ENVIRONMENTAL MANAGEMENT IN FORESTRY DEVELOPMENTS PROJECT CONSERVATION EVALUATION OF SOME NATURAL FORESTS IN SRI LANKA A Project of the Forest Department Ministry of Lands, Irrigation and Mahaweli Development in association with UNDP, FAO and IUCN-THE WORLD CONSERVATION UNION April 1993 The Environmental Management Component of the Environmental Management in Forestry Developments Project is being implemented by the Forest Department with technical assistance from IUCN - The World Conservation Union under contract No. DP/SRL/89/012-001/FODO to the Food and Agricultural Organisation of the United Nations. It is being funded by the United Nations Development Programme in accordance with the Project Document: Environmental Management in Forestry Developments (December 1989). This Component aims to strengthen the institutional capacity within the Environmental Management Division of the Forest Department and the Forestry and Environment Division in the Ministry of Lands, Irrigation & Mahaweli Development. It is located in the Forest Department. This manual is one of a series of documents prepared by IUCN consultants which provide a record of the activities undertaken by the project. Authors: Michael J.B. Green, International Consultant/Conservation Review (Parts A,C and D) E.R.N. Gunawardena, National Consultant/ Soil Conservation & Hydrology (Part B) Work ee -cngg CONTENTS Page CONMEISc 9 GedooodpooeuooonooadDUOO nO UC GH SDH ODS OOD Od De OOS i EXE CUMMVE;SUMMAR Yale cae ik ae ice nic ack nue eR eC oe ea ear ey orci oa Vv PART A BIOLOGICAL DIVERSITY 1. INTRODUCING THE CONCEPT OF BIOLOGICAL DIVERSITY 1.1 WHAT IS BIOLOGICAL DIVERSITY? ............ 0c eee ee eee eeeee 1 1.2 WHERE IS BIOLOGICAL DIVERSITY FOUND? ..............-2+-2+05- 2 1.3 LEVELS OF DIVERSITY: THE CONCEPT OF RARITY ..........------- 2 1.4 WHY CONSERVE BIOLOGICAL DIVERSITY? ........---2--2+ ee eeeee 3 1.5 HOW BEST TO CONSERVE BIOLOGICAL DIVERSITY? 1.5.1 Criteria for selection of protected areas .. 1... 2... ee ee eee eee eee 5 1.6 SRI LANKA’S BIOLOGICAL DIVERSITY 1.6.1 Status:and (distribution y-9-.-ege ei nba nck en aemeen nap 6 1.6.2 Past and present conservation imitiatives .........5++e eee ee eee 7 2 A METHODOLOGY TO ASSESS SRI LANKA’S BIOLOGICAL DIVERSITY Del JIN ODWKCTHION od slowest édelodso esos BED Oe Sob Oooo ODODOGCS 8 2.2 SURVEY DESIGN 2.2.1 (Gherbeset tis) iyyin Gials AAO cidlole 6 6 uididlblcic og dSiolocoodad5 9 DeAe?e Conservation evaluation ...........222 02 eee eee eee eee eee 9 2.3 (COINCIDING Ses gihlarotint gh aa Sleln ido ABI ER GIS 6 acco ses cD oc 10 3. SAMPLING BIOLOGICAL DIVERSITY IN SRI LANKA’S FORESTS 3.1 INTRODUGTION ee rr aeen eee ce aoe Nei a 12 3.2 CRITERIA FOR SELECTING FORESTS TO SURVEY ...........------- 12 3.3 GRADSEGIR SAMPIGING cp eiercie el tee ohne ieee sede t edie tietl een een 12 3.4 SAMPLING WITHIN PLOTS 3.4.1 11 eae ek olideci ob in. Seo ea cee ORs: OiGkd ong Or chiowcad acd Gore Gud 610) 15 3.4.2 Vel tire Ve ee anaes RE Ty Eto: GDL Scere DED BHC OnGEOLO TS Gig Alo Senshi Os0.000 18 3.5 OBSERVATIONS BETWEEN PLOTS ........--- 22+ eeeeeeeeereees 18 3.6 CONSTRAINTS 3.6.1 Sampling faunal diversity .....-.-- +--+ eee eee eset eee eeee 18 3.6.2 Sampling adequacy .....-.----- se eee ee tee ee eee eee 18 IREBERENGES ee ccc ene see foley oleate 0b elias erie momen = Malkame Selialfolalego Me ie 20 REFERENCES Annex 1 PLOT, DESCRIPTION FORM aioe sickle cl cicrs) ci siets) = ee oye en) oe Annex 2 SPEGIES INVENTORY FORM) G35 ccs ce se es we eke Annex 3 RESOURCE USE DESCRIPTION FORM ............-2-0 2+ ee eeeee Annex 4 BIOLOGICAL DIVERSITY ASSESSMENT: AN EXAMPLE ............. PART B SOIL CONSERVATION AND HYDROLOGY IMPORTANCE OF FORESTS FOR SOIL CONSERVATION AND HYDROLOGY 1.1 INTRODUCTION er iscyce eh fuck ure eae Rennie tm anomie Cleves oie 1.2 TROPICAL FORESTS AND SOIL CONSERVATION ...............-.- 1.3 HYDROLOGICAL IMPORTANCE OF TROPICAL FORESTS 1.3.1 IE EL SESS oa holo © cilojone Glord ai pie clo Gia: nla) Sarit Simon aanaetien este 1.3.2 Water yield les args sen ioush-Ucycucusiesd chewed te palerricdetisiyel Suonnys uej20es 2 aeenase 1.3.3 OOS 75. kept MeN Menem) eee kee o sre RCo 6: Mays. Race putts Serandacat ele, ASSESSING FORESTS FOR SOIL CONSERVATION AND HYDROLOGY 2.1 INERODU CRIONE sgarieror- ti mieRs Welt e Ben area nse oiteutons ions setissos, Sone se orem 729) SOIL EROSION 2.2.1 Assessment of the importance of forests for scil conservation ....... 2.2.2 Ranking forests for soil erosion ...............000e ee eas 2.3 HEAD WADERS IG.) gamsash eon) hc RCP eoacIC) CMM baa kort <# gsacns) oie eee 2.4 FLOOD HAZARD 2.4.1 Estimating flood hazard from catchment characteristics ........... 2.5 FOG INTERCEPTION AT HIGHER ALTITUDES .................... 2.6 EVALUATION 2.6.1 Preliminary ranking sch) nce eree ase Ghee se acatkents agoMorekel ciel cecue 2.6.2 einalorankin Secnccic- eae nent ors Wee erence seek sete ces Ia) Sao ae eee 2.7 CONSIRAINT Sraeeatenca- senor ceria ests whi eteueeeisneusyies Sees. giai'e.y, ics) & ote Gl See Annex 1 SOIL CONSERVATION AND HYDROLOGY ASSESSMENT: AN EXAMPLE ... li PART C THE CONSERVATION VALUE OF NATURAL FORESTS IN SOUTHERN PROVINCE: A PRELIMINARY REPORT 1. INTRODUCTION 1.1 1.2 PHYSIO-CLIMATIC CHARACTERISTICS OF SOUTHERN PROVINCE ....... METHODS 1.2.1 Biological(diversitysy-scv-ccloh iene okey ace ac iicuiod ds esac) or Menem tees 1.2.2 Soil conservation and hydrology ................-+-++++++8- 2. BIOLOGICAL DIVERSITY ASSESSMENT 2.1 2.2 2.3 2.4 2.5 2.6 SPECIES DIVERSITY WITHIN JNDIVIDUAL FORESTS ................. INFLUENCE OF FOREST SIZE 2.2.1 Species\diversity@o-g-n-n-i set en ee ene ona ken a eee Bon Died oD, Rare, and endemic species) ... 5) = 22-2 2s ee le ee ee SPECIES DIVERSITY WITHIN FOREST COMPLEXES 2.3.1 \Weody ents Wega oes coadodoacboqongo0bgcbon0 oO DDOD EE 2.3.2 NACE) egeen en ROR RONEN MCMRTOTe acre ONC OD OMOM O CIONO 6-50 U0 506 3. SOIL CONSERVATION AND HYDROLOGY ASSESSMENT 3.1 3.2 3.3 3.4 IMPORTANCE OF FORESTS FOR SOIL CONSERVATION .............--. HYDROLOGICAL IMPORTANCE OF FORESTS 3.2.1 Protection of headwaters of river systems ...............-+-.- 352-2 Protection from flooding. ...........-.-...202 + ese ee eeeeee 3.2.3 Hydrological importance... 2.2... ee ee ees IMPORTANCE OF FORESTS FOR SOIL CONSERVATION AND HYDROLOGY . SOIL CONSERVATION/HYDROLOGY AND BIOLOGICAL DIVERSITY ...... 4. DISCUSSION AND CONCLUSIONS 4.1 4.2 REFERENCES Annex 1 Annex 2 Annex 3 Annex 4 DISCUSSION 4.1.1 Costs of optimal networks... 1... 6.0 eee eee ee eee 4.1.2 Constraints sai ee en eee ne ee a (CONCLUSIONS omaiels pills da oop ob nll oe cinco woes bb omos do ooae OS LIST OF WOODY PLANT SPECIES ........- 222s eee eee eee eee eens LIST OF SPECIES OF SELECTED GROUPS OF ANIMALS............--.- NEW LOCALITY RECORDS OF RARE SPECIES OF WOODY PLANTS ...... NEW LOCALITY RECORDS OF ENDEMIC ANIMAL SPECIES ...........- ill PART D FUTURE STRATEGY AND PROGRAMME 1. FUTURE STRATEGY AND PROGRAMME TO COMPLETE THE NCR 1.1 INTRODUGIION@atsnel ee terete rein tenet etienote ceneterewcr el sietena che + 6 choker a 147 1.2 REVIEWSORNPROGRESS ict eye lletaiteelcliovole elle (ole leo) oeWeeiia| leNlemeh one ©) -moNaliet 147 1.3 IDENTIFICATION OF REMAINING FORESTS TO SURVEY ......-------- 148 1.3.1 Completion of wet zone ..........- 000 eee eee eter e tees 149 1.3.2 Completion of dry zone .......... 0.000 149 1.4 RU TURE SPRAMEG NGI s ratwenet a) cmeitelle lehchaten ct. c) ce tenelele ele ete icitene! «2m shiete lle 151 1.5 FUTURE PROGRAMME 1.5.1 JEFEILNSOTT 6 00 616.0 0 O1GG 6 8/4 010: 0-GadhOb0NO.0.5, 010 ORONCEDS Ouch aceonciceo Gio o 151 1.5.2 Identification of specimens ............0 00 eee eee eee 152 1.5.3 Database management ............. 2.000: eee eee ee eee 152 1.5.4 MaxONOMICHLiStsirewe wen eMehe ehh eMen ene cited eh oh cnet etch splonte Hem. 6 oMeloins 153 REFERENCES Md peed al P ay ctepoy-y Herre we eel heuer eer ohencl ieee) cranict i eMOMawei i a) soto tee a. ss, Sele 153 Annex 1 LIST OF SITES REVIEWED FOR THE NCR ..............2-2--000000 154 iv EXECUTIVE SUMMARY The recently created Environmental Management Division within the Forest Department is currently undertaking a National Conservation Review (NCR) of all remaining natural forest and related grasslands within Sri Lanka, as part of the Environmental Management Component of the Environmental Management in Forestry Developments Project. The NCR addresses the biological importance of natural forests, largely in terms of their species diversity, together with their value for soil conservation and hydrology. It is being carried out by a team of scientists comprising three national consultants (botanist, zoologist and hydrologist) and an Assistant Conservator of Forests, with technical assistance provided by an international consultant. This document consists of a description of the methodologies used to evaluate the importance of natural forests, a report on the results of fieldwork undertaken to date, and an assessment of the additional resources required to complete the NCR. A method of rapidly assessing biological diversity within natural forest has been developed by the NCR team and is fully described in Part A. Known as gradsect sampling, it is based on sampling along environmental gradients to provide a description of the full range of species diversity within forests, overcoming problems of inadequate representative sampling and accessibility while minimising survey costs. Sampling is limited to woody plants, vertebrates and selected invertebrate groups (molluscs and butterflies). Part A was originally prepared as manual for a workshop on Assessing the Biological Diversity of Sri Lanka’s Natural Forests, held in Sinharaja National Heritage Wilderness Area, 2-5 December 1992. Rapid techniques for assessing the hydrological value of forests and their importance for soil conservation have been developed using four main criteria. These are control of soil erosion and flooding, protection of headwaters of river systems and, in the case of forests at higher altitudes, contribution of additional moisture through interception of fog. The methodology is described in Part B. Southern Province, comprising Galle, Matara and Hambantota districts and representing approximately 10% of the country, has been surveyed to date. The results of this survey are presented in Part C. Many species of plants and animals, including endemics and rarities, have been recorded in new localities and some species thought to be new to science have been discovered. An analysis of species’ distribution patterns and topographic variables, such as rainfall, slope, soil type and stream frequency, shows that virtually all remaining natural forests in the Province are of considerable importance for biological diversity, as well as for control of soil erosion and flooding and for protection of headwaters. Optimal networks of forests which meet a range of conservation criteria are identified. The results of this survey are preliminary, however, until such time as the NCR is completed and the importance of these forests can be evaluated within a national context. The need to review the legal conservation status of many of these forests is clearly demonstrated. Progress achieved to date by the NCR is reviewed in Part D. It is estimated that remaining districts in the wet and dry zones will each take another two years of fieldwork. This is well beyond the resources of the present Project, and a strategy for completing the NCR within the overall time frame of the Environmental Management in Forestry Developments Project is elaborated. Natural forests to be surveyed are identified and a future programme of work is outlined. Completion of the NCR will represent a major achievement for the Environmental Management Division, enabling an optimal network of conservation forests to be defined and providing the basis for informed decisions to be made concerning the future use of forest resources. In the longer term, the information generated by the NCR represents an extremely powerful tool for evaluating the potential impact of proposed development projects on forests, for monitoring changes in the biota and for management planning, particularly with respect to zonation. Digitized by the Internet Archive in 2010 with funding from UNEP-WCMC, Cambridge http://www.archive.org/details/conservationeval93gree PART A BIOLOGICAL DIVERSITY “Y oP ur 7 1. INTRODUCING THE CONCEPT OF BIOLOGICAL DIVERSITY 1.1 WHAT IS BIOLOGICAL DIVERSITY? A country’s biological wealth can be measured in terms of its biological diversity, the product of millions of years of evolution and thousands of years of cultivation of plants and domestication of animals. It is extremely valuable, more important than the cultural or material wealth of a country (Baldwin et al., 1991). Biological diversity, or biodiversity, is the variety of life forms, the ecological roles they perform and the genetic diversity they contain (Wilcox, 1984). It is an umbrella term used to describe the total variety of life (microbes, fungi, plants and animals) on Earth, encompassing both the number and frequency of genes, species or ecosystems within a given assemblage (McNeely, 1988). This diversity of living organisms is so high that much of it has yet to be identified. Variously estimated at 5-100 million or more, only about 1.7 million species have actually been described (Wilson, 1988; WCMC, 1992a)!. It has become widespread practice to define biological diversity in terms of genes, species and ecosystems, corresponding to three fundamental and hierarchically-related levels of biological organisation (WCMC, 1992a): - genetic diversity is about the range of genetic material in the world’s living organisms. It is a concept concerning the variability within a species, upon which depend the breeding programmes necessary to protect and improve cultivated plants and domesticated animals as well as much scientific advance and innovation. It is measured by the variation in genes, the chemical units of hereditary information that may be passed from one generation to the next. - species diversity is about the variety of living organisms on Earth. It is measured by the total number of species” within a given area under study. The species level is generally regarded as the most natural one at which to consider whole-organism diversity, being the primary focus of evolutionary mechanisms. [NB This manual is concerned principally with species diversity.] - ecosystem diversity is about the variety of ecological complexes (habitats) within which species occur. Their health and conservation are crucial to the well-being and survival of the species which they support. It is difficult to assess ecosystem diversity because there is no unique definition and classification at the global level. Moreover, unlike genes and species, ecosystems explicitly include abiotic components, being partly determined by soil parent material and climate. While species diversity may be strongly correlated with ecosystem diversity, it is usually not possible to have both maximum species diversity and maximum genetic diversity. Genetic diversity increases with the size of a population, but a population increase in some species may lead to a decline in others, or even to a reduction in species diversity. Thus, strategies to conserve biological diversity must be directed towards maintaining the diversity of species and their associated habitats, while ensuring that no species falls below the minimum population level at which its future viability is severely at risk due to the loss of genetic diversity. 1Of these described species, approximately 250,000 are flowering plants, over 1 million are invertebrates (insects are the largest group with 950,000 species) and about 45,000 are vertebrates (WCMC, 1992a). 2A species is a group of actually or potentially interbreeding living organisms reproductively isolated from other such groups (Mayr, 1969). 1.2 WHERE IS BIOLOGICAL DIVERSITY FOUND? Biological diversity in the wild is not distributed uniformly across the planet. Most of it is concentrated in the tropics where conditions are hot and wet (McNeely, 1988; WCMC, 1992a). Lowland tropical terrestrial ecosystems tend to have the highest diversity, with diversity declining with precipitation and latitude (or altitude). Tropical forests, for example, contain over half of the world’s biological diversity. Similar generalisations apply to aquatic ecosystems. Coral reefs, lakes and wetlands in the tropics exhibit a higher diversity than temperate systems. Apart from precipitation and temperature gradients, biological diversity is also governed by nutrient and salinity levels in terrestrial and aquatic ecosystems, respectively. Islands or small areas of habitat tend to have fewer species than large areas of the same type of habitat (see Section 1.5.1), but geographically isolated islands often hold proportionately higher numbers of endemic species’ than elsewhere. Within these broad limits, some areas are centres of high biological diversity ("hot spots’) due to factors such as soil complexity, altitudinal variation, climate, and geological and anthropological history. Human influences tend to reduce biological diversity, particularly where they are intensive and long-standing, as with permanent agriculture. Diversity may increase, however, as a result of limited human activities, such as some systems of shifting cultivation at low human population densities. 1.3 LEVELS OF DIVERSITY: THE CONCEPT OF RARITY Species may be rare on account of their locai abundance, habitat specificity and/or geographic distribution (Bond, 1989; Ferrar, 1989). - Local abundance or population size may be influenced considerably by a variety of direct and indirect management techniques. A species that is rare within a community is known as an alpha rarity. - Habitat specificity is a highly complex and genetically determined attribute which largely falls outside the influence of conservation management. A habitat specialist is known as a beta rarity. - Geographic distribution may be influenced to some extent by conservation management, subject to the limitations of the adaptability of the species and the extent and distribution of habitable areas. A geographically restricted or narrowly endemic species is known as a gamma rarity. There are eight possible permutations of these three levels of diversity, giving seven types of rarity and one that is common to all levels. The classification of species according to these levels of diversity provides a basis for prioritising conservation action (Table 1). Thus, species which are rare for a single level of diversity should receive a lower priority than those rare for two attributes. Highest priority should be given to species rare for all three levels of diversity (i.e. highly specialised and restricted endemics, with markedly reduced populations). In most natural or near-natural systems the majority of species are relatively rare. This ’natural’ or ’static’ rarity is quite distinct from rarity induced by a decline in numbers. The reduction of rarity is a biologically valid goal for conservation only in the case of induced rarity, not natural rarity (Ferrar, 1989). Thus, while species naturally rare on account of their beta or gamma diversity need to be conserved, efforts to reduce rarity are valid only in the case of species with locally declining populations. *An endemic species is one which is restricted to a particular locality or region. 2 Table 1 Possible permutations of species distribution (adapted from Bond, 1989) alpha diversity beta diversity gamma diversity Local Habitat Geographic Conservation abundance specificity range priority: abundant generalist widespread none abundant specialist widespread low abundant generalist endemic low rare generalist widespread low abundant specialist endemic intermediate rare specialist widespread intermediate rare generalist endemic intermediate rare specialist endemic high 1.4 WHY CONSERVE BIOLOGICAL DIVERSITY? Biological resources provide the basis for life on earth. Their values in social, ethical, cultural and economic terms have been recognised by societies in religion, art and literature from the earliest days of recorded history. Their importance is reflected in the increasing efforts made by governments to formulate and implement policies, legislation and programmes to ensure that biological resources are conserved. The signing of the Convention on Biological Diversity‘ by 157 countries at the United Nations Conference on Environment and Development (Rio de Janeiro, June 1992), better known as the Earth Summit, highlights the worldwide importance given to the conservation of biological diversity and its high priority on the international agenda. Conservation is the management of human use of the biosphere so that it may yield the greatest sustainable benefit to present generations while maintaining its potential to meet the needs and aspirations of future generations (IUCN, 1980). Thus, conservation does not mean preservation but wise use, thereby contributing to sustainable development. Sustainability is the basic principle of all social and economic development because it optimises the social and economic benefits available in the present without jeopardising the likely potential for similar benefits in the future (McNeely er al., 1990). Preserving the diversity of biological resources ensures that present and future options for their wise use are maintained, and that the biosphere is kept in a state supportive of human life (WCMC, 1992a). The values of biological resources can be classified in terms of their direct and indirect benefits (McNeely, 1988): Direct Values - Consumptive use value is the non-market value of natural products, such as firewood, game and fodder, that are consumed directly, without passing through a market. - Productive use value is the value of natural products harvested commercially, such as fish, medicinal plants and timber. Indirect Values ‘The principal objectives of the Convention are the conservation of biological diversity, the sustainable use of its components, and the sharing of benefits that come from the use of genetic resources. 3 Indirect benefits are seldom accounted for in cost-benefit analyses, but they may far outweigh direct benefits. - Non-consumptive use value is concerned primarily with nature’s services rather than her goods through the proper functioning of ecosystems, such as watershed protection, photosynthetic fixation of solar energy, regulation of climate and soil production. It also includes recreational, aesthetic, spiritual, cultural, scientific and educational values. - Option value is concerned with maintaining as many gene pools as possible, particularly for those wild species which are economically important or potentially so, in anticipation of unpredictable events, both biological and socio-economic. It is the value of keeping options open for the future. - Existence value is the value of ethical feelings towards the very existence of wildlife. Biological resources have multiple values which are perceived in different ways according to needs. At the local level, consumptive use value is often the most relevant, while national governments tend to be most interested in productive use value, often in terms of revenue from foreign exchange earnings. Although many products of natural resources are traded internationally, the world community is also likely to be interested in non- consumptive use and existence values, particularly as it grapples with global issues such as climate change and nising sea level. Assessing the value of biological resources is an essential first step towards sound development, enabling planners and resources managers to address their conservation. The second step is deciding how best to conserve such resources, which is discussed in the next section. 1.5 HOW BEST TO CONSERVE BIOLOGICAL DIVERSITY? One of the best-known and most effective ways of conserving biological diversity in order to better meet the material and cultural needs of mankind now and in the future is through the establishment and management of protected areas’ (MacKinnon et al., 1986; McNeely, 1988). In a strategy paper recently produced by The World Bank, it is recognised that "setting up comprehensive and well-managed protected area systems is likely to be the most practical way to preserve the greatest amount of the world’s biological diversity and the ecological processes that define and mould it" (Braatz, 1992). Protected areas can be managed according to a wide spectrum of objectives ranging from strict protection to sustainable use, depending on conservation and development priorities. Very often it may be necessary to zone a protected area to provide for a range of management objectives and multiplicity of uses. Zonation can also be used to buffer ecologically sensitive core areas from external pressures. Such integration of strict protection with sustainable use forms the basis of the biosphere reserve concept, first launched in 1971 under the Unesco Man and Biosphere Programme and now being widely applied to resolve the often conflicting interests of conservation and development. The concept provides for the zonation of a biosphere reserve into areas of different use, with a strictly protected core area of high biological value buffered by concentric zones under progressively more intensive but sustainable forms of management towards its periphery (Batisse, 1986). Maintenance of biological diversity on land outside protected areas is also essential, particularly in the Asia- Pacific region where less than 4% of total land area is protected (Braatz, 1992). Some of the more innovative and cross-sectoral approaches to conserving biological diversity are reviewed by McNeely et al. (1990). Sas recently defined at the IV World Congress on National Parks and Protected Areas, Caracas, 10-22 February 1992, a protected area is an area of land and/or sea managed through legal or customary regimes so as to protect and maintain biological diversity and natural and associated cultural resources. 1.5.1 Criteria for selection of protected areas To ensure that protected areas function with maximum effect, they should be selected in accordance with principles of conservation biology. The following criteria provide a basis for the selection of protected areas. Size Protected areas should be as large as possible in order to (a) minimise risks of species’ extinctions and (b) maximise representation of species. (a) Given that protected areas are effectively islands of natural or near-natural habitat in a sea of humanity, they should be as large as possible to maximise the degree to which their contents retain their integrity and to minimise extinction rates. The larger a protected area is, the better it is buffered from outside pressures (Soule, 1983). While many of the human pressures on species and their habitats can be reduced or removed through more effective management, chance demographic and genetic events are more difficult to overcome. Since genetic variability is rapidly lost in small populations due to genetic drift (random changes in gene frequencies) and inbreeding (breeding among close relatives), populations should be maintained as large and diverse as possible (Wilcox, 1984). Thus, protected areas need to be large enough to support minimum viable populations of key species. Good candidates are umbrella species, whose conservation will provide a protective umbrella for other associated species, and ecologically significant species which occupy central positions in the food webs of communities (Wilcox, 1984). Populations of key species should consist of at least 500 genetically effective individuals, or a total population of about 1,000 individuals including juveniles and other non-breeders, in order to maintain sufficient variability for adaptation to long-term changing environmental conditions (Frankel, 1983; Soule, 1987)°. (b) Protected areas should encompass as wide a contiguous range of ecological communities as possible because few species are confined to a single community and few communities are independent from those adjacent to them (MacKinnon ef al., 1986). The more communities represented, the greater the number of species and the greater the complexity of ecological interactions. Maximum representation of communities is best achieved by ensuring that the entire range of an environmental gradient, such as altitude or soil type, is included. There is a relationship between the number of species within a relatively uniform area and the size of that area. As a general rule, for every 10-fold decrease in the size of an area, 30% fewer species are present (Wilcox, 1984). The relationship has been described by a variety of equations (see Nicholls, 1991), of which the most usually used are: species richness (S) as a power function of area (A), S = kA*(1+e) and its logarithmic transformation, In(S) = In(k) + zin(A) + In(1+e) where k and z are constants (or parameters) and e is the stochastic or random component of the model. Shape Protected areas should be of a compact shape in order to minimise ‘edge effects’, and their boundaries should be biogeographically meaningful. : ‘For short-term survival of serious inbreeding and its deleterious effects, the minimum viable population is estimated to be 50 breeding individuals (Soule and Wilcox, 1980). Edge effects, such as colonisation by invasive species from adjacent disturbed habitats or human encroachment, can be minimised by selecting compact shapes, preferably circular. Boundaries should follow natural topographic features, but watersheds are preferable to rivers because the latter often bisect essential terrestrial habitats of a range of species (MacKinnon ef al., 1986). Corridors and clusters Protected areas should be linked to each other by corridors of natural or semi-natural habitat or located in clusters to prevent them from becoming completely isolated from each other. Corridors and clustering of protected areas enable animals to move between adjacent sites, thereby maximising the exchange of genetic material between neighbouring populations. They also increase the effective size of protected areas. Representativeness The full complement of biolugical diversity within a region should be represented within a network of protected areas. Given that it is seldom possible to protect entire geopolitical units in their natural state, networks of geographically scattered protected areas need to be established which are representative of every ecological community within a region. Networks should be optimal in terms of the amount and uniqueness of biological diversity protected per unit area to make most efficient use of scarce land resources for conservation. This is best achieved by giving priority to centres of high species diversity. Pragmatic considerations should be incorporated in the selection and design of protected areas. For example, an area should only be protected for conservation purposes if there is a good chance of its ecological integrity being maintained. Thus, protected areas should only be established in areas where they can be afforded adequate protection (MacKinnon ef al., 1986). Other important considerations are the desirability to locate protected areas in areas where they can provide a variety of goods (e.g. firewood, minor forest products) and services (e.g. research, tourism, watershed protection), and to avoid establishing them in areas of high timber or agricultural production potential unless there are no suitable alternatives (Howard, 1991). 1.6 SRI LANKA’S BIOLOGICAL DIVERSITY 1.6.1 Status and distribution Sri Lanka, covering an area of 65,610 sq.km, is one of the smallest but most biologically diverse countries in Asia (Braatz, 1992). It has more species of flowering plants, amphibians, reptiles, birds and mammals per unit area than most other Asian nations (Baldwin ef al., 1991). Many of these species are endemic, for example 27% of flowering plants, 51% amphibians, 50% reptiles and 14% mammals, a reflection of the island having been separated from the Indian subcontinent since the late Mesozoic. Much of this biological diversity occurs within natural forests, particularly the rain forests of the south-west wet zone where, for example, 94% of the 830 endemic species of flowering plants are to be found. Forest cover in Sri Lanka has declined from an estimated 84% in 1881 to 24% (1.58 million ha) in 1989. It has declined most in the south-west where only 12% of the wet zone remains forested as compared to 30% in the north-east dry zone (Baldwin ef al., 1991). Closed-canopy natural forest is currently estimated to cover 20.2% (1.33 million ha) of the country (Legg and Jewell, 1992). Species extinction is the most serious consequence of forest clearance: although hydrological and climatic functions performed by original forest can be re-created in man-made vegetation, once lost a species is gone for ever (Sayer and Whitmore, 1991). This decline in forest cover is a reflection of the extremely high human population density, exceeding that of most other countries in Asia except Bangladesh and a few very small, essentially urban nations such as Hong 6 Kong and Singapore. The wet zone, occupying just 24% of the country, is under greatest pressure because it is settled by 55% of the island’s 15 million inhabitants. Not surprisingly, therefore, this has led to a trend of increasing isolation and fragmentation of forests in this zone. Knowledge about the status and distribution of Sri Lanka’s flora and fauna is limited to species inventories of a few of the better known conservation areas, such as Sinharaja in the wet zone and Ruhuna/Yala in the dry zone, and even these sites have yet to be comprehensively surveyed. The avifauna is the only group to have been systematically surveyed across the entire country, based on the national grid (10 x 10 sq.km). Thus, a national survey of the biological diversity contained within remaining natural forests is a prerequisite to sound land use planning. 1.6.2 Past and present conservation initiatives Sri Lanka has one of the oldest and most extensive networks of protected areas in Asia, extending over 14% of the country. Most of this network (9,053 sq.km) comprises national reserves and sanctuaries established under the Fauna & Flora Protection Ordinance and managed by the Department of Wildlife Conservation. The remaining 1,178 sq.km consists of Sinharaja, notified under the National Heritage Wilderness Areas Act, and a number of forest reserves (or parts thereof) designated as National Man and Biosphere (MAB) reserves, all of which are administered by the Forest Department (WCMC, 1992b). Following an accelerated review of the conservation importance of 30 forests in the wet zone (TEAMS, 1991), the Forest Department has earmarked 10 forests, covering 138 sq.km, for conservation (IUCN, 1992). A further 4 forests covering 121 sq.km have subsequently been added to this total. Building on its traditional concern for forests and associated wildlife, the Government and people of Sri Lanka have ensured that care for the environment features prominently in the Forestry Sector Development Programme. A moratorium on logging in the wet zone has been introduced pending an assessment of the conservation value of its remaining rain forests. This National Conservation Review, which extends to all remaining natural forests and associated grasslands throughout the country, is being carried out by the newly created Environmental Management Division of the Forest Department as part of the Environmental Management in Forestry Developments Project, the environmental component of the Forestry Sector Development Programme (FAO, 1989). The Project commenced in 1991 and is scheduled to run for five years. It is being implemented by the Food & Agricultural Organization of the United Nations and IUCN - The World Conservation Union, with funds from the United Nations Development Programme. The National Conservation Review is being conducted by a team of international and national consultants working alongside staff in the Environmental Management Division. The team includes a botanist, zoologist and hydrologist, and conservation and database specialists. 