2A490

ae UNIVERSITY OF 4 a Been ALTERRA (a Y ¢
‘4 CAMBRIDGE a f . enpitenmental pak WAGENINGEN NL ?)
: UNEP WCMC

REVIEW ON THE ECONOMICS OF
BIODIVERSITY LOSS:
SCOPING THE SCIENCE

ENV/070307/2007/486089/ET U/B2

FINAL REPORT (draft version as of 19 May 2008)

Balmford, A., Rodrigues, A. (University of Cambridge),
Matt Walpole (WCMC), ten Brink, P., Kettunen, M. (IEEP)
& Braat, L. and de Groot, R. (Alterra)

May 2008

This report has been produced for the European Commission under Contract
ENV/070307/2007/486089/ETU/B2.

Scientific lead:
Andrew Balmford and Ana S.L. Rodrigues

Conservation Science Group, Department of Zoology, University of Cambridge,
Cambridge, United Kingdom

Partners:
Patrick ten Brink and Marianne Kettunen
IEEP — Institute of European Environmental Policy, Brussels, Belgium
Matt Walpole
UNEP — World Conservation Monitoring Centre, Cambridge, United Kingdom
Leon Braat and Rudolf de Groot

Alterra, Wageningen University and Research Centre, Wageningen, The
Netherlands

External experts:

The expertes that provided direct input into this project are listed under each
theme.

Acknowledgements:

To all the experts that contributed as authors, reviewers, contributors or otherwise
provided information useful to the thematic reviews. Also to the interns that worked
on each task (see participant list in each theme)

To Ben Phalan and Bernardo Strassburg for helping with case-studies for the glossy
report. To the members of the Zoology Department that created the logistical
conditions for this project, in particular Julian Jacobs, Nanna, Neil, David Clarke, and
Keith Griffiths.

TABLE OF CONTENTS

1 DNPRODUG TION: sesctsrcccesscscsssaactcescccsucescsucn otaceseonendze toca ete nettteans noel ceateuauscescesuncesncsecsesessese 1
1.1 RACK ON OWING Ei cccescacesccsssesaccncescaceauconscerscesnconstermectecctentes tenaccnate tc tesceacecnateseceneraatreerence 1
1.2 This Scoping the Science project..............csccsssscescessesecssssssessssseesssseeseeseessesseesees 1
1.3 AMSG PO Hit seescecteenccess cece sei she cavatsenssueeceesacsctecsseeeterseetectaddtatstascheossussceseccacesscaecvecyoes 2

2 A CONCEPTUAL FRAMEWORK FOR THE REVIEW......cscssssssssssssssssssssssssscsssssssseesaseseesees 4
2.1 Why we need a conceptual framework.................cssssscsccsssssccssceceeseeesseeseeeseseees 4
2.2 Initial considerations and assuMptions,.................scsccsssscsseseesssscecsscesssssssesseseesess 4

2.2.1 Waltin Wd NOUUre nc .ccseccosceccencenconcsesscceseaceseezsencvessessontcccenescecesesesussastecrsasecse 4
Dele, SCOPE Of ING REVIEW scccccsessescesessssseresccstsccesosceasscessecresvatsuoestsreccesctecctsatsssttecesees 6
2.2.3 Valuuing marginal ChANGe...........ccecsecsssesssceceecsescssscescsescsesssecsscssscssessecsonceees /
2.2.4 The need to be spatially explicit ......c.sssssssessesscsessesscssessesesscssccsescesessesseeseeeees 8
2.3 The conceptual framework..............csscssccsccessessssssssssssecsescecesesessessesssesseesecseeseesees 8
2.3.1 Defining appropriate policy actions based on the drivers of loss............... 10
Deed, Defining the counterfactual states Of the WOT ...........ssssseeeeseseseeseessoees 11
2.3.3 Quantifying and mapping how the biophysical provision of benefits is
Affected by the policy ActiON ...........sesscsecseccesesccsccscesccsesceccesescsscescsecssesssecsecsecscesocseces 12
2.3.4 Quantifying and mapping the economic value of changes in benefits
derived from the Policy ACtiON..........s..cseoceesceesseesssessesssessseccsesssecssnscscssecesessscssecssnscenss 16
2.3.5 Quantifying and mapping the overall economic value of changes in
benefits derived from the Policy ACtON .......s.ssscseeseeseersessesssssesssescnsesscessesseessesseseasees 21
2.3.6 Quantifying and mapping the costs of Policy ACTION .....s.sssssseesseesereesereees 22

Deaf, Quantifying and mapping the net economic consequences of policy
action 23

2.3.8 Evaluating the adequacy/desirability of the policy ACtiON.L.........1.00.00.00000 24
2.4 Summary of key points from the conceptual framework ..................sssseeees 24
2.5 PAN CI CLP ANUS eesecesscecsescuecosecocescerseetssesctsuccccastessscadseacscnccaceccercacesarsstocascuccssttencscasses 25
3. A STRATEGY FOR REVIEWING KNOWLEDGE ON THE LINKS BETWEEN WILD
INATURE/ANDIFIUMAIN WELUBBING ceccorecccstncsscesesccstsesecssesroescssssesecsesesecttecscossessecsosaccescacceesee Di
3.1 Mhe nee dilo rials trate sy,ccccccce er ececceetcssoaneenacteacseacccecerastaasuassacesarmtscateatsanceesacccere 27
3.2 The Millennium Ecosystem Assessment as a starting POint .............cscseseeees 27
3.3 The need to avoid double COUNTING ...............s.ssssccssssssscsssessssssssesssccssssssseaseaesrees 28
3.4 Proposed classification of ecosystem processes and benefits ..............s.s0s00000 29
3.5 _ Partitioning the links between wild nature and human wellbeing as a
basis for the thematic reviewS...............ccsccssccscessssscsscsscsssssssscssssssscsssssssessessesseeseeseeseeeees 35
Op INCI TIC REVIEW Slecsccerscccecceccesessseetnaccaccessasscarsacccasncvessocesdeensacaseaenactacsatsassacsansaces 38
3.7 _ Relationship between the proposed classification and the Millennium
Ecosystem Assessment framework..............sccsscssscssssscsscsscescescesssscsscssesessnsesssasensesseences 40
3.8 Relationship between the proposed classification and the Total
Economic Value framework ..............ssccssccssccssessscssssssssssesscessescessesessncsenssesecesseesscensens 42
Seeman Ee ALGUICH PD ATLAS fee secescsnesacsatesencestaansasacveccdcnesaaransassasactassascerssechonvanescsaacesessessscessssaemnsea 45

iil

4

THEMATIC REVIEWS. ......sscssssssssssssesssssesecensesssssestesssssescessessesssscssnssscsacensecsecsensoessessaseass 46
4.1 Wild crop pollimation...............scssccsscsssssssssscssscssssssssssssscssssessseesesseessesasenssenseessens 48
4.1.1 Why is this process important for human wellbeing? .....s.scsesereererereees 48
4.1.2 What are the overall trends in the provision of wild crop pollination?..... 48
4.1.3 How is the provision of wild crop pollination affected by changes in
WEIN ALUT.C Oicncsesconsscsoncssaaccaccacscesscsonstotastostssodcetasetesestacsctaascocssbassssseceseccsnncessonssacnssanneta= 49
4.1.4 What are the main threats to the provisioning of wild crop
POLLINATION? ......cceccerrsscsessssccsscesccccsccescccnsccsscsaccnscscescssscseacsoncesscenseseassasensccecsscccnneenaeenees 52
4.1.5 Are abrupt changes likely in the provision of wild crop pollination?........ 52
4.1.6 Can we quantify and map the global provision of this wild crop
Pollination and how it Might ChANGE? ...ccscsccsscsersceseeeereserseesesserssssnsnseeesscensensesaees 53
4.1.7 Insights for CcOnOMicC VALUATION.......c.sccssccsssssseereerseesecesseescenssensssseeesesnseenees 57
4.1.8 SOMME HEVIMESOUTCES scestesscrsesrereeqcotescactaeceesecestsccs ranccstoeceocssececoccoscsscssancezesansees 57
4.1.9 IRATTICIDANISecresncenscstectrectrtrretetscatsacssecsscecuscesteasesecscccuccsnscnactscsautreassseseccees 58
4.2 Biological control im Crops ...........ccsccsssssessessessessessessessesnseccsccsncesessessessesscssesacenees 59
4.2.1 Why is biological control in crops important for human wellbeing?........ 59
4.2.2 What are the overall trends in the biological control Of CropS? .....100.100006 60
4.2.3 How is the provision of biological control in crops affected by
Changes in wild nature? ...........cccsccsssccsscssscsssssssecssesccessssscssssessssesscssasssssssscacssnsseesees 61
4.2.4 What are the main threats to the provision of biological control in
crops? 63
4.2.5 Are abrupt changes likely in the provision of this benefit/process?........... 63
4.2.6 Can we quantify and map the global provision of biological control in
Crops, ANA how it Might CHANGE? ..esceccssserssrssreereeeersecersersecsersesssssesecsesesessseseceeeeees 64
4.2.7 Insights for CCONOMIC VALUATION.......cscesssssceseesscsssessecescessssssesssessssssenseessees 65
4.2.8 SOME) EVI CSOUTCES ccccesecccssaccsecatsnccsccsessossecuctsezaccacacccesoosescsecossescassazsesecenseane 65
4.2.9 IROTTUCIPONISiecsnscsnsecocastacvoesacssctscceccestancecessecsesecsaccaccseresssissasaasaccaccaesceceucsaeeeees 65
4.3 Genetic diversity of crops and livestock ..............sccscssssssessscsssssssscsssssssssseseseees 66
4.3.1 Why is the genetic diversity of crops and livestock important for
WUMmanbw CHB CIN Ge rrrccsectcosttsesncchscoctsestenccctenceasoneteceacerenassucecesceeceesnastastesasececsesacevescarsaes 66
4.3.2 What are the overall trends in the genetic diversity of crops and
LEGO 5% cecececgs2c802000000- 00005 2600 CCE CEO DOC ECE ESOC OCRO CB CoS Sco GHOCHACCOCGSSCoSC ES EEA AE eeepc coocIacEC 67
4.3.3 How is the genetic diversity of crops and livestock affected by changes
EM WIE NOQLUTE? vccccecccsscesuscccacsotesessasacsecestanectases tavetoesettocseaasseziesdensssuecsid ccsdestoesesocttvotseosee 67
4.3.4 What are the main threats to the genetic diversity of crops and
UL VESLO CK Zasvensanssntesscstnices sssesstaanscszsateateascecenatatasneaneaseateacancatucecessarcarcarnatctertensserteceetecees 68
4.3.5 Are abrupt changes likely in the genetic diversity of crops and
VEVESLO CK idperecescnccnascentenrentsnteatonesasccasesssassareasedcesceacsstcatccsanecanscceerrateteecsccseieeseoc seer one ete 68
4.3.6 Can we quantify and map the global diversity of crops and livestock,
ANGHOWND MICH CHANGE essere sserscancecesncescencertcccereeceertectersectestsassscereecceesceceeoteteotnieee 69
4.3.7 Insights for CcOnOniic VALUATION .....1.scsccsssseseeresssssesersessessessasessasessscessacsnesses 70
4.3.8 SOME: ROY PESOUTCES i ocsxccacsacoccsecsurtccuucsuceactertsuctnevnssaston toast iaesesiivickatot eee atest 70
4.3.9 IP AVEICIPOMIS pandetantenteacntcccnssnenessarateiaes caraacuasnenseeasnes eres rereeceteer tee eeeeee eee 71

4.4 Soil quality for crop production. ..............sscscscscscsccesssecesssesesesesesesescsesesesesessscees 72

4.4.1 Can we quantify and map the contribution of wild nature for the
provision of soil quality for crops, and how it might Change? ....s.c.s.sssesseseeserereeeseeess 76
4.4.2 SOME/K CY FCSOUTCOS «0.5 ccessicscsucsccscsersasccte Rea Ae sce ess 76
4.4.3 ROT UICID GNU S\accnsceszscssessoncsssectescesterecsmteencveccterstescnceree teeter nese eeeteeseeeas Tif,
4.5 Wives CKaetacsssssncarccespnesasesessscceactoescotestastesseusseaeatesnaes saceneceecae site esacesseteteate reese sce 78
4.5.1 Why is the production of livestock important for human wellbeing?........ 78
4.5.2 What are the overall trends in the production of livestock? ........0..0000000006. 78
4.5.3 How is the production of livestock affected by changes in wild nature?... 78
4.5.4 What are the main threats to livestock production in natural
BTASSTANAS Tee Mines sate seeecon tee sce ecn acres tac s teeta cece aa ecserse cto nace 79
4.5.5 Are abrupt changes likely in the provision of livestock in natural
QT OSSIANAS PrevcccsancsvscssassesstocsunscsscstoCucsucveewesaseussicatestscsccereate eee teeter ete ee ak eakeses 79
4.5.6 Can we quantify and map the contribution of wild nature for livestock
Production, and how it might CHANGE? .....ssscesesesseerssssssssssecsssssssecessescssssssessssssessesees 79
4.5.7 Insights for CCONOMIC VALUATIONL.......scscesercesssesssscescessesseessesesseessesssssensssees 80
4.5.8 SOME KEY TES OUNCOS cc tzcsccescosccetsosocsorsusteiasteesiesaduseteeoeiotes eveate acaseaethetasiatse 80
4.5.9 PATULGID ANUS s28. Sosssece tt ce cectac acest sev eeaeeec ttn eeteantet eae seeaco ee ceatcasotent tier cas 81
4.6 aus Marine fish Cries). sccscssc.cdesccoeeccors<secrccctsecscestastaveasosasscscasesasceteterceasessoneottabeireesaeeee 82
4.6.1 Why are marine fisheries important for human wellbeing ?.......1..000000000 82
4.6.2 What are the overall trends in the provision of marine fisheries? ........... 82
4.6.3 How is the provisioning of marine fisheries affected by changes in
WHE QUITE? Sea ee ates tanta aa tae ansacdl teocaaa eee oanbek saat tie oeaaa 83
4.6.4 What are the main threats to the provision of marine fisheries? ........0.0.+ 88
4.6.5 Are abrupt changes likely in the provision of marine fisheries? ............0+. 89
4.6.6 Can we quantify and map the global provision of marine fisheries and
HOW Tl MUIBRECRONGE? covscsccscccetsccdecsrcesttsscactacvstenstesesacucvesaestesteateacsaciescrecseeeeteat sti iestses 90
4.6.7 Insights for economic ValuUation......c.sccccscsssesssesssessersssssesssssccsssacsccsceacsssacess 92
4.6.8 SOME KEY FESOULCES oc. -cescveesoctuncenesdedatsostvstwes ssucestectscssussesatesesecce necretexectersesse? 92
4.6.9 IPQTEICIPANES wvcscescossssssstsacsctstcrescesesntsncsesvessostecsectettessoctsdsewiacsaseeceetenee ieee tees 93
4.7 Inland fisheries and aquaculture..............sccsccsscsscssssssssscsscssssscsscscscsssssssssceseess 95
4.7.1 Why are inland fisheries and aquaculture important for human
WELIDCIDG trscrcerttccersccssscersessshccessaccessssences tatcacsscorarccasstaststersorsectacucensecteversesteceteeetasecensee 95
4.7.2 What are the overall trends in the provision of inland fisheries and
AQUACUILUTE yc cacssecsctscsnsestnseceeesetetacertstsstacnsctatsasees snasceceeteccontastecenockeaetattacscstostitectsoséasses> 96
4.7.3 How is the provision of inland fisheries and aquaculture affected by
GCHANGESiIN Wild MAtUTe 2 sccccectcesettentnocscctcescoaseateasesscccecesacctevcesietectessvetcacsscceseustoctaasaaseess 96
4.7.4 What are the main threats to the provision of inland fisheries and
CUTTER Ree ECP PEED TTT CCRE EE EERE CECE EEE PEE EEE CECE CEP RPE EEE 99

4.7.5 Are abrupt changes likely in the provisioning of freshwater fisheries
ACL OU OCHILUNG Aenvcnsenetnesusccacsuoctastuascastessaseasesennasuastatanvenceecactasepeucee:ceaeecettctetererseeeee 100
4.7.6 Can we quantify and map the global provision of inland fisheries and
aquaculture, and how it might ChANGe? ......1.seeeesssssessssessessesssssssssrsesssssenscssenscesees 100

4.7.7 Insights for CCONOMIC VALUATION... seccscesssessescesssssscesescessesssesssscassscssensesees 102

4.7.8 SOME/KEY TOSOULCES ss.cc.acssecee eae eater eh von bad sauhace eee obatnanesbbuctiebiestsstes 103
4.7.9 PQTUCIP ONES ii re hase ES ccs 104
4.8 Wild! animal products ssc.ccccccccseccceressersesester eset teeter reo aa toon seebaccencteasotessesees 105
4.8.1 Why is this benefit important for human wellbeing? ....c.cccscssssseeerereeeeees 105
4.8.2 What are the overall trends in the provision of wild animal products?... 107
4.8.3 How is the provisioning of wild animal products affected by changes
PAWID NOLUTE 2 mircsessckacsnsoscnsosdencncerssseeetee ee tt RT eee eens 108
4.8.4 What are the main threats to the provisioning of wild animal
POTTS Se ene rectpoer eco cco coc ICL ASS SCG GI o2C0030 0005220 Ne GOCE CORO CECEEEOS ECL PECECODCHCCECEB ECO PEESSS 113
4.8.5 Are abrupt changes likely in the provision of wild animal products?..... 114
4.8.6 Can we quantify and map the global provision of this benefit/process
ANGMOW TMB ht CRANGOL rerrecs-ccecscecensnereternesereenenctoteceenetersten er aseaceee tenet sas sacae staat easeee 115
4.8.7 Insights for CCONOmMIC VALUATION. ....ccccceccecessersersessesseessessessessessessessessessenses 119
4.8.8 SOME MEY TESOUTCES .: savesccsacsssestterrsrcencottaeteesed cena taceceea ee soeeeie cote tedetasenssts 120
4.8.9 DPATLICIPORIS oo coe cree ccasstete tee C Tee hii aca gasatsiee 120
4.9 Fresh water provision, regulation, and purification ..............ccsccccsssesesceeeees 122
4.9.1 Why is this process important for human wellbeing? .........s0ssseeeeeeeee 122
4.9.2 What are the overall trends in fresh water provision, regulation, and
DATTA LGU (teres ec OR ECE EE ECE CECECEE SE CPPCC ATCC PELE OPE EEC CED 124
4.9.3 How is the provision, regulation, and purification of fresh water
Affected by changes in Wild NAture?.....sssssceecessseecsssesssseccssccsecsssscsececsssessessesseessaces 124
4.9.4 What are the main threats to the provision, regulation and
DUTU{TCATONVOSA TES W ALCT, ooccecscencsucuccocvccsccotvcterectacectacecsvsesescsacssssasestsisssessisetsssacereveee 129

4.
4.
4.

4.9.5 Are abrupt changes likely in the provision and regulation of fresh
water? 130

4.9.6 Can we quantify and map the global provision, regulation, and

purification of freshwater, and how it might ChANge?.......scecscesscsessssssssssssessescenes 131
4.9.7 Insights for economic ValUation.......s.scsecescsessesssesscesscenssessssssecseacesscenceeses 134
4.9.8 SOME /KEY PESOUTCES -saceeccectszesccsecsusvasvasseesoscsetncssenesaceuuccscarestcssssseasasisaseessets 134
4.9.9 IROTULCIDANUSjesressencutesceasetcstescunccsenceccuscetcusteacetneeeraarnecasnacnrsecorsasesescurtoceevererts 135
NO Wialdliiplamtefibresiccs.ccscscrece-cuesscectcceasescuescsssecscesseactasoncotsnscssencorsasocscsersasetacsisscrs 136
MA Wildinvedicunallplamtsscsscc.crscecccetearesctensecesssestsnectoseresessactessasarsacessnccevescesssataece 137
12 Outdoors activities related to Mature .............scscsccscsssessctscssetscsssseeeseeeseeceaces 138
4.12.1 Why is this benefit important for human wellbeing? .......0..ssseseeeeseeees 138
4.12.2. What are the overall trends in the provision of this benefit?..............00 139
4.12.3 How is the provision of this benefit/process affected by changes in

WELD OLUTC Yownsccucecanectncer sesso een cotene ees senenteotesnectcnsnasecatnetursancatcesertacecsacestucassateseetats 140
4.12.4. Whatare the main threats the provision of this benefit? ...........s1sees000 143
4.12.5 Are abrupt changes likely in the provision of this benefit? ........1...0.1000 144
4.12.6 Can we quantify and map the global provision of this benefit and how

WL UGH CHANGED reccecceerssreeeccrsctst etter taatnseenteaneeneasucctescetanssesaecsesteescesuesccsncccsccsceesossesss 144
4.12.7 Insights for economic VAlUation.....s.sccscereereeresrerreerserscsrserserensenseesensseseesees 146

Vi

ANU ZESMS OMEIK CITES OUT CES -oxn.cczactcaseacersacsusessactasccarceesscocsesncuansuasvocsesaceascucceaesaaeseess 147

4.12.9 Participants in this SUb-review..........scssccsscesreerecesscssseesscesseesserseesesseesseess 147
4.13 Regulation of natural hazards .............scsscsscscscsssecsssssessscesssseessessesesesseeaeesees 149
4.14 One-off use benefits ...............cescsscecessscescsscsscsscsscsssersesssessscsessssseaseasessensensens 150
4.15 Nom-use bene fits.............cscccccsscsssssssssccsssscsscssscsscsssssssesessessscssessaeseesseasensseneeess 151
Global climate regulation. .................:ccsscssssssssssccesscsscssscscssessecsssssscsscsssessseesenssesssenseeass 152

4.15.1 | Why is global climate regulation important for human wellbeing?........ 152

4.15.2 How is global climate regulation affected by changes in wild nature?... 152
4.15.3 Can we quantify and map the value of wild nature for global climate

regulation, and how it might CHANGE? .....scsssscsreesessecseerscrscssceceeseessecsecsecsecseneesseescees 153
ASU SYA WMP AVLECLP AN Stcsrsctsscccsosssesersucsctsnesecsercentsnstencencseeenesonccaceestassacenssatsansansseseesoers 154
4.16 Unknown benefits or processes ...........s.ssssscsscesssesscsssssssscescsessessesessssesssesseesees 155
ANP S COM ANOS seetescentsessssarcasoatescecsnaccnccesaststecnactancsunseacsuatesarsnacsncecaecsuresatsustspessestaatsness 156
A SUSI BOT LL CLD ANUS) :cncsssscsscenaesasseseeareatsascarenttatseacsaneeasnavauscatesessescecacesttaceacasasasseaanees 162
4.18 Prioritisation of recommendations for future WOrK.............scsscccseessessessseeees 163
ANUS ST OSTLCID AMS |ecencescsasssaacsansseassenascatsonscarecssescvaconseccetassuccosatsnceuasteatscareatsectsnctes 166

5 INVENTORY OF RESEARCH ORGANIZATIONS, PROGRAMS AND RECENT
LITERATURE DEALING WITH ECONOMICS OF BIODIVERSITY LOSS ........scsscsecsesssssecseeeees 167
5.1 Write rate TO VICW cecessccessecesecceccececerectoresteresscscescasvscecsecssaccnscceeseorececesecuecsccesesees 167
5.2 Review of organisations, programmes and NetWOrks............ccscessesesresesers 167
GPIRERBRENGES esresecteecsseseescrccssssttacvorconcenseactaccacesssstacnncacccessescestessacenstrarnercaccnsesracemataasn= 170
Annex 1 — Cost and benefits of agriculture CONVErSION............ssscsscesecessesssssseseesens 207
Annex 2 — Framework development and consultation. ............scsssssssssssessesesseesees 212
Annex 3 - Practical strategy followed in the thematic reviewS............cscssecseseseeers 213
Annex 4 - Literature review “Economics of Biodiversity Loss” ...............ssssssesees 216

Annex 5 - Inventory of research organizations, programs and projects dealing
with “Economics of Biodiversity LOSS” ..............ssssccssscsrsssssssssesssssscesscsessnsesacsesseseens 233
Annex 6 - Key Internet Resources on Biodiversity and Ecosystem Services .......... 241

Vil

1 INTRODUCTION

1.1 Background

The recently released Stern Review on the Economics of Climate Change (Stern 2007)
concluded that it makes economic sense to invest in mitigating greenhouse emissions
to avoid the worst effects of climate change rather than face the consequences of
failing to do so. This proved highly influential in persuading politicians and
economists of the need to act on climate change now.

The G8 decided in March 2007 to initiate an analogous “Review on the economics of
biodiversity loss”, in the so called Potsdam Initiative: 'In a global study we will initiate
the process of analysing the global economic benefit of biological diversity, the costs
of the loss of biodiversity and the failure to take protective measures versus the costs
of effective conservation.'. This proposal was endorsed by G8+5 leaders at the
Heiligendamm Summit 6-8 June 2007.”

This study consists of two phases:

- Phase | is a preparatory stage including several studies to be ready for the CBD
COP9 (Conference of the Parties to the Convention on Biological Diversity) in
May.

- Phase 2 is the consolidation stage that will produce the full Review, to be ready
in October 2009.

The study is being supported by the European Commission (together with the
European Environmental Agency and in cooperation with the German Government),
notably through contracted studies such as the present one. Leading economist Pavan
Sukhdev as been appointed as an independent Review leader for Phase 2.

1.2. This Scoping the Science project

The “Review on the economics of biodiversity loss: scoping the science” project
(henceforth, the Scoping the Science project) is one of several Phase 1 projects,
running from 14/12/2007 to 30/4/2008. According to the invitation to tender:

“The objective of the current study is to provide a coherent overview of existing
scientific knowledge upon which to base the economics of the Review, and to propose
a coherent global programme of scientific work, both for Phase 2 (consolidation) and
to enable more robust future iterations of the Review beyond 2010.”
The specific tasks to be carried out in the context of the project are:

1. Elaborating the conceptual framework for the Review for biodiversity;

2. Establishing a working relationships with research networks and
programmes;

3. Reviewing existing ecological knowledge needed to carry out the Review:

4. Identifying critical gaps in knowledge;

5. Presenting key findings;

6. Elaborating a proposal for a 1-2 year programme of work to provide case
studies;

7. Elaborating a proposal for a 2-3 year programme of work to fill critical gaps;

8. Building an inventory of relevant research networks, programmes and
projects; and

9. Providing an inventory of internet resources.

The outputs of this Scoping the Science project will provide background and
recommendations for Phase 2, when the approach to be followed in the Review will be
defined in detail.

1.3 This report

This report presents the final results and recommendations from the Scoping the
Science project.

The Conceptual Framework for the Review on the Economics of Biodiversity Loss
(Task 1) is detailed in Section 2. Note that this has a significantly broader scope than
the remaining tasks in this project. The conceptual framework was developed for the
whole Review, covering the fuii integration of ecological and economic sciences. This
task was led by the University of Cambridge.

The bulk of this report covers the most substantial task of the project, of providing a
coherent overview of existing scientific knowledge (Task 3). Given the specific
mandate that such review should provide an adequate basis for the economic valuation,
we ensured that the definition of the review themes selected was appropriately defined
for this aim. Section 3 presents the conceptual background and justification for the
selection of the set of themes that form the basis of the review of the ecological
knowledge. Section 4 presents the results of the thematic reviews. Gaps in knowledge
(Task 4) and key findings (Task 5) are discussed in the report for each theme. An
extensive network of experts was created in this review (Task 2; see Error! Reference
source not found.). This work was led by the University of Cambridge, with the
support of UNEP-WCMC.

A synthesis of the recommended priorities for Phase 2 (Task 6) as well as in the longer
term (Task 7) is presented in section 4.18. This task was led by the University of
Cambridge, with the support of UNEP-WCMC.

The project also built an inventory of relevant research networks, programmes and
projects (Task 8), with the aim of expanding the relationships with these in the future
(Task 2). To support this exercise, a review of the recent literature (i.e. publications
post the 2005 Millennium Ecosystem Assessment) dealing with economics of
biodiversity loss was conducted (Section 5; with the list of identified publications and
networks presented in

Annex 4 and in Annex 5). This work was led by Alterra and the Wageningen
University.

Finally, the project also provides a review of key internet resources on biodiversity and
ecosystem services (Task 9). This task was lead by UNEP-WCMC and its outcomes
can be found in Annex 6. This review was specifically focused on web-available
material related to biodiversity research and ecology (i.e. as oppose to biodiversity
economics). Thus, its outcomes largely complement the information on research
initiatives / networks and literature on economics of biodiversity loss above.

2 ACONCEPTUAL FRAMEWORK FOR THE REVIEW
2.1 Why we need a conceptual framework

The scope of the Review on the Economics of Biodiversity Loss is extremely
ambitious: analysing the global economic benefit of biological diversity, the costs of
the loss of biodiversity and the failure to take protective measures versus the costs of
effective conservation (Potsdam Initiative). The scope of the Review is also very wide,
as the benefits that we obtain from biodiversity are extremely varied. Tackling this
immense task will require building from the knowledge and the skills of an extensive
network of research teams with very different expertises and backgrounds.

The conceptual framework is a proposed operational roadmap for Phase 2 of the
Review on the Economics of Biodiversity Loss. It presents a strategy for partitioning
the Review into well-defined and self-contained pieces that can be tackled by teams
with the appropriate expertise. It then presents the method for linking these pieces
together in order to provide a reliable answer to the Postsdam challenge.

The framework is particularly important for ensuring a smooth link between the
ecological and the economic aspects of the Review.

The Review will need to face substantial gaps in knowledge and so the results will
inevitably be plagued by uncertainty. Dividing the Review into modules allows for the
identification of the areas where the main gaps are. Understanding the links between
modules will indicate how uncertainty in a particular subject propagates within the
review to affect its final results. This facilitates the identification of research priorities
for better quantifying the links between biodiversity and economics.

This is a mechanistic framework, for understanding how changes in biodiversity cause
changes in economic costs and benefits, rather than simply for analysing how changes
in biodiversity co-vary with economic changes. That is, this framework will help to
distinguish causality from simple correlation in the relationship between trends in
biodiversity and in economic values.

2.2 Initial considerations and assumptions
2.2.1 Valuing wild nature

The term ‘biodiversity’ is often considered to refer to variability amongst organisms
(e.g., species diversity, genetic diversity). Indeed, the Convention on Biological
Diversity, defines ‘Biological diversity’ as “the variability among living organisms
from all sources including, inter alia, terresirial, marine and other aquatic ecosystems
and the ecological complexes of which they are part; this includes diversity within
species, between species and of ecosystems”.

However, the economic importance of wild nature does not rely solely on variability
(e.g. species and genetic diversity). Indeed, many of the benefits obtained from nature
rely much more on amount (e.g., the abundance of particular species) (Figure 1).

Figure 1 Illustration of the importance of variability and of amount for the provisioning of
different benefits from nature. a) High variability, low biomass fynbos ecosystem. b) Low
variability, high biomass improved pasture. Despite its lower biomass, the fynbos ecosystem
has a higher value for the provisioning of benefits that depend on diversity, such as compounds
for the pharmaceutical industry. c) High biomass, low variability cod-dominated marine
system. d) Low biomass, high variability marine system after overexploitation of cod. Despite
its lower variability, the cod-dominated system has a higher value for the provisioning of food,
a benefit that is highly sensitive to amount (biomass).

Furthermore, the provision of benefits often depends on the condition and extent of
ecosystems — incorporating many species and their interactions amongst them and with
their environment. Ocean fisheries provision, for example, is affected by the condition
of coral reefs and mangroves.

It is therefore proposed that the Review on the Economics of Biodiversity Loss should
aim to address not simply the effects of the loss in variability, but also the effects of
changes in amount, and in the condition and extent of ecosystems. To avoid confusion,
throughout we use the expression wild nature to refer to this broader definition of
biodiversity.

By ‘wild’ we do not mean ‘pristine’ (in which case it would apply to very few parts of
the world); we mean ‘non-domesticated’ (i.e. excluding for example livestock and
crops).

There are many ways in which wild nature contributes to human wellbeing. These
have frequently been called ‘ecosystem services’, particularly in the context of the

Millennium Ecosystem Assessment (MEA 2005). This term includes both ecological
processes (e.g. nutrient cycling, water regulation, pollination) and benefits (e.g. food,
water, medicinal products). As discussed in Section 3, benefits are the end products of
these ecosystem processes, which directly affect human wellbeing, and which can
ultimately be evaluated economically (e.g., clean drinking water). We we focus this
Review as much as possible on such benefits, because these can be directly valued. In
contrast, the value of ecological processes (e.g., pollination, water regulation) can only
be established by valuing their contribution to different benefits (e.g., food crops,
drinking water). However, this has not always been possible, and so throughout we
refer to benefits or (beneficial) processes as the ways in which wild nature contributes
to human wellbeing. This terminology, and how it relates to the framework of the
Millennium Ecosystem Assessment, is fully explained in Section 3.

2.2.2 Scope of the Review

The aim of this Review is to evaluate the economic consequences of biodiversity (wild
nature) loss. It does not address non-economic values of nature, such as those
stemming from what many consider the moral right of all species to exist, irrespective
of people.

Similarly, ecosystem functions (e.g., nutrient cycles, species interactions) are not
valued in their own right (or because they are important for other species), but
specifically for the direct or indirect benefits they provide to humans. For the purposes
of this Review, only changes in wild nature that have economic consequences are
considered.

Hence, the Review — being an economic analysis — will only be able to cover a fraction
of the value of wild nature. There are limits to the meaningful valuation (in monetary
terms) of nature and its total complement of services and consequent benefits to
humans (Turner 1999; Turner et al. 2003). While most use values can be adequately
captured with the economic calculus, controversy remains over the evaluation of so-
called non-use values (Pearce & Turner 1990). For some analysts, intrinsic values in
nature cannot be ‘captured’ within economic analysis (Pearce & Moran 1994) but
much depends on the precise definition of intrinsic value. If the interpretation is one
involving the assignment of intrinsic values by a human to nature (or to its
components) then techniques such as contingent valuation and choice experiments may
be able to shed light on the motivations and preference values involved (known as
‘existence’ value). On the other hand, if the interpretation is one in which no human
agent is involved then monetary valuation is replaced by moral imperatives (often
linked to non-human species rights and/or interests) (Norton 1992). A further
complication arises if one accepts that a certain minimum provision of ‘healthy’
functioning ecosystems is essential to ensure a sustainable flow of services and
benefits to avoid threshold effects and system collapse. There is then a ‘glue value’,
‘insurance value’, or ‘infrastructure value’ bound up with this (often unknown)
minimum ecosystem provision. Total ecosystem value will therefore always be greater
than total economic value (Turner 1991).

This document does not reflect our personal conviction, shared by many, that wild
nature has an intrinsic value which on its own justifies efforts to conserve it. However,
an evaluation of the economic value of wild nature is not incompatible with this
conviction. Indeed, if the results of such evaluation are that conservation results in a

net economic gain, then that simply adds an economic argument against wild nature
loss, alongside the moral argument. If the results are that conservation of wild nature
incurs a net economic loss, then that will provide the net size of the bill for conserving
wild nature.

Note that ‘economic’ does not mean simply monetary revenue. It also includes aspects
such as changes in livelihood conditions, economic structure, investment risk and
social aspects such as poverty, inequality in access, and benefit sharing.

2.2.3 Valuing marginal change

This Review is not about the economic value of wild nature as a whole. In one sense,
that calculation is trivial: biodiversity has an infinite value because no human life is
possible without it (Toman 1998). This Review is instead about the economic
consequences of a marginal loss of wild nature, which are substantially lower than the
total stock of nature. Indeed, even within the current biodiversity crisis we are
(fortunately) very far from erasing all life and the benefits we derive from it. Even
substantial transformations in natural habitats may result in only relatively small
changes in the provision of some benefits (e.g. water regulation benefits can be
retained in the absence of forest cover through appropriate soil conservation practices;
Bruijnzeel 2004) and in some cases changes in wild nature may actually improve the
provision of benefits (e.g. it is possible that wild meat production is higher in some
secondary habitats than in primary forest; Robinson & Bennett 2004).

It is also fundamental to understand that for some benefits the value of nature is to
contribute to the provision of a benefit, rather than to provide the entire benefit. For
example, a degree of water purification would take place even in the absence of life, as
water can be filtered by passing through soil and rock. It would therefore be incorrect
to attribute the value of all water purification in a watershed to its natural vegetation.
What is relevant for the purposes of this Review is the added (again, marginal) value
of natural vegetation to water purification.

The concept of marginality is key for making ecosystem service research policy
relevant because it is at the margin that policy and economic decisions operate (Turner
et al. 1998). Indeed, it would not be useful, for example, to calculate the total value of
the global forests as a tool for informing practical forest policy (Bockstael et al. 2000;
Fisher et al. in press-b). Having said this, the term ‘marginal’ is usually used in the
economics literature to refer to a very small change. Here we refer to changes that
while small in relative terms (compared with the full stock of wild nature) may be
quite substantial in absolute terms (e.g., thousands of hectares of change in land use).
This creates substantial challenges to economic valuation, particularly if the system
function is subject to abrupt, non-linear changes in function (e.g. by ‘flipping’ from
one equilibrium state to another). Incorporating marginality in ecosystem service
evaluation requires therefore a good understanding of the drivers and pressures on the
systems under study, as well as of how the system is changing or might change from
its current state into a different state under a particular policy action (Fisher et al. in
press-b). Here, we use the term ‘marginal’ to refer to changes that a single policy level
can foresee. Hence, the valuation of marginal changes is anchored into specific ‘states
of the world’ generated from counterfactual scenarios where a specific policy action is
either adopted or not (see below, and section 2.3).

The overall economic consequences of a marginal loss of wild nature can be evaluated
by investigating the economic consequences of adopting a specific set of actions that
can prevent that specific level of marginal loss. Such evaluation requires comparing
different ‘states of the world’ — one with more, and one with less wild nature.

The ‘state’ of a given area (e.g.: a | km plot in Europe; the entire world), is a
particular set of biotic and abiotic conditions for that area, comprising aspects such as:
land cover, climate, human distribution, human activities, and conservation actions in
place. There is a current state of the planet, a different state existed 20 years ago, and
there will be a different state in the future.

Scenarios can be used to conceptualise the state of the planet in the future (e.g., by
2010 or by 2050) or what it would have been today if decisions in the past had been
different. Scenarios are the tools (e.g., models, storylines) used to imagine states of the
world. Conversely, states of the world are the outputs of particular scenarios.

As discussed in section 2.3.2, measuring the consequences of a marginal change in
wild nature requires contrasting very specific states of the world (counterfactuals), in
which everything else is equal except for the implementation or not of a set of actions
aimed at reducing losses in wild nature. This has the advantage that it requires being
explicit about what the conservation goals are, and what the actions are required to
achieve those goals.

2.2.4 The need to be spatially explicit

A key characteristic of this framework is that it is spatially explicit. We think this is
essential, for three sets of reasons:

1. The production, use and flow of benefits from wild nature vary spatially, and
so it matters to human wellbeing where conservation actions are implemented.

2. A spatially-explicit framework requires stating the assumptions being made
when extrapolating across heterogeneous landscapes using limited data.

3. A spatially explicit quantification of benefits and costs allows makes explicit
the mismatch between winners and losers in different scenarios, and is thus
essential for designing effective and equitable policy interventions.

We develop these points in detail in section 2.3.3.

2.3 The conceptual framework

The proposed conceptual framework integrates ecological and economic knowledge to
evaluate the net socio-economic consequences of policy actions for halting/reducing
wild nature loss (Figure 2). This framework is therefore a practical tool for evaluating
the effectiveness of different policy actions.

The next section details, step-by-step, how the framework works.

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Throughout we refer to a ‘policy action’ but the framework can be used to test packages of
policy actions, as long as adequate counterfactual states of the world are created
accordingly.

The term ‘wild nature loss’ is used throughout to refer to losses in biodiversity (both variety
and amount) and ecosystem degradation (section 2.2.1).

2.3.1 Defining appropriate policy actions based on the drivers of loss

Biodiversity is being lost and ecosystems are being degraded through a
diversity of drivers, including habitat loss and degradation,
overexploitation, species invasions, and climate change (Baillie et al.
2004).

Drivers of
wild nature
loss

{ The starting point of the framework needs to be a good understanding
pa of these drivers. This is crucial to desinging and costing effective
policy actions for reducing/halting biodiversity loss and ecosystem
degradation (losses in wild nature).

policy action to
halt/reduce
wild nature

loss oe : : ; 9
For example, overexploitation is the main driver of losses in the

weeccccess =’ provision of marine fisheries (Pauly et al. 2005). Appropriate policy
actions for reducing/halting the decline in the provision of benefits from marine
fisheries address this driver directly by regulating fishing effort. This may include:

ereccecocoery
Seececeeececec?

- Regulation of the temporal distribution of fishing effort by setting fishing seasons
that minimise impacts on the fish stocks (e.g., avoiding the reproductive season).

- Regulation of the spatial distribution of fishing effort (e.g. creating marine protected
areas with no-take zones, banning of bottom dredging practices below 1000 m).

- Regulation of overall fishing effort, by setting fishing quotas.

- Regulation of fishing targets and impacts, by defining which fishing gear can be
used.

The first, and crucial, step in the framework is to make explicit what the overall
conservation goal is. For example, it may be “to prevent any additional fishing stock from
becoming depleted and to ensure the recovery of at least 30% of currently depleted socks
by 2030”. This step is crucial because this is where it is defined what specific marginal
change in wild nature is being considered in this evaluation (see section 2.2.3). The
economic consequences (both costs and benefits) of changes in wild nature (biodiversity
loss and ecosystem degradation) are completely determined by this definition.

Having defined this goal, the next step is the identification of an appropriate bundle of
policy actions, explicitly enough to be costed, that are likely to deliver the goal. For
example, these may include actions such as:

- Decommissing 80% of the tonnage in fishery X;

- Set aside 10% of region Y as reserve networks;

- Replace current fishing gear W with new gear Z.

Naturally, the definition of what the appropriate actions are needs to be done in close
collaboration with experts in the field.

2.3.2 Defining the counterfactual states of the world

Evaluating the effectiveness of a particular policy action (or bundle
world A: of actions) requires comparing two hypothetical states of the world:
no action

(business-
as-usuay 7

- World A (business-as-usual), where the action is not put in

) place.

- World B_ (biodiversity-friendly) where the action is
implemented.

The states of the world are obtained through appropriate scenarios.
The business-as-usual scenario generates predictions for the
: : plausible state of the world in the absence of the specific
bioaieean intervention being tested (e.g., likely situation of the fishing stocks
-friendly) and the fishing industry by 2030 given current predictions in
/ population growth, in demand for fish, in climate change, etc.). The
biodiversity-friendly scenario needs to be identical in everything
else to the business-as-usual scenario except but for the specific policy action being tested
(e.g. likely situation of the fishing stocks and the fishing industry by 2030 if the policy
actions are implemented, given current predictions in population growth, in demand for
fish, in climate change, etc.).

world B:

The scenarios (and the states of the world produced) need to have the right level of
information at the right spatial scale. For example, if the action is to implement new
protected areas, scenario A needs to state where and roughly how large they are: if the
action is to manage fisheries appropriately, scenario A needs to spell out how that would be
done, for example by creating no-fishing zones and setting sustainable fisheries quotas. If
the scenarios do not have the necessary detail, they will not provide clear answers about
how plausible changes in wild nature affect the benefits we derive from it, and they cannot
be costed appropriately.

While we refer throughout to ‘states of the world’, this framework can be applied to
evaluate policy actions at any scale, not just global. For example, it may be applied to
evaluate the consequences of implementing a particular protected area or a new dam. The
methods for comparing states with and without particular interventions are well-established
in the field of Environmental Impact Assessment, being for example a requirement in the

EU Directive on Environmental Impact Assessment.

Last, while we refer throughout to “two scenarios”, many scenarios can be developed and
contrasted, although each contrast needs to be between two appropriately matched
counterfactuals.

The risks of contrasting inappropriate states

It is fundamental that the scenarios are identical in everything else but the specific policy
action being tested. Otherwise, the economic results cannot be directly attributed to a

11

difference in the state of wild nature and therefore are not a measure of the economic
consequences of biodiversity loss and ecosystem degradation. For example, a comparison
between the state of the world today and a predicted state by 2030 is not appropriate for
understanding the net economic consequences of changes in wild nature over that period,
because many other conditions would be changing at the same time (e.g. population
growth, climate change, technology).

2.3.3 Quantifying and mapping how the biophysical provision of benefits is affected by
the policy action

quantify and map
us isheteS : Sy quantify and map
quantify and map difierences in
world A: a, risk to fisheries A fisheries
—

without ‘quantify and map
action water provision A, Guantify and map

This is the focus of Task 3 in this
Scoping the Science project (Sections 3
and 4 of this report).

The first step is to define what are the
benefits or beneficial processes that will
be evaluated - i.e. the different ways in
which wild nature contributes to human
world wellbeing. These need to be defined
with uanily and map carefully to avoid double-counting,
action water provision B y 4
—— which would compromise the results and
ee the credibility of the overall evaluation.
Section 3 of this report presents in detail
our proposal for a classification of the links between nature and human wellbeing that avoid
the double-counting problem and so provides a sound basis for economic valuation.

For each particular benefit (e.g. fisheries) or beneficial process (e.g. water regulation; see
section 3) one needs to understand how its provision is affected by the policy action (i.e.
what are the marginal benefits of the action). In order to do that, we need to be able to
quantify the predicted provision for each of the states of the world considered. For example,
we need to predict fisheries production under each scenario; these can then be compared to
understand how the policy action is likely to affect fisheries.

The importance of being spatially-explicit

It is crucial that the contrast between benefit provision under different scenarios is done not
simply in terms of the overall global value, but by attempting to quantify and map the
spatial variation in the provision of the benefit or beneficial process. Indeed, a key
characteristic of this framework is that it is spatially-explicit. This is essential, for three sets
of reasons:

i. The production of benefits or beneficial processes varies spatially, because it is
based on spatially-variable underlying ecosystem processes. For example, fisheries
production will depend on ocean productivity (section 4.6), while carbon storage
(contributing to carbon regulation; section 0) depends on the vegetation cover
(Figure 4). The flow of benefits from the point of production (e.g., an upstream
forest) to the point of use (e.g., a downstream city) is highly influenced by spatial
constraints. The use of these benefits is spatially heterogeneous too, depending
critically on the patterns of distribution of end users (e.g., cities, or agricultural
areas), which creates substantial spatial variability in the value of benefits even
within areas with similar natural production and flow. Finally, alternative states of

12

the world capturing different political interventions need to be spatially explicit, as
the results of such interventions depend critically on where they are implemented
(e.g., the location of no-take marine areas). Integrating all these components
(production, flow, use, and state) clearly therefore requires a spatially explicit
framework.

ii. Frequently, available information will be insufficient for the elaboration of detailed
maps of particular elements of the framework (e.g., value of wetlands for storm
protection). This does not render the framework less useful: quite the opposite.
Having to describe each element spatially requires stating the assumptions being
made when extrapolating from limited data. If a single data point is all that is
available, then the corresponding world map may have a single colour.
Oversimplifications become obvious, and so are more likely to receive attention in
future work. Further information will help to refine the spatial representations (e.g.,
if other data indicates that value of wetlands for storm protection is likely to vary
with Gross Domestic Product/km’). A spatially-explicit framework can always be
aggregated into coarser spatial units, for example to provide values per biome, or
global values. The reverse, however, is not possible.

iii. Services often flow from their point of production to users elsewhere (Figure 5) — so
the benefits of their conservation may accrue to different actors than the benefits
associated with their loss (Figure 6). For any given action there will likely be
winners (e.g., areas where fisheries production increases) and losers (areas where
production decreases). Information on these would be lost from the global
aggregated value, but is fundamental to an appropriate economic valuation (as in
most cases benefit value is context-dependent). A spatially-explicit approach is
therefore crucial to fully evaluate the broader social consequences of each action in
terms of impacts on livelihoods, development goals, and equity, and for designing
effective and equitable policy interventions.

Quantifying and mapping the production of benefits/processes

Obtaining global, or at least large-scale, maps of the production of a particular benefit (or
beneficial process) requires a good understanding the factors that drive production. These
factors will include a mix of abiotic (e.g., climate, soil type, topography) and biotic (e.g.
ecosystem type, species diversity) variables, as well as factors that are determined by
human actions (e.g. extent of habitat). If these relationships are well understood, it should
be possible to generate a mathematical model that can reasonably predict benefit production
given information on the underlying factors. Such a model corresponds to what in
economic terms is called a production function. At this stage, however, we are interested in
modelling the biophysical production of benefits (or beneficial processes) rather than their
economic value.

For example, timber production from natural forests per area, per year, may be given as the

forested area multiplied by a function (f) of factors such as primary productivity, forest
type, topography, etc. (Figure 3).

13

® |

|

| timber production = forest area x primary productivity, forest type, tree diversity, topography, ....) |

| & |
|

Figure 3 Hypothetical production function for timber form natural forests. Green arrows correspond
to biodiversity/ecosystem inputs; red arrows correspond to human inputs.

Note that for many of these benefits their production depends not only on wild nature, but
also on abiotic (e.g. topography) and anthropogenic inputs (e.g. forest area). The main aim
of this Scoping the Science project is to understand the marginal (or added) value of wild
nature for the production of these benefits.

Given such a production function and appropriate biophysical data (maps of primary
productivity, of forest types, of tree richness, topography) it would be possible to create a
global surface of potential timber production for each state of the world. From one state to
the other, some key variables would change (e.g., forest conversion), therefore resulting in
spatial differences in timber production. A contrast between these maps would then be used
to quantify, and subsequently value, the spatial variation in differences in timber production
between states of the world.

The production map should be expressed in the appropriate physical units for measuring the
wile . . . . io)
provisioning of the service in question (e.g., tons of carbon/km? for carbon storage, m* of
2) opis. . oe 2 . .
water/km*/y for water provisioning, numbers of visitors/km*/y for recreational services).

In practice, a detailed model of a production function will seldom exist at a global scale.
However, more simplified models do exist in some cases, or they can be produced from
available data. For example, a global model of wild meat production (section 4.8) has not
been created yet, but a reasonable number of individual studies have calculated productivity
for different areas around the world, and we believe that a first-cut global model could be
produced in time for Phase 2 (section 4.8). The key question we addressed in each of the
thematic reviews (section 4) was how far is scientific knowledge from being able to create
such models. In a few cases, maps of benefit/process production have already been
developed (e.g. Figure 4).

14

Figure 4 A preliminary map of the global distribution of current ecosystem productivity for the
service ‘carbon storage’ (Naidoo et al. in press).

Mapping (potential) sustainable production, rather than current use

For some benefits (those that involve harvesting, such as fisheries, timber production, wild
meat production) there is a potential for overexploitation leading to depletion. In this case,
the relevant production map needs to correspond to a map of sustainable productivity,
rather than current benefit flow. For example, when considering food production from
fisheries, the relevant map is one of sustainable fisheries production, rather than one of
current catches. Indeed, the latter is likely to reflect excessive fishing effort in some areas,
which over the long-run will lead to a reduction in benefits.

On the other hand, a map of current benefit flow would ignore areas where there is no
present use but which may be valuable nonentheless. For example, areas too remote to
contribute for current timber production may be considered to hold value for future timber
(option value, Section 3.7).

Quantifying and mapping risk

Biodiversity loss and ecosystem degradation affect not only the flows of services and
benefits, but also the resilience of systems. It is therefore crucial to attempt to quantify the
extent to which a particular action (or the lack of its implementation) affects the likelihood
that each service or benefit will be compromised. Knowledge on resilience and risk is even
scarcer than on benefit/process production, but whenever available is should be
incorporated in the economic valuation. In the thematic reviews (section 4), we collated
information on the possibility of non-linearities in the production of each benefit/process,
and particularly the possibility of threshold effects in which a small change in the state of
wild nature could result in a disproportionate change in benefit/process provisioning.

For example, there is now some information on the conditions that trigger collapse in
marine fisheries (section 4.6), and so it may be possible to obtain at least some indication of
the extent to which fisheries are at risk of collapse under each scenario. Again, whenever
possible, this information should be quantified in a spatially-explicit way, even if the
mapping units are likely to be quite coarse. For example, it will clearly not be possible to
map risk of fisheries collapse at a fine scale, but expert opinion could probably provide at

least a qualitative assessment of the risk of collapse for each of the FAO major fishing
areas.

Key here is considering the sensitivity of benefit/process productivity to likely changes in
biodiversity. It may not be vital (at this stage) to know, for instance, that reducing forest
cover from 2% to 1% of the land surface will probably trigger a collapse in timber
productivity if the relevant difference in states of the world (under policy action vs.
inaction) is 25% vs. 15% forest cover.

2.3.4 Quantifying and mapping the economic value of changes in benefits derived from
the policy action

The information on how the biophysical
production of each _ benefit/process
changes under each scenario becomes the
basis of the economic valuation, using
appropriate tools.

quantify and map
economic value of
differences in fisheries

quantify and map
differences in
fisheries

Guantify and map
differences in risk
to fisheries
production

quantify and map

economic value of
differences in risk to
isheries productio

It is beyond the remit of this project to
discuss in detail how such valuation can be
made, but we present here some
considerations that will affect the way the
valuation is done.

quantify and map
differences in
water supply

quantify and map
economic value of
differences in water suppl.

Different types of values

quantify and map
economic value of
differences in crop
pollination

quantify and map
differences in crop
pollination

Different benefits (or beneficial processes)
correspond to different types of economic
values, which affects the methods used to
<a = Cee quantify their economic value. For
example, benefits obtained through direct, consumptive use (e.g., fisheries, wild meat,
medicinal plants) can be valued using market prices directly or through replacement costs;
non-consumptive use benefits such as nature tourism can be valued using travel costs; and
hedonic price methods can contribute to the evaluation of indirect use values such as water
purification (Turner et al. 2003).

In section 3.7 we discuss how the benefits/processes proposed for the Review relate to the
framework of Total Economic Valuation (Pearce & Turner 1990, Pearce & Moran 1994).

Importance of understanding benefit flow to the economic valuation

Benefits often flow from their point of production to users elsewhere (Figure 5). For
example, water purification takes place throughout a watershed, including areas that may
not be populated; the benefits from this process (clean water) are used in downstream
populated areas such as cities and agricultural fields.

Benefit flow has a very substantial effect on valuation because it influences the degree of
offer and demand for each benefit. For example, water prices are determined at the regional
scale because water flows within watersheds (Figure 5d). So, given similar demand, it is
expected that water will be cheap in areas where there are abundant supplies and expensive
in areas where water is scarce. Given the same supply, water will be more valuable if there

16

is local higher demand (for example, large expanses of agricultural fields). In contrast, the
value of carbon (for global climate mitigation) is the same worldwide. Indeed, the
sequestration of a tonne of carbon (or the avoidance of the release of one tonne of carbon)
creates the same level of benefit irrespective of where in the world it happens (Figure 5e).

The flow of services may be affected by one or more possible mechanisms: physical
processes (e.g., currents, winds, diffusion); biological processes (e.g. bee movements, fish
migration); and anthropogenic processes (e.g. trade, governance). The spatial configuration
of service flow falls into five general categories (Figure 5):

e Locally produced benefits: when the point of service production is the same as the
point of use (e.g., soil production);

e Omnidirectional neighbourhood benefits: when service use takes place within a
buffer areaa surrounding the point of production (e.g., pollination);

e Directional neighbourhood benefits: when service use takes place in the
neighbourhood of the area of production, but only in a given direction (e.g., storm
protection);

e Long-distance directional benefits: when service users are located far from the point
of production, with services flowing in specific directions (e.g., water provisioning
flowing downstream); and

e Globally-distributed benefits: when the service can be used anywhere irrespective of
the point of production (e.g. climate change mitigation by carbon sequestration).

a) tally produced d) Long-distance

€g_ Soil production directional benefits
eg. water provisioning

b) Omnidirectional
neighbourhood benefits
€.g. pollination

e) Globally distributed
benefits

¢) Directional €.g. carbon sequestration
neighbourhood benefits
e.g. stom protection

Figure 5 General categories of service flow in relation to spatial configuration (adapted from Fisher
et al. in press-a).

Quantifying and mapping benefit use
While benefit production is essentially an ecological process, benefit use is intrinsically

human-centred. Socio-economic data are therefore fundamental to quantifying and mapping
the economic value of variations in benefit production.

17

First, socio-economic data are key to understanding benefit flow for some ecosystem
services. Some anthropogenic processes such as the establishment of particular governance
systems (e.g. marine economic exclusive zones, territorial boundaries) put constraints to the
natural flow of benefits, while others (e.g. trade) create routes for their distribution.

Second, the use of benefits (underlying their economic valuation) naturally depends on the
spatial pattern of users. Individual humans are the ultimate users of all benefits that are
valued economically, although value may be through direct use (e.g. drinking water),
indirect use (e.g., crop irrigation water), or even through non-use (e.g. water affecting
habitat quality of species that have intrinsic value). Even within the same area, different
people may have different levels of reliance on ecosystem benefits (thus valuing them
differently). For example, poor farmers may depend more directly on benefits provided by
nearby forests than other sectors of society.

Third, socio-economic data are key to understanding what drives demand for the benefits
obtained from wild nature. The ultimate value of benefits services depend on how much
people are willing to pay, which in turn depends on variables such as the relative scarcity of
the benefit, the income of the users, and the extent to which the benefit may be replaceable
or not.

Finally, the costs of conserving the biodiversity and ecosystems underpinning the benefit
will depend substantially on the human pressure in the areas where those benefits are
produced.

Relevant socio-economic data to be gathered for the Review are likely to include, amongst
others: maps of human population density; geopolitical maps; maps of landuse (with
agricultural and urban areas); data on relative income; poverty maps; and data on trade.

Patterns of benefit use will evolve from the interaction between the patterns of service
production, the mechanisms of benefit flow, the distribution of users, and the relative
demand for the benefit across different users. For example, the benefits of water
purification by a forested area are used downstream, given the mechanism of flow for this
service (Figure 6). In mapping benefit use it also becomes clear that not all productivity is
necessarily used. For example, water purification benefits by a given forest are not used if
there is no-one downstream (Figure 6).

Understanding determinants of demand

The economic value of benefits results from the interactions between availability and
demand. Such value is therefore likely to be highly dynamic, as both availability and
demand change over time. Changes in availability may result from either changes in
productivity (including natural fluctuations but also declines caused by overexploitation of
the ecosystem) or changes in flow (e.g., changes in governance or in trade). Changes in
demand may be caused by changes in flow (e.g., when new roads create a market for wild
meat, section 4.8); socioeconomic changes (e.g., population growth, increased/decreased
affluence); sociological changes (e.g. changes in diet or in preferences); technological
changes (either by creating alternatives to the benefit, for example water treatment plants,
which reduces demand; or by increasing the accessibility of benefits, such as deep sea
fisheries, thereby stimulating increased demand).

18

forests

a :
rivers

SSS populated

a) Schematic
representation of
two partially
forested
watersheds

b) Benefit
production

c) Benefit
use

d) Economic
value of
benefit:
where used

e) Economic
value of
benefit:
where
produced

f) Economic
costs of
conserving
benefit:
where paid

g) Economic
costs of
conserving
benefit:
where
generated

Figure 6 Illustration of the different
types of maps of benefit production
and costs considered under the
framework. a) The scheme
represents two forest patches, two
river systems, and a set of populated
areas. The beneficial process ‘water
purification’ is used as an example.
b) Benefit production: water
purification takes place upstream, in
areas with natural vegetation, such
as forest patches. Riparian areas
(near the river) are particularly
important (darker shades
correspond to higher benefit
production). c) Benefit use: users of
clean water are downstream
populated areas and their
agricultural fields. Given that the
benefit flows along rivers, only
those populated areas downstream
from a forest patch benefit from the
purification service provided by
forests (darker shades correspond to
higher levels of use). d) Economic
value of the benefit, where used:
clean water only has an economic
value where used, with value
depending on demand. e) Economic
value of the benefit, where
produced: the overall economic
value of clean water, established
according to its use (d) can be
‘back-mapped’ into the area of
production (b). Areas where
production is higher are attributed a
higher economic value. Only areas
producing services that are being
used have economic value. f)

Economic value of conserving the benefit, where paid: conserving the forests that help to purify the
water has costs. For example, a protected area may be created to prevent logging. Part of the costs
may be shared by tax payers throughout the area (light red). However, populations near the forest
may incur other costs, such as the opportunity costs of not being able to log the forest or to convert
it to agriculture (dark red). g) Economic costs of conserving the benefit, where generated: the
overall economic costs of conserving the benefit (f) can be ‘back-mapped’ into the forest. Costs of
conservation (shades of red) are in this example higher near population centres, where there may be
more conflict with other forms of land use (and hence higher opportunity costs).

19

While many of these changes are somewhat unpredictable, a good understanding of what
drives demand (coupled with a good understanding of what drives availability) is
fundamental for being able to predict/model variations in the value of benefits over space,
for alternative states of the world.

Quantifying and mapping economic value for each benefit

A map of the economic value of a particular benefit (or beneficial process) needs to be
anchored by actual data points of the value of benefits across space. These may include, for
example, studies of the local market value of particular goods (e.g., wild meat) or of the
damage costs avoided of certain processes (i.e. carbon sequestration). These can then be
extrapolated across space based on the knowledge of the drivers of economic value
(demand, offer, flows) combining spatial information on production and use.

The quality of this map — the extent to which the numbers contained in it are reliable — will
of course reflect the quality of the knowledge gathered in each of the previous steps. In any
case, a basic map can be produced even with very little information (and indeed this has
been done before; Costanza et al. 1997). If a single data point is all that is available on the
economic value of mangrove for storm protection, for example, then a global map could
have all mangroves of the world coloured in the same way. It is, however, very likely that
substantially better judgements can be made based on the information gathered in the steps
described before. For example, we know that protection from physical storm damage only
benefits the neighbourhood of the mangrove, in a directional way depending on the wind
(Figure 5). Socio-economic data should highlight which of those areas are more likely to
suffer substantial financial damage from storms (e.g., urban centres, industrial areas).
Understanding the drivers of demand should help to understand how the monetary value of
the service varies with, for example, income.

The value of the economic benefits for each service can initially be mapped onto the point
of use, where benefits are enjoyed. For example, economic benefits of pollination would be
mapped on the relevant agricultural areas, economic benefits of storm protection on the
relevant population centres, and economic benefits of water purification on relevant
downstream populations or crop fields (Figure 6d). The same overall value can also be
‘back-mapped’ onto the area where the ecosystem service is initially produced (forest plots
where crop pollinators are based, mangroves that provide valuable storm protection, forests
that regulate water for downstream populations; Figure 6e). Only areas producing services
that are being used (and therefore that have economic significance) would be included, and
their relative value would be defined according to their contribution to the overall economic
value (Figure 6d).

Value over time: discount values

Ideally, a map of the global value of a given benefit should be more than a static picture of
value at a given time, but show the overall value accumulated into the future. This would
produce a more valuable indication of relative value of the underlying ecosystems. For
example, areas where natural resources are currently subject to overexploitation (e.g.,
overharvested fish stocks) may be currently yielding high economic value at the expense of
compromising future productivity. In contrast, in areas where exploitation is being managed
sustainably (e.g., through fishing quotas), current production may be lower but more likely
to be sustained into the future. Aggregating values over time will require economists to
make decisions about discount rates (levels, shapes, fixed or declining, etc). These could

20

potentially defer to the decisions made in the Stern Review (Stern 2007) over the same
issues.

Different measures of economic value

Economic value is frequently interpreted in terms of monetary value. However, a diversity
of other measures can be considered that offer different insights into the way in which
changes in wild nature affect human wellbeing. For example, economic value may be
quantified as the fraction of local income, rather than in absolute (e.g. US$) terms. This
would highlight areas where the local value of the benefit is low in absolute economic
terms but high in terms of livelihoods. For example, the absolute economic value of wild
meat is not particularly high but it contributes significantly to the protein intake and food
security of millions of people in Africa (section 4.8).

2.3.5 Quantifying and mapping the overall economic value of changes in benefits
derived from the policy action

Once converted into a common currency, combining the

quantify and map

economic value of information for all benefits and services provides an overall
differences in fisheries : . . . .
quantification of the economic value of differences in benefit
uantfy and map and service flow, and in risk, from implementing the policy
economic value of z “4: g
differences in risk to action (world B) or failing to do so (world A). This
fishenes productior = e ci 5
calculation may account for differences in purchasing power
eee ere parity. This information is quantified in a spatially-explicit
SiGe way, indicating in which parts of the world the net value of
Guantiy and map benefits increased or decreased.

economic value of
differences in crop
pollination

The spatially-explicit approach followed in the framework
(eS) means that trade-offs between benefits are effectively
accounted for. Indeed, some benefits are competing, rather
than additive, such that an increase in the value of one benefit
may come at the expense of a reduction in the value of other.
For example, the protection of a forest from logging may
ensure the continuation of clear water delivery, at the expense
of reducing water quantity (section 4.9). This is dealt with in this framework by being
spatially explicit, and by the way states of the world are defined. Most of the differences in
benefit production will be noticeable through changes in land use from one state of the
world to the other, and these will in many cases indicate which services are being increased
and which ones are being lost. For example, a conversion of forest to agriculture will
predictably increase the production of services associated with crops (e.g., soil, biological
control, pollination) but will reduce services associated with forest (e.g., timber
production).

‘quantify and map’
economic value of
differences in benefit
flow and risk

More complex interactions between services could in principle also be accounted for — as
long as they are explicitly recognised. For example, if recreational value in a given area
(say, a lake) is only possible if regulation of water quality is also in place, then maps of
benefits of the two services may be combined by making the value of the former service
conditional on the existence of the latter. It is therefore possible to account for leverage
amongst ecosystem services (Where the occurrence of one service unlocks the conditions
for the occurrence of another).

21

For ‘extractive’ types of benefits (e.g. fisheries, timber, wild meat), the production will
depend on whether the extraction of natural resources is taking place in a more or less
sustainable way. Again, this will need to be considered within each scenario. It might be
particularly interesting to compare scenarios of sustainable production and of
overexploitation. As discussed above, the overall value of benefit production will need to
be measured not just for a particular year but integrating the value of production over time.
A key decision (likely to affect the results of such comparisons) will be what discount rate
to apply.

2.3.6 Quantifying and mapping the costs of policy action

The benefits derived from the conservation of ecosystem service

production are of course only part of information needed to decide if

world B: such conservation makes economic sense. As established by the
with Potsdam mandate, these need to be weighed “versus the costs of
action effective conservation”.

Implementing a policy or set of policies for halting/reducing
biodiversity loss and ecosystem degradation will have costs. These
include direct costs (e.g. management of marine protected areas) as
well as opportunity costs (e.g., compensation for decommissioning
part of a fishing fleet). These costs are marginal, because they only
exist for the state of the world for which the action is implemented.
quantify and And they vary across space, and so should be quantified in a
map costs of spatially-explicit way.
action

In a few cases, the exact values of those costs will be well
inventoried. In most cases, though, the values and geographic spread
of the costs will need to be modelled from a collection of data points. For example, data on
running costs for a set of protected areas around the world can be used to generate a model
for predicting approximate management costs for areas for which no data are available
(Balmford et al. 2003, 2004). Creating those models requires the integration of biotic,
abiotic and socio-economic data. The separate report “Review of the Costs of Conservation
and Priorities for Action”, by Bruner, Naidoo and Balmford (also an output of this Scoping
the Science project), reviews the state of knowledge for different types of conservation
costs.

Conservation costs are initially mapped to the point where they are paid. For example, the
running costs of protecting a forested area (e.g., paying forest guards) may be incurred by
taxpayers within a given country, while the opportunity costs of not being able to log the
forest or convert it to agriculture are more likely to be incurred by populations in the
vicinity of the forest (Figure 6f). The value of costs can also be ‘back-mapped’ onto the area
where those costs are generated. In this example, the costs would be mapped onto the
forest, being higher in those areas where human pressure is higher, such as near population
centres, as this increases opportunity costs (Figure 6g).

Again, an ideal map of the global costs of conserving a given ecosystem service should be
more than a static picture of costs at a given point in time, but the overall cost accumulated
over time. This would allow for a distinction between areas where initial costs are high and
then decline (e.g., one-off costs of establishing a protected area) and areas where costs are

22

likely to increase over time (e.g., where human pressure is increasing and so are pressures
on natural habitats).

2.3.7 Quantifying and mapping the net economic consequences of policy action

ecceccecess,
of <x

‘quantify and map
economic value of
differences in benefit
flow and risk

quantify and H
map costs of oe '
e
.

action

how does it
affect regional
stability?

how does it how does it
affect global affect national
GDP? GDP?

evaluate socio-economic

consequences of action to
halt/reduce wild nature loss

The net economic consequences of the policy action being evaluated are quantified by
comparing the costs of the action with the economic gains from benefits and processes
obtained from the additional conservation of biodiversity and ecosystems. It is this
comparison that will provide the ultimate answer to the question of the Review: what are
the economic consequences of global biodiversity loss?

A key advantage of adopting the current framework to address this question is that it can
help explore spatial variation in the answer. Irrespective of whether the net global result is
positive or negative, there will always be winners (where there are increases in benefits
and/or decreases in costs) and losers (where there are decreases in benefits and/or increases
in costs) from the implementation (or lack thereof) of particular actions.

Recognising these patterns is essential for fully understanding the socio-economic
implications of particular actions. A diversity of policy-relevant questions can be addressed
with this information, besides the global aggregated result. The spatially-explicit nature of
the assessment will help answer questions such as:

- How are national economies likely to be affected? Are the poorest countries the
ones that benefit/lose the most from a particular action?

- How does the action (or failure to implement it) affect development goals, such as
the Millennium Development Goals? Does it contribute to alleviate poverty or
access to water?

- How does the action (or failure to implement it) affect regional stability? Are
livelihoods improved in areas currently under great socioeconomic strain or are they
likely to worsen?

- How does the action (or failure to implement it) affect equity? Are the winners
amongst the richest and the losers amongst the poorest, or vice-versa?

Understanding spatial variation in costs and benefits also allows for the exploration of
conservation trade-offs, highlighting for example regions that are conservation bargains

(where benefits from conservation largely exceed the costs) and therefore good investments
for immediate conservation.

2.3.8 Evaluating the adequacy/desirability of the policy action

From the analyses of this set of policy-relevant

questions is then possible to make a better judgement of
the overall socio-economic consequences of a particular
action or set of actions, and therefore whether the action
contributes to improve human wellbeing or not. This in
turn informs whether the policy action make sense from
ecological, economic, and social perspectives, and
therefore whether its implementation is recommended
or not.

If the action is implemented, an understanding of the
socio-economic impacts provides the opportunity for adding mechanisms that make the
action fairer and more effective. For example, market tools such as payments for ecosystem
services may be developed to internalise the full costs and benefits of conservation. These
can help ensure that conservation takes place where it is needed (e.g. payments for
ecosystem services from cities to rural areas) while reducing inequity and social injustice
(e.g. when costs of conservation are imposed on, or costs due to loss of biodiversity are
incurred by, local populations without adequate compensation).

2.4 Summary of key points from the conceptual framework

In summary, the conceptual framework relies on the spatial assessment of the variation in
the marginal benefits and costs of biodiversity and ecosystems conservation. Marginality is
essential because at all times the relevant question is what is the difference in benefits and
costs fromthe implementation, or not, of a particular policy package. Being spatially
explicit is vital because we need to know how costs and benefits vary across space.

The rationale and key characteristics of the conceptual framework are:

a. A Review of the economic consequences of the loss of biodiversity and degradation
of ecosystems (throughout referred to as wild nature loss) is not about quantifying
the overall value of wild nature to human wellbeing; such value is infinite, because
we cannot live outside of nature. Instead the Review is about quantifying the

marginal costs and benefits associated with the loss of ecosystems _and_their
biodiversity.

b. Quantifying marginal costs and benefits requires contrasting two well-defined
situations, which we term states of the world. A ‘state of the world’ is a particular
set of biotic and abiotic conditions, including aspects such as land cover, human
distribution, human activities, and conservation actions in place. States of the world
are the end products of scenarios.

c. The two states of the world being contrasted in each case need to be carefully
matched, such that one is the counterfactual of the other, by being equal in

everything else except the implementation or not of a set_of actions aimed at

24

reducing losses in wild nature losses. Many possible pairs of matching states of the
world can be contrasted, each contrast evaluating the economic consequences of a

specific set of policies. For example, the contrast between worlds with and without
an effective network of marine protected areas would be adequate for quantifying
the economic consequences (costs and benefits) of reducing the loss of marine wild
nature through protected areas.

d. Given two matching states of the world, the calculation of the economic
consequences of losing wild nature by failing to adopt the policies that distinguish
the two states requires calculating:

o The difference in the provisioning of benefits from wild nature (marginal
benefits; higher in the biodiversity friendly world; e.g., higher long-term

fisheries production in the state of the world with marine reserves);

o The difference in the costs associated with the biodiversity-friendly policy
measures (marginal costs; higher in the biodiversity friendly world; e.g.

opportunity and management costs of establishing marine reserves).

The net consequences of biodiversity loss are obtained by comparing the marginal
benefits with the marginal costs.

e. The quantification of the benefits and costs must be spatially explicit. These spatial
considerations make the framework a useful tool for addressing key considerations

in the evaluation and development of adequate policy measures, by:

o Allowing for the quantification of costs and benefits to be contextualised
more appropriately, thereby shedding light on how different policy options

affect development goals.

o Requiring the explicit statement of the assumptions being made when
extrapolating across heterogeneous landscapes using limited data.

o Enabling an understanding of the mismatches between winners and losers
from particular policy actions. This is fundamental for understanding social
impacts, equity/fairness issues, and issues of responsibility and governance,
as well as for the development of market tools that internalise the full costs
and benefits of conservation, ensure that conservation takes place where it is
needed and improves the effectiveness of policy actions while reducing
inequity and social injustice.

o Enabling the exploration of trade-offs, highlighting for example regions that
are conservation bargains (where benefits from conservation largely exceed
the costs) and therefore good investments for immediate conservation.

2.5 Participants

Authors

Andrew Balmford, Ana Rodrigues (University of Cambridge)

Rhys Green (University of Cambridge and Royal Society for the Protection of Birds)

Kerry Turner, Brendan Fisher (Centre for Social and Economic Research on the Global
Environment, University of East Anglia, UK)

Robin Naidoo (Conservation Science Program, WWF-US, USA).
Contributors

Katie Bolt, Martin Jenkins, Lera Miles, Matt Walpole (UNEP-WCMC)
Acknowledgements

We are grateful to the following for providing opportunities for stimulating discussions
in the development of this conceptual framework: participants in the “Conceptualising
ecosystem services and human well-being” (15 January, Department of Geography,
University of Cambridge; see Annex 2) in particular those who provided written
comments: Bill Adams and Bhaskar Vira (University of Cambridge), Nigel Leader-
Williams (University of Kent); partners in the Scoping the Science project: Patrick ten
Brink, Marianne Kettunen (IEEP), Leon Braat (Alterra); Aude Neuville and Patrick
Murphy (DGENV); members of the Working Group on the Review on the Economics
of Biodiversity Loss.

3 A STRATEGY FOR REVIEWING KNOWLEDGE ON THE LINKS
BETWEEN WILD NATURE AND HUMAN WELLBEING

3.1 The need for a strategy

The bulk of this report (sections 3 and 4) covers the most substantial task of the Scoping the
Science project, of providing “a coherent overview of existing scientific knowledge upon
which to base the economics of the Review, and to propose a coherent global programme of
scientific work, both for Phase 2 (consolidation) and to enable more robust future
iterations of the Review beyond 2010.”

This task entails reviewing the vast literature on the many mechanisms through which wild
nature contributes to human wellbeing, each typically comprising several mechanisms by
which changes in biodiversity and/or in the state of ecosystems have economic
consequences. Some of these mechanisms are very direct, for example a decline in fish
biomass resulting in the decline in food obtained from fisheries. Others are rather indirect,
for example a decline in forests affecting water flow, resulting in increased erosion,
resulting in sedimentation of coral reefs, resulting in fish population declines, resulting in a
decline in food obtained from fisheries.

Partitioning the vast diversity of links between wild nature and human wellbeing into
themes is a critical step for organising the review of those links. Such a stucture needs to be
robust, ensuring that it covers the most important links while avoiding overlap (and
therefore, economic double-counting).

This section details the rationale and the strategy followed for creating a robust
classification of the links between wild nature and human wellbeing, and a coherent
partition of our review of ecological knowledge into manageable thematic reviews that
provide an adequate platform for the economic evaluation.

3.2 The Millennium Ecosystem Assessment as a starting point

The Millennium Ecosystem Assessment (MEA 2005) firmly established the concept of
ecosystem services as an important model for linking the functioning of ecosystems to
human welfare benefits.

The MEA framework (Figure 7) was built to demonstrate the importance of ecosystems for
the constituents and determinants of human wellbeing and has been very successful in
doing so. The MEA thus becomes the groundwork for the evaluation of the economic
consequences of biodiversity loss. However, the MA itself was not developed as a valuation
exercise, and it has been pointed out that its framework is not directly fit for that purpose
(Boyd & Banzhaf 2007; Wallace 2007; Fisher et al. in press-a,b).

Here we build from the MEA to propose a classification of the links between wild nature
and human wellbeing. As clarified before, the term ‘wild nature’ is used throughout to refer
to biodiversity (both diversity, such as genetic diversity, and amount, such as biomass) and
ecosystems. We start by presenting the proposed classification, and then clarify how it

Dil

relates to the MEA framework. This is, naturally, work in progress: the MEA is still very
recent, and much more work will still build from it.

ECOSYSTEM SERVICES

Provisioning
FOOD
FRESH WATER
WOOD AND FIBER
FUEL

Supporting Regulating
NUTRIENT CYCLING CLIMATE REGULATION
SOIL FORMATION FLOOD REGULATION

PRIMARY PRODUCTION peo Benen
WATER PURIFICATION

Cultural

AESTHETIC
SPIRITUAL
EDUCATIONAL
RECREATIONAL

Figure 7 Classification of ecosystem services followed in the Millennium Ecosystem Assessment
(MEA 2005).

3.3. The need to avoid double counting

Ecosystem services have been defined in the literature in a diversity of ways (see Fisher et
al. in press-a for a review), including the “conditions and processes through which natural
ecosystems, and the species that make them up, sustain and fulfil human life” (Daily 1997),
“the benefits human populations derive, directly or indirectly, from ecosystem functions
(Costanza et al. 1997), and “the benefits people obtain from ecosystems” (MEA 2005).
These definitions reveal a mix between ecological functions (e.g. pollination, water
regulation) which are the means for the production of benefits, and the ends (benefits)
resulting from these processes (e.g., food from crops, drinking water) (Wallace 2007).

The MEA classification of ecosystem services has proved highly useful as an educational
and policy tool. However, as many economists have pointed out since its publication, it is
not fit for the purpose of economic evaluation (Boyd & Banzhaf 2007; Wallace 2007;
Fisher et al. in press-a). By mixing processes (means) and benefits (ends) it is particularly
prone to double counting. For example, the regulating service “water purification’ provides
added value to the benefits that this process underpins, including drinking water, food
(through purification of water used in crop irrigation and in rivers producing freshwater
fisheries) and cultural benefits (e.g., aesthetic, spiritual and tourism benefits from a clean
river). Valuing separately ‘water purification’ and ‘drinking water’, ‘food’, and ‘cultural

28

benefits’ results in double counting of the value of water purification. Another example of
double-counting in the MA classification is between pollination or pest regulation and food
provision, because the value of pollination and crop pest regulation manifests itself through
added food production. The relationship between our proposed classification and that
followed in the Millennium Ecosystem Assessment is clarified in section 3.7.

The Total Economic Value framework (Pearce & Turner 1990; Pearce & Moran 1994) also
has to be interpreted carefuly if it is to be used as a basis for economic valuation, as there is
much scope for double-counting particularly between direct and indirect use values. The
relationship between our proposed classification and the Total Economic Value framework
is clarified in section 3.8.

3.4 Proposed classification of ecosystem processes and benefits

The classification presented here was developed with the explicit objective of providing an
adequate basis for economic evaluation, within the conceptual framework for the broad
Review on the Economics of Biodiversity Loss (section 2).

We make an explicit distinction between processes and benefits. We consider two types of
ecological processes: ‘core’ ecosystem processes, the basic ecosystem functions (e.g.,
nutrient cycling, water cycling) supporting the processes that provide benefits to
humankind (corresponding to “intermediate services” in Fisher et al. in press-a,b); and
‘beneficial’ ecosystem processes, the specific ecosystem processes that directly underpin
benefits for humankind (e.g. waste assimilation, water purification; corresponding to “final
services” in Fisher et al. in press-a,b). The benefits are the end products of these beneficial
ecosystem processes (e.g., clean drinking water) (Figure 8; Table 1; these are also called
benefits in Fisher et al. in press-a,b).

Benefits are therefore the discrete products of ecosystem processes that directly impact
human wellbeing, ranging from food to spiritual fulfilment. These can, in principle, be
valued in monterary terms. In theory, it is possible to predict how each ecosystem process
contributes to the production of benefits, by considering the relationships between core and
beneficial ecosystem processes, and between the latter and benefits (Figure 9). For
example, pollination is a beneficial ecosystem process contributing to the production of
some biofuel crops. Pollination in turn is potentially regulated by several ecological
processes: evolutionary processes resulting in the diversification of plants and pollinators,
and their co-adaptation; animal-plant ecological interactions; production supporting the
populations of pollinators and the other species they depend on (e.g. forest trees).

CORE ECOSYSTEM
PROCESSES

e Production

e Decomposition
e Nutrient cycling
e Watercycling

e Weathering/
erosion

e Ecological
interactions

e Evolutionary
processes

BENEFITS

* Food
o Crops
o Livestock
o Capture fisheries
o Aquaculture
o Wild foods

Fresh water
o Drinking
o Industry
Oo 5
Raw materials
Timber
o Fibres from crops/livestock
o Fibres from wild species
o Synthetic materials

fo)

e Energy
o Biofuels
o Coal/firewood
o Dung
o Working animals
o Hydroelectric energy
ro]

Property
°o Private property
o Infrastructure

ie)

Physical wellbeing

o Synthetic medicines

o Cultivated medicines
Medicines from wild species
Avoidance of injury
Avoidance of pollution
Avoidance of infection
Physical exercise

Psychological wellbeing
Tourism
Recreation
o Spiritual/cultural wellbeing
o Aesthetic benefits
o Nature watching
o Pets, garden plants

e Knowledge
o Research
o Education

io}

e Currently unknown
benefits

Figure 8 Classification of ecosystem processes and benefits followed in this study (lists of

processes and benefits are not exhaustive).

Table 1 Types of core and beneficial ecosystem processes, and of benefits (not an exhaustive list).

CORE ECOSYSTEM PROCESSES: basic ecosystem processes supporting ecosystem
services

Production: Production of plant and animal biomass.

Decomposition: Reduction of the body of a formerly living organism into simpler forms of
matter.

Nutrient cycling: Cycle by which a chemical element or molecule moves through both biotic
and abiotic compartments of ecosystems (e.g. nitrogen cycle, phosphorus cycle, carbon cycle).

Water cycling: Cycle of water through both biotic and abiotic compartments of ecosystems.

Weathering/erosion: Weathering is the decomposition (in sitw) of rocks, soils and their
minerals through direct contact with the atmosphere. Erosion involves the movement and
disintegration of rocks and minerals by agents such as water, ice, wind and gravity.

Ecological interactions: Inter- and intra-specific interactions between organisms (e.g.,
predation, competition, parasitism, and animal-plant interactions such as pollination).

Evolutionary processes: Genetically-based processes by which life forms change and
develop over generations (inc. evolution, speciation, adaptation).

BENEFICIAL ECOSYSTEM PROCESSES: ecosystem processes that directly underpin
benefits to people

Biomass production: primary: Production of plant biomass.
Biomass production: secondary: Production of animal biomass.

Pollination: Pollen transport (particularly by organisms). [Seed and fruit dispersal may also
be considered]

Biological control: Inter- and intra-specific interactions resulting in reduced abundance of
species that are pests, diseases or invasives in a particular ecosystem.

Other ecological interactions: Other inter- and intra-specific interactions, for example
competition and predation.

Formation of species habitat: Formation of the physical properties of the habitats necessary
for the survival of species (e.g., canopy structure in forests).

Species diversification: The production of genetic diversity across species.
Genetic diversification: The production of genetic diversity within species.

Waste assimilation: Removal of contaminants from the soil in an ecosystem (inc. through
biological processes such as decomposition or assimilation).

Soil formation: Process by which soil is created (including changes in soil depth, structure
and fertility).

Erosion regulation: Control of the processes leading to erosion (e.g. by controlling the effects
of water flow, wind or gravity).

Formation of physical barriers: Formation of structures that attenuate the energy of (or
block) water or wind flow (e.g., mangroves, dunes, forests).

31

Formation of pleasant scenery: Formation of landscapes that are attractive to people.

Air quality regulation: Removal of contaminants from air flowing through an ecosystem
(inc. through physical processes such as filtration or biological processes such as
decomposition or assimilation).

Regional/local climate regulation: Modulation of regional/local climate (e.g., of temperature,
or humidity).

Water regulation (timing): Regulation of the timing of water flow through an ecosystem
(e.g., attenuation of floods/droughts).

Water purification (quality): Removal of contaminants from water flowing through an
ecosystem (inc. through physical processes such as filtration or biological processes such as
decomposition or assimilation).

Water provisioning (quantity): Changes in the quantity of water flowing through an
ecosystem.

Global climate regulation: Modulation of global climate and ocean acidity through changes
in the concentration of greenhouse gases in the atmosphere.

Currently unknown beneficial processes: the possibility that wild nature contributes to our
current and/or future welfare in ways we currently do not realise. For example, the
contribution of forests to the regulation of global climate has only very recently been realised
as a beneficial process.

BENEFITS: the products of ecosystem processes that directly impact human
wellbeing (we are specifically interested in understanding the role of wild nature in
providing these benefits)

Food:
e From crops (including orchards, mushroom production, cultivated algae);
e From livestock (including poultry);
e From capture fisheries (marine and freshwater);
e From aquaculture;
e Other wild foods (including wild meat, mushrooms, invertebrates, etc);
e
Freshwater (for direct consumption; excludes irrigation water, covered in crops):
e Drinking water;
e Water for industry;
°
Raw materials:
e Timber (from natural forests and from plantations);
e Fibres from domestic plants (e.g., cotton), or from domestic animals (e.g., wool);
e Fibres from wild plants (e.g., rattan), or from wild animals (e.g., hides);

e Synthetic materials copied from/inspired by natural products;

32

Energy:
e Biofuels (e.g., palm oil, algae) from domestic plants;
e Coal/firewood from wild plants;
e Dung from livestock;
e Working animals (e.g. oxen, llama);
e Hydroelectric energy;
e
Property:
e Private property value and condition;
e Infrastructure condition (e.g., hospitals, factories);
e
Physical health:
e From synthetic medicines copied from/inspired by natural products;
e From cultivated medicines;
e From medicines harvested from wild species;
e By avoiding injury (e.g. from natural hazards);
e By avoiding pollution (e.g., air pollution);
e By avoiding contamination (e.g., contaminated water);
e By stimulating physical exercise (e.g., hiking, diving).
e
Psychological wellbeing:
e From tourism;
e From other recreation (e.g., hiking, diving);
e Through spiritual/cultural wellbeing (e.g., sense of wonder from nature);
e Through aesthetic benefits (e.g., pleasure to watch a beautiful landscape);
e From nature watching (e.g., bird watching, coral fish watching);

e From garden plants and pets;

Knowledge:
e Through research of the natural world;
e Through education about the natural world.

Currently unknown benefits: the possibility that wild nature provides/will provide benefits

currently unknown (e.g., algae now considered a promising biofuel).

33

CORE ECOSYSTEM Yn
PROCESSES }

oe
« Decomposition \

Weathering/
erosion

Figure 9 Core and beneficial ecosystem processes underpinning the provisioning of a biofuel crop

(lists of processes and benefits are not exhaustive).

34

Se IOS

°
°

A

fo)

fou Tc pagote fc igs 2

G

12)

Oo

BENEFITS
Food
Crops
Livestock
Capture fisheries
Aquaculture
Wild foods:

Fresh water
Drinking
Industry

Raw materials
Timber
Fibres from crops/livestock
Fibres fromiwild species
Synthetic materials

)

Coal/firewood

Dung

Working animals
Hydroelectric energy

Property
Private property
Infrastructure

Physical health

Synthetic medicines
Cultivated medicines
Medicines from wild species
Avoidance of injury
Avoidance of pollution
Avoidance of infection
Physical exercise

Psychological wellbeing
Tourism
Recreation
Spiritual/cultural wellbeing
Aesthetic benefits
Nature watching
Pets, garden plants

Knowledge
Research
Education

Currently unknown
benefits

3.5 Partitioning the links between wild nature and human wellbeing as a basis for
the thematic reviews

Ideally, we would have liked to evaluate the state of knowledge on the links between wild
nature and the production of each benefit amongst those listed in Figure 8. In practice, this
was not possible within the time frame of this project, and it is also unlikely that it could be
done in Phase 2 as well. We have therefore bundled these links into a smaller, but hopefully
manageable, number of thematic reviews.

Firstly, we considered general categories of benefits, rather than the more detailed ones in
Figure 8 and Table 1. These general categories were defined in terms of the way the
benefits are produced (e.g. through cultivation, through fisheries) rather than by type of
benefit (food, water, energy, etc.). That is, we grouped benefits that we assumed would
have a similar production function (Figure 3). The assumption was that benefits that are
similarly affected by ecosystem processes are also likely to be similar in their sensitivity to
changes in biodiversity and ecosystems. For example, we grouped all benefits produced
through cultivation (e.g., food crops, fibre crops, biofuel crops), because these are likely to
be similarly affected by ecosystem processes such as biological control, pollination, water
regulation and erosion regulation. If, in contrast, we had grouped benefits by main type
(food, water, energy, etc.; Figure 8) we would have had to consider together benefits
produced in quite distinct ways (e.g. food produced through crops and through marine
fisheries; energy produced through hydroelectric power and through biofuels). The
categories of benefits considered (rows in Figure 10) are:

e Crops: including for food (e.g., wheat, potatoes), fibres (e.g. cotton, linen), biofuels
(e.g. palm oil, sugar cane), timber and paper pulp production (e.g. pine and
eucalyptus plantations), ornaments (e.g. flowers), and stimulants (e.g. coffee,
cocoa). Benefits are mainly in terms of food, but also raw materials, energy
(biofuels), and psychological wellbeing (stimulants and ornamentals).

e Livestock: includes all domesticated animals raised for the production of food
(including eggs, milk), fibres (e.g., wool), or energy (dung and working animals).

e Marine fisheries: includes benefits extracted from animals (including invertebrates
such as crustaceans and molluscs) harvested from the seas and oceans, including
from mariculture. Benefits are mainly food (including indirectly through fishmeal),
but also include psychological wellbeing (recreational fisheries) and possibly
physical health (e.g. fish liver oil as a food supplement).

e Inland fisheries: includes benefits extracted from animals (including invertebrates)
harvested from freshwater systems, including from freshwater aquaculture. Benefits
are mainly food, but also psychological wellbeing (recreational fisheries).

e Wild animal products: includes benefits extracted from wild harvested animals
(including invertebrates), Benefits are mainly food, but also psychological
wellbeing (recreational hunting), raw materials (hides, fur) and physical wellbeing
(medicinal value).

e Drinking and industry water: fresh water that is directly consumed by people or
as a raw material in industry.

35

e Hydroelectric energy: the production of energy through the movement of water.

e Wild plant fibres: fibres harvested from wild plants, including timber, paper pulp,
rattan and bamboo from natural forests, for raw materials and energy (firewood).

e Wild medicinal plants: plants with medicinal value harvested from the wild,
providing physical health as benefits.

e Nature-related outdoor activities: how wild nature contributes to benefits such as
human health, psychological wellbeing and education through outdoor activities
such as nature tourism, hiking, cycling, diving, camping, species-watching, use of
urban parks, and gardening.

e Avoidance of injury and property loss: avoidance of personal injury or property
loss, for example through the prevention of hurricanes and mudslides. Benefits are
mainly in terms of physical health (avoided injury) and property (avoided property
losses), but also psychological wellbeing (sense of security).

e One-off use benefits: how wild nature contributes to health, psychological
wellbeing, physical comfort, and knowledge, by maintaining the biological diversity
from where ideas, chemical compounds, and images can be sourced. This only
requires one-off use of nature, as the elements are initially obtained from wild
nature but subsequently propagated outside of it. They include pharmaceuticals
compounds, raw materials and ideas inspired by/copied from nature, as well as
photography, films and art based on/inspired by nature.

e Non-use benefits: non-material benefits, in which appreciation of nature (species
and landscapes) results in improved psychological wellbeing, knowledge and social
relations without a direct use of biodiversity. These may translate into cultural
diversity and heritage, spiritual and religious values (inc. sacred species and
groves), knowledge systems, educational values, aesthetic values, social relations,
and sense of place (MA 2005). The benefits they deliver are mainly in terms of
psychological wellbeing (e.g. sense of wonder).

e Unknown benefits: present and future contribution of wild nature to human
wellbeing through benefits that are currently not realised. While these values are
difficult or even impossible to measure, they may be very substantial to our
wellbeing. By definition, they may contribute to all other types of benefits.

We would have liked to focus on these groups of end benefits as the basis for the economic
valuation. Focusing on benefits prevents double counting, as the value of different
ecosystem processes would be partitioned according to their contribution to each of the
final benefits (e.g., part of the value of water regulation would be attributed to drinking
water, part to crop production). In practice, we were unable to follow this strategy. Indeed,
while in some cases, the ecological knowledge is aligned with the production of benefits
(e.g. marine fisheries; rows in Figure 10), in others the literature focuses on processes (e.g.
water regulation; columns in Figure 10). We therefore partitioned our task into thematic
reviews that are a mix of benefits and processes (Figure 10). By plotting the links between
benefits and processes onto a matrix (Figure 10), we were able to group them in a way that
is a compromise between theoretical ideal and practical reality, while explicitly avoiding
double-counting.

36

FOOD

FRESHWATER

RAW MATERIALS

ENERGY

PROPERTY

PHYSICAL HEALTH
PSYC. WELLBEING

KNOWLEDGE

i <
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Crops

Livestock
Marine
fisheries

Inland
fisheries

Wild animal
products

Drinking and
industry water

Hydroelectric
energy

Wild plant
fibers

Wild medicinal
plants

Nature-related
outdoor activit
Avoid. injury,
property loss

One-off use
benefits

Non-use
benefits

Unknown
benefits

Figure 10 Review themes in this project (1 to 17), and how they relate to beneficial ecosystem processes (columns) and benefits (rows). See text details on

each number. Grey bars on the left indicate main types of benefit. Each review theme is represented by a block with a blue border. Colours indicate an
estimate of the importance of the link between each ecosystem process and each type of benefit: red — very important; orange — important; yellow — somewhat
important; white — relatively unimportant.

37

For example, we considered global climate regulation (including regulation of ocean
acidity) as a single (‘vertical’) theme, rather than considering it within each of the relevant
benefits (e.g. a part of crop production, of avoidance of injury and property loss, of the
production of marine fisheries; Figure 10). We decided to do so because the Stern Review
(Stern 2007) has, effectively, already valued the consequences of avoiding climate change
by considering its effects on each of a diversity of benefits, including food crops, injury and
property damage, and marine fisheries. Stern did not include all the possible links between
climate change and human wellbeing (e.g., how climate change affects recreation, tourism,
research and education, spiritual and cultural wellbeing), but we recommend that it would
be a better investment of resources in Phase 2 to build from the Stern Review rather than to
repeat their analyses (and invest those resources in any of the other themes, which are much
less advanced in terms of valuation). In order to avoid double-counting, the effects of
climate change on benefits have been excluded from all the other themes (Figure 10).

3.6 Thematic reviews

Overall, we considered 17 thematic reviews (Figure 10). Some of these have the same titles
as the benefit categories listed before, but often the review had a narrower focus/definition,
as explained:

e Wild crop pollination (1): how wild nature contributes to crop yields through wild
(unmanaged) pollination (section 4.1).

e Biological control in crops (2): how wild nature contributes to crop yields through
the control of crop pests and diseases (section 4.2).

e Genetic diversity of crops and livestock (3): how wild nature contributes to
agriculture and livestock production by maintaining a diversity of populations of
wild relatives and varieties (section 4.3).

e Soil quality for crop production (5): how wild nature contributes to crop yields by
contributing to soil formation and to preventing erosion regulation (section 4.4).

e Livestock (5): we focused specifically on the way wild nature contributes to
livestock production through the provision of natural grazing and browsing areas
(rangelands) (section 0).

e Marine fisheries (6): we focussed specifically on capture fisheries (mainly for
food), with some considerations on recreational fisheries (section 4.6).

e Inland fisheries (7): we focussed on capture fisheries and aquaculture (mainly for
food), with some considerations on recreational fisheries (section Error! Reference
source not found.).

e Wild animal products (8): we focused on wild meat, with some considerations on
recreational hunting (section 4.8).

e Fresh water provision and regulation (9): how wild nature affects freshwater
quantity, timing and quantity (section 4.9).

38

Wild plant fibres (10): we focussed on timber production (section 4.10).

Wild medicinal species (11): how wild nature contributes to human health through
the provision of a diversity of harvestable medicinal plants (section 4.11).

Nature-related outdoor activities (12): how wild nature contributes to benefits
such as human health, psychological wellbeing and education through outdoor
activities such as nature tourism, hiking, cycling, diving, camping, species-
watching, use of urban parks, and gardening (section Error! Reference source not
found.).

Natural hazard regulation (13): how wild nature contributes to avoid human
injury and property loss by avoiding or mitigating the effects of natural hazards,
including coastal storms, hurricanes, floods, avalanches, mudslides (section 4.13).

One-off biodiversity use values (14): how wild nature contributes to health,
psychological wellbeing, physical comfort, and knowledge by maintaining the
biological diversity from where ideas, chemical compounds, and images can be
sourced. We focused on pharmaceutical compounds (section 4.14).

Non-use values (15): non-material benefits, in which appreciation of nature (species
and landscapes) results in improved psychological wellbeing, knowledge and social
relations without a direct use of biodiversity. These may translate into cultural
diversity and heritage, spiritual and religious values (inc. sacred species and
groves), knowledge systems, educational values, aesthetic values, social relations,
and sense of place (MEA 2005). The benefits they deliver are mainly in terms of
psychological wellbeing (e.g. sense of wonder) (section 4.15).

Global climate regulation (16): how wild nature contributes to human wellbeing
by contributing to climate regulation (concentration of greenhouse gases in the
atmosphere). We focused on terrestrial systems (section 0).

Unknown benefits or processes (17): wild nature may potentially contribute to our
future welfare in ways currently not realised, including both through ecosystem
processes not currently known or valued (e.g., carbon storage and sequestration has
only recently been identified as a ecosystem valuable process) and through benefits
currently not predicted (e.g., algae are just starting to be considered as a promising
biofuel). While these benefits are difficult or even impossible to measure, they may
be very substantial (section 4.16).

In addition to these main review themes, we considered three cross-cutting themes:

Resilience: review the evidence for how wild nature contributes to the resilience in
the provisioning of each of the benefits and services described in points | to 17 (this
was covered under each main theme).

Scenarios: brief overview of main tools available for generating scenarios that
could be useful in Phase 2 (section 4.17). Under each main theme there is also a
brief description of the main type of information that scenarios would need to
produce.

39

e Prioritisation: a systematic assessment of the relative priority of the recommended
analysis under the main themes for Phase 2 of the review (and beyond) based on a
combination of predicted feasibility (given the state of ecological knowledge) and
importance (how they are likely to affect the results of the valuation) (section
4.18.1).

3.7 Relationship between the proposed classification and the Millennium Ecosystem
Assessment framework

The MEA followed a classification of ecosystem services into supporting, provisioning,
regulating and cultural (Figure 7). These types of services map into our proposed
classification as illustrated in Figure 11. Essentially, the MEA’s supporting services
correspond mainly to our ‘core’ ecosystem processes, although they also include some
‘beneficial’ services. The regulating services essentially correspond to our ‘beneficial’
processes, and provisioning and cultural services tend to correspond to our benefits.

The match is not perfect: the MEA provisioning service “genetic resources’ (underpinned
by our processes ‘genetic diversification’ and ‘species diversification’) is spread across a
diversity of benefits such as crop and livestock genetic diversity contributing to food
production, diversity of wild pollinators contributing to crop yields, and diversity of wild
species contributing to tourism. The MEA regulating service ‘natural hazard regulation’ is
partially covered by our process “fermation of physical barriers’, but also by water, climate
and erosion regulation. The benefits provided by these services include the avoidance of
personal injury, of damage to property and infrastructure, of damage to crops, and of
damage to tourism.

40

BENEFITS

BENEFICIAL
ECOSYSTEM PROCESSES

CORE ECOSYSTEM
PROCESSES

Biological cont
Other ecological interactions
e Formation of species habitat

o Working animals
o Hydroelectric energy

e Weathering/
erosion

°
e Pri

i

e Ecological
interactions

ell

e Evolutionary Physical health

processes

clit julati

e Currently unknown beneficial
processes

Psychological wellbeing

Correspondence with the MA classification
ee) Supporting services

eee Provisioning services

{HNN Provisioning services (partial match)

Rest Regulating services

A Regulating services (partial match)

.
ee Cultural services K

Currently unknown
benefits

Figure 11 Correspondence between the classification followed in this review (into core ecosystem
processes, beneficial ecosystem processes, and benefits) and the classification followed by the

Millennium Ecosystem Assessment (into supporting services, provisioning services, regulating
services, and cultural services).

4]

3.8 Relationship between the proposed classification and the Total Economic Value
framework

While the MEA is still relatively recent, and few economic papers have been published on
it, a longer and well-established approach for classifying the value of wild nature is the
Total Economic Value (TEV) framework (Pearce & Turner 1990, Pearce & Moran 1994;
Figure 12).

Here we clarify the relationships between the review tasks and the TEV framework (Figure
13). Throughout, we use the definitions in Defra (2007) for each type of value.

| Total Economic
Value

Non-use
value

RS SENTEN ES epson i For others Existence |
use value
Direct use | Indirect use Altruism Bequest |

J ges
ee

Figure 12 Total Economic Value framework (adapted from Defra 2007).

Direct consumptive use values

Direct consumptive use values are those obtained when individuals make actual or planned
use of an ecosystem by extracting resources from it. Amongst the benefits/processes we are
reviewing, this corresponds to those where there is direct harvesting from nature: livestock
grazing in natural areas (theme 5), marine and inland fisheries (including recreational
fishing; 6 and 7), harvesting of wild animal products (including recreational hunting; 8),
harvesting of wild plant fibers (10), and harvesting of medicinal plants (11).

Direct non-consumptive use values

Direct non-consumptive use values are defined as being obtained when individuals make
actual or planned use of an ecosystem without extracting any element from it. Nature
tourism and other forms of outdoors recreation related to nature are traditionally considered
within this category (e.g. Rockel & Kealy 1991), and we followed that approach (theme
12), although it should be acknowledged that these activities may have substantial impacts
on the places visited, even if not harvesting resources from them.

c
s
=
s
<=
=
5
a

=
=
5
z

Blomass production:

primary
Biomass production:

secondary
Biological control
Other ecological
Waste agsimilation
Erosion regulation
physical barriers
pleasant scenery
Water regulation
(timi

Interactions
species habitat

Pollination
Formation of
diversification
Genetic
diversification
Soll formation
Formation of
Formation of
Air purification
(quantity)
regulation
Unknown
processes

Spec!

"|| Water provisioning

~— || Global climate

= a= ——
Livestock

Marine
fisheries

Inland
fisheries

Wild animal
products

Orinking and
industry water {
Hydroelectric

energy 17

Wild plant
fibres

Wild medicinal
plants

Nature-related
outdoor activit

Avoid. injury,
property loss

Non-use
benefits

Unknown
benefits

17
| Total Economic } Non-use Option

Figure 13 How each main review theme relates to the Total Economic Value framework.

Indirect use values

Indirect use values refer to situations when individuals benefit from ecosystem processes
underlying the provision of direct benefits. For example, crop pollination (theme 1) is not
used as a benefit per se, but as a process towards the increased provision of crop yields.
We also included in this category: biological control in crops (2), maintenance of soil
quality for crop production (4), fresh water provision and regulation (9), global climate
regulation (9), and the regulation of natural hazards (13).

Option values
Option values are the values that people put on the option to use a resource in the future,

even if they are not current users. These future uses may be either direct or indirect. Option
values can be thought of as a form of insurance, but the economic literature is divided over

43

whether such values are a true component of TEV (Freeman 1993). Strickly speaking, we
should treat them as a separate concept related to a precautionary policy stance (theme 17)

Wholly within option values is genetic diversity of crops and livestock (theme 3), which
can be considered a form of insurance in the case of future need to add genetic diversity to
existing crop varieties or livestock breeds, for example in case of emergence of a new pest
or disease. Also within option values are benefits from one-off use (14), the set of the ideas,
chemical compounds, and images initially obtained from nature but subsequently
propagated outside of it. Indeed, by definition the only way in which wild nature is valuable
for these is through predictions of their future value. Data on the economic value of
previously extracted ideas, compounds and images can be used to obtain estimates of likely
future value, but not for measuring directly the value of existing wild nature (as those past
ideas, compounds and images are now fully independent from nature).

All tasks covering use values (direct and indirect) listed previously can potentially have a
component of option value. For example, not all known marine fish stocks need to be
exploited today (even sustainably). One possibility is that some stocks may be exploited
immediately and some left untouched as options for the future. These two types of values
are likely to be monetised differently in the subsequent economic valuation.

As an example of option values for indirect use, natural water purification processes have
no use value if nobody is using the water, for example in a region with no inhabitants.
Nonetheless, we may want to attach value to those processes (and the ecosystems
generating them) as providing options for future use.

Option values also apply to non-consumptive uses: we may want to set aside areas as
valuable for future tourism, even if currently they are not used as such (e.g. deep sea
systems may become attractive for tourism given future technological developments).

All considerations of resilience discussed within each task refer to option value, as they
relate to the concern of ensuring future use. For example, the diversity of marine functional
groups provides resilience to fisheries production (see section 4.6), and so there is value in
the conservation of such variety as a form of insurance against future changes.

Non-use value

Non-use value is derived simply from the knowledge that the natural environment is
maintained. There are three main components:

- Bequest value: where individuals attach value from the fact that a given natural resource is
passed on to future generations;

- Altruistic value: where individuals attach values to the availability of a given natural
resource to others in the current generation;

- Existence value: where individuals attach value simply from the existence of a resource,
even though the individual has no actual or planned use of it.

Theme 15 (non-use values) relates principally to existential non-use values.
Bequest values — which directly address issues of intergenerational equity — are implicit or

explicit in other aspects of the Review on the Economics of Biodiversity Loss. In particular,

44

we recommend that the comparison between states of the world for a given resource (e.g.
fisheries production, wild meat) is based on the capacity of ecosystems for the sustainable
production of the resource, rather than based on current flows that may not be sustainable
(see section 2.3.4). In valuation terms, bequest values and the associated concerns about
intergenerational equity will be explicitly incorporated in the decision of which discount
rate to apply when valuing future resources, as was also the case with the Stern Review on
the Economics of Climate Change (Stern 2007). They can also be addressed in the
estimation of values (based on contingent valuation methods).

Altruistic values, on the other hand, include (amongst others) concerns for intragenerational
equity. The emphasis placed on the spatial distribution of costs and benefits (see section
2.2.4) is aimed at addressing such concerns, allowing for the redistribution of benefits for
example through the establishment of payments for ecosystem services. Some of these are
not altruistic, though, as such redistribution may directly benefit the ‘donors’ by increasing
the likelihood of long-term provision of the resource or by reducing the deleterious social
effects of inequity (for example in terms of security). Purely altruistic values nonetheless
exist and can be integrated in the valuation component of the Review. But the only way to
assign monetary values to non-use motivations is via so-called stated preference
survey/choice-based methods. It is still an open research question as to how adequately
values such as existence value can be meaningfully expressed in monetary terms (Heal
2000; Turner et al. 2003; Barbier 2007). To the extent that non-use values are not captured
via economic analysis the TEV and the Total Ecosystem Value (TSV) will diverge with
TEV < TSV.