2. A METHODOLOGY TO ASSESS SRI LANKA’S BIOLOGICAL DIVERSITY 2.1 INTRODUCTION Biological surveys are necessary for rational land use planning and decision-making, and they are required to resolve land use conflicts. Much of the conflict between logging and forest conservation, for example, is concerned with which species and how many are present in a particular area. If that information is present the issue can be simplified. The conflict remains but it can be resolved on the basis of facts, not guesses or unsound extrapolations. Very often, however, the information is not available and the first step towards resolving the conflict is to conduct a biological survey to determine what species are present, where they are and how many. This is the prevailing situation in Sri Lanka as already outlined (Section 1.6.1). Financial resources for surveying biota are diminishing relative to planning needs, hence it is essential that surveys are cost-effective (Burbidge, 1991). To help ensure that costs are kept to a minimum, only relevant data should be collected and they should be available for re-use as required. It is necessary, therefore, to develop a method of surveying biological diversity that is both comprehensive and cost-effective (i.e. rapid) in order to meet the overall objective of the Conservation Review component of the Environmental Management in Forestry Developments Project: to assess the conservation value of remaining forests, mangroves and grasslands in Sri Lanka. This will enable decisions concerning future uses of these natural resources to be soundly based on scientific principles. 2.2 SURVEY DESIGN The most widely used scientific criteria for assessing conservation value are diversity, rarity, naturalness, size and representativeness (Margules and Usher, 1981; Usher, 1986; Margules et al., 1988). Indeed, as pointed out by Mackey et al. (1989), these criteria are used to identify natural properties under the World Heritage Convention. All refer, either wholly or in part, to a common underlying theme: the maintenance of biological diversity in perpetuity. Conservation planning in the past has usually be focused on ensuring adequate representation of communities or habitats, but many species cannot necessarily be perpetuated by the reservation of communities because of their dependence on disturbance, their peripatetic propensities (they may be nomadic among communities in response to environmental cycles or rare events) or their occurrence in chorological tension zones (i.e. community interfaces). Thus, the best conservation planning should encompass both species and communities, with priority, if any, being given to species (Kirkpatrick and Brown, 1991). In practice, the number of species has become the simplest and most commonly used measure of biological diversity (Bond, 1989), despite certain limitations (see Section 2.3), and forms the basis of this National Conservation Review. The National Conservation Review is designed to identify an optimal or minimum set of sites which is representative of Sri Lanka’s biological diversity, measured in terms of species richness. This approach will enable questions such as the following to be answered: (i) To what extent is Sri Lanka's biological diversity represented by its existing protected areas network? : (ii) Which additional forests, mangroves and grasslands should be protected in order to create a truly representative protected areas network, as well as to conserve soil and water resources? (iii) Are any sites within the existing network a luxury, contributing negligible additional biological diversity to the optimum network (as defined above) and having over-riding potential socio- economic value? In order to answer such questions it is necessary first to determine the distributions of species, and then to identify a optimum set of sites which encompasses all species through some form of pattern analysis. Surveys intended to provide data for identifying a representative protected areas network require a procedure which ensures that the full range of biological diversity is sampled. Such surveys are concerned with gathering information about species’ distributional patterns, rather than obtaining unbiased estimates of the abundance of individual species using standard statistical sampling techniques. Stratification is essential but practical problems of travel costs and accessibility must be incorporated into any cost-effective survey. An additional consideration is the importance of remaining forests for soil conservation and hydrology. This is considered in Part B of the manual. ys | Gradsect sampling Gradient-directed transect (gradsect) is the deliberate selection of transects which contain the steepest environmental gradients with maximum access present in an area (Austin and Heliger, 1991). It has been selected for the National Conservation Review as being the most appropriate technique for rapidly assessing the diversity of species contained within natural forests. Gradsect sampling is designed to provide a description of the full range of biological diversity within a region, overcoming problems of inadequate representative sampling and accessibility, while minimising survey costs. Gradsects are deliberately selected to contain the strongest environmental gradients within a region to optimise the amount of information gained relative to expenditure of time and effort. Sampling along a gradsect maximises variation between plots and accessibility can be enhanced by choosing localities with an adequate road network to reduce travel time. It has been shown statistically that gradsects capture more information than randomly placed transects of similar length (Gillison and Brewer, 1985; Austin and Heyligers, 1989). Previous studies (Austin 1978, Austin ef al., 1984) have shown that rock type, precipitation and temperature have a strong influence on the distribution of plant species. Temperature is closely correlated with altitude, which was has been chosen as the main variable for the National Conservation Review because of the ready availability of information on altitude from topographical maps (1:63,360). 2-2-2, Conservation evaluation Historically, the selection of protected areas has tended to be ad hoc, often depending on the availability of land unsuitable for other forms of land use and influenced strongly by perceived threat (Leader-Williams ef al., 1990). This is unsatisfactory because it results in a bias of the range of species protected. A widely-used, more systematic alternative is to rank candidate sites according to various criteria of conservation value, such as diversity, rarity, naturalness, size and representativeness as mentioned above. As discussed by Margules (1989), however, combining ranks for different criteria to derive an index of conservation value inevitably involves weighting of the criteria according to a subjective assessment of their relative importance. Moreover, a major draw back in ranking sites for their conservation value on the basis of a single application of a formula is that sites of lower conservation priority may duplicate species protected in sites of higher conservation priority (Kirkpatrick, 1983). Other problems associated with combining ranks to derive a conservation value index are reviewed by Margules ef al. (1991). An alternative approach being deveioped is to use patterns of species’ distributions to identify a set of sites which encompasses all species (Margules et al., 1988, 1991). This minimum set of sites in which all species (or other units of diversity) are represented at least once is the bottom line: the bare minimum. Anything less would constitute an inadequate representation of biological diversity. The algorithms can be constrained in various ways: for example, to ensure that each species is represented in at least two sites. The large area of land required to represent biological diversity, even when the number of sites is minimised, makes it unlikely 9 that all, or even most, species will be represented in the protected areas network. However, the minimum set approach identifies explicitly which sites are needed to maximise biological diversity and, therefore, which species will not be represented in a proposed network that does not include all of those sites (Margules, 1989). An iterative method (after Kirkpatrick, 1983) is being used to define a minimum set of sites necessary to conserve the biological diversity contained within Sri Lanka’s natural forests, mangroves and grasslands. An heuristic algorithm is being developed to select a set of forests in which all species occur at least once. It consists of the following steps (adapted from Margules et al., 1988): (i) Select all forests with any species which occur only once. (ii) Beginning with the rarest unrepresented species (ie. least frequent species remaining in the data matrix after the previous step), select from all forests in which it occurs the forest contributing the maximum number of additional unrepresented species. (iii) Where two or more forests contribute an equal number of additional species, select the forest with the least frequent group of species (defined as that group having the smallest sum of frequencies of occurrence in the remaining unselected forests). (iv) Where two or more forests contribute an equal number of infrequent species, select the first forest encountered. The fourth step is order-dependent but other conservation goals, embodying new concepts, can be introduced as necessary. For example, the criterion of naturalness could be introduced by selecting the forest with the least number of exotics, or proximity to other forests by selecting the next nearest forest. The algorithm can be further modified to determine the minimum set of forests needed to represent all species twice, three or more times. A complementary approach to consider in the National Conservation Review is the identification of a network representative of the full range of Sri Lanka’s physical environments, given the strong correlation between species’ distribution patterns and such physical conditions as soil, temperature and precipitation. Hunter et al. (1988) consider that the selection of protected areas should be more strongly influenced by the distribution of physical environments than by that of communities, which are transitory assemblages or co- occurrences among taxa that have changed in distribution, abundance and association in response to past climatic changes. Ideally, protected areas should encompass a sufficiently broad range of physical environments to allow organisms to adjust their local distribution in response to long-term environmental changes. Given that landscapes also change, albeit over a longer timespan, protected areas should be connected by corridors to allow species to modify their geographic distribution. In the interests of the long-term preservation of Sri Lanka’s biological diversity, it will be important to check that the minimum set of sites identified by means of the iterative approach contains the full range of physical environments, particularly given the likelihood of significant global climatic changes over the next century. 2.3 CONSTRAINTS The number of species is not an adequate measure of biological diversity because speciation is not necessarily correlated with ecological differentiation (Bond, 1989). Thus, the species in a genus or higher taxonomic unit may be ecologically monotonous. In contrast to large genera with many ecologically similar species is the case in which extraordinary variability is found within a single species, perhaps necessitating conservation of individual populations throughout its entire range. In order to prioritorise the selection of protected areas networks for biological diversity conservation, taxonomic distinctness needs to be measured in addition to species richness and complementarity (Vane-Wright et al., 1991a). Species richness (number of species) and complementarity (i.e. relative contributions of individual biotas to the network) are incorporated in the existing conservation evaluation procedure (Section 2.2.2). Thus, the ideal 10 first choice is the site having the most species (or rare species); subsequent sites are selected on the basis of their representation of the residual complement. The model does not, as yet, take into account taxonomic distinctness, the difference between species in relation to their place in the natural hierarchy. This can be evaluated by root weighting, whereby species are weighted for distinctness according to their position in the taxonomic hierarchy (Vane-Wright er al., 1991b). Systems are being developed which will enable taxic diversity measures to be combined with complementarity for a range of different organisms. It may be possible to apply them during the life of this Project. 11 3. SAMPLING BIOLOGICAL DIVERSITY IN SRI LANKA’S FORESTS 3.1 INTRODUCTION These guidelines provide the basis for estimating the biological diversity represented within natural forests (including mangroves) and related patanas (i.e. montane grasslands), as specified in the Project Document (FAO, 1989). They are written in the light of experience gained in surveying natural forests but they can be applied to montane grasslands (and other habitat types) with minimal modification. The sampling procedure involves the following steps, each of which is described in the sections below: - identification of study sites, - positioning of transects and plots along environmental gradients, and - inventorying of flora and fauna within plots. 3.2 CRITERIA FOR SELECTING FORESTS TO SURVEY The new 1:500,000 Forest Map of Sri Lanka (Legg and Jewell, 1992)’ provides the basis for identifying areas of remaining natural forest (including mangrove). Boundaries of forest and proposed reserves, together with protected areas under the jurisdiction of the Department of Wildlife Conservation, are marked on this map reproduced at a scale of 1:100,000. This enables those forests which are notified under either the Forest Ordinance, National Heritage Wilderness Areas Act, or Fauna & Flora Protection Ordinance to be distinguished from other state forests that lack any legal protection. The National Conservation Review covers all natural forests administered by the Forest Department (i.e. forest reserves, proposed reserves and national heritage wilderness area) and by the Department of Wildlife Conservation (i.e. national reserves and sanctuaries). It includes the 30 forests previously surveyed under the Accelerated Conservation Review (TEAMS, 1991), in which sampling was not systematic. In the case of proposed reserves and other state forests, only those exceeding 100 ha® in size are surveyed unless they are known to be of particular biological importance. 3.3 GRADSECT SAMPLING Transects are oriented along environmental gradients in order to sample the full range of biological diversity within a forest. Altitude has been selected as the main environmental variable for the purposes of the National Conservation Review, as mentioned in Section 2.2.1. Reference to the 1:63,630 series of topographic maps enables transects to be positioned at right angles to contours, ensuring that the full range of altitudes and aspects is covered by one or more transects within each forest (Figure la). In the case of extensive forests, Landsat Thematic Mapper false-colour images (scale 1:50,000) are used to differentiate between community types and ensure that each is sampled. While large-scale maps of the soil and vegetation may help in deciding where to align transects, such information is not available for the majority of forests. Aerial photographs may also be useful. 7This was not available at the start of the National Conservation Review. Thus, forests in Galle, Matara and Hambantota districts were identified from the 1:100,000 Land Use series. 84 threshold of 50 ha was applied to Galle, Matara and Hambantota districts at the outset of the National Conservation Review, but this was later raised to 100 ha to expedite the survey. 12 (a) (b) (c) (d) TRANSECT Figure 1 Transects are aligned along altitudinal gradients (a), taking advantage of access routes as appropriate (b). Narrow belts of vegetation, such as riverine forest, may be sampled in a zigzag direction (c), crossing between banks as conditions permit (d). 13 ySa104 yBiy (— \Sasoy yeyseoo -) Vai SEA FOREST EDGE Schematic diagram of plots aligned along transects at regular intervals in wet zone (a) and dry zone (b) forests (not to scale) Figure 2 14 Transects need not follow a straight line, but can change direction to maximise variability between plots and to enhance accessibility as necessary (Figure 1b). An effective way of sampling narrow bands of forest, such as riverine or coastal vegetation, is to run transects in a zigzag along the width of the forest (Figure 1c,d). A single transect with at least five plots should be adequate for sampling small forests of about 100 ha, but up to four or five transects may be required for those of about 10,000 ha, particularly in the case of rain and sub- montane forest where species diversity is higher. Having fixed the transect(s) on the map, the site is visited and any slight modifications to the original bearing are made before setting off along the transect. The transect is walked along fixed bearing(s) using a compass, with plots of 100 x 5 m placed at regular intervals. Transects and plots are permanently marked so that they can be revisited for checking data or carrying out further fieldwork. Trees are marked with yellow paint at 10-20 m intervals. The paint is applied to one side of trees between plots and to both sides within plots. Plots are further distinguished by means of coloured nylon rope tied out of reach round a branch of the first and last tree in each plot. The first plot is positioned at the beginning of the transect when starting on the coast, in a valley or on a ridge, but it should otherwise be placed 100 m inside the perimeter of the forest to avoid peripheral, disturbed areas. Plots are spaced 150 m apart (i.e. 4 plots per km of transect), the distance between plots being paced. This is generally adequate in rain forest and/or steep terrain, where species composition is fairly heterogeneous (Figure 2a). Plots may need to be spaced up to 400 m apart (i.e. 2 plots per sq.km) in the dry zone when the topography is fairly uniform (i.e. level terrain) and there are extensive patches of relatively homogenous forest (Figure 2b). Spacing of plots should not exceed 400 m because of the high investment in time and energy required to walk the transect. Occasionally, it may be necessary to space plots as little as 100 m apart, as in the case of narrow bands of coastal vegetation (Figure 2b). 3.4 SAMPLING WITHIN PLOTS Plots are 100 m long, aligned along the length of the transect, and 5 m wide (Figure 2). They are measured along the centre-line using a brightly-coloured nylon rope, which changes from one colour to another at its mid- point (50 m) to facilitate sampling (see below). The exact location of each plot is determined from a Global Positioning System (GPS)’. As dense canopy cover interferes with the receiver, it is often necessary to climb a tree in order to obtain an unobstructed fix from the satellites. Various physical parameters are measured at 0 m, 50 m and 100 m intervals along the plot and recorded on the Plot Description Form (Annex 1). The condition of the vegetation is also assessed. The fauna and flora within each plot are recorded on the Species Inventory Form (Annex 2). Noteworthy species seen along the transect but outside the plots may also be recorded, but such data are not used in the analyses. Inventory forms with a check-list of species and their codes are automatically generated from EIMS, based on inventories of one or more forests previously surveyed. This saves time writing species’ names in the field and speeds up data entry by using the species’ codes. The time required to inventory the species within each plot ranges from less than 1 hour in dry monsoon forest to 2-3 hours in rain forest. 3.4.1 Fauna The plot is first walked by the zoologist who records all vertebrate animals seen or heard within the range of visibility - this takes up to 30 minutes. The zoologist is followed by his assistant carrying the 100 m length of rope, and by a painter who marks both sides of trees at 10-20 m intervals along the centre-line. The zoologist and his assistant then retrace their footsteps either side of the fixed rope, disturbing the leaf litter and undergrowth as necessary to record animals, their tracks and signs within a 5 m belt (i.e. up to 2.5 m either side of the fixed rope). The edge of the plot is determined using a 2.5 m long stick held at right angles either As this was not available at the start of the National Conservation Review, plots in Galle, Matara and Hambantota districts were marked on topographic maps (1:63,630) and the geographic coordinates obtained. 15 No. animal species (selected groups) 100 a 263 @ 138 a 500 gm 499 35 gw 201 a 2 a 190 FAR 60 &0 100 120 140 160 180 200 No. woody plant species Figure 3 Relation between animal and plant diversity in natural forest, based on data from Matara District. Data points are labelled by protected area number. % new species 250 100 200 80 & a > Sey 60 i=} 2 Ss s 100 40 i= —) 3 < 50 20 0 ae eae eee OE To EE 0 0 § 10 15 20 25 30 No. plots _s— Accumulative no. species _e— (No. new species/Accum. no. species)x100 V7, 5% threshold for new species NOTE: Transect numbers are marked on the graphs for the first plot onlv. Figure 4 Species/area curve for gradsects in Sinharaja. Sampling is considered adequate once the number of new species falls below the 5% threshold for at least two consecutive plots. 17 side of the fixed rope. The faunal survey is restricted to mammals, birds, reptiles, amphibians and a few invertebrate groups (butterflies, molluscs and those species of termites which build mounds). Fishes are recorded opportunistically, as time and conditions permit. Species are recorded on a presence or absence basis from their tracks and signs, but the number of individuals is recorded in the case of direct observations. For termites, the number of termite mounds is recorded for two easily distinguished genera, Odontotermes and Eutermes (Annex 2). 3.4.2 Flora Once the centre-line of the plot is fixed by the rope, the botanist walks along the length of the plot recording all woody plant species within a 5 m width (i.e. up to 2.5 m either side of the fixed rope, measured with a stick). Specimens of unidentified species are collected and numbered for subsequent identification at the National Herbarium. The number of individuals exceeding 10 cm DBH is recorded for each species, but species with no individuals exceeding 10 cm DBH are recorded only on a presence/absence basis (Annex 2). The botanist is assisted by one person who checks the width of the plot using a 2.5 m-long stick and who collects, labels and presses the herbarium material. Trees may have to be climbed to collect suitable herbarium specimens. The floral survey is restricted to woody plants because of time constraints. It is of similar duration to the faunal survey, but tends to take less time in dry zone forest and longer in rain forest. In order to maximise efficiency, the first of either the floral or faunal parties to complete the plot travels along a fixed bearing to the next plot, marking trees on one side only with paint as it proceeds. 3.5 OBSERVATIONS BETWEEN PLOTS Noteworthy fauna and flora observed along a transect while walking between plots are recorded, and specimens collected if their identity is uncertain. Such data are entered into EIMS, but are not used in any of the analytical procedures because this would bias the results. The use of faunal, floral and land resources is also recorded along the length of each transect (Annex 3). Frequency of use is recorded up to a maximum of 10, more frequent use of a resource is recorded as 10+. 3.6 CONSTRAINTS 3.6.1 Sampling faunal diversity Given the rapidity with which it is necessary to survey forests because of overall time constraints, analyses are based on the plant data and supplemented by the animal data. This is necessary because faunal diversity cannot be adequately sampled without re-visiting sites at different times of day and in different seasons, which is well beyond the scope of the National Conservation Review. For analysis purposes, it is assumed that plant and animal diversity are strongly correlated: forests rich in woody plant species can be expected to be rich in animal species. This is demonstrated in Figure 3 using data from Matara District. Despite the limitations of the animal data, which are far from comprehensive, the correlation is extremely good. 3.6.2 Sampling adequacy The adequacy of sampling for species diversity is routinely assessed in the field by constant reference to the relationship between the accumulative number of woody plant species recorded and the total area sampled. Once the asymptote is reached, the majority of species will have been recorded and sampling is discontinued for that particular forest. In practice, sampling continues until the number of new species of woody plants recorded within at least 2 successive plots does not exceed 5% of the total number of species. An example of a species/area curve derived from data for Sinharaja, where fieldwork is ongoing, is shown in Figure 4. Further transects will reveal whether or not the asymptote has been reached at about 230 species. It is instructive to note that this total, recorded within 1.3 ha (26 plots), is well in excess of the 184 species recorded by Gunatilleke and Gunatilleke (1981) within a total area of 15 ha. They found that a minimum arza of 3.75 ha was required to sample woody plants in the rain forests of Sinharaja. Such comparisons provide an indication of the cost-effectiveness of gradsect sampling. The methodology is based on the premise that surveys of forests are comprehensive, with the full range of species sampled. In practice, this condition is unlikely to be met because of the limited resources available to survey all remaining natural forest in the country, particularly in the case of tropical rain and sub-montane forests where biological diversity is extremely high. Use of satellite imagery can help to ensure that transects pass through the full range of community types represented within each forest (see Section 3.3). Some gaps in the data can be filled by reference to published material and unpublished sources but such data must not be mixed with field data in any comparative analyses of forests because the results would be biased. Ultimately, the results of the National Conservation Review represent a preliminary attempt to assess the status and distribution of Sri Lanka’s flora and fauna, thereby enabling an optimum protected areas network to be identified for the conservation of this biological diversity. 19 REFERENCES Austin, M.P. (1978). Vegetation. In Land use on the south coast of New South Wales, eds Austin, M.P. and Cocks, K.D. CSIRO, Melbourne. Pp. 44-46. Austin, M.P. Cunningham, R.B. and Fleming, P.M. (1984). New approaches to direct gradient analysis using environmental scalars and statistical curve-fitting procedures. Vegetatio 55: 11-27. Austin, M.P. and Heyligers, M.P. (1989). Vegetation survey design for conservation: gradsect sampling of forests in north-eastern New South Wales. Biological Conservation 50: 13-32. Austin, M.P. and Heyligers, M.P. (1991). New approaches to vegetation survey design: gradsect sampling. In Nature conservation: cost effective biological surveys and data analysis, eds C.R. Margules and M.P. Austin. CSIRO, Australia. Pp. 31-36. Baldwin, M.F. (Ed.) (1992). Natural resources of Sri Lanka: conditions and trends. Natural Resources, Energy and Science Authority of Sri Lanka, Colombo. 280 pp. Batisse, M. (1986). Developing and focusing the biosphere reserve concept. Nature and Resources 12: 2-11. Bond, W.J. (1989). Describing and conserving biotic diversity. In Biotic diversity in Southern Africa, ed. Huntley, B.J. Oxford University Press, Oxford. Pp. 2-18. Braatz, S. (1992). Conserving biological diversity: a strategy for protected areas in the Asia-Pacific region. World Bank Technical Paper No. 193. 66 pp. Burbidge, A.A. (1991). Cost constraints on surveys for nature conservation. In Nature conservation: cost effective biological surveys and data analysis, eds C.R. Margules and M.P. Austin. CSIRO, Australia. Pp. 3-6. FAO (1989). Project document: environmental management in forestry developments. Mission Report SRL/89/012. 90 pp. Ferrar, A.A. (1989). The role of red data books in conserving biodiversity. In Biotic diversity in Southern Africa, ed. Huntley, B.J. Oxford University Press, Oxford. Pp. 137-147. Frankel, O.H. (1983). Evolutionary changes in small populations. In Conservation biology: an evolutionary - ecological perspective, eds Soule, M.E. and Wilcox, B.A. Sinauer, Mass. Pp. 135-149. Gillison, A.N. and Brewer, K.R.W. (1985). The use of gradient directed transects or gradsects in natural resources surveys. Journal of Environmental Management 20: 103-127. Gunatilleke, C.V.S. and Gunatilleke, I.A.U.N. (1981). The floristic composition of Sinharaja - a rain forest in Sri Lanka with special reference to endemics and dipterocarps. Malaysian Forester 44: 386- 396. Howard, P.C. (1991). Guidelines for the selection of forest nature reserves and their application nationally and locally. In Nature conservation in tropical forests: principles and practice, ed. Howard, P.C. Forest Department, Kampala. Pp. 69-85. Hunter, M.L. Jr, Jacobsen, G.L. Jr, and Webb, T. Jr (1988). Paleoecology and the coarse-filter approach to maintaining biological diversity. Conservation Biology 2: 375-385. 20 TUCN (1980). World conservation strategy: living resource conservation for sustainable development. TUCN/UNEP/WWF, Gland, Switzerland. 44 pp. IUCN (1992). Requirements for the preparation of management plans for nine wet zone conservation forests - Sri Lanka. A proposal submitted to the Forest Department, Ministry of Lands, Irrigation and Mahaweli Development. IUCN, Colombo. 56 pp. Kirkpatrick, J.B. (1983). An iterative method for establishing priorities for the selection of nature reserves: an example from Tasmania. Biological Conservation 25: 127-134. Kirkpatrick, J.B. and Brown, M.J. (1991). Planning for species conservation. In Nature conservation: cost effective biological surveys and data analysis, eds C.R. Margules and M.P. Austin. CSIRO, Australia. Pp. 83-89. Leader-Williams, N., Harrison, J. and Green, M.J.B. (1990). Designing protected areas to conserve natural resources. Science Progress 74: 189-204. Legg, C. and Jewell, N. (1992). A new 1:500,000 scale forest map of Sri Lanka. Forest and Land Use Mapping Project, Forest Department, Colombo. 12 pp. Mackey, B., Nix, H.A., Stein, J.A., Cork, S.E. and Bullen, F.T. (1989). Assessing the representativeness of the wet tropics of Queensland World Heritage Property. Biological Conservation 50: 279-303. MacKinnon, J., MacKinnon, K., Child, G. and Thorsell, J. (1986). Managing protected areas in the tropics. IUCN, Gland, Switzerland and Cambridge, UK. 295 pp. Mayr, E. (1969). Principles of systematic zoology. McGraw-Hill, New York. pp. Margules, C.R. (1989). Introduction to some Australian developments in conservation evaluation. Biological Conservation 50: 1-11. Margules, C.R., Nicholls, A.O. and Pressey, R.L. (1988). Selecting networks of reserves to maximise biological diversity. Biological Conservation 43: 63-76. Margules, C.R., Pressey, R.L. and Nicholls, A.O. (1991). Selecting nature reserves. In Nature conservation: cost effective biological surveys and data analysis, eds C.R. Margules and M.P. Austin. CSIRO, Australia. Pp. 90-97. Margules, C.R. and Usher, M.B. (1981). Criteria used in assessing wildlife conservation potential: a review. Biological Conservation 21: 79-109. McNeely, J.A. (1988). Economics and biodiversity: developing and using economic incentives to conserve biological resources. YUCN, Gland, Switzerland. 236 pp. McNeely, J.A., Miller, K.R., Reid, W.V., Mittermeier, R.A. and Wemer, T.B. (1990). Conserving the world’s biological diversity. TUCN, Gland, Switzerland; WRI, CI, WWF-US, and the World Bank, Washington, DC. 193 pp. Nicholls, A.O. (1991). Examples of the use of generalised linear models in analysis of survey data for conservation evaluation. In Nature conservation: cost effective biological surveys and data analysis, eds Margules, C.R. and Austin, M.P. CSIRO, Australia. Pp. 54-63. Sayer, J.A. and Whitmore, T.C. (1991). Tropical moist forests: destruction and extinction. Biological Conservation 55: 199-213. 21 Soule, M.E. (1983). Applications of genetics and population biology: the what, where and how of nature reserves. In Conservation, science and society. Unesco-UNEP. Pp. 252-264. Soule, M.E. (1987). Viable populations for conservation. Cambridge University Press, Cambridge. ?7? pp. Soule, M.E. and Wilcox, B.A. (1980). Conservation biology: an evolutionary - ecological perspective. Sinauer, Sunderland, Mass. 393 pp. TEAMS (1991). Review of forest management plans for environmental conservation. 2 volumes. Final report to Forestry Planning Unit, Ministry of Lands, Irrigation and Mahaweli Development. TEAMS, Colombo. Usher, M.B. (Ed.)(1986). Wildlife conservation evaluation. Chapman and Hall, London. 394 pp. Vane-Wnight, R.I., Humphries, C.J. and Williams, P.H. (1991a). Biodiversity reserves: setting new priorities for the conservation of wildlife. Parks 2: 34-38. Vane-Wright, R.I., Humphries, C.J. and Williams, P.H. (1991b). What to protect? - systematics and the agony of choice. Biological Conservation 55: 235-254. Wilcox, B.A. (1984). In situ conservation of genetic resources: determinants of minimum area requirements. In National parks, conservation and development: the role of protected areas in sustaining society, eds McNeely, J.A. and Miller, K.A. Smithsonian Institution Press, Washington, DC. Pp. 639- 647. Wijesinghe, L.C.A. de S., Gunatilleke, I.A.U.N., Jayawardana, S.D.G., Kotagama, S.W. and Gunatilleke, C.V.S. (1989). Biological conservation in Sri Lanka (a national status report). Natural Resources, Energy & Science Authority of Sri Lanka, Colombo. 64 pp. Wilson, E.O. and Francis, M.P. (Eds) (1988). Biodiversity. National Academy Press, Washington, DC. 521 Pp. World Conservation Monitoring Centre (1992a). Global biodiversity: status of the Earth’s living resources. Chapman & Hall, London. 594 pp. World Conservation Monitoring Centre (1992b). Assessing the conservation status of the world’s tropical forest: a contribution to the FAO Forest Resources Assessment 1990 (Draft). World Conservation Monitoring Centre, Cambridge, UK. 662 pp + 158 maps. World Conservation Monitoring Centre (1992c). Conservation status listing: Sri Lanka. WCMC, Cambridge, UK. 47 pp. 22 Annex 1 PLOT DESCRIPTION FORM DATE: NAME OF SITE: NO. OF SITE: LEGAL DESIGNATION: BRIEF DESCRIPTION OF SITE CONDITION: manseormorne. | | de eee GEOG. COORD. - LATITUDE - LONGITUDE TIME - START - END WEATHER - CLOUD COVER! - CONDITIONS? ALTITUDE (M) - MIN. - ak a oe ASPECT @ OM sd cal | soa Ea eed Ee ee 50: 70mm le pl i ei in sa ed eknorr coven: “eisess =| |] eons] gaillaon] 50% canopy removed) to direct observation 5 = 76-100% of mammals & birds Date of database entry: Signed: 23 Annex 2 SPECIES INVENTORY FORM DATE: NAME OF SITE: NO. OF SITE: LEGAL DESIGNATION: PLANTS!