3.9 Participants
Authors
Andrew Balmford, Ana Rodrigues (University of Cambridge)

Rhys Green (University of Cambridge and Royal Society for the Protection of Birds)

Kerry Turner, Brendan Fisher (Centre for Social and Economic Research on the Global
Environment, University of East Anglia, UK)

Robin Naidoo (Conservation Science Program, WWE-US, USA).
Acknowledgements

We are grateful to the following for providing opportunities for stimulating discussions
in the development of this section: partners in the Scoping the Science project: Patrick
ten Brink, Marianne Kettunen (IEEP), Leon Braat (Alterra); Aude Neuville and Patrick
Murphy (DGENV); members of the Working Group on the Review on the Economics
of Biodiversity Loss.

45

4 THEMATIC REVIEWS

Given time limitations, we were unable to fully develop all the themes listed in the previous
section. As a result, themes have been analysed at different levels of depth, from fully
developed thematic reviews that received expert support, to not developed at all. The level
of each review is indicated at the start of their respective section.

We tried to answer each of the following questions in each thematic review. However, for
the least detailed reviews that was not possible. At very least, we tried to obtain an answer
to the questio: Can we quantify and map the global provision of this benefit/process
and how it might change? We aimed to evaluate how far scientific knowledge is from
being able to produce a spatially-explicit quantification of the production of the benefit (e.g.
fisheries production) or of the function of the beneficial process (e.g., added value of wild
pollination for crop yields), that can act as a basis for the economic valuation contrasting
two states of the world. The information gathered was then used to guide the prioritisation
of recommendations for Phase 2 (section 4.18).

The questions are:

e Why is this benefit/process important for human wellbeing? A brief review of the
information supporting the relevance of each benefit/process for human wellbeing.
Any monetization presented here is to support the importance of the benefit to
human welfare, rather than to quantify the importance of wild nature for the
provision of the benefit. For example, the information that “floods affected 3.5
million people in Cambodia (with associated costs of US$145 million) in 2000”
should not be interpreted to mean that conserving wild nature could have prevented
those floods and therefore avoided those costs.

e What are the overall trends in the provision of this benefit/process? Review of
state of knowledge on the trends of the provision of the benefit/function.

e How is the provision of this benefit/process affected by changes in wild nature?
Here we describe the links between wild nature and the production of the
process/benefit. This section is tailored to each review task, discussing the most
relevant aspects of wild nature in each case. These may include ‘diversity’, such as
species richness, and ‘amount’, such as biomass, as well as ecosystem condition.
We specifically looked for information on which each of these components is
particularly important for resilience in the provision of the benefit/process. And we
specifically tried to understand what the relationship is between habitat area and the
provision of the benefit/function, which is important for estimating the likely
consequences of habitat loss.

e What are the main threats to the provision of this benefit/process? Information on
the drivers of loss is key to the development of scenarios that are fit-for-purpose
(section 2.3.1). Indeed, these will only be accounted for in the Review on the
Economics of Biodiversity Loss if they are different in business-as-usual and
biodiversity-friendly states of the world. The information on these threats is also
fundamental to calculating the costs associated it conservation, as costs will vary
depending on the type and intensity of threat (see separate report on “Review of the

46

Costs of Conservation and Priorities for Action”, by Bruner, Naidoo and Balmford,
also an output of this Scoping the Science project).

Are abrupt changes likely in the provision of this benefit/process? Here we were
aiming to understand if there is evidence that the provision of this benefit/ process
may be subject to thresholds/tipping points in the foreseeable future (in the
questionnaire to experts we used 2025 as the foreseeable future data). By
thresholds/tipping points we refer to a situation when a small (anthropogenic)
change in nature may have disproportionate effects on the provision of the
benefit/process. We drew from the information in the previous sections and on the
opinion of experts to make a prediction of the likelihood of thresholds or tipping
points. The result is a qualitative (and inevitably subjective) assessment of relative
risk of abrupt changes.

Can we quantify and map the global provision of this benefit/process and how it
might change? Here we evaluate how far scientific knowledge is from being able to
produce a spatially-explicit quantification of the production of the benefit (e.g.
fisheries production) or of the function of the beneficial process (e.g., added value
of wild pollination for crop yields), that can act as a basis for the economic
valuation contrasting two states of the world. At one extreme, a good model already
exists (a well-developed production function) and maps can easily be generated
from available data. At the other extreme, no good model and/or good data exist that
could form the basis of a global map. In most cases the state of knowledge was
somewhere in between, with at least a first cut being possible within one year by
building from existing studies. We also tried to obtain information on: the main
gaps in data/knowledge; what would be needed from scenarios for the model
proposed; who could potentially do the analysis; and what the required effort would
be (in researchers-months).

Insights for economic valuation The purpose of this section is to maximise the
value of our work as a basis for the economic valuation, helping readers to interpret
our recommendations by clarifying what our assumptions were on how the
ecological modelling and the valuation fit.

Some key resources Here we list key resources that we came across in our review
and which are likely to prove useful in Phase 2.

Participants The list of authors, contributors, reviewers and acknowledgements (see
Annex 3).

47

4.1. Wild crop pollination

This section is a fully developed review, including contributions by experts in the field, who
subsequently reviewed the text.

4.1.1 Why is this process important for human wellbeing?

Many plant species benefit for their reproduction on animals that transport pollen between
flowers. This biotic pollination is typically done by insects (particularly bees; also flies,
beetles, moths, butterflies and wasps), but in some species is performed by vertebrates
(particularly birds and bats). Pollination is therefore a key ecological process service, upon
which both natural and agricultural systems depend (Nabhan & Buchmann 1997). While
the extent to which staple food crops depend on pollinator services has been questioned by
some (Ghazoul 2005), Klein and colleagues (2007) found that fruit or seed numbers or
quality of 87 out of 115 leading global crops (representing up to 35% of the global food
supply) were increased upon animal pollination.

In many agricultural systems, pollination is actively managed through the establishment of
populations of domesticated pollinators, particularly the honeybee Apis mellifera. However,
the importance of wild (i.e. unmanaged) pollinators for agricultural production is being
increasingly recognised (e.g. Westerkamp & Gottsberger 2000; Kremen et al. 2007;
Kremen & Chaplin-Kramer 2007). For a diversity of crops, it has been found that wild
pollination increases the size and quality of harvests (Klein et al. 2007). Wild pollinators
may also interact synergistically with managed bees to increase crop yields (Greenleaf &
Kremen 2006). Furthermore, a diverse assemblage of native pollinators provides insurance
against year-to-year population variability or loss of specific pollinator species (Kremen et
al. 2002; Rickets 2004; Tscharntke et al. 2005), and might better serve flowers because of
pollinator-specific spatial preferences to a flowering plant or crop field (Klein et al. in
press). Given current declines in populations of managed honeybees (Colony Collapse
Disorder [Johnson 2007], and abandonment of beekeeping in regions affected by
‘Africanization’ of honeybees [Brosi et al. 2007]), the importance of wild pollination is
likely to increase.

Estimating economic value is difficult and controversial, but the global value of wild and
domestic pollination has been estimated at $120 billion per year (Costanza et al. 1997),
while Losey & Vaughan (2006) estimated that wild pollinators alone are responsible for
about $3 billion of fruits and vegetables produced in the United States.

4.1.2 What are the overall trends in the provision of wild crop pollination?

The Millennium Assessment indicated a low to medium certainty that pollination
ecosystem services are declining (Duraiappah et al. 2005). Direct evidence for the decline
in pollination services (i.e., evidence that global crop yields are being affected by a
reduction in wild pollination) does not seem to exist, and would be difficult to obtain given
that many other aspects of agricultural practices are changing simultaneously. Some
indirect evidence exists based on reported declines in the abundance or area of occupancy
of some wild pollinators, for example in North America (NRC 2007), and in Europe
(Biesmeijer et al. 2006; Goulson et al. 2008). On the other hand, some species have
extended their ranges (Ghazoul 2005). A continent-level assessment of pollinator declines

48

is still lacking (Diaz et al. 2005). Most of the evidence for a decline in wild pollinator
services is inferred from changes in land use known to affect pollinator communities,
particularly declines in the extent and condition of available natural and semi-natural
habitats, and the effects of agricultural intensification (e.g. Kremen et al. 2002). On the
other hand, an increase in organic agriculture (Willer et al. 2008) may be increasing
provisioning of pollination services (Holzschuh et al. 2008) in some parts of the world.

4.1.3 How is the provision of wild crop pollination affected by changes in wild
nature?

Many crops are self-compatible to different degrees or wind pollinated, and therefore
receive only small benefits from wild pollination services (Klein et al. 2007). For crops that
rely heavily on biotic pollination, there are several ways in which changes in biodiversity
can affect yields.

Wild pollinators often depend on natural or semi-natural habitats for the provisioning of
nesting (e.g. tree cavities, suitable soil substrates) and floral resources that cannot be found
within crop fields (Kremen et al. 2004). Consequently, the available area of natural habitat
has a significant influence on pollinator species richness (Steffan-Dewenter 2003),
abundance (Heard et al. 2007; Morandin et al. 2007), and pollinator community
composition (Steffan-Dewenter et al. 2002; Brosi et al. 2007). Accordingiy, habitat area in
the neighbourhood of crop fields has been found to be strongly related to a direct measure
of the pollination service measured here in terms of pollen deposition provided by bees
(Kremen et al. 2004; Figure 1a).

Besides area, the quality of the habitat, both in natural systems and in croplands, seems to
be important for pollinator services, particularly the extent to which they provide nesting
and floral resources (Klein et al. 2003a; Goulson et al. 2005; Potts et al. 2005). Not all
pollinators are dependent on ‘natural’ habitats (Ricketts et al. 2004) and some are able to
use resources within agricultural fields themselves (reviewed in Kremen et al. 2007) and
therefore can even profit from agricultural management (Klein et al. 2002, Westphal et al.
2003).

There is clear evidence that wild pollination is strongly related to proximity to natural or
semi-natural habitats. A recent quantitative review of 23 studies (Ricketts et al. 2008) found
an exponential decay in pollinator richness and native pollinator visitation rate with
distance to natural or semi-natural habitats. A decline in yield was less clear, possibly
because few studies measured it directly. Visitation rate declined more steeply (dropping to
half at 0.6 km) than richness (1.5 km). Despite the general exponential relationship found
by Ricketts et al. (2008), it may be that slightly different relationships apply to different
crops (e.g. linear for coffee, Klein et al. 2003a,b; log-linear for watermelon, Kremen et al.
2004) and to different types of pollinators (e.g. of different sizes; Klein et al. in press).

Density of wild pollinators at the crop site is considered a good proxy of visitation rates
(Kremen et al. 2007; Ricketts et al. 2008) and therefore key for crop production (Vazquez

et al. 2005). This is confirmed by a few experimental case-studies (Roubik 2002), although
results are potentially confounded by a positive relationship between numbers of individual
visitors and diversity of pollinator species (Kremen & Chaplin-Kramer 2007). Interestingly,
Klein et al. (2003b) found that numbers of individual visiting bees did not explain fruit set
but that diversity of visitor species did. Three potential mechanisms may lead to a
relationship between pollinator diversity and pollinaiton services, ‘Sampling effect’ a

49

greater chance of having a pollinator species that perfectly fits the flower morphology
leading to more reliable pollination; (2) ‘Niche complementarity’ all receptive flowers over
an extended blooming period receive optimal pollination service; (3) ‘Functional
facilitation’ synergistic interspecific interactions with domestic honey bees (Greenleaf &
Kremen 2006, Klein et al. in press).

The identity of the pollinator species matters, as different crops benefit from pollination by
different species (Klein et al. 2007). Some crops have specific pollinator requirements,
either certain guilds or certain flowering times, and so abundance of pollinator individuals
may not translate into high pollination services if they belong to the wrong guilds (so they
visit flowers but are inefficient at actually pollinating them), if their abundance is out of
synchrony with crop mass flowering (e.g. almonds in California typically flower before
many wild bees have built up significant populations) (Ricketts et al. 2006; Kremen et al. in
press; Klein & Kremen unpublished), or if honey bees are attracted to another flowering
crop adjacent to the target crop field but other pollinating species still prefer to forage at the
target crop e.g. for alfalfa pollination.Accordingly, crops with a narrow range of specialised
pollinators are more likely to experience pollinator shortage when grown in highly
modified landscapes (Klein et al. 2007). Overall, functional diversity (diversity of
functional traits) may be more important to crop yield than either pure abundance or species
richness (Klein et al. in press). Consequently, the order in which species are lost is likely to
affect pollination services differentially (Larsen et al. 2005). Unfortunately, large-bodied
bee and beetle species seem to be both most extinction-prone and most functionally
efficient, their loss contributing to rapid functional loss (Larsen et al. 2005). A current
NCEAS analysis into body size, nesting behaviour, trophic specialisation and sociality will
shed more light on this issue.

Pollinator diversity has been found to improve resilience of crop production, by buffering
pollination against asynchronous fluctuations of bee abundances between years, including
temporal variation in the relative abundance within native species (Kremen et al. 2002) and
a sharp decline in domestic honeybees (Ricketts 2004, Winfree et al. 2007b). Given that
different species are differentially effective as pollinators (both within and among crops),
managing for bee diversity could meet the pollination requirements of a greater number of
crops, provide insurance in the event of shortages of any specific pollinator (managed or
unmanaged), and provide options for new or alternative crops (Kremen et al. 2002).
Furthermore, different taxa are likely to respond differently to landscape isolation and
habitat characteristics (e.g. Steffan-Dewenter et al. 2002; Klein et al. 2003a,b), so a
diversity of pollinator taxa may help to reduce the effects of land use change on pollination
services (Ricketts et al. 2008). The current precipitous declines in managed honeybees in
the United States due to Colony Collapse Disorder are making clear the dangers of relying
on a single pollinator species (Johnson 2007; NRC 2007).

Relationship between habitat area and pollination

Most services from wild pollinators take place near the interface between natural/semi-
natural habitats and crop fields (Ricketts et al. 2008), and so landscape patterns have a
strong influence on pollination services.

There is strong evidence that both richness of pollinator species and rates of visitation by
native pollinators at crop sites decline quickly with distance to natural/semi-natural habitat
(Ricketts et al. 2008; Figure 14a) and so crop yields are also expected to decline with
distance.

50

»
—

richens/native visitation

4000
‘distance 6 nearest vabitat patch

)
fraction of habitat within 2.4 km of farm site ‘

pollen deposition on
farm site

benefits from pollination ©
on farm site

size of habitat patch adjacent to farm site

benefits from pollination &
across landscape

fraction of habitat within the landscape 1

0

51

Figure 14. Four aspects of the spatial
relationship between habitat area and
provisioning of pollination services.

a) Both species richness (blue) and the rates of
visitation by native pollinators (red) have been
found to decline with distance to nearest
habitat patch (Ricketts et al. 2008), and so
presumably the benefits from pollination (in
terms of crop yields) should also show a strong
relationship with distance.

b) Given a particular farm site (cross),
pollination increases with the fraction of
habitat (green) within 2.4 km of the site’s
neighbourhood (after Kremen et al. 2004).

c) Our prediction for the relationship between
benefits obtained from pollination at the farm
and the size of the habitat patch (green)
adjacent to a particular farm site (purple): very
small habitat patches (smaller than a minimum
viable area for pollinators) may not have any
effect; afterwards, pollination is predicted to
increase rapidly with area, but then to stabilise
as further increases in habitat happen
correspond only to “core” area (dashed).

d) Predicted relationship between the fraction
of overall habitat area (green) within a
landscape (square) and overall pollination
services for the crop (purple; Morandin &
Winston 2006): pollination values are zero if
there is zero habitat for pollinators; pollination
then increases as habitat area increases, but
declines again as habitat expansion results in a
decline in crop land at the interface with the
habitat; for 100% natural habitat, pollination
for crops has zero value. For a given overall
area of natural/semi-natural habitat, pollination
benefits may be higher (dashed line) or lower
(solid line) depending on the — spatial
arrangement of the habitat patches.

The fraction of natural/semi natural habitat area in the neighbourhood of crop fields has
been found to be strongly related to a direct measure of the pollination service, pollen
deposition (Kremen et al. 2004; Figure 14b). Concrete studies on species—area relationships
for pollinators (particularly in tropical forest fragments; Priess et al. 2007) are lacking, but
patch area of natural habitat is likely to influence species richness and abundance of
pollinator communities, as larger habitat fragments in many cases show higher species
richness and density (Fahrig 2003).

We therefore predict that, after a minimum threshold of habitat patch size, pollination at a
given crop site increases with the size of the adjacent habitat patch, both because the patch
itself becomes larger (and potentially has higher pollinator diversity and abundance), and
because a larger fraction of the site’s neighbourhood becomes natural/semi-natural habitat
(Figure 14c). This increase, however, is only expected up to a certain point, followed by
stabilisation, as additional area increases take place too far from crop fields to produce
pollination services (Figure 14c; however, Brosi et al. [2007] found non-significant
relationships between bee abundance or diversity and size of neighbouring forest patch). If
so, the overall benefits from pollination within a given landscape are expected to peak at
intermediate levels of natural/semi-natural habitat cover, at the point where the spatial
interface with crops is maximised (Morandin & Winston 2006; Figure 14d). The spatial
arrangement of the habitat patches within the landscape is likely to matter; modelling
(Keitt, in review) suggests that pollination benefits are optimised for maximized by
providing islands of nesting habitat where inter-island distance matches mean foraging and
dispersal distances of wild pollinators. Hence, strategically creating new patches of habitat
in areas of intensive agriculture (for the crops that benefit from wild pollination) can
increase services from wild pollination significantly. The size of these islands is likely to be
important, though, as pollinator communities may become unviable in very small fragments
(Keanrs et al. 1998).

For some pollinator species, agricultural areas (the crop fields themselves) are also
important habitats, providing nesting and floral resources. In this case, even small natural
or semi-natural habitats (too small to support pollinator populations by themselves), can
bolster populations of important pollinators.

4.1.4 What are the main threats to the provisioning of wild crop pollination?

Loss of suitable habitat is recognised as a key driver of declines in wild pollination (e.g.
Steffan-Dewenter 2003; Ricketts et al. 2004; Morandin & Winston 2006; Priess et al.
2007). Habitat degradation, for example through agricultural intensification, leads to
scarcity in critical floral and nesting resources for many species. The use of chemicals (such
as insecticides, herbicides and fertilisers) in conventional agriculture has been found to
reduce populations of pollinators (Kearns et al. 1998). Climate change is emerging as a
potentially new threat, as phenological shifts may result in the disruption of plant-pollinator
interactions (Memmott et al. 2007). Invasive non-native species (including plant, mammals
and insects) pose additional threats (Cole et al. 1992; Kearns et at. 1998).

4.1.5 Are abrupt changes likely in the provision of wild crop pollination?
It is possible that a threshold in pollinator species/functional diversity, exists below which

pollination services become too scarce or too unstable (Klein et al. 2007). Such a tipping
point might occur when, at a landscape context, sufficient habitat is destroyed that the next

32

marginal change causes a population crash in multiple pollinators. Modelling supports this
prediction (Morandin & Winston 2006; also Keitt in review).

Alternatively, a threshold in habitat loss may lead to the collapse of particularly important
pollinators, leading to a broader collapse in pollination services (pollinator keystones).
Supporting this prediction, Larsen et al. (2005) found that large-bodied pollinators tended
to be both most extinction-prone and most functionally efficient, contributing to rapid
functional loss with habitat loss.

Empirical data on changes in the provision of pollination services to agriculture are still
very sparse. However, studies in California clearly indicate that there is indeed a tipping
point in the provision of such services from wild pollinators (Kremen et al. 2002, 2004;
Larsen et al. 2005: Greenleaf & Kremen 2006; Klein et al in prep; Chaplin-Kramer et al. in
prep). From this work we can extrapolate that in most areas of California’s agricultural
region such threshold has already been surpassed and there is now little wild pollination.
This makes crop production substantially to entirely reliant on managed honey bees, whose
numbers show strong within-year variation and an overall declining trend due to diseases
and other factors (NRC 2006). The studies in California are illuminating because the region
has very strong gradients, ranging from small farms surrounded by natural habitat to some
of the most intensively managed agricultural landscapes in the world. Given global trends
in agricultural intensification, the California region is therefore very informative of the
possible future changes in the provision of pollination services worldwide.

A model currently being developed by the NCEAS working group could be used in the
future to explore such thresholds.

Overall, we predict that there is a medium to high probability that the provisioning of
wild pollination services is likely to be subject to thresholds/tipping points in the
foreseeable future (by 2025), with a very high probability that such thresholds will

happen in regions of very intensively managed agriculture.

What this will mean for actual crop productivity is less clear as there are substitutes
for wild pollinators and for pollinator-dependent crops.

4.1.6 Can we quantify and map the global provision of this wild crop pollination and
how it might change?

State of knowledge and data availability

Wild pollination is a valuable ecosystem process because it can increase the yield of
economically important crops. Phase II of the Review on the Economics of Biodiversity
Loss will attempt to assess how such services are affected by biodiversity loss (e.g. by
deforestation leading to declines in habitat for wild pollinators). An ideal answer to this
would involve producing a global map of pollination services (measured in units reflecting
the contribution to increased yields per ha per year). Such a map would be generated for
different scenarios of possible global changes (e.g. what if all remaining Andean cloud
forest is lost?), and differences in pollination services would be contrasted to evaluate the
extent to which pollination is affected by those changes. The economic valuation would be

53

done on top of this ‘biophysical model’, accounting for local/regional variation in crop
yields as well as for global aspects such as changes in commodity market prices.

Although a biophysical model of delivery of pollination services is not fully in use as yet,
two major efforts are currently being reconciled with the aim to have such a model in
operation by October 2008:

e Kremen et al. (2007) have created a conceptual model of ‘mobile agent-based
ecosystem services’, which forms the basis of work carried out by the NCEAS
Working Group on pollination services. The model includes interactions and
feedbacks among policies affecting land use, market forces, and the biology of
organisms involved. The group is conducting quantitative syntheses of the key
relationships within this model, which has already been done for the dependence of
world crops on pollinators by Klein et al. (2007) and for the relationship between
distance from natural habitat and pollination services by Ricketts et al. (2008), and
is underway for impacts of disturbance on bee abundance and diversity (led by Rae
Winfree), and pollinator functional traits (led by Neal Williams). This component
considers the sensitivity of pollinator species based on life history and other traits
(e.g., body size, trophic specialization, nesting habit) and explores the consistency
of resulting functional group responses among different types of disturbance. From
this, a quantitative model will be built.

e The Natural Capital Project is developing tools for modelling and mapping the
delivery, distribution, and economic value of ecosystem services and biodiversity.
The Project’s pollination module is developing a biophysical model for pollinator
abundance on crops in a landscape. It uses information on key pollinators, the
availability and location of their nesting and floral resources on the landscape, and
their flight ranges, to predict their abundances on crops. The result is a map of
relative pollinator abundance in agricultural fields or pixels. Predicted effects on
yield or quality of crops have not yet been calculated, but the group plan to do so.

Recently, these two efforts have merged, with key leaders of the NCEAS group helping to
devise the Natural Capital model. By October 2008, they plan to have the model formulated
and validated on three landscapes by comparing model predictions against empirical data.

In a related study, Priess and colleagues (2007) used empirical relationships on the effects
of forest distance on both pollinator diversity and fruit set of coffee (based on the results of
field experiments carried out by Klein et al. 2003a,b,c) to estimate future changes in
pollination services for different land use scenarios in Sulawesi, Indonesia.

These efforts indicate that it is possible to develop a landscape-scale spatially-explicit
model based on an empirical understanding of how different crops benefit from wild
pollination (Klein et al. 2007) and how pollination decays with distance from natural/semi-
natural habitat (Ricketts et al. 2008), ideally complemented with more detailed data on the
identity/guild of the pollinators, the specific pollinator needs of crops, and the value of
modified habitats (including the agricultural lands themselves) for nesting and floral
resources. Currently available data are far from perfect, but allow for broad generalisations,
and expert opinion could be used to fill some data gaps.

The key challenge would reside in extrapolating these models for application at a global
scale. Indeed, global maps would be needed not only on the distribution of the relevant

54

natural/semi-natural habitats, but also on the distribution of the relevant crops (that benefit
from pollination). Furthermore, these maps would need to have sufficient spatial detail to
be relevant to the fine-scale spatial dynamics that governs pollination, as foraging ranges of
pollinators have been empirically shown to be typically less than 1 km. Such maps
currently do not exist. The main limiting factor is probably the unavailability of a detailed
map of global crops distribution, with information for individual crops and with sufficiently
fine spatial resolution. But the production of fine scale maps of natural/semi-natural habitat
(with information on their relative value for pollination services) is also not trivial. Such
maps can be generated for particular regions (e.g. Natural Capital model now being applied
to the Valuing the Arc project in Tanzania) but certainly not for the world within the time
frame of the Review.

Two possibilities exist in the short-term for the development of a first-cut pollination model
at the global scale:

e A probabilistic approach, whereby the probability of each pixel being under a crop is
estimated from data on the pixel’s suitability for crops (Fisher et al. 2002) as well as
broader crop maps (Cassman et al. 2005; Ramankutty et al. 2008). The expected
benefits from pollination on each pixel would then be estimated using a generalised
biophysical model.

e A sampling approach, in which detailed models are developed for each of a set of
representative landscapes, applied using fine-scale maps of crops and habitats for
those landscapes. Expected global benefits from pollination, and predicted changes
for different scenarios, would then be obtained by extrapolation using global crop data
(Cassman et al. 2005; Ramankutty et al. 2008).

We believe the second approach is the most feasible as well as the most informative.The
probabilistic approach would allow for the consideration of uncertainty of what the relevant
crops are in each area, but the spatial resolution would not be sufficiently fine for the
adequate incorporation of the relevant spatial dynamics. The sampling approach would
benefit from finer spatial data on the distribution of both crops and natural/semi-natural
habitats, therefore resulting in substantially more reliable predictions. A choice of adequate
sample landscapes (including temperate and tropical regions, containing strong gradients in
agricultural intensification) would allow for a first-cut extrapolation to the global scale. We
therefore recommend that a global evaluation of the effects of biodiversity loss and
ecosystem change on pollination services builds from the work currently being done by the
Natural Capital Project, which is focusing on four landscapes: California, Tanzania. China
and Hawaii.

Even at the finer landscape scale, it is important to keep in mind the limitations of any
model that can possibly be developed and applied in the near future. Smaller plots of
agricultural land in close proximity to natural biodiversity, which are important for
pollination services, are unfortunately likely to be lost due to coarse resolution of the data.
Small field sizes and mixed cropping systems make Africa particularly vulnerable to this. A
model in the lines currently being developed and proposed here would be particularly
informative for the effects of changes in wild pollination services associated with changes
in land cover, an less useful to predict variation in other factors. Effects of variation in
pesticide use can somehow be incorporated in the classification of crop types (as intensive
crops tend to make more use of pesticides) but only up to a certain extent (as, within any
given crop type, level of pesticide use depends on individual farmer decisions). The model

55

would be unlikely to capture changes dues to invasive species, unless the value of invasive
floral resources for pollinators is know along with habitat associations of these plant species
(one example for which this could be known is Centaurea solstitialis, yellow star thistle).

Adequacy of scenarios

A probabilistic approach would require the generation of predicted global maps of land
cover, including predictions for the distributions of particular sets of crops, at a fine spatial
scale. Approaches such as those developed by the ATEAM Project (Advanced Terrestrial
Ecosystem Analysis and Modelling) for Europe (Schréter et al. 2004; Rounsevell et al.
2006) could potentially be expanded to the global scale (within 1 year?)

A sampling approach would require the generation of detailed maps of predicted land cover
for a set of representative regions, for each of the chosen scenarios. While these are not
trivial to generate either, results would be more realistic and a finer scale than for when
produced at a global scale. Approaches such as those used by Priess et al. (2007) seem to
have potential. They used ‘generalized cellular automata’ to allocate future land use based
on biophysical suitability (climate, soils, topography), allocation factors (e.g. distance to the
next river, land use on adjacent cells, preferred walking distance to field), demography
(population growth rate, migration), and the land use strategies of farmers (e.g. moderate or
high intensity agriculture, forest use) and other restrictions like the protection status of the
area. The extrapolation from study landscapes to the global scale would require linking
global coarse scenarios for changes in land use distribution with the finer landscape
scenarios. This could potentially draw on the methods and approaches that have been
developed for downscaling land use scenarios (e.g. Verburg et al. 2006). Model validation
is fundamental, by matching modelled predictions to exisiting data from targeted areas. A
potential approach would be to select existing data sets, while another would be to prioritize
study of pollination service at particular target areas for which other data are known.

A fundamental variable in the scenarios is the extent to which managed pollinators
(particularly honeybees) are made available in crops. The scenarios being contrasted would
need to consider that this is the same in both cases. Possible extreme scenarios include, on
the one hand, a situation in which wild pollination becomes the only available mechanism
for crops that depend on biotic pollination (in the remote possibility that Colony Collapse
Disorder eliminates all managed honeybees) and, on the other hand, a situation in which
managed pollination is provided whenever useful and the value of wild pollination is
simply by complementing (or interacting with) managed pollinators. Reality is somewhere
in between, and so scenarios could also consider an intermediate situation in which
particular crops, from particular regions, benefit from managed pollination and others do
not. The key consideration would be that any pair of scenarios being compared would need
to consider matching levels of managed pollination. The same applies for levels of pesticide
use.

56

4.1.7

Insights for economic valuation

Appropriate scenarios can generate maps of different states of the world, varying in their
spatial patterns of distribution of crops and natural/semi-natural habitat. A model along the
lines of those described above can then be used to create surfaces of pollination services
(measured in changes in the yields of particular crops) for each state of the world. The
contrast between these two maps will indicate areas where crop yields are expected to
change (either increase or decrease). Data on the market value of these crops (adjusted for
possible variations in global crop prices arising from differences in the scenarios) could
then be used to monetise the map of variation in crop yields.

4.1.8

Some key resources

The National Center for Ecological Analysis and Synthesis (NCEAS) Working
Group on “Restoring an ecosystem service to degraded landscapes: native bees and
crop pollination” is synthesising and analysing data on bee populations, pollinator
communities and pollination services across agro-natural landscapes, including the
development of models for the management of pollination services. The Group is
led by Claire Kremen and Neal Williams and composed of c. 20 experts, including
amongst others Alexandra-Maria Klein, Ingolf Steffan-Dewenter, Rachael Winfree

and Taylor Ricketts.

The Natural Capital Project, a joint venture among The Woods Institute for the
Environment at Stanford University, The Nature Conservancy, and the World
Wildlife Fund, is developing tools for modelling and mapping the delivery,
distribution, and economic value of ecosystem services and biodiversity. It includes
a pollination module (led by Taylor Ricketts), which is developing a biophysical
model for pollinator abundance on crops in a landscape.

Modelling for the NCEAS Working Group and the Natural Capital pollination
module is being led by Eric Lonsdorf.

' This is our recommendation for Phase II, based on the results of this review. It does not commit the leaders
of Phase II to follow it, and it does not commit the recommended research group to actually do such
work.

57

e The EU-funded ALARM (Assessing Large-scale Risks for Biodiversity with Tested
Methods) project, led by Ingolf Steffan-Dewenter, includes a research module on
pollination investigating the risks resulting from, and rates and extent of, loss of
pollinators. With a particular emphasis on Europe, this 5-year (2004-2009) project is
developing predictive models for pollinator loss and consequent risks.

e The Millennium Assessment (Cassman et al. 2005) used data on rain-fed and
irrigation croplands obtained from a cropland-focused reinterpretation of
GLCCDv2, based on methods described in Wood et al. (2000).

e Ramankutty et al. (2008) developed a new global data set of croplands and pastures
circa 2000, at 5 min (~10 km) resolution, by combining agricultural inventory data
and satellite-derived (MODIS-derived and GLC2000) land cover data.

e The Global Agro-Ecological Zones (GAEZ) assessment created a global database of

estimated yields for various crops by matching the soil type, terrain, and climate of
grid cells with productivity levels for 154 land-use types documented in places with
similar characteristics (Fisher et al. 2002). The 5-arc-minute grid generated by the
GAEZ assessment contains measures of agricultural suitability, assigned to eight
categories based on estimated productivity levels as a percentage of maximum
observed yields for a given crop.

e The International Pollinators Initiative, established by the Fifth Conference of
Parties to the Convention on Biological Diversity in 2000, declared an ‘urgent need
to address the issue of worldwide decline of pollinator diversity’.

4.1.9 Participants
Authors

James J. J. Waters, Ana S. L. Rodrigues, Andrew Balmford (University of Cambridge,
UK)

Taylor H. Ricketts (WWF-US, US)
Claire Kremen, Alexandra-Maria Klein (University of California, Berkley, US)
Neal M. Williams (Bryn Mawr College, US)

Contributors

Saul A. Cunningham (CSIRO, Australia)

58

4.2 Biological control in crops

This section is based on a quick literature review, and did not receive contributions from,
nor has it been reviewed by, experts in this field.

4.2.1 Why is biological control in crops important for human wellbeing?

Biological control is the process by which one organism reduces the population density of
another organism, for example through predation or parasitism. In nature, most organisms
are consumed by other organisms, which in many cases leads to drastic reductions in the
population of the prey species; in biological control, man exploits this ‘natural control’ to
suppress the numbers of pest species (Bale et al. 2008).

Here we focus on the value of biological control for reducing the effects of pests in
agricultural systems, and therefore contributing for increases (or preventing decreases) in
crop yields. We recommend a focus on biological control for crops mainly because of the
availability of data (e.g. Losey & Vaughan 2006) and the huge value of crop plants to
human well-being.

Agricultural pests cause significant economic losses worldwide. Globally, more than 40%
of food production is being lost to insect pests, plant pathogens, and weeds, despite the
application of more than 3 billion kilograms of pesticides to crops, plus other means of
control (Pimentel 2008). In the US alone, it is estimated that more than US$18 billion are
lost due to insect damage (including more than US$ 3 billion spent in insecticides), of
which about 40% ate attributed to native species and the remaining to exotic pests (Losey &
Vaughan 2006). These values, however, would be much higher if biological control was not
in place. Losey & Vaughan (2006) estimate that 65% of potential pest species are being
suppressed in the US, with a total value of pest control by native ecosystems around US$
13.60 billion. Through a predator removal experiment, Ostman et al. (2003) showed that the
presence of natural enemies increased barley yields 303 kg/ha, preventing 52% of yield loss
due to aphids.

Biological control in crops may be totally natural, without direct intervention from man, or
it may be enhanced through biological control interventions. Frequently, the term biological
control refers to the latter only, when an organism is used by man to reduce the population
density of another organism (Bale et al. 2008). Here, however, we are interested in the
contribution of ‘wild nature’ into both of these, and particularly in the natural control that
flows from unmanaged ecosystems.

Natural control of plant pests is provided by generalist and specialist predators and
parasitoids, including birds, bats, spiders, ladybugs, mantis, flies, and wasps, as well as
entomopathogenic fungi. In the short-term, this process suppresses pest damage and
improves yields, while in the long-term maintains an ecological equilibrium that prevents
herbivore insects from reaching pest status (Zhang et al. 2007 and references therein).

In biological control interventions, pest control is enhanced through a diversity of
approaches (Bale et al. 2008 and references therein):

- Inoculative control is used mainly against ‘exotic’ pests that have become
established in new countries or regions of the world and involves the collection and

59

release of natural enemies from the country or region of origin of the pest. This
works better with perennial crops (fruit plantations and forests), where the long-
term nature of the ecosystem enables the interactions between pest and natural
enemy to become fully established over a period of time.

- Augmentative control involves periodically reintroducing natural enemies that are
usually commercially produced for that purpose. It is particularly indicated for
short-term annual crops, as populations of natural enemy species may not persist
between the crop production cycles. This includes both ‘inundation’ (the mass
production and release of large numbers of the control agent) and ‘seasonal
augmentation’ (where the natural enemy population is built-up through successive
releases during the same growing season).

- Conservation control (see Landis et al. 2000 for a review) refers to the manipulation
of the environment to increase the effectiveness of indigenous predators and
parasitoids, usually against native pests. Various measures are implemented to
enhance the abundance or activity of the natural enemies, including manipulation of
the crop microclimate, creation of overwintering refuges (like ‘beetle banks’),
increasing the availability of alternative hosts and prey, and providing essential food
resources such as flowers for adult parasitoids and aphidophagous hoverflies.

Biological control reduces rather than eradicates pests, such that the pest and natural enemy
remain in the agro-ecosystem at low densities. A number of important pests can be kept at a
low population density by biological control over long periods of time. In other cases,
populations of pests are significantly reduced by natural enemies, but repeated additional
methods are needed to achieve an adequate level of control (e.g., resistant plants, cultural
techniques, physical barriers, semiochemicals and, as a last resort, the use of selective
chemicals) — this is the fundamental philosophy of integrated pest management (Bale et al.
2008).

Here we are particularly interested in the value wild nature to natural (unmanaged) pest
control and to (managed) conservation control. However, wild nature also contributes to
inoculative and augmentative pest control through the provision of natural enemies in the
regions of origin of exotic pests.

4.2.2 What are the overall trends in the biological control of crops?

The Millennium Assessment indicated a medium to high certainty that pest regulation
services are declining (MEA et al. 2005).

Agricultural intensification (reduction in the landscape complexity, increase in pesticide
use) and loss of crop genetic diversity increase the exposure of crops to major disease
outbreaks and therefore the importance of biological control.

At the same time, improvement in artificial pest control (through the use of pesticides) and
the development of pest-resistant crops reduce the need for biological control. However,
pesticide use has an associated array of problems (Pimentel 2008): they are expensive and
need to be applied frequently; public health and environmental impacts, with more than 26
million people are poisoned annually from pesticides, resulting in more than 220,000
deaths; and environmental impacts such as losses to non-target species, including natural
enemies of crop pests. Indeed, pesticides can be more damage to natural enemy population

60

than to pests, therefore exacerbating the very problem they were designed to solve (Kremen
& Chaplin-Kramer 2007). Furthermore, pesticide-resistant organisms evolve rapidly (e.g.
insects usually evolve resistance within about a decade of the introduction of an insecticide;
Palumbi 2001), while there are no known cases of evolved resistance of natural enemies
(Bale et al. 2008). The value of natural control should therefore not be underestimated or
presumed to be declining, even less so as demand for organic products is increasing (Willer
& Yussefi 2008).

Data on the populations of biological control agents are scarce but the trends are presumed
to be negative due to habitat transformation associated with agricultural intensification
(agricultural expansion, enlargement of field size, removal of non-crop habitat resulting in a
loss of natural landscape features required for maintaining their populations) and increase
in pesticide use. On the other hand, the increase in organic farming worldwide may help to
reverse this trend (Bengtsson et al. 2005; Willer et al. 2008).

4.2.3 How is the provision of biological control in crops affected by changes in wild
nature?

Biological control seems to be greatly determined by the abundance of natural enemies, and
indeed this is often used as a measure of the intensity of biological control (e.g. studies
reviewed in Bianchi et al. 2006 and Kremen & Chaplin-Kramer 2007). Several studies
demonstrated increases in predation rate with predator abundance, while for parasitoids the
majority of evidence for increased densities affecting pest control comes from exotic
introduction examples (see references in Kremen & Chaplin-Kramer 2007). However,
enhanced enemy abundance or richness does not necessarily guarantee improved pest
control (see below).

The diversity of natural enemies also seems to improve biological control, through a variety
of mechanisms, including: species complementarity, when more than one type of predator
or parasitoid adds to the control of a pest species; sampling effect, meaning a particularly
effective natural enemy is more likely by chance alone to occur when more species are
present; redundancy, having more species will buffer against disturbance or ecosystem
change; and idiosyncrasy, when the whole is greater than the sum of the parts due to
interaction between species (Tscharntke et al. 2005; and see Kremen & Chaplin-Kramer
2007 for a review). However, these expected benefits of diversity to pest control may be
countered by antagonistic effects between different natural enemy species, such as
competition and intraguild predation (Kremen & Chaplin-Kramer 2007). On the other hand,
intraguild predation could help supplement natural enemy diets when pest densities are low,
potentially maintaining a higher natural enemy population size more capable of controlling
sudden pest outbreaks. The overall effect seems to be context-dependent, being important
to consider both the diversity and the composition of natural enemy assemblages (Wilby &
Thomas 2002; Kremen 2005), as well as the characteristics and ecology of predators, and
the identity of their prey species (Kean et al. 2003; Wilby et al. 2005). While species
richness will be most important for the control of some pests, the presence of a particular
predator or parasite will be of utmost importance for others (Wilby & Thomas 2002). The
provision of biological control is crucially determined by the identity of biological control
organisms, which may even have a stronger effect than diversity (e.g. Straub & Snyder
2006).

Non-crop habitats are fundamental for providing habitat where many biological control
agents (predators, parasitoids) mate, reproduce, and overwinter (Zhang et al. 2007) as well

61

as additional, complementary, food (Kremen & Chaplin-Kramer 2007). The quality of the
habitat can make a significant difference (e.g. Thies et al. 2005; Tylianakis et al. 2007) and
this can be improved through habitat management (Landis et al. 2000).

Landscape diversity or complexity is generally positively associated with the abundance
and species richness of natural enemies and may be the most crucial factor driving
biological control services (for reviews see Bianchi et al. 2006; Kremen & Chaplin-Kramer
2007; Tscharntke et al. 2007). Landscape complexity can be measured by a variety of
factors, including the proportional area of natural habitat, distance to natural habitat or
perimeter to area ratio of the field (Kremen & Chaplin-Kramer 2007), and beta diversity
(species turnover) across natural habitat patches (Tscharntke et al. 1998). Landscape
connectivity (Tscharntke et al. 2007) and age of natural habitat (Tscharntke et al. 1998) also
seem to increase biological control. While there is evidence that increased landscape
complexity is correlated with diversity and abundance of natural enemy populations, this
does not necessarily translate into improved pest control, as pest densities may also respond
positively to landscape complexity (Thies et al. 2005). For example, while 75% of the
studies in Bianchi et al. (2006) found increased abundance of natural enemies in more
complex landscapes, only 45% showed lower pest pressure. Relatively very few studies
measured actual predation rates or yields through controlled experiments (e.g. Ostman et al.
2003; Williams-Guillen et al. 2008).

The proximity to semi-natural habitats greatly influences biological control by increasing
natural enemy abundance, diversity, predator/prey ratios and importantly predation risk or

parasitism for crop pests (Tscharntke et al. 2007; Bianchi et al. 2006). The distance of
enhanced parasitism varies from tens to hundreds of metres, depending on parasitoid
species (Tscharntke et al. 2005). Although there are many examples of parasitism rates
being higher at the edges of fields than the middle, they are also frequently found to be
unrelated, so that one cannot be sure whether the number of natural enemies or their
movement is limiting biological control (Tscharntke et al. 2007). Unfortunately, there are
few experimental studies (e.g. Tscharntke et al. 1998) quantifying the relationship between
distance from semi-natural habitat and biological control, and no work has investigated the
shape of a general relationship, as has been done for pollination (Ricketts et al. 2008).

Habitat fragmentation may also affect biological control (With et al. 2002), with a greater
effect on specialist parasitoids than generalist predators (Tscharntke et al. 2007), such that
generalist and mobile enemies may even profit from the high primary productivity of crops
at the landscape scale, their abundance compensating for losses in diversity.

Natural habitat may actually have negative effects (“ecosystem disservices’) on biological
control, particularly as a source of pests (Thies et al. 2005; Kremen & Chaplin-Kremer
2007). On the other hand, some studies have shown a greater effect of habitat loss on the
predators or parasitoids than on their herbivore prey and so the disservice of pest provision
may be negated by the benefits of natural enemies in large and well-connected habitats
(Kremen & Chaplin-Kremer 2007 and references therein).

Crop diversity can reduce the incidence of pests and diseases, both the increase in the

genetic diversity of individual crops (e.g. Zhu et al. 2000) and the increase in the diversity
of crop species in multi- or poly-cropping systems (e.g. Sutherland & Samu 2000).

62

Relationship between habitat area and biological control

Work quantifying the effect of the area of natural habitat on the provision of natural
enemies and biological control services is lacking (Kremen & Chaplin-Kramer 2007),
although results suggest that may be greater for parasitoids/predators than their prey (e.g.
Kruess & Tscharntke 2000). The question is complicated by the different dispersal abilities
of insect species: while a large uncultivated area may provide a large source of natural
enemy individuals and species, the same area distributed in smaller fragments among
cultivated fields would allow greater dispersal (Tscharntke et al. 2007). The optimal
resolution to this tradeoff will also depend on the attributes of the pests and the crops, as
ephemeral annual crops will benefit most from natural enemies moving quickly into the
area (Bianchi et al. 2006) whilst a larger refuge of diverse natural enemies might be more
suitable for perennial crops. Equally, highly mobile pests may be best controlled by natural
enemies in a landscape of well-connected natural habitats (Tscharntke et al. 2007).

4.2.4 What are the main threats to the provision of biological control in crops?

The main threat to the provision of biological control seems to be habitat loss and
degradation, particularly associated with agricultural intensification. This results in
landscape simplification, and natural habitat fragmentation leading to losses in the diversity
and abundance of natural enemies. Intensive agricultural management practices threaten
natural enemy populations (Naylor & Ehrlich 1997). Ironically, pesticide use has had
severe detrimental effects on natural enemies, often greater than on the target pests, and
may even result in the emergence of new pests (Kremen & Chaplin-Kramer 2007). Natural
enemy species will also be under threat from invasion of foreign species and the disruptive
effects of climate change.

4.2.5 Are abrupt changes likely in the provision of this benefit/process?

The relationship between densities of natural enemies and the biological control services
they provide is unlikely to be linear (Losey & Vaughan 2006). From the complexity of
interactions and non-linearity of the relationship between diversity and function mentioned
above, it seems likely that a function may decline disproportionately when a tipping point
in natural enemy diversity is passed. However, there does not seem to be empirical
evidence to prove this. Thresholds in landscape structure (interpatch distance) have been
shown to impact the aggregative response of predators, thereby generating a similar
threshold in pest populations (With et al. 2002). The importance of natural enemy
assemblage composition in some instances of biological control (Shennan 2008) indicates
that changes in composition can lead to disproportionately large, irreversible and often
negative shifts in ecosystem services (Diaz et al. 2005).

Overall, we predict that there is a medium to high probability that the provisioning of
biological control is subject to thresholds/tipping points in the foreseeable future (by
2025), with a very high probability that such thresholds will happen in regions of
very intensively managed agriculture.

63

4.2.6 Can we quantify and map the global provision of biological control in crops,
and how it might change?

Making assumptions on the proportion of insects that cause any damage, the proportion of
native pest species in the US, and the relative amount of pest suppression that is due to
insects rather than other causes, Losey and Vaughan (2006) illustrate how to use the ratio of
cost of damage due insect pests to estimate the value of native pest control services.
However, such a calculation is based on location-specific assumptions, and it is not clear
that it could be scaled up to a global calculation. It would also not be fine enough to
quantify the effects of changes in biological control associated with changes in states of the
world due to specific policy measures.

A model for biological control services has not been created, although Kremen et al. (2007)
created a conceptual framework for the effects of land-use change on ‘mobile-agent-based
ecosystem services’, including pest control. Initially developed for pollination (see chapter),
the model includes interactions and feedbacks among policies affecting land use, market
forces, and the biology of organisms involved.

Unlike for pollination (section 4.1), syntheses of the literature to quantify the key
relationships within this framework have not yet been carried out for biological control.
These include:

- The relative value of biclogical control to crop productivity, that is, their
vulnerability to a shortage of natural enemies. This can be identified by the intensity
of pesticide applications to those crops (Kremen & Chaplin-Kramer 2007).

- The relationship between the provision of biological control and distance to natural
habitat patch (related to the dispersal and foraging movements of natural enemies).

- The relationship between the provision of biological control and area of habitat
patch.

- The functional significance of species diversity in each system would also be a
requisite for determining natural enemy assemblage service provision.

These syntheses need to build from field data and (ideally) experiments. We are not sure if
the current body of knowledge is sufficient for such syntheses to be produced from
published information, or if additional field work would be required. A complication in
relation to pollination (in which natural habitat is always expected to have a positive effect),
is that natural habitat may simultaneously result in the increase in the populations of natural
enemies and of the pests. Hence, the measure of enemy abundance on its own is not an
adequate surrogate of biological control and instead information on the variation in crop
yields should be used.

Adequacy of scenarios

We suspect that the same variables required for modelling pollination (section 4.1) would
be needed for modelling biological control.

64

4.2.7 Insights for economic valuation

See pollination section (section 4.1)
4.2.8 Some key resources

This section was based on a (quick) overview of the literature, and we suggest it is further
developed by contacting experts in this field. We recommend the following as first points
of contact:

e Claire Kremen at the Department of Environmental Science, Policy, and
Management, University of California, Berkeley (USA) who has led development
of a model on ‘mobile-agent-based ecosystem services’, including pest control
(Kremen et al. 2007).

e Teja Tscharntke in the Department of Crop Science, University of Gottingen,
(Germany), whose group also includes Carsten Thies.

e David Pimentel at the Department of Entomology, College of Agriculture and Life
Sciences, Cornell University (USA).

e Felix Bianchi at the Functional and Spatial Ecology group in CSIRO Entomology
(Australia).

4.2.9 Participants
Authors

James Waters, Andrew Balmford and Ana Rodrigues (University of Cambridge, UK)

65

4.3 Genetic diversity of crops and livestock

This section is based on a quick literature review, and did not receive contributions from,
nor has it been reviewed by, experts in this field.

4.3.1 Why is the genetic diversity of crops and livestock important for human
wellbeing?

Food production and security depend on the wise use and conservation of agricultural and
livestock biodiversity and genetic resources. Crops and their wild relatives (plant genetic
resources for food and agriculture; FAO 1997) and livestock and their wild relatives
(animal genetic resources for food and agriculture; FAO 2007) have the genetic variability
that provides the raw material for breeding new crop varieties, through classical breeding
and biotechnological techniques. This diversity is fundamental to respond to environmental
and demographic changes (Esquinas-Alcazar 2005).

While thousands of plant and animal species have been domesticated for human use,
currently, barely more than 150 plant species are cultivated and most of mankind lives off
no more than 12 plant species (Esquinas-Alcazar 2005) and 30 crops supplying 90% of the
global calorie intake (Wood et al. 2005). Domestic animals provide 30% of food globally
but just 14 species represent 90% of the global production (Wood et al. 2005).
http://www.nature.com/nrg/journal/v6/n12/full/nrg1729.html - B3Global population growth
and corresponding rising demand for food favoured the adoption of highly productive
varieties. The introduction of modern farm machinery, marketing and transport methods
that require uniform crop and livestock characteristics have required the introduction of
standard, homogeneous plants and animals. This trend, which peaked during the so-called
'Green Revolution’, has made it possible to boost food production, but lead to the loss of
innumerable heterogeneous (and often locally adapted) traditional farmers’ varieties
(Esquinas-Alcazar 2005).

The loss of local species and varieties usually results in irreversible loss of the genetic
diversity they contain, known as genetic erosion. This has dangerously shrunk the genetic
pool that is available for natural selection, and for selection by farmers and plant and
livestock breeders, and has consequently increased the vulnerability of agricultural crops
and livestock production to sudden changes in climate, and to the appearance of new pests
and diseases (Esquinas-Alcazar 2005). For example, in the United States in 1970, the
fungus Helminthosporium maydis destroyed more than half the standing maize crop in the
southern part of the country. The crop had been grown from seeds that have a narrow
genetic base and are susceptible to this disease. In this case and others, the problem was
resolved by breeding resistant varieties using genetic resources that were obtained from
other parts of the world (Esquinas-Alcazar 2005).

The conservation of animal and plant genetic resources for food and agriculture relies on
the preservation of both the variety of domesticated species and their wild relatives. This
can take place ex-situ, particularly for plants, through the development of seed banks and
germplasm collections (FAO 1997; e.g. the recently inaugurated Svalbard Global Seed
Vault, already with 100 million seeds). Here, we are particularly interested in the in-situ
conservation of genetic diversity, through the protection of the natural habitats of wild
relatives or through the maintenance of traditional agricultural landscapes. Note that with

66

the exception of the wild boar (Sus scrofa) the ancestors and wild relatives of major
livestock species are either extinct or highly endangered and that therefore domestic
livestock are the only depositories of the now largely vanished diversity of the wild
ancestors. This is a major difference from crop species, in many of which the wild ancestors
are commonly found at the centres of origin and represent an important source of variation
and adaptive traits for future breeding programmes (FAO 2007).

The term ‘crop biodiversity’ is sometimes used to refer to the diversity of crops species
within one region (e.g. wheat and barley rather than just wheat; e.g. Di Falco & Perrings
2005). We are not addressing the effects of crop diversity in that sense.

Despite the widespread use of modern varieties, landrace (locally adapted, traditional
varieties) are still grown in many parts of the world. This is particularly the case in
marginal areas where locally-adapted varieties (e.g., to specific soil and water regimes)
thrive better. Landraces may also allow labour requirements to be spread (through
differences in maturing time of the varieties), be adequate for multiple usages (e.g.,
differences in stalks can make them better for use as fencing material or for feeding to
cattle), and reduce disease/pest risk (Cassman et al. 2005).

4.3.2 What are the overall trends in the genetic diversity of crops and livestock?

The genetic diversity of crops and livestock has been declining markedly, but the overall
rates of loss are not easy to quantify (FAO 1997). For example, in China nearly 10,000
wheat varieties were in use in 1949, but only 1,000 were still in use by the 1970s. In the
US, 86% of the apple varieties, 95 % of the cabbage, 91 % of the field maize, 94 % of the
pea, and 81 % of the tomato varieties documented as having been in use between 1804 and
1904 apparently no longer exist (FAO 1997). Of the 7,616 livestock breeds listed by FAO’s
Global Databank for Animal Genetic Resources for Food and Agriculture, around 20 % are
classified as at risk and 62 breeds became extinct during the 2001-2007 period — amounting
to the loss of almost one breed per month (FAO 2007).

The three main crops — rice, maize and wheat — that provide over half the world
population’s requirement for protein and calories (Bioversity International 2008) are
increasing reliant on a small number of modern varieties. Indeed 80% of the wheat area
sown in developing countries is modern semi-dwarf varieties and over 75% of all rice in
Asia is improved semi-dwarf varieties (Cassman et al. 2005)

4.3.3 How is the genetic diversity of crops and livestock affected by changes in wild
nature?

The genetic diversity amongst wild relatives of crop (and to a lesser extent livestock)

species is a direct result of the species and population diversity amongst these species.
They are therefore affected by changes that lead to species/populations extinctions, such as

loss and degradation of natural habitats.

Amongst domesticated relatives, loss of varieties and diversity within varieties is expected

from the loss of area under traditional agricultural practices and associated local breeds,
such as when these are either abandoned or converted to intensive agriculture.

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Relationship between habitat area and genetic diversity of crops and livestock

Losses in diversity species with area are likely to follow a non-linear relationship (linear
with log area) similar in shape to the species-area relationship (but probably much steeper
for the loss of intra-specific/intra-variety genetic diversity).

4.3.4 What are the main threats to the genetic diversity of crops and livestock?

The main threat to the genetic diversity of crops and livestock is the marginalization of
traditional production systems and the associated local breeds, driven mainly by the rapid

spread of intensive agriculture and livestock production, often large-scale and utilizing a
narrow range of crop varieties and livestock breeds (FAO 1997, 2007). The intensification

process has been driven by rising demand for agricultural and animal products, and has
been facilitated by the ease with which genetic material, production technologies and inputs
can now be moved around the world (Esquinas-Alcazar 2005; FAO 2007).

Acute threats such as major disease epidemics and disasters of various kinds (droughts,
floods, military conflicts, etc.) are also a concern — particularly in the case of small,
geographically concentrated varieties and breed populations (FAO 2007).

The loss of cultural diversity is also a factor in the loss of genetic diversity as cultural
knowledge about production of traditional varieties and extraction of wild relatives is also
lost (FAO 1997).

Wild varieties are threatened by habitat _loss_ and degradation, and overharvesting. We
presume that climate change may also affect not only wild relatives but also domestic
breeds and varieties, if it reduces the degree to which they are adapted to their local
environments.

4.3.5 Are abrupt changes likely in the genetic diversity of crops and livestock?

Given the likely non-linear relationship between area and genetic diversity, we predict that
in some cases a small change in area (of natural habitat, or of traditional agricultural lands)
may result in a disproportionate loss in genetic diversity of crops and/or livestock. This is
probably more likely in areas that have already suffered extensive habitat loss and land
conversion, where the remaining populations of particular varieties and breeds are quite
small.

Climate change may also have non-linear effects on genetic diversity of crops and
livestock.

-

Overall, we suspect that abrupt changes are likely in the genetic diversity of crops
and livestock in the foreseeable future (by 2025), particularly in regions subject to
extensive habitat loss and land conversion.

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4.3.6

Can we quantify and map the global diversity of crops and livestock, and how it

might change?

We predict that it would be possible to create a global, spatially-explicit, model of crop and
livestock diversity through the following steps:

I)

2)

3)

Map the distribution of wild relatives and of local varieties and breeds. There
seem to be a few databases on which this could be based, including for example

the Domestic Animal Genetic Resources Information System (DAGRIS), and the
databases under the Germplasm Resources Information Network (GRIN).

However, we are not sure if these are geographically and taxonomically
comprehensive and if the distributional information (which seems to be at the
country level) is sufficient.

Obtain an estimate of the economic value of each breed/variety as insurance
against pest and disease outbreaks. This would need to be estimated from statistics
on past outbreaks to predict the risk of future outbreaks. The economic
consequences of those outbreaks would be estimated from the predictions of
damage and of crop/livestock prices. The economic value of each breed/variety
would be calculated as the predicted avoided damage by using its genetic diversity
to prevent outbreaks. This would in principle follow similar principles to the
calculation of an insurance premium given the predicted probability of relatively
infrequent (but economically costly) events. We do not know if the data required
for these calculations exist.

The comparison between states of the world (with different maps of landuse,
including the replacement of areas of extensive with intensive agriculture) would
allow for an estimate of which breeds/varieties would be more likely to be lost
given their spatial distributions. Particularly relevant would be to understand
which of these breeds/varieties occur in specific areas and are not safe somewhere
else (e.g. in seed banks). The economic value of those losses would be calculated
from the estimate of the ‘premium’ values of each breed/variety.

We suspect that this model would be best for calculating changes in crop, rather than
livestock, genetic diversity, given that the latter are less likely to be directly related to
changes in landuse. For breeds/varieties conserved ex-situ (e.g. in seed banks) the relevant
comparison would be between one scenario in which the creation and maintenance of these
banks is ensured versus one where it is not — the costs of creation/maintenance would then
be compared with the benefits (predicted avoided costs due to disease and pest outbreaks).

69

Adequacy of scenarios

We predict that the key data that scenarios would have to produce is information on landuse
change, including on the intensification of agriculture. Scenarios could also specifically
assess the costs and benefits of maintaining breeds and varieties ex-situ.

4.3.7 Insights for economic valuation

The general model proposed would already provide results in economic terms.

4.3.8

Some key resources

Food and Agriculture Organization of the United Nations (FAO), Commission on
Genetic Resources for Food and Agriculture. Has published both The State of the
World’s Plant Genetic Resources for Food and Agriculture (1997) and The State of
the World's Animal genetic Resources for Food and Agriculture (2007).

International Treaty on Plant Genetic Resources for Food and Agriculture
(ITPGRFA)

Global crop diversity trust
Convention on Biological Diversity (CBD) — Agricultural Biodiversity

International Food Policy Research Institute (IFPRI)
International Livestock Research Institute (ILRI), that has build the database Domestic

Animal Genetic Resources Information System _(_DAGRIS), containing research-based

information on the distribution, characteristics and status of 871 breeds of cattle, sheep,
goats, pigs and chickens indigenous to Africa and Asia.

Consultative Group on International Agricultural Research (CGIAR) is a strategic
alliance of countries, international and regional organizations, and private

foundations supporting 15 independent international agricultural research centers.
Bioversity is one of these CGIAR-supported Centres. Bioversity International
(formerly IPGRI) is one of those centes.

Germplasm Resources Information Network (GRIN) within the U.S. Department of

Agriculture's Agricultural Research Service.

Possible experts to contact include:

70

e Melinda Smale, Senior Research Fellow at IFPRI. Her research emphasizes the
development of methods to assess the value of crop biodiversity and the
identification of policies to enhance the utilization and management of crop genetic
resource, particularly in developing economies.

e Jan Engels, Genetic Resources Science and Technology Group, at Bioversity
International.

e José Esquinas-Alcazar, FAO Commission on Genetic Resources for Food and
Agriculture.

4.3.9 Participants
Authors

Hannah Peck, Andrew Balmford and Ana Rodrigues (University of Cambridge, UK)

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4.4 Soil quality for crop production

This section is a very brief overview based on a quick literature review. It greatly benefited
from expert contributions (see below) but it has not been reviewed by experts in this field.

There seem to be four main ways in which wild nature contributes to the benefits that we
obtain from agricultural crops (e.g. food, fibres, fuels) by improving or retaining farmland
soil quality. We called these: internal effects (soil biota); conversion effects (when non-
agricultural land is converted to agriculture); neighbourhood effects (when neighbouring,
non-agricultural, systems contribute to soil quality in croplands); and wild fertilisers (e.g.
guano).

Note that the importance of soil for water regulation is covered separately (section 4.9).
Internal effects

The soil fauna and flora, and the communities occupying the agricultural land, affect the
soil properties and therefore the quality of the soil for agriculture. These ‘internal effects’
seem to be the main way in which wild nature affects soil quality, by affecting: nutrient
fixation (e.g. nitrogen fixation by bacteria); nutrient cycling (e.g. in organic matter
decomposition, by fungi, bacteria, dung beetles...); soil structure (e.g. by plant roots,
termites...); water regulation, particularly water holding capacity and drainage (e.g., by
plant roots, termites, micro-organisms and earthworms...); uptake of water and nutrients
(mycorrhizal fungi); erosion regulation (e.g. by vegetation cover and leaf litter);
suppression of pests and diseases (e.g. by mycorrhizal fungi).

More than species diversity, it seems that functional diversity (and its influence in trophic
interactions) is key to the decomposition, nutrient cycling, and stability of soil processes.
Soil diversity is important for resilience to stress and disturbance (Griffiths et al. 2000;
Brussaard et al. 2007).

As agricultural intensification occurs, the regulation of functions through soil biodiversity
is progressively replaced by regulation through chemical and mechanical inputs (Giller et
al. 1997; Swift et al. 2004). The type of agricultural management is the primary determinant
of the composition, diversity and functioning of the soil food web. So, to reap the rewards
of soil biodiversity in farming systems requires identifying those practices that best harness
the beneficial roles of the soil food web. One idea commonly proposed is that management
regimes that encourage a soil community that bears the closest resemblance to natural
ecosystems are most likely to require fewer inputs because of greater reliance on ecosystem
self-regulation (e.g. Bardgett & McAlister 1999; Mader et al. 2002). In tropical agricultural
systems undergoing intensification, large numbers of farmers have limited access to inputs,
and therefore the maintenance and enhancement of soil biodiversity may be particularly
relevant to such farmers (Giller et al. 1997). One key feature of natural ecosystems is a soil
community that is dominated by fungal-based pathways of decomposition, whereas intensive
agriculture tends to promote more bacterial-based pathways of decomposition. Therefore,
farming systems, for example non-tillage systems, cessation of fertiliser and organic farming,
that encourage fungi and their consumers might reap additional benefits in terms of nutrient
cycling, enhancement of plant diversity through the action of mycorrhizal fungi (Smith et al.

72

2008), carbon storage (e.g. Chivenge et al. 2007), and nutrient retention under stress (e.g.
Gordon et al. 2008).

We predict that the importance of ‘internal effects’ is increasing as agricultural land
expands and appropriate soil management becomes more important to ensure long-term
crop production (particularly when the inputs of chemical fertilizers and pesticides are
restricted for environmental reasons), and as organic agriculture expands (Mader et al.
2002).

We do not know the extent to which the economic consequences of different agricultural
management regimes on crop yields (specifically through their effect on soil quality) have
been quantified. We get the impression that while a fair amount of work seems to have been
done in comparing organic and conventional farming systems, this has mainly focused on
temperate regions. Even if information is available on how crop yields are affected by changes
in the soil biota resulting from different management regimes, it would still be difficult to
assess these effects globally. Indeed, the scenarios being produced to compare a business-as-
usual world with a more biodiversity-friendly world would need detailed spatial information
not only on the specific crops but also on the management regime for each crop. We suspect
this would not be possible for Phase 2.

Other relevant references:
e Cassman et al. (2005) — section on cultivated systems of the Millennium Ecosystem
Assessment (volume Ecosystems and Human Well-being: Current States and

Trends).

e Bloem et al. (2004) — Measuring soil biodiversity: experiences, impediments and
research needs.

e Bloem et al. (1997) — Food webs and nutrient cycling in agro-ecosystems
e Bloem et al. (2006) — Microbiological methods for assessing soil quality
e Schjonning et al. (2004) — edited volume on Managing Soil Quality: Challenges in
Modern Agriculture, particularly section by Brussaard on Biological soil quality
from biomass to biodiversity — Importance and resilience to management stress and
disturbance.
e FAO (1999) — Sustaining Agricultural Biodiversity and Agro-ecosystem Functions.
e Brussaard et al. (2007) — Soil biodiversity for agricultural sustainability
e Wood et al. (2000) — Pilot analysis of global ecosystems: Agroecosystems
Conversion effects
These are one-off inputs from wild nature, when non-agricultural land is converted into
agricultural land. This may happen as agriculture expands into areas occupied by natural
ecosystems (e.g. the Amazonian frontier), or as a cyclic events through shifting agriculture.

Here, crops benefit from the improvements in soil quality that were accumulated over time
in the previous form of land use (e.g. long-term soil improvement in forest systems).

73

Agricultural expansion into new areas often occupies terrains that are nor particularly
suitable for agriculture, and soil fertility may decline very quickly as crops effectively mine
the soil nutrients (Alfsen et al. 2001; Carr et al. 2006). Soil fertility may however be
retained under appropriate agricultural practices that ensure adequate replacement of the
nutrients used by the crops (Barber & Diaz 1994).

Shifting cultivation, also called ‘swidden’ agriculture or ‘slash-and-burn’ agriculture, is one
of the oldest forms of farming and consists of cropping on cleared plots of land, alternated
with lengthy fallow periods. These systems are the dominant form of agriculture in tropical
humid and subhumid upland regions and are typically associated with tropical rain forests.
Shifting cultivation is practiced on about 22% of all agricultural land in the tropics and is
the primary source of food and income for some 40 million people (Giller and Palm 2004),
including many of the world’s poorest. While the contribution to global food security is
negligible, given the low yields and general lack of infrastructure in areas where shifting
cultivation predominates, this method of cultivation has a potentially large impact on
regional and global ecosystem services through its effects on biodiversity, greenhouse gas
emissions, and soil nutrients. Although these systems are generally associated with soils of
low fertility, they are highly sustainable and resource-conserving in areas with low
population density (Kleinman et al. 1995). However, high population density increases the
pressure on available land and resources, reducing the time available for a regenerative
fallow between cropping cycles (Cassman et al. 2005), and effectively converting the
region into an agricultural frontier.

We predict that the importance of conversion effects is increasing as the agricultural
frontier expands in regions such as the Amazon. On the other hand, it is expected to decline
as population increases, rendering shifting agriculture non-viable.

In comparing different states of the world (created by different scenarios) it should be
possible to account for the economic value of soil fertility for crops in agricultural frontiers,
quantified as one-off subsidies in the conversion from natural ecosystems to agriculture.

As for shifting agriculture, by knowing the time needed for soil recovery and the number of
people that can be sustained per unit of area of cultivated plots it should be possible to map
potential for sustainable shifting cultivation worldwide and the maximum population
densities allowed for long-term sustainability. This would then be crossed with data on
demand (regions where populations are more likely to depend on this type of agriculture) as
a basis to calculate the potential economic value of a sustainable inputs of natural
ecosystems to farmland soil through shifting agriculture. Given current human population
densities, it is predicted that shifting cultivation can only provide a minor contribution to
food production (from a global perspective), although as mentioned poor people in some
regions will depend on this type of agricultural practice. The time needed for field to
recover is likely to be variable and will depend on soil type and climate.

Other relevant references:

e Moreira et al. (2006) — book Soil Biodiversity in Amazonian and Other Brazilian
Ecosystems.

¢ Noordwijk (1995) — book Below-ground Interactions in Tropical Agroecosystems.

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Neighbourhood effects

When neighbouring, non-agricultural, systems contribute to improve or maintain soil
quality in croplands. We envisage that this could include, for example, coastal sand dunes
sheltering inland agricultural fields from sea salt spray, or uphill forests protecting
downslope croplands from erosion (by reducing water runoff) or from landsides.

The importance of neighbourhood effects may be increasing as agriculture expands from
the most favourable lowlands into slopes, or declining as the size of fields expands and
therefore reduces the contact with other ecosystems.

Assuming that these effects are essentially related to the physical protection of farmland
soil by neighbouring ecosystems, it seems conceptually possible to model this effect, such
that, for example, one would be able to quantify the changes in farmland soil quality in a
watershed having either more or less coverage of upslope forest. However, we found no
evidence for the existence of such models.

Wild fertilisers

These are products obtained from wild nature, which are used as crop fertilisers, for
example: guano, seaweed, peat and fish. These subsidies to soil quality have been very
important historically (e.g. guano was the basis of the Peruvian economy in the mid 19"
Century) but their importance has declined substantially with the development of artificial
fertilisers. Wild fertilisers may still be important in areas where farmers cannot afford
artificial fertilisers, though.

We suspect there is not a centralised dataset on current use of wild fertilisers that could be
used as the basis of for a spatially explicit economic evaluation.

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4.4.1 Can we quantify and map the contribution of wild nature for the provision of

soil quality for crops, and how it might change?

What can be done in Phase 2? By whom?

Internal effects We predict that it is unlikely that even a first-cut quantification could
be possible of the value of wild nature for agriculture through internal effects. Indeed,
the type of agricultural management is the primary factor in this evaluation, and so
modelling the economic consequences of different policy options on soil fertility (and
crop yields) would require detailed information not only on how specific crops vary
across space but also on management practices in each plot. We suspect this is not a
feasible task for Phase 2.

Conversion effects We predict that it should be possible to obtain a first-cut model for
quantifying the value of wild nature for agriculture through conversion effects, both
frontier and shifting cultivation. We suggest further expert consultation (see below) to
evaluate the feasibility of this particular task and understand who the appropriate
experts would be.

Neighbouring effects While conceptually possible to quantify, we found no evidence
for researchers thinking along these lines and therefore suspect that this task would not
be feasible in the short-term.

Wildlife fertilisers We suspect there is not a centralised dataset on current use of wild

fertilisers that could be used as the basis of for a spatially explicit economic evaluation
on Phase 2.

4.4.2 Some key resources

FAO Land and Water Development Division, particularly its section on soil health

for food security. They maintain the Soil Biodiversity Portal: The role of soil
organisms in agriculture for better land management decision.

ISRIC — World Soil Information, including David Dent (Director) and Zhanguo Bai
(global assessment of land degradation and improvement).

Alterra, Wageningen University, The Netherlands, including: Peter de Ruiter, Jaap
Bloem and K.B. Zwart

Kenneth G. Cassman and John W. Doran at the University of Nebraska-Lincoln,
USA.

Stanley Wood at the International Food Policy Research Institute, Washington DC,
USA.

Rattan Lal at the School of Natural Resources, College of Food, Agricultural, and
Environmental Science, Ohio State University, USA.

76

e Peter Kleinman at the Department of Crop and Soil Sciences, Pennsylvania State
University, USA

e Paul Mader at the Research Institute of Organic Agriculture, Switzerland

e Richard D. Bardgett Soil and Ecosystem Ecology, Lancaster University, United
Kingdom

e Sara Scherr at Ecoagriculture Partners, USA
4.4.3 Participants
Authors

Ana Rodrigues, Antares Hernandez, Andrew Balmford (University of Cambridge,
UK)

Contributors

The following experts provided very valuable insights on which this section was based. We
apologise to them that time constraints prevented us from circulating the text for revision.

Kenneth G. Cassman (University of Nebraska-Lincoln, USA)
Jaap Bloem (Alterra, Wageningen University, The Netherlands)
Richard D. Bardgett (Lancaster University, UK)
Acknowledgements

Stanley Wood (IFPRI, Washington DC, USA) provided useful references.

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4.5 Livestock

This section is a very brief overview based on a quick literature review. It has not been
reviewed by experts in this field.

4.5.1 Why is the production of livestock important for human wellbeing?

Livestock and livestock products (e.g. milk, eggs) are estimated to make up over half of the
total value of agricultural gross output in industrial countries, and about a third of the total
in developing countries (Wood et al. 2005). Globally, it accounts for 40% of agriculture
gross domestic product, provides one-third of humanity protein intake, employs 1.3 billion
people and creates livelihoods for one billion of the world’s poor (Steinfeld et al. 2006).
Livestock are also a significant source of income and consumption in developing countries,
often providing a supplementary source of income and income stability, and frequently one
of the few assets available to the poorest.

Livestock production also affects human wellbeing negatively by: causing land degradation
and desertification, habitat loss, climate change (18% of greenhouse gas emissions), and
water pollution (see Steinfeld et al. 2006 for a very comprehensive review).

4.5.2 What are the overall trends in the production of livestock?

The global importance of livestock and their products is increasing with population growth,
changes in food preferences (associated with rising incomes and urbanisation), and
expanding global trade. Global production of meat and meat are projected to more than
double between 2000 and 2005 (Steinfeld et al. 2006).

4.5.3 How is the production of livestock affected by changes in wild nature?

Livestock production is the single anthropogenic user of land either directly through
grazing (26% of the ice-free terrestrial land surface) or indirectly through consumption of
fodder and feedgrains (33% of total arable land). In all, livestock production accounts for
70% of all agricultural land and 30% off all land surface area of the planet (Steinfeld et al.
2006).

An increasing fraction of livestock production is intensive, with crops provided as feed to
animals kept at high densities. Globally, it is estimated that 35% of all cereal use are fed to
animals. In this case, the contribution of wild nature for livestock production is indirect, via
the contribution of nature to crop production, including through pollination (section 4.1),
biological control of crops (section 4.2), soil quality for crops (section 4.4) as well as
through water regulation (section 4.9) and the regulation of natural hazards (section 4.13).

An important fraction of livestock production takes place in natural grasslands, and that is
the focus of this section. Here livestock production is being directly subsidised by the
primary productivity of natural ecosystems, and the species and ecosystem processes that
sustain it.

Wild nature also subsidises livestock production when pasturelands expand into other
ecosystems. Expansion of livestock production is a main driver of deforestation,
particularly in Latin America — 70% of the previously forest landed area in the Amazon is

78

occupied by pastures, and feedcrops occupy much of the rest (Steinfeld et al. 2006). Here,
wild nature is contributing by providing soil fertility, as discussed for the conversion effects
in section 4.4.

The contribution of genetic diversification to livestock production is discussed in section
4.3.

Relationship between habitat area and livestock production
We expect this relationship to be linear.
4.5.4 What are the main threats to livestock production in natural grasslands?

Overexploitation of natural grasslands, by keeping livestock at a higher density than what is
sustainable, leads to degradation of the grasslands that is not easily reversed. For example,
about 20% of the world’s pastures and rangelands, with 73% of rangelands in dry areas,
have been degraded to some extent, mostly through overgrazing, compactation, and soil
erosion created by livestock action. Drylands tend to be particularly affected as they have
lower natural productivity and livestock is often the only source of livelihoods to people
living in those areas (Steinfeld et al. 2006).

Climate change is likely to affect livestock production, both by affecting natural rangelands
(particularly in regions where predictions are of increased aridity) and by affecting feed
crop production.

4.5.5 Are abrupt changes likely in the provision of livestock in natural grasslands?

We found no evidence for future abrupt and disproportionate changes in the capacity
of natural systems (grasslands) to produce livestock.

4.5.6 Can we quantify and map the contribution of wild nature for livestock production,
and how it might change?

A first-cut map of the contribution of wild nature for livestock production already exists, as
a map of the annual production of livestock derived — at least in part — from grazing on
unimproved natural grasslands (Naidoo et al. in press). This was obtained by combining
global data on livestock distributions (Wint & Robinson 2006), producer prices, and current
and potential vegetation (Ramankutty & Foley 1999; Bartholome & Belward 2005)

To map livestock production on natural pastures, Naidoo et al. (in press) used recently-
developed 5° resolution global maps of livestock distributions (Wint & Robinson 2006),
which used regression-based methods to estimate the expected density of cattle, sheep,
goats, pigs, poultry, and buffalo across the earth’s surface. For each livestock type, they
used these density estimates and data on the mass of edible meat per animal (estimated by
country from FAOSTAT data) to estimate the tons of meat produced in each cell. A global
producer price was used to weight different livestock types; using these weights, an
aggregate index of livestock production was obtained by summing the weighted livestock
meat weights. Naidoo et al. (in press) then constructed a global map of natural pastures by
combining a 5’ resolution potential vegetation data set of savanna, grassland/steppe, and
shrubland biome types (Ramankutty & Foley 1999) and then masking out all known
human-altered landscapes using the Globa! Land Cover 2000 remote sensing data

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(Bartholome & Belward 2005). They intersected the livestock production index and the
map of natural pastures to produce a global map of livestock production on natural pastures.

Limitations of these data include the difficulties of mapping pastures from remotely sensed
imagery and the lack of spatially explicit weightings that reflect differences in the economic
values of livestock species in different regions of the world (Naidoo et al. in press). The
new pastureland maps by Ramankutty et al. (2008) provide more updated information than
Ramankutty & Foley (1999).

The map by Naidoo et al. (in press) provides an estimate of current production in
grasslands. However, this may overestimate the natural sustainable production rates. On the
one hand, existing livestock may in some areas receive other food supplements, and
therefore not depend completely on the natural grassland production. On the other hand,
livestock may be kept at densities that are too high, therefore degrading the natural
grassland productivity in the long term (Steinfeld et al. 2006). Hence the Naidoo et al. (in
press) map is not ideal, but we believe it is a very useful first cut to quantifying the value of
wild nature for livestock production in natural grazing areas. In the longer-term, it would be
useful to calibrate this map to account for the maximum natural productivity of land.

Adequacy of scenarios

Appropriate scenarios would need to predict landuse and demand for livestock products.
4.5.7 Insights for economic valuation

The map produced by Naidoo et al. (in press) has as units an aggregate index of livestock
production, but it could have equally have been produced in terms of estimated monetary
value of the livestock produced in each cell.

4.5.8 Some key resources
- Wint & Robinson (2006) — Gridded Livestock of the World, by the FAO.

- Ramankutty et al. (2008) present newly derived global maps of agricultural lands,
including pasturelands.

- Steinfeld et_al. (2006) present an assessment of the full impact of the livestock
sector on development and the environment, produced by the Livestock,
Environment and Development (LEAD) Initiative of the FAO.

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4.5.9 Participants
Authors

Ana Rodrigues, Andrew Balmford (University of Cambridge, UK)

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4.6 Marine fisheries

This section is a fully developed review, including contributions by experts in the field,
some of whom subsequently reviewed the text.

4.6.1 Why are marine fisheries important for human wellbeing?
This section focuses on marine capture fisheries, not covering marine aquaculture.

Marine capture fisheries are a globally important source of food: fish is highly nutritious,
rich in micronutrients, minerals, essential fatty acids and proteins, and represents a valuable
supplement to diets otherwise lacking essential vitamins and minerals. Globally fish
provides more than 2.8 billion people with at least 20 percent of their average per capita
animal protein intake (FAO 2006). Marine capture fisheries contribute 56% of the total
amount of fish available for human consumption (FAO 2006). Marine fisheries also affect
the provisioning of food indirectly, via the use of fishmeal and fish oil in aquaculture and in
poultry and pig production (Arthurton et al. 2007). Demand for fish is increasing with
population growth, rising wealth and changing food preferences as a result of the marketing
of fish in developed countries as part of a healthy diet (Arthurton et al. 2007).

Accordingly, marine capture fisheries are an important source of economic benefits, with
an estimated first-sale value of US$84.9 billion (FAO 2006), and important for income
generation, with an estimated 38 million people employed directly by fishing, and many
more in the processing stages (Arthurton et al. 2007).

Marine fisheries are increasingly valuable for recreation, particularly in developed
countries. In the US alone, in 2006 nearly 13 million anglers made more than 89 million
marine recreational fishing trips on the Atlantic, Gulf and Pacific coasts, capturing almost
476 million fish, of which 55 percent were released alive (Van Voorhees & Pritchard
2007). In the European Union (EU 15), an estimated 8 million recreational sea anglers
(Stevenson 2007) spend an estimated €25 billion a year, compared to a €20 billion value for
commercial landings in 1998 (Pawson et al. 2004).

4.6.2 What are the overall trends in the provision of marine fisheries?

The global trends in marine fisheries have been reviewed in the Millennium Ecosystem
Assessment (Pauly et al. 2005). Catches increased substantially during the twentieth
century as fishing fleets worldwide expanded. These trends continued until the 1980s, when
global marine landings reached slightly over 80 million tons per year; then they either
stagnated or began to slowly decline. However, regional landings peaked at different times
throughout the world, which in part masked the decline of many fisheries. This global
decline is despite an increase in fleet size, effort, and technology, in conjunction with an
expansion into previously unfished areas, depths and stocks (Pauly et al. 2005; Morato et al.
2006).

It is estimated that in 2005 around one-quarter of the stock groups monitored by FAO were
underexploited or moderately exploited (3% and 20%, respectively) and could perhaps
produce more. About half of the stocks (52%) were fully exploited and therefore producing
catches that were at or close to their maximum sustainable limits, with no room for further
expansion. The other one-quarter were either overexploited, depleted or recovering from

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depletion (17%, 7% and 1%, respectively) and thus were yielding less than their maximum
potential owing to excess fishing pressure exerted in the past, with no possibilities in the
short or medium term of further expansion and with an increased risk of further declines
and need for rebuilding (FAO 2006). The real extent of collapses in marine systems is
likely to be more dramatic than realised, as massive declines that took place before
systematic statistics began are likely to go unnoticed as perceptions are adjusted to what a
natural marine system is (the ‘shifting baseline syndrome’; Carlton et al. 1999; Jackson et
al. 2001b). A meta-analysis of collapsed fisheries has indicated minimal recovery even after
fishing moratoria are imposed (Hutchings 2000, Hutchings and Reynolds 2004). Loss of
production has mean loss of earnings which has been disportionately absorbed by
developing countries whose resources were the last to be fished down by multinational
fleets (Srinivasan et al. 2008).

4.6.3 How is the provisioning of marine fisheries affected by changes in wild nature?

Biomass of the exploited species is the key parameter determining catches in marine
fisheries, at least in the short-term. Global declines in exploited biomass are inferred from
declines in catches despite increased effort. In the North Atlantic, for example, current
overall biomass of high-trophic level fishes is estimated to be one third of what it was in
1950 (Christensen et al. 2003). Fishing pressure has reduced the biomass of some species to
less than 10% of the pre-exploitation level within a few decades, particularly species with
vulnerable life history traits such as large predatory fishes (Myers and Worm, 2003),
including sharks and relatives (Myers et al. 2007), and deep sea species (Devine et al.
2006). While commercially exploited species have been particularly affected (e.g. Cod,
Cook et al. 1997; bluefin tuna, Fromentin & Powers 2005), non-target species have also
suffered depletions as by-catch (e.g. leatherback turtles, Spotila et al. 2000).

Fishing efforts tend to be concentrated on high-trophic species first. As these become
depleted, the biomass of lower-trophic species may increase due to predator release
(Scheffer et al. 2005). Fishing efforts then move down the food web, and landings from
global fisheries have accordingly shifted in the last few decades from large piscivorous
fishes toward smaller invertebrates and planktivorous fishes, especially in the Northern
Hemisphere (Pauly et al. 1998). This may at first lead to increased catches, but overfishing
of lower-trophic species will also result in their declines, and so fishing down the food web
is unsustainable in the long-term (Pauly et al. 2005).

Provisioning of fishmeal for aquaculture and for meat production can be done using lower
trophic levels species but recreational fisheries benefit specifically from large, long-lived,
high-trophic fish species.

There is evidence that species diversity is important for marine fisheries, both in the short-
term, by increasing productivity, and in the long-term, by increasing resilience. A recent
review of fisheries trends in large marine ecosystems (Worm et al. 2006) found that the
proportion of collapsed fisheries decayed exponentially with increasing species richness.
Furthermore, the average catches of non-collapsed fisheries were higher in species-rich
systems. Diversity also seemed to increase robustness to overexploitation, with rates of
recovery positively correlated with fish diversity. This increased stability and productivity
was attributed to a portfolio effect (Tilman et al. 2006), whereby a more diverse array of
species provides a larger number of ecological functions and economic opportunities,
leading to a more stable trajectory and better performance over time (Worm et al. 2006).

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The diversity of genetically distinct populations, adapted to particular regions, is also likely
to be important, especially in terms of the sustainability and resilience of wild stocks to
longer-term natural and anthropogenic change (Hilborn et al. 2003a; Worm et al. 2006;
Hiddink et al. in press). Maintaining the genetic diversity of marine fisheries keeps also
options open for future farming of marine species.

Given that marine fisheries are sustained by lower-trophic species, they depend directly on
the biomass of other, non-targeted, species, including phytoplankton and zooplankton. The
diversity of non-target species, such as prey species, may also be important: experiments
have shown the importance of diverse food sources for secondary production and for the
stability of populations across trophic levels in marine ecosystems (Worm et al. 2006).

The removal (or depletion below a certain level) of populations of particular species or
functional groups has been shown to have dramatic effects on some marine ecosystems and
the associated fisheries. Predators in particular (‘top-down control’) seem to be very
influential in shaping and maintaining various habitat states or population levels (Myers et
al. 2007). Many examples of regime shifts associated with biodiversity losses have been
documented (reviews in: Jackson et al. 2001b; Bellwood et al. 2004; Folke et al. 2004;
Agardy et al. 2005) and include: kelp forest losses due to urchin population explosions after
declines in large predatory fishes and sea otters; die-offs of turtlegrass beds following turtle
population collapse; sudden catastrophic coral mortality due to overgrowth in algae after
grazers (herbivorous fishes, urchins) have been lost (Mumby et al. 2007); trophic cascades
following the collapse of cod and other large-bodied predators in the northwest Atlantic,
transforming a highly productive system dominated by benthic fish into systems dominated
by pelagic fish and macroinvertebrates with poor fisheries productivity (Frank et al. 2005);
and two successive regime shifts in the Black Sea triggered by overfishing and
compounded by euthrophication, firstly after depletion of top predators and secondly after
depletion of planktivorous fish, resulting in a jellyfish-dominated, fisheries-poor system
(Daskalov et al. 2007). Recovery following change may be slow and may follow a different
trajectory from the one observed during the decline (hysteresis; Hughes et al. 2005).

Fisheries records provide compelling evidence of dramatic declines in the stocks of many
exploited species. Substantially less is known about trends in non-targeted species. Marine
species have long been assumed to be resilient to extinction, and therefore until recently
been neglected in extinction risk assessments (Baillie et al. 2004). This is now changing,
with ongoing assessments demonstrating that as human activities take over the world’s
oceans (Halpern et al. 2008) marine systems are suffering serious declines in species
diversity, both locally (particularly in coastal habitats; Jackson et al. 2001b; Worm et al.
2006) and globally (e.g. predatory fish communities; Myers and Worm 2003). The largest
ongoing assessment of species conservation status is the Global Marine Assessment (due to
assess 20,000 marine species by 2010), which is assessing the threat status of every marine
vertebrate species, and of selected habitat-forming invertebrates and plants.

In addition, experimental evidence suggests that a loss of biodiversity increases

vulnerability to the establishment of invasive species (Stachowicz et al 1999, 2007; Worm
et al. 2006).

Marine fisheries are vulnerable to the decline in the extent or quality of particular marine
habitats that play important roles in the provisioning of key resources (e.g. food, shelter) for

targeted species. These include, amongst others:

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Coral reefs, which directly support fisheries that constitute 9-12% of the world’s total
fisheries (up to 25% in some parts of the Indo-Pacific), providing livelihoods for
millions of people in tropical coastal regions. A large number of offshore fisheries also
rely on the supporting services of reefs as breeding, nursery or feeding grounds
(Moberg and Folke 1999; Agardi et al. 2005). Cold water reefs are also important to
fisheries (Fossa et al. 2002; Freiwald et al. 2004), although much less is known about
these. Coral reefs are one of the world’s most threatened habitats, particularly in the
Caribbean (Gardner et al. 2003). Coral reefs are suffering a worldwide decline in
quality and extent (Pandolfi et al. 2003; Bellwood et al. 2004, Newton et al. 2007), with
some regions particularly degraded (e.g. Caribbean, Gardner et al. 2003; SE Asia,
Middle East and Indian Ocean, Wilkinson 2000). They are also one of the habitats most
at risk from climate change (Hoegh-Guldberg et al. 2008).

Seamounts are very poorly known systems, but thought to be important for the
provision of food, as sites for spawning aggregations, and nurseries/refuges for juvenile
open ocean fish (Moore & Jennings 2000; Stocks 2004). Out of an estimated 100,000
seamounts (>1 km high), only about 350 have been sampled biologically (many more
have no doubt been fished out), and less than 100 of these have been studied in any
detail; the ones that were revealed extraordinary levels of species endemism
(EuroCoML 2008). Seamounts are among ‘newly’ targeted ecosystems that have been
intensively fished since the second half of the 20th century, particularly through highly
destructive bottom trawling (Watson & Morato 2004). However, their long-lived, slow-
growing, species are particularly vulnerable to over-exploitation, and so reports of
drastic collapses are accumulating (Devine et al. 2006). Whole populations have been
known to be depleted in just a few years (Devine et al. 2006) while few deep-sea
fisheries have recovered from bottom trawling even two or three decades after fishing
ceased (Morato et al. 2004; Watson & Morato 2004).

Seagrass meadows are highly productive systems, thought to have a fundamental role in
maintaining populations of commercially exploited fish and invertebrate species, in
both tropical and temperate regions, by providing a permanent habitat, a temporary
nursery area for the development of the juvenile stages, a feeding area for various life-
history stages, and/or a refuge from predation (McArthur et al. 2003; Agardy et al.
2005). They are also thought to maintain fisheries indirectly by providing organic
matter that is incorporated into coastal nutrient cycles (Jackson et al. 2001a; Gullstrom
et al. 2002). Major losses of seagrass habitat have been reported from the
Mediterranean, Florida Bay, and Australia (Duarte 2002). Present losses are expected to
accelerate, especially in Southeast Asia and the Caribbean (Burke et al. 2001; Duarte
2002), as eutrophication increases, algal grazers are overfished, and coastal
development increases.

Kelp forests are highly productive temperate systems, with a complex biological
structure organized around large brown algae, supporting a high diversity of species and
species interactions. Kelp support fisheries of a variety of invertebrate and finfish
species, particularly by providing nursery and shelter habitats, and the kelp itself is
harvested for food and additives. Overfishing of predator species has led to regime
shifts in many regions towards systems that are totally dominated by urchins and of
substantially less value for fisheries (Agardy et al. 2005).

Marine fisheries are also vulnerable to the declines in the extent or quality of coastal
habitats, including:

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Mangroves are found in intertidal zones and estuarine margins in tropical and
subtropical regions, where they can be important to fisheries as refuge areas from
predators or physical disturbance, or via food input (Agardy et al. 2005). Mangroves are
particularly important for supporting artisanal fisheries in some regions; in Bangladesh,
for example, the artisanal fishery (contributing 85-95% of the total coastal and marine
catch) is highly influenced by mangroves (Islam & Haque 2004). Furthermore,
mangroves may be ecologically linked to seagrass beds and coral reefs, with synergistic
effects for fish productivity (Mumby et al. 2004). Much of the coastal population of the
tropics and sub-tropics resides near mangroves, and it has been estimated that 35% of
mangrove forests have been loss in the recent past principally due to conversion to
agriculture, aquaculture, salt pans, and urban and industrial development (Valiela et al.
2001; Agardy et al. 2005).

Estuaries and coastal wetlands play a key role in marine fisheries provision, particularly
as nursery areas for fisheries (Beck et al. 2001), but also by providing habitat for
commercially important molluscs and crustaceans, and in regulating water quality
(Agardy et al. 2005). The loss of these habitats has been linked to the collapse of
fisheries in North America, North Africa and elsewhere, although further quantitative
studies are required (Agardy et al. 2005). Estuaries and coastal wetlands have been
degraded, altered, or eliminated in many areas, particularly through reduction of the
estuaries watershed through agricultural conversion, eutrophication, pollution by
pesticides and herbicides, overfishing, and biological invasions (Agardy et al. 2005).

Marine fisheries are furthermore affected by changes in inland ecosystems that affect the
quality, volume and timing of water inputs as well as erosion regimes:

Forests, wetlands and riparian habitats play an important part removing excess nutrients
from freshwater runoffs (see section 4.9). This role is becoming increasingly important
as agricultural intensification is increasing nutrient loads, leading in some regions to
eutrophication responsible for extensive “dead-zones’ that can impact seriously on the
production of fisheries (Rabalais et al. 2002; Agardy et al. 2005).

The volume and timing of freshwater inputs to marine systems affect the salinity in
estuarine and other coastal environments, in turn affecting the fisheries that depend on
them. Inland changes in the regulation of the timing and volume of freshwater input are
therefore likely to affect the breeding and recruitment of fisheries species dependent
these environments. A dramatic example of fisheries declines associated with changes
in freshwater regime can be found at the mouth of the Colorado River (Kowalewski et
al. 2000; Rowell et al. 2005).

Coral reefs, seagrass meadows, mangroves and estuaries can be degraded or even
destroyed by increasing sedimentation caused by landuse change, and so inland erosion
regulation is important for the conservation of these habitats and of the species that rely
on them (Agardy et al. 2005). For example, a decline in the tropical fisheries of the
Caribbean and Pacific has been linked to the degradation of coral reefs, seagrass beds,
and mangroves from sedimentation (Rogers 1990).

Relationship between habitat area and fisheries provisioning

This question can be addressed in at least three ways:

i)

Are larger areas of suitable habitat (e.g. larger coral reefs, larger mangroves) more
productive in terms of fisheries per unit of area than smaller ones?

86

ii) How does fisheries productivity in a region vary with the fraction of area reserved?

iii) Are larger reserves more effective at improving fisheries than smaller ones?

Answering the first question is important to understanding whether the effects on fisheries
yields are proportional to area change when habitat declines (e.g., when part of a coral reef
is dredged). We found only one study that directly investigated what a fisheries-area
relationship looks like (Watson et al. 1993) but there are likely to be more. We suggest that
the relationship is likely to be linear at least for larger areas, such that doubling habitat area
doubles overall yields (Figure la). However, we also predict that there is likely to be an
area threshold (Roberts et al. 2003), below which yields decline disproportionately with
habitat loss. Whether the threshold corresponds to a large (purple line) or a small area (blue
line) is likely to depend on the extent to which there is low or high species redundancy for
fisheries production, and the extent to which the system has top-down regulation. For high
redundancy and bottom-up regulation, fisheries per unit area are likely to depend essentially
on the primary productivity of the habitat, and will likely remain unaffected by habitat loss
up to very small areas, when area finally becomes too small to retain viable populations of
any exploited species. For systems with low redundancy and/or strong top-down
community regulation, a threshold effect is likely to take place as the area tips below the
minimum viable area needed to support the most sensitive targeted species and/or keystone
species. As discussed above, there is evidence that marine systems are at least in some
cases affected by strong top-down regulation (e.g., by predators, large herbivores; Jackson
et al. 2001b; Folke et al. 2004). There is also evidence that increasing species diversity
increases fisheries productivity (Worm et al. 2006), indicating low functional redundancy
(Micheli & Halpern 2005). This suggests that there may be a relatively high threshold in the
relationship between area and fisheries provisioning (Figure la: purple line). These
thresholds are likely to become even higher when considering the long-term resilience of
fisheries productivity.

We found no empirical tests investigating how the fraction of a region that is reserved (in
no-take zones) affects overall fishing productivity in the region. Theoretical metapopulation
models (Botsford et al. 2003) suggest that the effect will vary with the overall fishing effort.
For relatively low fishing effort, high catches are obtained even in the absence of reserves;
they can be improved by a low level of reservation, but beyond that an increase in reserve
area reduces catches as it reduces the area available for fishing. For high fishing efforts,
though, low levels of reservation may be unable to avert population collapses, and therefore
null catches (this is because the movement of animals and cheating of fishers around
boundaries depletes stocks near the boundaries even within a reserve, and so if the reserve
is too small there is no protection rendered); in this case, it may pay to set aside a
substantial fraction of the region as reserves.

We did not find studies that investigated directly how the size of individual marine reserves
(no-take zones) affects fishing yields in the surrounding areas. There have however been
several studies that measured differences in fish communities (diversity, biomass and/or
density) inside and outside protected areas. It is assumed that positive differences result in
higher spill-over effects resulting in improved fisheries in the surrounding areas (Roberts et
al. 2001; Halpern 2003). Recent reviews have investigated whether these differences vary
with reserve size: most found no relationship (Coté et al. 2001; Halpern 2003; Guidetti &
Sala 2007) but one found a positive relationship (Claudet et al. in press). If there is no
relationship with area, then many small reserves are predicted to be a better option for
maximising fisheries within a region (as they provide higher edge:area ratios and therefore

87

more gain from spills over for the same area), although larger reserves are likely to be
needed to ensure long-term viability (Roberts et al. 2003). Even if in general there is no
relationship between reserve effect and area (Figure 1b, blue line), there is likely a
threshold below which the effect declines very quickly with area (see discussion above)
below which the effect declines very quickly with area, for the same reasons as described
above. The size of such threshold has not been determined (Roberts et al. 2003). Reserve
effect may also plausibly increase with reserve size, as overall species richness increases
(Neigel 2003), but it is likely that it does so with diminishing returns (Figure 1b, purple
line).

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Figure 1. Hypothetical relationship between habitat area and fisheries production,
represented in three ways. a) Relationship between area of a given habitat and fisheries
production, for lower (blue) and higher (purple) thresholds. b) Relationship between fraction
of a region reserved and fisheries production across the entire region, for low (blue) and high
(purple) fishing effort. c) Relationship between the size an individual reserve and reserve
effect (difference between fish biomass/density inside and outside the reserve, assumed to be
an indicator of fisheries catches), assuming no relationship (blue) but an area threshold, or a
positive relationship (purple).

4.6.4 What are the main threats to the provision of marine fisheries?

Human activities have now affected all oceans to various degrees (Halpern et al. 2008).
Marine fisheries are under threat from different, but often interdependent and self-
reinforcing, pressures (Pandolfi et al. 2005).

Overfishing is the most widespread and the dominant direct impact on marine fisheries
(Pauly et al. 2005). Fishing pressure is increasing as technological advances allow
industrial fleets to fish with greater efficiency, farther offshore, and in deeper waters to
meet the global demand for fish. Overfishing is in many parts of the world stimulated by
subsidies which either reduce the costs of fishing or increase the net revenue fishers obtain
(Pauly et al. 2005). Illegal, unregulated, and unreported ([UU) fishing is estimated to
constitute a substantial fraction of the overall catches, undermining efforts for sustainable
fisheries management (Pitcher et al. 2002).

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Habitat destruction affects the provisioning of fisheries by marine systems. Most is caused
by highly destructive fishing techniques, including bottom trawling and coral reef
dynamiting. Expansion of coastal infrastructure may affect marine habitats directly (e.g.,
dredging during port construction) or indirectly (e.g. by increasing sedimentation).
Underwater mining activities (minerals, gas and oil) pose an increasing threat. Some
systems may recover fairly quickly, but others, such as cold-water corals and seamounts,
may take hundreds of years to recover (Pauly et al. 2005).

Pollution is particularly prevalent in coastal habitats, including through sewage discharge
and agricultural runoff. In some regions, the extremely high nutrient loads trigger hypoxia
conditions known as dead zones (Malakoff 1998; Rabalais et al. 2002). The open seas can
also be affected by pollution, for example oil spills. Past waste dumping, particularly
radioactive waste, poses a significant threat to deep-sea ecosystems should the containers
leak, which seems likely over the long term (Pauly et al. 2005). There is still poor
understanding of the effects of persistent organic and inorganic pollutants on marine
fisheries (Pauly et al. 2005).

Climate change is predicted to have both direct and indirect impacts on marine fisheries.
Direct effects act on physiology and behaviour and alter growth, development, reproductive
capacity, mortality, and distribution of targeted species. Indirect effects alter the
productivity, structure, and composition of the ecosystems on which fish depend for food
and shelter. Some of the changes are expected to have positive consequences for fish
production, but in other cases negative impacts are predicted (Brander 2007). Ocean
acidification is predicted to have dramatic impacts for calcifying organisms, including coral
reefs (Hoegh-Guldberg et al. 2007) as well as some types of zooplankton, particularly at
high latitudes (Orr et al. 2005).

Other current threats include biological invasions, such as by competitors (e.g. Daskalov et
al. 2007) or pathogens (e.g. Mumby et al. 2007), while future threats may include larger-
scale projects for carbon sequestration through iron enrichment (Pauly et al. 2005).

4.6.5. Are abrupt changes likely in the provision of marine fisheries?

Fisheries yields of individual species are well-known to be subject to sudden collapse
following overexploitation, with stocks of slow-growing, slow-maturing species in
particular often failing to recover to former levels of abundance (e.g., Hutchings 2000;
Watson & Morato 2004).

There are many documented examples of recent sudden regime shifts in marine systems
(e.g. Jackson et al. 2001b; Bellwood et al 2004; Frank et al. 2005; Daskalov et al. 2007),
with implications for fisheries provisioning. Such shifts seem to be particularly likely in
ecosystems that are or have been under intense fishing effort, and which have been
simplified by the loss of one or more higher-trophic functional groups. Many apparently
stable systems have very little resilience, with shifting between stages possibly depending
on small changes in the abundances of one or a very few species (e.g., Mumby et al. 2007).

While the collapse of entire fisheries has been observed across relatively large areas (e.g.
Black Sea; Daskalov et al. 2007) more often the collapse of a particular species or set of
species results in a shift in fishing effort towards other species (often further down in the
food web; Pauly et al. 1998) or towards other regions/ecosystems (e.g. towards increasing
depths; Pauly et al. 2005). These shifts mask the underlying sequential collapses from

89

ocean-level or global fisheries statistics. Under current knowledge, it is therefore unlikely
that a synchronised global collapse will be observed by 2025, but it is very likely that the
steady decline that has been observed since the mid-1980s will continue, with some local-
or regional-sclae collapses (Pauly et al. 2005).

Climate change (Brander 2007) and related ocean acidification (Orr et al. 2005) are the
greatest sources of uncertainty in predictions of marine fisheries, and potentially could be
responsible for sudden, large-scale, changes in the foreseeable future.

4.6.6 Can we quantify and map the global provision of marine fisheries and how it
might change?

State of knowledge and data availability

Fisheries management has traditionally relied on single-species models aimed at guiding
harvesting decision for particular species of interest (Hilborn et al. 2003b). In the last two
decades, there has been a shift towards ecosystem-based fisheries management, stimulated
by the development of ecosystem modelling (Christensen & Walters 2005). These are
particularly relevant as tools for evaluating scenarios and trade-offs associated with
particular policy options, and as such particularly relevant for the Review on the Economics
of Biodiversity Loss. A diversity of models have been developed and these are reviewed
and compared in Plaganyi (2007).

One of the most widely used models is the Ecopath with Ecosim (EWE) modeling approach
(Christensen & Walters 2005), with development spearheaded by the University of British
Columbia’s Fishery Centre (www.ecopath.org). EWE combines software for ecosystem
trophic mass balance analysis (Ecopath), with a dynamic modeling capability (Ecosim) for
exploring past and future impacts of fishing and environmental disturbances as well as for
exploring optimal fishing policies. Recent versions of the software have brought Ecosim
much closer to traditional single-species stock assessment, by allowing age-structured
representation of particular, important populations and by allowing users to ‘fit’ the model
to data. Ecosim models can be replicated over a spatial map grid (Ecospace) to allow
exploration of policies such as marine protected areas, while accounting for spatial
dispersal/advection effects and migration (Christensen & Walters 2004). EwE has already
been applied for regional-scale policy exploration aiming to achieve economic, social and
ecological sustainability objectives (Pitcher & Cochrane 2002).

90

Based on the EwE approach and software, the University of British Columbia’s Fishery
Centre has just developed a global marine model to explore scenarios for the world’s
oceans (Alder et al. in press). EcoOcean is spatially defined by the 19 FAO fishing areas
covering the world’s oceans and driven by effort of five fleets. The model was constructed
using 43 functional groups, selected with special consideration for exploited fish species
but intended to include all major groups in the oceans. The model relies on global fisheries
datasets available through the Sea Around Us Project (SAUP) and FAO websites, in
particular fish catches, ex-vessel prices, and fleet statistics. The model output from
EcoOcean can be used to describe how landings, profits, the marine trophic index and
depletions may change under different policy scenarios in different areas of the world. For
the first time, EcoOcean provides a common reporting platform so that the outcomes of the
different scenarios can be compared within and between geographic areas, as well as for
fleets and fisheries.

Fishing effort is the most important driver for the ecosystem model simulations, and so
EcoOcean can be applied to investigate the consequences for fisheries of policy actions
under different scenarios, such as a variation in the area covered by marine reserves, or the
implementation or not of fishing quotas for particular groups of species. It also tracks
changes in community structure, for example in the Mean Trophic Index (Pauly et al. 1998)
However, currently the finest spatial resolution possible for the scenario comparison is that
of the 19 FAO fishing areas used to develop the model. The model is therefore not
appropriate for investigating the consequences of finer-scale changes, such as the
implementation of particular marine reserves, the loss of a coral reef, or local changes in
water quality as a result of terrestrial runoff, for example leading to the creation of dead
zones. The next version of EcoOcean has a planned spatial resolution of half-degree cells
(Alder et al. in press), which will allow greater sensitivity to these changes. Further ongoing
developments in the quality of the dataset by the SAUP will improve the accuracy and
reduce the uncertainty associated with the models.

These limitations notwithstanding, EcoOcean seems a good candidate for evaluating the
consequences of policy measures towards reducing biodiversity loss on the provisioning of
marine fisheries. Indeed, EcoOcean has already been applied to explore the scenarios posed
in the Global Environment Outlook 4 (UNEP 2007) and the International Assessment for
Agricultural Science, Technology and Development, demonstrating its usefulness as a
policy tool. The outputs from these two assessments have provided policy makers with
plausible results on which to base future decisions regarding management of fisheries and
marine ecosystems.

For other models of marine ecosystem management, see Plaganyi (2007).
Adequacy of scenarios

Adequate scenarios for application of marine ecosystem management models must specify
fishing effort across space, and for each functional group.

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4.6.7 Insights for economic valuation

Appropriate scenarios will generate different states of the world, with corresponding
predictions of fisheries intensity across the world’s oceans, for different functional groups.
EcoOcean can be used to investigate how these translate into long-term trends in fisheries
yields for each state of the world. Data on the market value of those fisheries, and
appropriate discount rates, can be used to monetise the difference in fisheries yields
between different states of the world. This market value should reflect predicted variations
in demand for different regions/functional groups. For example, increasing demand in
fishmeal for aquaculture may result in increasing prices for small, pelagic fish. An increase
in recreational fisheries, on the other hand, may lead to increasing value in the stocks of
large, high-trophic species.

4.6.8 Some key resources

e Fishery Centre at the University of British Columbia, Vancouver, Canada, is a
leading centre of research on global marine systems, including the development of
modelling tools such as Ecopath with Ecosim (EwE), a freely available
ecological/ecosystem modelling software suite. The Fishery Centre is also the home
of the Sea Around Us Project (SAUP) that actively collates and analyses data on
global fisheries catches, fishing effort and costs of fishing to investigate the impact
of fisheries on the world's marine ecosystems to support the development of
sustainable, ecosystem-based fisheries policies. SAUP is led by Daniel Pauly and
includes amongst other leading experts Jackie Alder, Villy Christensen, and Reg
Watson.

e The Food and Agriculture Organisation (FAO) Fisheries and Aquaculture

Department collates and distributes global data on catches, value, consumption,
fleets and trade for marine fisheries.

e United Nations Environment Programme (UNEP) Global Marine Assessment:

following from a call at the World Summit on Sustainable Development (WSSD)
for the "establishment by 2004 and under United Nations of a regular process for
global reporting and assessment of the state of marine environment, including socio-
economic aspects, both current and foreseeable, building on existing regional
assessments", and subsequent approval by the United Nations General Assembly,

> This is our recommendation for Phase II, based on the results of this review. It does not commit the leaders
of Phase II to follow it, and it does not commit the recommended research group to actually do such
work.