/ANIMALS? (delete as appropriate) ' For plants, the number of individuals exceeding 10cm DBH is recorded for each species. A plus indicates the presence of a species but with no individual exceeding 10cm DBH. 2 For animals, the number of individuals of all vertebrate groups except fishes (i.e. mammals, birds, reptiles and amphibians) and certain invertebrate groups (i.e.. butterflies, molluscs and termites which build mounds) directly observed or heard ig) recorded. A plus sign indicates that a species is present. In the case of indirect observations the following code is used: defaecation F rubbing site T B L burrow D lying site R feeding sign H = heard tracks 3 Species identified in the field are recorded by their scientific name or species code. | Unidentified species are collected for subsequent classification and recorded by their accession or herbarium number. | | Date of database entry: Signed: 24 Annex 3 RESOURCE USE DESCRIPTION FORM NAME OF SITE: : ; NO. OF SITE: TRANSECT NO: TRANSECT LENGTH: . km DATE: MEAT-HUNTING - guns (illicit) - dogs - gun trap - pitfall trap - deadfall trap - noose sprung noose OTHER (specify) a ee CHENA - temporary (1/2 yrs) (illicit) - permanent MINING - gemming (illicit) - other (specify) PLANTATION - tea - other (specify) SETTLEMENT - temporary (wadia) - permanent WILDFIRE - deliberate (in last 1-2 yrs) — natural - unknown BAMBOO —- licit - illicit - unknown FOOD - fruit - root - other (specify) KITUL - licit - illicit - unknown MEDICINE - fruit - root - other (specify) RATTAN - licit - illicit - unknown TIMBER (EX SITU) TIMBER (IN SITU) - illicit unknown OTHER (specify) ae ere Date of database entry: Signed: Annex 4 BIOLOGICAL DIVERSITY ASSESSMENT: AN EXAMPLE This annex provides an assessment of the biological value of nine forests selected from Galle and Matara districts. It is based on preliminary plant data obtained as part of the National Conservation Review. The data are held in the Environmental Information Management System (EIMS), maintained by the Environmental Management Division, Forest Department. EIMS is capable of performing a range of analyses, including those featuring here. 1. BIOLOGICAL DIVERSITY SUMMARY The total number of woody plant species recorded within each forest is given in Table 1, together with various other statistics concerning levels of endemicity and threat. Diyadawa, for example, has the most species, as well as endemic species, but the forest with most unique species is Kalubowitiyana. Kekanadura has fewest species, none of which is unique to this site. Similar statistics can be generated for animals recorded within these forests. Table 1 Summary of woody plant species diversity for selected forest in Galle and Matara districts. Unique species are those recorded only within a single forest. Nationally threatened species are those listed as such by Wijesinghe et al. (1989); globally threatened species are those registered as such by WCMC (1992c). Species Threatened species No Forest Families Genera Species Unique Endemic Un/End. National Global 497 Kalubowitiyana 116 119 142 19 48 1 6 4 37 Beraliya (Akuressa) 119 121 166 13 82 a 15 6 69 Dellawa 132 134 172 10 80 5 8 5 77 Diyadawa 128 129 174 7 85 2 15 6 178 Kanumuldeniya 71 73 82 4 36 0 5 3 190 Kekanadura 63 63 66 0 25 0 4 3 263 Masmullekele 89 90 105 5 43 1 7 4 329 Oliyagankele 73 715 91 5 50 3 9 6 388 Rammalakanda 123 126 161 18 72 7 13 9 od 2. DEFINING AN OPTIMAL NETWORK OF FORESTS Two procedures have been developed within EIMS to use species’ distribution patterns to identify a minimum number of forests in which all species are represented. The first method ranks forests in order of their contribution of species to the network (Table 2). Thus, the forest selected first in this example is Diyadawa, the one which is richest in species. Beraliya is ranked as second because, of the remaining forests, it has the highest number of additional species not already represented in the first site, and so on. The analysis demonstrates that eight of the nine forests need to be conserved in order that each species is represented in the network. Kekanadura does not contribute any additional species to the network, all of its species being found in one or more of the other forests. Various criteria can be applied to the model. For example, it may be desirable to ensure that each species is represented in at least two forests, or it may be necessary to consider only endemic species due to socio-economic constraints. 26 Table 2 Minimum network of forests requiring protection in order to conserve 314 species of woody plants. Forests are ranked according to their contribution, in terms of woody plant species, to the network. _—————————SSSaSa=a=a=ES=S=SE= OS __E—— eee Species represented in network Unrepresented species Rank No Forest New % New Cumulative No. % Total Total 1 77 Diyadawa 174 55.4 174 55.4 140 2 37 Beraliya (Akuressa) 52 16.6 226 72.0 88 3 497 Kalubowitiyana 35 11.1 261 83.1 53 4 388 Rammalakanda 22 7.0 283 90.1 31 5 69 Dellawa 11 3.5 294 93.6 20 6 178 Kanumuldeniya 10 3.2 304 96.8 10 7 263 Masmullekele 5 1.6 309 98.4 5 8 329 Oliyagankele 5 1.6 314 100.0 0 9 190 Kekanadura 0 0 314 100.0 0 The second method weights for rarity by ranking forests in order of their contribution of rare species to the network. In this example, Kalubowitiyana is ranked highest (Table 3) because it contains most rare species, despite its species diversity being lower than for some of the other forests (see Table 1). Diyadawa, the most species-rich forest, is ranked fifth. Table 3 Minimum network of forests requiring protection in order to conserve 314 species of woody plants. Forests are ranked according to their contribution of rare (i.e. unique) species of woody plants to the network. Unique Species represented in network Unrepresented Rank No Forest Species New % New CumNo. % Total Species 1 497 Kalubowitiyana 19 142 45.2 142 45.2 172 2 388 Rammalakanda 18 68 21.7 210 66.9 104 3 37 Beraliya 13 51 16.2 261 83.1 53 4 69 Dellawa 10 24 7.6 285 90.8 29 5 71 Diyadawa 7 9 2.9 294 93.6 20 6 263 Masmullekele 5 7 22 301 95.9 13 7 329 Oliyagankele 5 6 1.9 307 97.8 7 8 178 Kanumuldeniya 4 7 2.3 314 100.0 0 9 190 Kekanadura 0 0 0 314 100.0 0 ——llEESEEeEEeESSsS=_N™_E_E™““™E™“™=—EEEaaEeEEEEEEeee_O3 _ ee ——— This example demonstrates just a few of the analyses to which species data can be subjected. Procedures can be modified accerding to conservation priorities and constraints on land use as appropriate. NOTES 28 PART B SOIL CONSERVATION AND HYDROLOGY 1. IMPORTANCE OF FORESTS FOR SOIL CONSERVATION AND HYDROLOGY 1.1 INTRODUCTION Forests contribute to the stability of watersheds by protecting the soil surface from the direct impact of intensive tropical rain storms. Rainfall is intercepted by the vegetation canopy and the balance falls on the forest floor. Additional storage is provided by organic litter and the surface soil, which are very porous due to the action of roots and the soil fauna. Water rapidly percolates vertically into the subsoil and drains laterally into the streams. The removal of forest cover exposes the soil to erosion and compaction. Reduced vegetation cover allows more water to reach the soil more rapidly. Less infiltration caused by surface sealing and compaction increases surface run-off and, hence, erosion which quickly reduces the depth of the soil and its capacity to store water. For example, an average of 300 mm of soil has been lost from Sri Lanka’s upper Mahaweli watershed during the last 100 years (Krishnarajah, 1982), thereby reducing the capacity of the soil profile to store water by about 60 mm. With increasing surface run-off, the ground water table is progressively lowered and processes of desertification set in. Steep hill sides become more prone to landslides as tree roots rot, perennial streams become ephemeral (intermittent), and floods more frequent with increased surface run-off and accumulating sediment in the river beds. Inevitably, the repercussions of increased sediment loads and higher flood peaks are experienced kilometres downstream from the deforested headwaters. In the long term, sedimentation of reservoirs may substantially reduce their capacity to store water for hydropower and irrigation, jeopardizing the ago-industrial base of the economy. 1.2. TROPICAL FORESTS AND SGIL CONSERVATION Deforestation is a major cause of soil erosion in many tropical countries. The popular view that trees check soil erosion, particularly when planted in stands, is scientifically proven. Besides erosion, deforestation can lead to various problems downstream, such as reservoir siltation, sedimentation of irrigation works, and higher flood peaks. It is necessary to distinguish between surface erosion, gully erosion and landslides because the ability of forests to control these various types of erosion differs considerably. In a review of 80 studies of surface erosion in tropical forests and tree crop systems, Wiersum (1984) concluded that surface erosion is negligible in those systems where the soil surface is adequately protected from the impact of raindrops by a well-developed litter and herb layer. Erosion is slightly higher if the understorey is removed, but rises dramatically with the removal of the litter layer. Studies in India have shown that sediment loads are about five times higher in deforested than forested catchments (Haigh et al., 1990). In Kanneliya, which lies in Sri Lanka’s wet zone, it has been shown that sediment loads are lower (0.15 t ha! yr') for natural forest than selectively logged forest (0.27 t ha! yr‘) (Ponnadurai et al., 1977). Sediment loads for pines (0.49 t ha’ yr’) and grasslands (3.3 t ha” yr") are still higher (Gunawardena, 1989a), but nowhere near the high loads (100 t ha’ yr') recorded from formerly forested, badly managed agricultural land (Stocking, 1984). Accelerated soil erosion on hill sides shortens the effective life span of reservoirs. A study of 17 major reservoirs in India, for example, shows that they are filling at about three times the expected rate because of the vast areas deforested (Tejwani, 1977). Similarly, high rates of siltation (13,500 nr km” yr") have been recorded from deforestation in Tanzania (Kunkle and Dye, 1981). In Sn Lanka, serious concer was expressed in 1873 about soil erosion caused by indiscriminate conversion of forest for plantation agriculture. A recent study shows that 15 millions tons of sediment passed through the Peradeniya gauging station in the upper Mahaweli watershed during the period 1952-1982 (NEDECO, 1984). Almost 44% of the capacity of the Polgolla Barrage, sited about 4 km downstream from this station, was silted by 1988, only 13 years after its commissioning (Perera, 1989). There is every indication that the reservoirs built under the Accelerated Mahaweli Development Project are silting up at a much faster rate than predicted at the feasibility stage. 29 Hill roads cause many landslides and are a major source of sediment. It has been estimated that roads in the Central Himalaya produce 430-550 m3 km" yr'' of sediment (Haigh et al., 1990). Similarly, studies carried out in Sri Lanka have identified landslides as a major source of sediment (Gunawardena, 1987). 1.3 HYDROLOGICAL IMPORTANCE OF TROPICAL FORESTS The influence of forests on the hydrological cycle has been studied for a long time, but many contradictory views persist. While there is general agreement about the beneficial effect of forest on microclimate, air and water purification processes, and control of run-off and erosion, the influence of forest and forest reclamation on the water balance within a region remains the subject of much controversy. The way in which forest influences rainfall, water yield and flooding is discussed in the following three sections. 1.3.1. Rainfall Opinions differ regarding the effect of forest on rainfall. Some authorities maintain that forest has marginal effect on rainfall, while others consider that the removal of forest results in desertification. The consensus among hydrologists is that changes in land use have no effect on rainfall, except in extensive areas such as the tropical watersheds of the Congo and Amazon (Bruijnzeel, 1986; Meher-Homji, 1988; Pereira, 1989). Although the impact of deforestation on rainfall is contentious and difficult to quantify at present, there is rather more agreement about its repercussions on storm intensity and associated soil erosion (Meher-Homji, 1988). Dickenson (1980) reviewed a number of studies and concluded that deforestation could increase the intensity and reduce the duration of tropical rainfall, enhancing run-off without necessarily changing the total amount of rain falling during a given period. Long-term observations of private rubber plantations in Malaysia provide circumstantial evidence of profound and lasting changes arising from forest clearance (Unesco, 1978). Total rainfall remained unchanged following large-scale forest clearance: rainfall frequency decreased but its intensity increased. Similarly, Meher-Homji (1980) has shown that large-scale deforestation tends to reduce the number of rainy days rather than the total volume of the rainfall. The result is enhanced soil erosion and, consequently, a higher incidence of major disasters. If rainfall remains unchanged in total amount but becomes sporadic, with a higher incidence of torrential downpours, the consequences are more flooding and greater siltation of river beds and reservoirs. Greater erosion reduces the capacity of soils to store water, with the result that streams dry up more quickly if drought conditions persist. 1.3.2. Water yield A common misconception is that the complex of forest soils, roots and litter behaves like a sponge, soaking up water during rainy spells and releasing it gradually during dry periods. Although forest soils generally have higher infiltration and storage capacities than soils with less organic matter, most of this water sustains the forest rather than stream flow. Moreover, appreciable quantities of rainfall (20%) are intercepted by the canopies of tropical forests, 30% in the case of wet zone forests in Sri Lanka (Ponnadurai et al., 1977), and released back into the atmosphere. Hibbert (1967) was the first to review the effect of forest clearance on water yield. On the basis of 39 studies, he concluded that: a) reduction of forest cover increases water yield, b) establishment of forest cover on sparsely vegetated land decreases water yield, and c) response to treatment is highly variable and, for the most part, unpredictable. Subsequent work by Bosch and Hewlett (1982), however, has shown that water yield can be predicted. They examined 94 case studies, including those reviewed by Hibbert, and concluded that coniferous forest, deciduous hardwood forest, brush and grass cover, in that order, have decreasingly less influence on water yield from the source area compared to bare ground which has none. Other work by Hamilton and King (1983) and by Oyabande (1988) supports these findings. The only undisputed case of forests having a positive influence on water yield is along coastal belts and at high elevations where the incidence of cloud or fog is high. Such ’cloud’ forests, which comprise almost 5% of the total area of closed moist tropical forest (Bruijzeel, 1986), intercept significant quantities of "horizontal precipitation’ from cloud or fog. Typically, in the humid tropics, this represents from 7-18% of normal, 30 vertical precipitation recorded during rainy seasons to over 100% during dry seasons. Preliminary studies conducted at 2100 m in Sri Lanka have shown that cloud forests contribute an additional 17% of normal precipitation (Mowjood and Gunawardena, 1992). Thus, conversion of cloud forest to agricultural land in the humid tropics will cause a marked and usually irreversible reduction in stream flow and ground water storage. This, and the fact that they often constitute unique ecosystems, provides a strong case for their conservation. 1.3.3. Floods Forest clearance in tropical uplands has often been considered to be the major cause of severe flooding, particularly in Asian countries such as northern India, China and the Philippines (Bruijzeel, 1986), and it is widely believed that upland forestation represents the solution to this pressing problem. On the other hand, Bosch and Hewlett (1982) concluded from their review of mainly North American research that deforestation does not significantly increase the volume of water flowing in large streams, although significant increases in stream flow usually occur in small streams and these are related to the proportion of the catchment area that is cleared. It has been shown that the presence or absence of forest has little effect on the magnitude of flooding, which is mainly the result of too much rain in too short a time. The degree of flooding is also a function of basin and channel geometry. Recent studies in the tropics still suggest, however, that afforestation greatly reduces flood peaks by reducing surface flow. Studies conducted at Kanneliya, in Sri Lanka's low-country wet zone, show that the ratio of run- off/rainfall was increased by 29% in a logged catchment (Ponnadurai er al., 1977). Comparative studies conducted at Wewelthalawa in the mid-country wet zone show that pine plantations produce 48% less surface run-off compared to grasslands (Gunawardena, 1989b). Similarly, in India, deforestation may account for the increase in extent of flood prone land from 20 to 40 million ha in the ten years up to 1989 (Haigh ef al., 1988). 31 2. ASSESSING FORESTS FOR SOIL CONSERVATION AND HYDROLOGY 2.1 INTRODUCTION The importance of forests for soil conservation and hydrology is assessed on the basis of the four criteria identified in the previous section as follows: Soil Conservation - soil erosion Hydrology - flood hazard - protection of headwaters - fog interception Similar criteria have been identified by IUCN (1986) for the selection of areas in need of protection for their hydrological functions. Whereas forest is entirely beneficial for the prevention of erosion, its presence has both positive and negative impacts on the hydrological cycle as discussed in Section 1.3. Thus, the importance of forest for soil conservation is given priority weighting over its hydrological value in the methodology. 2.2 SOIL EROSION Two approaches to assess erosion at the reconnaissance level have been identified by Morgan (1980). The first is based on regional variations of various erosion indices, such as gully density and drainage density. The second uses rainfall to calculate an erosivity index based on kinetic energy. The latter, physically-based method has been universally accepted as a standard technique. It was used in a case study to assess the erosion risk of Peninsular Malaysia (Morgan, 1980). A similar procedure has been adopted in the present study to determine rainfall erosivity. In addition, some of the parameters usually considered in more detailed erosion assessment procedures are also incorporated in the methodology. 2.2.1 Assessment of the importance of forests for soil conservation Soil erosion by water is influenced most by the amount of rainfall. In regions of very low mean annual rainfall, little erosion is caused by the rain running off the land because most of it is absorbed by the SOIL and vegetation. In other regions of very high rainfall (> 1000 mm yr’), as in Sri Lanka, dense forest typically develops which protects the soil from erosion by water. Removal of this natural forest cover usually results in severe erosion. However, it is not only the amount of rainfall that affects soil erosion but also its intensity. The intense downpours characteristic of the tropics have a very greater impact than the gentler rains of temperate climates. The zone of intensive or ‘destructive’ rain lies between approximately 40° North and 40° South within which Sri Lanka is situated. The severity of soil erosion also depends on the susceptibility of a given soil to erosion. This attribute of soil is influenced mostly by its physical characteristics, such as texture, structure, infiltration capacity and permeability. Soil structure is largely determined by the organic matter content of the soil: the higher the level of organic matter, the less susceptible the soil is to erosion. Thus, the amount of erosion depends upon a combination of the power of the rain to cause erosion and the ability of the soil to withstand erosion (Hudson, 1981). In mathematical terms, soil erosion is a function of the 32 erosivity of the rain and the erodibility of the soil, or Erosion = f(Erosivity)(Erodibility). This relationship is expressed by the Universal Soil Loss Equation (USLE), developed by Wischmeir and Smith (1965) and widely accepted as a predictive equation to estimate the mean annual soil erosion. In its basic form, USLE is: ASE Ke val yivonave yridibtrches 28 atedugorwpm) 6 wt daduleny: beers! nancies Se eppind tenia (1) where A = mean annual soil loss (t ha” yr‘), E = erosivity, and K = erodibility. Erosivity can be defined as the potential ability of the rain to cause erosion. For given soil conditions, one storm can be compared quantitatively with another based on a numerical scale of values of erosivity. Erodibility is defined as the vulnerability of the soil to erosion and, for given rainfall conditions, one soil condition can be compared quantitatively with another, based on a numerical scale of values of erodibility. Erodibility has three components: first the fundamental or inherent characteristics of the soil, such as texture, structure and organic matter content, which can be measured in the laboratory; secondly, topographic features, especially the slope of the land; and thirdly, the way in which the soil is treated or managed. In the present study, the third factor is assumed to be a constant because the only type of land use under consideration is natural forest. Thus, the following equation is used to predict soil loss from natural forest: Ac=sE:K.S?0U1) goiuel™ ve bedinowh exaieanng od? gaiew benkerrsiok aiaqeis aces et (2) where S = slope factor. Estimating rainfall erosivity Rainfall erosivity is a function of the size and velocity of the rain drops. It can be predicted from its proven relationship with rainstorm intensity (Wischmeier and Smith, 1958; Hudson, 1981; Lal, 1976; Morgan et al., 1982). But rainfall intensity data are not available in many countries, including Sri Lanka, due to a lack of recording rain gauge stations'. This problem can be overcome by developing models to predict rainfall erosivity from meteorological parameters, such as total annual rainfall and the Fournier Index, which are commonly available from non-recording rain gauge stations (Arnoldus, 1980). A similar approach has been used in Sri Lanka (Premalal, 1986). Ten meteorological stations with recording Tain gauges were selected to sample the mid- and up-country zone (i.e. above 300 m) and 12 for the low-country zone (i.e. below 300 m). An erosivity index was calculated using the procedure described by Hudson (1981). A regression equation was derived for the mid- and up-country to predict erosivity from mean annual rainfall, modified Fournier Index and altitude. For the low-country, it was found that mean annual rainfall alone is strongly correlated with erosivity. However, these two equations for the mid/up-country and low-country zones can be used to estimate erosivity for a given forest only if it has a single value for altitude and lies within one zone. Consequently, the original data for mid-, up- and low-country were pooled and a multiple step-wise regression performed on erosivity with a variety of independent variables, including mean annual rainfall, modified Fournier Index and altitude. This showed that mean annual rainfall alone accounts for about 75% of the variation. The resultant regression equation is given below: Bj 972:75%4+:9:95:MAR; (Ri-v0:75)in. cnly those while Gee. be aimed .cr ae (3) ‘A recording rain guage automatically records the amount of rainfall with time. 33 where E = Erosivity (J m? Yr'), and MAR = Mean annual rainfall (mm). Two hundred and twenty ’station years’ of rainfall data were used to derive the above equation. This is a considered to be an adequate sample size, being very much larger than the 11 ’station years’ of data used to derive a similar erosivity index for Peninsular Malaysia (Morgan, 1980). It should be emphasized that the above equation (3) is valid only for Sri Lanka. An estimate of mean annual rainfall is a prerequisite to calculating erosivity for a given forest using equation 3. The isohyetal method (Linsley et al., 1982) was used to estimate mean annual rainfall (see Annex 1) in preference to Theissen’s polygon method of weighting which is not suitable, particularly in the case of smaller forests. Estimating soil erodibility The erodibility of the major soil types in Sri Lanka is given by Joshuwa (1977). Erodibility values are obtained from the soil map which is superimposed on the forest map in order to assign the respective erodibility value to each forest. This is necessarily crude because more detailed soil maps are not available. The alternative of surveying soils within individual forests to obtain a more detailed information on soil type was not warranted in view of the time involved in obtaining adequate sample sizes. Furthermore, since there is only a twofold difference between the highest and lowest erodibility values of the major soils in Sri Lanka, more precise estimates would not make a significant difference to the overall assessment of the hydrological value of forests. Determining slope factor The mean slope is determined using the procedure described by Fleming (1975). Each forest is located on the 1:63,360 series of topographic maps and overlaid by a grid, effectively dividing it into smaller units. The perpendicular distance is measured from the contour at each grid point to the nearest stream (or paddy field when there is no stream in the vicinity). This value is divided by the altitudinal difference to derive the slope from which an arithmetic mean is obtained. The procedure, described in Annex 1, is modified in the case of dry zone forests where stream density is lower due to the reduced rainfall and to the terrain being less steep. In such cases, slope is estimated by dividing the difference in altitude between two points by the distance for a range of different aspects and calculating a mean value. The slope factor in equation 2 has a value of 1.0 when the slope is 9%. Soil erosion increases exponentially with the slope. It has been widely accepted that in the tropics the exponential term for the slope is equal to 2 (Hudson, 1981). Thus, the slope factor in the USLE should be substituted by mean slope as follows: SES i(S/9) yen Sete rig gira 8 Wet ue ior Leet See (4) where S = slope factor, and S = mean percentage slope. 2.2.2 Ranking forests for soil erosion Substitution of the erosivity, erodibility and slope factor into equation 2 enables the mean annual soil loss to be estimated for a given forest under standard conditions, which are as follows: (i) slope length is 22.6 m, (ii) land use is bare cultivated fallow, and (iii) land is ploughed up and down the slope. In other words, the value of the mean annual soil loss represents the worst case scenario when the forest is removed and the land is badly managed. These erosion values can be reduced substantially by introducing conservation practices and covering the bare soil with vegetation. Forests with very high soil erosion rates are those most important for soil conservation; they rank high in priority for protection measures. 34 2.3 HEADWATERS Policy within the Sri Lanka’s Commission on Land Use demands that (i) stream sources and headwaters of river systems, (ii) water divides, and (iii) stream reservations and riparian land be protected for soil and water conservation purposes. The importance of a forest for protecting stream sources is evaluated by counting the number of streamlets originating from within a forest using the 1:63,360 topographic series of maps. A second criteria used is the number of major river catchments protected by the forest, based on the standard system of river catchments defined by the Irrigation Department (Navaratne, 1985). Assessing the importance of a forest in terms of its proximity to the headwaters of a river and for protecting stream reservations and riparian land is based on the distance from the forest to the outlet of the river. Distance was selected as the criterion because water originating from a given forest will sustain flora and fauna throughout its course to the sea. It is measured from the headwaters stream closest to the centre of the forest, along its course to the outlet, using the 1:63,360 maps. In summary, the assessment is based on: (i) the number of streamlets originating from the forest, (ii) the number of river catchments protected by the forest, and (iii) the distance (km) from the headwaters stream nearest to the centre of the forest to the outlet. 2.4. FLOOD HAZARD In the absence of flow records for a forest, a preliminary estimate of the mean annual flood or other flood statistics may be obtained from the relationship between floods and catchment characteristics using maps (NERC, 1975). However, this indirect method is generally less reliable than any direct analysis of flood Statistics. Research in the UK has provided a set of equations relating mean annual flood to catchment area alone, with provision for regional variations. Other investigations have taken into account climate and slope (NERC, 1975). The use of multiple regression techniques to study the relationship between mean annual flood and its coefficient of variation and catchment characteristics is widely accepted. One reason why such techniques have been so useful in assessing flood hazard is the strong relationship between mean annual flood and other variables, such as slope and channel network (i.e. number of streams per unit area), which can be estimated easily from topographic maps. 2.4.1 Estimating flood hazard from catchment characteristics In the absence of any previous research on estimating flood hazard from catchment characteristics for Sri Lankan conditions, a model developed in the UK has been adopted for the present study. This is justified because the behaviour of most of the variables concerned with the flooding component of the hydrological cycle appear to be the same. Details of the analysis and the flood prediction equations are given in a Flood Studies Report (NERC, 1975). In the UK study, catchment characteristics were obtained from 1:63360 and 1:25000 topographic maps. Climatic variables were also used. Of these variables, only those which can be measured or estimated accurately were selected for this study, namely mean annual rainfall, catchment area (or forest area for the purposes of this study) and stream frequency. These three variables account for 86% of the variation of mean annual flood. 35 Most of the rainfall stations in Sri Lanka have more than 30 years of rainfall data from which mean annual rainfall can be calculated. Area can be measured using a planimeter. Stream frequency is measured by counting the number of channel junctions within a forest, using the 1:63,360 topographic series, and dividing by its area. A stream without any junctions is given a value of one, as if it had a single junction. It is best to work progressively up along each tributary; the running total is noted at each major junction. This value is then divided by the area and presented as stream junctions/knr (adapted from NERC, 1975). Stream frequency was selected in preference to drainage density which cannot be reliably sampled by grid or other methods (NERC, 1975). It is simpler to measure and it is strongly correlated with drainage density (Melton, 1958). Moreover, 1:63,360 maps are sufficiently detailed for measuring stream frequency. In the UK study, there was a very high correlation (0.89) between stream frequency measurements from 1:63,360 and 1:25,000 maps for 55 catchments. The predictive equation for mean annual flood is given below: BESMAF = 4.53*10° AREA°™ STMFRQ*™! SAAR“ ww we ee ee ee eee (5) where BESMAF Mean annual flood (m s"'), AREA = Area (kr), STMFRQ = Steam frequency (stream junctions km”), and SAAR = Mean annual rainfall (mm) It should be noted that the values estimated from the equation will not provide absolute values of mean annual flood since the regression equation was derived from a different set of data. However, the values indicate the "flood response" of each forest and should be used for ranking purposes only. 2.5 FOG INTERCEPTION AT HIGHER ALTITUDES Altitude has been identified as a criteria for identifying forests for conservation in the past mainly because the headwaters of major rivers originate from higher elevations. However, the importance of forests for protecting stream sources and headwaters of river systems is assessed using measures other than altitude, as described in Section 2.3. Recent research has shown that altitude has a direct effect on the hydrological cycle through the contribution of additional moisture from cloud forests (Juvik et al., 1978, Gunawardena, 1991; Mowjood and Gunawardena 1992). Since a given forest may extend over a range of altitudes, altitude needs to be adjusted accordingly, using a procedure similar to the isohyetal method (Linsley ef al., 1982). Calculation of the mean elevation enables the percentage of additional moisture contributed through fog interception to be calculated using the following equation, which was derived from experimental studies conducted in Hawaii: Nee SESE SIOOARK oR or ee ee (6) where Y = additional percentage moisture contributed by fog, and X = altitude (m). Mean annual rainfall for a given forest is multiplied by Y (equation 6) to estimate the annual fog contribution (mm). The equation is valid for forests above 1500 m, there being no significant fog interception at lower altitudes. Application of this equation, derived from fog studies in Hawaii, to conditions in Sri Lanka is justified elsewhere (Gunawardena, 1991). Similar studies are underway in Sri Lanka which, once completed, will enable the coefficients in equation 6 to be modified. 36 2.6 EVALUATION The following evaluation is applied to all natural forests except those which have already been designated for strict protection by the government because they lie in the immediate catchment of hydroelectric and water supply reservoirs. 2.6.1 Preliminary ranking Forests are ranked according to four main criteria as a measure of their importance in soil conservation and hydrology. The four criteria are: (i) erosion hazard, (ii) importance as headwaters of rivers, (ili) flood hazard, and (iv) additional moisture contributed by fog interception. Erosion hazard is measured for each forest as mean annual soil loss (t ha! yr!) using equation 1 which treats (a) erosivity, (b) erodibility and (c) slope as independent variables. The importance of a forest for protecting headwaters of rivers is measured in terms of a) the number of streamlets originating from the forest, b) the number of river catchment areas protected by the forest, and c) the length along the river from the centre of the forest to the river outlet. Each forest is ranked in descending order for criteria a, b, and c, separately. The values of the three columns of ranks are added horizontally, and the lowest value is assigned the highest rank. If two values are equal, priority is given in the following order: a), b), c). Mean annual flood, which is used as an index of flood hazard, is estimated for each forest by substituting (a) mean annual rainfall, (6) area and (c) stream frequency in the predictive equation 5. Fog interception is measured as the additional moisture (mm yr’) contributed by a forest (above 1,500 m). 2.6.2 Final ranking The four main criteria (Section 2.5.1) are classified into two groups, namely: (i) soil conservation (criterion i) (ii) hydrological importance (criteria ii, iii, and iv) highest. If there are two equal values, priority is given in the following order: criteria ii, iii, iv. Ranks for soil conservation and hydrological importance are added and their values ranked,the lowest value being ranked highest. If two values are equal, priority is given to soil conservation (see Section 1.3). A worked example for selected forests in Matara and Galle districts is given in Annex 1. 2.7 CONSTRAINTS The methodology developed for this study is adequate for a rapid assessment of the importance of forests for soil conservation and hydrology. Although some field checking is carried out to validate the results, subjectively interpreting them as necessary, more detailed, quantitative studies of soil and hydrological conditions in Sri Lanka are required in order to refine the methodology. Plans are underway to examine soil erosion and hydrology in the upper catchment areas of the country using a Geographic Information System. Once the relevant data have been digitised, they can be used to verify the findings of the present study. 