92

effort is underway to evaluate all the recent assessments at regional and national
scales for fisheries as well as other marine resources/issues. A first report Survey of

Global_and Regional Marine Environmental Assessments and Related Scientific
Activities has been completed by UNEP-WCMC.

e A Global Environmental Facility funded Large Marine Ecosystems Strategy is
underway for the assessment and management of international coastal waters, as a
joint effort between the IUCN -— The World Conservation Union, the
Intergovernmental Oceanographic Commission of UNESCO (IOC), other United
Nations agencies, and the US National Oceanic and Atmospheric Administration
(NOAA). This programme is led by Ken Sherman at NOAA.

e The Global Marine Species Assessment is based at Old Dominion University, VA,
USA, coordinated by Kent Carpenter. A partnership between the IUCN Species
Survival Commission and Conservation International’s Centre for Applied
Biodiversity Science, this project will be the first global review of the conservation
status of every marine vertebrate species, and of selected invertebrates and plants.
The project involves a range of partners in compiling and analyzing all existing data
on approximately 20,000 marine species, and will determine the risk of extinction
according to the IUCN Red List Categories and Criteria. The goal is to complete
Red List assessments of approximately 15,000 marine species by the year 2010.

e The Census of Marine Life a global network of researchers in more than 80 nations
engaged in a 10-year scientific initiative to assess and explain the diversity,
distribution, and abundance of life in the oceans. The world's first comprehensive
Census of Marine Life-past, present, and future-will be released in 2010.

e FAO Fisheries and Aquaculture Department provides statistics on fisheries
commodities and trade, global production, fishing fleets, fishers, and consumption
of fish and fisheries products.

e SAUP is actively collating and making freely available data on global fisheries
catches, fishing effort and costs of fishing.

e The Stock Recruitment Database consisting of maps, plots, and numerical data
relating to over 600 fish populations (over 100 species) from all over the world,
collated by the late Ransom Myers at the Department of Biology at Dalhousie
University in Halifax, Nova Scotia, Canada.

e FishBase is was developed at the WorldFish Center in collaboration with the FAO
and other partners. FishBase is a relational database with information on the
ecology, distribution, and conservation of ~28,500 marine and freshwater species
(practically all fish species known to science).

4.6.9 Participants
Authors
Kelly Flower, Ana Rodrigues, Andrew Balmford (University of Cambridge, UK)

Reg Watson (SAUP, University of British Columbia, Canada)

93

Contributors/Authors (currently reviewing)
Jackie Alder (SAUP, University of British Columbia, Canada)
Boris Worm (Dalhousie University, Canada)
Acknowledgements

Daniel Pauly and Rashid Sumaila (SAUP, University of British Columbia, Canada)
provided contacts and suggestions for additional experts.

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4.7 Inland fisheries and aquaculture

This section is a developed overview based on a literature review and expert consultation.
It greatly benefited from expert contributions (see below) but it has not been reviewed by
experts in this field.

4.7.1 Why are inland fisheries and aquaculture important for human wellbeing?

Inland capture fisheries and aquaculture provide globally important sources of food and
income. Catches from inland waters, about 90 percent of which occur in Africa and Asia,
account for 27% of global fisheries production, including include 9.6 million tonnes (7%)
from capture fisheries and 28.9 million tonnes (20%) from aquaculture (70% of which
come from China; FAO 2006). They employ more than 50 million people directly and
many more indirectly (Finlayson et al. 2005). These fisheries are almost entirely finfish,
with some regional exploitation of crustaceans and molluscs (Wood et al. 2005). Three
types of freshwater fisheries are covered in this section:

Subsistence and artisanal fisheries are important for their roles in nutrition, food security,
income generation and informal employment. They are particularly important in certain
regions, such as SE Asia and Africa, where they can be the only source of animal protein
(Finlayson et al. 2005). These fisheries rely on a broad diversity of species and
corresponding diversity of fishing gear (Coates et al. 2003). In Asia, many people practise
integrated agriculture-aquaculture, maintaining and fishing the fish fauna of rice paddies as
an additional income or food source (Dugan et al. 2006). There are very few reliable
official statistics available, and the available figures are thought to be large underestimates
(Finlayson et al. 2005; FAO 2006). For example, Coates et al. (2003) estimates the total
fisheries production of the Lower Mekong Basin to be between 2.6 and 21 times higher
than official statistics. This is mainly due to the diffuse nature of small-scale fisheries (both
temporally and spatially), with data often based on household surveys and creel census; and
the fact that many rivers and lakes are part of more than one country (FAO 2006). Many
African countries have poor statistical information, despite the importance of inland
fisheries to their economies and food supply — for example, in 2004 the FAO had to
estimate the catch for over half of the countries where inland fisheries were known to occur
(FAO 2006).

Commercial fisheries are mainly centred on the Great Lakes of North America and Africa,
although some do occur on the world’s major rivers (Finlayson et al. 2005; Coates et al.
2003). For example, the Great Lakes of North America have supported one of the largest
freshwater fisheries for a century or more, valued (along with sport fishing) at $4 billion
annually (Finlayson et al. 2005). Freshwater commercial fisheries are mostly locally traded,
with a few species (e.g. salmon) making it to the international market. In addition,
developed countries tend to trade within themselves, whereas developing countries export
mostly to developed ones (FAO 2006).

Within commercial fisheries, there is a marked trend towards aquaculture-based systems, as
inland water systems (particularly lentic) are managed more intensively via feeding and
restocking activities to produce a higher yield (‘agriculturalisation’ of freshwater systems;
Wood et al. 2005). Often, there is a focus on a few productive species, rather than the
ecosystem as a whole. For example, the Great Lakes fisheries in North America consist of a

mix of native and introduced species, which are maintained by regular stocking of cage-
reared individuals (Finlayson et al. 2005), whereas the Lake Victoria fishery, which was
once based on over 500 species, now relies on the exotic Nile Perch for ~90% of their catch
(Njiru et al. 2008). There is a continuum of increasing levels of food and stocking input in
freshwater systems — from purely capture fisheries to high-input, high-technology
aquaculture — that makes these two types of fisheries difficult to separate in practice.

Recreational fisheries are dominant in northern temperate countries, where they contribute
significantly to the economy. In Europe, for example, the inland recreational fishing
industry has been valued at $25 billion/year. This move towards recreational fisheries is
also increasing in developing economies (e.g. Brazil, India, Argentina, and several states of
the Zambezi River basin), often to boost the local tourism economy (Dugan 2006). These
fisheries tend to focus on a few prize species, with a substantial proportion being catch-
release. In some areas, there is an increasing trend to manage freshwater systems (including
through stocking, feeding, and habitat management) to favour recreational species,
particularly in reservoirs (Finlayson et al. 2005).

4.7.2 What are the overall trends in the provision of inland fisheries and
aquaculture?

Catches from inland waters, have shown a slowly but steadily increasing trend (about 2% a
year) since 1950, owing in part to stock enhancement practices. However, there are
different trends across regions. Catches are declining in Europe, Canada and the United
States; for example, Europe has seen a 30% decline in catches since 1999 (FAO 2006). In
some cases, increases are due to better recording of subsistence fisheries, for example in the
Lower Mekong Basin (Dugan et al. 2006).

Aquaculture continues to grow more rapidly than all other animal food-producing sectors,
with an average annual growth rate for the world of 8.8 percent per year since 1970.
However, there are signs that the rate of growth for global aquaculture may have peaked,
although high growth rates may continue for some regions and species (FAO 2006).

Recreational fisheries are increasing as a result of urbanisation, increasing affluence and
larger populations, in both developed and developing economies. A significant proportion
of the catch from inland waters in developed countries is from recreational fisheries (FAO
2006), and they too have the potential for dramatic impacts on inland fish stocks (Cooke &
Cowx 2006).

It is thought that subsistence and artisanal fisheries are in decline, due to a pressure to
commercialise and the shrinking of rural populations. However, trends are difficult to
assess due to the paucity of reliable official statistics.

4.7.3 How is the provision of inland fisheries and aquaculture affected by changes in
wild nature?

The provisioning of inland fisheries ultimately depends on the biomass of targeted species.
In most systems this is sustained ‘bottom-up’ by the productivity of primary producers,
which in turn are largely influenced by physicochemical factors (however, some deep lake
demersal fisheries also depend on detrital fallout). ‘Top-down’ trophic interactions at
higher levels also regulate primary productivity and maintain conditions for higher trophic
level species (Carpenter 1987; Carpenter & Kitchell 1988).

96

The overall increases in inland fisheries catches mask the underlying decline in the biomass
of many individual stocks as most inland fisheries in the developing world are
overexploited (Finlayson et al. 2005; Wood et al. 2005; Dugan et al. 2006). As with marine
fisheries, there has been a decline in the mean trophic level of inland fish catches over the
last few decades (‘fishing down the food web’; Welcomme 1999; Wood et al. 2005). This
tends to result in the replacement of larger, slower-reproducing species towards smaller,
fast-reproducing species (including invertebrates), whilst the total productivity may remain
constant or even increase initially as predators disappear (Welcomme 1999). Hence
individual species declines are masked as fisheries switch to other species, as well as by the
use of enhancements (such as stocking or feeding) which are increasingly required to
maintain yield, and by the general rising production of all types of aquaculture (Finlayson
et al. 2005; FAO 2006). Hence, there is no evidence for a generalised collapse in inland
fisheries, even if there have been instances of local collapses related to modifications of
freshwater systems, such as dam building (e.g. the loss of migratory guilds after the
building of a dam on the Mekong; Amornsakchai et al. 2000).

There does not seem to be much evidence for a relationship between species diversity and
freshwater fisheries yields. Indeed, increases in productivity have been observed in some
systems despite dramatic declines in diversity; for example, catches from Lake Victoria
increased from 30,000 mt to an average of 500,000 mt since the 1970s, despite the loss or
near-loss of about half the native species, with the introduced Nile Perch now making
around 90% of the landing’s volume and value (Njiru et al. 2008). However, it is likely that
species diversity (both of targeted species and others) improves the resilience of capture
fisheries and aquaculture to anthropogenic and environmental changes, as seems to be the
case with marine fisheries (Worm et al. 2006; section 4.6). Indeed, inland fisheries are
being simplified to depend on a very few species that do well in the modified habitats that
we are creating, and while this seems to be working well for fisheries in the short-term it
may be risky in the long-term if conditions (e.g. climate) change.

In the transition to more intensively managed freshwater fisheries, the tendency is to focus
on a few more desirable and/or more productive species. However, the genetic diversity of
managed populations remains very important, both as a source of variation for breeding in
desirable traits and to increase the resilience of farm fish to disease. Also, there is still a
very broad set of freshwater species being domesticated/selected for aquaculture
production, and so there is a greater reliance on species diversity than in other food
production sectors (FAO 2006).

A diversity of species is also important for the colonisation of new habitats (such as
reservoirs) created by freshwater modification. Often, species from the previous habitats
(e.g. flowing water environments) are unable to establish themselves, and in this case other
species are frequently introduced, such as cichlids and carps (Finlayson et al. 2005).

Particular keystone species can be very important in some freshwater systems. For
example, the loss of andromadous salmon from rivers results in a substantial reduction in
nutrient input (Allan et al. 2005; also McIntyre et al. 2007) and beavers’ activities
substantially change freshwater habitats (Rosell et al. 2005; Collen & Gibson 2001).

97

Freshwater biodiversity is highly threatened, including for example 56% of endemic fish in
the Mediterranean basin (Smith & Darwall 2006), and 54% of endemic fish in Madagascar
(Darwall et al. 2005).

Freshwater communities are highly affected by loss, degradation, and transformation of
freshwater habitats (Finlayson et al. 2005). Furthermore, many freshwater species use
distinct habitats for breeding, hunting and nurseries (e.g. hundreds of species migrate from
large rivers/lakes to streams in order to breed). This renders them particularly vulnerable, as
they can be affected by the degradation of any of those habitats, as well as by factors
affecting the linkages between habitats, such as through the construction of dams and
through the separation between the main river channel and the floodplain. Communities in
lotic systems (rivers, streams) are frequently dependent on particular hydrological regimes,
which can be disturbed by land use changes (e.g. logging), water abstraction, and dam
building (Finlayson et al. 2005; Welcomme & Halls 2004). These systems are also affected
by river canalisation, separation from the flood plain, and water pollution. Lentic systems
(lakes, reservoirs, wetlands) are particularly vulnerable to eutrophication (e.g., in Lake
Victoria a doubling of nutrient input led to an 6-8 times increase in algal biomass; Balirwa
2007), sedimentation (e.g. in Lake Malawi forest loss resulted in increased sedimentation,
and reduced quality habitat for cichlid species; Rusuwa et al. 2006), the introduction of
invasive species (e.g. Nile perch in Larke Victoria; Njiru et al. 2008; Balirwa 2007), water
abstraction (e.g., Aral Sea; Finlayson et al. 2005), and loss/decline of area due to land use
change (e.g. Lake Victoria has lost many wetland habitats at a current rate of ~3.8%
annually; Njiru et al. 2008). Floodplains are key habitats for many species, particularly for
breeding, and they are being threatened by disconnection from main river channel and by
habitat loss as the floodplain is appropriated for agriculture and human settlements.

Changes in the habitats within the water catchment area of freshwater systems (including
riparian habitats) can have important and widespread effects on the quality, extent and
composition of freshwater ecosystems (Finlayson et al. 2005). Terrestrial systems can
contribute to the regulation of energy and material transfer between the riparian and aquatic
ecosystems, particularly in small water bodies (Pusey & Arthington 2003), affecting water
quality (e.g., via leaf litter input; Allan & Flecker 1993), water turbidity and sedimentation
(e.g. increasing erosion following deforestation and road building; Alin et al. 1999), and
hydrologic regimes (e.g., and changes in quantity and seasonality of water flow following
deforestation; Allan and Flecker 1993; Welcomme & Halls 2004). Terrestrial habitats can
also provide nutrition to freshwater species (e.g. fruit eating fish guilds) and contribute to
the structure of freshwater habitats (e.g. by affecting sedimentation and the presence of
wood debris; Wright & Flecker 2004).

While the negative effects of habitat changes on freshwater communities are well-
established, it is often less obvious what the effects are on freshwater fisheries proper. In
some cases, the creation of new habitats favours particular species, which in turn can be
exploited in fisheries. Examples include reservoirs following dam construction, and rice
fields following the conversion of floodplain habitat (Dugan et al. in press). In many cases,
it would seem that some species benefit whilst others lose out, and so the
advantages/disadvantages for fisheries are dependent on the role of species in the
ecosystem, and their importance to fisheries.

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Relationship between habitat area and the provision of inland fisheries

We predict that in principle marine fisheries decline linearly with the area of relevant
habitat (e.g. floodplain, rivers). However, reality is likely to be much more complex, given
the intrinsic interconnectivity of freshwater systems. Hence, a stretch of river cannot be
retained on its own if the upper stream river is lost, while a floodplain area cannot exist in
isolation from the main river channel.

4.7.4 What are the main threats to the provision of inland fisheries and aquaculture?

Water abstraction (particularly for agriculture) is perhaps the main threat to inland fisheries
and aquaculture, altering flowing regimes and reducing river flow in riverine systems
(sometimes completely, as in sections of the Colorado River), reducing the area of available
habitat in lentic systems (e.g. Aral Sea, where water volume has been reduced by 75% since
1960; Finlayson et al. 2005), and allowing saline water intrusion in coastal freshwater
systems (Finlayson et al. 2005). Water translocation is also an increasingly important
consideration, as water is diverted from one river basin to another (e.g. Yellow River in
China). Fisheries are directly impacted as the flood cycle and water availability are
disrupted, and there is an increased threat from invasive alien species as river basins are
linked.

River_canalisation and separation from the floodplain changes hydrologic regimes
substantially, affecting communities in both river systems and floodplains. Floodplains are

also directly threatened by habitat loss, as the land is appropriated for agriculture and
human settlements. Indeed, floodplain systems are one of the most threatened habitats in
the world; Europe, for example, has lost up to 95% of its riverine floodplains, and those that
are left are often decoupled from the hydromorphological dynamics of the river (Tockner &
Stanford 2002).

Dam building destroys habitats upstream, causes major disruption to hydrological and
sedimentation flows downstream, creates a barrier to species migrations, and fragments
resident species populations. Fisheries based on migratory species may be totally
compromised (e.g. sturgeon fisheries). However, fisheries may improve in the reservoir
area when there is restocking with suitable (frequently alien) species (Finlayson et al.
2005).

Overexploitation leads to population reductions and even extinction of the targeted species.
A FAO major assessment of inland fisheries (1999, in Finlayson et al. 2005) reported that
most inland capture fisheries that rely on natural reproduction of the stocks are overfished
or are being fished at their biological limit.

Water pollution and eutrophication (particularly due to agriculture, but also urban and
industrial run-off) significantly affect freshwater communities. Eutrophication, caused by
increased nutrient loads (particularly due to fertilisers in agriculture runoff) is the most
widespread problem in lakes and reservoirs, and also one of the most difficult to abate.
Toxic contaminants (e.g. heavy metals and organochroline compounds) may render
fisheries inappropriate for human consumption (Finlayson et al. 2005)

Landuse changes in terrestrial habitats (e.g. deforestation) affect water and sediment regime
flows and water quality.

99

Invasive species may affect fisheries directly, by predating, competing with, or parasiting
the targeted species, or indirectly, by affecting habitat characteristics (e.g. water hyacinth)
and water regimes (e.g. mimosa; Finlayson et al. 2005)

Climate change is likely to affect inland fisheries and aquaculture by altering hydrologic
flows.

Many aquaculture practises also contribute to the degradation of inland water systems, via

pollution, the spread _of diseases and the introduction of alien species (Finlayson et al.
2005).

It is important to keep in mind that some anthropogenic changes are also resulting in
increased fisheries, due to stocking and feeding (particularly of small lakes and reservoirs)
and management of habitat structure for particular target species (Finlayson et al. 2005;
Lake Victoria example: Njiru 2008).

4.7.5 Are abrupt changes likely in the provisioning of freshwater fisheries and
aquaculture?

As discussed above, there does not seem to be evidence of widespread collapse in
freshwater fisheries as a result of overharvesting. It is not impossible that the ‘fishing down
the food web’ process may lead to such collapses, as is happening in marine systems
(section 4.6), but on the other hand. freshwater systems may follow a different trajectory
given the tendency towards increased management (feeding, restocking etc).

Human-induced eutrophication can trigger sudden shifts in lakes and reservoirs from clear
to turbid due to algal blooms. These blooms may include toxic cyanobacteria and affect
freshwater fisheries and recreational use of the water bodies. Reduction of nutrient
concentrations is often insufficient to restore the original state, with restoration requiring
substantially lower nutrient levels than those at which the regime shift occurred (Scheffer et
al. 2001). Particular species invasions may also cause localised disruption to fisheries and
aquaculture.

Climate change has perhaps the potential to cause widespread disruption to inland fisheries
and aquaculture, but currently this is essentially speculation.

4.7.6 Can we quantify and map the global provision of inland fisheries and
aquaculture, and how it might change?

There is currently no overall global model for the productivity of freshwater fisheries and
aquaculture. Different models exist for different systems in different areas, including for
example:

100

e Tropical river floodplain — model investigating the impact of hydrological changes
on fish yield/productivity, combining a hydrological and population model to
simulate production changes with different water regimes (Halls & Welcomme
2004).

e Northern temperate lakes — the Morphoedaphic Index (Ryder 1965) has been
extensively used as a predictor of fisheries yields based on total dissolved solids and
depth of lake. However its accuracy is contested, and there are many subtle
variations in analysis.

e African water bodies — models which predict fisheries production have been
generated by the Africa Water Resources Database. These models use physico-
chemistry and climatological maps to predict production based (mainly) on surface
area.

It is likely that different freshwater systems will need to be modelled separately. As with
marine fisheries (section 4.6), it is key that models reflect sustainable production, rather
than current catches, as the latter are likely to be unsustainable in the long term.

Except for some well-studied systems, the state of knowledge regarding freshwater
fisheries is very poor. FAO has historically managed global statistics, but they are currently
decentralising for freshwater fisheries and so they are not the central repository anymore.
The leading role in freshwater statistics is now taken by World Fish Centre. The best
available data are being compiled at regional scales (e.g. Mekong River Commission

Fisheries Programme).

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What can be done in Phase 2? At what cost? By whom??

The experts we consulted were of the opinion that a reasonable model, or at least a
first estimate, could be generated within one year, by reviewing existing studies on
sustainable freshwater fisheries production across the world and extrapolating from
those. Two possible tiers were proposed:

a) Engage a group of 12-15 experts covering different regions, including people
with good skills in GIS and statistical modelling (see suggested names under
key resources), based on existing models and statistics, ask them to provide a
predictive collective opinion of how different states of the world would differ
in inland fisheries and aquaculture production. One or several collective
meetings (NCEAS-style workshops) would be needed.

As in a), but proceeded by regional meetings to collect and collate existing
(but currently dispersed) regional information on which to base finer
estimates for productivity change.

It is estimated that it would take around 36-48 researcher-months for a suitable team
to compile a reasonable global estimate from existing knowledge (tier a). Resources
would need to be allocated for meetings.

Adequacy of scenarios

In order to be useful in predicting the impacts of changes in wild nature on the provision of
freshwater fisheries and aquaculture, the scenarios would ideally provide information on:

e The availability of water (temporally and spatially), particularly affected by
abstraction for agriculture and hydroelectric projects.

e Land available in the floodplain, closely linked to the expansion of agriculture and
urban development.

e The connectivity of water systems, particularly the construction of dams.
e The canalisation of rivers.
4.7.7 Insights for economic valuation

The subsequent economic evaluation, based on the maps of biophysical production, would
need to carefully consider what the end use of each type of fisheries is. Different valuation
methods would be necessary in each case:

3 This is our recommendation for Phase II, based on the results of this review. It does not commit the leaders
of Phase 2 to follow it, and it does not commit the recommended research group to actually do such work.

102

For subsistence and artisanal fisheries, data are likely to come from household
surveys and creel census (i.e. Asia, South America, Africa)

For commercial fisheries, most data can be taken from the market in fish and fish
produce (African and North American Great Lakes, some large rivers).

Recreational fisheries are likely to need valuation using data from licenses and
expenses, utilising Contingent Valuation and Travel Cost methods (North America,
Europe, Australia, some parts of the tropics).

Some key resources

Patrick Dugan, at the World Fish Centre, would be in a good position to oversee this
project.

R. Hilborn, at the University of Washington (USA) and E. Jeppesen (Denmark)
could be valuable with regards to statistical modelling of fisheries-ecosystem
relationships.

Robin Welcomme, Institute of Fisheries Management, UK.

Will Darwall, Freshwater Biodiversity Unit, SSC/IUCN, Cambridge, UK: leading the
global freshwater assessment.

Peter Mcintyre, School of Natural Resources and Environment, University of
Michigan, USA.

Gerd Marmulla, FAO.

Robin Abell, Senior Freshwater Conservation Biologist, Conservation Science,
WWE-US, Washington DC, USA.

Carmen Revenga, World Resources Institute, Washington DC, USA.
Regional expertise:

o Mekong/Asia: Eric Baran (CGIAR), Chris Barlow (Mekong River
Commission Fisheries Programme); Ashley Halls (Aquae Sulis Ltd, UK).

o Africa: Patrick Dugan, Africa Water Resources Database.

o Latin America/South America: Miguel Petrere (Universidade Estadual
Paulista, Rio Claro, SP, Brazil; for Brazil and much of Latin America);
Oriana Almeida (IPAM, Amazonian Institute of Environmental Research,
Brazil; for the Amazon); Angelo Antonio Agostinho (Universidade Estadual
de Maringa, PR, Brazil).

o Europe: tan Cowx (University of Hull, UK)

o Australia: Angela Arthington (Griffith University, Australia)

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4.7.9 Participants
Authors

Kelly Flower, Andrew Balmford, Ana Rodrigues (University of Cambridge, UK)
Contributors

The following experts provided very valuable insights on which this section was based. We
apologise to them that time constraints prevented us from circulating the text for revision.

Will Darwall (SSC/IUCN, Cambridge, UK)

Patrick Dugan (World Fish Centre)

Peter McIntyre (University of Michigan, USA)

Robin Welcomme (Institute of Fisheries Management, UK)
Acknowledgements

Robin Abell (WWF-US) provided useful suggestions for experts.

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4.8 Wild animal products

This section is a fully developed review, including contributions by experts in the field, most
of whom subsequently reviewed the text.

4.8.1 Why is this benefit important for human wellbeing?

The harvesting of wild animal products is defined here to include the capture of terrestrial
wild animals or their products, including mammals, birds, reptiles, amphibians and
invertebrates (for example snails and insects, including honey harvesting). Wild animal
harvesting can be broadly classified into three categories: subsistence, commercial, and
recreational (Table 2). In subsistence harvesting, the benefits obtained from wildlife
(particularly food) are directly consumed or used by, and play a very significant role in the
subsistence of, the harvester and its family (e.g. Peres 2000). Commercial harvesting takes
place when most of the products are sold for profit (e.g. caiman meat trade; Silveira et al.
1999; chameleon pet trade; Carpenter et al. 2004). Recreational hunting refers to activities
in which the main objective is the personal enjoyment of the hunter, rather than food or
profit (e.g. trophy lion hunting in Tanzania; Whitman et al. 2004). There is substantial
overlap between these categories. The differentiation between subsistence and commercial
harvesting may be subtle (with, for example, subsistence hunters often selling the excess or
the most valuable species as a source of income; Wilkie & Carpenter 1999b), and the
transition between the two may happen quickly as markets open (e.g. Sierra et al. 1999).
Recreational hunting may also have roots in traditional (either subsistence or commercial)
hunting activities (McCorquodale 1997).

Table 2 Comparison of main attributes of subsistence, commercial and recreational hunting.

—
Attribute Subsistence hunting Commercial hunting Recreational hunting

Purpose Food Profit Sport
Users Rural people, poor Rural and urban High income
Volume High Very High Low
Area Often close to residence, Distant from residence Distant from residence,
permanent variable
Frequency Year round Year round Weekends, hunting
seasons
Principle Prey Mostly mammals Mostly mammals Mostly birds, but medium
to large mammals also.
Legality Variable according to Often illegal Usually legal
country
Control Difficult but may be Often very difficult Feasible
feasible
Social Value _ High Low Medium

The main benefit obtained from the harvesting of wild animals is arguably food (e.g. Fa et
al. 2003), with other benefits including raw materials such as hides (e.g. Iriarte & Jaksic
1986), medicines or substances traditionally considered to have medicinal value (e.g. Alves
& Rosa 2005; Ntiamoa-Baidu 1997), pets (e.g. Carpenter et al. 2004), and personal

105

enjoyment (e.g. Wilkie & Carpenter 1999a). Here we focus particularly on harvesting for
food (referred throughout as ‘wild meat’) in tropical regions, with some discussion of
harvesting for personal enjoyment (‘recreational hunting’).

Wild meat is a valuable source of nutrition across the globe, but its importance as food is
particularly high in tropical regions (with some forest-living peoples obtaining up to 90% of
animal protein from wild animals; Fa et al. 2003). The literature focuses on large
vertebrates (> | kg), which seem to contribute the most to wild meat provision (particularly
mammals, followed by birds, reptiles and amphibians) and this is the emphasis of this
section as well. Invertebrates also have a very significant nutritional role in some areas, but
these are frequently ignored in studies of wild food harvesting, and traditional consumption
is declining due to a negative bias by Westerners (deFoliart 1999). deFoliart (1999)
presents a comprehensive review of the use of insects as food across the world, including
for example a study that found that 65 species of insects furnished 10% of the animal
protein intake in the Democratic Republic of Congo (compared to 30% from game and 47%
from fishing), and the case of Thailand, where more than 80 species of insects (35 families)
are used as food, and locust and grasshopper catching are becoming alternatives to pesticide
spraying during plague periods.

Wild meat is important for the poor and landless, particularly during times of the year when
agricultural production is lower (de Merode et al. 2004), and periods of famine and
insecurity or conflict, when normal food supply mechanisms are disrupted and local or
displaced populations have limited access to other forms of nutrition (Elliott et al. 2002;
Wood et al. 2005). In some regions, though, reliance on wild meat is permanent (see
Ntiamoa-Baidu 1997 for a review of wild meat use across Africa). While the dependency
on wild meat is greatest in rural communities, townspeople can also be major wildlife
consumers (Milner-Gulland et al. 2003). In the transition from rural to urban consumption,
wild meat consumption becomes less of a nutritional need (in which case people will
readily switch to domestic meat, if it becomes more readily available) and more a matter of
preference based on tradition or status (in which case at least some consumption persists
even when wild meat becomes substantially more expensive than domestic alternatives)
(Milner-Gulland et al. 2003; see Goudarzi [2006] for an extreme example of African wild
meat being sold as a luxury product in US and European markets). Urban demand creates
the opportunity for trade, and therefore wild meat harvesting is also an important source of
income for local people (e.g. Silveira et al. 1999; Ntiamoa-Baidu 1997).

Estimates of the annual wild meat harvest, mainly mammals, include 23,500 tonnes in
Sarawak, 67,000— 164,000 tonnes in the Brazilian Amazon, and 1—3.4 million tonnes in
Central Africa (Milner-Gulland et al. 2003 and references therein). Wild meat harvest and
trade are often excluded from official statistics (Wood et al. 2005) but the economic value
of annual trade has been estimated at, for example, over US$175m for the Amazon Basin
and US$200m for Céte d’Ivoire (Rao & McGowan 2002 and references therein).

Recreational hunting is the dominant form of terrestrial wild animal harvesting in the most
economically affluent countries. The nutritional value of wild meat is negligible given wide
access to cheaper alternatives. And, quite the opposite of being a source of income, hunting
becomes a substantial expense as hunters spend considerable sums for the pleasure and
social status of doing it. Regions in Europe and the United States with higher fractions of
rural population tend to have a higher frequency of hunters, supporting the hypothesis that
the motivation to hunt is based on cultural roots (Heberlein et al. 2002). In the United
States, 5% of the population 16 years old and older went hunting in 2006, spending US$22

106

billion (USFWS 2007). In the European Union, there are more than seven million hunters
(FACE 2007), estimated to spend €10 billion per year (Pinet 1995). Trophy hunting by
foreign tourists generates at least US$201 million per year in sub-Saharan Africa, from a
minimum of 18,500 clients (Lindsey et al. 2007), while in Eurasia US$33-39 million
dollars are generated from 45,000 to 60,000 foreign hunters (Hofer, 2002).

Harvesting of wild animals has costs to human wellbeing as well. It is widely
acknowledged that overexploitation is resulting in a worldwide depletion, and sometimes
the complete extirpation, of many species. Historically, harvesting has already contributed
to the demise of 55 terrestrial animal species (listed by the 2007 IUCN Red List of
Threatened Species as Extinct or Extinct in the Wild), and 792 additional species are
threatened with extinction from hunting (IUCN 2007). The Convention on International
Trade in Endangered Species of Wild Fauna and Flora (CITES), an international agreement
between governments that aims to ensure that international trade in specimens of wild
animals and plants does not threaten their survival, has listed about 5,000 animals
(including terrestrial, marine, and freshwater) for which trade regulations are in place
(CITES 2008). Species extinctions preclude the provision of other present and future
benefits. The large-bodied, slow-growing, species that are the most vulnerable to
overharvesting (Bodmer et al. 1997) are often charismatic species that can attract tourists
and funding, such as tigers, elephants, rhinos, and gorillas (Walpole & Leader-Williams
2002). Hunting also raises ethical and animal welfare issues (e.g. Gilbert 2000), and there is
increasing resistance to the idea of killing animals for sport among urban residents in the
industrialised world, as highlighted by the recent ban on fox hunting in the UK (Lindsey et
al. 2006). Outbreaks in zoonotic diseases (ie shared between humans, domestic animals,
and wildlife; e.g. SARS, West Nile virus), causing hundreds of billions of dollars of
economic damage as well as mortality in humans, livestock and wildlife, have been
attributed to the global wildlife trade (Karesh et al. 2005).

4.8.2 What are the overall trends in the provision of wild animal products?

The Millennium Ecosystem Assessment (MEA 2005) considered that there is a medium to
high certainty that wild food provisioning is declining, as natural habitats worldwide are
under increasing pressure and wild animal populations are exploited at unsustainable levels
(Wood et al. 2005).

The most direct measure of trends in the provision of benefits from wild animal harvesting
is derived from the flow of wildlife products, for example through statistics on consumption
or trade. However, often these measures cannot be used to extrapolate future (even near-
future) trends. Indeed, a steadily increasing trend in consumption or in market volumes may
persist for some time despite depletion of the source populations. This may occur if there is
increased harvesting effort (e.g. traps being replaced by guns), if harvesting extends to
species not previously exploited, or if harvesting is expanding to new areas (Crookes et al.
2005). The higher the demand, the greater the economic incentive to source wildlife
products further away, so a thriving market may persist for a substantial period. Ultimately,
though, the widespread depletion of animal populations will inevitably result in declines in
the supply of wildlife products.

Overexploitation of wild animals is typically linked to human population growth, and
consequently it is currently felt most in Asia, followed by Africa and by Latin America
(Milner-Gulland et al. 2003). Depletion of animal populations is often associated with
habitat loss, but may also occur in forests that otherwise appear to be intact (‘empty forest

107

syndrome’; Redford 1992). In Asia, a thriving trade of wild animal products for food, pets
and traditional medicines results in widespread population declines and local extirpations
(The World Bank 2005; Corlett 2007), with overexploitation being one of the most
importants threat to some groups of species, such as amphibians (Stuart et al. 2004) and
freshwater turtles and tortoises (van Dijk et al. 2000). In Vietnam, for example, 12 large
vertebrates have been extirpated in the past 40 years largely because of hunting (Bennett &
Rao 2002). In Africa, the scale of wild meat hunting has accelerated over the last few
decades, reaching particularly worrying levels in Central and West Africa (e.g. Fa et al.
2006). For example, hunting has been identified as the main cause for the collapse in gorilla
and common chimpanzee populations in western equatorial Africa (Walsh et al. 2003). In
Latin America, where human population densities and resulting rates of exploitation are
still considerably lower than in Africa (Fa et al. 2002), even subsistence hunting can
substantially affect population densities and faunal community structure. In the Amazon,
the aggregated biomass of the most sensitive species was reduced by half between non-
hunted and light-to-moderately hunted sites, and reduced 11-fold between non-hunted and
heavily hunted sites (Peres and Palacios 2007). Given that wild meat exploitation in tropical
forests is often substantially higher than natural productivity (e.g., Fa et al. 2002) supplies
in wild meat protein are predicted to decline (e.g. by 81% in the next 50 years, in the Congo
Basin; Fa et al. 2003).

On the other hand, game ranching (the maintenance of wild animals in defined areas
delineated by fences) is expanding as a method of wild meat production, particularly in
Sourthern Africa, although the main aim of these farms is to attract wildlife tourism or
trophy hunting (Ntiamoa-Baidu 1997).

As for trends in recreational hunting, participation in the US has declined by about 10%
between 1996 and 2001 (USFWS 2007), while numbers of hunters in France have declined
by about 3% per year (RSPB 2006). These declines may be explicable by increasing
urbanisation (Heberlein et al. 2002), often associated with increasing animal welfare
concerns. However, there is evidence that trophy hunting is expanding in Southern Africa
and Tanzania, but stable or declining in Central and West Africa (Lindsey et al. 2007).

4.8.3 How is the provisioning of wild animal products affected by changes in wild
nature?

The supply of wild meat is largely determined by the ecosystem’s productivity of the
targeted species. Often there is little consumer preference amongst species and they are
mutually interchangeable as protein sources. Hunter effort varies spatially according to
drivers such as market access and alternative livelihoods, and combines with overall animal
abundance to determine supply. Hunting history affects current supply (Peres 1999).

Large-bodied (average adults > 1 kg) primates, ungulates and rodents account for most of
the wildlife biomass hunted by humans for food across the tropics, with ungulates typically
making up by far most of the biomass. Robinson & Bennett (2004) reviewed data on the
standing biomass of these taxonomic groups across a diversity of relatively undisturbed
ecosystems, finding a peak for intermediate levels of rainfall. The increase in biomass with
rainfall reflects a positive association with in primary productivity with rainfall, while the
decline for highest rainfall levels may reflect a reduction in food availability, particularly
for ungulates, with increasing forest density (as much of the plant biomass is in the form of
inedible tree trunks and toxic leaves, with high proportion of the primary productivity high
in the canopy; Robinson & Bennett [2004] and references therein). Accordingly, they found

108

that ungulates predominate in open habitats while primates are realtively more common in
forested areas. Overall, their results indicate that mammalian standing biomass is low
below 100 mm of annual rainfall, that grasslands with more than 500 mm can commonly
support mammalian biomasses of between 15,000 and 20 000 kg/km?, and that the total
mammalian biomass in tropical forests rarely exceeds 3,000 kg/km? (but see Fa & Peres
2001 for much higher values in Africa). Within a given habitat type, soil fertility can have a
major effect on primary productivity and, therefore, on the standing biomass of mammal
species; for example, Peres (2000) found that nutrient-rich floodplain forests consistently
contained a greater game biomass than nutrient-poor unflooded forests.

The sustainable production of wild meat is affected not only by the standing biomass but
also by the species’ rate of natural increase. The percentage of standing biomass that can be
harvested without resulting in population decline (the maximum sustainable offtake rate) is
typically lower for long-lived species, estimated by Robinson & Bennett (2004) to exceed
50% for some rodent species, be around 20% for ungulate species, and usually under 5%
for primates. Given that, as described above, ungulates and (to a lesser extent) rodents
dominate in savannah habitats, while in forest primates play a more dominat role, the
maximum sustainable offtake is likely to be higher in the former (maybe as much as 20% of
the standing biomass) than in the latter (perhaps 10%; Robinson & Bennett 2004). A higher
sustainable offtake combined with a larger standing biomass means that savannahs are
typically more productive than closed forests, and therefore have a higher likelihood of
being harvested sustainably (Robinson & Bennett 2004).

While a wide diversity of species is usually harvested, the bulk of the wild meat biomass
captured in any one location tends to be dominated by a few species (e.g. Fa et al. 2005,
2006). These, however, change in space and time. At least in South America, large species,
such as tapirs, tend to be targeted by hunters first, with efforts turning to smaller species,
such as squirrels or rats, as these disappear. This preference for larger-bodied species seems
to be explained by a maximisation of return (kg of meat) per unit of hunting effort, rather
than intrinsic cultural preferences for particular species (Jerozolimski & Peres 2003).
Unfortunately, large species tend to have life-history traits (low reproductive rates, low
population densities, long generation time, and long lifespans) that make them particularly
vulnerable to overexploitation and less likely to recover once depleted (Bodmer et al. 1997;
Jerozolimski & Peres 2003). As the most desirable species disappear, hunters’ selectivity
declines and they tend to diversify their prey base (Jerozolimski & Peres 2003). Depletion
of the large-bodied species may not result in a decline in wild meat supplies; theoretically,
it may even result in an increase, as their reduced density allows faster-growing (more
productive) species to dominate the ecosystem (density compensation; Peres & Dolman
2000; Jerozolimski & Peres 2003). Sustainable exploitation, in the sense of a future
continuous supply of wild meat, may therefore become a more achievable aim in these
conditions (Cowlishaw et al. 2005; but see Waite 2007), although this process cannot be
considered sustainable in the ecological sense if it requires the extinction of a subset of
species.

Habitat loss inevitably results in the extirpation of habitat-dependent species and the wild
food obtained from them. Nonetheless, a temporary increase in the supply of wild meat
may be observed during the process of habitat loss, for example as new logging roads are
opened, due to increased accessibility to wildlife. In the longer-term, though, supply is
expected to decline as populations are depleted by the synergistic effects of hunting and
habitat loss (Peres 2001). Habitat fragmentation reduces the habitat available to species

109

averse to the matrix and to edges. Peres (2001) calculated that the minimum fragment area
needed to maintain a sustainable harvest of 46 hunted species in the Amazon (given levels
of extraction documented to date) was as high as 2000 km?, with tapirs as the most
spatially-demanding species. This is in sharp contrast with the observed size of fragments
in the region of the deforestation frontier, with a modal area of less than | ha (Peres 2001).
However, it is not clear that species diversity per se affects wild meat supply (see below).

Fragmentation also isolates populations, thereby reducing or precluding colonisation of
overharvested areas from nearby non-harvested or underharvested regions, disrupting the
source-sink dynamics that help maintain the long-term sustainability of game harvest over
large spatial scales (Novaro et al. 2000. 2005). This may explain why surprisingly few local
extinctions have been reported in persistently overhunted (but finite) harvested areas within
large, non-harvested, forest landscapes (Peres 2001; Ohl-Schacherer et al. 2007).

Accordingly, the spatial configuration of harvested and non-harvested areas can affect the
resilience of populations to harvest levels (McCullough 1996).

Robinson & Bennett (2004) hypothesised that the conversion of primary forest to secondary
habitats (such as forest—farm—fallow mosaics) may increase the supply of wild meat (see
also Wilkie 2005). As discussed above, in dense forests much of the plant biomass is in the
form of inedible tree trunks and toxic leaves, with high proportion of the primary
productivity high in the canopy. Habitat disturbance opens the canopy, increasing the
amount of browse and graze available for herbivorous animals such as ungulates, which can
in principle increase the standing biomass of wild animals. However, disturbance is usually
associated with increasing human presence (through opening of roads and logging),
resulting in a decrease in the biomass of wildlife because of hunting and the introduction of
competing livestock. On the other hand, hunting also shifts the composition of the faunal
community, so that the biomass of large-bodied, slow reproducing species (including
predators) declines, while the biomass of more adaptable, rapidly-reproducing, small-
bodied species, might increase. Robinson & Bennett (2004) propose that the consequence
of this shift is that even though overall biomass might decline, biomass production might,
under certain circumstances, increase with the transition between primary forest and more
disturbed habitats. This remains unresolved, with the studies reviewed by Robinson &
Bennett (2004) providing some evidence in support of this currently open hypothesis. The
effects may differ between continents depending on their respective faunal assemblages. In
the Neotropics, most large vertebrates are primarily or partially frugivorous or granivorous
(Robinson & Redford 1986b), and so a shift from fruit-based to browse-based plant food
resources may not necessarily lead to an overall increase in the standing biomass and/or
productivity of wild meat. Irrespective of whether secondary habitats are more or less
productive than primary forest, they are certainly not empty of wildlife and they play an
important role in wild meat supply in many areas (Daily et al. 2003; Naughton-Treves et al.
2003).

In theory, a wider diversity of targeted species could directly improve the supply of wild
meat in two possible ways:

- By increasing the secondary productivity of the ecosystem, if different species
explore ecological resources in a complementary way. This effect has been
predicted theoretically and demonstrated in experiments with simplified plant
systems (Hooper et al. 2005) but we found no evidence that it may happen for the
provision of wild meat.

110

- Through a portfolio effect, by providing a diversity of options to hunters. Variety
does not seem to be a major factor from the consumer’s perspective, with the most
abundant species often being the most preferred (but see East et al. 2005). A
diversity of species could also allow hunters to switch to other species as some
become too scarce. Indeed this is the case, but species disappearance tends to follow
a nested pattern, with the most sensitive only being present in species-rich systems
and the most tolerant species dominating species-poor systems. As discussed below,
we found no evidence that the latter are less productive than the former.

Species diversity may nonetheless affect the provision of wild meat indirectly, by
influencing ecosystem composition, spatial structure, and ultimately resilience. The species
that tend to be the most affected by hunting often play key roles in maintaining tropical
forests, performing ecological functions (grazing, browsing, trampling, seed dispersal and
excavation) that are disproportionately large relative to their total numbers (Robinson et al.
1999; Wilkie & Lee 2004). In particular, depletion and extinction of frugivorous species
seems to have a significant effect on seed dispersal and consequently on the recruitment of
particular plant species (Roldan & Simonetti 2001). Large-bodied primates (woolly
monkeys and spider monkeys), in the Amazon are key to the recruitment of many large-
seeded tree and liana species (Peres & Palacio 2007), while tapirs are key to the recruitment
of a large-seeded palm in the Amazon (Fragoso 1997). In the Congo Basin, forest elephants
are keystone species, ecosystem engineers, and the bulk of mammalian biomass where they
occur. The decline of these species (all of them favoured by hunters) may therefore result in
long-term changes in the forest communities, both in terms of floristic compositional and of
spatial structure (Stoner et al 2007).

The abundance and diversity of other (non-targeted) species can theoretically affect wild

meat production through either bottom-up or top-down processes (Sinclair & Krebs 2002).
Bottom-up effects are more straightforward in that a higher abundance of food species can
improve animal productivity. Diversity is also likely to have a positive effect, increasing the
stability of food provision across the year. Top-down effects are more contentious. The
effect of a higher number of predators is the decline in prey abundance, including many
species (such as ungulates, rodents, primates, and squirrels) that are the basis of wild meat
supply. And indeed Michalski & Peres (2007) found that a decline in predators leads to an
increase in wild meat supply through predator release. However, a diversity of predator
species may have a positive effect, if top predators prey on other predator species
(mesopredators). Theoretical studies (Palomares et al. 1995) and some empirical results
(Crooks & Soulé 1999; Terborgh et al. 2001) provide some evidence for this effect.

Recreational hunting (and trophy hunting in particular) is more selective in terms of the
species targeted, and so it is likely to be more affected than harvesting of wild meat by the
loss of particular species.

Relationship between habitat area and wild meat provision

The diversity of harvested species is predicted to increase with area (MacArthur & Wilson
1967), and indeed Michalski & Peres (2007) found a classic species-area relationship
(linear with the logarithm of area) for mammals species in a fragmented landscape in the
southern Amazon (Figure 15a). Peres (2001) predicted a non-linear relationship between
harvested vertebrates and logarithm of forest fragment area: small fragments (<20 ha) retain
practically no species (with or without hunting pressure); larger areas are required to retain
the same number of species when hunting pressure is higher (e.g. 90% of the species

111

retained in fragments of 3000 ha in the absence of hunting, but required about 11,000 ha if
exposed to moderate to heavy hunting); and areas as high as 2000 km” required to maintain
the most spatially-demanding species (tapirs) (Figure 15a). These studies also found that as
area declines, a non-random, nested simplification of fauna takes place, with large slow-
growing species, or those that live in large wide-ranging groups that occur at low density,
disappearing first.

The relationship between area and species richness is not particularly informative, though,
as we found no evidence that species richness of harvested species drives wild meat
provision. The relevant relationship is one between area and wild meat productivity
(biomass that can be harvested, a product of standing biomass and the maximum
sustainable offtake rate). We did not find any study which explicitly investigated this
relationship. In principle, the relationship between wild meat productivity and area can be:

a) Neutral: productivity per unit of area is the same irrespective of whether that unit of
area belongs to a small or a large habitat fragment. This is plausible if we assume
that animal productivity reflects primary productivity, and that this remains the
same irrespective of fragment size. In this case, wild meat production increases
linearly with area (Figure 15b, blue line).

b) Positive: larger fragments have larger productivities per unit of area. This can be
the case if the reduction in species richness for declining fragment area translates
into a decline in overall wild meat productivity (for example, if it means that some
ecological niches become empty, ‘wasting’ some of the primary productivity). In
this case, productivity increases faster than area, at least until the point where area
becomes large enough to maintain the complete faunal assemblage (Peres 2001
found this to be about 200,000 ha in Amazonian forests) (Figure 15b, purple line).

c) Negative: smaller fragments have larger productivities per unit of area. This is
plausible if as faunal assemblages become more simplified for smaller areas, they
become dominated by species that are on average more productive (e.g., if slow-
reproducing species are replaced with fast-growing species). Predator release
(Terborgh et al. 2001) may also result in higher productivity in smaller habitat
fragments. In this case, productivity increases slower than area, again at least until
the point where area becomes large enough to maintain the complete faunal
assemblage (Figure 15b, green line).

Edge effects may also affect the shape of the relationship between area and the productivity
of wild meat:

d) If the matrix habitat is unfavourable (e.g. a population sink due to increased
mortality) it is expected that smaller fragments (with more pronounced edge effects)
will have lower productivities per unit of area, and productivity will increase faster
than area while the fragment is small enough to have all of its area affected by edge
effects (this will vary with the distance at which edge effect are felt; for 1 km deep
edge effect in a circular fragment, the area would be 314 ha) (this produces the same
shape as for Figure 15b, purple line, but the threshold would be a different area).

e) Ifthe matrix habitat is favourable (e.g. by providing additional sources of nutrition)

it is expected that smaller fragments (with more pronounced edge effects) will have
higher productivities per unit of area, and again productivity will increases slower

112

than area while the fragment is small enough to have all of its area affected by edge
effects (Figure 15b, green line).

b
a) 4 )
40 4
SS
=
o
—
ee OE 2
2 =
s g
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i coe 9
® fom
oO
® o ‘
Qa @O ‘
Oo £ :
U :
2

1 10 100 1000 10000 100000

forest fragment size (ha) forest fragment size (ha)

Figure 15. a) Relationship between forest fragment size (note the logarithmic scale) and the
diversity of mammal species (from Michalski and Peres 2007). b) Possible shape of the
relationship between wild meat productivity (in biomass per year) and forest fragment size
(see text for details).

Michalski & Peres (2007) found a negative relationship between forest-patch area and two
measures of the aggregate abundance of mammal species, and no variation for a third
measure. However, overall abundance (number of individuals per unit area) is not an ideal
measure of wild meat supply because it does not account for variation in body size across
species. Indeed, if larger species are dominant in larger habitat patches, then a negative
relationship is expected between area and abundance, even if standing biomass remains
constant. Michalski & Peres (2007) found that larger species (e.g., tapir, giant armadillo)
only occurred in the largest fragments. On the other hand, larger-bodied species tend to
reproduce slower, and so for the same standing biomass productivity may be higher for
smaller fragments if they are dominated by small, fast-reproducing species (supporting
hypothesis c, above).

Michalski & Peres (2007) also found that for several species, there was a hyperabundance
in small patches. They explained this as possibly resulting from: subsidies by matrix
habitats (e.g. presence of exotic fruit trees; supporting hypothesis e); the outcome of density
compensation (increase in the abundance of one species as competition with other species
declines; supporting hypothesis a); or the outcome of release from top predators (increase in
abundance due to predator removal; supporting hypothesis e).

In summary, wild meat production likely increases linearly with area, but a threshold
habitat size may exist below which the relationship is non-linear.

4.8.4 What are the main threats to the provisioning of wild animal products?

Overexploitation is the main threat to the provision of benefits from wild animal harvesting,
frequently resulting in the exhaustion of the source populations. Hunting pressure in

113

tropical forests has risen dramatically in recent decades, concomitantly with forest loss,
increasing human population, greater access for hunters and traders to remaining forests as
a result of road building and forest fragmentation, the use of efficient modern hunting
technologies (especially firearms and wire snares), loss of traditional hunting controls, and
greatly increased commercialization of hunting (Milner-Gulland et al. 2003). Hunter access
to inaccessible forests is exacerbated by commercial logging operations: these create an
extensive network of roads linked to the national road system, and the trucks that travel
them become conduits for a vast commercial trade in wild meat, transporting it from remote
areas to towns for sale (Robinson et al 1999).

Overexploitation can lead to the local or global global extinction of particular species, even
if these have become so scarce that it would be uneconomic to pursue them individually.
This is because hunters pursuing other more common species will still kill scarce species
encountered (e.g. Clayton et al. 1997). Even the collection of other non-timber forest
products (e.g. Brazil nuts and forest vines) may exacerbate the possibility of this
“piggyback extinction’ to take place, by lowering the opportunity costs of hunting (L. Parry,
unpublished data).

Increased demand for wild meat may also result from a lack of availability of alternative
protein sources (Rowcliffe et al. 2005). Brashares et al. (2004) found that a declines in fish
supply in West Africa were associated with increased hunting in nature reserves and
ultimately the sharp declines in the biomass of 41 wildlife species.

The introduction of livestock may reduce wild meat supplies in savannah systems, if the
domestic species compete with the wild ones (Robinson & Bennett 2004), but on the other
hand it may reduce demand for wild meat (and therefore prevent overharvesting) if it
provides a more readily accessible source of protein (Bennett 2002). Outbreaks of human
commensal species (for example baboons) may cause the decline of the harvested species
and prevent them from recovering even if the hunting activities are subsequently reduced or
ceased (J. Brashares, personal observation).

Increased contact between people, livestock and wildlife increase the likelihood of disease
outbreaks in any of these groups (Karesh et al. 2005; Rizkalla 2007), therefore posing risks
not only to wild meat provision but also to public health and to economic activities.

Social and economic disruption can affect the provision of wild meat by creating the
conditions for overharvesting, for example by displacing people and promoting illegal
trade. They can also affect recreational hunting, particularly international, by reducing
expenditure and willingness to travel.

4.8.5 Are abrupt changes likely in the provision of wild animal products?

At the single-species level, overexploitation can lead to sudden transitions from large
harvests to population collapses (Barnes 2002). We predict that benefits derived from a
single or just a few species (e.g. a particular type of medicine or fur) will undergo sudden
collapses as well. However, benefits from wild meat production, often based on harvesting
a broad set of species, boom-and-bust trajectories of individual species are likely to be
obscured as hunting effort extends to other species. Therefore, changes in overall wild meat
supply are likely to be gradual, except when faunal communities become very simplified
and hence a sudden collapse may be expected if the last few species all decline abruptly.

114

As described above, large-bodied species that are most sensitive to overharvesting play key
roles in maintaining the habitats themselves (e.g. through seed dispersal), and so the
disappearance of these species will result in a permanent change in ecosystem structure and
composition (Fragoso 1997; Robinson 1999; Fragoso et al. 2003; Peres & Palacio 2007),
which in turn may influence habitat quality for other species, thereby affecting the
provision of wild meat in the long-term. This process, which is irreversible once species are
permanently lost, may take decades or even centuries for its effects to become obvious.