37 REFERENCES Amoldus, H.M.J. (1980). An approximation of the rainfall factor in the universal soil loss equation. Assessment of Erosion. John Wiley & Sons, Chichester. Pp. 127-132. Bosch, J.M. and Hewlett, J.D. (1982). A review of catchment experiments to determine the effect of vegetation changes on water yield and evaporation. Journal of Hydrology 55: 3-23. Bruijnzeel, P.S. (1986). Environmental impact of deforestation in the humid tropics: a watershed perspective. Wallaceana 46: 3-13. Dickinson, R.E. (1980). Effects of tropical deforestation on climate. In Browning in the wind: deforestation and long-range implications. College of William and Mary, Department of Anthropology, Williamsburg, USA. Pp. 411-441. Fleming, G. (1975). Computer simulation of hydrology. Elsevier, New York. Pp. 70-78. Gunawardena, E.R.N. 1987. Computer simulation of runoff and soil erosion from small agricultural catchments in Sri Lanka. Ph.D. thesis, Silsoe College, UK. Pp. 147-155. Gunawardena, E.R.N. (1989a). Identification of landuse and physiographic parameters in computer simulation of soil erosion. In Agricultural engineering: land and water use, eds Dodd A. and Grace, M. Balkema Publications, Rotterdam. Pp. 757-764. Gunawardena, E.R.N. (1989b). Hydrological and soil erosion studies on Pinus in Sri Lanka. In: Reforestation with pinus in Sri Lanka, eds Gunasena,H.P.M., Gunatilleke S. and Perera, A.H. Proceedings of a Symposium organised by University of Peradeniya and the British High Commission on behalf of the Overseas Development Administration, UK, 15-16 July, 1988, Kandy. Pp. 46-55. Gunawardena, E.R.N. (1991). Priorities in forest hydrology research in the uplands. In Multipurpose tree species in Sri Lanka: research and development, ed. Gunasena, H.P.M. Pp. 19-29. Gunawardena, E.R.N. and Taylor, J.C. (1988). Application of the Stanford watershed and sediment models to small agricultural catchments in Sri Lanka. In Computer methods and water resources: fluvial hydraulics. Computational Publications, UK. Pp. 247-259. Hamilton, L.S. and King, P.N. (1983). Tropical forested watersheds: hydrological and soils response to major uses or conversions. Westview Press, Boulder, USA. 155 pp. Haigh, M.J., Rawat, J.S. and Bisht, H.S. (1988). Hydrological impact of deforestation in the central Himalaya. In Hydrology of mountain areas. Pp. 419-433. Hibbert, A.R. (1967). Forest treatment effects on water yield. Proceedings of the International Symposium on Forest Hydrology. Pergamon Press, Oxford. Pp. 527-543. Hudson, N.W. (1981). Soil conservation. Betsford, London. Pp. 33-38, 47-74. IUCN, (1986). Managing protected areas in the tropics. IUCN, Gland, Switzerland. Pp. 46-47. Joshuwa, W.D. (1977). Soil erosive power of rainfall in the different climatic zones of Sri Lanka. [ASH-AISH Publication No. 122. Pp. 51-61. Juvik, J O. and Eker, P.C. (1978). A climatology of mountain fog on Mauna Loa. Hawaii Island. Water Resources Research Centre, University of Hawaii, Technical Report No. 118. 70 pp. 38 Krishnarajah, P. (1982). Soil erosion and conservation in the upper Mahaweli watershed. Paper presented at the Annual Session of the Soil Science Society of Sri Lanka, 13 November 1982. 15 pp. Kunkle, S.H. and Dye, A.J. (1981). The effects of forest clearing on soils and sedimentation. In Tropical agricultural hydrology, ed. Lal, R. and Russell, E.W. Pp. 99-109. Lal, R. (1976). Soil erosion on alfisols in western Nigeria. Effects of rainfall characteristics. Geoderma 16: 389-401. Linsley, R.K., Kohler, M.A. and Paulhus, J.L.H. (1982). Hydrology for engineers. 3rd Edition. McGraw- Hill, New York. Pp. 71-74. Meher-Homji, V.M. (1980). The link between rainfall and forest clearance: case studies from western Kamataka. Transactions of the Institute of Indian Geographers 2: 59-65. Meher-Homji, V.M. (1988). Effects of forests on precipitation in India. In Forests, climate and hydrology, eds Reynolds E.R.C. and Thompson F.B. The United Nations University, Tokyo, Japan. Pp. 51-77. Melton, M.A. (1958). Correlation structure of morphomeric properties of drainage systems and their controlling agents. Journal of Geology 66: 442-460. Morgan, R.P.C. (1980). Soil erosion. Longman, UK. 113 pp. Morgan, R.P.C., Morgan, D.D.V. and Finney, H.F. (1982). Stability of agricultural eco-systems: documentation of a simple model for soil erosion. Soil erosion assessment. IJASA Collaborative Paper CP-82-59. Mowjood, M.I.M. and Gunawardena, E.R.N. (1992). Designing and testing of a rotating screen fog collector. Agricultural Engineering 4(1). In press. Navaratne, N.M.G. (1985). Hydrometric network. In Some aspects of water resources of Sri Lanka with special reference tc hydrology. The National Committee of Sri lanka for the International Hydrological Programme. Irrigation Department, Colombo, Sri Lanka. pp 1-12. NEDECO (1984). Sediment transport in the Mahaweli Ganga. Irrigation Department, Colombo. 74 pp. NERC (1975). Flood studies report. Vol. 1. Hydrological studies. Whitefrias Press, London. 550 pp. Oyabande, L. (1988). Effects of tropical forests on water yield. In Forests, climate and hydrology, Eds Reynolds, E.R.C. and Thompson, F.B. The United Nations University, Tokyo, Japan. Pp. 16-49. Pereira, H.C. (1989). The role of forests in watershed management. In Policy and practice in the management of tropical watersheds, ed. Pereira, H.C. Westview Press. Pp. 57-77. Perera, M.B.A. (1989). Planned landuse for the tea country. Sri lanka Journal of Tea Science 58(2): 92-103. Ponnadurai, D.K., Gomez, G.E.M. and Kandiah, A. (1977). Effect of selective felling on the hydrological parameters of a wet zone catchment. Paper presented at the Asian Regional Meeting of the International Hydrological Programme National Committee of Unesco and WHO Regional Meeting of Working Group on Hydrology, Colombo, Sri Lanka. 10 pp. Premalal, W.P.R. (1986). Development of an erosivity map for Sri Lanka. A research report submitted for the requirement of the B.Sc. degree. Department of Agricultural Engineering, University of Peradeniya, Sri Lanka. 65 pp. 39 Stocking, M.A. (1984). Land use planning - Phase II, Sri Lanka. UNDP/FAO AGOF: SRL/84/032. Report submitted to Ministry of Lands and Land Development, Colombo. 55 pp. Tejwani, K.G. (1977). Trees reduce floods. Indian Farming 26(11): 57. Unesco (1978). Tropical forest ecosystems. Unesco/UNEP/FAO, Paris. Wiersum, K.F. (1984). Surface erosion under various tropical agroforestry systems. In Proceedings of the Symposium on Effect of Forest Landuse on Erosion and Slope Stability, eds. O’Loughlin, C.L. and Pearce, A.J. Pp. 231-239. Wischmeir, W.H. and Smith, D. (1958). A rainfall erosion index for a universal soil equation. Journal of the Soil Science Society of America 23: 246-249. Wischmeir, W.H. and Smith, D. (1965). Predicting rainfall erosion losses from croplands east of Rocky Mountains. US Department of Agriculture, Agricultural Handbook 282. 47 pp. Annex 1 RANKING FORESTS FOR SOIL CONSERVATION AND HYDROLOGY This annex provides a worked example for nine forests selected from Galle and Matara districts. It is based on preliminary data obtained as part of the National Conservation Review. 1. EROSION HAZARD (a) Estimating rainfall erosivity Rainfall erosivity is estimated from the following equation: Bi = 972.75: +9195 MAR, (R2\=.0:75) ie ve oe. + cea (1) where E = Erosivity (J m? Yr"), and MAR = Mean annual rainfall (mm). The isohyetal method is used to estimate mean annual rainfall. This is illustrated in Figure 1 for Dellawa using mean annual rainfall data from nearby rain gauge stations (Table 1). Data for each station are plotted on a Table 1 Data from rain gauge stations in the vicinity of Dellawa Rain gauge station Mean annual Location Rainfall (am) = ———______ Latitude Longitude 1. Panilkanda Estate 3208 06-21-33N 80-37-38E 2. Tawalama 4731 06-20-28N 80-21-22E 3. Millawa Estate 3749 06-17-35N 80-27-41E 4. Morawaka 3386 06-15-25N 80-29-25E 5. Opatha 4080 06-16-05N 80-24-29E 6. Vedagala 3564 06-27-15N 80-25-32E 7. Dependene Group 3483 06-27-45N 80-32-56E 8. Lauderdale Group 3478 06-25-08N 80-36-23E 1:63,360 map, enabling contours of equal rainfall (isohyets) to be drawn. Mean annual rainfall is estimated for each area between adjacent isohyets, weighted by its percentage area. Summation of the mean values for each area provides an estimate of mean annual area for the entire forest, as follows: A (Ry, + Rg)/2 + B (Ry + Ro/2 + C(Re + Rp) + MAR = - (2) 100 where MAR = Mean annual rainfall, R,, Rg, Rc, and Rp are the rainfall isohyets, and A, B, and C are the % areas enclosed within isohyets R, - Rp, Rg - Rc, and Re - Rp, respectively. 41 witirsme pat aqupo' MORAWAK KORALE DISTRICT Mean annual rainfall isohyets for Dellawa Figure 1 42 Substituting the values from Figure 1, the mean annual rainfall for Dellawa is calculated as follows: 3.1(4050) + 14.2(3950) + 20.8(3850) + 23.5(3750) + 34.8(3650) + 3.4(3550) MAR a RE Ea RE SE OE eS AE ES 100 3759 mm The value for mean annual rainfall is substituted in equation 1 in order to calculate an erosivity value for Dellawa as follows: E 972.75 + (9.95 x 3759) 38375 J m? yr! The erosivity index (383.75) is obtained by dividing the erosivity value by 100. It indicates the ability of the rainfall to cause soil erosion. Mean annual rainfall and the respective erosivity values are given in Table 2 for each forest. Table 2 Mean annual rainfall and erosivity index for selected forests in Galle and Matara districts —S”RF—E—Dz236—6808?80800O————————— OEE eee Forest Mean annual Erosivity index rainfall (mm) 1. Beraliya (Akuressa) 2671 275.49 2. Dellawa 3759 383.75 3. Diyadawa 3410 349.02 4. Kalubowitiyana 4189 426.53 5. Kanumuldeniya 2432 251.71 6. Kekanadura 1721 180.97 7. Masmullakele 2076 216.29 8. Oliyagankele 2465 254.99 9. Rammalekanda 2837 292.01 (b) Estimating soil erodibility The major soil type(s) within a forest can be determined by superimposing the forest boundary onto the soil map (published by the Survey Department of Sri Lanka). Erodibility values of the major soil types are given by Joshuwa (1977). If there is more than one type within a forest, the erodibility value should be weighted according to the area covered by the different soils. All forests selected in this example have red yellow podzolic soils, for which the erodibility value is 0.22. (c) Determining slope factor Forest boundaries are marked onto 1:63,360 maps and a 10 mm x 10 mm grid drawn over each forest, as shown in Figure 2 for Dellawa. The perpendicular distance is measured from the contour at each grid point to the nearest stream. This distance is divided by the altitudinal difference between the contours at the grid point and the stream in order to calculate the slope. Slope values are summed and divided by the number of grids in order to obtain a mean value. 43 A _B Cc D E LM N O SEE Te Sa RES OTe RSS Niger Perac Zama. pat Py Ba er) a Ajie @ N o (24) - wo to Re Po. Me a pater | ZENS re ome LS Nie MC AY) oa Seo ACARI eh ah RN Raa Se el Figure 2 Overlaying a 10x10 mm grid onto a contour map to estimate mean slope in Dellawa Table 3 Slopes at grid points in Dellawa B € De E F G H I! J K L M N 3 0.38 0.41 0.47 - - - - - - - - - - 4 - 0.12 0.34 0.47 0.47 0.37 - - - - - - - 5 - - 0.39 0.0 0.32 0.3 0.47 0.3 - - - - = 6 - - 0.34 - - 0.24 0.12 0.41 0.47 - - - - 7 - S = - - 0.24 0.0 0.32 - - - - - 8 - = = - - 0.30 0.24 0.36 - - - - - 9 - - - = - - 0.47 - 0.0 - - - - 10 - - 2 = - - - - 0.0 0.28 - = = 11 - c © - - - - - 0.47 0.36 0.24 - - 12 - - - = = - 0.32 0.09 0.63 0.0 0.47 0.0 - 13 - - = - = 0.39 0.47 0.12 0.16 0.47 0.59 0.42 0.32 14 - = e - - - - - 0.06 0.32 0.24 - 0.0 1S - = = - - - - - - - 0.09 0.24 - The slope at each grid point is given in Table 3 for Dellawa. Summation of the slopes (14.38) and division by the total number of grid points (52) gives a mean value of 0.28, or 28%. The slope factor is calculated as follows: S= (s/9)? (28/9)? 9.68 where S = slope factor, and Ss = mean percentage slope. Mean slope and the slope factor are given for each forest in Table 4. Table 4 Mean slope and slope factor for selected forests in Galle and Matara districts Forest Mean slope (%) Slope factor 1. Beraliya (Akuressa) 20 4.94 2. Dellawa 28 9.68 3. Diyadawa 33 13.44 4. Kalubowitiyana 48 28.44 5. Kanumuldeniya 14 2.42 6. Kekanadura 3 0.11 7. Masmullakele 12 1.78 8. Oliyagankele 14 2.42 9. Rammalekanda 23 6.53 (d) Calculating soil erosion The erosion hazard, or mean annual soil loss, is calculated by substitution of the values of the (a) erosivity index, (b) erodibility, and (c) slope factor into equation 4. 45 A =EKS. hid wag Aa cma. . eS (4) where A = mean annual soil loss (t ha” yr'), erosivity index, erodibility, and = slope factor. E K S Substituting the respective values for Dellawa forest, A = 383.75 x 0.22 x 9.68 817.23 t ha” yr! Values of the erosivity index, erodibility, slope factor and erosion hazard are given in Table 5 for each forest. These results show that Kalubowitiyana has the highest erosion hazard. This forest has the highest erosivity index as well as the highest slope factor, making it most prone to erosion. Table 5 Erosion hazard for selected forests in Galle and Matara districts, together with values of erosivity index, erodibility and slope factor Forest Erosivity index Erodibility Slope factor Erosion hazard t hatyr! 1. Beraliya (Akuressa) 275.49 0.22 4.94 299.3 2. Dellawa 383.75 0.22 9.68 817.2 3. Diyadawa 349.02 0.22 13.44 1032.3 4. Kalubowitiyana 426.52 0.22 28.44 2669.1 5. Kanumuldeniya 251.71 0.22 2.42 134.0 6. Kekanadura 180.97 0.22 0.11 4.4 7. Masmullakele 216.29 0.22 1.78 84.6 8. Oliyagankele 254.99 0.22 2.42 135.8 9. Rammalekanda 292.01 , 0.22 6.53 419.6 2. HEADWATERS (a) Streamlets originating from a forest The number of streamlets originating from a forest is counted, using the 1:63,360 series of maps. The value for Dellawa is 77. (b) Number of river catchments protected by a forest Streamlets originating from a forest are traced to major rivers, identified as such by the Irrigation Department (see Section 2.3). The number of major rivers into which flow the streamlets of a forest is counted. Dellawa contributes water to two major rivers, namely Nilwala and Gin Ganga. (c) Distance from headwaters of centralmost stream to its outlet The stream closest to the centre of a forest is selected and the distance measured from its headwaters to its mouth, using a thread laid along its course. If there are two river catchments, the values of the respective distances are summed. Values for Dellawa are as follows: Distance along Nilwala River = 58 Distance along Ginganga = 87 Total distance = 145 km The number of streamlets and river catchments, and the total distance between headwaters and outlets are given in Table 6 for each forest. Table 6 Distance of stream headwaters from river mouths for selected forests in Galle and Matara districts, together with the number of streamlets and major rivers Forest Streams Major Headwaters distance (km)” rivers Nil Gin Pol Kir Total 1. Beraliya (Akuressa) 37 2 37 - 27 - 64 2. Dellawa 77 2 58 87 - - 145 3. Diyadawa 73 24 64 95 - - 158 4. Kalubowitiyana 5 2 61 79 - - 140 5. Kanumuldeniya 5 ] - - - 30 30 6. Kekanadura 0 - - - - - - 7. Masmullakele 1 | 17 - - - 17 8. Oliyagankele 0 - - - - - - 9. Rammalekanda 43 2 64 - - 39 103 "Nil = Nilwala; Gin = Ginganga; Pol = Polwatta Ganga; Kir = Kirama Oya 3. FLOOD HAZARD Mean annual flood for a given forest is used as an index of its response to floods, and hence provides a measure of its role in reducing flood hazard. It is estimated using the following predictive equation: BESMAF = 4.53*10° AREA°™ STMFRQ**! SAAR! (5) where BESMAF = Mean annual flood (m s"), AREA = Area (km’), STMFRQ = Steam frequency (stream junctions km), and SAAR = Mean annual rainfall (mm) (a) Area This information is available for most forests that have been notified as forest reserves under the Forest Ordinance or as national reserves and sanctuaries under the Fauna and Flora Protection Ordinance. It is also available for proposed reserves. For other state forests, for which such information may not be available, the area is measured directly from 1:63,360 maps using a planimeter. The area of Dellawa is 22.36 km’. (b) Stream frequency Using 1:63360 maps on which forest boundaries have been marked, the number of stream junctions within each boundary is counted. Streams without any tributaries are considered as one junction. 47 The total number of stream junctions within the 22.36 km’ Dellawa is 65. Thus, its stream frequency is: STRFRQ = 65/22.36 = 2.9 (c) Mean annual rainfall Determination of mean annual rainfall is described in Section 1(a). The mean annual rainfall (SAAR) for Dellawa is 3759 mm. (d) Calculating flood hazard Substituting values of a), b) and c) into equation 5, the mean annual flood for Dellawa is: BESMAF = 4.53*10° 22.37°% 2.9°5! 3759! = 65.47 (m’ s") If a forest does not have a stream originating from it, the mean annual flood is considered to be equal to zero. The Values of area, stream frequency, mean annual rainfall and mean annual flood are given in Table 7. Table 7 Flood hazard for selected forests in Galle and Matara districts, together with values of area, stream frequency and mean annual rainfall Forest Area Stream freq. Mean annual rainfall Flood hazard km? km? mm t ha'yr! 1. Beraliya (Akuressa) 16.46 1.80 2671 25.11 2. Dellawa 22.36 2.90 3759 65.47 3. Diyadawa 24.48 2.52 3410 57.72 4. Kalubowitiyana 2.72 1.84 4189 10.23 5. Kanumuldeniya 6.79 0.64 2432 6.21 6. Kekanadura 3.80 0.00 1721 0.00 7. Masmullakele 6.18 0.14 2076 2.14 8. Oliyagankele 4.86 0.00 2465 0.00 9. Rammalekanda 14.07 2.22 2837 26.55 4. FOG INTERCEPTION Assessment of fog interception applies only to forests above 1500 m. All forests selected from Matara and Galle districts for purposes of this worked example lie below this altitude. Hence, the additional moisture contributed by fog interception is zero. For illustrative purposes, the procedure for estimating fog interception for a selected forest, Hakgala, is given below using equation 6. WoS1=38:5) + O10 FO. eee ee ee ee (6) where Y = additional percentage moisture contributed by fog, and X = altitude (m). 48 { The mean altitude of Hakgala is 1,980 m, and its mean annual rainfall is 1701 mm. Thus, Y = -38.5 + (0.4.x 1980) Y = 41% and Annual fog contribution = 1701 x 41/100 = 697 mm. 5S: EVALUATION Preliminary ranking Forests are ranked according to a single criterion, erosion hazard, as a measure of their importance for soil conservation, and according to three criteria for assessing their hydrological value, namely importance as headwaters of rivers, flood hazard and additional moisture contributed by fog interception. The importance of a forest for protecting headwaters of rivers is assessed in terms of three sub-criteria: the number of streamlets originating from it, the number of river catchments protected by it, and the distance from the headwaters to the river mouth. Each of these sub-criteria is ranked separately and the values of the three columns of ranks are added horizontally to derive an overall headwaters value which is then ranked. If values are equal, priority is given in the order: streams, major rivers and distance. The results of this ranking procedure are given in Table 8. Table 8 Selected forests in Matara and Galle districts ranked for their importance as headwaters catchments Forest Streams Major rivers Distance Headwaters Headwaters rank rank rank total rank 1. Beraliya (Akuressa) 4 1 5 10 5 2. Dellawa 1 1 2 4 1 3. Diyadawa 2 1 1 4 2 4. Kalubowitiyana 5 1 3 9 4 5. Kanumuldeniya 5 2 6 13 6 6. Kekanadura 7 3 8 18 8 7. Masmullakele 6 2 7 15 7 8. Oliyagankele 7 3 8 18 8 9. Rammalekanda 3 1 4 8 3 Hydrological importance is determined by adding the rank values of headwaters importance, flood hazard and fog interception and assigning the highest rank for the lowest total. If two or more values of the total are equal, priority is given to the value of the headwaters rank. The results of this ranking procedure are given in Table 9. Table 9 Selected forests in Matara and Galle districts ranked for their hydrological importance 8 Forest Headwaters Flood hazard Fog interception Hydrology Hydrology rank rank total rank 1. Beraliya (Akuressa) 5 4 - 9 4 2. Dellawa 1 1 - 2 1 3. Diyadawa 2 2 - 4 2 4. Kalubowitiyana 4 5 - 9 5 5. Kanumuldeniya 6 6 - 12 6 6. Kekanadura 8 8 - 16 8 7. Masmullakele 7 7 - 14 7 8. Oliyagankele 8 8 - 16 8 9. Rammalekanda 3 3 - 6 3 Final ranking The rank values of erosion hazard and hydrology are summed for each forest and the total ranked to obtain an overall ranking for soil conservation and hydrology. If two or more totals are equal, priority is given to erosion hazard. The results of the final ranking are given in Table 10. Table 10 _—‘ Final ranking of selected forests in Galle and Matara districts for their importance in soil conservation and hydrology Forest Erosion hazard Hydrology Erosion hazard + hydrology rank rank total rank 1. Beraliya (Akuressa) 5 4 9 5 2. Dellawa 3 1 4 2 | 3. Diyadawa 2 2 4 1 4. Kalubowitiyana 1 5 6 3 5. Kanumuldeniya 7 6 13 6 6. Kekanadura 9 8 17 9 7. Masmullakele 8 i 15 8 8. Oliyagankele 6 8 14 7 9. Rammalekanda 4 3 7 4 This worked example shows that Diyadawa is overall the most important of the selected forests for its role in conserving soil and maintaining the hydrological regime. Dellawa is hydrologically the most important forest, mainly due to its dense stream network. Kalubowitiyana ranks highest for soil conservation alone, a reflection of its high rainfall and steep terrain, but it is relatively less important for hydrology due to its small size. The extremely high erosion hazard, more than double that of any other forest (Table 5), might appear to be exaggerated but checking in the field showed that erosion is high, as evidenced by a recent landslide. 30 PART C THE CONSERVATION VALUE OF NATURAL FORESTS IN SOUTHERN PROVINCE: A PRELIMINARY REPORT = ogc f a Yay 4 = A of <3 ‘ e ler, i ay i 4 fi © ‘ ~ Zz y | a a. ot ~ i. ITS: A ere ee F ily) wee aide ‘i - wis oh faint Sethe Peta ee“? coy ' cl ere : net tg? OAR ad ade irate eat i wh Sew a a Raa hy wouh mp Soiegye ; 5 ; i i {im een en eee eee : “a , ‘ B ws gl 1 : Peon: ¥ Hage ‘ « * reer? 4 Sentient mite hp ee 8 a ee y im —_ permit iret ne a i i as 7 she ae, i haa 5 i : } gh ores aI ee st Ce ae 1. INTRODUCTION The National Conservation Review (NCR) component of the Environmental Management in Forestry Developments Project addresses the biological importance of natural forests, together with their value for soil conservation and hydrology. Included are all natural forests within politically accessible parts of the country (i.e. excluding Northern and Eastern provinces). The NCR follows on from an earlier Accelerated Conservation Review (ACR) of 30 lowland rain forests, managed as 13 units and covering some 480 knr (TEAMS, 1991). Scientific information on their conservation importance was urgently needed because these forests had been earmarked for logging. Thus, the review was necessarily rapid, with fieldwork conducted on an opportunistic basis over a three month period. Some 359 km? have since been allocated for conservation management by the Forest Department (see Part A, Section 1.6.2). Implicit in the Project Document (UNDP/FAO, 1989) is the requirement that the ACR be extended to remaining natural forest not covered by the ACR, which is based on the assumption that the methods used for the ACR would be applied to the NCR. The methods used for the ACR were reviewed but considered to be inadequate for purposes of the NCR, given the need to be able to prioritorise forests for conservation. The ACR is constrained by the lack of any systematic approach to sampling biological diversity and by biases in sampling intensity. The hydrological component of the ACR focuses mainly on soil erosion and its impact on irrigation, hydropower and water supply schemes resulting from sedimentation. The importance of forests in also controlling floods, protecting headwaters of river systems and providing additional moisture to the hydrological regime through the interception of fog is not taken into account. Unfortunately, the ACR methods cannot be fully evaluated with respect to the NCR because they are not sufficiently well documented. In view of the different procedures developed for the NCR, it was considered essential to resurvey those forest previously covered by the ACR in order to be able to compare sites for their conservation importance. This decision is proving to be amply justified (see Section 2.6). The NCR commenced in April 1991. Initial months were spent putting together a team and developing sampling techniques. Fieldwork commenced in September 1991, with priority given to the wet zone as conditions in the field permitted. Work on the soil conservation and hydrology component of the NCR began late in 1992 but has since caught up with the biological diversity studies. The entire Southern Province, comprising Galle and Matara districts in the wet zone and Hambantota in the dry zone, was surveyed by the end of 1992. This represents approximately 10% of the country. The results of this survey are presented in this preliminary report. Findings are tentative because the importance of forests within Southern Province cannot be evaluated within a national context until the NCR is completed. Despite this limitation, those forests which have been surveyed can be compared with each other in order to assess their relative importance and to identify an optimal conservation areas network for the Province. 1.1. PHYSIO-CLIMATIC CHARACTERISTICS OF SOUTHERN PROVINCE Galle, Matara and Hambantota districts lie within the wet, wet and intermediate, and dry zones, respectively. The wet zone receives a mean annual rainfall of 2000-5000 mm, with substantial rain falling during both the south-west and north-east monsoons. In the dry zone, which receives 1000-2000 mm annually, rain falls principally during the north-east monsoon. The mean annual rainfall in the intermediate zone' is 1300-3500 mm. Considerable variation in the magnitude and timing of the monsoons can result in persistent wet and dry periods which may extend over several years. In the dry zone, months of zero rainfall are very common. The availability of water has a major influence on the nature of the vegetation. The vegetation is most luxuriant in the wet zone, which supports rain and montane forests. By contrast the intermediate and dry zones tend to ‘For purposes of this study, the intermediate zone is not distinguished. Galle and Matara are considered to be in the wet zone and Hambantota in the dry zone. 51 support jungle or scrub vegetation as a result of the high water deficit. Water deficits of up to 500 mm may be experienced from July to December and in February in the intermediate zone, and up to 800 mm from February to September in the dry zone. In Galle and Matara districts, the terrain consists of steeper hills towards the north and low hills and undulating plains close to the coast. The main rivers are the Gin and Nilwala which originate from within Galle and Matara, respectively, and drain southwards. Discharge is sustained throughout the year, with peak flows during the south-west monsoon. Hambantota lies almost entirely in the dry zone except for the extreme north-western corner, which comprises 5% of the district. Its highest hills lie along the western boundary. 1.2 METHODS 1.2.1 Biological diversity Surveys were conducted between September 1991 and December 1992 using the procedures described in Part A (Section 3) of this document to identify and sample natural forests. A total of 52 forests (including three mangroves) of 50 ha” and above were surveyed for biological diversity in and adjacent to Southern Province (Table 1). Sinharaja and Ruhuna, in the wet and dry zones, respectively, lie mostly outside the Province but they are included in the analysis because they are large and diverse, providing useful standards against which other forests can be evaluated. Of the 52 forests, only 16 are considered to have been adequately surveyed (Table 1), based on a 5% threshold for the number of new woody species recorded in the penultimate and last plots, expressed as a percentage of the total number of species (see Part A, Section 3.6.2). In order to accommodate this constraint, forests in close proximity to each other and lying within the same catchment were treated as a single forest complex. The data were combined accordingly for a total of 18 forest complexes, each meeting the 5% criterion for the last two plots. Beraliya (Akuressa) was treated singly, being adequately sampled and somewhat isolated from other forests. The analysis is based on a total of 21,841 records of 657 species of woody plants and 6,211 records of 365 species of selected groups of animals (Table 2). Over 1,300 plant specimens were collected from the field, most of which have since been identified. Not included in the present analysis are 105 unidentified plant records (representing 13 genera and 27 species) and 573 unidentified animal records (representing 29 species). The large number of unidentified animal records is due to the many records of reptiles and molluscs of known genus awaiting species determination, and to the many records of mammal genera based on indirect observations (tracks, droppings, feeding signs efc.). In most cases of the latter, species determination is not possible. Given that faunal diversity is subject to biased sampling and likely to be grossly underestimated due to the rapidity with which it is necessary to survey forests because of overall time constraints (see Part A, Section 3.6.1), the analysis is based primarily on the data for woody plants and supplemented with those for animals. It can be assumed, however, that plant and animal diversity are closely correlated. As demonstrated in Figure 1, forests rich in woody species are rich in animal species and vice versa. Figure | also shows that woody plant diversity is much higher in the wet zone than in the dry zone, most wet zone forests having a higher diversity than all but the largest of the dry zone forests (i.e. Ruhuna). This does not appear to be the case for faunal diversity, there being no marked difference between wet and dry zone forests. But it could be an artifact, perhaps due to animals being more easily observed in forests in the dry zone as compared to the wet zone. The analysis has two main objectives. The first is to assess the diversity of each forest (or forest complex) in terms of species richness. This is done by totalling the number of species of woody plants and animals within each site. The second objective is to identify a minimum conservation areas network in which each species is 2This threshold was raised to 100 ha subsequent to the survey of Southern Province in order to expedite the NCR. 52 Table 1 List of natural forests surveyed for biological diversity in Southern Province, together with the number of transects, plots and days devoted to each. Onginal estimates are given in brackets. Forests in bold are considered to have been adequately surveyed (i.e. number of new woody plant species recorded in penultimate or last plot <= 5% total number of species); the rest have been insufficiently sampled. FOREST Desig- Not. Present Transects Plots Fieldwork New species No. Name nation area area” Pen. Last (ha) (ha) (no.) (no.) (days) (%) (%) GALLE (N=19) 509 Auwegalakanda OSF 250.0 250.0 1 (2) 4 (8) 1 (1) 10:9) 8357 511 Bambarawana OSF 248.0 248.0 1 (1) 4 (8) 1 (1) 17.6 Tet 37 Beraliya (Akuressa)” PR 1859.9 1645.5 2 (3) 20 = (24) 4 (3) 2.1 15 38 Beraliya (Kudagala) PR 4241.1 2571.8 1 (1) 8 (16) 2 (2) 3.0 2.4 62 Darakulkanda PR 457.6 141.7 1 @G) 6 (8) 1 (1) 16.9 4.6 65 Dediyagala® FR 3789.9 3789.9 1 (1) 17 (24) 5 (3) 0.0 0.0 69 Dellawa” PR 2034.0 2236.3 3 (3) 15 (40) 4 (5) 35 2.4 120 Habarakada PR 209.6 209.6 1 (0) 4 (0) 1 (0) 1B83) > ONT=1 508 Hindeinatw OSF 200.0 200.0 1 (2) 4 (8) 1 (1) 20.8 7.4 507 Homadola OSF 300.0 300.0 1 (0) 5 (0) 1 (0) 6.8 5.8 173 Kandawattegoda PR 404.7 358.6 1 (1) 5 (8) 1 (1) 6.5 11.6 175 Kanneliya FR 6114.4 6024.5 1 (2) 24 = (32) 7 (4) 13 0.4 208 Kombala-Kottawa PR 2289.7 1624.6 2 (2) 14 (16) 4 (2) 0.0 1.6 253 Malambure FR 1012.3 929.8 1 (2) 7 (16) 2 (2) 8.5 1.2 303 Nakiyadeniya PR 2292.1 2235.5 2 (2) 12 (16) 3 (2) 2.1 0.8 369 Polgahakanda FR 862.3 577.4 1 (0) 8 (0) 2 (0) 12 0.0 414 Sinharaja” NHWA 11187.0 11187.0 8 (4) 48 (42) 7 (10) 05 90.5 505 Tawalama OSF 1000.0 1000.0 1 (0) 5 (0) 1 (0) 8.0 8.0 506 Tiboruwakota OSF 600.0 600.0 1 (1) U (8) 2 (1) 2.9 525 MATARA (N=19) 501 Aninkanda OSF 75.0 75.0 1 (0) 5 (0) 1 (0) 14.4 9.0 24 Badullakele FR 182.3 147.7 1 (1) 5 (4) 1 (1) 3.8 8.1 60 Dandeniya-Aparekka FR 560.0 348.3 1 (1) 5 (12) 1 (2) 2.2 7.2 500 Derangala OSF 50.0 50.0 1 (1) 4 (4) 1 (1) 12.9 4.1 77 Diyadawa FR 2578.2 2447.7 2 (3) 17 (28) 4 (3) 2.0 15 138 Horagala-Paragala OSF 1800.0 1800.0 1 (1) 4 (24) 1 (3) 12.5 8.9 497 Kalubowitiyana OSF 100.0 100.0 1 (1) 5 (4) 1 (1) 14.5 3.8 178 Kanumuldeniya” FR 678.7 678.7 1 (1) 5 (8) 1 (1) 2.5 10.1 190 Kekanadura FR 401.7 379.9 1 ql) 5 (8) 1 (1) 8.9 3.7 201 Kirinda Mahayayakele FR 374.1 252.7 1 (1) 6 (8) 1 (1) 11.5 3.3 498 Kurulugala OSF 175.0 175.0 1 (1) 4 (8) 1 (1) 12.5 5.9 263 Masmullekele FR 805.4 618.0 1 (1) 6 (16) 1 (2) 7.4 ie) 293 Mulatiyana FR 3277.5 3148.9 4 (4) 29 = (40) 7 (4) 05 1.9 329 Oliyagankele FR 488.6 486.0 1 (1) 6 (8) 1 (1) 8.1 8.3 343 Panilkanda FR 588.1 588.1 1 (3) 5 (20) 1 (2) 6.9 7-3 388 Rammalakanda’ FR 1698.1 1406.7 2 (3) 16 = (16) 4 (2) 1.0 3.4 499 Silverkanda OSF 1000.0 1000.0 1 (2) 5 (16) 1 (2) 88 2.6 453 Viharekele FR 825.1 625.1 1 (1) 6 (12) 1 (2) 7.8 7.3 471 Welihena FR 333.1 296.8 1 (1) 6 (12) 1 (2) 8.7 4.6 ADJOINING DISTRICTS (N=!) 129 Haycock FR 362.0 362.0 1 (0) 6 (0) 2 (0) 10.3 3.8 TOTALS (WET ZONE) 55.705.5 51.116.8 56 (58) 367 (522) 83 671) DRY ZONE DISTRICTS HAMBANTOTA (N=9) 44 Bundala Ss 6216.0 6216.0 1 (1) 5 (6) 1 523 Kahanda Kalapuwa’ OSF 200.0 200.0 1 (0) 3 (0) % (1) 0.0 0.0 | 164 Kalametiya Kalapuwa’ Ss 712.0 712.0 1 (0) S (0) 1 (1) 80.0 16.7 | 186 Katagamuwa s 1003.6 1003.6 1 (1) 6 (6) 1 (1) 2.4 12.5 237 Madunagala FR 975.2 975.2 1 (1) 3 (6) 1 (1) 20.8 17.2 q 525 Miyandagala OSF 300.0 300.0 1 (1) 2 (6) 1 (1) 100 21.6 524 Rekawa Kalapuwa’ OSF 50.0 50.0 1 (0) 3 (0) % (1) 20.0 0.0 398 Ruhuna Block 1 NP 136792 136792 2 (-) 24 (¢-) 5 (-) 1.400 4.1 7 464 Wedasitikanda” FR 1343.4 1343.4 1 (2) 5 (12) 1 (2) 0.0 14.6 ' ADJOINING DISTRICTS (N=4) | 187 Kataragama s 837.7 837.7 it -@ Ge (6) R96 45 83 | 399 Ruhuna Block 2 NP 9931.0 9931.0 2 (-) 13 (-) 2 (-) 35 5.0) 400 Rubuna Block 3 NP 40775.4 40775.4 4 (-) 28 () 6 () 2.9 0.9 401 Ruhuna Block 4 NP 26417.7 26417.7 1 (1) 5 (12) 2 (2) 7.1 16.0 © TOTALS (DRY ZONE) 102,441.2 102,149.8 18 (ew) 107 (48) 23 eet) *Accounts for lands released subsequent to notification. | “Lies in one or more adjacent districts. | ’Mangrove ; | Table 2 Status of plant and animal records for Southern Province | Woody plants Animals” Total number of records 21,946 6,784 Number of identified records 21,841 6,211 | Number of identified genera 341 244 Number of unidentified genera 13 0 Number of identified species 657 365 Number of unidentified species 27 29 “Selected groups (i.e. vertebrates, molluscs, butterflies) 54 Number of unidentified records 105 573 Number of animal species (selected groups) 4 Dry Zone es Wet Zone Note: Data points are labelled by forest mumber. 5 a 4 Dry Zone « a Wet Zone Note: Data points are labelled by forest number. Number of endemic animal species (selected groups) 4 » 100 150 200 Number of endemic woody plant species Figure 1 Relationship between animal and plant diversity for (a) species and (b) endemic species 55 represented in at least one forest complex using the woody plant data. It is achieved by means of an algorithm which comprises the following iterative procedure: ; (i) The forest complex with the most species is selected as the first site. (ii) | The forest complex with most species not already represented in the first complex is selected as the second site. (iii) | The forest complex with the most species not already represented in the previously selected complexes is selected as the next site. (iv) | The previous step (iii) is repeated until all species are represented in one or more forest complexes. Various modifications to this algorithm were explored to demonstrate the range of options available. For example, in order to reduce the risk of species extinctions, the algorithm was modified to ensure that each species is represented within at least two forest complexes. Another option is to restrict the analysis to endemic species. A second algorithm was used to weight for rare species. This comprises the following steps: (i) | The forest complex with the most unique species (i.e. species recorded only in that site) is selected as the first site. (11) The forest complex with most unique species not already represented in the first complex is selected as the second site. (iii) | The forest complex with the most unique species not already represented in the previously selected complexes is selected as the next site. (iv) The previous step (iii) is repeated until all unique species have been selected. (v) The forest complex with the most species not already represented in the previously selected complexes is selected as the next site. (vi) | The previous step (v) is repeated until all species are represented in one or more forest complexes. Having defined a minimum complex of forests with respect to woody plant diversity, it was optimised to ensure that faunal diversity 1s adequately represented. 1.2.2 Soil conservation and hydrology The methodology is described in Part B (Section 2) of this document. It is designed to predict the potential soil erosion and flooding caused by conversion of natural forests to other forms of land use, and to assess their importance for protecting headwaters of river systems. Soil erosion and flood hazard are estimated in absolute terms, while importance for headwaters protection is based on a ranking procedure which takes into account the number of streamlets and catchments, and the distance to the outlet. Fog interception is not relevant to the present study because all forests lie below 1,500 m, the threshold below which interception of moisture from fog is insignificant. A total of 45 natural forests in Southern Province were evaluated with respect to their role in soil conservation and hydrology. It includes all forests covered by the biological diversity study (see Table 1), with the exception of Sinharaja, Ruhuna Block | and the three mangroves (Kahanda, Kalametiya and Rekawa) in the Province and those forests lying in adjacent districts (Haycock, Kataragama and Ruhuna Blocks 2-4). Included in the total but not covered by the biological diversity study are Yakdehikanda and Keulakada Wewa (yet to be surveyed) and Mahapitikanda (too degraded to warrant surveying). 56 The results of this evaluation have been presented in a separate report (Gunawardena, 1993). The main findings have been incorporated within the present report, and integrated with the results of the biological diversity study. Complete integration is not yet possible because only 42 of the forests are common to both surveys. 37 2. BIOLOGICAL DIVERSITY ASSESSMENT 2.1 SPECIES DIVERSITY WITHIN INDIVIDUAL FORESTS Species recorded within individual forests are listed in Annexes 1 and 2 for woody plants and selected animal groups, respectively. Many species of plants and animals have been recorded in new localities, extending their known distributions. Some rare species have been found to be more common than previously thought, and a number of woody species believed to have become extinct or not collected for a very long time have been recorded and collected for the first time this century. Two species of lizard have been discovered, and it is thought that the faunal collection awaiting identification may include one or more species of frog new to science. Details of some of the new locality records are given in Annexes 3 and 4 for plants and animals, respectively. The total number of species recorded within each forest is summarised in Table 3 for (a) woody plants and (b) selected groups of animals’. Sinharaja, Nakiyadeniya, Kanneliya, Mulatiyana, Dellawa, Rammalakanda and Diyadawa are the most diverse forests for woody plants, with some 200 or more species recorded in each. All of these lie in the wet zone. The most diverse forest for woody plants in the dry zone is Ruhuna Block 3 with over 100 species (Table 3a). Mulatiyana, Diyadawa, Dellawa, Sinharaja and Rammalakanda are among the most diverse wet zone forests for animals, together with Ruhuna Blocks 1-3 and Bundala in the dry zone (Table 3b). All of these forests are among the largest and, with the exception of Bundala, are considered to have been adequately sampled (Table 1). The only adequately sampled forest that contains fewer woody plant and animal species than might be expected on account of its large size (> 1,000 ha) is Beraliya (Kudagala). This forest is twice the size of Bereliya (Akuressa) (Table 1) but supports markedly fewer species (Tables 3a,b). Sinharaja contains both the greatest number of woody species (277) and endemic woody species (150). Together with Rammalakanda and Ruhuna Block 3, it has the most unique species (i.e. species not represented in any other site). Kanneliya is notable, having among the highest percentage of endemic woody species (60%) of any forest, as well as the most nationally and globally threatened woody species. Haycock, Oliyagankele and Welihena (all insufficiently sampled), with half the number of species, are also notable with respect to their high proportions of endemic and threatened woody species. Of the dry zone forests, Ruhuna Blocks 3-4 have the most endemic woody species (Table 3a). Ruhuna Block 1 is marginally the most diverse forest for animals, closely followed by Ruhuna Block 3. Together with Ruhuna Block 4 (insuffiently sampled), they are the most important forests in the dry zone for endemic and threatened species of animals. Sinharaja has the most endemic and threatened species of animals, other important forests in this respect being Beraliya (Akuressa), Dellawa, Kanneliya, Mulatiyana and Rammalakanda. Haycock and Homadola (both insufficiently sampled), with much lower diversity, are notable for having the highest percentage (>40 %) of endemics (Table 3b). 2.2 INFLUENCE OF FOREST SIZE 2.2.1 Species diversity In general, the larger the size of a forest, the greater the number of species contained within it up to a threshold which is a function of its complexity or heterogeneity. Such a trend is evident for woody plants (Figure 2a) and animals (Figure 2b), despite the limitation that many forests have not been adequately sampled (see Section 1.2.1). As discussed previously (Section 1.2.1), the distinction between wet and dry zone forests is marked in the case of woody plants but not animals. These data endorse the maxim that conservation areas should be as large as possible to maximise representation of species (see Part A, Section 1.5.1). 3Faunal statistics should be treated with caution because of sampling constraints (see Section 1.2.1). 58 Table 3(a) Summary of woody plant diversity recorded within natural forests in Southern Province. Forests are listed in descending order for species diversity, those in upper case having been adequately sampled. Species recorded within individual forests are listed in Annex 1. —_—_— SSS SSS No. Forest name Families Genera Species s p e c i e s Unique Endemic Threatened National Global 414 SINHARAJA 166 170 27 11 150 2% 7 303 MAKI YADENIYA 161 164 27 5 117 24 11 175 KANNELIVA 147 149 PL 2 140 26 16 293 MULATIYANA 149 151 205 8 97 18 6 69 DELLAMA 16h 147 203 0 101 9 6 388 RAMMALAKANDA 141 148 199 1 39 12 6 7 DIYADAWA 138 142 199 1 106 16 6 208 KOMBALA-KOTTAMA 131 132 192 0 108 17 11 37 BERALIYA (AKURESSA) 136 137 191 2 102 5 6 65 DEDIYAGALA 126 126 189 1 112 18 10 129 Haycock 124 125 182 2 105 16 12 506 Tiboruwakota 129 130 180 1 93 16 6 369 POLGAHAKANDA 124 126 170 0 90 10 7 38 BERALIYA (KUDAGALA) 123 125 166 6 90 12 5 253 Malambure 125 128 163 1 89 13 7 497 Kalubowi tiyana 126 129 158 4 59 10 4 507 Homado|a 107 110 154 0 83 12 6 505 Tawalama 108 111 150 fy) 90 16 8 511 Bambarawana 114 117 140 1 58 8 6 120 Habarakada 101 104 135 0 71 9 2 508 Hindeinattu 114 117 135 0 59 1 6 509 Auwega lakanda 104 105 134 0 74 12 7 62 Darakulkanda 108 111 130 1 58 9 5 138 Horagala-Paragala 102 104 123 0 56 9 2 501 Aninkanda 9 101 122 2 57 6 3 173 Kandawat tegoda % 99 121 0 58 7 4 498 Kurulugala 89 90 117 5 55 6 3 263 Masmul Lekele 97 99 116 0 55 9 4 499 Silverkanda 86 86 112 4 54 6 3 329 Oliyagankele 82 85 108 2 63 10 6 453 Viharekele 87 90 108 0 58 9 2 343 Pani Lkanda 5 % 108 1 43 3 2 471 Welihena 78 81 107 0 68 13 5 400 PRUHUNA BLOCK 3 6B 7 105 1 8 2 1 60 Dandeni ya-Aparekka 81 8&3 96 2 47 7 2 500 Derangala & 87 96 2 36 4 1 201 Kirinda Mahayayakele 72 74 89 0 44 6 2 178 Kanumuldeniya 7 79 87 0 38 4 2 24 Badul Lakele 72 73 8&4 0 39 7 3 190 Kekanadura 76 76 82 0 36 6 4 398 PRUHUNA BLOCK 1 58 59 7% 8 3 1 1 399 PRUHUNA BLOCK 2 49 49 60 5 4 2 2 401 °Ruhuna Block 4 42 42 50 1 8 2 1 186 “Katagamuwa 4“ 41 48 2 5 i) 1 464 Wedasitikanda 32 34 41 4 4 1 1 525 Mi yandagala 29 29 37 4 2 1 1 44 °Bundala 34 35 36 5 3 0 0 237 °Madunagala 25 26 29 1 2 1 0 187 °kataragama 20 21 24 1 0 0 ) 164 “kalametiya Kalapuwa’ 12 12 12 0 0 0 i) 524 °Rekawa Kalapuwa’ 9 9 10 4 1 1) 0 523 PKAHANDA KALAPUMA’ 10 10 10 5 0 1 0 Dry Zone (the rest are in the Wet Zone) ‘Mangrove Table 3(b) Summary of diversity for selected groups of animals recorded within natural forests in Southern Province. Forests are listed in descending order for species diversity, those in upper case having been adequately sampled. Species recorded within individual forests are listed in Annex 2. No. Forest name Families Genera Species s p e c i e s Unique Endemic Threatened National Global 398 PRUHUNA BLOCK 1 78 84 % 10 8 15 6 400 PRUHUNA BLOCK 3 7% 7” % 6 1 13 6 293 MULATIYANA 6) 80 89 4 27 24 6 7 DIYADAWA 70 74 88 2 22 20 3 69 DELLAWA 64 71 82 5 27 26 5 414 SINHARAJA 64 68 7 1 29 31 5 44 "Bundala 54 65 5 1 1 4 2 388 RAMMALAKANDA 60 64 72 3 26 3 3 399 °RUHUNA BLOCK 2 58 60 67 2 3 8 3 37 BERALIYA (AKURESSA) 56 60 67 3 26 21 4 186 °Katagamuwa 55 57 66 4 3 5 3 175 KANNELIYA 51 55 64 4 26 18 5 164 “Kalametiya Kalapuwa’ 52 55 62 10 1 2 0 303 NAKIYADENIYA 48 49 60 2 22 17 4 208 KOMBALA-KOTTAWA 43 47 57 3 23 19 8 464 Wedasitikanda 48 50 56 3 4 3 2 65 DEDIYAGALA 45 47 51 3 17 14 4 38 BERALIYA (KUDAGALA) 45 46 50 2 12 8 2 401 PRuhuna Block 4 39 41 47 1 8 9 5 524 °Rekawa Kalapuwa’ 34 37 45 9 1 1 0 369 POLGAHAKANDA 38 40 4h 0 13 14 5 253 Malambure 37 38 43 1 19 11 4 129 Haycock 37 39 43 1 20 14 3 343 Pani Lkanda 36 39 42 0 13 13 2 497 Kalubowi tiyana 34 34 40 0 14 9 2 187 ’kataragema 37 38 40 0 4 7 2 138 Horagala-Paragala 32 33 38 0 10 9 0 263 Masmul Lekele 36 36 38 0 8 5 0 120 Habarakada 33 33 36 0 12 9 1 525 "Mi yandagala 26 30 35 0 ) 1 2 500 Derangala 28 29 35 2 5 6 1 501 Aninkanda 31 32 34 1 10 12 2 506 Tiboruwakota 28 28 33 0 14 10 0 505 Tawalama 28 29 32 1 8 8 3 499 Silverkanda 26 28 31 1 12 10 0 62 Darakulkanda 30 30 31 0 7 4 1 237 "Madunagala 27 27 31 0 3 4 2 329 Oliyagankele 26 29 30 3 8 5 2 498 Kurulugala 25 26 28 1 12 8 1 508 Hindeinattu 22 24 28 0 8 4 2 453 Viharekele 25 235 28 0 10 9 3 201 Kirinda Mahayayakele 24 25 27 0 7 6 1 507 Homadola 24 24 27 0 14 8 1 173 Kandawat tegoda 24 25 26 0 8 3 1 523 °KAHANDA KALAPUWA’ 21 21 24 2 0 0 ) 471 Welihena 22 22 24 2 4 4 1 24 Badul Lakele 22 22 3 2 7 5 1 511 Bambarawana 19 19 22 2 5 2 1 60 Dandeni ya-Aparekka 22 22 22 0 3 2 1 178 Kanumuldeni ya 19 19 20 0 4 2 0 190 Kekanadura 17 17 20 0 8 5 0 509 Auwegalakanda 15 15 18 0 6 3 0 "Dry Zone (the rest are in the Wet Zone) ‘Mangrove g 8 8 sarseds yuejd Apoom jo Joquiny 8 & | R zs & ¢ & (sdnosd poysejes) sotseds Teumue jo JoquINN & 10 Forest size (ha) Note: Data points are labelled by forest number. a Dry Zone a Wet Zone Relationship between species diversity and forest size for (a) woody plants and (b) selected animal Figure 2 61 oe . -) vt sooeds juejd Apoom onbrun jo soquinn he 4a 3< Ra Rte (sdnoxd poyoojas) sotoeds ewtue onbrun jo Joquinn ef ARRGRE Forest size (ha) Note: Data points are labelled by forest mumber. a Wet Zone 4 Dry Zone Relationship between the number of unique species and forest size for (a) woody plants and (b) selected animal groups Figure 3 62 & 2 & 8 sa1deds juejd Apoom ommapua Jo asejusolag i=} e 8 8 2 (sdnoid poyejas) saioeds jeurue ofWspus Jo osejus0Iag Forest size (ha) Note: Data points are labelled by forest number. 4 Dry Zone as Wet Zone Relationship between the percentage of endemic species and forest size for (a) woody plants and (b) selected animal groups Figure 4 63 2.2.2 Rare and endemic species Many forests contain one or more rare species that are uniquely restricted in their distribution to that particular site (Tables 3a,b). There is no clear relationship between the number of species unique to a particular forest and its size in the case of either plants (Figure 3a) or animals (Figure 3b). This emphasises the importance of protecting small, as well as large, forests in order to conserve the entire spectrum of species. Kalubowitiyana [497] and the mangrove of Rekawa [524] are prime examples of small forests (<= 100 ha) with as many or more unique species than many larger forests (> 1,000 ha). Both are other state forests and, therefore, receive negligible legal protection. Levels of endemism are high in wet zone forests, ranging from 37% to 64% for woody plants (Figure 4a) and from 14% to 52% for animals (Figure 4b). They are much lower, usually below 10%, in dry zone forests with the notable exception of Ruhuna Block 4 (insufficiently sampled) at about 16% for both woody plants and animals. While small forests tend to contain fewer endemic species than large forests, their proportion of endemics may be as high or even higher (Figure 4a,b). The 297 ha forest of Welihena [471] (insufficiently sampled), for example, ranks 33rd for woody plant diversity (Table 3a) but it has the highest proportion of endemics (64%), as shown in Figure 4a. Similarly, Homadola [507] (insufficiently sampled), covering some 300 ha and ranking 43rd for faunal diversity (Table 3b), has the highest proportion (52%) of endemic animals (Figure 4b). Other small forests with exceptionally high levels of animal endemism include Haycock [129] and Kurulugala [498], the latter being the type locality for a new species of lizard (Ceratophora sp. nov.) discovered during the NCR. As in the case of unique species, there is no clear relationship between levels of endemism and forest size for either woody plants (Figure 4a) or animals (Figure 4b), further emphasising the relative importance of small forests for endemic species. In defining an optimal conservation areas network, therefore, it is vital that small forests are not overlooked in deference to larger forests because they may be important for rare and endemic species. 2.3 SPECIES DIVERSITY WITHIN FOREST COMPLEXES In order to overcome problems of sample size, forests were amalgamated into 18 complexes (see Section 1.2.1). Each complex is numbered according to the number of one of its constituent forests. The biological diversity represented within each complex is summarised in Tables 4a and 4b for woody plants and animals, respectively. 2.3.1 Woody plants The most diverse site for woody plants is KDN er al. (Kanneliya, Dediyagala, Nakiyadeniya and others), the largest of the complexes (13,400 ha). It also has the most endemic and threatened (nationally and globally) species. This is particularly remarkable since much of KDN has been logged for timber in the past. KDN is considered to be the most important lowland rain forest in the country, warranting strict protection (P.S. Ashton, in litt., 1992). Sinharaja et al. (12,962 ha), second most diverse complex, contains most unique species among wet zone complexes. Also notable for its high complement of unique species is Horagala- Paragala/Mulatiyana/Rammalakanda (6,356 ha). Other highly diverse complexes, with over 250 species of woody plants, are Dellawa/Diyadawa/Kalubowitiyana (4,784 ha) and Habarakada/Haycock/Auwegalakanda/Bambarawana (1,070 ha) (Table 4a). Of the dry zone forests, Ruhuna National Park (i.e. Blocks 1-4) is outstandingly important, with nearly twice as many woody species as any other dry zone forest and double the number of unique species compared to any dry or wet zone forest. Doubtless, this reflects its very large size (90,803 ha). The complex comprising Bundala and the three mangroves (9,172 ha) also contains many unique species (Table 4a). 2.3.2 Animals Highest faunal diversity’ was recorded in Ruhuna National Park, followed by Bundala/Mangroves. Together, these two complexes account for over half of the 129 species unique to a particular complex. Of the wet zone complexes, the most diverse are KDN et al., Horagala-Paragala/Mulatiyana/Rammalakanda, 64 Table 4(a) Summary of woody plant diversity recorded within natural forest complexes in Southern Province. Forest complexes are listed in descending order for species diversity. Forest complex Families Genera Species s ) e c i e s No. Subsidiary nos. Name Unique Endemic Threatened National Global 175 65,303,500,505,507,508 KDN et al. 203 207 357 12 185 43 19 416 498,499,506 Sinharaja et al. 194 198 337 28 173 31 11 138 293,388 Hora-Para/Mula/Ram 191 196 293 21 131 22 8 69 77,497 Della/Diya/Kalu 190 194 290 6 133 21 8 120 129,509,511 Haba/Hay/Auw/Bamb 177 179 263 3 131 23 13 253 369 Malam/Polga 148 150 211 1 113 17 9 38 62 Berel/Daru 147 149 211 7 104 16 7 173 208 Kanda/Komb-Kott 142 144 210 0 112 18 1 37 Beraliya (Akuressa) 136 137 191 2 102 15 6 343 501 Panil/Anin 126 128 155 4 70 7 3 329 471 Oliy/weli 97 100 145 2 89 18 8 60 190,201 Dand/Keka/Kiri 112 112 145 3 B 13, 5 398 399,400,401 °Ruhuna NP 100 103 143 56 13 5 2 24 263 Badu/Masmu 111 112 142 i) 66 12 6 178 453 Kanu/Vihi 103 106 128 0 65 10 3 186 187,464 °kata/Kata/Weda 59 61 B 10 7 1 2 237 525 Madu/Miya 45 45 55 5 3 1 1 44 164,523,524 °Bund/Mangroves 49 49 53 21 4 1 0 pry Zone (the rest are in the wet zone) Table 4(b) Summary of animal diversity (selected groups) recorded within natural forest complexes in Southern Province. Forest complexes are listed in descending order for species diversity. Forest complex Families Genera Species s p e c i e s No. Subsidiary nos. Name Unique Endemic Threatened National Global 398 399,400,401 °Ruhuna NP 119 122 145 27 14 20 44 164,523,524 °Bund/Mangroves 95 107 133 41 2 6 2 175 65,303,500,505,507,508 KDN et al. 92 96 128 12 41 32 9 69 77,497 Della/Diya/Kalu 91 97 119 9 35 34 6 138 293,388 Hora-Para/Mula/Ram 92 103 119 7 39 37 7 414 498,499,506 Sinharaja et al. 86 90 106 6 41 33 5 186 187,464 °kata/Kata/Weda 88 90 106 7 7 10 4 173 208 Kanda/Komb-Kott 54 57 68 3 26 20 8 120 129,509,511 Haba/Hay/Auw/Bamb 56 59 67 3 26 19 4 37 Beraliya (Akuressa) 56 60 67 3 26 21 4 253 369 Malam/Polga 55 57 64 1 23 20 7 38 62 Berel/Daru 54 57 63 2 16 10 3 237 525 wadu/Miya 39 43 54 0 3 4 3 343 501 Panil/Anin 47 50 54 1 17 18 2 329 471 Oliy/Weli 40 42 46 5 11 8 3 24 263 Badu/Masmu 43 44 46 2 10 6 1 60 190,201 Dand/Keka/Kiri 38 39 45 0 13 10 1 178 453 Kanu/Vihi 34 34 37 0 13 11 3 "Dry Zone (the rest are in the wet zone) 65 Table 5(a) Network of forest complexes required to conserve woody plant diversity in Southern Province. Forest complexes are ranked in descending order of their selection, based on their contribution of species to the network. Those below the dotted line do not contribute any additional species to the network. Feeo) if ue) 2s) t c om pl ie x Protected species Unprotected —_— species Rank No. Subsidiary nos Name New % Total % Total 1 175 65,303,500,505,507,508 KDN et al. 357 54.3 357 54.3 300 2 398 399,400,401 °Ruhuna NP 118 18.0 475 72.3 182 3 414 498,499,506 Sinharaja et al. 60 9.1 535 81.4 122 4 138 293,388 Hora-Para/Mula/Ram 35 «5.3 570 86.8 87 5 44 164,523,524 °Bund/Mangroves 23 «3.5 593 90.3 64 6 186 187,464 "kata/Kata/Weda 16 2.4 609 92.7 48 7 69 77,497 Della/Diya/Kalu 12 1.8 621 94.5 36 8 38 62 Berel/Daru 11.7 632 96.2 25 9 60 190,201 Dand/Keka/Kiri 5 0.8 637 97.0 20 10 237 525 "Madu/Miya 5 0.8 642 97.7 15 1 120 129,509,511 Haba/Hay/Auw/Bamb 4 0.6 646 98.3 1 12 329 471 Oliy/weli 4 0.6 650 98.9 7 13 343 501 Panil/Anin 4 0.6 654 99.5 3 14 37 Beraliya (Akuressa) 2 0.3 656 99.8 1 15 253 369 Malam/Polga 1 0.2 657 100.0 i) 16 178 453 Kanu/Vihi 0 0.0 657 100.0 0 17 173 208 Kanda/Komb-Kott 0 0.0 657 100.0 0 18 24 263 Badu/Masmu 0 0.0 657 100.0 0 "Dry Zone (the rest are in the wet zone) Table 5(b) Network of forest complexes required to conserve woody plant diversity in Southern Province. Forest complexes are ranked in descending order of their selection, based on their contribution of unique species to the network. Those below the dotted line do not contribute any additional unique species to the network. FAO mt JO unS galt CL eon mS pial...e.x Unique Protected species Unprotected species § —— ——___________ species Rank No. Subsidiary nos Name Total New % Total % Total 1 398 399,400,401 °Ruhuna NP 56 143° 21.8 143 21.8 514 2 414 498,499,506 Sinharaja et al. 28 319 48.6 462 70.3 195 3 44 164,523,524 °Bund/Mangroves 21 2 3.8 487 74.1 170 4 138 293,388 Hora-Para/Mula/Ram 21 55 8.4 542 82.5 115 5 175 65,303,500,505,507,508 _KDN et al. 12 51 7.8 593 90.3 64 6 186 187,464 "Kata/Kata/Weda 10 16 2.4 609 92.7 48 7 38 62 Berel/Daru 7 11. «1.7 620 94.4 37 8 69 77,497 Della/Diya/Kalu 6 12 1.8 632 96.2 25 9 237 525 Wadu/Miye 5 5 0.8 637 97.0 20 10 343 501 Panil/Anin 4 4 0.6 641 97.6 16 11 60 190,201 Dand/Keka/Kiri 3 5 0.8 646 98.3 11 12 120 129,509,511 Haba/Hay/Auw/Bamb 3 4 0.6 650 98.9 7 13 37 Beraliya (Akuressa) 2 ay (Heb 653 99.4 4 14 329 471 Oliy/weli 2 3. «0.5 656 99.8 1 15 253 369 Malam/Polga 1 1 0.2 657 100.0 0 16 178 453 Kanu/Vihi 0 0 0.0 657 100.0 0 17 173 208 Kanda/Komb-Kott 0 0 0.0 657 100.0 0 18 24 263 Badu/Masmu 0 0 0.0 657 100.0 0 Dry Zone (the rest are in the wet zone) Table 6(a) Network of forest complexes required to conserve endemic woody plant diversity in Southern province. Forest complexes are ranked in descending order of their selection, based on their contribution of endemic species to the network. Those below the dotted line do not contribute any additional species to the network. FesOtste Cla iSa sot C00. JBL Pll ens Protected species Unprotected SEE EEUU species Rank No. Subsidiary nos Name New % Total % Total 1 175 65,303,500,505 507,508 KDN et al. 185 73.7 185 73.7 66 2 414 498,499,506 Sinharaja et al. 26 10.4 211 84.1 40 3 138 293,388 Hora-Para/Mula/Ram 12 4.8 223 88.8 28 4 38 62 Berel/Daru 6 2.4 229 91.2 22 5 398 399,400,401 PRuhuna NP 6 2.4 235 93.6 16 6 60 190,201 Dand/Keka/Kiri 5 2.0 240 95.6 1 7 329. 9 471 Oliy/weli 4 1.6 244 97.2 7 8 253 369 Malam/Polga 2 0.8 246 98.0 5 9 343 «501 Panil/Anin 2 0.8 248 98.8 3 10 44 164,523,524 Bund/Mangroves 1 0.4 249 99.2 2 11 120 129,509,511 Haba/Hay/Auw/Bamb 1 0.4 250 99.6 1 12 186 187,464 "Kata/Kata/Weda 1 0.4 251 100.0 0 13 237 =525 "Madu/Miya 0 0.0 251 100.0 0 14 178 453 Kanu/Vihi 0 0.0 251 100.0 0 15 173 208 Kanda/Komb-Kott 0 0.0 251 100.0 0 16 69 77,497 Della/Diya/Kalu 0 0.0 251 100.0 0 17 24 263 Badu/Masmu 0 0.0 251 100.0 0 18 37 Beraliya (Akuressa) 0 0.0 251 100.0 0 "Dry Zone (the rest are in the wet zone) Table 6(b) Network of forest complexes required to conserve endemic woody plant diversity in Southemm province. Forest complexes are ranked in descending order of their selection, based on their contribution of unique endemic species to the network. Those below the dotted line do not contribute any additional unique species to the network. Fe Ome Cee Smet c om ple x Unique Protected species Unprotected species § ——__ species Rank No. Subsidiary nos Name Total New % Total % Total 1 414 498,499,506 Sinharaja et al. 15 173 68.9 173 68.9 78 2 138 293,388 Hora-Para/Mula/Ram 8 18 7.2 191 76.1 60 3 175 65,303,500,505,507,508 KDN et al. 5 32 12.7 223 ~88.8 28 4 398 399,400,401 °Ruhuna NP 5 6 2.4 229 91.2 22 2 38 62 Berel/Daru 2 6 2.4 235 93.6 16 6 60 190,201 Dand/Keka/Kiri 2 5 2.0 240 95.6 11 7 329 471 Oliy/Weli 2 4 1.6 244 97.2 7 8 343 501 Panil/Anin 2 2 0.8 246 98.0 5 9 44 164,523,524 °Bund/Mangroves 1 1 0.4 247 98.4 4 10 120 129,509,514 Haba/Hay/Auw/Bamb 1 1 0.4 248 98.8 3 1 186 187,464 Kata/Kata/Weda 1 1 0.4 249 99.2 2 12 253 369 Malam/Polga 1 2 0.8 251 100.0 0 13 237 525 "Madu/Miya 0 0 6.0 251 100.0 0 14 178 453 Kanu/Vihi 0 0 60.0 251 100.0 0 15 173 208 Kanda/Komb-Kott 0 Oo 60.0 251 100.0 0 16 69 77,497 Della/Diya/Kalu 0 0 60.0 251 100.0 0 17 24 263 Badu/Masmu 0 0 0.0 251 100.0 0 18 37 Beraliya (Akuresse) 0 0 0.0 251 100.0 0 Dry Zone (the rest are in the wet zone) Table 7 Contribution of woody plant species and endemic woody plant from individual forests to forest complexes. Forests are listed in the order of their selection for woody plant species, those in upper case having been adequately sampled. SN eee F Son «rem -srt Species Endemic species No. Name Rank No. % Rank No. % 303: KDN et al. 303 NAKIYADENIYA 1 237 66.4 2 24 13.0 175 KANNELIYA 2 71 #419.9 al 140 75.7 065 DEDIYAGALA 3 17 4.8 3 10 5.4 500 Derangala 4 12 3.4 5 3 1.6 508 Hindeinattu 5 9 2.5 6 2 algal 505 Tawalama 6 7 2.0 4 4 2.2 507 Homadola 7 4 atoal 7 2 1.1 Species totals 357 185 414: SINHARAJA et al. 414 SINHARAJA 1 277 82.2 1 150 86.7 506 Tiboruwakota 2 29 8.6 3 8 4.6 499 Silverkanda 3 21 6.2 4 4 2.3 498 Kurulugala 4 10 3.0 2 11 6.4 Species totals 337 173 138: HORA-PARA/MULA/RAN 293 MULATIYANA 1 205 70.0 1 97 74.0 388 RAMMALAKANDA 2 71 24.2 2 27 20.6 138 Horagala-Paragala 3 17 5.8 3 7 5.3 Species totals 293 131 69: DELLA/DIYA/KALU 69 DELLAWA 1 203 70.0 2 19 14.3 497 Kalubowitiyana 2 49 16.9 3 8 6.0 77 DIYADAWA 3 38 13.1 1 106 79.7 Species totals 290 133 120: HABA/HBAY/AUW/BAMB 129 Haycock 1 182 69.2 1 105 80.2 Sjalat Bambarawana 2 49 18.6 3 9 6.9 509 Auwegalakanda 3 20 7.6 4 5 3.8 120 Habarakada 4 12 4.6 2 12 9.2 Species totals 263 131 253: MALAM/POLGA 369 POLGAHAKANDA 1 170 80.6 1 90 79.6 253 Malambure 2 41 19.4 2 23 20.4 Species totals 211 113 38: BEREL/DARU 38 BERELIYA (KUDAGALA) 1 166 78.7 1 90 86.5 62 Darakulkanda 2 45 21.3 2 14 13.5 Species totals 211 104 173: KANDA/KOMB-KOTT 208 KOMBALA-KOTTAWA 1 192 91.4 1 108 96.4 173 Kandawattegoda 2 18 8.6 2 4 3.6 Species totals 210 112 37: BERELIYA (AKURESSA) 37 BERELIYA (AKURESSA) 191 102 68 343: PANIL/ANIN 501 Aninkanda 343 Panilkanda Species totals 329: OLIY/WELI 329 Oliyagankele 471 Welihena Species totals 60: DAND/KEKA/KIRI 60 Dandeniya-Aparekka 190 Kekanadura 201 Kirinda Mahayayakele Species totals 398: RUHUNA NP 400 RUHUNA BLOCK 3 398 RUHUNA BLOCK 1 399 RUHUNA BLOCK 2 401 Ruhuna Block 4 Species totals 24: BADU/MASMU 263 Masmullekele 24 Badullakele Species totals 178: KANU/VIHI 453 Viharekele 178 Kanumuldeniya Species totals 186: KATA/KATA/WEDA 186 Katagamuwa 464 Wedasitikanda 187 Kataragama Species totals 237: MADU/MIYA 525 Miyandagala 237 Madunagala Species totals 44: BUND/MANGROVES 44 Bundala 523 KAHANDA KALAPUWA 524 Rekawa Kalapuwa 164 Kalametiya Kalapuwa Species totals PWNHe WNr Ne NR WNr &WNr 69 122 155 108 145 78.7 11.3 WPENE NWP NH aN WNP mNWH Whore NonwW RmOrOW Table 8(a) Network of forest complexes required to conserve woody plant diversity in Southern Province, with species represented in at least two complexes. Forest complexes are ranked in descending order of their selection, based on their contribution of species to the network. Foor e@ st c om ple x Protected species Unprotected — species Rank No. Subsidiary nos Name New % Total % Total 1 175 65,303,500,505,507,508 KDN et al. 0 0.0 Oo 0.0 657 2 414 498,499,506 Sinharaja et al. 274 41.7 274 «41.7 383 3 398 399,400,401 PRuhuna NP 13 2.0 287 43.7 370 4 138 293,388 Hora-Para/Mula/Ram 42 6.4 329 50.1 328 5 186 187,464 "kata/Kata/Weda 40 6.1 369 56.2 288 6 69 77,497 Del la/Diya/Kalow 26 4.0 395 60.1 262 7 44 164,523,524 °Bund/Mangroves 9 1.4 404 61.5 253 8 38 62 Berel/Daru 14 2.1 418 63.6 239 9 237 525 Madu/Miya 14 2.1 432 65.8 225 10 120 129,509,511 Haba/Hay/Auw/Bamb 13° 2.0 445 67.7 212 11 60 190,201 Dand/Keka/Kiri 6 0.9 451 68.6 206 12 37 Beraliya (Akuressa) 5 0.8 456 69.4 201 13 173 208 Kande/Komb-Kott Uwe 463 70.5 194, 14 329 471 Oliy/weli 5 0.8 468 71.2 189 15 343 501 Panil/Anin 2 0.3 470 71.5 187 16 253 369 Malam/Polga 4 0.6 474 72.1 183 17 24 263 Badu/Masmu 1 0.2 475 72.3 182 18 178 453 Kanu/Vihi 1 0.2 476 72.5 181 "Dry Zone (the rest are in the wet zone) Table 8(b) Network of forest complexes required to conserve endemic woody plant diversity in Southern Province, with endemic species represented in at least two complexes. Forest complexes are ranked in descending order of their selection, based on their contribution of endemic species to the network. FOP CG BG c om pl ie x Protected species Unprotected jj species Rank No. Subsidiary nos Name New % Total % Total 1 175 65,303,500,505,507,508 KDN et al. 0 0.0 0 0.0 251 2 414 498,499,506 Sinharaja et al. 147 58.6 147 58.6 104 3 138 293,388 Hora-Para/Mula/Ram 11 4.4 158 62.9 93 4 38 62 Berel/Daru 11. 4.4 169 67.3 82 5 120 129,509,511 Haba/Hay/Auw/Bamb 10 4.0 179 71.3 72 6 60 190,201 Dand/Keka/Kiri 4 1.6 183 72.9 68 7 173 208 Kande/Komb-Kott 6 2.4 189 75.3 62 8 398 399,400,401 °Ruhuna NP 1 0.4 190 75.7 61 9 69 77,497 Della/Diya/Kalow 5 2.0 195 77.7 56 10 329 471 Oliy/weli 2 0.8 197 78.5 54 1 44 164,523,524 Bund/Mangroves 2 0.8 199 79.3 52 12 253 369 Malam/Polga 2 0.8 201 80.1 50 13 343 501 Panil/Anin 1 0.4 202 80.5 49 14 37 Beraliya (Akuressa) 2 0.8 204 81.3 47 15 186 187,464 ’Kata/Kata/Weda 1 0.4 205 81.7 46 16 24 263 Badu/Masmu 1 0.4 206 82.1 45 17 178 453 Kanu/Vihi 0 0.0 206 82.1 45 18 237 525 Madu/Miya 0 0.0 206 82.1 45 "Dry Zone (the rest are in the wet zone) 70 Table 9(a) Network of forest complexes required to conserve: faunal diversity in Southern Province. Forest complexes are ranked in descending order of their selection, based on their contribution of species to the network. Those below the dotted line do not contribute any additional species to the network. Fores _t corm “prt te x Protected species Unprotected wa species Rank No. Subsidiary nos Name New % Total % Total 1 398 399,400,401 °Ruhuna NP 145 39.7 145 39.7 220 2 175 65, 303,500,505 ,507,508 KDN et al. 75 20.5 220 60.3 145 3 446 164,523,524 Bund/Mangroves 59 16.2 279 76.4 86 4 69 77,497 Della/Diya/Kalu 30 «8.2 309 84.7 56 5 138 293 , 388 Hora-Para/Mula/Ram 16 4.4 325 89.0 40 6 414 498,499,506 Sinharaja et al. ibs Sc0) 336 92.1 29 7 186 187 , 464 "Kata/Kata/Weda 7 1.9 343 94.0 22 8 329 471 Oliy/Weli 1.6 349 95.6 16 9 173 208 Kanda/Komb-Kott 4 1.1 353 96.7 12 10 37 Beraliya (Akuressa) 3 «60.8 356 97.5 9 11 120 129,509,511 Haba/Hay/Auw/Bamrd 3 0.8 359 98.4 6 12 24 263 Badu/Masmu 2 0.5 361 98.9 4 13 38 62 Berel/Daru 2 0.5 363 99.5 2 14 253 369 Malam/Polga tO eS 364 99.7 1 15 343 501 Panil/Anin 1 0.3 365 100.0 0 16 237 525 Madu/Miya 0 0.0 365 100.0 0 17-178 453 Kanu/Vihi 0 0.0 365 100.0 0 18 60 190,201 Dand/Keka/Kiri 0 0.0 365 100.0 0 "Dry Zone (the rest are in the wet zone) Table 9(b) Network of forest complexes required to conserve endemic faunal diversity in Southern Province. Forest complexes are ranked in descending order of their selection, based on their contribution of endemic species to the network. Those below the dotted line do not contribute any additional endemic species to the network. F} io Ahasenr is) ct c o m plex Protected species Unprotected Sata species Rank No. Subsidiary nos Name New % Total % Total 1 175 65 ,303,500,505 ,507,508 KDW et al. 41 49.4 41 49.4 42 2 138 293 ,388 Hora-Para/Mula/Ram 13° 15.7 54 65.1 29 3 69 77,497 Della/Diya/Kalu 9 10.8 63 75.9 20 4 398 399,400,401 PRuhuna NP 9 10.8 72 86.7 11 5 414 498,499,506 Sinharaja et al. 6 7.2 78 94.0 5 6 173 208 Kanda/Komb-Kott 2 2.4 80 96.4 3 7 37 Beraliya (Akuressa) 1 1.2 81 97.6 2 8 44 164,523,524 °Bund/Mangroves i [men Er! 82 98.8 1 9 329 471 Oliy/wWeli ie er 83 100.0 0 10 343 501 Panil/Anin 0 60.0 83 100.0 0 11. 253 369 Malam/Polga 0 © 60.0 83 100.0 0 12 237 525 "Madu/Miya 0 0.0 83 100.0 0 13. 186 187,464 °’Kata/Kata/Weda 0 60.0 83 100.0 0 14 178 453 Kanu/Vihi 0 0.0 83 100.0 0 15 120 129,509,511 Haba/Hay/Auw/Bamb 0 0.0 83 100.0 0 16 60 190,201 Dand/Keka/Kiri 0 0.0 83 100.0 0 17 38 62 Berel/Daru 0 0.0 83 100.0 0 18 24 263 Badu/Masmu 0 60.0 83 100.0 0 "Dry Zone (the rest are in the wet zone) Dellawa/Diyadawa/Kalubowitiyana, and Sinharaja et al. in descending order. KDN ef al., Horagala- Paragala/Mulatiyana/Rammalakanda and Sinharaja et al. are similarly the most important complexes for endemic and threatened species. KDN et al. and Dellawa/Diyadawa/Kalubowitiyana hold the most unique species (Table 4b). 2.4 DEFINING A MINIMUM CONSERVATION AREAS NETWORK The results of applying the two algorithms described in Section 1.2.1 to the woody plant data are presented in Tables 5a,b. A total of 15 forest complexes, covering 63,798 ha or 11.6% of Southern Province, are required in order that each species is represented at least once. The same network is selected irrespective or whether or not the iterative procedure is weighted for rarity (i.e. unique species), although the order in which complexes are selected differs. The three complexes which do not contribute any additional species to the network are Badullakele/Masmullekele[237], Kandawattegoda/Kombala-Kottawa| 173]and Kanumuldeniya/Viharekele[ 178]. Similarly, if the algorithms are constrained to endemic woody species, only 12 complexes are necessary irrespective of any weighting for rarity (Tables 6a,b). They cover a total area of 56,093 ha or 10.2% of the Province. The three additionally redundant complexes are Beraliya (Akuressa) [37], Dellawa/Diyadawa/Kalubowitiyana [69] and Madunagala/Miyandagala [237]. In order to check for redundancy within forest complexes, the first algorithm was applied to each complex to assess the contribution from each forest in terms of species and endemic species of woody plants. There are no cases of redundant forests with respect to woody species and only three cases with respect to woody endemics, namely Kataragama and two of the mangroves, Kahanda and Kalametiya (Table 7). Neither Kataragama nor Kalametiya, however, was adequately sampled. This result underlines the unique importance of most forests, based on surveys conducted to date. While it is sound conservation practice to ensure that each species is represented in at least two forest complexes, this condition cannot be met for either all woody species (Table 8a) or endemics (Table 8b). Even if all 18 complexes are protected, only 73% of woody species or 82% of endemic woody species would be represented within at least two complexes. It may be unrealistic, however, to attempt to meet this criterion as certain species may prove to be extremely localised in their distribution. This will only become apparent as forests in adjacent districts are surveyed. 2.5 OPTIMISING THE CONSERVATION AREAS NETWORK FOR FAUNA In the previous section, minimum networks of 15 and 12 forest complexes were identified for 100% representation of woody species and endemic woody species, respectively. These networks (Tables 5a and 6a) are very similar to those identified for 100% representation of animal species (Table 9a) and endemic animal species (Table 9b). In order to achieve 100% representation of both woody plants and animals, the total number of complexes required is 17, covering 6,6547 ha or 12.1% of the Province. Kanumuldeniya/Viharekele [178] in the wet zone is the only redundant complex not contributing any additional diversity. Complete representation of endemic plants and animals is achieved with 15 complexes, covering 64,206 ha or 11.7% of the province. Redundant complexes are Kanumuldeniya/Viharekele [178] and Badullakele/Masmullekele [24] in the wet zone, and Madunagala/Miyandagala [237] in the dry zone. 2.6 COMPARISON BETWEEN ACR AND NCR Early on in the NCR, it was decided to re-survey all natural forests previously covered by the ACR to ensure consistency in data collection. To date, 15 forests have been re-surveyed. A comparison between the two surveys shows that in all but one case (Viharekele) more woody species have consistently been recorded in the NCR than in the ACR (Table 10). The marginally greater number of species recorded in the ACR than the NCR in Viharekele probably reflects the fact that this site, along with Kekanadure and Oliyagankele, was surveyed twice during the ACR (A.H.M. Jayasuriya, pers. comm.). 72 Table 10 Comparison between ACR and NCR for woody plant species and endemic woody plant species recorded in forests in Southern Province. No Name of forest Endemism Common Unique Unique Total to both to ACR to NCR spp. (%) (%) (%) 37 Beraliya (Akuressa): all spp. 27.9 8.2 63.9 208 endemic spp. 31.3 3.0 65.7 99 38 Beraliya (Kudagala): all spp. 27.9 18.6 53.4 204 endemic spp. 32.7 14.3 53.1 98 60 Dandeniya-Aparekka: all spp. 31.3 26.7 42.0" 131 endemic spp. 25.9 16.7 57.4! 54 69 Dellawa: all spp. 34.8 17.8 47.4 247 endemic spp. 46.5 16.7 36.8 114 71 Diyadawa: all spp. 34.5 16.4 49.2 238 endemic spp. 38.1 14.4 47.5 118 173 Kandawattegoda: all spp. 29.7 26.7 43.6" 165 endemic spp. 29.7 31.1 39.2° 74 190 Kekanadura: all spp. 36.1 24.1 39.8" 108 endemic spp. 33.3 22.2 44.4° 45 201 Kirinda Mahayayakele: all spp. 26.6 30.5 43.0" 128 endemic spp. 32.1 20.8 47.2" 53 208 Kombala-Kottawa: all spp. 36.4 14.7 48.9 225 endemic spp. 43.5 13.0 43.5 115 263 Masmullekele: all spp. 41.1 28.8 30.1" 163 endemic spp. 39.4 25.4 35.2" 71 329 Oliyagankele: all spp. 37.3 29.4 33.3" 153 endemic spp. 37.5 27.5 35.0" 80 343 Panilkanda: all spp. 32.1 22.9 45.0" 140 endemic spp. 32.1 26.4 41.5* 53 369 Polgahakanda: all spp. 23.6 14.6 61.8 199 endemic spp. 26.3 15.2 58.6 99 453 Viharekele: all spp. 40.3 32.1 27.7" 159 endemic spp. 44.0 28.0 28.0% 75 471 Welihena: all spp. 28.5 13.0 58.5° 123 endemic spp. 28.6 8.6 62.9" 70 ooo “ACR survey was incomplete. "NCR survey inadequate (i.e. number of additional species in penultimate or last plot > 5% total no. species). 