Overall, the key factors that are more likely to either result in or prevent a very quick
depletion of wild meat supply are socioeconomic in nature, rather than biological. These
include rights and access to the resource, management of the resource, availability of
alternative protein sources, markets, economics of harvest, and incentives to use species
sustainably.

Recreational hunting, and in particular international trophy hunting, is also particularly
sensitive to socioeconomic changes, likely to collapse as a result of political instability in a
region (e.g. war, civil disruption). While its reliance on a smaller set of species could in
principle make this activity more prone to disintegrate, in practice it is unlikely in cases
where the exploited species and populations are actively managed.

Overall, we predict that there is a very high probability that individual populations of
harvested species (particularly those harvested for subsistence or commercially) will
collapse in the near future. However, there is a low probability that this will translate
into a sudden collapse in the provision of wild meat.

Socioeconomic changes are the most likely factors that could result in a sudden
change in the supply of wild meat or in recreational hunting.

4.8.6 Can we quantify and map the global provision of this benefit/process and how it
might change?

We aim to obtain a method or model for generating, from a given map of land cover, a
surface of the estimated maximum sustainable production of wild meat. Given two maps
(two ‘states of the world’) we would then be able to estimate the difference in the provision
of wild meat between one state and the other. The economic value of this difference would
then be calculated using information on demand, having into account that value (prices) are
a function of both supply and demand.

A first cut model would calculate local production values (kg/km*/y), derived from
empirical data for particular sites, and then generalised from local production estimates to
larger-scale patterns, in order to create a map of potential wild meat production for the
regions analysed, under current circumstances. This map would be heavily biased by the
fact that production is dynamic, with different population sizes producing different levels of
offtake at different hunting pressures; in order to generalise robustly about maximum
sustainable yield values, a time series of population size and offtake estimates is needed
from which to calculate sustainable offtake rates - of which there are very few in tropical
forest areas.

115

We suggest following Robinson & Bennett (2004) in focusing on the extraction of large (>
lkg) mammals, as these dominate supply of wild meat as well as available data on species
densities. We also suggest restricting the analysis to Central and South America, sub-
Saharan Africa, and tropical Asia and Australasia, as these are the main regions where wild
food is particularly important for human wellbeing.

A first approach for calculating local production rates is the widely used Robinson and
Redford (1991) model (e.g. Peres 2001; Fa et al. 2002; Robinson & Bennett 2004). This
predicts the maximum sustainable wild meat offtake for each species in a given habitat, that
is, the percentage of standing biomass that can be harvested without resulting in population
decline. Calculating this offtake requires information on:

- The carrying capacity of the habitat for each species, typically measured as the
density (number of adult individuals per unit area) of the species in unexploited
areas. This can also be estimated from empirical relationships between density, diet
and body size.

- The maximum intrinsic rate of natural increase (7max) for each species, the maximum
rate of increase that a population can achieve under natural conditions without
significant intraspecific competition (Robinson and Redford 1986). This is very
difficult to measure directly (Milner-Gulland & Akgakaya 2001). Fast-reproducing
species such as ungulates and rodents have higher intrinsic rates of natural increase
than primates.

- A correction factor to account for natural rates of mortality (generally varying
between 0.2 and 0.6), that is lower for longer-lived species.

Both rmax and carrying capacity can be roughly estimated from their allometric relationships
with body size, corrected by taxonomic group and geographic location (Rowcliffe et al.
2003).

Using Robinson and Redford’s (1991) model, the maximum sustainable offtake rate results
in percentage values that are typically lower for long-lived species: annual offtake rates for
some rodent species exceed 50% of the standing biomass; are generally lower, but
frequently above exceed 20%, for ungulates; and are very low, usually under 5% of
standing biomass, for primates (Robinson & Bennett 2004).

The overall production of a given area (wild meat biomass that can be extracted per year)
therefore depends on the particular set of species in the community, as their life history
traits, body size and relative abundance affect the final result. For example, using this
method Fa et al. (2002) calculated a production of 1,111 kg/km*/year for the Congo Basin
and of 488 kg/km’/year for the Amazon.

There are, however, important and fundamental limitations with this approach and we
strongly recommend that a more suitable model (or ideally, a battery of different models) is
used to calculate sustainable production rates (Milner-Gulland & Akgakaya 2001).

The model by Robinson & Redford (2001) assumes that the aim is to maintain populations
at their original size, yet harvest may bring populations to any lower population size and
still be sustainable if the population remains stable. These lower population sizes may be
more desirable to improve long-term profitability or to maintain pest populations at low

116

densities. Deciding at which population size the “maximum sustainable harvest” can be
taken is therefore a priority. One option is to assume that all target populations should
remain above some percentage of their carrying capacity. The tradeoff is then between
maximising production (at a population size of 50% of carrying capacity, under the
simplest assumptions) and reducing the risk of population extirpation in the face of
environmental stochasticity (at a population size greater than 50% of carrying capacity). A
number of authors have suggested 75% of K as a suitable precautionary reference point
(Roughgarden & Fraser 1996).

Having decided the target population size, one needs to estimate how much wild meat could
be produced sustainably. This requires understanding the rate at which populations can
grow. Robinson & Redford (2001) use the maximum intrinsic rate of natural increase, but
this overestimates the rate at which populations can grow unless they are at a very low
levels. For higher population sizes, the growth rate tends to zero as carrying capacity is
approached, due to intra-specific competition (density dependence). Population growth
rates should therefore be scaled according to population size (i.e. how close it is from
maximum carrying capacity). A first approach for a growth model incorporating density-
dependence is the logistic function (Milner-Gulland & Akcakaya 2001). Assuming logistic
growth, the maximum sustainable production at 50% of carrying capacity is “4 of the
intrinsic rate of increase multiplied by the carrying capacity. However this is a single-
species model which would be unsuitable for complex multi-species systems such as
bushmeat harvesting. There has not yet been any work done to calculate a maximum
sustainable production rate for such systems.

A substantial difficulty with the application of any of these models is for areas that are
already being harvested, which is the case for the vast majority of tropical forest areas.
Here, the current population densities cannot be considered to be the population size at
carrying capacity. Measures of offtake need to include not only reported captures but also
collateral mortality — instances in which animals are fatally wounded by hunters yet not
recovered. In one game harvest study in the Amazon, modern colonists hunting with
shotguns caused an estimated 9% collateral mortality (L. Parry, unpublished data), while
wastage rates in snares in Africa have been estimated at up to 28% (Muchaal & Ngandjui
1999).

Finally, estimates of productivity should incorporate uncertainty in parameter estimates,
and appropriate methods are becoming available for doing so (Milner-Gulland & Akcakaya
2001).

Moving from local estimates of population productivity for individual species to large-scale
surfaces of overall wild meat productivity could take two possible routes. One could
generalise first on a species-by-species basis, using information on habitat suitability to
infer changes in species density, to produce a map of potential productivity for each
species. These could then be added to produce overall productivity maps. A second route is
to aggregate the productivity of all species at a given site to obtain an overall estimate of
wild meat productivity at current population sizes and harvest rates, and then generalise
those across space based on bioclimatic and land cover data. We recommend the second
route, as the former would require much finer, species-by-species, data that are unlikely to

be available, as well as making untested assumptions about the interspecific interactions
between species.

117

High quality empirical data would inform better models, as more explanatory variables can
be integrated to explain local production. For example, the incorporation of adequate soil
fertility data will improve production models since nutrient-rich environments have a
greater game biomass than nutrient-poor ones; as shown by Peres (2000) for floodplain and
unflooded forests in the Amazon. Additionally, livestock densities will affect the density of
wildlife through competition, thus livestock distribution maps can be incorporated, as well
as information derived from the Human Footprint Map on the degree of human disturbance,
fragmentation and alteration (Sanderson et al. 2002).

Data limitations mean that only a very crude model can be generated. Existing data on local
species composition and densities of individual species (e.g. Fa & Purvis 1997; Peres 2000;
Robinson & Bennett 2004) are still very patchy, although improving. Studies also
integrating accurate measures of offtake are even scarcer, as are studies for secondary
habitats in general (including secondary forest and agricultural land).

We suspect there is a vast wealth of data on the productivity of different habitats (e.g.,
forests, wetlands, mountain areas) for species of birds and mammals harvested through
recreational hunting in Europe and North America. This is expected given the degree to
which hunting is generally managed in these regions. However, we do not know if such
data are concentrated or dispersed. If the former, models of productivity for recreational
hunting would not only be possible but also substantially better than the models for wild
meat harvesting in tropical regions, as they would rely on better data and apply to much less
complex communities (with fewer species). Modelling of recreational hunting of migratory
species (such as ducks) would however pose specific challenges.

As for international trophy hunting, we are under the impression it mostly takes place in
private land or concessions that are actively managed for the target species. Sustainable
harvests are likely to be ensured by managers, who not only control harvest directly but
also enhance the habitat to increase species’ productivities. The economic value that can be
gained from this activity is therefore determined much more by the capacity of operators to
attract tourists than by biological constraints. In this sense it is much more like international
tourism (section Error! Reference source not found.), and should be modelled and valued
accordingly.

Adequacy of scenarios

The key information that the scenarios need to produce is maps of landcover. Other layers
likely to be important include: human density, transport routes, livestock density, and the
bioclimatic variables used to model variation in productivity across space.

118

What can be done in Phase 2? At what cost? By whom*?

A first-cut, crude, pan-tropical, model is possible given the available analytical tools,
and could be developed within one year given 24 researcher-months. Several
research groups would be well positioned to do this work (see key experts below).

The quality of the model would rely crucially on the underlying data. Maximising
the quality and quantity of the data that could be made available would be best done
by establishing collaborations across institutions working in different areas. Our
recommendation is therefore that this work builds from a consortium, brought
together through two NCEAS-style workshops.

We suspect that a model of recreational (domestic) hunting is possible for Europe
and North-America, if data on game productivity are concentrated in particular
institutions (perhaps CIC, see key resources). We recommend investing some time in
pursuing this possibility as if such data repositories exist, creating a model would be
both feasible and economically relevant.

4.8.7 Insights for economic valuation

Published economic valuations of wild meat harvesting are typically based on the current
rates of extraction (e.g., Gram 2001; de Merode et al. 2004), yet these are known to be
frequently unsustainable (e.g., Fa et al. 2002). Spatially-explicit models of the potential
value of habitats for bushmeat production cannot therefore be built from studies on current
values of wild meat extraction. Instead, biophysical models of sustainable production could
be generated first, based on very crude models built using allometry to estimate rmax and
carrying capacity, and the few studies that exist on population sizes in unhunted and lightly
hunted areas. An estimate of the associated uncertainty could also be included.

These predictions could be compared with spatially explicit maps of current bushmeat
production, drawn from broad-scale surveys (such as are being developed by the Bushmeat
Crisis Task Force, www.bushmeat.org). Crude extrapolation from existing data to
unsurveyed areas on the basis of human population density, vegetation type and transport
routes would be possible, and these could highlight areas in which hunting is most likely to
be highly unsustainable.

Current economic values could be obtained by overlaying data on current prices and
consumption. However because supply and demand are interlinked, and dependent on
availability of both bushmeat and alternatives (both for consumers and producers), as well
as income and tastes, use of these same data to estimate the potential economic value of
sustainable bushmeat harvest would involve heroic assumptions.

* This is our recommendation for Phase II, based on the results of this review. It does not commit the leaders

of Phase II to follow it, and it does not commit the recommended research group to actually do such
work,

119

Further extrapolation to alternative scenarios might be possible, but only if an
understanding of the underlying drivers of changes in bushmeat hunting pressure and value
were obtained. This has not yet been done even at the regional or local level, although there
are datasets available for which this could be done.

4.8.8 Some key resources

Wild food:

e WCS Hunting and Wildlife Trade Program, led by Liz Bennett. Also at WCS are
John Robinson and Kent Redford.

e Imperial College Conservation Science group, led by E.J. Milner-Gulland.

e Durrell Wildlife Conservation Trust, with John Fa as director of Conservation.

e Bushmeat Research Programme at the Zoological Society of London, led by Marcus
Rowcliffe and Guy Cowlishaw.

e Justin Brashares group at University of California Berkeley, including Karen
Weinbaum.

e Carlos Peres group at University of East Anglia, including Luke Parry.
e Washington-based Bushmeat Crisis Task Force, led by Heather Eaves.
e Robin Naidoo, Taylor Ricketts and Neil Burgess at WWF-US.
Recreational hunting:
e FACE-Europe, a European federation of national hunters’ associations of the
Member States of the European Union and other Council of Europe countries

(representing the interests of some 7 million European hunters).

e Ducks Unlimited, a North-American NGO focused on the conservation of
waterfowl and wetlands for hunting purposes.

e CIC — International Coucil for Game and Wildlife Conservation a Hungary-based
non-governemental organisation focused on the management of wild-living
resources.

4.8.9 Participants

Authors
James J.J. Waters, Ana Rodrigues, Andrew Balmford (University of Cambridge, UK)
E.J. Milner-Gulland (Imperial College London, UK)

John Fa (Durrell Wildlife Conservation Trust, UK)

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Contributors
John Robinson
Acknowledgements

Guy Cowlishaw and Elizabeth Bennett provided references and contacts.

4.9 Fresh water provision, regulation, and purification

This section is a developed overview based on a literature review and expert consultation.
It greatly benefited from expert contributions (see below) but it has not been reviewed by
experts in this field.

4.9.1 Why is this process important for human wellbeing?

Fresh water is fundamental to the survival of every single human, for direct consumption
and to maintain an adequate food supply (Pimentel et al. 2004). Although water covers 71%
of the planet surface, globally freshwater is a scarce resource: only 2.5 % of water is fresh,
and nearly three quarters of that is frozen, and most of the remainder is present as soil
moisture or lies deep in the ground. The principal sources of fresh water that are available
to society reside in lakes, rivers, wetlands, and shallow groundwater aquifers—all of which
make up but a tiny fraction (tenths of 1%) of all water on Earth. This amount is regularly
renewed by rainfall and snowfall and is therefore available on a sustainable basis
(V6résmarty et al. 2005; Arthurton et al. 2007). Furthermore, water is unevenly distributed
across the planet’s surface, with abundant supplies in regions such as the wet tropics and
absolute water scarcity across the desert belts and in the rain shadow of mountains
(V6résmarty et al. 2005).

Fresh water is essential first and foremost for drinking. Each person needs only 20 to 50
liters of water free of harmful contaminants each day for drinking and personal hygiene to
survive, yet there remain substantial challenges to providing this basic service to large
segments of the human population. Half of the urban population in Africa, Asia, and Latin
America and the Caribbean suffers from one or more diseases associated with inadequate
water and sanitation (V6rdsmarty et al. 2005). Globally, more than 1.1 billion people lack
adequate access to clean water and 2.6 billion people — half of the developing world’s
population — lack access to adequate sanitation. Every year some 1.8 million children die as
a result of diarrhoea and other diseases caused by unclean water and poor sanitation. The
world’s governments set as a global target under the Millennium Development Goals to
halve by 2015 the proportion of world population without sustainable access to safe
drinking water and basic sanitation (Goal 7, target 10) (UNDP 2006).

Household water requirements represent a very small fraction of water use. Water use is
dominated by agricultural withdrawals (70% of all use and 85% of consumptive use),
including livestock production, followed by industrial and only then domestic applications.
For example, approximately 1000 litres of water are required to produce | kilogram of
cereal grain, and 43,000 litres to produce | kilogram of beef (Pimentel et al. 2004). Fresh
water is also fundamental for inland fisheries, including aquaculture (section 4.7), for
recreation (section Error! Reference source not found.), for the production of
hydroelectric energy and for transport (waterways). Indirectly, freshwater water flows also
contribute to marine fisheries production (section 4.6) (Table 3).

Over the last few centuries, global water use increased roughly exponentially, and
substantially quicker than population growth, as a result of both population growth and
economic development, and in particular the expansion of irrigated agriculture (V6résmarty
et al. 2005). Global water withdrawals now total ~3900 km’ yr, or ~10% of the total
global renewable resource, and the consumptive use of water (not returned to the

watershed) is estimated to be ~1800 to 2300 km* yr! (Foley et al. 2005). Water use
efficiency is now improving, with per capita use rates dropping as of around 1980 from
about 700 to 600 cubic meters per year (V6résmarty et al. 2005). The aggregate global
withdrawal continues to increase, though, with water withdrawals predicted to increase, by
2050, by 50% in developing countries, and 18% in developed countries (UNESCO-W WAP
2006).

While demand is increasing, freshwater resources are under threat by unsustainable water
extraction and pollution. Particularly serious is the uncontrolled rate of water withdrawal
from aquifers, which in many regions (especially arid ones) is significantly faster than the
natural rate of recharge. This mining of groundwater reserves poses a serious threat to water
supplies in world agricultural regions, especially for irrigation (Postel 1999). Increases in
pollution of surface and groundwater resources not only pose a threat to public and
environmental health but also contribute to the high costs of water treatment, thus further
limiting the availability of water for use (Postel 1999).

Ecosystems play important roles in the hydrological cycle, contributing to water provision
(quantity, defined as total water yield), regulation (timing, the seasonal distribution of
flows) and purification (quality, including biological purity as well as sediment load)
(Dudley & Stolton 2003; Bruijnzeel 2004; Brauman et al. 2007). Different benefits are
affected differently by each of these aspects of water regulation (Table 3). The production
of these benefits may be synergistic or competitive, with water management for one benefit
possibly affecting others (e.g., improvement of water quality for drinking purposes through
reforestation resulting in a decline in water quantity for hydroelectric production). And use
may be extractive (water is removed from the system), in-situ (when the benefits are
enjoyed without requiring water to be extracted), and indirect (when water contributes to
the benefit provision only indirectly).

Table 3. Fresh water provision and regulation contributes to the production of a diversity of
benefits. Water provision (quality), regulation (timing) and purification (quality) are not
equally important for each of these benefits (estimates of relative importance: + somewhat
important, ++ important, +++ very important).

Type ofuse _—_ Benefits Water Water Water

quantity _ timing quality
Extractive Domestic water consumption + lata ctataaha
use

MONO PADD BAU OM orwell te en att la a
aliivestocksproduction _ 07 ee a eh ee
Industrial use ite sti +

Ine Seeulase | tulancittshenes,(ineludin aquaculture) Sit Sec ta
abiydnocleeniernerey production ee Gass a ae

Transport: waterways +++ +++ +

Recreation (sport, tourism) ++ ++ +4++

Indirect use Marine fisheries ++ 4+ aaere

4.9.2 What are the overall trends in fresh water provision, regulation, and
purification?

The Millennium Ecosystem Assessment (MEA 2005) considered that there is a medium to
high certainty that freshwater provisioning, regulation and purification has been degraded
in the recent past, caused by unsustainable use for drinking, industry and irrigation, and that
there has been a decline in water purification services. Dams are increasing our capacity to
use hydroelectric energy and water regulation services have changed in a variable way,
depending on ecosystem change and location.

4.9.3 How is the provision, regulation, and purification of fresh water affected by
changes in wild nature?

Water provision (quantity)

In reviewing what is known about the impact of biodiversity on water quantity, we rely
extensively on the exhaustive review Bruijnzeel (2004). Here we summarise key points
about the influence of forest cover on regional climate (rainfall) and total water yield; in
later sections we précis what is known about its effects on the timing of water yields, and
on erosion and sedimentation.

The quantity of water delivered from a watershed is conventionally measured only as
surface water output and reported as mean annual watershed yield. Ecosystems, however,
affect the available quantity of both surface and groundwater (Brauman et al. 2007).

Vegetation, and forests in particular, significantly affect the quantity of water circulating in
a watershed, although not necessarily in the way that is popularly perceived. Dealing first
with precipitation, while it is commonly assumed that forests generate rainfall, this is not
straightforward to test, particularly in tropical regions, because rainfall can be highly
variable in space and time (Bruijnzeel 2004). The rationale for this effect is that the higher
evapotranspiration and greater aerodynamic roughness of forests (compared to pasture and
agricultural crops) leads to increased atmospheric humidity and moisture convergence, and
thus to higher probabilities of cloud formation and rainfall generation. Early demonstrations
of this effect have however been questioned, with the suggestion that observed higher
rainfall in forested areas was an artifact of orographic effects (remaining forests tend to be
in uplands where clouds are forming because of atmospheric cooling of rising air) or of
differences in exposure of measurement instruments to wind and rain. Bruijnzeel’s (2004)
review concluded that there is increasing evidence that large-scale land use conversion
affect cloud formation and rainfall patterns, although the effects may be small and they
depend on the particular land use conversion. For example, simulation predicts large-scale
Amazonian forest conversion to pastureland would result in a mere 7% reduction in annual
rainfall; conversion between primary forest and secondary forest or even some crops (e.g.
tea plantations) is expected to have little effect on levels of evapotranspiration; and forest

conversion to rice paddies is expected to increase cloud formation and rainfall (Bruijnzeel
2004 and references therein).

It is well-established that, in coastal or montane regions subject to wind-driven fog or
clouds (cloud forests), the presence of tall vegetation can significantly increase the amount
of water reaching the forest floor as canopy drip (Bruijnzeel 2004). It has been also been
suggested that deforestation of upwind lowland forests can reduce cloud cover on adjacent
upland cloud forests, affecting moisture interception by the canopy (Lawton et al. 2001).

In contrast with popular belief, it is well-established that the dominant impact of vegetation,
and of forests in particular, is one of reducing the surface water output (which ultimately
forms rivers), thereby reducing watersheds yield (Dudley & Stolton 2003; Bruijnzeel 2004;
Brauman et al. 2007). This effect is due to evapotranspiration, whereby plants draw water
from the soil into the atmosphere as part of photosynthesis. Trees generally use more water
than other plants, as their roots allow them to absorb water from deeper in the soil. Also,
being aerodynamically rougher than short, smooth vegetation, they increase the efficiency
of gas exchanges with the atmosphere (Brauman et al. 2007). Under mature tropical rain
forest, typically 80-95% of incident rainfall infiltrates into the soil, of which ca. 1000 mm
per year is transpired again by the trees when soil moisture is not limiting, whereas the
remainder is used to sustain streamflow (Bruijnzeel 2004).

Bruijnzeel (2004) reviewed the results of controlled experiments comparing water yield
before and after deforestation, finding that in all cases the removal of more than 33% of
forest cover resulted in significant increases in overall annual streamflow during the first 3
years, with changes in water yield roughly proportional to the fraction of biomass removed.
As forest regenerates, or is replaced by forest plantations, water yields tend to decline again
to pre-clearing levels, a process which happens faster in tropical regions (where vegetation
grows more quickly) than in temperate areas. In contrast, conversion to cropland or
pastureland may result in permanent increases in annual water yields. This reflects not only
the diminished capacity of short vegetation to intercept and evaporate rainfall (given its
lower aerodynamic roughness and generally smaller leaf area) but also its reduced capacity
to extract water from deeper soil layers during periods of drought (because of more limited
rooting depth) (Bruijnzeel 2004 and references therein). While there are no reported
declines in annual streamflow totals following lowland tropical forest removal, from the
opposite appears to hold for the conversion of montane cloud forests to cropland or
pastureland. This is because evapotranspiration in generally low in cloud forests, while
cloud forest-clearing typically results a reduction in flows generated by cloud and fog
interception (Bruijnzeel 2004; Postel & Thompson 2005).

Water requirements are different for different plant species, so species composition of
vegetation may affect water yields. Young and invasive plants generally have
disproportionately large impacts on water quantity because vigorously growing vegetation
tends to use more water than mature vegetation. In arid areas, adaptations such as dry-
season senescence of native vegetation may limit its water use, whereas an introduced
species that lacked these traits would consume water over longer periods during the year
(Brauman et al. 2007 and references therein). In South Africa, for example, the spread of
non-native eucalyptus, pine, black wattle, and other thirsty trees into the native fynbos
(shrubland) watersheds of the Western Cape is having negative consequences for water
supplies, as their evapotranspiration requirements greatly exceeds that of the low-stature
and drought-tolerant fynbos vegetation (van Wilgen et al. 1996).

125

Lakes and freshwater wetlands store about 50 times as much water as rivers (Vérésmarty et
al. 2005) and, if well-managed, provide a stable water supply for domestic, agricultural, and
industrial use (Finlayson et al. 2005). Some wetlands provide an important role in aquifer
recharge, therefore contributing to groundwater supply. However, some wetlands exist
because they overlie impermeable soils or rocks and there is, therefore, little or no
interaction with groundwater (Fenlayson et al. 2005). Wetlands evaporate more water than
other land types (including grassland, forests, and arable land) and therefore reduce annual
water yields (Bullock & Acreman 2003).

A less obvious role of biodiversity on water provisioning is through the provision of ice
nucleators (atmospheric particles that serve as condensation and ice nuclei in clouds) that
lead to precipitation, a substantial fraction of it are bacteria (Christner et al. 2008).

Water regulation (timing)

In areas with seasonal rainfall, the distribution of streamflow throughout the year is often of
greater importance than total annual water yield. This is particularly important to
agricultural production (as irrigation is most important during the dry season), to
hydroelectric production (as energy supply is not be possible if streamflow is insufficient),
and to transportation (as waterways cannot be used if water level is too low) (Table 3).
Bruijnzeel (2004) reviewed the available evidence on the effects of vegetation on the
seasonal distribution of water flows, and this section draws heavily on this reference.

Forest clearance often has dramatic consequences to the soil’s characteristics. Bare soil
becomes exposed to intense rainfall, topsoil is often compacted by machinery or
overgrazing, soil faunal activity decreases dramatically, and often part of the area is
occupied by impervious surfaces such as roads and settlements (Bruijnzeel 2004, and
references therein). All of these contribute to gradually reducing rainfall infiltration in
cleared areas. As a result, catchment response to rainfall becomes more pronounced, with
large storm runoff during the rainy season translating into lower recharging of the soil and
the groundwater reserves that feed springs and maintain baseflow (deep groundwater
outflow). This reduced recharge associated with deforestation typically exceeds reductions
in evapotranspiration, so that overall, forest clearance leads to diminished dry season (or
‘minimum’) flows (Bruijnzeel 2004). Note that this effect is attributed to changes in the
soil, and not directly to the forest loss per se. Indeed, if soil surface characteristics after
clearing are maintained sufficiently to allow the continued infiltration of (most of) the
rainfall, then the reduction in evapotranspiration associated with forest removal will result
in an increase in dry season flow. Infiltration properties may be conserved through the
establishment of a well-planned and maintained road system plus the careful extraction of
timber in the case of logging operations, or by applying appropriate soil conservation
measures after clearing for agricultural purposes (Bruijnzeel 2004 and references therein).
Effects of deforestation on baseflows are expected to be more pronounced following severe
surface disturbance in the case of deep soils with large storage capacity than in the case of
more shallow soils which have little capacity to store water anyway (Bruijnzeel 2004).

The same conditions that reduce water infiltration (resulting in lower baseflow) result in a
higher surface runoff (saturation overland flow), resulting in higher peakflows during the
wet season. However, even with minimum soil disturbance, there will still be increases in
peakflows after forest removal, because the associated reduction in evapotranspiration will
cause the soil to be wetter and therefore more responsive to rainfall (Bruijnzeel 2004). The
fraction of water that flows as surface runoff also depends on the antecedent soil moisture

126

status, and storm characteristics: the runoff response is higher if the soils are already very
wet from previous rainfall; and once the soil becomes saturated any further rainfall will
increase the saturation overland flow (see section 4.13 for a discussion of the effects of
forests on flooding).

Reforestation may not immediately restore the modulating effects of the original forest in
enhancing low flows in the dry season and reducing peak flows in the wet season, as the
soil storage and infiltration capacities lost with deforestation may take years to recover. In
fact, the most immediate effect of reforestation may be a decline in water yields that is
particularly felt in the dry season, as a result of a rapid increase in evapotranspiration. This
is particularly likely to occur for fast-growing (frequently exotic) tree plantations. Cloud
forest reforestation, on the other hand, is more likely to quickly result in enhanced low
flows as a result of moisture interception by the vegetation (Bruijnzeel 2004).

Inland waters, such as lakes and wetlands, are traditionally considered to be very important
for the temporal regulation of water flow, both by accumulating water during wet periods
(reducing peak flow) and providing a reserve of water during dry periods that maintains
base flow in adjacent rivers (Finlayson et al. 2005; V6rdsmarty et al. 2005). A
comprehensive review by Bullock & Acreman (2003) found support for the former effect,
but evidence against the latter. Evidence that wetlands have an effect in reducing or
delaying floods is particularly convincing for floodplain wetlands, but less so for headwater
wetlands (e.g., bogs and river margins), for which a substantial minority of studies report
wetlands are associated with increased flood peaks. In contrast, most (two-thirds) of the
studies concluded that wetlands reduce flow in the dry season, backed by overwhelming
evidence that evaporation from wetlands is higher than from non-wetland portions of the
catchment during dry periods (Bullock & Acreman 2003).

Water purification (quality)

Water quality is a measure of the chemicals, pathogens, nutrients, salts, and sediments in
surface and groundwater. The importance of water quality to domestic use, particularly to
drinking supply, is obvious (Dudley & Stolton 2003). But water quality is also very
important for food production (including crops, livestock and inland and marine fisheries)
and for recreational use (Table 3) (V6rdésmarty et al. 2005). Sediments reduce the storage
capacity of reservoirs, thereby affecting water supply and hydroelectric production (Postel
& Thompson 2005; Arthurton et al. 2007).

Ecosystems with intact groundcover and root systems are considered very effective at
improving water quality. Vegetation, microbes, and soils remove pollutants from overland
flow and from groundwater by: physically trapping water and sediments; adhering to
contaminants; reducing water speed to enhance infiltration; biochemical transformation of
nutrients and contaminants; absorbing water and nutrients from the root zone; stabilising
eroding banks; and by diluting contaminated water (Brauman et al. 2007).

Streamside ecosystems such as riparian forests reduce nutrient movement to streams,
therefore playing a key role in controlling nonpoint sources of pollution by sediments and
nutrients in agricultural watersheds (Naiman & Decamps 1997). These ecosystems can trap
sediments and sediment-bound pollutants in surface runoff (e.g. removing 80-90% of
sediments leaving agricultural fields; Naiman & Decamps 1997) as well as absorb nutrients
dissolved in the water, such as nitrogen and phosphorus (e.g. reducing local nitrate
concentrations from cropland runoff by 5% to 30% per meter width; Brauman et al. 2007).

127

These buffer ecosystems can thus potentially reduce water treatment costs for downstream
users.

The macrophytes and microbes that promote denitrification and other biochemical
processes that improve water quality are particularly abundant in wetlands, which are so
reliable at removing suspended solids, phosphorus, and nitrogen from wastewater that they
are regularly integrated into treatment plants (e.g. Sundaravadivel & Vigneswaran 2001).
Wetland biota can also remove waterborne toxins and heavy metals from the water (e.g.
Simpson et al. 1983). The effects of wetlands in environmental filtration are potentially
very large. It has been estimated that converting less than 10% of the Mississippi Basin to
wetlands and riparian forest would reduce 10% to 40% of the nitrogen currently creating
the hypoxic zone in the Gulf of Mexico (Mitsch et al. 2001).

Forests and other mature ecosystems generally improve water quality in a catchment, by
reducing surface erosion and increasing water infiltration and therefore soil filtration of
pollutants. Surface erosion is rarely significant in areas where the soil surface is protected
against the direct impact of the rain through a litter layer maintained by some sort of
vegetation (Bruijnzeel 2004). Erosion rates can increase very significantly with
deforestation, as soils tend to lose organic matter and become compacted and crusty, with
impaired infiltration capacity (see above). Increased surface runoff increases erosion and
watershed sediment yields. Fast water flow as surface runoff also means that pollutants
built up in ecosystems (e.g. through decomposition, fertilizer application) are quickly
transported to rivers, rather than being filtered through the soil (Brauman et al. 2007).

It seems to be the case that there is high functional redundancy in the effects of species on
the provision and regulation of freshwater water, for example in the effect of plants in
protecting against soil erosion. Some species or groups of species, however, may have
particularly important roles. Freshwater mussels are particularly effective biofilters,
filtering suspended particles (such as clay, silt, bacteria and phytoplankton and small
zooplankton) and bio-deposit the particulate matter to the sediment floor, increasing water
quality and clarity (McIvor 2004). Beavers are bio-engineers, creating dams that affect
hydrological flow and nutrient cycling, improving downstream water quality (Naiman et al.
1986).

What is the relationship between habitat area and the impact of ecosystems on the
provision, regulation and purification of fresh water?

It is likely that a minimum area is needed for ecosystems to have a significant effect on
water provision and regulation. Studies suggest that a riparian buffer needs to be a
minimum 25 m wide to remove nutrient and pollutants and a minimum of 50 m to provide
detritus removal and bank stabilisation (Scherr & McNeely 2008).

In an analysis of 27 US water suppliers, treatment costs for drinking water deriving from
watersheds covered at least 60% by forest were half of the cost of treating water from
watersheds with 30% forest cover, and one-third of the cost of treating water from
watersheds with 10% forest cover (Ernst 2004; Postel & Thompson 2005). Re-plotted as a
function of water treatment costs avoided (Figure 16a) this indicates somewhat diminished
returns for increasing fraction of watershed covered with forest.

Combining these observations with those minimum area effects, we speculate that the
overall relationship between percentage of watershed covered by a particular beneficial

128

ecosystem (e.g. forests, wetlands) and the regulation of water quality is such that no benefit
is noticeable for small areas, followed by a quick increase that tends to flatten out for larger
ecosystem coverage (Figure 16b).

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% of watershed covered by ecosystem % of watershed covered by cloud forest
% of watershed covered by forest

Figure 16. Relationship between area covered by an ecosystem and its impact on the provision
and regulation of freshwater. a) Relationship between percentage of watershed covered by
forest and avoided water treatment costs (from Postel & Thompson 2005). b) Speculated
relationship between ecosystem cover in a watershed and regulation of water timing and
quality. c) Speculated relationship between cloud forest cover and water quantity.

We did not find references with the shape of the relationship between regulation of water
timing and ecosystem coverage, but suggest it will be similar to that for water quality, with
very small or negligible effects for very low coverage and diminishing effects for high
coverage (Figure 16b).

As for water provision, as mentioned above the effect for both forests and wetlands seems
to be one of declining total water yields for increasing ecosystem coverage. If
evapotranspiration increases linearly with area, this effect might perhaps be linear. As for
cloud forests, assuming that most water interception takes place in the most exposed areas
of the forest (with core areas of the forest sheltered from cloud/fog contact by surrounding
forest), we again speculate that the quantity of water captured is likely to increase rapidly
for a small amount of forest, and then increase with diminishing returns for larger forest

coverage (Figure Figure 16c; although note that we know of no studies that have looked at
this).

4.9.4 What are the main threats to the provision, regulation and purification of fresh
water?

Water provision (quantity)
Global deforestation is estimated to have reduced global vapour flows from land by 4%

(3000 km*/yr), increasing water yields, although these gain s have been mainly offset by
increased vapour flow caused by irrigation (2,600km*/yr) (Gordon et al. 2005).

No global data seem to exist on trends in cloud forest cover in particular, but these, and
their water provisioning services, are threatened by deforestation (e.g. for coffee plantations
and timber extraction) and by climate change (which is raising the altitude of the cloud cap)
(Bubb et al. 2004).

Invasive species are in some areas compromising local water yields (e.g., Enright 2000;
Zavaletta 2000).

Unsustainable exploitation (mining) and pollution of water deposits, particularly of
aquifers, is reducing the capacity of ecosystems to provide what should be a renewable
resource (Pimentel et al. 2004).

Water regulation (timing)

Land conversion, including widespread loss of forests (Mayaux et al. 2005) and wetlands
(Zedler & Kercher 2005) and changes in soil properties due to inappropriate soil
management (Bruijnzeel 2004), are affecting the capacity of ecosystems to regulate
seasonal water flow, resulting in increases in peak flows and declines in dry flows in many
regions (Postel & Thompson 2005).

Water purification (quality)

Again, land conversion, including !oss of forests and wetlands and soil degradation, is
affecting the capacity of ecosystems to filtrate and purify water. At the same time,
agricultural, urban and industrial effluents are adding large quantities of pollutants aquatic
systems in particular, including groundwater (Postel & Thompson 2005).

Mussels are declining globally, affecting their role as biofilters, as result of widespread
changes such as damming of rivers, land use change, and pollution and invasive species
(Aylward et al. 2004).

4.9.5 Are abrupt changes likely in the provision and reguiation of fresh water?

Most changes to the capacity of ecosystems to regulate and provide freshwater seem to
derive from, and be generally proportional to, land use change. There are however
situations in which a relatively small additional change may trigger a disproportionate — and
sometimes difficult to reverse — response from ecosystems’ hydrological function (Gordon
et al. 2008). A few examples are presented below.

Human-induced eutrophication can trigger sudden shifts in lakes and reservoirs from clear
to turbid due to algal blooms (Scheffer et al. 2001). These blooms may include toxic
cyanobacteria and causes major problems in water treatment works, particularly those
where treatment is by direct filtration (Hitzfeld et al. 2000). They also affect freshwater
fisheries and recreational use of water bodies. Reduction of nutrient concentrations is often
insufficient to restore the original state, with restoration requiring substantially lower
nutrient levels than those at which the regime shift occurred (Scheffer et al. 2001).
Eutrophication may also trigger sudden shifts in estuarine or coastal ecosystems with the
creation of dead (hypoxic) zones, affecting fisheries (Rabalais et al. 2002).

Deforestation on steep slopes may result in changes in soil properties that are not easily

reversed as topsoil is removed by erosion, thereby making it difficult for new vegetation to
get established. In particular, if gullies are not treated at an early stage, they may reach a

130

point where restoration becomes difficult and expensive, as the moderating effect of
vegetation on actively eroding gullies is limited and additional mechanical measures such
as check dams, retaining walls and diversion ditches become necessary (Bruijnzeel 2004).
Active gully erosion substantially increases catchment sediment yields, affecting water
quality as well as the storage capacity of reservoirs.

Cloud forest loss may also result in a regime shift that may be largely irreversible. In some
areas, such forests were established under a wetter rainfall regime, thousands of years
previously. Necessary moisture is supplied through condensation of water from clouds
intercepted by the canopy. If the trees are cut, this water input stops and the resulting
conditions can be too dry for recovery of the forest (Folke et al. 2004).

Climate change can bring potentially trigger sudden changes, particularly in regions where
ecosystems are already highly water-stressed.

Overall, we predict that there is a medium to high probability of localised regime
shifts in the capacity of ecosystems to regulate and provide freshwater and the

benefits we derive from it. Regional-scale shifts are more likely to be associated with
water eutrophication. Global-scale shifts associated with climate change are perhaps"
possible.

4.9.6 Can we quantify and map the global provision, regulation, and purification of
freshwater, and how it might change?

There does not seem to be a large-scale hydrological model that simultaneously integrates
changes in water quantity, quality and timing. The Natural Capital Project (Nat Cap) is
developing models that account for all three aspects of water provisioning and regulation.
Nat Cap is using a tiered approach where the first models (currently under development)
will use commonly available data and simple models to estimate the relative provisioning
of water yield, baseflow (dry season flow) support, flood regulation, water quality and
sediment yield. Higher tier (more advanced) models are being developed which will be
quantitative and allow valuation.

Most previous modelling efforts have focused on mapping water yield, often in order to
compare with water demands for evaluating shortfalls, or the impacts of climate change on
water availability.

Water yield maps are usually generated from a water balance map for individual grids
across the Earth’s surface, taking into account water input from precipitation, output from
evapotranspiration, and water flow across cells.

A number of global hydrological models exist (see D6ll et al. 2003 for a review), including:
WaterGap2 developed at the Universities of Kassel and Frankfurt, Germany (Alcamo et al.
2003a,b, 2005, 2007; Dall et al. 2003); Macro-PDM, developed by Nigel Arnell (Arnell
1999, 2004); the VIC model developed at the University of Washington (Liang et al. 1994;
Nijssen et al. 2001); and the WBM model developed by the Water Systems Analysis Group
at the University of New Hampshire (VGrésmarty et al. 1998). All of these models are

131

driven by monthly climatic variables and they all use a spatial resolution of 0.5° (the
highest resolution of available climatic input data). They vary in the way they treat
variables such as soil water storage, surface runoff and groundwater recharge. They all
require calibration/correction against observed water flows in order to produce a good
agreement of model results, which suggests shortcomings in the models or the way they are
applied. Whilst the basic hydrological concepts used are sound and have been seen to work
at hillslope to river basin scale, the application at large-scales relies on gridded data that
does not match well the scale of hydrological processes.

While the focus of most research has been on climate change, some of these models do
incorporate land use, albeit in a very crude way (e.g. categories such as agriculture, forest,
scrub, wetlands). None of them seems to have been successfully validated for its capacity to
simulate the effect of landuse changes on water yields. So while theoretically one can
change the landuse parameters in a model and it will respond with changes in estimated
river flows (e.g. Douglas et al. 2007), it is not clear how meaningful such results would be.
Adequately modelling the impacts of specific changes in land use on water provision
therefore requires further development.

With these caveats in consideration, if it is considered acceptable to use such calibrated
models with their remaining high error bands (especially in snow affected regions) then
water yield is the aspects of water provision and regulation that can best be modelled
globally.

Global mapping of water regulation (timing) does not seem to have been attempted yet,
although those models which distinguish between surface and groundwater resources may
be able to be adapted to look at flood mitigation/dry season flow sustainability. The
standard temporal resolution of the models is monthly, but they are typically only validated
for long-term annual averages, so estimated changes in monthly monthly values (e.g.
Douglas et al. 2007) are likely to be associated with high error margins.

A different type of model investigates biosphere-hydrosphere interactions. These include
the LPJ Dynamic Global Vegetation model, currently being developed at the Potsdam
Institute for Climate Impact Research (PIK) in Germany (Gerten et al. 2004). This model
estimates impacts of changes in vegetation types on the terrestrial water balance,
accounting for example for the effects of changes in CO on vegetation evapotranspiration
and therefore water yields. It is therefore more responsive to changes in types of vegetation
cover than stand-alone hydrological models. These models have focussed on natural
vegetation (Gerten et al. 2004), although an agriculture mode is now under development.
Global vegetation models are less effective at modelling hydrological processes, not
accounting, for example, for groundwater flows (which are key to modelling the timing of
water provision).

Regional models of water quality include the SWAT (Soil and Water Assessment Tool)
model, developed to assist water resource managers in assessing the impact of management
and climate on water supplies and non-point source pollution in watersheds and large river
basins (Arnold & Fohrer 2005). The SWAT2000 includes as model components weather,
hydrology, erosion/sedimentation, plant growth, nutrients, pesticides, agricultural
management, stream routing, pond/reservoir routing, bacteria transport routines and urban
routines. However, SWAT this relies on detailed data not available at the global scale. No
global mapping/modelling of water quality seems to have been developed yet. Such models
are complex in that they depend not only on water yield models but also on correctly

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defined hydrological pathways (e.g. rapid surface and subsurface flow, versus long term
groundwater). Impacts on water quality as often very localised in time and/or space, and so
the models need to be able to respond to different types of pollution sources, including
point (e.g. industrial) and diffuse (e.g. agricultural) sources as well as event-based (e.g. oil
spill) and long-term chronic impacts (e.g., leakage from mining fields).

In summary, global hydrological models are less developed for water quality and timing
than for quantity, which is unfortunate as the former are precisely those aspects of water
regulation that seem to be most closely linked to ecosystem extent and condition. A global
valuation of the impacts of biodiversity and ecosystem change on water resources therefore
requires additional modelling efforts.

Ideally, the different aspects of water provision and regulation should be addressed in an
integrated way (rather than by having independent models for quantity, for timing and for
quality) as there are likely to be synergies and tradeoffs between these components. We
therefore recommend that Phase II builds from the efforts currently being developed by the
Natural Capital Project to model the effects of landuse change on water services. The
hydrology module of the Natural Capital Project is based at Cranfield University, where
previous work also included the development of methodologies for large scale risk
assessment of diffuse source contaminants (e.g. Kannan et al. 2007a, b).

h

ondition and «

Adequacy of scenarios

The scenarios being assessed in Phase 2 need to produce:
- global maps of forest cover, distinguishing cloud from other forest types
- global maps of wetland distribution

- global maps of agricultural production (inc irrigated)

> This is our recommendation for Phase II, based on the results of this review. It does not commit the leaders
of Phase II to follow it, and it does not commit the recommended research group to actually do such
work.

1333)

- global maps of sources of pollution, including different types of pollution sources,
including point (e.g. industrial) and diffuse (e.g. agricultural) sources

4.9.7 Insights for economic valuation

Assuming it is possible to predict the direction and magnitude of the impacts of land-use
changes on a watershed’s hydrological services, valuation of these services needs to
consider that water flow is directional, and so users are typically downstream. The specific
attribute of water provision (quantity, timing or quality) and its value will depend critically
on the type of use (e.g. urban water supplies, irrigated agricultural production, , etc. — Table
1). Importantly, the value of a change in total water yield, in its timing, or in quality, will
depend on how far these constrain benefits — for example, the value of an extra litre of
water for crop irrigation will vary depending on whether irrigation is currently practiced,
and if so, whether water availability (rather than, say, soil fertility) limits crop yields.

There are likely to be trade-offs within catchments, with the same change in land use (e.g.
declining forest cover) increasing the provision of one hydrological service (e.g. total water
yield) but reducing the provision of another (e.g. water purification). Hence, the
determination of the net effect of a land-use change on the overall value of hydrological
services would need to consider impacts on water quality, water timing, and water quality,
and how these impacts in turn affect both downstream users and activities. To date, such
comprehensive analyses have rarely been done (see Aylward 2005 for a review).

4.9.8 Some key resources

e The hydrology module of the Natural Capital Project is being developed by Sue
White at Cranfield University and Guillermo Mendoza at Stanford University.

e The WaterGap2 (Water — Global Assessment and Prognosis) model is being
developed by Joseph Alcamo at the Center for Environmental Systems Research at
the University of Kassel, and Petra D6ll at the University of Frankfurt, Germany.

e The WBM model is being developed by Charles Vérésmarty by the Water Systems
Analysis Group at the University of New Hampshire, USA.

e The Macro-PDM model developed by Nigel Arnell, currently at the Walker Institute
for Climate System Research, University of Reading, UK.

e The VIC model is being developed by Dennis Lettenmaier at the University of
Washington (Liang et al. 1994; Nijssen et al. 2001).

e The Global Water System Project (GWSP - http://www.gwsp.org/) is currently
running a project to compare various global water models, including those
mentioned above.

e LPJ Dynamic Global Vegetation model, currently being developed at the Potsdam
Institute for Climate Impact Research (PIK) in Germany.

e SWAT (Soil and Water Assessment Tool), a public domain model actively
supported by the USDA Agricultural Research Service at the Grassland, Soil and
Water Research Laboratory in Temple, Texas, USA.

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4.9.9 Participants
Authors
Hannah Peck, Ana Rodrigues, Andrew Balmford (University of Cambridge, UK)
Sue White (Cranfield University, UK)
Contributors
Bernhard Lehner (McGill University, Canada)
Sandra Postel (Global Water Policy Project, USA)
Ed Tanner (University of Cambridge, UK)
Acknowledgements

Neil Burgess, Robert Naiman, and Charles Vérésmarty provided useful contacts and
references.

135

4.10 Wild plant fibres

Not developed

136

4.11 Wild medicinal plants

Not developed

137

4.12 Outdoors activities related to nature

Fully developed review led by an expert in the field.

4.12.1 Why is this benefit important for human wellbeing?

Outdoor activities related to nature (nature-based tourism and recreation) can be categorised
in numerous ways. For the purposes of this review we consider two means of
categorisation; outdoor recreation vs wildlife-based tourism, and local vs. longer-distance

travel.

General outdoor recreational pursuits such as walking, cycling, horse-riding and the
general enjoyment of being in green spaces, the countryside and wilderness areas.
Beach tourism, in as much as it relies on natural landscapes and natural processes,
could also be included here. These kinds of recreation rely on natural or semi-
natural landscapes but biodiversity is not the focus of the attraction. They can take
place almost anywhere with scenic or wilderness value, including transformed
landscapes such as the English (agricultural) landscape, managed woodlands or
even urban parks and gardens.

Wildlife-based tourism, whether non-consumptive (e.g. photographic, birdwatching)
or consumptive (hunting and fishing). This is more closely connected to biodiversity
in that it has specific elements of biodiversity (i.e. selected species) as the focus of
the recreational attraction. This kind of recreation usually relies on more pristine or
untransformed landscapes, and is most often (but not exclusively) associated with
protected areas or other designated sites or large-scale wilderness areas, including
the marine environment.

Local recreation, where people travel short distances to enjoy nature, is primarily
likely to be general outdoor recreation and enjoyment of nature, as per the first
bullet point above, although it will also include visits to nearby nature reserves by
local residents. This segment of the market is likely to be more heavily affected by
biodiversity loss or the loss of natural spaces, because such loss will reduce access
and increase the cost of access for users. As biodiversity is lost locally there will be
fewer or no alternative substitutable sites locally.

Longer distance recreation, where people travel hundreds or thousands of miles to
their destination, will primarily be for wildlife viewing as per the second bullet
point above although beach tourism and some outdoor pursuits will also be included
here. This form of recreation will be less affected by local biodiversity loss, since
there will be alternative substitute sites with equivalent access or travel costs
elsewhere in the world.

There are many reasons why the provision of outdoor recreational activities is beneficial.
Time spent experiencing nature is enjoyable, but also benefits our mental and physical
health, fosters well-being and sense of fulfilment (Bird 2005; Brown & Grant 2005), and
aids education (Fjortoft 2001). The psychological benefits of access to nature and natural
environments, to a primarily urban, sedentary population, and the resultant improvements
in productivity of the workforce, has been recognised since the industrial revolution:

138

‘thousands of tired, nerve-shaken, over-civilised people are beginning to find out
that going to the mountains is going home; that wilderness is a necessity; and that
mountain parks and reservations are useful not only as fountains of timber and
irrigating rivers, but as fountains of life’ (Muir 1898).

There is a body of evidence that strongly suggests that contact with nature is beneficial to
mental health and wellbeing (Henwood 2003), aiding patient recovery, reducing stress and
anxiety (Ulrich et al. 1991), improving worker concentration and reducing crime and
aggression (Bird 2007). Horticulture is widely used as a form of therapy in prisons and
hospitals, and BCTV’s ‘green gym’ volunteers gain more benefits than just physical health
improvements from manual conservation work (Brown & Grant 2005).

Beyond the psychological are demonstrable physical benefits of outdoor recreation. Many
of the outdoor activities that people enjoy rely on nature to provide the rich landscapes and
stimuli that underpin these experiences, ranging from gardening and walking in the park to
hiking and wildlife watching. Many of these are active pursuits, and as such are extremely
valuable in tackling ill-health in the face of a modern cultural environment that encourages
overconsumption of food and discourages physical activity (Hill & Peters 1998).

Currently around 1.6 billion adults are overweight worldwide, with in excess of 400 million
adults clinically obese, more than 115 million of whom are from developing countries
(WHO). This pandemic brings with it not only serious health disorders but also social and
psychological problems, all resulting in an enormous economic cost, conservatively
estimated at $117 billion annually in the United States alone (Stein & Colditz 2004).
However research indicates that the obesity epidemic in the United States could be halted
by increasing time spent walking by only 2-3 minutes per day on average (Veerman et al.
2007). Access to green public spaces is important to urban senior citizens’ longevity
(Takano et al. 2002), while accessible, large, and attractive public spaces demonstrably
increase the frequency of walking, with respondents describing the importance of trees and
birds amongst the features important to them (Gilles-Corti et al. 2005).

Although there is currently no evidence of additional physical health benefits from
exercising outdoors per se (Henwood 2002), “Health Walk’ and ‘Green Gym’ outdoor
exercise schemes in the UK have demonstrated that participants are more likely to continue
with this type of exercise than indoor gym-based exercise routines (Brown & Grant 2005).

Finally, the tourism industry, incorporating nature-based tourism and outdoor recreation, is
one of the world’s largest and fastest-growing industries, supporting millions of jobs
worldwide and contributing significant proportions of the GDP of many developing and
developed countries (www.worldtourism.org). Almost a century ago the scenic value of
Switzerland’s natural landscapes was valued in excess of $200 million annually
(Chamberlain 1910), a fact which helped to fuel the massive global expansion of national
parks and protected areas and with it the birth of the modern conservation movement
(Runte 1987). Nature-based tourism is one of the only non-consumptive uses of protected
areas/ecosystems/biodiversity that generates tangible economic values (Ceballos-Lascurain
1996; Gossling 1999), thereby making it an important ecosystem service (MEA 2005).

4.12.2 What are the overall trends in the provision of this benefit?

With increasing ease and affordability of travel, better access and communications, and
increasing affluence and leisure time, the global tourism industry is growing rapidly. Travel

139

and tourism account for 10% of world GDP, 8% of jobs and 12% of global investment
annually (WTTC 2007). Tourism also has the highest potential for growth of any industry —
currently running at more than 4% per annum, with an average of 3% in developed
economies and more than 7% in emerging markets” (WTTC 2007). Within this, nature-
based tourism and recreation is considered to be an increasingly significant sub-sector,
although there are little consistent aggregate data (Goodwin 1996; Eagles 2002). Although
there have been numerous studies of the magnitude (Goodwin et al. 1998; Eagles et al.
2000; Wade et al. 2001) and value (e.g. Wells 1993; Moran 1994; Shrestha et al. 2007) of
nature-based tourism at local and national scales, to date there has been no attempt at a
global assessment. This is in part because there is no consistent approach to recording and
reporting visitor arrivals to parks and protected areas (Eagles 2002) and other forms of
outdoor recreation go largely unrecorded in any form other than periodic site-based or
regional surveys.

Although recent high-profile research suggests that nature recreation is declining per capita
in US and Japan (Pergams and Zaradic 2008), this trend is not mirrored in much of the rest
of the world where growth in visitation to protected areas is growing at least as fast as
international tourism as a whole (Balmford et al., unpublished data). Less data are available
for other types of outdoor activity, though it has been estimated that each year over half the
population of the UK makes over 2.5 billion visits to urban green spaces (Wooley & Rose
2004), and 87 million Americans participated in wildlife-related recreation in 2006, an
increase of 13% over the decade (USF WS 2007).

As people show a universal preference for natural environments over built environments, an
increasing amount of outdoor recreation would be expected with projected increases in
world population and growing urbanisation (de Groot et al. 2005). This will place an
increasing reliance (and therefore value) on remaining natural landscapes.