73 These results demonstrate the efficiency of the gradsect procedure in sampling for species diversity, and justify the decision to re-survey ACR sites using a systematic sampling procedure to enable comparisons to be made between sites. The comparison also reveals, however, that in many cases a large number of species observed in the ACR were not recorded in the NCR. This partly reflects the fact that 9 of the 15 sites are considered to have been inadequately sampled for purposes of the NCR. Comparative data from the ACR provide a valuable means of assessing the efficiency with which species diversity is sampled by the NCR. In the case of the six adequately surveyed sites, up to 18.6% (Beraliya Kudagala) of the total number of species for combined reviews were recorded only by the ACR. Thus, it can be concluded that gradsect sampling accounts for no more and probably less than 80% of woody species within a forest. As the NCR progresses, it should be possible to obtain better estimates of the efficiency of the sampling procedure with respect to woody plants, using the few sites for which comprehensive inventories already exist as standards. Examples of well-inventoried forests include Hakgala, Ritigala, south-western Sinharaja and Udawattakele. 74 3. SOIL CONSERVATION AND HYDROLOGY ASSESSMENT 3.1 IMPORTANCE OF FORESTS FOR SOIL CONSERVATION The estimated mean annual soil loss for each of the 45 forests under standard conditions (defined in Part B, Section 2.2.2) is given in Table 11. Kalubowitiyana has the highest erosion hazard due mainly to its high rainfall and steep slopes. The predicted proneness of this forest to erosion was borne out during a field visit when a landslide was observed. By contrast, Bundala ranks as the penultimate site. Despite its soils being much more erodible than any other forest, its rainfall and slope values are extremely low. In general, forests in the wet zone are much more prone to erosion than those in the dry zone on account of much higher rainfall and steeper terrain. Exceptions are forests such as Badullakele, Dandeniya-Aparekka and Kekanadura, which lie within low rainfall isohyets in the coastal lowlands. The acceptable level of soil erosion in Sri Lanka is about 9 t ha! yr! (Krishnarajah, 1984). All forests in Hambantota in the dry zone are at or below this threshold with the exception of Wedasitikanda, a reflection of its steep terrain. 3.2 HYDROLOGICAL IMPORTANCE OF FORESTS 3.2.1 Protection of headwaters of river systems This part of the hydrological assessment is based on the number of streams and catchments, and the total distance to the outlets of rivers. Walues for each forest are given in Table 12. Diyadawa is the most important forest fer headwaters protection, a reflection of it encompassing the headwaters of the Gin Ganga and Nilwala Ganga, and the substantial number of streamlets originating from this forest. It was observed during a field visit that a number of government and private organizations obtain water directly from streams originating from this forest, including water for a sprinkler system in a tea plantation. The adjacent forest of Dellawa ranks as the second most important forest. All forests in Hambantota District rank lowest in pnority for headwaters protection. Lying on the district boundary between Hambantota and Matara, Rammalekanda receives a comparatively high rainfall. Forty three streams originate from this forest and sustain two rivers, placing it in sixth place in the rank order. The majority of forests in Hambantota, such as Bundala, Katagamuwa, Madunagala and Miyandagala, have no streams at all, while those having one or two streams, namely Keulakada Wewa and Wedasitikanda, respectively, have no perennial streams. Such streams in the dry zone carry water only during the monsoon season, remaining dry for the rest of the year. 3.2.2 Protection from flooding The mean annual flood for each forest, estimated from its mean annual rainfall, area and stream frequency, is given in Table 13. Kanneliya should receive the highest priority for conservation with respect to flood hazard, reflecting its larger area, higher stream frequency and higher rainfall than most other forests. Other important forests for flood control include Beraliya (Kudagala), Dediyagala, Dellawa, Diyadawa, Mulatiyana and Nakiyadeniya. Habarakada has a particularly high stream frequency and rainfall compared to most other forests, but its mean annual flood is fairly low due to its small size. Forests with no streams, such as most of those in the dry zone plus Badullukele, Kekanadura and Oliyagankele in the wet zone, are of negligible importance in preventing floods. In contrast, the majority of forests in the wet zone have a significant role in flood control. 75 Table 11 Importance of natural forests in Southern Province for controlling soil erosion (Source: Gunawardena, 1993) —————————————————E———E————— ESS Es eSlSlSlSS§ No. Forest name Rainfall Erosivity Slope Erodibility Erosion Erosion mm yr’ Jom? yr mm’ t ha’ yr” rank GALLE 509 Auwegalakanda 4500 45748 0.380 0.22 1794.2 3 511 Bambarawana 4150 42265 0.310 0.22 1103.2 6 37 Beraliya (Akuressa) 2671 27549 0.200 0.22 299.3 235 38 Beraliya (Kudagala) 3660 37390 0.190 0.22 366.6 21 62 Darakulkanda 3457 35370 0.100 0.22 96.1 34 65 Dediyagala 3437 35171 0.190 0.22 344.8 22 69 Del lawa 3759 38375 0.280 0.22 817.1 12 120 Habarakada 4397 44723 0.410 0.22 2041.9 2 508 Hindeinattu 3083 31649 0.340 0.22 993.7 9 507 Homadola 3800 38783 0.220 0.22 509.8 15 173 Kandawattegoda 2550 26345 0.170 0.22 206.8 28 175 Kanneliya 3984 40614 0.190 0.22 398.2 19 208 Kombala-Kottawa 2680 27639 0.120 0.22 108.1 33 253 Malambure 3937 40146 0.280 0.22 854.9 11 303 Naki yadeniya 3561 36405 0.180 0.22 320.4 24 369 Polgahakanda 4051 41280 0.160 0.22 287.0 26 328 Tawalama 4340 44156 0.200 0.22 479.7 16 506 Tiboruwakota 4350 44255 0.340 0.22 1389.5 4 510 Yakdehikanda 3600 36793 0.320 0.22 1023.3 8 MATARA 501 Aninkanda 3250 33310 0.350 0.22 1108.3 5 24 Badul lakele 2070 21569 0.050 0.22 14.6 39 60 Dandeni ya-Aparekke 1839 19271 0.070 0.22 25.6 38 500 Derangala 3500 35798 0.300 0.22 875.1 10 7 Diyadawa 3410 34902 0.330 0.22 1032.3 7 138 Horagala-Paragala 3209 32902 0.210 0.22 394.1 20 497 Kalubowitiyana 4189 42653 0.480 0.22 2669.1 1 190 Kekanadura 1721 18097 0.030 0.22 4.4 41 178 Kanumuldeniya 2432 25171 0.140 0.22 134.0 32 201 Kirinda-Mahayayakele 1940 20276 0.080 0.22 35.2 37 498 Kurulugala 3400 34803 0.210 0.22 416.9 18 263 Masmul lekele 2076 21629 0.120 0.22 84.6 35 293 Mulatiyana 3034 31161 0.200 0.22 338.5 23 329 Oliyagankele 2465 25499 0.140 0.22 135.7 31 343 Pani lkanda 3133 32146 0.270 0.22 636.5 13 388 Rammalekanda 2837 29201 0.230 0.22 419.6 17 499 Silverkanda 3380 34604 0.260 0.22 635.3 14 453 Viharakele 2444 25291 0.150 0.22 154.6 29 471 Welihena 2723 28067 0.140 0.22 149.4 30 HAMBANTOTA 44 Bundala 878 9709 0.007 0.48 0.3 44 186 Katagamuwa 1100 11918 0.006 0.27 0.1 45 526 Keulakada Wewa 1300 13908 0.018 0.27 os) 42 237 Madunagala 1150 12415 0.047 0.27 9.1 40 249 Mahapi takanda 2368 24534 0.080 0.22 42.6 36 525 Miyandagala 1050 11420 0.013 0.27 0.6 43 464 Wedasitikanda 1062 11540 0.270 0.27 280.4 27 Table 12 _ Importance of natural forests in Southern Province for protection of headwaters of river systems (Source: Gunawardena, 1993) No. Forest name Streams Catchments Outlet distance Sum of Final qc“ — ranks rank No. Rank No. Rank No. Rank GALLE 509 Auwega lakanda 10 19 2 2 149 3 24 9 511 Bambarawana 6 20 1 3 44 26 49 27 37 Beraliya (Akuressa) 37 9 2 2 64 18 29 14 38 Beraliya (Kudagala) 43 7 2 2 71 16 25 10 62 Darakulkanda 4 22 1 3 33 29 54 31 65 Dediyagala 111 2 2 2 102 11 15 5 69 Dellawa 7 4 2 2 145 4 10 2 120 Habarakada 18 13 1 3 58 21 37 22 508 Hindeinattu 4 22 2 2 69 17 41 24 507 Homadola 15 15 1 3 47 24 42 25 173 Kandawat tegoda 4 22 1 3 7 34 59 37 175 Kanneliya 245 1 2 2 109 8 11 3 208 Kombala-Kottawa 17 14 3 1 59 20 35 18 253 Malambure 36 10 1 3 52 23 36 20 303 Nakiyadeniya 46 6 3 1 131 6 13 4 369 Polgahakanda 20 12 2 2 99 12 26 11 428 Tawalama 33 11 2 2 118 7 20 7 506 Tiboruwakota 12 17 2 2 154 2 21 8 510 Yakdehikanda 4 22 1 3 57 22 47 26 MATARA 501 Aninkanda 4 22 1 3 7% 14 39 23 24 Badul lakele 0 26 0 4 0 35 65 39 60 Dandeni ya-Aparekka 6 20 1 3 22 32 55 33 500 Derangala 1 25 1 3 45 25 53 29 7 Diyadawa 16) 5 2 2 158 1 8 1 138 Horagala-Paragala 41 8 1 3 61 19 30 15 497 Kalubowi tiyana 5 21 2 2 140 5 28 12 178 Kanumuldeniya 5 21 1 3 30 30 54 30 190 Kekanadura 0 26 0 4 0 35 65 39 201 Kirinda Mahayayakele 4 22 1 3 27 31 56 35 498 Kurulugala 6 20 1 3 99 12 35 19 263 Masmul Lekele 1 >) 1 3 17 33 61 38 293 Mulatiyana 96 3 1 3 52 3 29 13 329 Oliyagankele 0 26 0 4 0 35 65 39 343 Pani lkanda 6 20 1 3 7 13 36 21 388 Rammal akanda 43 7 2 2 103 10 19 6 499 Silverkanda 11 18 1 3 108 9 30 16 453 Viharekele 10 19 1 3 33 29 51 28 471 Welihena 3 3 1 3 3% 28 54 32 HAMBANTOTA 44 Bundala 0 26 0 4 0 35 65 39 186 Katagamuwa 0 26 0 4 0 35 65 39 526 Keulakada Wewa 1 23 1 3 41 27 55 34 237 Madunagala 0 26 0 4 0 35 65 39 249 Mahapi takanda 13 16 2 2 72 15 33 17 525 Miyandagala 0 26 0 4 0 35 65 39 464 Wedasitikanda 2 24 1 3 30 30 57 36 Table 13 —_ Importance of natural forests in Southern Province for flood control (Source: Gunawardena, 1993) No. Forest Name Rainfall Area Stream freg. Mean flood Flood mm yr ke? streams km ms! rank GALLE 509 Auwegalakanda 4500 2.50 3.20 13.91 18 511 Bambarawana 4150 2.48 2.82 11.62 21 37 Beraliya (Akuressa) 2671 16.46 1.80 25.11 12 38 Beraliya (Kudagala) 3660 25.72 1.48 50.42 6 62 Darakulkanda 3457 1.42 2.11 4.91 31 65 Dediyagala 3437 37.90 2.70 87.22 2 69 Dellawa 3759 22.36 2.90 65.47 3 120 Habarakada 4397 2.10 8.10 18.70 16 508 Hindeinattu 3083 2.00 2.00 5.47 28 507 Homadola 3800 3.00 3.67 13.86 19 173 Kandawat tegoda 2550 3.59 1.11 5.13 30 175 Kanneliya 3984 60.25 3.77 186.05 1 208 Kombala-Kottawa 2680 16.25 1.11 19.50 15 253 Malambure 3937 9.30 3.12 34.61 10 303 Nakiyadeniya 3561 22.36 1.74 46.93 7 369 Polgahakanda 4051 5.77 3.12 24.08 13 328 Tawalama 4340 10.00 3.20 42.46 8 506 Tiboruwakota 4350 6.00 1.83 20.85 14 510 Yakdehikanda 3600 1.00 3.00 4.62 32 MATARA 501 Aninkanda 3250 2.3 1.79 6.08 27 24 Badul lakele 2070 1.48 0.00 0.00 39 60 Dandeni ya-Aparekke 1839 3.48 1.07 3.17 33 500 Derangala 3500 0.75 1.33 2.31 35 77 Diyadawa 3410 26.48 2.52 57.72 4 138 Horagala-Paragala 3209 18.12 2.15 38.11 9 497 Kalubowitiyana 4189 2.72 1.84 10.23 22 178 Kanumuldeniya 2432 6.79 0.64 6.21 26 190 Kekanadura 1721 3.80 0.00 0.00 39 201 Kirinda-Mahayayakele 1940 2.53 0.91 2.40 34 498 Kurulugala 3400 2.85 2.11 8.62 24 263 Masmul lLekele 2076 6.18 0.14 2.14 36 293 Mulatiyana 3034 31.49 2.25 57.55 5 329 Oliyagankele 2465 4.86 0.00 0.00 39 343 Pani lkanda 3133 5.88 1.58 12.25 20 388 Ramma | ekanda 2837 14.07 2.22 26.55 11 499 Silverkanda 3380 7.25 1.52 15.86 17 453 Viharakele 2444 6.25 0.94 7.10 r+) 471 Wel jhena 2723 2.97 1.36 5.30 29 HAMBANTOTA 44 Bundala 878 62.16 0.00 0.00 39 186 Katagamuwa 1100 10.04 0.00 0.00 39 526 Keulakada Wewa 1300 3.00 0.33 0.96 38 237 Madunagala 1150 9.75 0.00 0.00 39 249 Mahapi takanda 2368 7.22 1.52 9.81 3 525 Miyandagala 1050 3.00 0.00 0.00 39 464 Wedasitikanda 1062 13.43 0.15 1.73 37 78 a XX EK REX EX XX XA SYS TS yyy ys ss yy) OC Z2SZSANZS ASAP SPITS SS SASS) 199 aaa S'S’ S'S a S'S S 4 BaGBeeo48sesesesses 0.9.9.9.9.0.9.9.9.9.9.F.F LGBESESESBSEEEBEESET A A A a A AY a oi OV OV OVO DRGGGGGGOBEEBEBET 185 190 4 \@'@'@'@ G'S 2 2'6 22'S SSSSESSEEESEEBEBESB! uw BI DS 16 RAR AAA ARAARAAARASA Ba@eeeweeseases’ WA A AA OS") DBRGEEGEEEEES SSSSSOS SCS SC OSG G2 LBEBEBEBEEBEEESE | RAAAAAAAAALAAY LY SYS See ys sy) 44" 2* "4" 4"4"S* 4° "LG GBECEEESB EET °.9.9.9.9.9.9.9.9.9.0.7.7T SB@esees_sesas' eV eV aVaVeVaVeleVeVe: BEGEGGEESEEESE! @ 2 2 @ 2 2 2 2 2 d.GBSeS@eGeB™esesesat SF .8.8.S8.8.8.8.8.7-F DLHGGOGGEESEEB VA OA bv OV OV OOS DEGGEGGOGOSES' RAARAAAAAASAA YS SS SS SS 17% 41 3 1B 3 @ ZB OA OSA SS" REGEEEE' CRRA RAASL SAY SY SS SSS SY aVeVeVe¥e¥.Ve" REGEEEEEEY Note: Bars are labelled by forest number. Su ‘&' &' @ 2S 2'2'2'S2SBBGSE EEE" ?.9.9.8.9.9.9.0. 7 B@eBewst A AO ON OVO" DEG o' 2 22 2 22 1. SS8SESEaav RAAAAAY SYS SS Sy WA AA Ses BEGEES Hydrology rank Cx x KES SS SS SS) 5LAAAAAAY SSS 3 4°4*4°4"S"1. GSS RARARAAYY SY Ver PRE EEY CY YY ¥ YS SS SES AAAAA TS CALZ SS SS ‘@ 2 1SSSBe" LAAAATS 3 pase 6 BS HSH Sw ee wR a DBD DI a CVWy Ts a A XA SY BAZ SS) CWI S33 KATY a R 8 8 £ & 8 a 8 a R § a x a ) g a a ccs Tro me WM BH Re wD BP ww GS) & AD 123 4S5SE7TVI9WUNNBMEUENBIDARBKHASBVWBBONDUNRBUMUUMVIBHDBDWDHDHDHH Hydrological importance of natural forests, showing the relationship between headwaters protection and flood hazard 3 :) a 3 3 a @ r) 2 9 a Ri 8 8 R 4 Fi g R R R 8 g By 000'T ¥ (a4eqn) OOT X (#/ta-ns) UOISOIO [IOS pooy jenuue uesyy Note: Bars are labelled by forest number. 8. 22 He Bee Sse ac quel siqqempeoy yues poor] 10 2a 30 “0 x» Figure 5 719 Soil conservation/hydrology rank 123 45678 IWIULNIMSCITCEIDALBDNSBNBIBDUVUBMSHMUNBAHOMLGUS Relationships between soil erosion, flood hazard and overall importance for soil conservation and hydrology Figure 6 3.2.3. Hydrological importance Assessment of the overall hydrological importance of forests is based on ranking the combined values of the ranks for headwaters importance and flood control. Figure 5 shows the relationship between hydrological importance and the two criteria (headwaters importance and flood hazard) used to assess it. The very good symmetry about the x axis clearly demonstrates that headwaters importance and flood hazard are complementary as indicators of hydrological importance. 3.3. IMPORTANCE OF FORESTS FOR SOIL CONSERVATION AND HYDROLOGY Evaluation of the overall importance of forests for soil conservation and hydrology is based on ranking the combined values of the ranks for erosion hazard and hydrological importance. Since flood hazard provides an adequate measure of hydrological importance, given its close relationship with headwaters importance (see Section 3.2.3), it can be examined in relation to soil erosion in order to identify criteria, based on absolute values, to classify forests according to their importance for soil conservation and hydrology. Figure 6 shows the relationships between soil erosion, flood hazard and the ranked importance for soil conservation/hydrology. The position of the dotted lined marks the division between forests considered to be important for soil conservation and/or hydrology to the left, with an erosion hazard >300 t ha" yr' and/or a flood hazard >10 m’s", and those estimated to be less important to the right, with an erosion hazard <300 t ha' yr! and a flood hazard <10 m? s’. Table 14 — Importance of natural forests in Southern Province for soil conservation and hydrology. Districts are in brackets. (Source: Gunawardena, 1993) VERY IMPORTANT MAY BE IMPORTANT LESS IMPORTANT Soil Erosion (>300 t ha™ yr’) For Erosion Hazard only Soil Erosion (<300 t ha” yr’) Mean Annual Flood (> 10 m° s*) (>300 t hat yr") Mean Annual Flood (<10 m? s*) 77 Diyadawa (M) 501 Aninkanda (M) 453 Viharekele (M) 506 Tiboruwakota (G) 508 Hindeinattu (G) 249 Mahapitakanda (H) 69 Dellawa (G) 510 Yakdehikanda (G) 471 Welihena (M) 509 Auwegalakanda (G) 498 Kurulugala (M) 178 Kanumuldeniya (M) 497 Kalubowitiyana (M) 500 Derangala (M) 173 Kandawattegoda (G) 175 Kanneliya (G) 464 Wedasitikanda (H) 120 Habarakada (G) For Flood Hazard only 62 Darakulkanda (G) 428 Tawalama (G) (>10 m' s") 329 Oliyagankele (M) 388 Rammalakanda (M) 60 Dandeniya-Aparekka (M) 253 Malambure (G) 369 Polgahakanda (G) 201 Kirinda Mahayayakele (M) 65 Dediyagala (G) 37 Beraliya (Akuressa) (G) 263 Masmullekele (M) 38 Beraliya (Kudagala) (G) 208 Kombala-Kottawa (G) 24 Badullakele (M) 303 Nakiyadentya (G) 526 Keulakada Wewa (H) 511 Bambarawana (G) 237 Madunagala (H) 499 Silverkanda (M) 190 Kekanadura (M) 138 Horagala-Paragala (M) 525 Miyandagala (H) 293 Mulatiyana (M) 44 Bundala (H) 343 Panilkanda (M) 186 Katagamuwa (H) 507 Homadola (G) Forests are classified according to these criteria in Table 14. Those 19 forests considered to be very important for controlling soil erosion and flooding warrant high priority as conservation areas. All of them lie in the wet zone and they include a number of large, other state forests or proposed reserves, which have minimal legal protection status. Examples are Beraliya (Kudagala), Dellawa, Horagala-Paragala, Nakiyadeniya, Silverkanda and Tawalama, all of which are at least 1,000 ha in area. Less important for soil conservation and hydrology are all those forests lying in the dry zone, together with some in the coastal lowlands of the wet zone. The latter tend to lie within low rainfall isohyets and/or lack streams, such as Badullukele, Dandeniya-Aparekka, Kekanadura and Oliyagankele. 3.4 SOIL CONSERVATION/HYDROLOGY AND BIOLOGICAL DIVERSITY In order to examine relationships between soil conservation/hydrology and biological diversity, is first necessary to treat individual forests as part of larger complexes by combining data on soil erosion and hydrology in the same way as done for the biological diversity study (see Section 2.3). It should be noted, however, that the present analysis is restricted to 15 forest complexes, these being common to both the soil conservation/hydrology and biological diversity studies. Not included are Sinharaja ef al., Ruhuna NP and Bundala/Mangroves because they have yet to be assessed with respect to their soil conservation and hydrological values. It can be assumed, however, that these three complexes will need to be part of any optimal conservation areas network in view of their great importance for biological diversity (see Section 2.3). Combined soil conservation/hydrology data for each forest complex are given in Table 15. There is an extremely close agreement between these results and those of the biological diversity survey (Table 4a). Both soil erosion and flood hazard are closely related to biological diversity, as shown in Figures 7a and 7b, respectively. KDN et al. [175] is outstandingly important for biological diversity and for control of soil erosion and flooding*, followed by Dellawa/Diyadawa/Kalubowitiyana [69] and Horagala- Table 15 Importance of forest complexes in Southern Province for soil erosion control, headwaters protection and flood control. Forest complexes are ranked in descending order for their overall importance for soil conservation and hydrology. Headwaters No. Subsidiary nos Name Area Erosion Stream Catch. Dist. Flood Rank ha t ha'yr' no. no. km ms" 175 65,303,500,505,507,508 KDN et al. 13546.4 390 455 3 621 384 1 69 77,497 Della/Diya/Kalu 4712.2 1025 155 2 443 133 2 138 293,388 Hora-Para/Mula/Ram 6775.6 372 180 2 216 122 3 120 129,509,511 Haba/Hay/Auw/Bamb 1069.6 1626 34 3 251 44 4 253 369 Malam/Polga 1874.6 638 56 4 151 59 5 38 62 Berel/Daru 4698.7 353 47 2 104 55 6 343 501 Panil/Anin 663.1 766 10 i 151 86 7 37 Beraliya (Akuressa) 1859.9 299 37 2 64 25 8 173 208 Kanda/Komb-Kott 2694.4 126 21 3 66 15 9 178 453 Kanu/Vihi 1503.8 144 15 2 63 13 +10 186 187,464 Kata/Kata/Weda 3184.7 161 2 1 30 a 11 329 471 Oliy/wWeli 821.7 141 x) 1 34 5 Ye 60 190,201 Dand/Keka/Kiri 1335.8 20 10 1 49 6 13 24 263 Badu/Masmu 987.7 71 2 1 17 fats 237 525 Madu/Miya 1275.2 6 1 1 41 1 a15 RR ‘Only flood hazard is considered here as a measure of hydrological importance, the other indicator being headwaters protection to which flood hazard is closely related (see Section 3.2.3). 81 Total erosion (t/yr) Flood hazard (cu. m/sec.) Figure 7 Note: Data points are labelled by forest complex number. a Dry Zone (7) a Wet Zone a Note: Data points are labelled by forest complex mumber. 4 Dry Zon a Wet Zone 100 190 200 250 300 350 Number of woody plant species Relationship between (a) total soil erosion and woody plant diversity, and between (b) flood hazard and woody plant diversity 82 suoTTTW (147) uoIsosO [RO], (‘oas/ui no) prezey poor 155 No. woody plant species 10 11 Y 13 14 15 Woody plant species rank 9 Figure 8 _ Relationships between total soil erosion, flood hazard and woody plant diversity Paragala/Mulatiyana/Rammalakanda [138]. The close relationship between erosion or flood hazard and biological diversity is likely to reflect rainfall and terrain. Species richness is correlated with rainfall and altitudinal range. Thus, forests with steep terrain and high rainfall support a greater wealth of species and are more prone to erosion and/or flooding than forests with a fairly uniform terrain and little rainfall. It should be possible, therefore, to predict biological diversity from importance for soil conservation and hydrology, the advantage being that the latter assessment is much more rapid than the former. Such predictions have limited value, however, because they are concerned only with total diversity. Species distribution patterns are not predicted, preventing minimum conservation areas networks from being defined. The relative importance of a complex with respect to soil erosion, flood hazard and biological diversity is shown in Figure 8. Flood hazard is plotted on the -y axis to facilitate comparison with total erosion (y axis). Thus, Dellawa/Diyadawa/Kalubowitiyana [69] is second in importance to KDN et al. [175] for both erosion and flood control, but it is marginally less diverse than Horagala-Paragala/Mulatiyana/Rammalakanda [138] which ranks third in importance for erosion and flood control. Complexes with a low diversity (< 150 woody species) tend to be of negligible importance for erosion and flood control, the exception being Kanumuldeniya/Viharekele [178] for flood control. Katagamuwa/Kataragama/Wedasitikanda [186] has a higher total erosion than other low diversity complexes but it is below the critical threshold of 300 t ha! yr’. There is also a close relationship between the rank orders of forest complexes for soil conservation/hydrology and biological diversity with respect to both woody species (Figure 9a) and endemic woody species (Figure 9b). The dotted lines mark the positions of the soil erosion and flood criteria used to classifiy forests into three categories of importance (see Section 3.3). Thus, complexes below both dotted lines are classified as being very important for soil conservation and hydrology, those lying between the lines may be important for either soil erosion or flood control, and those above both lines are less important. Superimposed on these figures are the minimum networks of complexes (solid symbols) required to represent all woody species (Figure 9a) and all endemic woody species (Figure 9b). Thus, a network representative of all woody species in Southern Province and including all complexes important for soil conservation and hydrology would comprise 14 of the 15 complexes, the only redundant complex (open symbol) being Badullakele/Masmullekele [24] which is among the least diverse sites and of negligible value in controlling soil erosion and flooding. Redundant in terms of the biological diversity assessment, but possibly important for flood control are Kanumuldeniya/V iharekele [178] and Kandawattegoda/Kombala-Kottawa [173]. Similarly, a minimum network of 13 complexes can be defined to include those important for soil conservation and hydrology, and to be representative of all endemic woody species. Complexes redundant with respect to biological diversity and of negligible value for soil conservation and hydrology are Badullakele/Masmullekele [24] and Madunagala/Miyandagala [237]. The three complexes of possible value for flood control (Beraliya Akuressa [37], Kanumuldeniya/Viharekele [178] and Kandawattegoda/Kombala-Kottawa [173]) are also redundant in terms of their biological diversity. Although Dellawa/Diyadawa/Kalubowitiyana [69] does not contribute any additional endemic species to the network, it is one of the most important complexes for soil erosion and flood control. Further optimisation of these networks is necessary in order to ensure adequate representation of faunal diversity’. All 15 complexes are necessary for 100% representation of animal species, including Badullakele/Masmullekele [24], which is redundant with respect to woody species and of negligible value in controlling soil erosion and flooding. The network of 13 complexes identified above for endemic woody species requires no further modification with respect to endemic fauna because neither of the two redundant complexes, Badullakele/Masmullekele [24] and Madunagala/Miyandagala [237], contain any additional endemic fauna not already represented in the network (see Table 9b). 84 Soil/water conservation rank Soil/water conservation rank Figure 9 Note: Data points are labelled by forest complex number. Erosion < 300 tha/yr Flood < 10 cums Erosion > 300 tha/yr Flood > 10 cums as 0 5 10 15 Woody plant species rank Note: Data points are labelled by forest complex number. 0 =) 10 15 Endemic woody plant species rank Relationship between rank orders of forest complexes for soil conservation/hydrology and biological diversity with respect to (a) woody plants and (b) endemic woody plants. A solid symbol indicates that the complex is part of a minimum network for woody species; complexes redundant for woody species are denoted by an open symbol. 85 4. DISCUSSION AND CONCLUSIONS 4.1. DISCUSSION The NCR is the first ever comprehensive and systematic evaluation of the importance of Sri Lanka’s natural forests for biological diversity, soil conservation and hydrology. It is also a unique project, unparalleled elsewhere in the tropics, and may provide a useful model for possible application in other tropical countries. Considerable progress has already been achieved, with about 10% of the country surveyed to date. Quite apart from the institutional aspects, immediate technical benefits are already apparent in the detailed knowledge of species’ distributions obtained from the thousands of records of plants and animals collected in the field. Many species, including endemics and rarities, have been recorded in new localities, and several species new to science have been discovered. Systematically inventorying plants and animals within individual forests, combined with estimating the role of forests in controlling soil erosion and flooding and in protecting the headwaters of river systems, is providing a sound information base for informed decisions to be made concerning their future use. While the immediate application of this information is to design an optimal national network of conservation areas with respect to natural forests, in the longer term it represents an extremely powerful tool for evaluating the potential impact of proposed development projects on forests, for monitoring changes in the composition of flora and fauna and for management planning, particularly with respect to zonation, including buffer zone development. The ultimate value of having undertaken a nationwide survey will be realised when the impact of any proposed development can be assessed in terms of which species are likely to be threatened or disappear, and how much soil erosion and flooding can be expected to occur as a result. Only then can the real costs of such developments be balanced against the benefits. 4.1.1 Costs of optimal networks The various conservation options and their associated costs in terms of land requirements are summarised in Table 16. This preliminary analysis shows that all 18 forest complexes, representing 12.3% of the total area of Southern Province, should be protected if maximum conservation criteria are to be applied (Option 8), namely 100% representation of woody plant species and certain groups of animal species, and protection of all forests that are important or may be important for soil conservation and hydrology. Even within this network, only 73 % of woody species, or 82 % of endemic woody species, would be represented in at least two complexes (Table 8). Representation of species in two or more sites may be an unrealistic goal, however, given the very localised distributions of some species, compounded by the way in which remaining natural forests have become increasingly fragmented and isolated. It should also be noted that no forest within any complex is superfluous (Table 7). Each forest is uniquely important, contributing species not found in other forests of the same complex. Of the various other options, all of which are less costly in terms of the amount of land required for conservation, Option 9 should be considered as a minimum policy in order to safeguard all endemic species of plants and animals, as well as protect complexes important for soil conservation and hydrology (Table 16). Any network comprising fewer than these 16 complexes could jeopardise the future security of some species endemic to Sn Lanka. Such a network is only marginally smaller than that representative of all species, and comprises 11.9% of the total area of the Province. Optimal networks of forest complexes defined by Options 8 and 9 are shown in Figures 10 and 11 for 100% representation of species and endemic species, respectively. The importance of each forest for woody plants, animals, and soil conservation and hydrology is shown as a series of overlays using a geographic information system. Unfortunately, the maps cover only cover Galle and Matara districts because forest cover is still in the process of being digitised at 1:50,000. Forest cover data were provided by courtesy of the Forest and Land Use Mapping Project, a Government of Sri Lanka/UK Overseas Development Adminstration venture. 86 Table 16 Minimum networks of forest complexes required for conservation, based on various criteria with respect to biological diversity and soil conservation/hydrology Cniteria Minimum network of complexes Redundant complexes No.’ Area (% prov.) Woody plants-—-——----—-------—______ 1. Species 15 63,798 (11.6%) [24], [173], [178] 2. Endemic species 12 56,093 (10.2%) [24], [37], [69], [173], [178], [237] Woody plants + Animals --------------— 3. Species 17 66,547 (12.1%) [178] 4. Endemic species 15 64,206 (11.7%) [24], [178], [237] —---------------------------------- Soil /Water Ls 12 60,562 (11.0%) [24], [44], [60], [186], [237], [329] Woody plants + -------------- Soil/Water 6. Species 17 67,085 (12.2%) [24] 7. Endemic species 16 65,510 (11.9%) [24], [237] Woody plants + Animals + Soil/Water 8. Species 18 67,851 (12.3%) none 9. Endemic species 16 65,510 (11.9%) [24], [237] Where appropriate, minimum networks include Bund/Mangroves [44], Ruhuna NP [398] and Sinharaja et al. [414], none of which has yet been assessed for soil conservation/hydrology, on the basis that [398] and [414] are of importance and [44] is of negligible value for soil conservation and hydrology. Area of Southern Province is 69,645 ha (Galle = 160,733 ha, Matara = 129,931 ha, Hambantota = 259,432 ha). The adjacent forests of Haycock (362 ha), Kataragama (838 ha), Ruhuna Blocks 2-4 (77,124 ha) and approximately two-thirds of Sinharaja (7,383 ha), which lie outside the Province, are excluded from the total area of the network in order to calculate its percentage of the Province. These networks, like others defined in Table 16, are conservative and will need to be reviewed within the national context once the NCR is completed. The distributions of many species are not confined to Southern Province and, therefore, a network representative of 100% of the Province’s biological diversity is likely to comprise fewer complexes as more species are recorded in forests outside the Province. There is an absolute minimum requirement of 8-12 complexes, however, if soil conservation and hydrological criteria are to be met. Seven complexes are estimated to be very important for soil erosion and flood control, and an additional three complexes are important for one or other of these parameters (Figure 9a). To this total should be added Sinharaja et al. and Ruhuna NP, both of which have yet to be assessed. They are likely to meet the criteria for classification as very impertant and may be important complexes, respectively, but not Bundala/Mangroves which is of negligible importance for soil erosion and flood control (see Table 14 for Bundala). The eight complexes vitally important for soil erosion and flood control are almost without exception the most diverse sites for woody species (see Table 4a). They include Sinharaja [414], a World Heritage site, and KDN [175] which is the second largest remaining rain forest after Sinharaja®’. KDN is considered to be the most important lowland rain forest in the country, its rocky terrain and acid soils being of special importance for many endemics. Despite having been logged and degraded, its diversity is extremely high and it is possible that SSinharaja National Heritage Wilderness Area (11,187 ha), itself, is smaller than KDN (present total area = 12,050 ha) but it lies within a much larger block of forest that includes other reserves such as Dellawa PR (2,236 ha) and Diyadawa FR (2,448 ha). 87 VA Forest network for ezosion / flood control E]| Forest network for woody species IN Rorest network for animal species e g e Figure 10 Optimal network of closed canopy natural forests in Galle and Matara districts, with 100% representation of species of woody plants and animals (selected groups) and including forests vital for soil conservation and hydrology [Erratum: The forest complex label 77 should read 69.] 88 68 [69 peer pjnoys 11 jaqu] xejdwioo ysosoy oY], supe) AZojospAy puw oONBasesMOd [10s Joy [BIA s}SJ0y Surpnjour puw (sdnoss p}99]95) sjpuiiue pre syuejd Apoom jo satoods ormapue jo uonByaasasdes % OO] WIM ‘SOUYSIP BBB] puE a]BH UI sjsosoy jenjeu Adouws Pesojs jo lomjou jeundg |, andy 5 4 = Sods [wuITE ofUApUD JOY OMITU OID] KY Jarmo, poog / Cowal Joy FACT Wo (7) OOO00r ‘I HIVOS little may yet have been lost. This would be unlikely to remain the case if further logging took place, and it is thought that the forest will take a long time to recover from past logging, even with strict protection (P.S. Ashton, in litt., 1992). Immediately to the north-west of KDN is another important complex [120] of isolated hills including Hinidunkanda, which lies in Habarakada and is the highest hill in Southern Province. Some extremely rare woody species have been recorded from this site, as well as Auwegalakanda and Bambarawana (see Annex 3). Dellawa and Diyadawa [69] are relatively large, contiguous tracts of fine rain forest. Not only do they rank as the two most important forests in the Province for protection of headwaters (see Section 3.2.1), but they represent an important extension to the adjacent Sinharaja, effectively increasing its size by over 40% (4,684 ha). Rammalakanda, lying within complex 138 and straddling the border of Matara and Hambantota districts, is an extremely interesting forest with typical wet zone characteristics despite being adjacent to the dry zone. The discovery of several Dipterocarpaceae extends the distribution of this family to Hambantota District. Similarly, the presence of Ceratophora aspera, Calotes leolepis and Balanopsis ceylonensis represents the easternmost boundaries of these strictly wet zone species of reptile. Further preliminary details on the biological importance of these and other forests are documented by Jayasuriya et al. (1992, 1993). The scientifically proven need to conserve most, if not all, remaining natural forests in Southern Province, subject to further review on completion of the NCR, precludes them from being converted to other forms of land use. But it does not prevent them from being sustainably used provided that control measures are adequate and properly enforced. It is possible to integrate conservation and socio-economic requirements through appropnate planning and management. Zonation is a major tool whereby conservation areas can be divided into areas having different management objectives and regimes. In the knowledge that a particular forest is important for certain rare endemics, for example, provisions for their conservation can be made by establishing core zones for their strict protection, while at the same time creating other resource use zones in which forest products may be sustainably harvested. Given the overriding conservation importance of most natural forests in Southern Province, their legal protection status needs to be reviewed and in many cases upgraded. Of the 52 forests evaluated in this study, 1 (Sinharaja) is a national heritage wilderness area, 20 are forest reserves, 8 proposed reserves, 15 other state forests, 4 sanctuaries and 4 (Ruhuna Blocks 1-4) are national parks. The National Heritage Wilderness Areas Act, 1988 and Fauna & Flora Protection Ordinance, 1937 (amended 1970) provide for the protection of habitat and wildlife in national heritage wilderness areas and in sanctuaries® and national parks, respectively. While the Forest Ordinance, 1907 (amended 1966) provides for the protection of forests and their products within forest reserves, its primary function has always been to provide for controlled exploitation of timber. Proposed reserves are essentially other state forests earmarked for notification and managed as de facto forest reserves. Clearly, the legal status of the vast majority of these remaining forests is inadequate for conservation purposes and will need to be reassessed in the light of impending changes to the Forest Ordinance. Such changes include proper provisions for forests as conservation areas. 4.1.2 Constraints While every attempt has been made to develop rigorous procedures for evaluating the conservation importance of natural forests and accommodate shortcomings, such as inadequate sampling of the fauna, there remains a major constraint. The biological diversity assessment is aimed at inventorying all woody plant species within each forest but, in practice, possibly only up to 80% of the woody flora is sampled (see Section 2.6). This does not invalidate comparisons made between forests and forest complexes because sites are sampled at approximately the same levels of intensity. However, it does mean that minimum conservation area networks defined on the basis of woody species’ distributions may not be representative of certain unrecorded species. Such networks may also be larger than the minimum required because species thought to be unique to a particular forest may be present, though unrecorded, in other sites selected as part of the network. This emphasises the importance of carrying out more detailed surveys of individual forests as part of the management planning process, particularly those of lower biological value that are likely to be assigned to a multiplicity of uses. The importance of additional species recorded as a result of such surveys can then be evaluated with respect to their known distributions and representation elsewhere in the conservation areas network. ‘In sanctuaries the habitat is totally protected on state land but traditional human activities may continue to be practised on private land. 90 4.2 CONCLUSIONS The NCR is the first ever systematic evaluation of Sri Lanka’s natural forests for biological diversity and for control of soil erosion and flooding, based on comprehensive field surveys. It is a unique project, unparalleled elsewhere in the tropics, and may have useful applications elsewhere. While its immediate value is to enable an optimal network of conservation areas to be defined with respect to natural forests, in the longer term the information generated by the review provides an extremely powerful tool for evaluating the impact of proposed development projects, for monitoring changes in the biota and for management planning. All forests of 50 ha and larger within Southern Province, representing 10% of the country, have been surveyed to date. Many species of plants and animals, including endemics and rarities, have been recorded in new localities and a few species new to science have been discovered. The results of this survey are preliminary because the importance of forests cannot be evaluated within a national context until such time as the NCR is completed. Analysis of species’ distribution patterns and climatic and topographic variables, such as rainfall, slope, soil type and stream frequency, shows that virtually all remaining forests in the Province are of considerable importance for biological diversity, as well as for control of soil erosion and flooding and for protection of headwaters of river systems. In order to represent all endemic species and include forests important for soil conservation and hydrology, a network of 16 forest complexes would need to be conserved. This represents 11.9% of the total area of the Province. All 18 forest complexes, covering 12.3% of the Province, would be required for representation of all species. This reflects the very localised distributions of some species, with the result that each complex is uniquely important, contributing to the network one or more species not found within other complexes. The networks defined from the results of this study, however, are conservative: less extensive networks may be identified once the NCR is completed and species’ distribution patterns are fully determined. In general, the larger the size of a forest, the higher its diversity of species. This finding emphasises the principle that conservation areas should be as large as possible to maximise representation of species (as well as viability of populations). An outstanding case is Dellawa and Diyadawa, both extremely diverse forests important for watershed protection and adjacent to Sinharaja. Their designation as conservation areas would effectively increase the size of Sinharaja, thereby enhancing its biological value. It is vital, however, that small forests are not overlooked when defining conservation area networks because they may be important for rare and endemic species. In view of the considerable importance of all remaining forests in the Province, their future conservation role must now be addressed. The legal status of many of these forests will need to be upgraded, particularly those currently designated as other state forests (including proposed reserves). Conservation requirements need not preclude sustainable use of forest resources, provided that it is properly managed and controlled. Forests providing a multiplicity of services will need to be zoned, with core zones for strict protection of biologically important biota, resource use zones for harvesting forest products, and buffer zones for absorbing the impact of peripheral human activities. 91 REFERENCES Gunawardena, E.R.N. (1993). A report on the importance of natural forests in Galle, Matara and Hambantota districts for soil conservation and hydrology. IUCN/EMD Report No. 15. 19 pp. Jayasuriya, A.H.M., Karunaratne, G.P.B. and Abayawardena, S.D. (1992). National Conservation Review Interim Progress Report: January - June 1992. TUCN/EMD Report No. 13. 11 pp. Jayasuriya, A.H.M., Karunaratne, G.P.B. and Abayawardena, S.D. (1993). National Conservation Review Interim Progress Report: July - December 1992. TUCN/EMD Report No. 16. 14 pp. Krishnarajah, P. (1984). Soil erosion and conservation in the upper Mahaweli watershed. Paper presented at the International Workshop on Soil Erosion and its Counter Measures. Chiangmai, Thailand, 11-19 November. TEAMS (1991). Review of forest management plans for environmental conservation. 2 volumes. Final Report to Forestry Planning Unit, Ministry of Lands, Irrigation and Mahaweli Development. TEAMS, Colombo. UNDP/FAO (1989). Environmental Management in Forestry Developments. Project Document. Mission Report SRL/89/012. December 1989. 92 + + + D : : sn}no309 es 1weuy . . . . . . . . . . . . . . . . . . . . . . . . . . eue 1 ybim snss190)aduy A 0 0 = 0 6 0 . . . a a . s 6 . . my in a + < 6 m . i $14e]oyos @1U03s]¥ + + + + : + + + ° : + + + : + + + + + + + + : + + + 8) )Aydousew B1u0js)y a A . . : . . . . . . * a . : + 0 : + + : 0 C + + : @1)0stduedaues auydepoas yy a A a . a A a . 6 . 5 . . Q . . ‘ 5 a « é . : s C edie20ua}9s easuoud)y + + + + q + + + E : + + + ; + + 2 + + + + : . + + : snoitue)Aaz sn)Audo))V~ . . . . . . . . . . . . . . . . . . . . . ry . . . . sue ue sn)Aydo) 1 a . 0 . < ; = o 5 0 é * . . : 5 . ‘ ‘ 8 0 ‘ A a ewissjeJopo eLZIqiy . e . * 3 . A . A é d A 6 6 . é ' c A : . é . G 3 sisuaulyo elziqqy . . . . . . . . . . . + . . . + . . . . . . . . . . WNL }OSLLATeS win {6ue }¥~ ti ot . + 9 + + q . + + : 0 + + 2 + + z + > : + + : {4aupse6 BIply 5 a . . 5 . 5 . . . . . . . . + . . . . . . . . ry . eo1pul sAyoeysi3souby . . A . . + * . : . . + . : : + . . - + L + 5 cs + + e39814109 sAyoe3S1}SOJBy, A . . . . A, . . : . . . + . . . : . . G + : . F rs : eotunsad sAyoe3slysouby . . . . . . . . . . . . . . . . . . . . . . . . . . eaptoubeae)a e1e)6y . . . . . . . . . . . . . . . . . . . . . . . . . . $0) ABu09 e1e)6y + + : + : + + q - + + + $ : : + + + + + : . : + + + eduesoide e1e)6y, ns : 5 rs Q 6 5 . O e : e 0 . . * + 5 & 5 3 n . rm . e esowA2 ewsoueby . . . . . . . . . . . . . . . . . . . 5 . . . . . . uinje)no}us09 seaoiGay . . . 5 ° 5 . . . . . . . . . O . . . . . . . a . . 8)ejado1se] eupueulpyy ° . . re : . . . . 5 . . ri é a e - ; o < c A - . 2 40}091q esayjueuapy, + + . . . + . ° . . . + a . « . e . + + 5 . . . 4 . euet}4y6im eluepy . . . . ° . . . . . . * . . . . . . . . . . . + + . e)epuoy Bluepy : : : : 0 c . S : . : : A “ c . 2 A E ' é p E < : sueba)a auydepout32y4 qi : : a : . 3 S « O 2 ° « A A d . . - . r i ® “ euea)}opueo auydepout joy . . . . . . . . . . . + . . . . . . . . . . . . . + suoJ}iq)e auydepoul33y~ + + : + : + + : ; ; + + 2 + + + + + * : é . ; + + epiqye auydepou! 3V» . . . . . . . . . . . . . . . . . * . . . . . . . . StsuasJay6) Leu 8) 1ydajoy : + : + : + : + Z . + : + 4 , " 8 : + B : + : + + + @3e8)nouNpad eLydAuouoy : . : Q O . 5 5 4 . 5 . A 8 A A cs c s a 7 a < Q 5 SNLJOSLIIDL snyjuedy, . q : : : i A . . . 7 a 6 ' cs cl 3 . e A 4 c : é eauunga eloeoy . * . G 5 0 0 : : O s 0 5 . 5 Q c J ' : ' 5 A 0 c e1saeo eloeoy , ° ‘ : . o G 0 . . . : ni . i a 6 6 c A : a A é 2 wnsooi3nu} uo) Lanqy . . . . . . . . . . . . . . . . . . . . . . . . . . wno13e1se uo) Lyngy . . . . . . . . ° . . . . . . . . . . . . . . . . . snisojeseud sniqy . . . . . . . . . . . . . . + . . x a . . . . . . . eulwabiq BW JEQY » £0£ £62 £92 £62 L£2 B02 102 061 LBL 981 BLL SL £2L 991 BEL 62 O2L ZZ 69 S9 29 09 4 BF LE 42 satoads snuay i ‘Joquinu sop Aq pajouap ae sysoJ0.4 *ysLa\se ue Aq poyJew oe sonuepuq SaI0ddS LNV1Td AGOOM AO LSIT | xouuy “+ + £0£ “+ + + £62 £92 “+ £S2 ge o+ 902 + Loz 061 281 981 “+ + + B2L + SLt £L1 791 e+ + Sel v6 + + + + + 5 . . 5 . . . 5 . 5 + + + + + + . + + a 5 + 5 5 . O si a O + + + + + + - 2 + + . . . . 5 . . O O . . O 5 O 5 fe . ns . . 5 5 5 A D . 5 6 5 a . 5 o . 5 5 5 a 5 b . . 5 5 5 5 5 a © . A . 5 a A = 3 G 5 5 . 5 O + . 5 9 a a G . 5 5 5 . . A O o . + + + + + + + 5 + + . 5 5 5 . 5 a . 5 a . . 5 “ 3 . * O ° 5 + + + + + + + x + + 5 rs i ° 6 a 6 5 a 5 a a A O . ° . 0 . a 5 5 . . 0 : o . . . 5 © 5 A ny o . 5 5 + ° + + + 2 . 2 + + + + + + + + * + + + * + + + + os i + + . 5 5 5 A 5 5 . 5 9 + + + + + + + i + + + + + + + + ‘ ? + i 5 5 . i 5 5 ci a . . + + + + + + + F + + . . 5 . . 5 . . . ° 6et O2t 22 69 SOC 9 09 7 en ee ++ “+ 92 wn313d1))a e)nbuexas eZ 1 ysouwAB eo.tueael esnjai ttuoow BaePl-SIZLA eqiao eo1yeqe)ew 838) }squin eo1ue)Aao @1)0}1ps0os edtyeqe)}ew e@sojUaU0) esowsle4 esowaleJ @)nBue nse eyjueses3a3 Bot pul e21uU8)Aaz eulsew 8) )Aydouow e1ue)Aao snjed)e} St) 1qou sn) )Aydosazay snue | zowo6 snoiue)Aaz suabuls fisazemMy e3nssiy nyoa3e2 @40)419ned 1 tuoow {uaupse6 @}@)0a9uUe) ewsadsoipseo 8381)031q esonbi)}1s sniung wnptoe wn3e | Jqnuew eBsowenbs Sap towoweuu | > snjewey satsads wn) )AydossAgy esainbng @uainbng eaonig Bt yopisa BL )apligy e1uAaig xequiog @lsawyaog e1yse)g BSouBs eAssag e1uobag etutyneg etuiyneg @1u0} bul seg e1uoj6ul seg ewizy ejyoesipezy BupueulXyy euUadLAy e1que)eIy eljuejery sn6esedsy sndie20}4yy sndses0juy sndueso0jsy sAsjoqgeqiy @1490)03Sl4y @1asABsyy elasABsy eoa4y elsipiy @LSIPIVy BISIPIVy esosody, esouody, eiueydy ewedy ewsaptjuy ewsapizuy uoupuapouy euouuy 8a) )Aydos tuys SNPB}I0IISLIUYy snua) S6 + : a S . * : * : + 2 * 83e}NI Lune eisse) 0 . . f 6 c 6 O n A 0 Gi . a o o é Q 5 o Q 6 “ . O f unone}6 auisse) + + : : + : E s : . . : p 2 : ° . + 2 : 2 g + 7 : e91ue}Aa0 eaunodisse) + . . + . . . . . . . * . . . . . . . . . Q . . . . e21ue)Aaz elyeese) . . . . . . . . . + . . . . . . . . . . . . . . . . eo1ydijy\a e.sease) + + + + : + + + ¢ : o + + : + a + + + + + + o + + + suaun 830Ase9 : 5 0 : . . A ci b . a 5 : O . ci é Gi ‘ 5 . . O 5 : esnja euowue . . . . + . . . + + . . . . . . . . . . . . + . . . @))Aydous iw euowse) . . . . . + . . + + . + . . . . . . . . . . . . . - wnseuids essise9 + ‘ : + = + : ‘ ‘ : : + os E © + g + + + . a 3 . , euioA)e9 @1))8489, : % + + 5 + . E = :- “4 5 * B + % + + + + = a * + + > eyeryseug @1))eue9 . . . . . . . . . . . . . . . . . . . . . . . . . . 821ue)Aaz siuedde) . . . . . . . . . . . . . . . . . . . . . . . . . . elueidas sisedde) . . . ° O iQ . . 5 e . . . . ° : O . . O . 6 © 5 . : @so)nounpad stuedde9 . + . . . . . . . . . . . . . . . . . . . . . . . . tLuocow sisedde9 . . . . . . . . . . . . . . . . . . . . . . . . . . sipues6 stuedde9 . . : . . . . . . . . . . . . . . . . . . . . . . . . eJed1JeAIp stuedde9 . . . . . . . . . . . . . . . . . . . . . . . . . . eoonpeq stuedde9 . . . . . . . . . . . . . . . . . . . . . . . . . . tipaays wnt yjued . . . . . . . . + + . . . . . . . . . . . . . . . . wn )nsaqnd wniyjue9 + + + + : + ® + - + = + z J + s SS + + + + i 8 + > 5 wn33031p wniyjue), A . . a : 5 5 . 5 A . 5 : ‘ . 0 O 5 O 5 ti G . : 5 5 wn 1 )apuewos0a uniyjue9 . + . . . . . . . . . . . . + . . + . + . . . . . . wnje }nuedwes wnty ued, + + + + : + + + ° * + + + = + + + + + + + + ; + + + wno1ue)Aaz wntueue), + + < + : + 3 4 * ¥ " + 5 s A + + + + + : : + + : eo1ue)Aaz ewuadsoudwe), . . 6 D . ‘ D 5 * 5 O . b & a c . ao a Ql . S . a O Sisuauls et) )awe9 + + + + : + p + : : + + + s ° + + + + + + + : : + + 11saz1emy3 win) )Aydo 7282» . . . . . . . . . . . . . . . . . . . . . . . + . . 1433e)Nnos wn} )Aydo)e9 + + a + i + + ‘ ? : i + + = - + + + + + a Rc + + e tLuoow wn) )Aydo)89~ . . . . . . . . . . . . . . . . . + . + . . . . . . unBuo)qo-0}eps09 win) )Audo}e2». . . . . . . . . . . . . . . . . . . . . . . . + . . eqe}e9 wn) )Ayudo)e94 + + P + ; + : ; ‘ : ‘ + : + + + + + + 3 : : + + : wnje3}9e1q win) )Aydo) e805 + + 7 + > + . 5 5 * 4 + + < + + + + + ? 3 + ‘ ri + : snpise wn) 1Aydo 129% . . - . . . . . . . . « . . . . . . . . . . . . . . . esojuawo} edses1})e9 + + + + : + + : : . : + + ; + + + + + + 4 2 : + + : snoiue)Aaz Smue] 2D» . * 5 . . . . 0 . a ) c) n O ny : a A n . . 0 a A A 0 11sa31eMy3 snue}e9 5 + + + ¥ P + + " , + + ‘ 9 + + + + + + + + 2 = + + SL}BALJ snwe)ed% . . . . . n D O 5 0 S 5 o O A : . 5 o 5 . o . a 6 O snjeipes smule ]8) + . . + . i. . . . . . + . . . . . ‘e A ¥ . . . . + . Sinua3-opnasd snwe)}e9 . O O ‘ . ° 0 . 0 0 . ° . . 0 . . o a a . 5 3 0 0 O snap 1oao snue ]89% + + . + e + 4 4 “ * ‘ + % 7 + Ss ° o + + E i . ~ + @ sn}e316ip snwe]89, “ + . + : + R ‘ = ‘ 3 + + : 2 + E + B + : . + + . snjnje51 )ap Snwe] ed . . . . . . . . . . . . . . . . . . . . . 0 : . : : eduesouawAy e.uidjesaeg . . . . . . . . . . . . . . . . . . . . . . . . . . Bysiuo etuidjesae9 . . . . . . . . . . . + + . . . . . . + . . + . . . onpuoq e.uidjesae9 i Nir es a Nt lg I £0£ £62 £92 £52 L£2 B02 102 O61 Z28L 9BL BLL SAL E/b OL BEL 62t O21 22 69 S9 29 09 % Bf LE v2 satoads snuad 96 + . . : ewOOYIIP Bipsoy . + . . . . . . O . . . 5 . 5 5 . . 5 5 5 5 . . . ° sndsesouow snueuuo3 + + + + - + + + s . + + + - + + + + + + + + é + + + t Luo tdweys snJeuuo), . . ° . . 6 O a 0 5 . . s a 5 a . 5 . : q * o . 5 A e3epneo esoyd m0) . . . . . . . . . . . . . . . . . . . . . . + . . . eue 1 3yBim @ajj09 . A . . . R : a é : 0 6 . c : . a 6 a ie 6 . . ‘ : SisuasooueAes3 Bajo rm Fi . 6 6 C 5 5 a . . A 6 : K 6 x i be Q . : é . . Q equity eiwapt)3 . . . . . . . . . . . . . . + . . . . . . . . . . . un3e}no1ued wnspuapo.a}9 9 : : . ci 0 a . O oO . : . . d . O a fe 0 . rs . qi 6 auuaut unspuaposa)9 + + + + S| + ; + 7 . 3 + + + 5 + + + 5 e + + = + + . wnjzeunzJojul wnJpusposa)) ‘, A . . . . . . . . . . . . ay . : + + : 0 3 C + 0 + snejejnosado xA]@903S19)9 x . . i. . a . . . : : #5 : : : + : . : + + : : + 0 . snjnjed sny3ue3sia)) a ae s : o : : Z Oe. 2 er ste : : ® PR Oe 2 : 3 : eS 0 snpt} ed SNYIUBIS 139} I» st + . + . + : : 7 0 . + : : . + + : + + : + 3 + + 3 snaut6nisay SNY}UBIS19}Iy . . . . . - . . . . . + . . . . . . 4 . . . . A . . eeutuncse @1UJOYB9 I~ . . . . . . . . . . . . . . . . . . . . . . . . . . wnuo}4t21ds uotpia}) *, 4 . : ‘5 . . . . . * “a * 0 A a fs 6 7 0 : c . . 0 Bo 1put euasne)) . . . . . . . . . . . ie . . . . . + . . . . . Pn, . . eje}Uep Buesne)) . . . . . . . . . . . . . . . . . . . . . . . . fs . Buel y6im snssi9 + + . . . i . . . . . . + . . + . . . . . . . + . . @38qQ0) 143 snssi9 . . . . a . . . a + . . . . . . . : : . . . + . 0 * s1ue)n6uespenb snssi9 + + + + : + + : . : : + : 3 + + + + + + + + . + + : eueauday snsst9 . . . . . + . . . . . . . . . a “ . . . + . . . + . ejeuiwnse SNSSIDy . . . . . . . . . . . . . . . . . . . . . . . . . . wno ue )Aaz wnwoweUU td . + . . . + + . . . . . + . + . . . . . . . . . + + WNJ3A wnwoweUU td m . . 5 . . . . . . . : . : . + a : a + : . 7 0 0 stsuafeseyuls WNWOWBUU LD» + + + + : + + + 3 : + + + : + + + + + + + 5 : + + : wniqnp WINWOWEUU t a 5 a 5 O . . . : . . . . . . . . . : . . . : . . . apuoso2 -nunddes WNWOWBUU LD» . . . . : . o . x A : ; : . . A a 9 : 3 : C e a euLgnjaa eisesyny) + : : + : + + + z = : * . : * . S : + : + S + + : wnje )}oadue } wn) )AydosAsy9 *. . . . . . ° . . . . . . . . . . . . . . . . . . . suesbeuy eydsowauoy) . . . . . 5 . . es iS . . . . . . 5 5 . . . . 5 5 5 . e1uazaIMs uo} Axo40)y9 . + . . . . . . . . . + . . . . . . . . . . . . . . es0)stpiqje sny3ueuoiy), . 5. OL Oo . . . . . . . ° 5 5 5 5 o a A 5 ° . 5 . 5 9 ° sap10}Axo1ydo e.yessey 5 : x A é A a A . . is . . . . + . : + + + . . + + . snaoe 1409 sndse90jaeydy A x ie x . fy iS a . . i a A . A + + + + + + + S + + + sndsesouejseo sndses0jaey9 . . . * 5 . A . 8 5 Q 6 6 a dl 6 D ‘ 5 . A < 5 we} }0po eyaquag + + . . . . . . . . . . . . . . . . ° . . . . . . . Sisuasowly $13)99 . . . . . . . . . . . . . ¥ . . . . . . ° . . . - . BLyOpt4y e1jesAed 4 6 : : Q 4 . . . . . . . . . : . : . : . . + . 0 J esoutds weBayeunje) . . . . . . . . . + . . . . . . . . . . . . . . * . e21yeqe)ew we6Baseunje) . . . . . . . . . . . . . . . . . . . . . . . . . . B40} esse) . . . . . . . . + i . . . . . . . . . . . . . . . . eawels esse) . . . . . . . . . . . . . . . : . . . . . . . . . . ttyBunqxos eisse) . . . . . . . . as + . . . . . . . . . . . . . . . . eBynysiy eisseg £0£ £62 £92 £52 24£2 B02 102 O61 ZBL 9BL BLL SAL £2b 99 BEL 62t O2t 22 69 S9 29 OF Bf LE 7 saltoads snued + + + + + + : + + . + 5 c . m ns ° 9 < 5 . . . a . O rs 5 i 5 . O O 5 7 + + : + * + 5 5 5 D s : + + U + : + a ° . . rs . + + + + + + a . = 0 5 . . © ns O . a + + + + : + . . . . D ° + + : + $ + . . . . . . 5 a Q . . Q + + + + $ + O G 5 . . . + + + + ; + + + + : * + + 5 + 2 ‘ + + + + + $ + 5 a . 5 O . is 5 . * d . a rs . . o . . 3 a a a O + + M4 + . + 2 + + + a + ° 5 0 5 5 5 “+ + o+ Con L6 ait + + eet ++ + “+444 “++ tL Luoow eue}UOW eolseqe jew saptouBisut stu6tsut eynssty B@aJsa} wnuaga ejeuawnss edses0jyaeyo ejnoe 8407}1q)e ejenuaz Ee ejeuiwnse sn) 1eqe)6 esnjas @adauld e91ue}Aaz wno ue }Aaz wnueJas ay sapio.uo)ab @1jOptsua wnap toao e91ue)Aaz sue6a)a suapueos @40)41Ased Si suaseued eueiyoi)]eM oossis-opnasd siyeutsuio sno.ue)Aez eueosAesod e91ue}Aa0 euetyBbim snueisa3 1emMy} tLuooWw 4ajto0e) snoijzewoue esolBl ja wnjnosniuqeos wnjes3saua} 81) 0$16u0)qo e@910u0W yeuey6 soJAdsoldy sosAdsoig souAdsoigq souAdsoig sosAdsoig soJAdsol dy souAdsoig soJAdsoig sosAdsoigq souAdsol dy soJAdsol dy soJAdsoldy soJAdsotdy sosAdsotdy xA)es0ydsowg @1U9)] 1d» sAyoe}sosyoig 9Y43UB) LYDL dy wn)e,edeysid, wn)ezadeyoig wnjejyadeysig e) euetg wntyerd SOWSI Cy SOWS3 Gy st4usaq st4saqg st4saq et seabaiqeq e164aq\eq seoA9 xA}e9043eAD 21U9)] NI» 21U9)] NI» eAses03dAu9 u0}019 U0}019y uo}049 03019 @Aa}e19 wn 1qo]2)A309y wntutsso9 Blpso) elpso09 eipsog OE TTT eee £0£ £62 £92 £52 L£2 802 102 061 281 981 SZ sZt £LL 991 SEl 621 02L Zl 4) 09 "9 gf 2 92 saioads snuay £0£ £62 £92 £52 L£2 802 + “+ + “+ LO02 061 “+ + 98L 82 “++ + ++ i i re + ++ SLL gel + + + 621 “+ LEC 02L 86 + + Zl + + ee ot “+ $+ 69 s9 29 “+ : e3e9}981q e1uabng : wno tue )Aaz wnuwsadsos4y3Au3y : wnd1ue}Aaz wn) Axouy Aug : wnt }041sn3qo wn Axouy3Ad3y + 1 tuoow wn )Axosy3As3 $ wno iput win Axosy Aug + e3e)nd1ued aqioAs3 Q asuedeo wnway3UeI3 : e91ue)Aaz epejug O ejyeuiwnoe wny3UeSsOd LUZ, seqis Bt yequa : @1jOst3e) snuBeae }3 q snso}}] lAqns sndie509e | 3% : snjessas sndseso0ae}3 ° 4331 )Npue 16 sndses0ae ) 3, 0 snuaowe sndses0ae)3 : SLAde] e1jasyg : wnut43sn6t) wnt joqo3 awo}ytsAd wn)AxosAq + BWIOJLILY wn) Axoshq © e1ueidas sajadAug : tt yBungxou sajyadAsg + Lisaz1emy eusesesg : e21Uue)Aaz eucog + eso ]NuaA eucog : BL ost zadesy euoog : e6uo)qo BUu00g, : BSOAJaU eucog g 8) )Aydossew eucog : luaupse6 BU00d, : 240)4$13Sa6u09 eucog + stutyye eucog + snoiue)Aaz sndses0sa3dig, q stu6tsut sndse30J93d 1 dy + snpidsiy sndse30433d1 dy : snso)npue 6 sndses0Jaidi dy 3 SNSOINJIIA snosipo)dig + 1494)eM soJAdso1 dy + Lisa, Ley soJAdsot dy + @9138A]AS sosAdsoig = esoweseJ sosAdsoig + @31saenb soJAdsol dy a @1)0}1]8A0 souAdsoig : @1}0$131S0ddo soJAdsol dy . @1 104 16u0)Go soJAdsot dy 1g saioeds snua9 + + + + i + + + : + * + + + f = Z + . n . ° 5 c + + + + . + FR 5 . és © 5 + + : + : + 6 O A 5 o A 5 : . . . . . . . . : 5 . . . 5 . . . . . 5 . . . a . 5 ci . A . 5 5 . . . 0 5 . . . . . . + . . + + + fa = 4 . + . . 5 . . . . ° . . 7 . e . . . + + + + é 2 + ° z + ° + + + 2 rs ; + . . 5 5 5 5 . a . : : . + + + + = + i . 5 ° in 5 * ° = ° . i. + + : + . + . A O 5 . . . 5 . “s : q + a 9 . 5 5 > . 5 ° 5 O + . 5 4 o . . 5 5 . 5 5 +++ + + ++ 8+ “+ “++ “++ + + “+ +++ ejeoids e@}1saenb @) )as0w tLuowsay eduesoulysa 1494) eM sueutBea easo eJeI1JaAIp 1424) eM Botpul eotpul @83810]09 suaidisap @140}9uUL3 esolbl jas BsowsaseJ e@SOAJau St)) OW eduesossiw SLAa@) epidsiy tiuosnBsay S1WJO}LSU9AIP edues0}neo eso))e9 ew Juadse e21ue)Aaz JouLw e31Uue] 199 ey90))e6e ejzeutwnse euebu0}) wnonbi3ue wnjesopo 1494) em tise, lemyy epuayue -nun) ttmouap) im 11S9}18MY 8) Aydoudsay BA)N}-OjNS WNJO}NALI euqe)6 BAN} e1ulase9 BlULseDy eBluLose9 Blulosey,y e1ulsse9 @49u}43e9, @Jauz49e9, 849u} 4985, e4auzsae5 BlyaursAes4 8148) )96e)4 @134n09e)4 eueiwsiy WNtt ytd snot4 snoid snot4 snoid snoig snoi4 snot4 snot SNItdy SN3 tds snotd snot4 snot @rzrayuasyey eljLayuasyey eaes6ey @14e893909x3 eAung e@tuoydng eiquoydng wnisojzedn3 snwAuon3, snwAuon3y @1pon3 e.uabn3, BLuabng e1uabn3, e.uabn3, eiuabn3, e.uabn3,y e.uabn3, hn a a aaa ee eee £0£ £62 £92 £52 LE2 902 Lo2 06L 28 981 SZ S/L £Lt 991 SEL 62L 021 2d 69 29 09 99 sf rhs 92 saioeds snue5 ooT + + + + ; + + + : 2 + + + S + + + + + + + + : + + + @1 yoy tune) ®813p)oquinn . a A a fe 5 a A A a ci ri a lo A 0 O 0 G 6 6 . 5 « 6 Gj weysAw e1uoBnH + + + : : 0 : : 5 + : 2 : : + + + + + + + 5 : + + eaulBnssay @LUOBNHy : é a 0 : : : a 6 o A : : . : : a a a d . 0 5 epunqi 10} 4 B1U03JOHy + + + + 6 + + + s 6 + + a + ee ee ° + $ + + : BAdt @1pjalysson ; 5 x : » f : a 0 0 * is : S i 0 . 5 x . . . . 0 0 e3sapou eadoy . + . . . . . . . . es + . . “ + . fe * + . . . + + . epunon[ eadoH, 7 0 : . : : 0 0 A d 0 6 G C : 3 0 5 : : 7 . o : : 40]09S1p eadony . - ny B p ‘: A 6 O : 7 A ° ° x - ws A a A 4 . 0 . : 2 una tue Aaz wnt) ewoH " i C 0 3 0 3 J . S 0 e . : S . 6 : 0 a . 6 a 0 Si}iw euaysJe OH, . . . . . ° 5 ° 5 . . + . 5 . . . . . . 4 . . . . . Sisua)eybuaq abeydiy 6 O : 0 : 7 O fe 0 Q G 0 . 5 c : : 0 4 F : : 6 : sna2et)3 SN9Siqty 2 0 0 oO : J 0 0 2 0 0 . 7 6 6 : C o : 3 7 a 9 a : SNULY20 ] Ow Sn2siqiH . . . . . . . . . . . . . . . . . . + . : ° . . : . snjeouny SNISIQLH . . ° d ns = 0 6 . J d Ei a : 0 a o : 3 6 a o . . 6 . snduea0 14a snosiqt . . . . . . . . . . . . . . . . . . . . . . . ° . . eo1ue)Aao B191)aH» id i 0 0 . n 0 0 0 . 0 . 0 5 : Re fy i : : . 0 esoo143nd} s130Apay 3 5 0 b c . : d in a J 0 : 0 0 0 . . : d a . 0 snuediJawe sndues04A9 + + + + = + + + : 2 + + + : + + + + + + + + ; + + + 8) ) em sdou!4A9 ° . . . x . . . . + . . . . . . . . . . : . 0 0 0 rs e3euLbsawe @1sodsouwA9, . . . . . . . . . ° . . . . . ° . . . . . . . . . . 343SaA)As BWaUWAD 0 0 : 0 n 0 . : : 6 . . : 6 . 7 . D 0 C Q : 6 6 11yjou BIMau5 . : 5 0 4 . q . cj . . d 6 . a 0 6 . . 0 6 : 0 O ¢ 6 $1 )ejuatuo B1mad5 . . in . . ° ns . ° . + A 9 ei 5 ° . 5 a . 5 5 : 5 A G e1]0}1utdseo eimau5 . . . ° . . ° . . . . 5 . ° . * 3 . . : 0 é 0 o G . eo1ue)Aao BLUOPJOD» + + : + P + + + é ; + + + . + + + + + + + + : e + : fisaz1emMy Snwe e430 1U09 ; : + 3 3 + R + . G a + , : a + ; : : + ; : u + c : !Luoswoy SMW] EYIOLUODy : : : . . A . o : 6 . x . 6 a s . . é A " 0 . 5 : 5 euloi)es snue ]B4301U0Dy . A . . . . . . . . . . . . . . . . . . . . . . . + t4ayooy snwe }8430LU0D, + + + + : + + + : : + + : : + + + + + + + + o + + ‘ e3e1Jas B1yduioy s o . : 6 Q 5 . : : 7 . . 6 G 3 . . . : 0 : . . : 29138188 eul aw + + + + : + + : 2 + ‘ + : + + : + + + + ; 7 + + + 8) )Aydequad $1WsS09h19 : a : . 0 O a . : i Ns 6 . é S 0 : é 3 3 : : a . 3 : uel tunew $1Ws09A]9 : 0 0 0 Q ‘ Q . : 0 7 : 0 9 : Q 6 0 6 é : 0 i 0 81 }0313Sn6ue S1wso2A79 : 0 + : : 3 : + : : : + + ; : : : 9 + + 5 + 4 + . wn2e@) 93S UO LP LYy90)9 + + : + : + . : Dp : : + : . + + + + + ; ‘ é : + + : 3) e40weU UO LPL Y90} 5% . a 3 : S . : . D ss . 6 6 0 : 5 Ke 0 5 6 5 : : 1 uoow UO 1P1490]9% : x 7 7 : . 6 : é 6 é f 6 a a a 6 : : : a 0 0 : 6 wnt }o413n2e U0 LP1Y90]9% + . 5 . . 5 5 5 5 5 5 5 a 5 5 . 5 . 5 A si 5 6 5 5 . e6nf tun Ba 1UU2) D4 = 0 C a . A . . . : . : Fs : a + + : : : + : ° + + + eptuqeos e4atuuost9 6 : 0 7 : 6 0 . . a a R . : 7 0 : 0 7 : : 6 6 G 6 21 )0$138) Biuapse9 . . . . . . . . . . . os . . . + . . . . . . . + + . tyawes9 eluapsed, . . . + . . . . . . . . . . . . . . . . . . . . . . eo.tue)Aaz Blulqeo,y . . . . . . . . . . . . . . . . . . . . . . . . . . tisaz1emy3 e1ulqued, ; i . . . A . . . . . “ . . . ra x + + A . : . + + . @) )Aydoudsa3 BIlULIIEDy £0£ £62 £92 £52 £2 B02 102 O6L ZBL 9B BLL SAL 2b OL BEL Get O2t 22 69 S9 29 OF % BE LE satoads snue5 £0¢ + + + ++ ++ £62 £92 + “+ £S2 2&2 "++ 44 902 Loe + 061 281 “+ + 98L ++ ++ TOT “++ “+ “+ "++ ++ 82 SLL £LL 991 8£l 62L 021 69 s9 4) 09 94 gf +> “++ 2 + + 92 eA)N 838} ad euA6 ip @1)0313snbue esowsse4 @)o91dns eyjuesew euel}joue S1]es0ueuU 81)0416u0} auydepoa}t ew saqe)6 tyaupse6 ewissipioe 838) )a31deo @))Aydeija3 B1)0;191 duis Sisua)ebauas CEEERT) eS 1pul Stwsaut snueJjay) eM sny3ueb! )o snnbi qo 1 tuoow | Jaupse6 B4as1q yng evewed eo1ue)Aaz 1494) eM @31 90493904 e891U0}aq 91 1xe15 wnje)nd1une wnt 0} 13sn6ue $1saz1eMy epunon[ Basoque eo1ue)Aaz @38)}099uUe) B14ozoury eo1ue)Aaz @3eUaUdA @Jpue320 eo1ue)Aaz satoads BONUPEWy eBueseseq ebuesecen, eBUNnANI, e4az3 1uun) $N93090X01,. @))a14auasa07 8) )al4auasao7 ETS CEES Wi 23531 1x 8983114 898311, Bluow! ) eruapul tq, sayjuesiday, sayjuesiday, sayjuesiday sayjuesiday e207 e1uosme} sny3uelse, snyjueise, snyjuetsey snyjuelseq, SNYUBLSE Ty @a3J0de) euejuey BUu00}0¥ BLyxd1Ipusy einspey elotysner wnutuwser wnutwser wnutuser e4OX] BJOXI BOX] e@upueuos] eupueuos] eJajsObi pul x91 sndsesoupAy sndsesoupAHy @1493UunH snua5 cot . . . . . . . . . . . . . . . . . . . . . . . . . . snoiue)Aaz uopoyosiw : A 6 0 . A 0 é : 6 G c : . é - * 5 é : . e : 16ua)a sdosnwiw . + . : . . . : . . . . . . . . . . . + . . . . . . e91uUe}Aaz BSN!) t We . . . . as . . . . . . . . 5 . : . . . . . . . . : . eolput esnijiW a . . . . . . + . . . + : . : : . 0 o 0 + + s + : 0 eue1ydi))em sidoj}oJ91W K . . . . és . . . . . . . . . . . . . . . . . . . . 1sa3lemy3 ensay + + + q : : + + 3 : + + + C : + : : + + + + : 2 + + 8) a49)nd ensew . . . . . . . . . . . . . . . . . . . . . . . . 5 5 Bassa} ensay . d 0 + 0 0 . 0 0 : s + g : ; Q + + + + + + . . + : suelJed U0 ) Ada . . 4 . . . . . . . nm ° . . . . . . . . . x . . . . un3e}0a0In U0 ]AdaWaN» . . . . . . . . . . . . . . . . . . . . . . . . . . wn3e))}2@qun uo | A2auKW + : + + ; + . . . 2 + + : 2 + , + S * + + x : 0 + = wn313eA)As U0 )Ad0Ua Wy + + + : = + + + 2 a + + + e + + + + + + + + i. + + + wnje4s3Sou U0 )AdauRaWy . . . . . . . . . . . + . . ° + . . . . + . . iy . . ase nals U0 )AdaUR Wy . + . . . . . . . . . . . . . . . . . . . . . . . . win) )Aydout ys Uo) Adah» O 5 . . . x . . . . . + + : . . . . : + O 0 . ° 0 . wnJa20sd uo) Aaa Wy . . . . . . . . . . . . . . . . . . . . . . . . . . wnje}o13ad uo )AdawaWy . . . . . . . . . . . . . . . . . . . . . . . . . . auejnoiquo U0 )A2 aula Wy . . . . . . 4 . . . . . 5 e . . . ° . . . + ° 5 5 5 wn) )Aydossew U0 )AdaWa Wy . . . . . . . . . . . A . . . . . . . . . . . . . . wndues0u2ew Uo )AdawaWy . . . . ° . . . . . . . . . . . . . . . . . . . A . I4ayooy Uo )Adawa» . . . . . . . . . . . . . . . . . A, . . . . . As . . wna3ue6i6 Uo )AdallloWy. . . . . . . . . . . . : . . . . . . . A . . . . . . apuei6 Uo ]Adawa Hy + * + + 5 . + = ¥: = ( 8 ‘ 8 : r ; 4 - - e : + + suadsaasny uo Ad aW2aW» . . . . . . . . . . . . . . . . . . . . . . . . . . wno13di7)a U0 ]A0aua Wy . : . . . . . . . . . . . . . . . . . . . . . . . . 40]09S1p U0 )AdauaWy A 0 . . . — . . . . : * i . . A : A + + : . C + . : wnueayJe}9 Uo )AdaueNy “ . . . . . . . . . . . . . . . . ns . . F . . * . . wn3e))a31deo U0 )AdaWaWy . . . . . ° . . . . . . . . . . . . . . . . . . . = B1)op1o1jdwis ewSO! an + : : + 4 + : : 5 ; : + + : + + a + + + ( : 3 + : 5 wind tJyzege ) ew BOISE} OW . . . . . . . . . . . . . . . . . . . . . . . . : . 838 )noew 8) ) 1ULPans + + : + : + : ; : ; : + p : + + + + + + + g + + eupuesza9 eIxtysewW rs . . . . . . . . . . 4“ . . . . . . . . . . . . . . tyewiu eLxXl3seW . Q'S 0 . . . . . . ° . . . . . + . . . . . . . . . . @) )Aydossew BLXL SEW» . She exis . . . . . . . . . . . . . . . . . . . . ° 0 * Basoque @LXLISEWy . . . . es . . . . - : . . . . . . . . . . . a . . . eupuexay eve) 1ueW + + + + + + + é : + + + . + + + + + + + + 3 + + + e21ue)Aaz e494) 16ueW, 7 . . . . . . . . . . . . . . . . . . . . . . . : . ae4a4)eM $30} ]eW r . . . . . . . . . . . . . . . . . . . . . . . . . $n2909e433}3 $N}0)]eW : $ : : + : : ; + + Z + : : & i ‘ g % + é ? + + + : snt jo} tuweys $n}0))eW . + + . . . . . . . . . . . + . . . . . . . . . 7 . sisuaddi )1yd $N}0)]®W + + + + : + + . z : + + + ss + + + + + + + + 7 4 + + suadsaosny SN}0) ]EWs . . . . . . . . . . . . . . . . . . . . . . . . . . sndueso0iua $N30)]eW A . . . . . . . . . . . . . . . . . + . . . . . . . eue i 3a} JOJJaed BsoeW . . . . . . . . . . . . . . . . . . . + . . . . . . ttuoow BONYypeWs» £0£ £62 £92 £52 L2E2 B02 102 061 ZBL 9BL BLL SAL EAL 79L BEL 62 O2L 22 69 S9 29 O09 7 BE LE satoeds snua9 €oT + . . . 5 . . . . ° ° . . . ° . . . . . . . sn} )AudA}od snyjue})Aud 5 . . . + ° 5 . . “ 5 : . : . . O . . 5 . . A . . . snjeuuid snyjue) ]Aud . . . . + . * 5 . + . o 6 . . * 5 . 5 5 . . . . . . snoiput snyjue}]Aud + + + + g + + + : : + + + * + + g + * + + + 2 + + enbi}qo SNY}UBDLUZ0Udy 6 a 5 0 a a 7 5 . s s A a : : . . a : A A . . A 22981409 snyjuediuaoud, : . : . 3 : 6 A . . 0 2 . . Q . O . 2 * . . A x . : eo1ue)Aaz XLU30Udy % 6 . . . : . . : . + : . . : + : : : + 5 . + + : e91ue}Aa0 BLYdI8dy + . 2 a a QD : ; : + 4 : : + + + + + + : : 2 * 2 : eyjuesew easJad : . . . : : : : . : : : . : : : : 5 : : + : q . , 5 eue 1uoow Sisdodisad a a . ° 5 . . : 5 . 5 . . 2 . . i . 5 ‘ : C ‘ : ed 1pul 8330Aeq . . . ° . a 5 . 6 . F . : 9 o . * o 6 a . > : : . epue}q CEREYCP . . : . . . . . . . . 6 : a . . : : 6 . . : c r st)npa B40) }1SSeq . . . . . . . . . . . . . . . . . . . . . . . . . . @) )Aydouow eAuB6 twesed a A G . = + . . . 5 . . + 5 a O . 0 . 5 5 O 5 5 + = ejewue eAUB LWwetedy + + + + 4 + 3 ; : ‘ : : + : : + . + + + + + . + + + tisaz1emMyy snuepued, . . nm . ° . 5 A . . ° i 5 * * . = . a i ° < 0 es . . Weervicnith wninbe ed, . . . . . . . . . . . ce . ° . . . . . a . . . . . . wnsout6iqns wninbe)ed + + + + a + + 4 q . + + + si + + + + + + + + * + + m ase)oljad wninbe]@dy 5 fy > . : . A x . : + : . . & : : + + 3 : : 0 cl + : wnJo}41oned wninbe)ed . A : 5 . . . . . . . é . é . . . “a is di a c A < A 0 appad jou y wninbe}ed . + + . . “ . . . . . “ + . . . . . ay . . . + + . apue6 wninbe)ed, . ° ° : é . . . . a . . « . a 4, . . . . . F a . . ch wn3e]N31}eued uninbe}edy . * i 6 . . . . . . * ° Ry 5 . : és A A : : + : : : + BwO 0491p eyjueibed . . . . . . . . . . . . . . . . . . . . . . . . . . {4aupse6 81] WSO, ° . 5 : a ° ° : . . . 3 . . . A A fi 5 . . 5 a . : : euadse 8149aqS0 . 4 q . . . . A . 5 . 2 é & . . A A r 5 . : BY a r ' t1ua}] Ip e13undo + = = + r G S g : ; : + 8 : e + : A 7 4 3 : : : + . wnjejns1osey ewisadsooud, + . . + . . . . . . . . . . . + + . . . . . . . A . eo1ue)Aaz xe8}10 . . : . . . . : . . . . . . . . . . . . . . . . . . wnwiss13eJ6 wnw 30 ° . A . : . . : = . . é d . . . 5 . 6 . . . : 3 * é 2}e]0909Ue) euy20 + + : . . + : + : q : + ; ‘ + : : p : + : * . + : ey idejoqe! BUY90~ + + + + : + : + 9 a q + + a + + + + s + + + ‘ + + + @)npisys e4pue] 490% + + + + % + + : a + + + 5 + + + + + + + : + + ‘ Lawoppeq e@16adoyjon . . . . . . . . . . . . . . . . . . . . . . . . . . eue Lucu LU sajApodeyjon . . ° x . A . : : : : ne ss : : + + : : : + : ° + . ° epiyaojy saqApodey3on ° c . : a 4 0 A . 3 : 5 < a . a ' a . é A : : sueotyni} ediNn . . . . . . . . . . . . . . . . . . . . . . . . + . uinasedde } wnt jayden + g ; + ; + > s : : $ + : 7 > + + + + + 4 B * + : : @14038))13SIp sayjuaden, + + . + O + Q a Q . + = : + + ‘ + + + + + ; + + 5 eisseo 29S} 1 ]08N» + + E : z + 3 a 5 5 ; + : : + + + + ‘ + : : 5 5 + ‘ eduesousew OLpaBleNny + + + + yi + + + o : + + + RB + + + + + + + + + + + sapio}Ajoep BILISLIAW 0 + + s q q + + . 9 + + + . 9 . 0 : + + s + 0 ‘ + + 636198 )6 epusessnw + . + . . . . . . . . . . . . . + . + . . . : + + 0 esopuoty epusessnw . . . . . . . . . + . . . . . . . . . . . . . . . . t 161ua0cy eAesinW . . . . . . . . . + . . . . . . . . . . . . . . . . @t)oytased euA6eJ3 iW £0£ £62 £92 £52 LE2 B02 102 061 L28L 9BL BLL Slt S4bL OL BEL 62 O21 22 69 S9 29 09 % Bf LE v2 saloeds snued vot . . . . . . . . . . . . . . . . . . . . + . . . . . esouloodeu B49)}J949S . . . . . . . . . . . . . . . . . . . . . . . . . . e3eu16uews 843)}}}949S, . . . . . . = . 5 7 5 c . . . 5 . ip 5 . = . . . . ni Sap1oyjueso yo eupueoJes . . . . . . . . . . . . . . . . . . . . . . . . . . eotue)Aaz 22909485 j : s ns . . c . « A . D : 5 . G . . . : . . : 5 a : unptuqeos eusoudes, is iy . . 5 : oO 5 : . “i Q : s : ci 5 A, Si . 9 G dj 6 * a uno tput ewsoudes . . . 5 . . ° . : a : A : ° . 5 . A hy . 6 . . ° a sua}a0} ewsoudes . . . . ri : . . : . s 6 . : é . . . a . A . 5 5 6 5 snjeulBsews snpuides . . . . . . S . . : : * . 2 : : . : 6 . . . a 8 2 auBisut unides fy 5 . A 5 . : . : : i a ° . * é 5 . . . ¥ . - . . . eo1pul euapewes . . . . . . . . . . . . . + . . . . . . . . + . . . eoisuad esopeayes + + + + + + + 2 + + + + + : + + + + + + + : 5 + + + BJ |NIIjo4 e19e)es . . . . . . . . . . . . . . . . . . . . . . . . . . eBuo)qo e19e)es + + ee © ie + * 2 : * bs + a A + + + x. + : + f e + + eupue lp 8198)e8s s + + 5 ° 5 + + g 2 + + : u + + + + + + + F : 2 : 7 Joulw easnoy . . . . . . . . . . . . . . . . . . . . . . . . : . B3eBILA BIJOULYy ns . . . . ° . . 6 5 . . * ci . . A . : . . ‘ : . . . 2] }owsad wnysazoyouAyy ° . . . . . . . 5 5 5 . * j s . 5 . 5 A . : . 5 . ‘ e}euouonW eyoydoziyy . . . . . . . . . 5 . . . 3 5 . 6 . 5 : . * : ‘ Q eo1pul e1juesstay . . . ‘ . . . . . 5 ° . 5 . . 5 ° * . . . : 5 . . 8ysngos eauedey . . . . . . . . + . . . . ° . . . . . . ° . . . . . eo1seqe)ew elpuey . . . . . . . . . . . . . . . ° . . . . . . . . . * eotue)Aaz BAL [UBsINdy + . . . . . . . . . . . . . . rs . + . + . . . . + . tisaz1emy SiXAdoysAjdy rs ay A : : . . A : . . . : : + : : + + : 0 : : . + + suadsaued wnuw4adsoja3d . K . * 5 . . ° 5 . : ° : = ° A ry ° a . . : a : : c tiseem B14}0Y9ASdy . . . . . : . . 5 5 : . A . ° a 0 S s a A 9 a s c 8) )Aydouays @1430Y9ASdy + . . + . x + + ° . . + + . i: + + . . . . . . . . . eso}UuaWwues B@1u30YyIASq + + + + 2 + + + = ¥ + + + 7 + + + + + + + + : . + + eu6iu @LsOYIASd . . . . . . . . . . . . . . . . . . . . . + . . . . @uay1 )npue)6 @14JOYIASdy . . . . . . . . . . . + . . . . . . . . . . . + . . eiqnp @1sOYIASdy . + . . . . . . . . . . . . . . . . + . . . . . . . t tuo tdweys edesesopnasd, . * . . . . . . . . . . . . - . . + « ze . . . . + . 1494]eM SNUNJd» . . . . . + . . . . . . . . . . . . . . . . . . . . eupuesja} SiLJauo ews lid . . . . . ° . . . nm A s . . 5 7 A ° Q a g bs n F a 5 eS0}UAOy BUI . . . * . . . . gj . . . : c : rn ‘ . o < * : : c a ‘ 8105132) eUwatd . * . . . . . . . . 5 ° . 2 . : 5 0 D : é A 6 0 ‘ . ejeuuid e1weBuodg . = . . . re é . 5 . . A . : : A A, . S : A A : A é a es0}Uaw0} BL jawog . . . . 5 . . . . . Q a . a dj . : . S 2 5 A 0 0 : Fi esouaqns 8143) eA}0d . . . . . . . . . . . . . . . . . ° . . . . . . . . 