Two other trends are worth noting. The first is a shifting emphasis in global tourism
towards emerging, developing world destinations, and with it an increase in domestic
tourism in the developing world as urban incomes rise. The second is an increasing
diversification of the agricultural sector in Europe and beyond into recreational service
provision. At the same time community-based tourism in rural areas in the developing
world is expanding nature-based tourism beyond protected area boundaries (Walpole &
Thouless, 2005). Whether these trends are increasing the overall magnitude of nature-based
recreation is unclear, but they are undeniably expanding the area and range of ecosystems
which provide recreationa! opportunities.

4.12.3 How is the provision of this benefit/process affected by changes in wild nature?

There are almost no reliable quantitative data on the extent to which changes in biodiversity
influence recreational benefits. The value derived from nature by those experiencing and
using it for recreational purposes is influenced by a wide range of factors, of which
biodiversity is one. Whilst it underlies the provision of key elements of a recreational
experience, and of supporting services, its marginal value is difficult to determine and
varies depending on the type of recreation and the perspective of the individual user. The
following sub-sections review some areas where biodiversity is likely to play a role.

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Provisioning of beautiful landscapes/seascapes

Outdoor recreation users rely on the provisioning of aesthetically pleasing landscapes as a
stage for their experience (De Groot et al. 2005), landscapes which are created and
regulated as part of naturally functioning ecosystems. Examples include the deposition of
the calcium carbonate tests of foraminiferans and reef-building organisms which, together
with mollusc shells, supply material for many beaches (Yamano 2000).

Studies have shown a clear preference for natural landscapes over urban ones, and people
tend to prefer greener, healthy ecosystems, but more detailed judgements of landscape
quality such as ‘wildness’ are likely to be more culture-dependent (de Groot et al. 2005).
They are also activity-dependent. Much outdoor recreation is physical in nature and relies
on the physical (slope, terrain) rather than biological (species and habitats) qualities of the
landscape. Moreover landscape ‘beauty’ is in the eye of the beholder and for some can be
enhanced by modification (the English countryside being a good example) which may
reduce biodiversity or reduce the importance of biodiversity in the recreational experience.
A study of forest recreational user preferences in the UK revealed the importance of man-
made facilities for enhancing use and enjoyment amongst general and activity-based forest
visitors, with only dedicated nature watchers prioritising access to biodiversity (Christie et
al. 2007).

The creation of the natural landscapes that make outdoor pursuits attractive is clearly a key
benefit provided by biodiversity, yet there is currently insufficient knowledge to be able to
define which attributes of the landscape are key to peoples’ experiences, let alone to
quantify links between specific aspects of biodiversity and outdoor benefits. Typically such
benefits are linked only to very general descriptions of nature such as visual complexity or
perceived naturalness (Brown & Grant 2005), which are difficult to translate into biological
terms or a concrete metric of biodiversity. However, land use change from natural to
intensively cultivated (or urbanised) landscapes will clearly reduce or eliminate the
recreational amenity value of an area. This will impact on the provision of local outdoor
recreational opportunities where access/distance is a constraining factor.

Provisioning of focal attractions: species diversity or presence of particular species

Humans derive great enjoyment and benefit from interaction with and observation of wild
animals and plants, clearly demonstrated in our love of gardening, and the popularity of
wildlife watching. E.O. Wilson has argued that the desire for such experiences is the
product of an innate love of living things which he terms “biophilia’, which drives our need
to seek connections with other life-forms (Wilson 1984).

Charismatic species such as gorillas and cetaceans clearly impact demand for outdoor
recreation, even generating tourism industries in areas not previously seen as a destination,
such as Rwanda. The annual recreational value of wildlife watching in Lake Nakuru
National Park, Kenya has been estimated at between US$7-15 million, with flamingos
creating more than one third of the value (Navrud & Mungatana 1994). The reintroduction
of wolves to Yellowstone has attracted additional tourists to the national park, with
consequent economic and social benefits estimated at between US$6-9 million per year
(Donlan et al. 2006). Interestingly, the presence of the wolves alone increases the
enjoyment of the park experience for tourists, regardless of whether or not they see any
wolves (Montag et al. 2005).

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Charismatic bird species have a significant influence in generating wildlife tourism in the
UK, with an estimated 290,000 people visiting osprey nesting sites each year, bringing an
extra £3.5 million to local economies, while the presence of sea eagles on Mull attracts an
extra £1.4-1.6 million of spending each year (Dickie et al. 2006). In a study of nature
tourists visiting Uganda Naidoo & Adamowicz (2005) found a tangible demand for bird
diversity and a willingness to pay for it. Similarly, in Komodo National Park in Indonesia,
visitors’ willingness to pay increased entrance fees was positively correlated with the
number of dragons they had seen during their visit (Walpole, 1997).

Biodiversity is perhaps even more significant in marine tourism. There are around 10
million active scuba divers who generate around $1.2 billion annually, and reef-related
tourism is growing rapidly at around 20% per year (Cesar et al. 2004). For these outdoor
users the diversity of species found on a coral reef is clearly paramount to the enjoyment of
a diving experience. In the aftermath of the mass coral bleaching event of 1997-8 this was
demonstrated by the losses to the dive industry in regions hit by coral bleaching and the
associated loss of reef species, for example in Palau, where as much as 10 percent of the
diving industry’s producer surplus was lost (Schuttenberg 2001). A survey of tourists in
Bonaire revealed that 80% would not be willing to return in the event of coral bleaching
(Uyarra et al. 2005).

These examples all reveal the importance of biodiversity, in terms of species presence
(which often relies on broader ecosystem health). It is clear that wildlife-based recreation is
generally more sensitive to the availability of pristine environments, species abundance and
diversity than broader outdoor recreation. Moreover some of the highest profile attractions,
the top predators and other large animals, require intact, functioning ecosystems to persist.
Yet the relationship between changes in these elements of biodiversity and the provision
(and value) of recreational benefits is complex, case specific, rarely linear and sometimes
counter-intuitive.

Special features of tourism as an ecosystem service

There is no linear relationship between magnitude of visitation, expenditure or other
measure of value and biodiversity. A reduction in bird diversity in a tropical forest may
impact on bird-watchers who are motivated by, amongst other things, species richness.
Likewise a decreasing chance of seeing a rare species such as the Komodo dragon, or the
disappearance of any of the ‘big five’ African mammal attractions might deter some from
visiting a particular park. However in general there is likely to be significant elasticity of
demand, with a change in number of flamingos, or wildebeest, or lions in a park having
little immediate impact on visitor numbers (Walpole & Thouless 2005). In other cases
increasing rarity and risk of extinction may increase tourism demand for certain sites and
species, with the mountain gorilla, tiger and giant panda as obvious examples (Entwistle et
al. 2000).

Tourism is still growing and is not currently limited by availability of natural attractions.
Increasing availability of nature-based tourism attractions (for example due to the
establishment of new protected area destinations} increases choice, substitutability and
competition between destinations. It may thus alter the geographic distribution of economic
benefits from tourism as new destinations capture a portion of the market. However it will
not necessarily increase the overall value of tourism at global scales (Walpole & Thouless,
2005).

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The value of tourism as a rationale for ecosystem conservation is limited by the size of the
(albeit growing) global market, but in the short term is unlikely to be constrained by a
general reduction in biodiversity and availability of recreational sites until such point as
access, overcrowding or species disappearance cause a tipping point to be reached. Such
tipping points may be reached at the local level, particularly in terms of access to general
outdoor recreation where travel cost is likely to be a limiting factor. In theory, if local sites
for outdoor recreation decline (with increasing urbanisation or agricultural expansion),
there will be less opportunity for people to obtain this benefit and welfare will decline.

Overall welfare will not decline as long as substitutable opportunities are available
elsewhere. This is generally more likely to be the case for longer-distance wildlife tourism
than for more general outdoor recreation. Most tourists will simply choose an alternative
destination. Unlike other ecosystem services, tourism benefits do not flow to the
beneficiary but rather the reverse — beneficiaries travel to the point where the service is
produced. For international tourism at least, there is not the same distance or access
constraint as for some provisioning, regulating or cultural services, and substitutability is
possible.

Provisioning of regulatory services

Tourism and recreation rely on a range of regulatory and supporting services provided in
part by biodiversity, including climate regulation, water flow, waste management, coastal
protection and erosion control. Without the many regulating services provided by wild
ecosystems attractive environments for outdoor recreation would not be maintained. This
has been seen in some coastal areas in Mauritius, where seagrass beds were removed in the
belief that they were not aesthetically pleasing to tourists, resulting in a worse environment
for swimmers, with increased turbidity and loss of infaunal biodiversity (Daby 2003). The
role of biodiversity in these ancillary services are dealt with elsewhere in this review and
thus are not considered further here.

4.12.4 What are the main threats the provision of this benefit?

With increasing global population, urbanisation and land transformation the provision of
remaining natural landscapes will increase in importance, particularly for the growing
proportion of humanity that does not have any exposure to natural settings. Many natural
environments, such as coral reefs, wetlands and montane environments are already
becoming significantly degraded and transformed. Coastal areas are also under threat from
erosion, over-development and sea level rise. Intensifying conversion of wild habitat to
agricultural land will drastically impact outdoor recreation for many, particularly where
access to outdoor recreation becomes constrained, although there is insufficient knowledge
of landscape preferences to predict the net effects on all of humanity.

At the same time the proportion of the world’s surface under designated protection
continues to increase (UNEP-WCMC, 2008), and natural areas continue to be added to the
UNESCO World Heritage List (http://whe.unesco.org). Although such designation does not
guarantee protection (and many protected areas and wildlife populations remain threatened
by development pressures, poaching, logging, settlement, etc) it does raise profile and
makes such areas more likely to become tourism destinations. This is in effect increasing
the provision of relatively pristine natural environments upon which much nature-based
tourism is based.

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Globally 32% of amphibian species, 23% of mammals, and 12% of bird species are
threatened with extinction, while the status of reptiles, plants and fish is uncertain. Within
these groups, charismatic taxa such as primates, carnivores and albatrosses are particularly
at risk. In recent years, the extinction risk for birds and amphibians has increased (Baillie et
al. 2004) and the pattern is likely to be mirrored in other taxonomic groups. WWF’s Living
Planet Index also suggests that, overall, wild species abundance is also in decline, implying
that even the less threatened wildlife may become harder to find.

4.12.5 Are abrupt changes likely in the provision of this benefit?

Abrupt changes in the provision of tourism benefits can occur for a range of reasons. Some
of these may be ecological, as systems reach tipping points. Key wildlife populations may
collapse through disease or other factors, fire may destroy picturesque landscapes, corals
may bleach with sudden temperature shifts, systems may suddenly change from one
(attractive) to another (less desirable) stable state. Some of these will be reversible, others
may be more permanent.

Abrupt shifts may also (and perhaps more often) be socially instigated. War, terrorism,
socio-political disruption, natural disasters and health crises all tend to rapidly and
negatively affect international tourism demand, as evinced for example by Kenya,
Zimbabwe, Bali, Egypt and Nepal in recent times. Likewise events such as the foot and
mouth outbreak in the UK in 2001 may have dramatic impacts as people are prevented from
visiting the countryside for recreation.

In some cases it has also been shown that abrupt changes in management of areas, such as
degazetting parks or increasing entrance fees, can result in a sudden drop in recreational
demand (Goodwin et al. 1998). The current rapid rise in oil prices (and thus aviation fuel
costs) and potential carbon taxes on flying, may have similar impacts on international
tourism if such changes are too sudden.

4.12.6 Can we quantify and map the global provision of this benefit and how it might
change?

We are a long way from being able to map the production of benefits from nature-related
outdoor recreation based on changing states of biodiversity. Firstly there is little known
about the link between features of biodiversity and outdoor recreation demand on which to
base such models. Indeed the analysis of outdoor recreation in general is hampered by a
great scarcity of data on outdoor users, and where monitoring does exist is often unreliable
and patchy.

Secondly and in such cases where the link has been demonstrated or recreational value
estimated, it is not clear how far we can generalise such a relationship from very localised
case studies. In contrast to the general terms such as ‘perceived naturalness’ which are used
to describe the desired aspects of landscapes for general outdoor recreation in some
literature, valuation studies focussing on the importance of particular species or ecosystems
to certain types of outdoor visitor are too context specific to allow generalisation.

It is difficult to tie down universal preferences for biological attributes from such a diverse
group as nature-based recreational users, so linking biophysical attributes to amenity value
globally is problematic. Part of the difficulty may lie in that such preferences are context-
specific, that is, they are dependent on the physical and cultural environment from which

144

people originate. It has been found that tourists’ perception of environmental quality is
influenced by their past experiences, socio-economic background and culture (Petrosillo et
al. 2006).

Thirdly, most studies focus on total economic valuation rather than marginal values and so
the application of scenarios to model the impacts of change is constrained.

Already developed integrated models

Existing models of nature tourism demand are few and far between, and are unable to
include any aspects of biodiversity amongst their variables. Brainard and colleagues (2001)
have produced models predicting demand for recreation in English woodlands based on
accessibility, local population, and facilities present. Likewise Jalale (1993) used
conventional demand modelling to identify factors affecting visitation to national parks in
Malawi and found access and facilities to be important. Balmford and colleagues
(unpublished) have been the first to attempt to model visitor numbers to protected areas
globally, using a database of site-specific and national data. None of these models were
able to include significant variables relating to biodiversity. This may reflect both the
importance of non-biotic factors (such as access), as well as the range of motivations for
nature-based tourism, including various aspects of naturalness and biodiversity that cannot
easily be distilled into adequate quantitative variables (Goodwin & Leader-Williams 2000).

In the UK Carver and colleagues (2002) have developed a method for mapping a subjective
judgement of landscape, in this case wilderness. By allowing users to weight the
importance of underlying spatial variables such as distance from nearest road, the different
preferences of various groups for landscape qualities could be determined, and potentially
the relative importance of those qualities produced by biodiversity could be measured. It is
possible that a global map of a general subjective descriptor such as ‘natural attractiveness’
could be pieced together in this manner by allowing local weighting of the underlying
landscape qualities. Such a map could then be combined with other recognised variables
influencing tourism demand such as accessibility to create a model for general domestic
outdoor recreation potential.

For wildlife-based tourism it will probably be necessary to create individual models for
clearly distinct categories such as scuba divers and bird watchers, and to limit any model
extrapolation to those areas where tourism currently exists (e.g within national parks rather
than across natural ecosystems more broadly. The challenge is that likely (and relatively
simplified) scenarios based on land use change outside protected areas would not trigger
marginal changes in protected area tourism that could be valued, unless models included
the impact of neighbouring land use change on protected area biodiversity. Even if it did,
the likelihood of substitutable alternative destinations as menetioned above would limit the
impact on global provision of tourism benefits.

Availability of adequate data

e Further data on the magnitude (and value) of different types of tourism in different
ecosystems is a pre-requisite for any modelling exercise.

e Further work examining how nature-based recreation demand (either general

outdoor recreation with a primarily local catchment or nature-based tourism with a
potentially much broader regional or global catchment) responds to changes in

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biodiversity (or land use type at the very least) is required before any global
scenario models can be created.

Adequacy of scenarios

e None currently available for nature-based tourism and recreational activities.

What can be done in Phase II? At what cost? By whom®?

We recommend that in the short term, activities are two-fold, both focusing on the value of
specific recreational types in different habitats. First, more in-depth literature-based
syntheses of well-studied activity subtypes, namely scuba-diving and bird watching, where
the relationship between recreational benefits and biodiversity is more clearly defined and
where valuation studies are more advanced.

Second, and of potentially greater value, modelling change in the availability of local
outdoor recreation with changing land use/urbanisation and thus increasing distance to
recreational sites. The importance and impact of access/distance on tourism demand is
already well modelled, and numerous organisations in the UK have expertise in this area.

There is also benefit in modelling tourism demand and marginal value for terrestrial
protected areas. This work has recently begun in Cambridge, and investment will be
required in two areas: (1) refining the global visitor model for protected areas, and (2)
extending it to include economic valuation. This will require improving the coverage and
comparability of protected area visitor and finance data, besides a more targeted literature-
search for valuation case studies. For this, UNEP-WCMC in partnership with Cambridge
University, the International Centre for Responsible Tourism and the IUCN WCPA are
well-placed to lead.

These two approaches would need a minimum of 24 researcher-months, and could in theory
and in combination provide some kind of partial marginal valuation that could be included
in a global scenario modelling exercise.

4.12.7 Insights for economic valuation

The valuation of recreation can be done based on actual financial flows, although
distinguishing what proportion of expenditure during a tourism excursion should be
attributed to natural attractions can be challenging. Where there is no direct expenditure,
other evaluation methods such as travel cost can be used.

Since a propotion of the value of recreation is not captured in markets it is common in
recreational valuation studies to use contingent valuation methods to estimate ‘user

© This is our recommendation for Phase II, based on the results of this review. It does not commit the leaders
of Phase II to follow it, and it does not commit the recommended research group to actually do such
work.

146

surplus’, the proportion of the value of the experience retained by the tourist rather than
captured in entrance fees, for example. Such methods are problematic, but the issue of
where to draw the line in defining what to include in an evaluation of economic benefits is a
very important one to resolve at the outset of any global exercise.

4.12.8 Some key resources

Web resources are extremely limited since, apart from UNEP-WCMC and the University of
Cambridge (and the Natural Capital Project) no one is exploring this issue at a global scale.
The following is broken down into some key organisations and some web-available
literature:

Organisations

e Natural Capital Project: www.naturalcapitalproject.org/

e World Database on Protected Areas: www.unep-wemc.org/wdpa/
e World Commission on Protected Areas: www.iucn.org/wcpa

e International Centre for Responsible Tourism: www.icrtourism.org/
e World Travel Organisation: www.world-tourism.org/

Reports, publications and journal articles

e RSPB. 2007. Wellbeing through Wildlife in the EU.
http://www.birdlife.org/eu/pdfs/Wellbeing EU final version 2mb.pdf

e Pretty J, Griffin M, Peacock J, Hine R, Sellens M and South N. 2005. A countryside
for health and wellbeing: The physical and mental health benefits of green exercise.
University of Essex, Colchester. Available online at:
http://www.countrysiderecreation.org.uk/pdf/CRN%20exec%20summary.pdf
http://195.92.230.85/Images/Hine_tem2-30031.pdf

e Bird W. 2004. Natural Fit: Can Green Space and Biodiversity Increase Levels of
Physical Activity? RSPB. The RSPB. Available at:
http://www.rspb.org.uk/Images/natural_fit_full version _tem9-133055.pdf

e Collins, S. 2006. The Makuleke model for good governance and fair benefit sharing
Steve Collins in IUCN. Policy Matters
http://www.iucn.org/themes/ceesp/Publications/newsletter/PM14-
Section%201V.pdf

e Health Council of the Netherlands. 2004. The influence of nature on social, physical
and psychological wellbeing. Part 1: review of current knowledge. Report to the
Minister of Agriculture, Nature and Food.
http://www.rmno.nl/files_content/Nature%20and%20Health.pdf

e UNEP/CMS Secretariat (2006). Wildlife watching and tourism. Bonn. Available
at:

http://www.cms.int/publications/pdf/wildlifewatching_text.pdf

4.12.9 Participants in this sub-review
Authors
James Beresford (University of Cambridge, UK)

Matt Walpole (UNEP-WCMC, UK)

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Contributors

Andrew Balmford, Ana Rodrigues (University of Cambridge, UK)

148

4.13 Regulation of natural hazards

Not developed

149

4.14 One-off use benefits

Not developed

4.15 Non-use benefits

Not developed

151

Global climate regulation

This section is a very brief overview based on a quick literature review. It did not receive
contributions from, nor has it been reviewed by, experts in this field.

4.15.1 Why is global climate regulation important for human wellbeing?

There has been extensive work on the ways in which climate change influences human
wellbeing (and therefore the ways in which a reduction of the effects of climate change
affects wellbeing). It is very well-established that climate change will have profound effects
on wellbeing including through effects on food and water provision, health, property and
infrastructure, global security and impacts on ecosystems and the benefits we derive from
them. A disproportionate burden of these impacts falls on poor regions of the world, and so
climate change is predicted to affect global development goals very significantly. Key
references include:

e Stern (2007) — Stern Review on the Economics of Climate Change. This review has
collated information on how climate change affects: water provision, food provision
(including marine fisheries), health, land (including effects of sea-level rise),
property/infrastructure, and the environment (including species extinctions). It
further analysed how climate change affects development at global and regional
scales.

e House et al. (2005) — the section of the Millennium Ecosystem Assessment: Current
State and Trends on Climate and Air Quality, particularly the sub-section 13.6 on
Impacts of Changes in Climate and Air Quality on Human Well-being.

e [PCCWGII (2007) — Climate Change 2007 — Impacts, Adaptation and Vulnerability
(Working Group II contribution to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change), including an assessment impacts on
fresh water resources, ecosystems, food, coastal systems, human health, industry
and an overview of impacts per continent.

4.15.2 How is global climate regulation affected by changes in wild nature?

There has also been extensive work on the mechanisms driving climate change, including
the ways in which natural ecosystems affect the concentration of greenhouse gases, and the
ways in which human activities are impacting those processes. It is well-established that
terrestrial and aquatic ecosystems play key roles in the global geochemical cycles affecting
the concentration of greenhouse gases in the atmosphere. Ecosystems also influence climate
at a regional scale by affecting radiation (e.g. albedo), cloudiness, and surface temperature.
Accordingly, it is also well-established that anthropogenic changes on ecosystems have
significant impacts on climate change. Land use changes affecting forest ecosystems
(deforestation, reforestation, afforestation) are particularly influential to the balance of
carbon emissions, sequestration, and storage. Forest fires also contribute to climate change
by releasing aerosols. Other ecosystems also play important roles including: wetlands and
reservoirs (with net methane emissions); grasslands (with variable degrees of underground
carbon storage); peatlands and mires (important carbon storages); agricultural lands
(particularly nitrous oxide emissions, also methane). Oceans are substantial carbon

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reservoirs and sinks, but these seem to be stable (presumably not currently being affected
by human activities?). There has been plenty of work on the quantification of carbon
storage, sinks and emissions for different ecosystems (including above and below ground)
and these can be mapped at the global scale.

Key references include:

e SCBD (2003) report Interlinkages between biological diversity and climate change:
Advice on the integration of biodiversity considerations into the implementation of
the United Nations Framework Convention on Climate Change and its Kyoto
protocol discusses ways in which climate change affects biodiversity and also ways
in which changes in natural systems affect greenhouse gases emissions.

e IPCCWGI (2007) — Climate Change 2007 — The Physical Science Basis (Working
Group I Contribution to the Fourth Assessment Report of the Intergovernmental
Panel on Climate Change), in particular chapter 7 on Couplings Between Changes in
the Climate System and Biogeochemistry.

e IPCCWGIII (2007) — Climate Change 2007 — Mitigation of Climate Change
(Working Group III Contribution to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change) in particular chapter 9 on Forestry.

e Foley et al. (2005) includes a discussion of the ways in which land use change
affects global climate.

e IPCC (2001), the IPCC Special Report on Emissions Scenarios, particularly Chapter
3: Scenario Driving Forces which discusses agriculture and land-use emissions and
how these affect climate change scenarios.

e Pregitzer & Euskirchen (2004) discuss the role of forests in carbon cycling and
storage.

e McGuire, A. D., Sitch, S., Clein, J. S., Dargaville, R., Esser, G., et al. (2001)
Carbon balance of the terrestrial biopshere in the twentieth century: Analyses of
CO2, climate and land use effects with four process-based ecosystem models.
Global Biogeochem Cycles 15, 183-206.

e 3). Tian, H., Melillo, J. M., Kicklighter, D. W., McGuire, A. D. & Helfrich, J.
(1999) The sensitivity of terrestrial carbon storage to historical climate variability
and atmospheric CO2 in the United States. Tellus Ser B Chem Phys Meteorol 51,
414-452.

4.15.3 Can we quantify and map the value of wild nature for global climate regulation,
and how it might change?

There has been important work on the calculation of the economic implications of climate
change and the costs of climate change mitigation. This information is fundamental for
deriving a price for carbon, which can be created through both tax and trading. The first
international emissions trading scheme was created by the European Union in 2006. A
carbon price can be used to quantify the economic consequences of different policy actions

153

by assessing how they differ in terms of carbon storage, emission and sequestration by
different ecosystems.

Key references include:

e Stern (2007) — Stern Review on the Economics of Climate Change, particularly
section 15: Carbon Pricing and Emissions Markets in Place.

4.15.4 Participants

Authors

Hannah Peck, Ana Rodrigues, Andrew Balmford (University of Cambridge, UK).

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4.16 Unknown benefits or processes

Not developed

155

4.17 Scenarios

Scenarios provide a methodology for exploring alternative future environments in which
today’s decisions might be played out. In practice, scenarios are stories, written or spoken,
built around carefully constructed plots often termed narrative storylines. Scenarios are not
predictions; instead, scenarios are an approach to help manage the inherent uncertainties of
decisions by examining plausible, internally consistent alternatives of how the future might
unfold and comparing the potential consequences of decisions in different future contexts
(SEI, n.d.; Verbug et al. 2006). Scenarios may be purely qualitative, or may generate
quantitative results through the use of one or more simulation models.

The generation of appropriate scenarios is a key component of Phase 2. Without these, the
relevance and accuracy of all results obtained will be questionable.

Throughout we refer to two scenarios, but many more can and should be generated to
assess different policy packages. It is, however, fundamental that in any quantification of
the economic consequences of biodiversity loss and ecosystem degradation, two scenarios
are compared. These need to be identical in everything else but the specific policy package
being tested. Otherwise, the economic results cannot be directly attributed to a difference in
biodiversity/ecosystems. For example, an appropriate contrast would be between the state
of the world by 2025 generated by a business-as-usual (BAU) scenario, and an alternative
state of the world generated by an otherwise identical scenario that includes the policy
option of “a comprehensive global network of marine protected areas”. These two scenarios
could be contrasted to evaluate the specific economic consequences (both the costs and the
benefits) of implementing the marine protected area network, as everything else (e.g..
population, technology, consumption) would be the same. It would not be appropriate to try
to evaluate the consequences of establishing a comprehensive marine reserve network by
contrasting a 2008 state of the world (with its incipient network of marine protected areas)
with a 2025 state of the world generated by the policy option scenario. Such a contrast
would not take into account the changes in many other variables, including total human
population, demand for marine fish, technological changes in fishing methods, etc.

In order to be useful, the scenarios need to have the right level of information at the right
spatial scale. For example, if the action is to implement new protected areas, the scenario
needs to state where they are; if the action is to manage fisheries appropriately, the scenario
needs to spell out how that would be done, for example by creating no-fishing zones and
setting sustainable fisheries quotas. If the scenarios do not have the necessary detail, they
will be inappropriate to test how changes in biodiversity/ecosystems affect the benefits we
derive from them.

Scenario development has become a popular tool for the assessment of policy options, at
scales ranging from national and regional (e.g. Ferreras et al. 2001; Green et al. 2005;
Johansson et al. 2007; Rounsevell et al. 2006; Sandker et al. 2007; Soares-Filho et al. 2006)
to global (e.g. House et al. 2002; Kindermann et al. 2006; Msangi et al. 2007; Sathaye et al.
2003; Ten Brink et al. 2007) and there is a growing literature on scenario generation and
implementation. Recent large-scale scenario exercises likely to be relevant to the present
assessment include: ATEAM (for Europe); the global and regional scale the Millennium
Ecosystem Assessment; Global Environment Outlook exercises; the International
Assessment of Agricultural Knowledge, Science and Technology for Development; the

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Intergovernmental Panel on Climate Change fourth assessment; World Energy Outlook;
and the OECD Outlook series (environment, agriculture, economics).

It is beyond the capacity of the current project to review the extensive literature on scenario
development, to recommend which types of methods/approaches for generating scenarios
are the most suitable for the current assessment, or to propose in any detail which specific
scenarios should be contrasted. We emphatically recommend that this is a major priority for
Phase 2 of the Review, the task that underlies the development of all other work.

For each review theme, we have made a general assessment of whether the scenario results
currently being generated in global to regional scale modelling exercises are likely to
produce the adequate level of information needed for the biodiversity response
models/approaches that we have proposed for quantifying changes in benefit/services
provision.

In addition, we provide a brief overview (not an exhaustive list) of main scenarios that we
hope covers the major models used in global scale scenario exercises.

Table 4 Main scenarios used in policy evaluation

Model /
approach

Global
climate
models —
Hadley
Centre,
CSIRO,
etc.

Example
scenario
exercises*

IPCC
(Nakicenovic
& Swart
2000)

Output
variables

Climate
variables such

as temperature,

precipitation,
relative
humidity

Spatial
scale of
future
simulation

Multiple
degrees -
downscaling
approaches
permit finer
scale results

Theme
relevance

Scope to
improve

Ongoing
scope to
improve
integrated
land cover
models

Reference

Parry et al.
2007;
Solomon et
al. 2007

GEO;
regional
exercises

Land use
including
biofuel
production;
agricultural
production

Surface runoff
and river
discharges

Air quality —
pollutant
emissions
(NOx, SO2,
CO)

Land use
demand, land
cover

5 degree
grid — Asia-
Pacific
region

5 degree
grid — Asia-
Pacific

regi

Grid (SGale
8) — Asia-
Pacific
region

Flexible —
depends on
inputs

157

(Land use /
cover)

(Freshwater)

(Land use /
cover)

Finer
resolution

NIES 2008

Verburg e¢
al. 2002

EcoSim /
EcoPath

GLOBIO

GUMBO

IMAGE

GEO, GBO,
OECD

Environment
Outlook

‘Big Gov /
Eco-topia /
Mad Max /
Star Trek’ set

— Costanza services (see simulation of
(2000) model services and

Carbon price
scenarios

GEO, GBO,
IPCC, MA,
IAASTD

variables: other models | resolution

CO2 emissions

Fish
production;
biomass

Freshwater
fisheries
simulation

Pauly et al.
2002

Biodiversity Additional Alkemade et
indicator indicators al. 2006
“mean may be

abundance of developed

original
species’

Change in 0.5 degree (Land use / Modelling
ecosystem (fractional cover) approach is
extent cover per relatively

cell) basic

Land use Global (11 (Land use /
biomes) cover)

Boumans et
al. 2002

Add spatial
component

Ecosystem Direct

description their
below) marginal
value

National
scale

Multiple socio-
economic
variables
(economics,
food demand,
population,
etc)

Useful to More direct Hughes
provide environmental | 2001;
internally limits Hughes &
consistent Irfan 2007
sets of
drivers for
other models

Environment Various; but | Finer

remaining typically

fossil fuels, have finer
area of forested resolution
land, water
usage, and

Kindermann
et al. 2006

Forest area & (Land use / Finer
biomass cover); resolution
carbon

Finer Bouwman et
resolution al. 2006

(Land use /
cover)

Land cover — 0.5 degree
including
cropland,
pastureland,
forest, urban...

Soil 0.5 degree (Land use /

degradation cover)
NPP 0.5 degree Carbon
storage

158

Timber 24 regions Forest
production of world (?) | services

Nitrogen 0.5 degree
deposition

Risk of water 24 regions (Land use / Potting &
erosion of soils |} (?) cover) Bakkes
2004

IMPACT Crop, livestock | 69 regions (Land use / Finer Rosegrant et
consumption of world cover) resolution al. 1995
and production

Fish Fisheries
consumption
and production

River runoff 0.5 degree (Freshwater / | Integrate Arnell 2003
marine) effects of land
cover change

Exports of River basin | Fisheries; Linkage with | Seitzinger et
nutrients from mouths oceanic oceanic al. 2005
rivers to carbon transport

coastal zones sequestration | model

WaterGAP Water flow and | 0.5 degree Freshwater D6ll et al.
storage 2003
(surface runoff,
groundwater
recharge, river
discharge,
water storage
in soil,
groundwater
and surface
water bodies)

Water use 0.5 degree Freshwater
(withdrawals

for irrigation,

livestock,

households,

thermal power

plant and

manufacturing)

* Acronyms — scenario exercises

GEO — Global Environment Outlook (4)

GBO — Global Biodiversity Outlook (2)

159

IAASTD - International Assessment of Agricultural Knowledge, Science and Technology
for Development

IPCC - Intergovernmental Panel on Climate Change assessment reports (4°)

MA — Millennium Ecosystem Assessment — Carpenter et al. 2005

Model descriptions
(Quotation marks indicate text directly from paper or webpage describing model)
Global climate models.

Atmosphere-Ocean General Circulation Models designed to reproduce current and
historical climate, and project the response of future climate to changes in variables such as
the Earth’s atmospheric composition, land cover and solar forcing. Confidence is higher for
changes in temperature than in precipitation. See IPCC reports for further details.

AIM -— Asia-Pacific Integrated Model

An integrated assessment model, designed to assess policy options for stabilising and
adapting to changes in global climate, particularly in the Asian-Pacific region. Developed at
National Institute for Environmental Studies, Japan.

CLUE /CLUE-S

Developed for the analysis of land use at a national to continental scale. CLUE-S is a
variant for smaller regional scale applications (e.g., a watershed or province) at a fine
spatial resolution. Demand is simulated with one module, and spatial distribution with
another — it is possible to supply this second module with demands from a different external
scenario. Model uses decision rules such as conversion probabilities between land uses.
Developed at Wageningen University.

EcoSim/ EcoPath / EcoSpace

Simulates biomass within marine food web models under the impacts of competing fishing
fleets. EcoSpace simulates the influence of protected area networks. Developed at
University of British Columbia.

GLOBIO3

Developed for the analysis of impact of policy options on biodiversity. Indicator is “mean
abundance of original species of ecosystem’ — hence a relative indicator of biodiversity
compared to a reference ‘natural’ state, rather than an absolute indicator of biodiversity
compared between locations. Based on a set of cause-effect relationships grounded in the
literature on the impacts of pressures on biodiversity. Developed at the Netherlands
Environment Assessment Agency / UNEP/GRID Arendal / UNEP-WCMC.

160

Gumbo - Global Unified Metamodel of the Biosphere

“Gumbo consists of five sectors or spheres: Atmosphere, Lithosphere, Hydrosphere,
Biosphere, and Antrophosphere (human systems). It is also divided into 11 biomes or
ecosystem types which encompass the entire surface area of the planet: Open Ocean,
Coastal Ocean, Forests, Grasslands, Wetlands, Lakes/Rivers, Deserts, Tundra, Ice/rock,
Croplands, and Urban. Global energy, carbon, nitrogen, and water dynamics are
incorporated in Gumbo, along with soil erosion and formation, fossil fuel formation and
use, plant and animal productivity and harvest, human population, energy, biomass, and
water use, economic production and welfare, and changes in natural, built, human, and
social capital”. Simulates ecosystem services on a global scale, based on flows and storages
in the model. The services are: gas regulation, climate regulation, disturbance regulation,
water use, soil formation, nutrient cycling, waste treatment, food production, raw materials,
and recreation/cultural. Developed at University of Vermont.

IFs - International Futures

“The broad purpose of IFs is to serve as a thinking tool for the analysis of near through
long-term country-specific, regional, and global futures across multiple, interacting issue
areas”. The overall model incorporates different sub-models, including: Population,
Economics, Agriculture, Education, Energy, Socio-Political, the International Political,
Environment, Technology, and Health. Model relies on simulation of macroagents (e.g.
governments) and markets. Developed at University of Denver.

IIASA forest model [precise name unclear]

“Calculates differences in net present value of different land uses using a spatially explicit
integrated biophysical and socio-economic land use model. Key model parameters, such as
agricultural land use and production, population growth, deforestation and forest product
consumption rates were calibrated against historical rates. Land use changes are simulated
in the model as a decision based on a difference between net present value of income from
production on agricultural land versus net present value of income from forest products.
Assuming fixed technology, the model calculates for each 0.5° grid cell the net present
value difference between agricultural and forest land-uses in one-year time steps”

IMAGE - Integrated Model to Assess the Global Environment

The IMAGE model simulates direct and indirect pressures on human and natural systems
resulting from industry, housing, transport, agriculture and forestry. Socio-economic
activities and drivers of change are allocated to 24 regions of the world, and the climate,
land-cover and land-use change-related processes are represented on a 0.5 degree grid.
Developed at the Netherlands Environment Assessment Agency.

IMPACT - International Model for Policy Analysis of Agricultural Commodities and
Trade

IMPACT has as its basis a set of national supply and demand equations for 32
commodities, with each country model being linked to the rest of the world through trade.
“To explore food security effects, IMPACT projects the percentage and number of
malnourished preschool children (0 to 5 years old) in developing countries as a function of
average per capita calorie availability, the share of females with secondary schooling, the

161

ratio of female to male life expectancy at birth, and the percentage of the population with
access to safe water. A wide range of factors with potentially significant impacts on future
developments in the world food situation can be modeled based on IMPACT. They include:
population and income growth, the rate of growth in crop and livestock yield and
production, feed ratios for livestock, agricultural research, irrigation and other investments,
commodity price policies, and elasticities of supply and demand. For any specification of
these underlying factors, IMPACT generates projections for crop area, yield, production,
demand for food, feed and other uses, prices, and trade; and for livestock numbers, yield,
production, demand, prices, and trade”. Developed at International Food Policy Research
Institute.

Macro-PDM

Global scale river runoff model designed for use with IPCC scenarios. Changes in runoff
are largely driven by change in precipitation, offset by increases in evaporation. Each 0.5
degree cell is treated as an independent catchment. Developed at Tyndall Centre.

NEWS model suite - Global Nutrient Export from Watersheds

Simulates river nutrient export from watersheds. “The Global NEWS system includes
models that can be used to predict export of sediments, DIN, DIP, DOC, DON, dissolved
organic P (DOP); particulate organic C (POC), PN, and PP” — based on factors such as
inputs of fertiliser, manure and sewage. Developed through a workgroup of UNESCO’s
International Oceanographic Commission.

WaterGAP

“The overall aim is to investigate current and future world-wide water availability, water
use and water quality. Whereas water availability and water use have been implemented, a
water quality module is currently under development and constitutes the next major goal. In
particular, Water-GAP is concerned with the various impacts of global change on water
availability and water demand, and to determine the development of water stress conditions
on different spatial and temporal scales. Further research subjects are the variations of the
water balance components on the hydrological large scale, and the future development of
extreme conditions - such as floods and droughts”. Developed at University of Kassel /
University of Frankfurt.

4.17.1 Participants

Author: Lera Miles (UNEP — WCMP, Cambridge, United Kingdom)

162

4.18 Prioritisation of recommendations for future work

This section builds from the results of the thematic reviews (sections 4.1 to 4.16) to
prioritise amongst the analyses recommended those that are most important and most
feasible for Phase 2.

Scenario development

The first priority for Phase 2, underlying all subsequent work, is the development of
appropriate scenarios, built as counterfactuals to test the specific policy package that is
being tested.

As emphasised in sections 2.3.2 and 4.17, it is fundamental that in the evaluation of the
economic consequences of biodiversity loss and ecosystem degradation, two scenarios are
compared. These need to be identical in everything else but the specific policy package
being tested. Otherwise, the economic results cannot be directly attributed to a difference in
the state of biodiversity/ecosystems. Key steps in scenario development include spatially-
explicit quantitative descriptions of the likely state of biodiversity under business-as-usual
and under a set of policy interventions deemed to be sufficient to conserve biodiversity; and
the identification and costing of those interventions. Care must be taken to ensure that the
quantitative, mapped descriptions of the alternative states of the world are fit-for-purpose —
that is to say that they address all of the key biophysical and socioeconomic input variables
needed to feed into the models of the production and flow of each process and service of
interest.

Prioritising recommendations for Phase 2

The thematic reviews (sections 4.1 to 4.16) present an assessment of the feasibility, for each
particular theme, of quantifying the provision of ecosystem processes or benefits in a
spatially-explicit way as a basis for the economic valuation in Part 2. Recommendations are
presented for how such quantification can be done, and the resources estimated to be
necessary for each analysis. Here, we prioritise amongst those recommended analyses,
based on two criteria (Figure 17):

- Importance to human wellbeing: The degree to which these processes/benefits are
likely to affect the overall results of the Review. This is based on a qualitative
assessment of the predicted magnitude of the overall economic value of each
process or benefit. Recommended analyses were coded, in decreasing order of
importance, as A, B, or C. We have marked with an asterisk (*) those benefits that
are particularly valuable for local livelihoods (which may have low overall
economic value but may directly influence the wellbeing of millions of people).

- Feasibility in Phase 2: The likelihood that the particular analysis recommended can
be successfully undertaken in one year. Recommendations were coded, in
decreasing order of feasibility, as 1, 2 or 3.

The combination of these two criteria produces an overall priority ranking (Figure 17),
from very high priority, when the analysis is both highly important and highly feasibly
(A1), to very low priority, when the analysis is of lower importance and not particularly
feasible in Phase 2 (C3).

163

importance to
human wellbeing

higher <————_—__ lower Very high priority

ener 6s
inPhase? | 2 ||‘ x SE tow rny

3 O x xX x Very low priority
ike

Figure 17 Prioritisation of future research lines according to their importance to human wellbeing
(A to C) and feasibility in Phase 2 (1 to 3). Priority levels range from very high priority (A1) to very
low priority (C3).

higher High priority

To do if resources available

Table 5 presents the results of the prioritisation for each of the analyses recommended for
the benefits/processes considered in this project.

Prioritising recommendations for research in the longer-term

For the longer-term, priorities are defined based on importance alone. Indeed, all of the
analyses proposed are feasible, and so should all be done if time is less of a limitation. In
this case, the focus should become to ensure that the most important benefits or processes
are covered first.

The priority then becomes to tackle analyses coded as A first, followed by B and by C.

Table 5 Prioritisation of the recommended analyses for each category of benefit or process
considered in this review.

Benefit/process | Recommended analysis (see corresponding | Priority | Priority
section for more detail)

Scenario development Essential | Essential

Wild crop Global pollination model building from
pollination landscape-scale assessments

Biological Global crop biological control model building
control in crops from landscape-scale assessments

164

Genetic diversity | Global risk assessment of crop/livestock disease | B2?-3?

of crops and from loss of genetic diversity

livestock

Soil quality for e Global valuation of improvement in soil quality | B3
crop production from changes in soil biota (internal effects)

e Global valuation of soil subsidies when | B1*
agriculture expands into natural ecosystems
(conversion effects)

Global model of the protective effects to crop | C2*
soils of neighbouring natural habitat
(neighbouring effects)

Global model of value of wildlife fertilisers. C3*

Livestock e Global model of rangeland contribution to | BI*

livestock production

Marine fisheries e Global model of marine fisheries provision Al*

Inland fisheries e Global model of inland fisheries provision | B2*
including aquaculture

Wild animal Bl*

td a Ma

e Pantropical model of wild meat provision

e European and North-American model of
domestic recreational hunting

products

EF
co ©

Fresh water e Global hydrological model for water provision | Al*
provision and (quantity)
regulation

© Global hydrological model for water regulation | A2*

(timing)

Global hydrological model for water | A2*

purification (quality)

Nature-related e Global model of tourism in protected areas B3
outdoor activities

e Global model of the value of green areas for | B2
local recreation

165

Global climate e Global terrestrial model of carbon storage and

regulation sequestration

4.18.1 Participants
Authors
Ana Rodrigues, Andrew Balmford (University of Cambridge, UK)

Matt Walpole (UNEP-WCMC, Cambridge, UK)

166

5 INVENTORY OF RESEARCH ORGANIZATIONS, PROGRAMS AND
RECENT LITERATURE DEALING WITH ECONOMICS OF
BIODIVERSITY LOSS

As a contribution to the Scoping the Science project, an inventory was made of the main
current research programs, organizations and literature dealing with economics of
biodiversity loss. This Chapter presents the main results of these reviews and Annexes 6
and 7 list more detailed information on the results. It must be stressed that both the
literature search and inventory of organisations and programs is not exhaustive, thus to
obtain a more complete overview it is foreseen that this identification tasks will be
continued as a part of the upcoming EU initiatives on economic of biodiversity loss.

5.1 Literature review

An inventory was made of the main publications since 2005 (i.e. post the release of the
Millennium Ecosystem Assessment). The research has been realised using the following
search engines: Science direct, Scopus and Google scholar (succinctly), as well as direct
search on the home pages of the Journals Ecological Economics, Nature and Science.

A combination of key words was used during the research for each search engine. These
included “Biodiversity + Economic Loss”, “Biodiversity + Economic Cost” and
“Biodiversity + Economic Valuation”.

From these key words the articles including all the terms in the text and published between
2005-2008 (included) have been selected. A total of 132 documents have been gathered,
showing an increasing trend: 2005: 29 articles, 2006: 36 articles, 2007: 46 articles, 2008: 13
articles published and 8 articles in press.

The selected articles come from different sources the main ones (more than 5 articles)
being: Ecological economics (21), Science (14), Biological Conservation (10), and
Conservation Biology (6).

5.2 Review of organisations, programmes and networks

The inventory of organizations, programs and projects was carried out by using the main
well-known websites as a starting point (see box 1). Most of these websites have a section
with ‘links’ and by following these in a systematic way it is possible to obtain a good
overview of the main organisations and programs.

Similar to the literature search, keywords used were the link between “biodiversity” and
“economics / economic loss / economic cost / economic valuation”. When going “deeper”
into the various organisations and programs, many links are found to more specific subjects
(e.g. programs, projects and organisations focusing on specific ecosystems (e.g. watershed-
services, coral reefs, forests, etc), services or regions). Within the timeframe of this project
it was not possible to explore these links in any detail but that could be possibly
recommended as one of the future follow-up exercises.

167

BOX A. Main networks and websites dealing with economics of biodiversity (loss)

Biodiversity Economics (www.biodiversityeconomics.org)

Ecosystem Service Database (ESD) (//esd.uvm/edu)

Ecosystem Services (www.esa.org/ecoservices)

Ecosystem Services Database (esd-worldbank.org/eei/)
Ecosystem Services project (www.ecosystemservicesproject.org)
Ecosystem Valuation (www.ecosystemvaluation.org )

EnValue: www.epa.nsw.gov.au/envalue/

EVRI (www.evri.ca)

MILLENNIUM ECOSYSTEM ASSESSMENT (www.maweb.org)

Natural Capital Project (www.naturalcapitalproject.org)

Nature Valuation & Financing Network (NV&F) (www.naturevaluation.org)

In Annex 6 an overview is given of the main organisations and programs, organised by type
of organisation including the following categories: main networks/websites; multi-lateral
institutions, programs, treaties etc.; government supported initiatives; NGO’s; universities,
research organizations & programs; and business supported initiatives. Per category a
distinction is made between international and national organisations and programs. Within
these categories, organizations are listed in alphabetical order for easy reference.

This overview does not aim to be exhaustive, however in combination with the links given
in Box A on the main networks & websites it should enable quick and easy access to the
main organisations and programs on this topic’. In the future, some resources could be
directed to identifying relevant networks at the national level. Ideally, a formal “network-
analysis” should be performed to obtain more quantitative information on the main “nodes”
in the national and international networks.

One aim of this inventory has been to establish active links and working relations between
the Scoping the Science project and the main organisations. Facilitating communication
among the main Networks is an important objective of the Nature valuation and Financing

"In case you miss an (important) organization or program, please send an email to do/f.degroot@wur.nl

168

Network (NV&F) (www.naturevaluation.org) which already has a quite extensive list of links
and active working relations and could possibly be used to facilitate this process in the
future.

169

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Annex 1 — Cost and benefits of agriculture conversion

If is not straightforward to decide how to deal with systems (such as agricultural fields) that
despite being heavily modified by human activities still hold substantial biodiversity and
produce ecosystem services. For example, it is not clear if the economic value of wheat
production should be considered a benefit of the ecosystem service ‘food production’ or an
opportunity cost of not converting a forest to a wheat field.

This raised considerable discussion in each of the three consultations we had on the
conceptual framework. This discussion turned out to be extremely productive, as it became

clear that it does not matter how outputs from agricultural fields are classified — what is

fundamental is to ensure the adequacy of the ‘states’ being compared (Section 2). Indeed, as
long as these states are equal in everything else but the implementation or not of a set of

conservation actions, the end economic consequences are the same irrespective of whether
wheat production (for example) is treated as beneficial service enhanced by habitat
conversion, or as an opportunity cost of not converting.

We illustrate this with a simplified example of a parcel of land with two possible states: a
forest plot and a wheat field. Taken separately, each of these states has a set of running
benefits and costs (lists of costs and benefits have been simplified for illustration purposes).
The benefits of the forest plot are that it can produce a sustainable timber harvest (FB), stores
carbon (FB2) and it provides habitat for wild bees that pollinate nearby crops (FB3). The costs
of running the forest are expenses in forest management such as patrolling (FC,) and paying
compensation to nearby farmers for the damage caused by forest animals (FC2). The benefits
of the wheat field are wheat production (WBj), and some carbon storage (WBz), while the
costs are those related to crop management, such as ploughing the field (WC,) and paying to
clean the pollution from agricultural runoff to nearby rivers (WC).

Note that ‘timber production’ and ‘wheat production’ are here simply as economic benefits of
particular types of land use, irrespective of whether they are considered ecosystem services or
not. In the same way, “damage to nearby crops’ and ‘pollution runoff are treated here as costs
rather than ‘ecosystem disservices’.

Suppose the question being asked is: what are the net consequences of converting forest to
wheat?

As described in the general framework (Section 2), a comparison between states can be done
by investigating differences in benefits and in costs in both states. When converting from
forest to wheat (Figure A1), the differences in benefits are:

e The loss of the benefit of timber production (- FB1);

e The added benefit of wheat production (+ WB1);

e The difference in carbon storage (+ WB2 - FB2); and

e The loss of the benefit of pollination of nearby crops (- FB3).
In addition, there is a one-off benefit of conversion, the windfall of timber harvesting when

removing the forest (+ TH).

207

The net difference in benefits (A B) is the sum of each of these parcels.

rte

hdd

« fost timber production = ~ FB, « changed management costs = + WC, ~ FC,
added wheat production = + WB, * avoided damage to nearby crops = — FC;

.
+ changed carbon storage = + WE,-FB, + added pollution runott tc naarby nvers = + WC,
¢ ost pollination of nearby crops = - FB,

Differences in benafits (€}- Differences in costs (€): isa

tel: One-off cost of conversion {€}:
* windfall of one-off timber harvest = + TH * costs of physical conversion = + PC

Mot diffarances in boncfits (€): Net differences in costs (€}:
4B =—FB, + WB, + WB,- FB, - FB, + TH « AC=WC, -FC,—FC, + WC, + PC

he nnnurenneansenenennanmesean ae

A€= AB - AC =— FB, + WB, + WE2- FB, - FB; + TH- (WC; —FC, FC, + WC; * PC)
= WE,+ WB, + FC, + FC; + TH — WC,-WC; - FB,-FB:—FB; + PC
ee ee

added wheat avelded = 1-off timber added lost forest 1-céf physical
bereits foreatecats harvest wheat costs benefits converaion

|

advantages of conversion Gaadvantages cf conversion

IF: AE <0 IF: AE >0

nat differences In bonofis < nat differences Im coats net differences im benofits > nat Giforences In costs
sdvetlages of commrsion < dikmelwarlages of Conversion adhraMagia of converion > Gadvanbagea of coneralon

net € loss from conversion s* net € gain from eS

economic recommendation: not convert economic recommendation: convert

Figure Al: Evaluating the net consequences of converting a forest plot to a wheat field.

When converting from forest to wheat, the differences in costs are:

e The difference in the costs of managing wheat instead of forest (+ WC1 - FC1);

208

e The avoided costs of compensating for damage to nearby crops (- FC2); and
e The added costs of cleaning the pollution from agricultural runoff (+ WC2).

There is also a one-off cost of physically converting the land to make it suitable for wheat
production (e.g., removing tree roots; + PC).

The net difference in costs (A C) is the sum of each of these parcels.

As per the general framework (Figure 2, Section 2), the net consequences of conversion (A €)
can be assessed by comparing the net differences in benefits (A B) with the net differences in
costs (A C). If the former are larger than the latter, there is a net economic gain (A € > 0) from
conversion, hence it makes economic sense to convert.

The equation for the difference between net benefits and net costs can be rearranged to
indicate as positive terms all the advantages of conversion (the added benefits from wheat, the
avoided forest costs, and the one-off windfall in timber) and as negative terms all the
disadvantages of conversion (the added costs from wheat, the lost benefits of forest, and the
one-off costs of land conversion). If the advantages are larger than the disadvantages, it makes
economic sense to convert (Figure A1).

Note that ‘wheat production’ was simply an added economic benefit of converting forest to
wheat, irrespective of whether it is classified as an ecosystem service or not.

Suppose the question being asked is: what are the net consequences of not converting
forest to wheat? Importantly, the question is not “what are the net consequences of retaining
the forest” (these would simply be a continuation of state A), but the net consequences of
actions put in place to conserve the forest plot from conversion to a specific alternative form
of land use. Only by framing the question in this way it is possible to account for the
opportunity costs of conserving the forest.

The differences in benefits when not converting to a wheat field are perfectly symmetrical to
the differences in the benefits of conversion, and so are the differences in costs (Figure A2).
When computing the net consequences of no conversion, all parcels that were previously the
advantages of conversion (Figure Al) are now the disadvantages of no conversion (Figure
A2), and vice-versa. The end result is exactly the same: the conditions that would result in the
recommendation not to convert identical whichever way the question is put (Figure Al cf
Figure A2). In the latter figure, the value of ‘wheat production’ simply becomes an
opportunity cost of maintaining the forest.

yas

State A: Forest plot _

Differences in benefits (€}:
* retained timber production =+ FB, + changed managamant costs = — WC, + FC,
* prevented wheal production = — We, * maintained damage to nearby crops = + FC;

+ changed carbon storage = — WB, * FB; = avoided pollution runoff to nearby rivers = — WC,

* retained pollination of nearby craps = + FBS

* prevented windfall of timbar harvest =-— TH * avolded costs of physical conversion =-PC

Net differences in benefits (€): Net differences in costs (€):
AB = FB, —WE,— WE, + FB, + FB,—-TH AC = —WC, + FC, +FC,-Wwec, -Pc

RASS OO RRR SERRE SOR ARORESRORR ERE R ADEE ERODE Eee nennne ene e ADRES ORER REAR RRROn sR annensenennoonsnnennl

Peeeeret rrr reer e rt eeee errr

4
} Prevented one-off benatit ot conversion {€): (€):
:
H
F

A€ = AB — AC © FB, - WB, — WB, + FB, + FB;— TH —{- WC, + FC, * FC;—WC2-PC}
= FB, +FB;+ FB, + WC, + Wo; PC - FC,-FC, - WE,-WB, - TH
te ed

_———
prevented prevented 1-off

fornat benefits whestcasta T-of physical forest costs whest benefits winctall of
conversion timber harvest

nD |
adwartages of nat converting disadvantages of not converting

IF: A€ > 0 IF: A€<0

net differences in Senate > nat differences i coma net differences tm benefits < met differences In costs
advantages of not converting > disadvantages of not convening 3¢ advantages of not converting < disadvantages of not convarting

net € gain from not converting galt net € loss from not converting eo
economic recommendation: not convert economic recommendation: convert

Figure A2. Evaluating the net consequences of not converting a forest plot to a wheat field.

As demonstrated, it is irrelevant which parcels are considered benefits from ecosystem
services or not. The relevant information is whether they are benefits or costs of particular
states. Accordingly, it does not matter what classification of ecosystem services is being used.
Indeed, this same framework could have been used to assess the net economic consequences

210

of converting (or not converting) a forest to a car park, or traditional extensive wheat
production to intensive wheat production.

As discussed in Section 2, the fundamental step in ensuring that these comparisons address
the remit of the Review is to ensure that the states being compared contrast two specific
situations: one where conservation action to prevent wild nature loss has been put in place,
and another where it has not. In the previous example of forest plot and wheat field, the
comparison is relevant if it is deemed that conserving the forest is the action required for
preventing biodiversity loss. The net economic consequences of conversion (Figure Al) can
therefore be interpreted as the economic consequences of failing to protect biodiversity,
while, conversely, the net economic consequences of no conversion (Figure A2) are the
economic consequences of biodiversity loss.

Annex 2 — Framework development and consultation
We had three opportunities to present and discuss the framework with different audiences:

- Seminar “Conceptualising ecosystem services and human well-being”, 15 January,
Department of Geography, University of Cambridge. We presented a talk to an
audience dominated by environmental economists and geographers and solicited
additional comments by email.

- Presentation “Framework for a Stern-like review of the economics of biodiversity
loss”, 16 January, Department of Zoology, University of Cambridge. We presented a
talk in one of our departmental lunchtime seminars. There were about 20 attendees,
mainly biologists.

- Zoology workshop: the framework was discussed in a small (8 people) workshop of
key experts.

A detailed account of these discussions is presented in the Inception Report to this project.

The systematically raised concern was “the agriculture issue” — to include or not agricultural
food production as an ecosystem service?). In these discussions we found what we believe is
the solution for that problem (Section 2; Annex 1). Several other interesting issues were
raised and discussed. Overall, the framework has proved remarkably robust. The discussions
have also been very useful in making us think more carefully about the details of how the
framework is applied (e.g., how to consider interactions and trade-offs between services).

212

Annex 3 - Practical strategy followed in the thematic reviews

This Scoping the Science project required reviewing very quickly a wide literature, most of
which out of the immediate areas of expertise of the members of the team. We therefore had
two limiting factors: expertise and time.

To make sure we access the relevant expertise, we had to draw on the support of experts for
each theme, who know the literature well and have a broad perspective of the state of
knowledge in their respective fields. Ideally, we would have wanted a team of experts in each
theme producing the respective reports. This would have required a substantially longer and
better-funded project than the current one. What was possible to obtain in the current project
was more or less detailed guidance from a diversity of experts.

Following such guidance required time, to being able to pursue the relevant references and to
compile the overall information into a coherent report. The best way in which we were able to
obtain researchers-time in such a short project was by hiring five interns. They were: James
Beresford, Kelly Flower, Antares Hernandez, Hannah Peck, and James Waters. We were
fortunate to hire five bright young biologists, but being very recent graduates they had only
limited research experience. It would have been impossible to hire more experienced
researchers in such short notice and given the limited funds available. This meant that their
outputs had to be substantially edited before being sent back to the experts.

Given time limitations, we were unable to fully develop all the themes considered. We
considered that it was better to invest in producing fewer, but high-quality, reviews than to
present lower quality reports for all themes. We therefore present eight fully developed
thematic reviews, presenting only brief overviews for the remaining nine themes.

The strategy adopted for those themes that were fully developed consisted of the following
steps:

Questionnaire A questionnaire (tailored to each task) was sent to a set of experts to try to
obtain key pointers (to references, resources, other experts) as well as to capture the experts’

experience and opinion across a range of questions.

First draft An intern reviewed the literature and the information provided by experts, and
produced a first draft.

Second draft Given the limited experience of the interns, the first drafts produced required
considerable editing and double-checking of the information. This was done by Ana
Rodrigues. A second draft was then produced.

Review The second draft was sent back to the experts.

Final report Comments from the experts are incorporated into a final report, that becomes a
chapter in the final report of the Scoping the Science project.

The contribution of experts is acknowledged in the following way:
e Authors: experts who have contributed with substantial information and which

reviewed and approved the final report.

213

e Contributors: experts who provided initial information but were unable to review the
report, or experts that considered that this was a more suitable description of their
contribution.

e Reviewers: experts who reviewed the report but were not listed as authors. We gave
experts the option to reproduce their comments at the end of each section if
substantive comments were provided that we could not incorporate (e.g., given time
limitations). Hence, being listed as a reviewed did not have to be considered an
endorsement of the text.

e Acknowledgements: experts who contributed with references and/or contacts but not
with specific information, and who did not see the final text.

For some the themes for which it was not possible to develop a full review, we present only a
very brief overview of what we believe the state of the knowledge is, based on our own
(limited) knowledge, as preliminary guidance for Phase 2.

The overall level of expert support was extraordinary, given how busy these experts
inevitably are. People could only afford to take the time because they understand the
importance of the Review on the Economics of Biodiversity Loss.

However, the extent to which we feel that we have succeeded in representing the state of
knowledge in each field was very variable. In some cases (such as pollination) we feel that we
have been able to tap into the knowledge and advice of the key people in the field, that they
understood the purpose of this exercise, and that they took the time to provide substantial
input. In other fields (e.g. wild harvested fibers) we feel that we were unable to obtain advice
from key people and that therefore we may only present a partial view of the state of
knowledge.

Table Al indicates the level of information for each theme. For those that were fully
developed, we also indicate the level of expert support. This is an assessment of the extent to
which we feel that we have tapped significantly in the existing overall expertise in the field,
and for which we are more confident that the review results are representative of the overall
state of knowledge.

Table Al Team (intern + head), level of information collected and level of expert support in each
thematic review.

Level of information Expert
support

JJ Waters + Ana Fully developed review Very good
Rodrigues

Wild crop pollination

Biological control in JJ Waters + Ana Brief overview
crops Rodrigues

Genetic diversity crops | Hannah Peck + Ana Brief overview
and livestock Rodrigues

Soil quality for crop Antares Hernandez + | Brief overview
production Ana Rodrigues

Livestock JJ Waters + Ana Brief overview
Rodrigues

Marine fisheries Kelly Flower + Ana Fully developed review
Rodrigues

Inland fisheries Kelly Flower + Ana Fully developed review
Rodrigues

Wild animal products JJ Waters + Ana Fully developed review
Rodrigues

Fresh water provision Hannah Peck + Ana Fully developed review

and regulation Rodrigues

Timber from natural Antares Hernandez + | Not developed

forests Ana Rodrigues

Wild medicinal plants | Antares Hernandez + | Not developed
Ana Rodrigues

Nature-related outdoor | James Beresford + Fully developed review

activities Matt Walpole

Regulation of natural James Beresford + Not developed
hazards Ana Rodrigues

Very good

Reasonable

Antares Hernandez +
Ana Rodrigues

One-off use benefits Not developed

Non-use benefits Ana Rodrigues Not developed

Global climate Hannah Peck + Ana Brief overview
regulation Rodrigues

Unknown Ana Rodrigues Not developed
benefits/processes

215

Annex 4 - Literature review “Economics of Biodiversity Loss”
The research has been realized on the following search engines:

e Ecological Economics

e Nature
e Scopus
e Science

e Science direct

e Google scholar (succinctly)
A combination of key words was used during the research for each search engine:

e Biodiversity + Economic Loss

e Biodiversity + Economic Cost

e Biodiversity + Economic Valuation
From these key words, the articles including all the terms in the text and published between
2005-2008 (included) have been selected. A total of 132 documents have been gathered,
comprising:

e 2005: 29 articles

e 2006: 36 articles

e 2007: 46 articles

e 2008: 13 articles

e Inpress: 8 articles
The selected articles come from different sources the main ones being:

e Agriculture, Ecosystems and Environment (5)

e Agricultural and Resource Economics Review (2)

e Biological Conservation (10)

e Coastal Engineering (2)

e Conservation Biology (6)

216

e Ecological economics, (21)
e Ecological Modeling (3)
e Environmental Resource Economics (4)
e Environmental Modeling and Software (2)
e Environmental Science and Policy (3)
e Forest Ecology and Management (3)
e Journal of Sustainable Forestry (5)
e Land-Use Policy (2)
e Marine Policy (4)
e Marine pollution bulletin (3)
e Nature (2)
e PLOS Biology (2)
e Science (14)
The selected articles have been divided into two groups:
e the articles about topics that concerns general issues (Table A2)

e the articles related to case-studies in specific areas (Table A3)

Authors

Linda Scholten, Emmanuelle Noirtin & Rudolf de Groot (Environmental Systems Analysis
group, Wageningen University)

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SHOH.LNV

Annex 5 - Inventory of research organizations, programs and projects dealing
with “Economics of Biodiversity Loss”

As a contribution to the Scoping the Science project, an inventory was made of the
main current research programs and organisations dealing with “economics of
biodiversity loss” whereby 5 categories were distinguished:

e 0 - Main networks/websites

e 1 - Multi-lateral institutions, programs, treaties, etc

e 2-Government supported initiatives

e 3-NGO’s

e 4- Universities / Research organizations & programs
e 5- Business supported initiatives

The inventory was carried out by using existing and well-known websites as a starting
point (category 0 in below table). Most of these websites have a section with “links”
and by following these in a systematic way it is possible to obtain a good overview of
the main organizations and programs.

Keywords used for the search were the link between “biodiversity” and “economics /
economic loss / economic cost / economic valuation”.

When going “deeper” into the various organisations and programs, many links are
found to more specific subjects (e.g. programs, projects and organisations focusing on
specific ecosystems (e.g. watershed-services, coral reefs, forests, etc), services or
regions). Within the timeframe of this project it was not possible to explore these
links in any detail but it would be very interesting and useful to carry out a full
“network-analysis” as one of the future follow-up activities.

Below, a summary is given of the main organisations and programs, organised by type
of organisation. Per category a distinction is made between international and national
organisations & programs. Within these categories, organisations are listed in
alphabetical order for easy reference.

This overview does not aim to be exhaustive, however in combination with the links
given under the “Main Networks & Websites” (category 0) it should enable quick and
easy access to the main organisations and programs on this topic. In the future, some
resources could be directed to identifying relevant networks at national level.

NOTE: In case you miss an (important) organization or program, please send an
email to dolf.degroot@wur.nl]

0 - Main (long-term) networks / websites / databases

(are also listed under respective country or organization for easy cross-reference)

Economics

World Bank
Email: EnvEc@worldbank.org

Ecosystem Service Database
(ESD)

http://esd.uvm/edu

Ecosystem Services

WWW.€Sa.Org/ ecoservices

Ecosystem Services Database esd-worldbank.org/eei/

CSIRO
Www.cse.csiro.au/ecoservices

www.ecosystemvaluation.org Univ. Maryland

www.epa.nsw.gov.au/envalue/
(start 1995) (EPA New South Wales)

EVRI www.evri.ca Env. Canada & EPA-US (also
(since 1997) World Bank & EC are involved)
MILLENNIUM
www.maweb.org

ECOSYSTEM
Natural Capital Project www.naturalcapital project.org Stanford University

ASSESSMENT(2001-2005)
Nature Valuation &

Financing Network (NV&F)

Ecosystem Services project
(Australia)

www.ecosystemservicesproject.org

Ecosystem Valuation

EnValue

www.naturevaluation.org

1 — Multi-lateral Ints. / Org. / Programs / Treaties / etc.

Name Website Additional information
e Environmental Services and
Z : Sustainable Use of Forests-program
CIFOR / ICRAF www.cifor.cgiar.org

e RUPES program (Rewarding Upland
Poor for Environmental Services)

Convention on_ Biological www.biodiversity.org
Diversity - CBD or www.cbd.int o EGISS.GI ATES:

e Env. Economics [program]
ec.europa.eu/environment/enveco

EXTERNE -— calculating the costs of

externalities ec.europa.eu/research/

European Commission ec.europa.eu

call for evidence on economics of
biodiversity loss

EURECA (European Ecosystem
Assessment)

European Environment

Agency

www.cea.curopa.eu

www.eib.org

Topic: Biodiversity (esp. irt health

European Investment Bank loss); collaborate with IUCN

e Financing Strategies for Sust. Forest
Management (together with IUCN and
CCAD)

(www.fao.org/forestry/mecanismosfinancieros)

MILLENNIUM
ECOSYSTEM www.maweb.org
ASSESSMENT (2001-2005)

e Guidelines for Wetland Valuation (in

Ramsar Convention www.ramsar.or: collab. with CBD)

unstats.un.org/unsd/envaccoun e UN Committee of Experts on

UN CEEA 5 : ; F
ting/ceea Environmental Economic Accounting

e Biodiversity portfolio (240 projects;
Gustavo Fonseca)

UNDP - GEF www.undp.org/gef

UNEP Finance Initiative
(www.unepfi.org)

www.unep.ch UN Committee of Experts on Env.
Economic Accounting (UNCEEA)

WCMC (www.unep.wems.org)

WWE UNESCO project about
Financing Mechanisms for Protected

UNESCO Www.unesco.org Areas

(in Argentina) :
www.vidasilvestre.org.ar

UN-FCCC Impact CC on ecosystem services

e Ecosystem Services Database — World
Bank (esd-worldbank.org/eei/)

e + environmental economics,
environmental valuation, PES

WORLD BANK www.worldbank.org

2 — Government supported initiatives

[see also section 4: univ. and research programs]

CSIRO Ecosystem
Australia Services project [see also: main Networks]

e Env. Canada & EPA-US
(also World Bank & EC
are involved)

Additional information

Www.cse.csiro.au/ecoservices

www.evri.ca
(since 1997)

e established EEPSEA
(Economy & Env.
Program for SE Asia) in

1993

IDRC (Int. Dev. Research
Centre)

Canada www.idre.ca

235

supported by Royal
Danish Min. of Foreign

pANID AGES

Germany GTZ

MNP -_ Netherlands
Environmental
Assessment Agency

Netherlands

Ecosystem Services
Project

www.defra.goc.uk/environment

Env. Economics Publ

Atlas of Ecosystem
Services

Committee on Assessing

www.nationalacademies.org and Valuing Ecosystem
Services

National Research
Council

3 - NGO’s (organizations and programs)

International

Name Additional information
of

Association

Environmental and www.aere.org
Resource Economists

e Started in 2002, last update 2006 -
seems not very active anymore

www.conservationfinance.org e Joint project of 12 org., among
others: CI, IUCN, WWF, World
Bank etc

Conservation ‘cansenvaiGner mapping and valuing ecosystem
International (C1) ME eee services
ae e Economic analysis of benefits and
deft org aa A :
SEO TEIIE costs of biodiversity concervation
RSITAS www.diversitas-
DIVERS international.org

East-West Centre

Conservation Finance

Alliance (CFA)

e Ecoservices program (Charles
Perrings)

www.eastwestcenter.org

European’ Centre for
Nature Conservation
(ECNC)

rest Trends www. forest-trends.or. e see Katoomba Grou
Fores 1)
5 > e development of payment
i www.guianashield.or, e P
Guyana Shield program mechanisms for ecosystem services

236

e Biodiversity for Sustainability
program / Business & Biodiv.

www.ecnc.nl

IEEP (Inst. European Env.
Policy)

Wwww.ieep.eu

e Env. Economics Program (EEP)

e Support el. Newsletter on Payments
for Watershed Services
(www.flowsonline.net)

www.lied.org

e Funded by Env. Canada in 1990

IISD (Int. Inst. For Sust.
Development)

www.lisd.org)

e Topics among others “Valuing
Natural Capital”

ISEE (Int. Society for
Ecological Economics)

www.ecoeco.org or
www.ecologicaleconomics.org

Support many activities irt Ecosystem
Services and Economics:

e Economics Division (since 1998)
e Business and Biodiversity Program
(since 2000) (cms.iucn.org/etc ..)

e Global Economics and Env.
Program (GEEP) > 2009
(cms.iucn.org/etc ..)

e IUCN-Regional Env. Economics
Program Asia

° WANI (PES Watershed services)
(Wwww.iucn.org/themes/wani)

e CEM-Ecosystem Services

www.iucn.org

e CEM-Ecosystem Approach

www.katoombagroup.org
[see also Forest Trends

e Ecosystem Market Place
(ecosystemmarketplace.com)

Katoomba Group

Restoring Natural Capital RRO TCA AHGeTOne
(RNC) Alliance: i ae a a

SANDEE (South Asian
Network for Dev. and Env.
Economics)

www.sandeeonline.org

Society for Ecological WWW. Ser.org,

Restoration (SER).

WCMC (see also UNEP) p.wemc.org e¢ mapping ecosystem services
Wetlands International | wewetands.org | e Biorights program

Worldl uiResources. | Inst e People and Ecosystems Program

(WRI) (www.wri.org) www.wri.org/ecosystems/ecosyste
m-services

237

Many activities eg:

e (Centre for) Conservation Finance
(www.worldwildlife.org/conservatio
nfinance)
e WWF Macro-economics Program
Office www.panda.org/etc..
WWE www.worldwildlife.org e Meta-analysis of wetland values ...

e Natural Capital Project (with
Stanford Univ)

e WWE UNESCO project about
Financing Mechanisms for Protected
Areas

e §©6 Argentina: www. vidasilvestre.org.ar

National

NB: many national organizations were found but the list is rather incomplete and unbalanced
and is therefore not included here (can be provided at request); an exception is made for the
Ecological Society of America which made an interesting website with Toolkits on Ecosystem
Services: www.esa.org/ecoservices

4 — Universities / Research programs (& assessments)

International

Website Additional information
EVE concerted action www.landecon.cam.ac.uk/eve

e EU funded project (2007-2009)

e “Rationalising Biodiversity
Conservation in Dynamic
Ecosystems”

RUBICODE

www.rubicode.net

National [STILL VERY INCOMPLETE]

gene ee

Australian Nat.
University Economics
and Environment
Network

Australia een.au.edu.au Australian Nat. University

e a.o. host of global network of
environmental economists

238

e PREM (Poverty Reduction and
Netherlands - www.ivm.falw.vu.nl Env. Management)

www.premonline.nl

Netherlands | Tilburg Univ www.tilburguniversity.nl Et ee

e SELS-program
(www.ecosystemservices.nl):
WUR (Wageningen research program on Ecosystem &
Netherlands | Univ. & Research Wwww.wur.nl Landscape Services
Centre)

Wageningen International
(www.wi.wur.nl): many projects

CEEPA (Center for Env.
South Africa | Pretoria Univ . -up.ac. Economics and Policy in Africa)

(www.ceepa.co.za)

Beijer Int. Inst.
Environmental www.beijer.kwa.se
Economics

Env. Economics Unit

Env. For Development Initiative
(EfD) (www.efdinitiative.org)

Sweden Gothenborg Univ. WWW.gu.se

supported by SIDA

Sweden Stockholm Univ ae Dept. Systems Ecology

CSERGE Centre for
Social & Economic Wwww.uea.ac.uk/cserge
Research) UEA

Wwww.greeneconomics-
Green Economics Inst o org.uk e Center for Env. Management

e Various projects, including
MIMES; the Ecosystem Service
Database (ESD) (esd.uvm/edu)

Gund Inst. Ecological

Economics-, UVM Snape eel e to be linked to ARIES-project (an
interactive data base & DSS-tool),
and the EcoValue Project
(ecovalue.uvm.edu/evp)

239

e Ecosystem Valuation
Maryland, Univ www.umd.edu

www.ecosystemvaluation.org

{__
Natural Capital Project

(www.naturalcapitalproject.org)
(G.Daily et al),together with The

Nature Conservancy & WWF

© Ini
Stanford: Unis NatCap Network & Natural

Capital Database

InVEST — tool to model value of
ES and Trade-offs

Wyoming, Univ Dept.

of Economics & | business.uwyo.edu/econfin
Finance

5 - Business Supported Init. (Banks/ Consultants, etc )

Here only a few examples are listed but there are many more private organizations
becoming increasingly involved in economics of biodiversity, and biodiversity loss

“Building Biodiversity
www.shell.com Business” Publication together
with IUCN — 2008)

Valuation Studies & Payment
mechanisms

TripleE www.tripleee.nl

Economics for the
Environment Consultancy www.Eftec.co.uk
Ltd

Exist since 1992, set up
UKNEE (in 2004)

Author

Rudolf de Groot (Env. Systems Analysis Group, Wageningen Univ.)

240

Annex 6 - Key Internet Resources on Biodiversity and Ecosystem Services

General: Biodiversity and Ecosystem Services

Key institutions, groups and experts

Center for Agri-Environmental Research, (University of Reading ) — The biodiversity and ecosystem
services research group carries out rigorous research on the linkages between agricultural land-use,
biodiversity, ecosystem function and service provision and how respond to global change and their
value to man. http://www.reading.ac.uk/caer/theme_services.html

Defenders of Wildlife — Defenders' Conservation Economics Program focuses on objective and
transparent economic analysis of the benefits and costs of biodiversity conservation design of economic
incentives for wildlife conservation Ecosystem Services.

http://www.defenders.org/programs and_policy/science_and_economics/conservation_economics/inde
x.php

Defra’s Ecosystem Services Project (UK)- Defra research project on ecosystem services aims to
establish the basis for an ecosystems approach and how it may be used to make effective
assessments of the benefits that the natural environment provides.

http://www.ecosystemservices.org.uk/index.htm

Ecology and Society - An electronic, peer-reviewed, multi-disciplinary journal devoted to the rapid
dissemination of current research on intergrative science for resilince and sustainability (formerly
Conseration Ecology).

http://www.ecologyandsociety.org/

Ecological Society of America and Union of Concerned Scientists. Ecosystem Services Toolkit — Tool
for scientists to engage the public. Tool Kits have been completed on the following services:
Pollination and Water Purification. Tool kit on Flood Damage Control is in development.

http://www.esa.org/ecoservices/

Ecosystem Services Project - The Ecosystem Services Project was initiated by CSIRO Sustainable
Ecosystems and The Myer Foundation. The Project is a collaborative natural resource management
project studying the services people obtains from their environments, the economic and social values
inherent in these services and the opportunities that can arise from considering these services more
fully in land management policies and

decisions. http://www.ecosystemservicesproject.org/html/aboutus/index.htm

Ecosystem Services Management & Restoration - Online Database on Ecosystem Services: Linking
Valuation and Financing of Ecosystem Services to Sustainable Management. This database on the
website of the Nature Valuation & Financing (NV &F) network (www.naturevaluation.org) provides a
list of ongoing and completed case studies, initiatives and projects from around the world that link the
valuation and financing of ecosystem services to sustainable management.
http://topshare.wur.nl/naturevaluation/73766

241

Millennium Ecosystem Assessment. - Designed by a partnership of UN agencies, international
scientific organizations, and development agencies, this ongoing study assesses the capacity of
ecosystems worldwide to provide goods and services that are important for human development.
http://www.millenniumassessment.org/

Natural Capital Project. 2006. “Toolbox.” A joint venture among the Woods Institute for the
Environment at Stanford University, the Nature Conservancy and the World Wildlife Fund is
developing tools for modeling and mapping the delivery, distribution and economic value of ecosystem

services and biodiversity. Online at: http://www.naturalcapitalproject.org/toolbox.htm|

Global Biodiversity Outlook (GBO) - The GBO, is a periodic report on biological diversity, which
provide a summary of the status of biological diversity and an analysis of the steps being taken by the
global community to ensure that biodiversity is conserved and used sustainably, and that benefits
arising from the use of genetic resources are shared equitably. http://www.cbd.int/gbo/

Global Environment Outlook (GEO) - The Global Environment Outlook (GEO) project is the
implementation of UNEP’s mandate to keep the global environment under review. GEO is both a
process and a series of reports, analyzing environmental change, causes, impacts, and policy responses.
It provides information for decision-making, supports early warning and builds capacity at the global
and sub-global levels. GEO is also a communication process that aims at raising awareness on
environmental issues and providing options for action. http://unep.org/GEO/

Global Environment Outlook Year Book Series - The GEO Year Book series, produced annually by the
United Nations Environment Programme in collaboration with many world environment experts keeps
abreast of environmental issues as they unfold (i.e. an annual survey of the changing global
environment). The Year Book includes global and regional overviews. It also highlights the most

significant environmental developments in the year. http://www.unep.org/geo/yearbook/yb2008/
World Resources Institute — People and Ecosystems Programme.

http://www.wri.org/ecosystems/ecosystem-services

Reports, publications and journal articles

Ecosystem Services: A Primer - Ecological Society of America (ESA) — This online article highlights
the importance of natural ecosystems and the services they produce upon which humans are dependent.

http://www.actionbioscience.org/environment/esa.html

FAO. 2003. Biodiversity and the ecosystem approach in agriculture, forestry and fisheries Proceedings
of the satellite event on the occasion of the Ninth Regular Session of the Commission on Genetic
Resources for Food and Agriculture - Rome 12 - 13 October 2002.

http://www.fao.org/docrep/005/y4586e/y4586e00.htm

FAO.2007. The State of food and agriculture: Paying farmers for environmental services. Rome. Italy.

http://www.fao.org/docrep/010/a1200e/a1200e00.htm

Issues in Ecology, "Ecosystem Services: Benefits Supplied to Human Societies by Natural Ecosystems,
No. 2, Spring, 1997, Ecological Society of America. Available on ESA's website at:

http://www.esa.org/science_resources/issues/FileEnglish/issue2.pdf

Mainka, S., McNeely, J and B, Jackson. 2005. Depend on Nature: Ecosystem Services Supporting
Human Livelihoods. World Conservation Union. Available at: http://www.undp.org/pei/pdfs/IUCN-

DependonNature.pdf

Millennium Ecosystem Assessment, 2005, Living Beyond Our Means: Natural Assets and Human
Well-being. Statement of the Board.

http://www.millenniumassessment.org/documents/document.429.aspx.pdf

Millennium Ecosystem Assessment, 2005. Ecosystems and Human Well-being: Wetlands and Water

Synthesis. World Resources Institute, Washington, DC.

http://www.millenniumassessment.org/documents/document.358.aspx.pdf

Millennium Ecosystem Assessment, 2005. Ecosystems and Human Well-being: Synthesis. Island Press,
Washington, DC.

http://www.millenniumassessment.org/documents/document.356.aspx.pdf

Millennium Ecosystem Assessment, 2005. Ecosystems and Human Well-being: Health Synthesis.
Island Press, Washington, DC.
http://www.millenniumassessment.org/documents/document.357.aspx.pdf

Millennium Ecosystem Assessment, 2005. Ecosystems and Human Well-being: Desertification
Synthesis. World Resources Institute, Washington, DC.

http://www.millenniumassessment.org/documents/document.355.aspx.pdf

Millennium Ecosystem Assessment, 2005. Ecosystems and Human Well-being: Opportunities and
Challenges for Business and Industry.

http://www.millenniumassessment.org/documents/document.353.aspx.pdf

Naidoo, R and Ricketts, T.H. 2006. Mapping the Economic Costs and Benefits of Conservation PLoS
Biology 4, 11. (2006). Available at:

http://biology.plosjournals.org/perlserv/?request=getdocument&doi=10.1371/journal.pbio.0040360
RAND. Nature's Services: Ecosystems Are More Than Wildlife Habitat
http://www.rand.org/scitech/stpi/ourfuture/NaturesServices/section|.html

UNEP-WCMC. 2007. Biodiversity and Poverty Reduction. The importance of biodiversity for
ecosystem services. Available at: http://www.unep-

weme.org/latenews/Biodiversity%20and%20Poverty%20Reduction%20UNEP-WCMC. pdf

World Bank, 2006. Where is the Wealth of Nations? Measuring Capital for the 21st Century.
Washington, D.C.: The World Bank.

http://siteresources. worldbank.org/INTEEI/214578- 11 10886258964/20748034/All.pdf

World Resources Institute, 2005. World Resources 2005: The wealth of the poor — managing
ecosystems to fight poverty. Washington DC: WRI.

http://pdf.wri.org/wrr05_lores.pdf

Giller, P., and G. O’Donovan, 2002. Biodiversity and Ecosystem Function: Do Species Matter?
Proceedings of the Royal Irish Academy. 102B 3 129-139.

http://www.ria.ie/cgi-bin/ria/papers/100066.pdf

Crop pollination

Key institutions, groups and experts

CBD. Agricultural Biodiversity International Initiative for the Conservation and Sustainable Use of
Pollinators. Established by the Fifth Conference of Parties to the Convention on Biological Diversity in
2000. Declared an urgent need to address the issue of worldwide decline of pollinator diversity?

Available online at: http://www.cbd.int/programmes/areas/agro/pollinators.aspx

Ecological Society of America (ESA). Pollination Fact Sheet: Pollination: an essential ecosystem
service — Revealing secrets about the birds and the bees
http://www.esa.org/ecoservices/comm/body.comm. fact.poll.html or
http://www.esa.org/ecoservices/PollinationFactSheet.pdf

European Pollinator Initiative (Hope Page) provides information on a wide range of activities which

243

helps to conserve and manage pollinators to enhance the servives they provide.
http://www.europeanpollinatorinitiative.org/

IUCN. Task Force on Declinining Pollination of the Species Survival Commission of IUCN. Provides
information on pollination as an ecosystem service in the conservation and sustainability of natural

systems. http://www.uoguelph.ca/~iucn/
North American Pollinator Protection Campaign (Home Page) http://www.nappc.org/

The Arizona-Sonora Desert Museum, Forgotten Pollinators Campaign, Provides information on
pollinators and links to key websites on this subject.

www.desertmuseum.org/pollination/introduction.html

USDA-ARS Bee Biology and Systematics Lab Research provide information based on various research
projects on: the development and improvement of management systems for bee populations, biological
studies of bees, plant-pollination systems, and bee biosystematics. www.LoganBeeLab.usu.edu/

Apimondia Journal. APIMONDIA (International Federation of Beekeepers' Associations) promote
scientific, ecological, social and economic apicultural development in all countries and the cooperation
of beekeepers’ associations, scientific bodies and of individuals involved in apiculture worldwide. Has
various standing committees and publications on pollination.

http://www.beekeeping.com/apimondia/index_us.htm

Reports, publications and journal articles

Alexandra-Maria Klein et al., 2007. Importance of pollinators in changing landscapes for world crops.
Proc. R. Soc. B (2007) 274, 303-313. Available online at:

http://www.environnement.ens.fr/perso/claessen/e3/tpe/kleinetal2006.pdf

C. Kremen, N. Williams, M.A. Aizen, B. Gemmill-Herren, G. LeBuhn, R. Minckley, L. Packer, S.
Potts, T. Roulston, I. Steffan-Dewenter, D. Vazquez, R. Winfree, L. Adams, E. Crone, S. Greenleaf, T.
Keitt, A.M. Klein, J. Regetz, T. Ricketts. Pollination and Other Ecosystem Services Produced by
Mobile Organisms: A Conceptual Framework for the Effects of Land Use Change. Ecology Letters 10,
4: 299-314. (2007) Available at: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1461-
0248.2007.01018.x

Daily, G.C, Ehrlich, P.R, and C.H. Sekercioglu. 2004. Ecosystem Consequences of Bird Declines.
Proceedings of the National Academy of Sciences, USA 101, 52: 18042-18047.
http://www.pnas.org/cgi/reprint/101/52/18042

Eardly, C .Pollinator —A conservation priority. Science on Africa Magazine. Available online at
http://www.scienceinafrica.co.za/pollinator.htm

Goulson, D. 2003. Conserving wild bees for crop pollination. Food, Agriculture & Environment Vol.1
(1): 142-144. Available online at:
http://www.sbes.stir.ac.uk/people/goulson/intjourfoodagricenvir2003.pdf

Kearns, C.A., and D. Inouye. 1997. “Pollinators, Flowering Plants and Conservation Biology,”
BioScience 47: 297-397. Available at: http://www.jstor.org/pss/1313191 [Accessed 25 April 2008]

Kremen, C., Williams, N. M , and R. W. Thorp. 2002. Crop pollination from native bees at risk from
agricultural intensification. PNAS. December 24, 2002. Vol. 99, no.

26http://www.pnas.org/cgi/reprint/2624 13599v1
RAND. Pollination Services: No Food Without Them
http://www.rand.org/scitech/stpi/ourfuture/NaturesServices/secl_pollinators.html

Ricketts, T.H 2004. Tropical Forest Fragments Enhance Pollinator Activity in Nearby Coffee Crops.
Conservation Biology 18, 5: 1262-1271. (2004). http://www.blackwell-

synergy.com/doi/abs/10.1111/j.1523-1739.2004.00227.x
APIMONDIA Standing Commission on Pollination and Bee Flora -

244

http://www.beekeeping.com/apimondia/index_us.htm

Foraging Behaviour Of Honeybee On Parental Lines Of Hybrid Cauliflower Pusa Selvakumar.
http://www.apimondia.org/apiacta/slovenia/en/selvakumar.pdf

The Variability Of Yield Structure Of Black Currant Cultivars (Ribes nigrum L) In Different
Pollination Conditions Denisow http://www.apimondia.org/apiacta/slovenia/en/denisow.pdf
Induction Feeding Of Honey-Bees To Improve Actinidia deliciosa Pollination Gardi.
http://www.apimondia.org/apiacta/slovenia/en/gardi.pdf

Honeybee Pollination In Sunflower Hybrid Seed Production Yadav.
http://www.apimondia.org/apiacta/slovenia/en/yadav.pdf

Reforestation With Major Bee Food Trees In E] Salvador Sandker
http://www.apimondia.org/apiacta/slovenia/en/sandker.pdf

Genetic diversity of crops and livestock

Key institutions, groups and experts

Biodiversity International (Home Page)- Bioversity International is the world's largest international
research organization dedicated solely to the conservation and use of agricultural biodiversity.

http://www. bioversityinternational.org/. Carries research projects and publish extensively on genetic
diversity of crops and livestock.

http://www. bioversityinternational.org/Themes/Agricultural_ Ecosystems/index.asp
UK Agricultural Biodiversity Coalition (UKabc) Home Page:

http://www.ukabe.org/ukabe3.htm
The EU Biodiversity in Development Project

www.wemce.org.uk/biodev

CGIAR agricultural biodiversity research centre

Www.cgiar.org/ipgri
Open Directory Project links on Agricultural Biodiversity

www.dmoz.org/science/environment/biodiversity/agricultural

Reports, publications and journal articles

Daily, G.C, Ehrlich, P.R, Michener, C. and T.H. Ricketts. 2004. Economic Value of Tropical Forest to
Coffee Production. Proceedings of the National Academy of Sciences, USA 101, 34: 12579-12582.

Available at: http://www.pnas.org/cgi/reprint/101/34/12579
Cromwell, E., Cooper, D and Mulvany, P. Agriculture, biodiversity and livelihoods: issues and entry

points for development agencies. http://www.ukabe.org/odi_agbiod.pdf

FAO. 1997. The state of the world’s plant genetic resources for food and agriculture. Rome: Italy.
Available at:

http://www. fao.org/WAICENT/FAOINFO/AGRICULT/AGP/AGPS/Perfa/pdf/swrfull.pdf
ITDG. Agricultural biodiversity: farmers sustaining the web of life. Farmer’s World Network Briefing.

http://www. practicalaction.org/docs/advocacy/fwn_bio-div_briefing.pdf
Sustaining Agricultural Biodiversity and Agro-ecosystem Functions: Opportunities, incentives and

approaches for the conservation and sustainable use of agricultural biodiversity in agro-ecosystems and
production systems. Report of the FAO/CBD Agricultural Biodiversity Workshop, 2-4 December 1998,

Rome. http://www. fao.org/WAICENT/FAOINFO/SUSTDEV/EPdirect/EPre0063.htm
Jn Situ Agricultural Biodiversity Conservation Project A research project of the Intermediate

245

Technology Development Group (ITDG) and the Overseas Development Institute, UK (ODI).
http://www.ukabe.org/abe.htm

Developing Diversity: European NGOs' PGRFA activities Illustrated keynote paper presented to the
1998 European PGRFA Symposium, Braunschweig, 30th June 1998. By Patrick Mulvany,

Intermediate Technology, ITDG. http://www.ukabc.org/bschweigNGO.htm#p

Breeds of Livestock resource presented by the Department of Animal Science at Oklahoma State
University - an educational and informational resource on breeds of livestock throughout the world.
http://www.ansi.okstate.edu/breeds/

Heifer Project International Information on livestock projects. http://www.heifer.org/

Marine Fisheries

Key institutions, groups and experts

Communication Partnership for Science and the Sea: Marine Ecosystem Services - COMPASS is a
collaborative effort to advance marine conservation science and communicate scientific knowledge to
policymakers, the public, and the media.

http://www.compassonline.org/marinescience/ecosystem.asp
http://www.compassonline.org/

FAO. Fisheries and Aquaculture Department ( Hope Page) —Inland Aquatic Ecosystems and Coastalk
and Marine Information

http://www.fao.org/fishery/

The FAO Fisheries and Aquaculture Department collects, analyzes and disseminates information on the
sector operations (catch, production, value, prices, fleets, farming systems, employment). It also
develops methodology, assesses and monitors the state of wild resources and elaborates resources
management advice.

http://www. fao.org/fishery/about
World Resources Institute (WRI) 2006. The Value of coastal ecosystems

http://earthtrends.wri.org/updates/node/118

Reports, publications and journal articles

Duffy, J Emmett (Lead Author); Walker Smith (Topic Editor). 2006. "Marine ecosystem services." In:
Encyclopedia of Earth. Eds. Cutler J. Cleveland (Washington, D.C.: Environmental Information
Coalition, National Council for Science and the Environment). [First published in the Encyclopaedia of
Earth October 10, 2006; Last revised October 16, 2006; Retrieved April 24, 2008].
http://www.eoearth.org/article/Marine_ecosystem_services

Boris Worm, Edward B. Barbier, Nicola Beaumont, J. Emmett Duffy, Carl Folke, Benjamin S.
Halpern, Jeremy B. C. Jackson, Heike K. Lotze, Fiorenza Micheli, Stephen R. Palumbi, Enric Sala,
Kimberley A. Selkoe, John J. Stachowicz, and Reg Watson. Impacts of Biodiversity Loss on Ocean
Ecosystem Services. Science 314, 5800: 787-790. (2006).
http://www.sciencemag.org/cgi/content/abstract/3 14/5800/787

Issues in Ecology, " The Role of Nearshore Ecosystems as Fish and Shellfish Nurseries, No. 11, Spring,
2003, Ecological Society of America. Available on ESA's website at:

http://www.esa.org/science resources/issues/FileEnglish/issue 1 1.pdf

Timber from natural forests

Key institutions, groups and experts

The FAO Forestry Web site provides literally thousands of pages of information, access to all of FAOs

246

forest-related databases, detailed country profiles and links to documents on all aspects of forestry
including new sites on forest fire, national forest programmes and forest reproductive material, among
others. http://www. fao.org/forestry/en/

Information on specific forest products and services can be found at:

1. Wood energy http://www.fao.org/forestry/site/energy/en/
Pulp, paper and wood industries http://www.fao.org/forestry/site/harvesting/en/
3. Trade in forest products and services http://www. fao.org/forestry/site/trade/en/
4. Non-wood forest products http://www.fao.org/forestry/site/nwfp/en/

N

Nature-related outdoor activities
Reports, publications and journal articles
e RSPB. 2007. Wellbeing through Wildlife in the EU.

http://www.birdlife.org/eu/pdfs/Wellbeing EU_final_ version 2mb.pdf

e Pretty J, Griffin M, Peacock J, Hine R, Sellens M and South N. 2005. A countryside for health and
wellbeing: The physical and mental health benefits of green exercise. University of Essex, Colchester.

Available online at: http://www.countrysiderecreation.org.uk/pdf/CRN%20exec%20summary.pdf
http://195.92.230.85/Images/Hine_tcm2-3003 |.pdf

e Bird W. 2004. Natural Fit: Can Green Space and Biodiversity Increase Levels of
Physical Activity? RSPB. The RSPB. Available at:
http://www.rspb.org.uk/Images/natural_ fit full version _tem9-133055.pdf

e Collins, S. 2006. The Makuleke model for good governance and fair benefit sharing Steve Collins in
IUCN. Policy Matters

http://www.iucn.org/themes/ceesp/Publications/newsletter/PM 14-Section%201V.pdf

e Health Council of the Netherlands. 2004. The influence of nature on social, physical and psychological
wellbeing. Part 1: review of current knowledge. Report to the Minister of Agriculture, Nature and

Food. http://www.rmno.ni/files_content/Nature%20and%20Health.pdf
e UNEP/ CMS Secretariat (2006). Wildlife watching and tourism. Bonn. Available at:

http://www.cms.int/publications/pdf/wildlifewatching text.pdf
Soil Quality

Key institutions, groups and experts
e FAO. Soil Biodiversity Portal
http://www. fao.org/ag/AGL/agll/soilbiod/index_en.stm
Reports, publications and journal articles

e Coleman, D.C and Whitman, W.B. 2004. Linking species richness, biodiversity and ecosystem
function in soil systems. http://www. fao.org/ag/AGL/agll/soilbiod/promotxt.stmInterational
symposium on impacts of soil biodiversity on biogeochemical processes in ecosystems, Taipei,
Taiwan, 2004

http://www.umanitoba.ca/faculties/afs/soil_science/MSSS/Ecology/Graduate/Coleman%20and%20Whi
tman%202005.pdf

Biological control of crop pests and diseases

Key institutions, groups and experts

247

HRDA. Natural Pest and Disease Control —Carries out research on biological control of pest, deseases
and weeds. http://www.gardenorganic.org.uk/research/ir_pdw_man.php

Natural hazard regulation

Key institutions, groups and experts

Dartmouth Flood Observatory keeps a Global Active Archive of Large Flood Events (1985-present)
using information derived from a wide variety of news, governmental, instrumental, and remote
sensing sources.

International landslide centre is maintaining a worldwide landslide fatality database.

The Hawai'i Solar Observatory has data on tropical storm paths worldwide stretching back to 1995,
and a facility to calculate probability of strike given the co-ordinates of the location.

Reliefweb records past natural disasters worldwide, including the extent of destruction and human
suffering (but good maps of the area affected are not always available).

UNEP-WCMC has compiled a World Atlas of Seagrasses (Green & Short 2003), and a World Atlas of
Coral Reefs (Spalding et al. 2001). A World Mangrove Atlas (Spalding et al. 1997) is currently being
updated.

Global Lakes and Wetlands Database developed through a partnership between WWF and the
University of Kassel in Germany (Lehner and D6ll 2004).

Reports, publications and journal articles

Bruijnzeel, L., van Dijk, A. I., van Noordwijk, M., Chappell, N. A., & Schellekens, J. 2007. Tropical
deforestation, people and flooding. CIFOR. [online]
http://www.worldagroforestry.org/water/downloads/bea_bruijnzeel.pdf

Danielsen, F., M.K. Sorensen, M.F. Olwig, V. Selvam, F. Parish, N.D. Burgess, T. Hiraishi, V.M.
Karunagaran, M.S. Rasmussen, L.B. Hansen, A. Quarto, and N. Suryadiputra. 2005. “The Asian
Tsunami: A Protective Role for Coastal Vegetation.” Science. 310(5748): 643. Online at:

http://www.sciencemag.org/cgi/content/abstract/3 10/5748/643

FAO, & CIFOR. 2005. Forests and floods. Drowning in fiction or thriving on facts? Bogor Barat,
Indonesia: Center for International Forestry Research and Food and Agriculture Organization of the
United Nations. Retrieved April 14, 2008, from http://www.fao.org/docrep/008/ae929e/ae929e00.htm
MA. 2005. Regulation of Natural Hazards: Floods and Fires.
http://www.millenniumassessment.org/documents/document.285.aspx.pdf

NOAA. 2008. NOAA Pacific Tsunami Warning Center. http://www.prh.noaa.gov/pr/ptwe/

Medicinal Plant Species

248

Key institutions, groups and experts

e Biodiversity and Human Health: http://www.ecology.org/biod/

e CIFOR Livelihood Briefs: Available at: http://www.cifor.cgiar.org/Publications/Briefs/Livelihoods/

e CIFOR Forest and Health Initiative -
http://www.cifor.cgiar.org/Research/Livelihoods/MainActivities/F orestHealth/introduction.htm

Reports, publications and journal articles

e Chivian E. et al., (2004) Biodiversity: Its importance to human health. The Center for Health and the
Global Environment Harvard Medical School.

Available online at: http://chge.med.harvard.edu/publications/documents/Biodiversity_v2_screen.pdf

e Colfer, C.J.P., Sheil, D and M. Kishi. Forests and Human Health: Assessing the Evidence Center for
International Forestry Research (2006)

Available at: http://www.cifor.cgiar.org/publications/pdf_files/OccPapers/OP-45.pdf
Husselman, M. 2008. Beekeeping in Zambia. CIFOR Livelihood Brief 7. 4p.

http://www.cifor.cgiar.org/publications/pdf_files/livebrief/livebrief0801.pdf

Shackleton, S.; Kaschula, S.; Twine, W.; Hunter, L.; Holding-Anyonge, C.; Petheram, L. 2006. Forests
as safety nets for mitigating the impacts of HIV/AIDS in southern Africa. CIFOR Livelihood Brief 4.

4p. http://www.cifor.cgiar.org/publications/pdf_files/livebrief/livebrief0604e.pdf

Colfer, C.J.P.; Sheil, D.; Kishi, M.2006. Forests and human health: assessing the evidence. CIFOR
Occasional Paper No. 45. Center for International Forestry Research (CIFOR), Bogor, Indonesia. 11 1p.
Available:

http://www.cifor.cgiar.org/publications/pdf_files/OccPapers/OP-45.pdf

Colfer, C.J.P.; Sheil, D.; Kaimowitz, D.; Kishi, M.2006. Forests and human health in the tropics: some
important connections. Unasylva 57(224): 3-10 online [URL]
ftp://ftp.fao.org/docrep/fao/009/a0789e/a0789e02.pdf and
http://www.fao.org/docrep/009/a0789e/a0789e00.htm

Koppert, G.J.A, Dounias, E, Froment, A and Pasquet, P. 1993. Food consumption in the forest
populations of the southern coastal area of Cameroon: YASSA — MVAE — BAKOLA. In: Hladik,
C.M., Hladik, A., Linares, O.F. Pagezy, H., Semple, A. and Hadley, M. (1993). Tropical Forests,
People and Food. Biocultural Interactions and Applications to Development. Man and the Biosphere
Series, 13 (ParisE: UNESCO et CamforthE: The Parthenon Publishing Group), pp. 295-310. Available

online at: http://www.cifor.cgiar.org/publications/pdf _files/research/forests_health/17.pdf

Shanley, P and Luiz, L. 2003. The Impacts of Forest Degradation on Medicinal Plant Use and
Implications for Health Care in Eastern Amazonia. Vol. 53 No. 6 BioScience 573. June 2003.

Available online at: http://www.cifor.cgiar.org/publications/pdf_files/research/forests health/22.pdf

Non use values
Key institutions, groups and experts

e Amazon Conservation Team - work in partnership with indigenous people in conserving biodiversity,
health, and culture in tropical America. http://www.amazonteam.org/publications.html

e Sacred Mountains Program is a program of the Mountain Institute's which mission it is to advance
mountain cultures and preserve mountain environments.
http://www.mountain.org/work/sacredmtns/index.cfm

e The Theme on Indigenous and Local Communities, Equity, and Protected Areas (TILCEPA), was set
up in 2000 by the World Commission on Protected Areas (WCPA) and the Commission on

249

Environmental, Economic, and Social Policy (CEESP) of the World Conservation Union (IUCN). It
advocates, in all countries, the recognition of community conserved and managed areas that are
significant from biodiversity point of view, and the development of management partnerships with the
communities resident in or surrounding official PAs.
http://www.iucn.org/themes/ceesp/Wk /TILCEPA/TILCEPA.htm

The IUCN Commission on Environmental, Economic and Social Policy (CEESP), is an inter-
disciplinary network of professionals whose mission is to act as a source of advice on the
environmental, economic, social and cultural factors that affect natural resources and biological
diversity and to provide guidance and support towards effective policies and practices in environmental

conservation and sustainable development. http://www.iucn.org/themes/ceesp/index.html
The Forum on religioin and Ecology is the largest international multireligious project of its kind. With

its conferences, publications, and website it is engaged in exploring religious worldviews, texts, and
ethics in order to broaden understanding of the complex nature of current environmental concerns.

http://environment.harvard.edu/religion/main.html

IUCN WCPA Task Force_on Cultural and Spiritual Values of Protected Areas (CSVPA)
http://www.iucn.org/themes/wepa/theme/values/values.html

World Water Day. 2006. Facts and figures about water religions and beliefs.
http://www.worldwaterday.org/page/442

Freshwater

Key institutions, groups and experts

Ecological Society of America (ESA). Water purification fact sheet: Water purification an essential
ecosystem services - Revealing secrets about natural water purification

http://www.esa.org/ecoservices/comm/body.comm.fact.wate.html
http://www.esa.org/ecoservices/WaterPurificationFactSheet.pdf
Environment Canada: The Great Lakes Fact Sheet
http://www.on.ec.gc.ca/wildlife/factsheets/fs_wetlands-e.html

The hydrology module of the Natural Capital Project is being developed by Sue White at Cranfield
University and Guillermo Mendoza at Stanford University.

Reports, publications and journal articles

Alcamo, J., Vuuren, D. V., Ringler, C., Cramer, W., Masui, T., Alder, J., & Schulze, K. 2005. Changes
in nature’s balance sheet: model-based estimates of future worldwide ecosystem services. Ecology and

Society 10: 19. [online] URL: http://www.ecologyandsociety.org/voll 0/iss2/art19/

Issues in Ecology, " Sustaining Healthy Freshwater Ecosystems. No. 10, Winter, 2003, Ecological
Society of America. Available on ESA's website at

http://www.esa.org/science_resources/issues/FileEnglish/issue 10.pdf
RAND. Nature's Services: Ecosystems Are More Than Wildlife Habitat: Watershed -
http://www.rand.org/scitech/stpi/ourfuture/NaturesServices/sec]_watershed.html

Inland fisheries

Key institutions, groups and experts

Environment Canada: The Great Lakes Fact Sheet

http://www.on.ec.gc.ca/wildlife/factsheets/fs_wetlands-e.html
FAO. Fisheries and Aquaculture Department ( Hope Page) — nland Aquatic Ecosystems and Coastalk

250

and Marine Information: http://www. fao.org/fishery/

Reports, publications and journal articles

e Issues in Ecology, " Effects of Aquaculture on World Fish Supplies. No. 8, Winter, 2001, Ecological
Society of America. Available on ESA's website at:

http://www.esa.org/science_resources/issues/FileEnglish/issue8.pdf
Wild Meat
Key institutions, groups and experts

e Bushmeat Trade POSTnote, Parliamentary Office of Science and Technology.
http://www.parliament.uk/documents/upload/POSTpn236.pdf

e Conservation Science Group, Imperial College. http://www.iccs.org.uk/

e ODI Wild Meat, Livelihoods Security and Conservation in the Tropics. This research project focus on
the human and social dimensions of hunting wild meat for consumptive use in tropical forests.
http://www.odi-bushmeat.org/#home_research

e UK Tropical Forest Forum Bushmeat Working Group. http://www.forestforum.org.uk/tradee.htm

e WCS Hunting and Wildlife Trade Program. World Conservation Society Hunting and Wildlife Trade
Programme. http://www.wes.org/international/huntingandwildlifetrade

e ZSL Bushmeat and Forests Conservation Programme. http://www.zsl.org/field-conservation/bushmeat-
and-forest/

Reports, publications and journal articles

e DFID, Wildlife and Poverty Study, DFID Livestock and Wildlife Advisory Group London, 2002.
http://www.dfid.gov.uk/pubs/files/wildlifepovertystudy.pdf

e Bowen-Jones, E. What are the impacts of the bushmeat trade on biodiversity, and what entry points can
the EU most effectively use to reduce these. European Association of Zoos and Aquaria (EAZA).

Available online at: http://www.eaza.net/download/summebj.PDF
Livestock
Key institutions, groups and experts
e Agriculture and Agri-food Canada: Biodiversity and Livestock Production
http://www.agr.gc.ca/pfra/biodiversity/grazing_e.htm
e The UK agricultural biodiversity coalition (UKabc) www.ukabc.org
e The EU Biodiversity in Development Project. www.wemce.org.uk/biodev
e CGIAR agricultural biodiversity research centre www.cgiar.org/ipgri
e Open Directory Project links on Agricultural Biodiversity.
www.dmoz.org/science/environment/biodiversity/agricultural
Reports, publications and journal articles

e DFID, Wildlife and Poverty Study, DFID Livestock and Wildlife Advisory Group London, 2002.
http://www.dfid.gov.uk/pubs/files/wildlifepovertystudy.pdf

Global Climate regulation

251

Key institutions, groups and experts

e CIFOR. TroFCCA: Tropical Forests & Climate Change Adaptation Project.
http://www.cifor.cgiar.org/trofcca/_ref/home/index.htm

e The World Agroforestry Center (ICRAF).
http://www.worldagroforestry.org/sea/Networks/RUPES/index.asp

e IUCN Forests and Climate Change Initiative.

http://cms.iucn.org/about/work/programmes/forest/fp_our_work/fp_our_work_thematic/fp_our_work_f
cc/index.cfm

e UNEP-WCMC. Biodiversity and Climate Change. http://www.unep-weme.org/Climate/

Authors

Abisha Mapendembe & Matt Walpole (UNEP-WCMC, Cambridge, UK)

D2,

4

ie