8104 16U0) @143)8A}0d + + + + * + + : ‘ + + + + : + + + + D + + + : + + + 13UL404 8143 ]8A}0d . . . . . . . . i. + . . . . . . . . . . . . . . . . saptosesao @1y3)eA}0d . . . . ° . . . : . * A 5 6 O : . . F « & a 6 6 9 8} 1soddo 81 )Aysouna)d . . . ° es . . . a fs . A : . . c B 3 . : A A a . : wn3e)e un iwuadsota|d . . . . . . . + . . . e . . . . . . . + . . . . . . uounauls3 Jadidy . . . . . . . . . . . . . . . . . . . . . . . . . . Saploxe} SAwe ] 40 } yAud £0 £62 £92 £52 L2e2 B02 102 O61 ZBL 9BL BLL GL f2t 91 BEL 62L O2t 22 69 S9 29 09 Bf LE 4 sa.oads snua) set te + “+ + “+ + + £0£ £62 £92 £52 LE2 902 “+ + “+ Ot oc» + “+ + + “+ b02 061 28L 98L S82l “++ “+ SLL Oe £Lt + “+ gel 62 7+ 021 SOT “+ + ZZ 69 + “+ eueu BL Soquos3s sapltoxe sn)qas3s sadse sn)qas3s wn euosied wnwJsadsoaiays sey6ue)eq @1)n94335 eo.uodef etueydays snspuesja3 SNsnuowsa3s s1)eo1de SNINUOWSIS» wnt}04tps09 wn tuoydtsoua3s§ (12 46iM snsodouows3s, sn}e)nI13a0 snsodouowa3s, sisuaAt }auuey snsodouows3s snze)ndt)eued snsodouowai sy snjeutunse snsodouows3s, Si4se)oaseo @1}3esauU0S suadsaqnd wnue }0S e91ue)Aaz x8) LWs 84351 )04Nd xe) LWS siseyndiys BaJOUSy suadsa})ed BaJOYS, @1)0416u0)qGo BaJOUSy @))Aydossi) @dJOUSy eppiue}ny BaJ0Ys 14aAp BaJOUS, BYydiysip BIOS, 1494)em snduesawes, #383) adqns sndsedawes, suadsaqnd sndiedawes, sn} eui Baws -opnasd snduesawes, @1 Op 1Aued sndsedawes, @3eA0go snduesaues, SIPt4tA-ou6iu sndjedauas, 1 1uoow sndsesawes, t4aupseb snduesauas, ejeuiwnse sndsedawas, snsAdoona} eBaultsnoas ed 1jaws auowedas eauunig ayjueul nos, eut sAw @13n9s e))1snd e1do}09S, ejeutwnse e@1do}09s, wnJpuejuad wnJAdo4a 95 @1)0s1aue}seo e@Lsayoewnyos, @1)0}13sn6ue e@Lsayoeunyos, @s03]0 e@4ayota}Y49S satozads snua5 ++ “+ + + £0£ ++ “+ £62 £92 Oar 2 “+ + £S2 £2 802 02 06L 281 98L gZl ++ “++ “+ ot SZt £Zb 991 8£l 62t 90T + + + + + + ¥ % + + + + + + + + + Ee + . i . 5 d O . o a 5 5 . 5 3 A 5 6 O + + + + + + 9 5 4 A 9 . 0 a Ke 5 5 O + + + + Ss > ss + + + < . 9 % 5 5 Q . 6 5 O 5 a + + + + 2 + Ss 5 ns 9 O c O , 5 5 5 5 5 a 5 “ 5 : E + + + + & 5 % 5 “ 5 ¥ . + + + - 0 . . 6 5 . P + + + + + Oct AL) 569) °S9 eo) 09. “+ “+ 99 sf 2 92 eo13e1se 8) }asoque! wnotsi6e) tu esojuawses @J0)}tAsed eynqayo e914a)]aq eunfse eaundind BAe) e013e81S8e Bo tpul wno ue )Aaz wnue 13 46im wnzeuiqung unpundiqn wn3n}OAaJ wnue 1 saau wny3ueJ2 iw ynyew wn} )Aydoss 1} wn3 tayds tweay apuel6 tyaupseb WNW ty wnotuput yAo tuiuwino wnt jo} ipso wnze) )AydoAueo unanbe oqn)e euyojnd @) )Aydousew e)npidsiy e}eaun9 @3eu0s09 Stsuautyosutyo0o 8) )Aydossew @3e}099uUe) @1)0313sn6ue wnso3e 0d eyjuesoiw @1) 04 twoweuuld @3e@)no1ued ewtsuadse satoads BL) eppo) SNLUOWL Ly ew6iysesjal euasejal BL jeulusal,y el yeuiwmsas el yeutwmias et jeutmuay ersosydas euuase) euuae | snpulsewe) wni6AzAs wnt 6AZzAS wni6AzAsy wnt 6AzAs wniBAzAsy wniBAZzAsy wni6AZAS, wnt BAZAS, wniBAZzAsy wni.6AzAs wnt 6AzAs wnt 6AZASy wntBAZASy wnt BAZASy wni6AzAs wn t6AZzASy wniBAZASy wnt 6AzAs wniBAZASy $020) dwhs $090 |} dwAs $020) dwAS, s090)dwhs, $090)duAs,y $090 )duAs Bua aINS epoBbauns epoBains souydAs3§ SOUYIAIISy SOUYIAIISy S9YIUB) 1GOIJISy sayjue)iqos3s snua) + £0£ £62 £92 £S2 fe “++ “+ + 802 Loe 061 28 + + 981 8Zl ote eet SLL £21 791 g£l Lot + + + . 5 . . . 5 S “3 4 + + - + + + * r .. + + + + + . i 2 + + + + * > + + + + + + 2 3 " + + 5 + + 2 e 5 5 ns os 5 5 0 . 2 + + + + 2 + " . a o < 5 s 5 . 5 < 5 n , 5 5 : + F + + + 7 + ‘ > + + + + o = » n ‘ a D 5 5 Q é a a - D O o D - 5 . ns 5 o O “ a 5 * a *y 5 5 5 c é + + + + + - 7 . . * “ 5 < 5 . 5 a ns O a o * + 5 + + + + + : . . e 5 ° A 5 . G o 0 5 O 6 * A 6et O2b 22 69 S9 2@9 O09 + “+ gf Ze 92 81 )douso eoadeu euel3 lunew wnjepnes @1 03 LAued suedts61u tLuotdweys wnJo)4 tulwad @1)0s13sn6ue Bsjanbis3 Bsaylyssto ejepidsnsiq SL] LQn]OA @3@1 0} 149 etptosid ewissi3)e eas0que euejyedsesapew ta )queb Stulsse e@4a41))ed09 eduesouayds @1 10} tduesawas wnueu eduesousew e3epsoo wna .ue)Aaz wndt3di})a eot3di})2 eo1seqe jew euodsouy3Aua {)192)ep eo1ue)Aez 9)e3uUalJo tyaupseb satoads snyddz1Z snyddztZ» snydAz1z wn )Axoyjuez, e@1do}Ax 81do)AXy B1doyAXxy wn) )Aydoyjuex B12 4B LIM» @LWJOMy B1aqy6n)) IM» @tpue)pusy eyey823e4 euns)e4 ens )eM XI1A BLUOUJaA 068) 1}uUaA 068) 13UaA BOLIEAy BLUaIGAy BLICANy @LIBANy eLuean BLJeAn etuean wn} )Aydosn, wn) }Aydosn» @L4BOUNy etuiduny BtsA)edtily BLSA]BIlI1y euapeyrisiy ewe) snyjuedA jo, snua9 3 t ; + + + + + + + + + : + + z + + 3 ‘ y r + + + + @328)099uUe) esosody, g : ke + + 2 + + + e + a 2 3 ‘ s + : : iG : 2 + + u ewiadsoipued esoJody, 0 3 : . j ‘ . ° Q a js . . Q J 5 ; A a . A 0 : c a 2381] 0314 e.ueydy : : : + + + + + + + + + + + + : + + : + + + + esonb1}1s ewedy . . . . a 2 3 : . 6 f b i . . G . “ a ‘ 5 : 5 . F fs sniunq euisapijuy : : : + + + + + + + + + + + + g : + 2 q g : + + + + wnp toe eusapt uy : . . : A G a . . ° . s ° . . 5 . 5 g 0 . A 5 d . wn3e taqnuew uospuapouy . . . . . . . . . . . . . . . . : . . . . A . . . . esowenbs euouuy g s : + + + + + + + + + . + + : + + i q q y + + + + Sep owoweuuls 8a) )Aydostuy, : . 6 . 5 . . 3 . a 3 3 . G . O . : . 5 A 5 G A . snjewey snpe} 9043S 1ouyy A . . ° ° a O ° . . : . 5 : : . . ‘ Q d 0 . a a . snjna209 @34.weuy . . G . ‘: . d G . 0 3 . * d 3 0 A . A 8 . 9 : : 7 euet3y6in snss190)adwy . . . . : 0 ° 9 . = A : 5 A . : 5 . f° 5 O 5 . ° o stue}oyas @1U03S)¥ , e : + + + + 2 + ‘ + . + + ; ° + 3 : ‘ + + + 8) )Aydousew 81u03S)}¥ 5 3 : 5 + + : + : + + : + R + 2 + ; § + : i 81 )0}1duesauas auydepoas jy a . . 5 ° 5 . . . . . . . . . a G fc . . Ci é 5 A edies0sa)9s easuoyd)y q 2 g : + + + + + + . + + + + : q + ‘ ; : ‘ + + + + sn3.ue)Aaz sn) Ayo) 1V» . . . . ° . 5 5 ° . . . . = : 0 O . 9 0 s 6 o s sue LJeA sn) Audo}1¥ g . . . . o c a ° . . : . . : . : 5 5 é . o * a 5 0 ewissi3esopo BIZIG)y 7 5 ° A 6 : . . . . . . . . ° . 5 s o 0 . 5 9 . 6 sisuautyo elziqqy . . . + ° + . + . . . . . + . . . . . . . . + . . . WNL }OJLLAJES wni6ueyW» . e . + . ° . + . . . . . . . . + . . . . . . Ay “ . | uaupse6 BIpty . . . . . . . . . . . . . . . . . . . . . . + . . . eopul sAyoe3si3sos6y : S - o $ : 5 + * + + + + % + 2 Me + q : ¢ Q 2 9 2 + 89981409 sAyoe3sijsosby, . . : ns . . ‘i . : . . . . . . : + a : : 7 . 0 0 5 eo 1wnJsas sAyoe3S13soiby . . . . . . . . . . . . . . . . . . . . + zy . . . . eapioubeae)a e.e)by . . . . . . . . . . . + . . . . . . . . . . . . . . so ABuo> e1e)by Q : . 2 + + + + + + G + + + . : + + 4 : 0 + + + + eduesoide e1e)6y, 0 Fi . . G . . : . * . . . “ . 3 O a . . 3 > si A esowAd ewsoueby . + + . . . . . . . . . . . . . . . . . . . . . . . wnje}nd1uso0o seed bay . . . . . . . . . . . . . . . . . & . . 0 . : : . . e)e}ado01se) BupueU py, . . . . . . . * + . . . . . . . . + . . . . . . . . 4J0}091q esayzueUapy, . . . . . + . . . . ° . . . . . . . . . . . . . . . euei y6im e@luapy A oe 4 a“ . . : A a . . in 5 5 a Q : é 0 d ° x * . : e)epuoy e1uapy 0 3 O . : . . a . “ . 5 . + . . . + . . : . . . . . sue6a)a auydepoul 32y4 . . . . . . . . . . . . . . . . . . . . . . . . . . euea) }opues auydepoul joy . . . . . . . . . . . . . . . . . + . . . . . . . . suoJjiqye auydepoul joy, q . : o + . + = + R + + + . + ‘ + + i s 5 + + + + epiq)e auydepoul32yy . . . . . . . . . . . . . . Fe . . . . . . . . . . . StsuasJsay6) tau @)1ydajoy D 5 5 5 5 O 5 a 5 5 Q a 5 . . O O a . oO 0 5 es As + a 838 )nounpad eLysAuosoy . + . . . . . . . . . . . . . . . . . . . . . . . . SNL]OJLILYL sny uesy,y . . . . . . . . . . . . . . . . . . . i. . . . . . . eausnga e1sesy 5 . . . . . . .) . ° . a) . . . . a 5 6 Q . . a . . . e@1saed e1oeoy . . . . . . . . . . . . . . . . . . . + . . . . . . wnsoot3ni} uo) L3nqy + . . . . . . . . . . . . . . . . . . . . . . . . : wnd13e1Sse uo) tangy . . . . . . . . . . . . . . . . . . : + . . . . . . sntsojedaud sniqy . . . . . i. . . . + 5 . . + 5 . . . . . . . + + . . euliwebiq Swe JEqVy Ses 2S £25 LIS 60S 80S 40S 90S SOS 10S 00S 66% 86% 267 I” 99% £Sy YY 10% 007 66 B6£ BBE 69 EE 62E setoads soe) . 6 % 5 o . ° . . O Ps . : + + O . 5 . 5 . . ° . . . . . . 5 ° 5 5 . . . ° . 5 . . . 0 6 - a my 6 . . ° . é 0 Ks : O Ses 92S £25 + 7 + + + F . . ° O + 5 Q a ; a . 5 . * 3 . ‘ 5 . 5 6 A ci - 9 + % + + + . 0 ° . 5 5 5 . . 5 . a + + + + : + + + + + ; + 5 5 a D Q i. O 5 5 . . . . 5 . . . . + + e + + + . . . . . . . . . . 5 5 . ° . . . . . 5 6 . . 5 5 . 5 . . ° . . . ° . . 3 + + . + + . . e 5 5 . C3 7 e . 8 5 + + + 7 + + A . ° . a 5 + + + + + + a 3 5 . - . 5 . a . . 0 2 s 5 “ : 5 tLS 60S 80S 20S 90S SOS zs vs + 5 Q . i + a + + : 3 . a . Py 6 . Q 5 a 5 . . 5 . 5 . . 5 . 5 * 6 oi 6 . . . ° . . . ° . . + + + $ + . ° . . fs . . . . . . . . . ° A . . O e . . 5 . ° ° . ° 5 . . . . a 6 . 5 . . 6 “A A . . 5 . 5 5 a . . ° . . . + + $ * + + . A . & . 5 A . . + + + + + A Q 6 a . . . 4 d es Re G 5 A Gj . 5 . “ . 5 . a . ‘ + + + . 2 LOS 00S 667 86% 164 60T . . A, és . . é A . 5 . s 5 is . és . . . Z 5 5 . ; . . . rs + " + + . . . “s . 5 . + + * + + . i . A + E 5 + . . 6 F + ‘ + + + 5 + + . . ° xs A 5 ° + . . * a 1249 799 £S9 LY i : Fs =. + + + + a O . . a A 5s 6 a 5 . 5 5 2 0 * A A . . . . n . . . 5 5 es e a ny 0 5 A ° ns . 6 . 5 2 . 5 a A @ . . 5 . Q “ c 5 5 “ ci 5 . O a O 5 * 2 % S + + + + ° a Fs rs G 5 A . 5 by - Fy © 5 . cs . rs . 5 o 0 . a . o « < as 0 0 = D . 3 a 5 5 3 5 5 ny A a o Oi D . 5 . 5 - 5 O 5 D d 6 A O x r a fe 5 6 5 o a s “ O . c . O o a “A 0 5 3 " “ + + + + 5 5 Q 6 o A Fs 5 fs G 5 5 n . O . 5 * 5 O a A, ~ . . 6 0 O A . 5 5 . 5 « 5 5 a a 6 10% 007 66£ 86£ BBE 69f EYE 62k bso. 1emyy SL]CALS snje.pes Sinuaj-opnasd snaptoao sn3e3161p snjnjeo1 ap eduesouauAy esto onpuog wn 13di7)a e)n6uexas eZ tL ysouwA6 eo.ueael esnjau t1ucow BSePl-SILA eqiao eo. seqe)ew 8328) )39qQun eo1ue)Aao @1)04tpsos eo1eqe jew BSo}UAauIOy esowaceJ esowaceJ e)nBueynse eyjueses3a3 eo tpul eo1ue}Aaz eulsew 8) )Aydouow ed1ue}Aao snjeo)e} St) !qou sn) )Aydouazay snue i zewo6 snaiue)Aaz sua6bulJ bisazLemyy eynssiy nysa3e9 @40)419Ned ttuoow tuaupseb saioads snwe}e9 snwe)e)y smwe}e), snwe}e9 snwe]8), snwe}8), snwe)e), etuidjesaeg etuidjesae9 etuidjesae9 wn} jAydossAgy esaininig esa inGnug eaonig @lyepiig G1 ]epligy e1uAaig xequiog @lsawys0g e1yse)¢ Bsougy eAusag e1uobag eruryneg Blutyneg e1uoybulsseg e@1u0yBulseg ewizy e3yoes ipezy eupueulXxy, e1uuadiAy etjueyeyy e.jueyeyy sn6euedsy sndses03J1yy sndues0jy sndies0j4y sAujoqeqiy B1y90)03sS ly e1asABsyy erasABuy LEER] Bisiply BLSIPIV, BISIPIV, snua9 OTT + + + + + + + és . . 2 + a + + G S : 2 + + + + snduesoueysed sndses0jaey) A dj x : ° 6 . E E o 3 fe ui ti 3 5 a . 0 a . 0 0 5 6 we] }opo euaquay a 0 : Q ¢ a f 5 a . o : i : d A iG D . a é 0 : F a S1SUaJOUwL3 $1318) . . . . . . . . . . . . : . . . . . . . . . . . . . B1jOsi43 e1jesde9 . . . . . . . . . . . . . . . . . . . + + a . . . . esouids weBaseunjye) . . . . . . . . . . . . . . . . . . + + + . . . . . eo1ueqe)ew we6aueunje) a : a 7 D . . O 0 . 7 : a é 6 ei 9 : 3 é ¢ 6 O . 9 e403 eisse) . A 0 6 6 . 6 is . : 0 a . . . . . G 0 a & 0 qj 5 eawels eisse) . . . . . . . . . . . . . . . . . . . a . + . . . . t tyBunqxou eisse) en : . . 9 0 Q . A : é 0 é . cd ° A i é 0 a : e)nasiy eisse) mn 0 : fs : . . 0 6 . 0 : . 3 a 6 0 0 . A K K : 0 5 d e3e)n914ne Bisse) + . . . . . . . . . . . . . . ot . . . A + + . . . . wnone 6 auisse) . . . . . a . . . . . . . . . . . . K * . . es . . . eaiue)Aao easnodisse) e . . . = + * . . . . + . C 5 . . . e + és < a . iS 5 eo1ue)Aaz elueasey . . . . . . . ° . . . ° . . . . . . . . . . . . . . eo13d1))a eluease) Q 2 O + + + : + + + + + $ + : : + + . . & . + + + suain @30Aue9 5 . : 3 . O 6 a . 5 O : : . . 7 . 5 . A, 6 re 6 é c 0 esnjau euowue) + : . . . : . . . . . . . . : + . . : + + + O 0 0 e) )Aydousiw euowse) + . . + 5 e . . . . . ° ° . 5 . Q ° + a # A 0 a 5 ° unseuids essise) . . . . . . A + + . . . . . + . . + . . . . . . . . euloA}eo BL) )GJeDy 4 ; : : : : + + ; = : : : : : : + + . : . + + + 3 Bye1yseiq @1) e489 . . : . d 6 : 5 5 6 O . 9 6 5 a A, x 0 D 5 0 e91Ue)Aaz stsedde . . . 5 Q . . 5 o . 0 3 0 a é o 0 . 6 K i i 0 0 0 elJeidas stuedde . . ip . Q : . . 5 5 . b é . a . Q a . r, 0 0 0 . . eso )naunpad stuedde . a . . 7 D F . 5 D 6 0 . . o 6 . . . . . 0 . 0 . . | tuoow stuedde9 . : d : . . . rs ° 0 . . 0 . 6 . . 5 . a Pe q 0 . q sipues6 stuedde9 0 . o . . . 5 : 5 . . . . . 0 . . . : Ky a . 6 . 83891 JeAIp stuedde9 . . . a . . . . . . . . . Oo . 5 = . . . + + Oo . 5 . eoonpeq stuedde9 . p . a : é 7 . a O O D A O 0 . f 4 . . é Q a . O tpaays wntyjue + . . . . . . . . . . . . . . . . . + a . . . . . . un} nsaqnd wnt yjuey + A : + * + q + + + $ + + + + . = + + + + = + + + + wn33031p wnty ued, + . . + . . . . . ° . . + . . : . 9 . + + + + : S 2 wind | )apueWwos0d wnty3ue) . . . . . ° . . . . . . a . . 5 . * . 5 . . . “ 0 ny wnje )nuedwes wnt y3uedy : 5 ‘ + + + ++ + + + + : : + + : + + : : : : + + ° + wna tue) Aaz wnt JeUe dy 3 $ ‘ c + : + + + + : $ : + + # + + : . : e . + ° + eo1ue)Aaz ewsadsoudue), a . c Q . 6 o . . . - é rs 6 0 co 7 . 0 3 6 a 0 6 0 0 sisuauis e1))awWe9 = $ : + + + + + + : + : * : + Z + + ; : : : + + : + tisazlemy3 wn) )Aydo}e0% . . . . i. . + . . . . . . . . . . . . . . . . . . . 14338)n0s un} }Aydo}e9 A 6 é A 4 - 6 S ; 6 . . é 5 ¥ é 4 ra a : : 6 é nm . a LLucow wn) )Aydo)e9, . . . . . + . . . . . . . . . . . . . . . . . . . . unBuo )qo -0eps09 wn) )Aydo)e2, . . A ° - 5 Fs . : : a . . . a . 0 5 a : 4 . eqe]e9 un} )Audo]e9% - : ‘ : + + + + + + : : + + + : + + : : 3 a + + + 2 wnjea32e4q wn} }Aydo 89% : £ z + : : + + + 5 + + : : : : + : : . . + + + : snptoe wn} )Aydo 895 . . . . . . . . . . . . . + . . . . . . . . . . . . e@sojuUawo? edueot 1789 . é . : + : + + + + : + 2 + + : : + + : o . + + . + snoiue)Aaz smwe 894 SeS 72S £25 ILLS 60S 80S 20S 90S SOS 10S 00S 66% 86% 16” Il 9% §£S» Ly 107 00% 66£ 86£ BBE 69f EYE 62E satoeds snue9 Ses 2S £2S bLS 60S 80S 20S 90S SOS ot + + “+ e+ LOS 00S 66% 86% 264 + 1249 999 $S¥ LY TTT + “++ i 2 + + “+ + “+ “+ snueisa} (emyy LLuoow 4aj1998) snoijewose eso1Bl)jau wn)nosnisqeos wnj3es}sauay ®10416u0)qGo B89 10U0u yesey6b eulo2049 1p snduesouow t tuo tdweys ejepnes euel.ybim SltsuasooueAes eq4ty wnjze)noLued awuaul wnjeunzsojut sneje)nd4ado snjnjzed snp1) ed snaui6niasay ejeutunse wnjo}}toids eo tpul e3ejuap eue i ybim @3eq0) 143 sue )}n6ueupenb eueauday ejeuiwnse wnd tue }Aaz WwNJaA sisuafeseyuis wntqnp apuosos -nunddes BULIN]9A wine }0a5ue) sues6ei} e1uajzaims B40} ;1piqye sapto)Axoiydo snade 1402 saioads u0}019 03019, u03049 u03049 BAa eID wn 1qo}3]A309% wntutaso9 Bipso) Blp4oa @tpso9 BLpso) snseuuo) SNJBUUODy esoud tuu05 893409 893309 Btwept)9 winupuaposa}9 unJpuapoJa)3 wnupuapoJa)9 xA]8903819)9 snyjue4sia}9 SNy3uejsSia}dy SNY}UBIS139]Iy e1usoy6a)5, UoIpl9)]9 euasne)) euasne)) snssi9 snssi9 snssi9 snssi9 SNSS1Dy wnwoweuu t9 wnwoweUuU 9 WNWOWEBUU Ly WNWOWEUU I» WNWOWEUL | D5 e1sesyny9 wn) )AydosAuya eydsowauoy) uo }Ax040)Y9 SNYyUBUOL YD» e1yessey9 sndie50398y9, ee ee ee 107 00% 66£ 86£ 88 69E EYE 62E snuay Ses 92eS £es “+ “+ “+ + + bLS 60S + + + 80S “+ “+ + +++ 20S +++ + 90S ~+ ++ sos ++ bos 00s 669 869 +++ + 269 +++ t+ 129 999 + “+ £S9 ett +++ + 9 Lov “+ “+ 009 + + 66£ + “+ +++ 4 ++ +t ++ “++ + 86£ 88e 69£ £7E 62k snso)npue 6 snsoonsJaa 1494)eM bisaalemy @2138A)AS esowsles @31saenb 81]0$1)@A0 81 03131sdddo 81103 16u0)qGo {1uoow euejUuOW eo1seqe)ew saptouBbisul siuBisut eynssiy Bassas wnuaga ejzeuawns> edses0jyaeyo e3noe e40)31q)e e3enua}3e ejeuiunse sn} 12qe)6 esnjau easaulo eo1ue)Aaz wnd tue )}Aaz wnueJas)a4 sapio1u0)a6 @1joytsua wnap toao eo1ue)Aaz sueba)a suapueds @40)}1Ased sisuaseued eue1ydt))eM oossis-opnasd styeursato sno.ue)Aaz eueosAeso eo1ue)Aao euel ybin saioeds sndued0Jajdiq, snostpojdig sowAdsol dy souAdsol dy soJAdsoig souAdsoig sosAdsot dy soJAdsoig soJAdsol dy soJAdsol dy soJAdsoldy souAdsoig souAdsoig sosAdsoig souAdsoig soJAdsol dy souAdsoiq souAdsoig souAdsoig soJvAdsol Qy sosAdsol dy soJAdsol dy sosAdsol dy sotAdsol dy XA] B90ydsowig @1U9)) 1d, sAyoe3sojysig 9Y43UB) L491, wn)ejadeyst dy wn}e3adeyo! a uinjejedeyoia 8) )aue1G wnt yetg SOWSAQ, SOWS9 Cy s14saq §1449G st4saq eiseabaugeg e1649q)e80 seoA9 xA}e0043eA9 B1U9)] NI» B1U9)] NI» eAye903dAu9 snua9 ses 92eS £2s bLS 60S “+ 80S + ++ 20S + to + + 90S sos Los + 00S Tce “+ 669 “+ 869 + 169 129 799 + + "+ +e + + £S4 €TT “+ “+ + Che rot A Celt 5 98 cin oo > + LY Lov 009 66£ 86£ “+ ++ get “+ + + 69£ £ve + “++ “+++ + 62£ 1494) eM snwAuon3, tisaz1emy3 smwAuon3,y epuayue-nun) eB1pon3 tiMouap) im e1uabn3, Lisa} 1eMy3 e.u26ng 2) )Aydoudsay e1uabn3, BA)N}-O}nd e1uabn3, wnJo}NALs e1uabn3, esqe}6 e1uabn3, PANS @tuabn3, e3e939e/q e@tuabn3 wn ue )Aaz wnwJedsouy3AJ3y wno1ue)Aaz wn) Axosy3As3 wnt }0}1sn3qGo wn )Axos4}Ad3y 1 tuoow wn) Axouy3As3 wnd Lput wn) Axosy3As3 eje)notued aqtoAu3 asuadeo wnwey UeJ3 e51ue)Aaz epejug ejeuiwnse wny3uesod1u3, SeqtJ @1 equa 1705138) snu6eae )3 snso}}) LAqns sndie309e} 3, snjessas sndies0ae}3 4a}. )npue 6 sndues008) 34 snuaowe sndses0ae)3 SLAae) e@1jasy3 wnut43sn6t} wnt }0qo3 aw4os1sAd wn)AxosAq SWIOPLILY wn )AxosAq e.seidas sajedAuq t LyBungxos saqyadAuq tLsaz1emMyy eusesesg eo1ue)Aaz eucog @S0 )NuaA eucog @1yopizadesy eu0cog e6u0)qo BU00dy e@SOAJaU eu0og @) )Aydosoew euoog (saupseb eUu00dy @40)$13Sa6u09 eucog sturjye eucog snoiue)Aaz sndues0433d1 dy stuBisut sndsed0493d 1 dy snpidsty sndse50433dt dy saioads snua9 vtt + - Z + + + f a 2 + z 3 2 + 2 a a Fs ¥ r % . 9) eJ0WeU UO IPLYy90159% + + ttuoow UOLPLY9015% : 0 : : : 0 0 : a . 0 ; : : : 0 0 : c 0 : 0 6 wnt }0413n98 U0 LP1420]9% . . : . : . . . . . . . . . . . . + . . . . 5 . . . eBnf tun Ba 1UU9)D» 3 ° 5 . . . . a : . : : A he * : + + : : : : + + + eptsqeos Bsa 1UUOJI9 9 0 6 c 6 . e : . 6 é . . . O oO 0 : 7 6 3 6 @1]0413e) e|uapse9 ¥ : 6 s 6 , 0 0 0 i 0 6 5 0 . o 0 é 0 O a 0 6 {uaweJo eluapep, . . . . ° . . . . . . . . . . . . . . . . . . . . . eo1ue)Aaz eluioue9, : 5 . 6 6 . . 0 A 0 A . 0 6 o é : : 0 0 Q 5 . o 5 ” tisa3. Ley BIULDIEDy 0 ao . : K, 6 a - * 6 0 . . 3 : . : A . 6 5 0 0 re 3 o 2) )Aydouduaa etulouey, Q 9 Q 6 : 6 Q 6 6 6 6 6 6 : . 7 0 0 A i . . 0 0 eje21ds eluiouey : . : + + + 2 + + + + + + + + : + + = 3 5 . + + + + 83 1saenb B1ulsed, : 2 ‘ + + 3 : + + + . : + + + z + + 2 . . : + + . 7 @) }940W e1uLoseg R s : + + + + + + + : ‘ . + : . : + Z 3 s : + : 2 ‘ tLuowday BLULIIEDy . . . . . . . . . . . my + . . . . + . . . . + . . . eduesoui ya eluLsse9 : . : . . . 6 0 . 0 0 a4 oi 0 : 0 0 : a 0 : . . é 1494) eM 84334389, ; 6 . i. A A A a . . . : + + : + + : : : . + + Q + sueul Bea @19U} 4989, : 0 a . . . . . ns . - A . : : : fa . : : : + . a + Basod @49U} 1989, ) 9 eJedLJaAIp @4auyI9e9 + + * 1494)eM erjaursAasy Bo ipul e1ye))a6e)4 5 5 6 . . . 5 O . . 0 o 9 Q i 7 0 e91pul e134n02e}4 . . . “++ 44 . . . + . . + + . “+ . . . . . . . . . . . . . . . . . . . os . . . . . . 83840)}09 eue twig . 5 . %* . . . a“ . . . . . + . 5 . . . . . . . . + . suaidisap WNtdtyld . . . . . . . . . . . . . i. . 5 . . . . . . . . . . 814039U13 snoig . . . . . . . . . . . . esoiBi jas snot4 . . . . . . . . . ° . . . . . . . . . . . . . . . . esouwa2eu snotd n . ~ . . : a . : : 5 : . : . + + : : e@SOAJaU snot4 ms A r) 5 5 . . . . . . . St) )Ow snoigj . . . . . . . . . . + . . . . . . . . . . . . . . . edues0u91w snot4 . . . . . . . . . . . . . . . . . . . . . . fs . . . SLAGB)} snot4 . . . + . . + + . . . . . + . . . re . . . . + + . . ep tds t y sno t 4 . . . . . . . . + . + . . +. . . . + . . . . . . . . tiuosnBuay SNIL 4x . .- . 8 . . ° . . . . . . . . . . . . . . . . . . . . S1WJOJ1SIAALP SNILdx . . = ae . . . . . . . . . . + . . . . . . . . . . . . edse20)neo snoig . . . + . . . . . . . . eso) )e9 snoig . . . . . . . . . . . . . . . . . . . . . . . . . . ewissadse snoi4 ; F z 2 * + + + + + + + + + s 5 + e a % 2 + 5 + 2 e21ue)Aaz Blyrayuasyey . . . . a . . . a . . . . . ° . . . . . ° . : . : . sourw eljlayuasyes : = : + : : 4 + r + + : 5 Z e + - . i : + + : = eo1ue) 199 eaebey . + + . . . . . e . . . . . . . . . . . . + . . . . ey90) )e6e 8148990993 . . . . . . . . . . . + + . . . . + . . . . . . + . ejeuiwnse eAung 2 : . + + + + + + + : : = + : : + + : 3 : + + + + eueBuo } eisoydn3 . . . . . . . . . . + . . . . + . . . + + . . . . . wnvonbt jue eiquoydn3 FS . . x . 0 . . . ; : : - 0 . : 3 . a 3 Q 9 : 9 . un3eJopo wntsojedn3 S¢S 925 £2S 1LS 60S 80S 20S 90S SOS 10S 00S 66% 864% 16% Illy 799 £Sy %y 10” O07 66£ B86 BBE 69£ EYE 62k satoads snuag STT i o : : Q cs 0 : Q 0 A . c 6 5 : 6 2 . 5 0 3 Lisazremya e40x] R p 4 + + + + + + 8 + 2 + y + B + + 7 2 R 5 : + : + epunont BJOXI y ' a a 5 . O : . : 5 } 0 O : . 0 3 n n A * : i a c easoque @40X] = 2 2 + + . + + + + + © : ¥ + e ; + 3 Fi 2 * + + : = eo1ue}Aaz eupueuos] 9 s ‘ 5 S + + + + 3 5 . ‘ : + : 2 + ° E S Y % - + 838 )099ue) Bupueuos] : ' O r . % F 5 . D ' c : : ‘ ' : D © b co O c . C1 . 814039uU13 13j061pul . . . + + . . + . . . . . . . . . . . . . . . . . . e21uU8)Aaz xa] . . . . . . . . . . . . . . . . . . . . . + . . . . e}euauUaA snduesoupAy P P + + + + + + + : + + + + : + + 2 e : : + + + + eupue,s0 snduesoupAH, F ‘ : » + + : + + . , ‘ % + + E + + D R : 5 + : : + e21ue}Aaz @14393unH - ; ; + + + + + + : + 3 E + “ - + + : : = - a + + e 81 )joslune) 813p)oquiny Q . . . . 5 . . 5 . < D 0 5 O " ‘ 5 A g 3 : 6 . . . xeqysAw e1uobny ; P : + ‘ + + + ° * 4 > 7 + 2 s + + = - 7 = + + + = Baul Bnisaj e1uobNHy 5 : . . a . 5 6 . . r ' 0 . O G b nm 0 ‘ O : : o Q a epunq!Jo}} @1U03JOHy . . 7 : z A 3 - x 5 0 c. : n, hi 0 : e . b o F ci . . . BAdt B1p}alssioH . . . + . + + + . . . . . + . . . . . . . . . + . . eBJsapow eadoy 8 : D ¥ im s % ¥ “ 2 a c a 0 ; A, z % q . F . is . . epunon{ eadoHy . . . . . . . . . . . . . . . . . + . . . . . . . . 40)09sIp eadou, . . . . . . . + . + . . . + . . . . . . . . + . + . uno ue )Aaz wnt] ewoH : O . 0 7 . O . . O . D . E c o . O . C 9 Q 7 a Cj 3 si3tw euaysJe}OHy . . . . . . . . . . . . . . . . . . . . . . . . . . Sisua)eybuaq abeydiy . . a . D a . . O 5 O Es . D c 6 Q 0 0 0 ' : 5 3 : snaet)t3 Sndsiquy 9 : b 3 5 5 O 5 5 D 5 a ' 9 O “ Q 0 : 0 ci c c a fi snutyso}ow SNS iqtl OQ . 5 = 5 Fy - 5 O ° . 0 A, : 5 o : c : G 7 c b ' 6 snjeouny SNosiqth . . . . . . . . . . . . . . . . . . + + . . . . . . sndseso0isa snos (Qty . . . . . . . . . . . . + . . . . . . . . . . . . - e91ue}Aao BLIL)OHs . . . + + . + . . . + . + . . . . + . . . . . . . . Bsod1 yn} S1}0ApaH . . . . . . . . . . . . . . . . . . . . . . . . . . snuedtJjawe sndse.04A5 : 7 S + + + + + + + + + + + + ‘ + + : . E 5 + + + + e))e4 sdoutJA9 + . . . . . . . . . . . . . . + . . . . + . . . . . eeu Bowe 21 4yodsouwA5, + . . . . . . . . . . . . . . . . . . . . . . . . . 943SsaajAs ewauwdAy im 0 Q : 9 . O ej . - o c Q c : és D . A S 6 ; c f 9 : Liyjou eiMad5 A . 0 . Q 0 . cu . . . 6 : 2 A . : O 5 0 6 0 c : . $1] e]Ua L410 Bimad5 . . . . . + . . . + . . . + . . + . . . . . + . + . et yopytuidues @LMaI5 . . . . . . . . . . . . + . . . . . . . . . . . . . eotue}Aao BLuopsoy, 2 3 = = % + + + + + + + + 4 + : + + p * . + + + 53 (LSazLemyy Snwe}84301U09 : ; b O 0 . “ “ a . dj Q O 5 F 0 in ' 0 5 ‘ oj : 4 L1uosuioy3 snuve }84301U09y : : . 5 5 D a Q O b c . 9 . . 3 a 0 C Q . A : . eulai jes sme }8Y301U0Dy D . 0 : 0 :. D : : : aq . - rs 9 : a q : 5 0 n 0 0 0 \dayooy sme }84301U09y 2 : : + + + + + + + + ? + + + . + + 2 2 5 °. + + + + 8yesJas BL yduiog : : 0 a . . a Q Q . : a b : o 6 a O A : iS : Q e913e1Se Bul aug Q : Q + : + D P + + D + + + © + + + S 5 : + + + + 8) )Aydejuad S1WS03A]5 + . . . . . . . . . . . . . . . . . + * . . . . . . eue lz lunew SlWso9A}5 . . . . . . . . . . . . . . . a . . + + . . . . . . @1)0313sn6ue S1WSOIA}9 0 O 0 i. . mn O b j 0 o mn 5 vy O 0 : Q : 6 ' x c 0 0 wnje))23s uo 1p1y9079 a i nr er Bt Bis Bi ee en I ee S¢S 92S £25 11S 60S 80S 20S 90S SOS 10S 00S 66% 86 46% I> 99% £5» Ly LOY O0Y 66f 86 BBE 69£ EYE ézE satoads snus9 9TT + : . . . : : ° . i . 0 0 - : + : . + + + + : s 9 2 Bupuexay e4ey] LUeW : S é + + GC + + + + . : . . + : + + e ; - y + + + + eo1ue)Aaz 8494 16UeH, . a . . ° 5 : ° O . & 6 5 é . . ° ° c A < 5 F . A A 38194] $30) )eW ' . < . 3 C : . . . A : . * . . . . : . . . ° . « . sn2202e43a3 $30] )8W . . . . : = A . . . : a = a . A . 6 a ry 5 » A i“ . SNt}o} :uweys snjo}]eW . . . . . . . . . . . . . . . . . . . ° . : . . . sisuaddi 1d sn}0})]eW . : + : + : * + . + : . + + 2 + + aC = : 0 . + . + suadsadsny $N}0} ens . . . . . . . . . . . . . . . . . A = 5 . . . 5 5 sndses0iua sno} ew . . a 6 + c G G . A 0 a + a . 6 n . . G . 6 O : a eue13a330J4ad esaey . . . * 3 . ° A 5 . a a 5 . . . . 0 c 0 cd 5 : 5 a A ( tuoow Bonupely . . . . . 5 a R ns . . A A . . . s A O . 0 0 9 A 6 0 ®A)N} eonupen, R si Tee 4 40s oe or) yes a 4 a. re : eS : a : a a a” 8383) ad ebuesesey 5 . . . . . . . . . . . . . + . + . . . . ° 4 . . % euA6 ip eBuewecen, : s : : + : + + + + c + . + * + + 2 : : . + + + : 81 )0313snBbue eBUuNAN Ty, . K . . . . ° . . . . . . 5 . . . . = . . < : . . c esouiadeu e492} (uu : . . . - . . : . . . iS : + . : 5 : . 0 9 O 0 a 0 2 eyooidnu $N29090x0}, . . . ° ° 5 : A 5 . . 5 . . * . . . . . . G ny . . eyjuevoew @])aluauasacy . . . . . ° . m . . . ci 4 G 5 5 . 2 G . 3 6 . . 5 euel330ue @]]a14auasaoy a o . G . . : ° . . . . . . . 5 a : é A : : : a a St] eJsowau eaS3 11s 2 2 3 + : + + + + + : + + + + : + + 2 ? ro F + + + B @17)0316u0) CoS Ty . . e ° A ° O . . . w A + . 5 A . A . Q 5 - 5 5 5 5 auydepoa3t e983! 1s ° . . . . . . . . . . . . . . . . . . . . 5 A . . . ew! Juaqe)6 @3S31 1s . . . . . . + . . . . ry + . . . . e . . . . a . . . tuaupse6 89S} 11s in . . . . . . . . ° 5 . . . 4 G : O : a * i. : 0 : o ewissip12e BLUOWL . . . . + . + . + . + + + . . . a + . . . . . +. . a @3e))a31de9 e.uapul ty, eS le Dac eto ne On SAU ESR) pe CGF Osan 4 Pe oi em ree Oy ONCE RE Tee oes Mey OF Srey re 28) )Aydesyaa sayjues!day, . . . s . + . . . . . . . . . . . . . . . . a % . . B1]0,191)dwis sayjuesidat, 5 . . . . . . . . . . . . . . + . . . . . . . . . . stsua)ebauas sayjuesiday . D + * c + + . + 0 0 : : : * + + s s : 2 + + + + 839919 sayjuesiday . . . . 5 ; x ° . . . . ‘A . ra . A ns A 5 5 : x fs 5 Bd 1pul 823} . . . ° . . . . 5 . 5 a . . . : . < A a o * : . 3 : stugaut e1uosmey . . . . . . . . . . . . . . . . . . . . . . . . . + snueJay) eM snyjuelse, . : . . . . . . . . . . . . . . . + . . . . + me O 9 snyjuebi}0 snyjuetse), . . . . . . . . + + . . . . . . . + . . . . + . + . snnbi}qo snyjuetse : ‘ : : + + + + + . r + F ; + si + + : + : ° + 1 Luoow snyjuelse yy . . . . . . . . . . . . + . ° . . . . . . . . . . . {uaupseb snyjuelseq, . . . . * . . ° . . a ° ‘ . 5 a . é 5 c 0 : fs . 84a31q)Ng eaj40de] ; . . mn * « : a a : A a a : : ms 0 : G a : 7 : ° : c Buewes euejuey . . . . A . . ¥ . : . : . . + : . : : : p . Q : a + eo1ue)Aaz 8u00}04 . . . . . . . ° . ° . . 0 3 . : d oO A 7 : 0 * 0 6 14a4)eM 81491 upuay . 6 . . . % ie ys oi A . a “ a . . . A . . . : + ns + 0 831) 904a30y4 eunspey . . * * * . 5 ° D * 0 a : : . A . 0 0 : c ° . 5 291U03aq e1otasne ;: : , + : + : + : £ + + + ‘ 3 ° + : : y : + : = a1 Lxa1) unuWse fn . . . . . . . . . . . . . . . . . . . + . . . . . . wnje)ndL4une wnuiwser + . . . . . . . . . . . . . . . . . + + . . . . . . wnt }o}13sn6ue wnuiwser S2S 42S £25 LIS 60S 80S 20S 90S SOS 10S 00S 66% 867 267 LLY 9% £67 LY LO” 00% 66£ 86£ BBE 69E EYE bef saisads snueag + . + . . . . + + + + + + . . . + + . + + + + + + . . . + . + . + . . . . + . . . . . . . . . . . + “+ + “+ + D A . 5 5 . 5 . 5 . . 5 ‘ + + + ? ¥ + + + + + + + + ~ + e 2 ° Fy . . . : 5 5 . . * a . . . O O O + Re 3 + + e 5 . . . . . : . J Q i : a . 5 . . A ° . 5 5 . . + + . + = + r g + + + + ° . . . . = + + . . . + ‘ + + % + + + + + . . . a . . . . . +. . . . . + . . . + . . . . . ° . . . . . . . . . . . . . A . . . . . . . . . . . . . . . . . . . . . . . . . és . : ° = a : O . 5 a ° 5 . . . + : “ . x . + = + + + : . : . a + . . . . + + . . + . + . * + + . . . . . . + . . . + + + . . . ay . . . . . . : 5 . = 5 5 * 5 . = . $ $ + + + + 5 5 A 5 F 5 . . 6 ° rs d ; . A “ . ° 5 5 5 x . A 3 . iy A 5 5 . O . ¢ A, * a O 5 F 5 . dj . A 4 = F * % 5 * O 0 . O 5 ° 5 . A 5 . . a . 5 D O m 2 c * 5 3 3 A 0 a . a A 5 x A * 0 . . 5 . . . . . a 5 0 5 . * x 4 s a O 5 . . rs . . + + . . . . . . . . . . . . . + . . . . . * . . . . . . a . . + + + . . . . . . . . . . + . . . . . . . + + . . suedi3niy unasedde | @14038))13SIp eisseo eduesossew sapto}Ajsep ejes9e)6 e@sopuos} 116 1Ua04 e105 1Ased snoiue)Aaz 16ua)a e91ue)Aaz eotpul euetydi))em bisa, Lemy @)ayo)nd Bassa} sue lJeA wn3e}0a9INn win3e) )@quin wn313eA)As wnje43soJ aue}NAts wn) )Aydout ys wnsa90ud wnje}013ad 348 )NIIqQuo wn) )Aydousew unduesousew t4ayooy wna ue616 apues6 suaasaosny wndi3di})2 4J0)09Sip wnugay7Je)9 wnje))a31deo @1 jos 191 duis wno14y3eqge)ew e3e)new eupuesja3 tyewtu @) )Audousew e@asoque edin wnt )a4ydan sayjuedan, 23S}! )09N, B1pabseny BILISI IAW epusessny epusessnw eAesInW eudAbes31W uopoys iW sdosnwitwW BSNL] tH» esnty th Sidos}0491W ensew ensaw ensaw U0) AdaWaWy U0) AdaUaWy uo) AdauaW U0 )AdaWaHy U0 | A2aUAW U0] AD5Ua Wy U0 }A2aWaWy U0 )Adaua Wy U0 )AD3UaHy uo )AdauaWy U0) A202 Wy U0] AD9U8Wy uo) Adaula Wy U0 )A2a0WaWy U0 )Ad0WaWy U0 )AD2UAH Uo )AdawaW uo) AdawaW» uo )A2aU8Hy U0 )Adaua Hy ewSOl)aW BwO}Se)aW @)) LULPOWy BIxXlJseW eixtysew BLXLISENy BIXLISEWy Ses eS £25 ILS 60S 80S 20S 90S SOS LOS 00S 66% 86% 26% 1249 9799 £S% LY 10% O07 66£ 86£ SBE 69F EYE 62k satseds snua9 * a 0 ie 0 0 ; 0 3 Ses 72S £2S 0 . 0 a . = 2 0 5 a A 0 . . . . . 5 + + + + s + 5 O . : . . 5 . 5 : . . . . . 0 . J + q + S . + . 5 . D a O . 0 . : . 5 . O a A 5 O 9 . 5 . A, . O ° . . ° . . 5 . J . . . 5 . 5 o O i. 5 ; . . 5 + + + + + Se a 5 . 6 * Q + + 3 + + + . . . 7 . A . 5 A . . . 5 a . a 5 + 5 5 O A A si i . . O a . 5 . . ° . 5 + + + . = : Q i 5 . a A . . . . 5 . o A és D Q . = : 5; + + + + : + + + $ . 5 ° . 5 0 + = + + + + bLS 60S 80S 20S 90S SOS A 5 5 . % = + [ + + Fs + s + + . . 5 . 5 + o + . + . . . ° 5 . . 5 5 5 . . . 5 + . . 5 5 5 : i. 5 . } + e + + + + + rs + . . 6 5 a + 2 + + % G 5 5 5 . + + 2 . + . . ° . 6 5 } 5 a n O Q a i 5 . A . . . + + + + e + - + = + = e + + + LOS 00S 66% 8647 264 8Ttt . 5 5 . 4 0 5 ~ + > + + 0 as 5 5 5 . a 6 5 a a O + 3 + + . 5 5 ci 5 . . A . . 0 és . . ° + 6 O és 5 . . . G + 5 2 + + Z + . 5 6 O a & O 4 * 5 . . a 5 5 6 a B G « A . A a A . . O 5 5 o A R 129 999 £S9 LY 5 si 7 Bi . . 6 Q 8 = . 5 ‘ i % 5 A a 0 A 0 Bi A a A 5 as 4 s . 5 O + + . + + + $ + 7 * 5 a : 5 . 3 a . a a 4 A A D a 5 A 6 . 5 . . a &s 5 . Q . b O + + * A 5 D a . . O o O « a A A . ° 5 5 5 5 a QO 5 5 O 5 3 O . R 5 O ° 6 as A O 5 . A a Ks 5 6 6 D é O . O ne i rs O O 0 5 5 x a A 0 . . 2 5 rs a . H . . 5 o a 5 ns ¢ O Q . o 5 5 5 A 5 . 5 5 x As a c . 6 a 5 A . . D 5 5 . O . As G 6 & A 5 . . o o Q A a x i 3 3 a . 5 5 6 o A a 5 Q D D 5 O A A ns C 5 . 5 ° A . . 5 5 * O O a a ° . 107 009 66£ 86£ BBE 69F LYE 62k ejeuuid e@S0jUaUI0y e@souaqns @1)0$16u0) 13uLsoy sapltosesao ®31soddo wnje)e uounauts3 saploxe sn) )AydAjod snjeuuid sno ipul enbi)qo e998 1409 e21ue}Aaz e21ue}Aao eyjuesew eue 1ucow eo 1pul epue)q St \npe ®) )Aydouow eewse tisaz1emy3 11sa} 1emy3 wnsout6iqns aueyolijad wnJo}41toned appad) oun ty apues6 wnje)NI1)eued Bul0 JOYS IP {saupseb esadse {1ua)) Ip wnjze jn Losey eo1ue)Aaz wnwiss!3es6 2#38)099Ue) eyidejogel e)npis3s tewoppeq eue 1 UCU LU @plya0oy t ! saioeds e1weBuog BLJoulod @143)8A}0d 8143 ]@A]0d @143)8A}0d 8143 ]8A]0d 81 )Ajsouna)d wn tw4adsota}d dadidy sAwe 490) Aud snyjue) Aud sny3ue) Aud snyjue) Aud sny}UuedLUa0dy sny}uediuaodd, X1U90Udy BLYdI98d, @asJad sisdodisad 833aAed @33aAed 840) 4S1SSEd eAu6 weied eAuBb 1wesed, snuepued, uninbe}ed» uninbe)ed wninbe}@dy wninbe}ed wninbe}ed wninbe}@dy wninbe}@d, eyjueibed @L)ewso, @1499qS0 e1ijundo ewJadsooud, xe]0 wn | 90 euys0 BUYI0, @upue) 4904 e.Badoyjon sajApodeyjon sa Apodey on snus) 6TT . . . . . . . . . . . . . . . . . . . + 7. . . . . . snuAdoona} eBaulsnoas 0 A : fs QO 0 ci : 0 o 0 : 9 . a : : rn : : 6 6 é 8 e91}aWa auowesas c 0 0 : c . : A c a . 6 o 6 . . . : A 3 o b eauuniq ayjueutynos, rs . . . . . . . . . . . . . . . . . + + in + . . . . eul AW B13n95 . . . . . . . . . . . . . . . . . . . . . . . . . . 8) ]1snd e1do}00s, : , s + + + + + + + . : + + $ + + + t : 5 : + + + + eyeulwnse 81do}09S4 7 O O * O 6 5 A 0 6 Ss 5 6 in 6 a 5 O < 6 O Gs ns a 6 G unJpuejuad unsAdowa}25 : - p + + + + + + + + + + + + ; + + : D : : + + + + @1)0;lauejsed BLISYSeUNy oS . . . + + e . . + . . . . . + . . . . . . . . + . oy @1)0413sn6ue e@Lsayoeunyrsy : : G a : 5 ° * A é o . 6 6 : dj - c 7 : : : S @s0a}0 e4ayo1a}49s ° : 7 0 0 c . . . : . : . 6 : Q . A 3 : c 0 é : : D esouases 843)]}4a495 . . . . . . . . . . . . . . . . . + . . . . . . . . ejeulBuews 813)}4$J2949Sy : . : : . : : : : : . : : + . : : + : : 0 + E + : saptoyjueso)yo espuedies . . . . . . . . . . . . . . . . . * . . . . . . . . eo1ue)Aaz e99091eS . . 7 0 a . . 5 . : 5 ° 5 % 6 . c : . . ° 9 . 0 r unp | uqeas ewsoides, D . . ns : : ee . : = A ri : : . . n : : : : : : : : : wind tpul ewsoJdes 5 . : ° @ . : é . . i % 5 . : ; 6 ci : c ri 0 G q sua}a0} ewsoudes . . . . . . . ° . ° . . . . . . . . + + . . . . . . snjeulBbsews snpuides . . . . . . . . . . . . . . . . . . . + + . . . . . auBisut wnides : : : : fs : ° . : : PS 5 6 5 c 6 : G < 5 . . 0 Ki 3 eo 1pul evapewes . . . . . . . . . . ° . . . . ° . . . + oy + . . . . eo1sued eJOpeAyes , : : + + + + + + + + + + + + : + + : ; b + + + + + ee )nNI1Jad e19e8)es . . . . ° . ° ° . ° . . . . . + . . + + . . . . . . eGu0}qo e19e]es . . . . . . ; + . . ° + * + . . + + . . . . . + + . eupueip e19e)es ? P i + + + 3 + + ' + + + = 3 i + + % % 5 * ‘. 5 * " Joulw eainoy . . . . . . . . ° ° . . . . . . . . + + . . . . . : @2eBI1A BaJOULYy : : O 0 : O 0 0 . 6 0 0 : = . 5 5 . A 0 D 6 . 0 6 3) ]owsad unysaz0yauAyy . z 0 0 D 0 : 0 : : 0 . . : s a a : a o : 0 6 a 3 ej euosonw evoudoz1 yy : : O . : o . . : 6 : : 6 3 6 0 O 6 Fy - d 6 : 6 : ed 1pul e1juessiay . c : : 7 : : : 5 . = PS 3 . . . 6 6 5 . 6 . 0 : é e3snqos eauedey . 5 5 o . : : 5 b 3 . : : : ° 6 a 6 6 . : S : : . 6 eo 1ueqe]ew e.puey . . . . . . . . . . . . . . . . . + + . . . . . . . edtueyAaz BAL fUBIINds ‘ é 3 A O 3 O . 5 “ : H : c i 5 a * ri ‘ . n . : : LisazLemya stxAdoyrAi dy . sone . . ns . . : + . : . S + 0 + + : : + B + : suadsaued wnwJjadsosa3d . . . . . . . ‘ ; . : . : . . . . . ° a . . . + . + tiseem @14ZOYIASdy . 5 : : q a z Q a an 0 6 : : . - 6 . 6 . 0 ; o 0 8) )Aydouaas @1u,0Y9ASdy . . : . . . i. : . . + : z : . : : - . . C D 2 + o : esojuawues @143049ASq : 2 ? + + + + + + + + + + + + , + + : 5 ; 3 + + + 5 euBiu 8143049ASd . . . . . . . . . ° . . . . . . . . . . . . . . . . @43a)1 )npue)6 @14OYIASdy 5 . . . . . . : : ry c 0 A . . . . by : : . : a : : + eiqnp BLsOYIASdy . : : . : : . ; : . + . . : + : + + ° : c : . p C ‘ tuo tdweys edeseoopnasd, : : : + : + : P + % 4 + 4 + < P ‘ + : B 5 B + + + ‘ 149]eM SNUNds . . . . . . . . . . . . + . . . . + . . . . . . . . eupuesjay Si JawoeUS iid . . . . . . . . . . . . . . . . . . . A, + + . . . . @S0}UaUI0} Budd d . . . . . . . . . . . . < . . . . . . + * = . . . . @1)0513e) euwaId S¢S 92S £25 LIS 60S 80S 20S 90S SOS 10S 00S 66% 867 267 Il” 9 £59 417 LO” 00% 66£ 86 BBE 69£ EYE 62k satoads snua5 Ses 92eS £2S “+ 84+ LLS 60S “+ + “+ 80S 20s “+ 90S “+ + + + sos tos 00s bid 669 867 Oct + + + iB 5 Q ° ¥ . . . O n mn * . A 5 is . . . A O . . . 5 a } . O 5 A a i. 0 5 . . 5 5 5 = 6 a 5 5 O D . 5 cl 5 . K . . O i if A 5 o . G . 6 D 6 O is . A 5 . 5 5 a . . 0 & . a . + + : + + ‘ 7 a : + + + ° a 5 . ° . 6 é Q 5 . A 5 . cj * ° . 5 6 A 5 . a 5 a . a a A 7 5 G . a 6 + = = % + ~ 2 + + + F. + 5 A 3 5 S C 6 . 5 és 5 a a 5 ° a + 5 5 5 7 a A 6 + + S + + . A < . - + 7 . . a ° . 5 4 O O . . . 5 © 5 dj as O D D 5 A ° 9 A . 5 5 A 9 . D 5 a b o * 5 A . . 5 . . . . . 5 a 5 . 5) A D O ° a as 4 * . o 5 a ; D O 4 . a 5 A 5 o a . . 0 5 & ° . ns . 5 ° . . A 5 5 . a . x 7 A 5 . . A 5 a é . 5 0 . . ° . a 5 5 5 5 * . A 5 O 5 ° . ns O 5 . o 5 a 5 . ms . 7. 5 o oO . . n a A 5 5 5 5 * . O ) . 5 5 . A 5 5 x O . 9 5 a O 6 + + 2 + + z : : . + + £ 5 a . . 5 si . A, 4s 5 : . . A 5 O c * . o A o . 5 A . 6 7 . 5 a . dj + . . + + * 2 + : : $ : + + + . 5 5 6 A 5 . 5 a a Fy A 467 127 999 £S% 17 LO” 00% 66£ 86£ BBE 69F EE 62k @3eU0109 Sisuaulyoutyso9 8) )Aydousew @32@)0399Ue) ®1}0J$13sn6ue wnjojejod eyjuesoiw @1)041woweuuld e3e8)n91ued ewiuuadse eueu sapioxe Jadse wnjeuosiad sey6ue}eq eo1uodef snupues3a3 s.)eotde wnt}0}Lpsos 112461 snjenoijes StsuaAi jauuey snje)nd1 }eued snjeuiwnoe Siue]oased sua dsaqnd e31ue)Aaz Quast oud siueyndiys suadsa))ed 8104 16u0)qGo ®@) )Aydoss1 } eppiue ny 1434p BYydIISIp 14997] eM 38} )}adqns suadsaqnd snjeui Bawa -opnasd 810 1Ased @38A0go SIPt4tA-ou6 iu | Luoow tyeupseb ejeuiwnoe satoeds $090) dus, $090) dus @1uazaiMs epoBauns epoBauns souysAJ3s SouYydAI3Sy souysdAJ3Sy S2Y4IUB) 1qOo13Sy Sayjue)iqosjys BL soquiodys snjqe4as sn]q943s unwJjadsoasa3s CTO LEPC S elueydays SNJNUOW2IS SNsnuowe3S, wn tuoydtsouas SNJOGOUWaIS » snuodouows sy snsodouowsays snsodouows 3S, snsodouowa3s, @1}e4sauU0S wnue}0s x8] LWs x8) LWS BaJOUSy @aJ0USy BaJOUSy BaJOYSy Basoys @dJ0USy BasOUS, snduesauwas, snduesauas, snduesawas, sndsedawesy snduesawas, snduesawas, sndsesawes, sndsedawes, sndsesauwes, sndiesawes, snua) + D ; + 8}eps09 eLueAn a 5 F 3 Q 9 O 4 c 0 . 0 : . c 3 . 5 : : a 5 wna 1ue]Aaz wn) }Audoun ees eet a ae ee SE ee Ca Cin See Ce Oo Oe OF ho Ge wno tdi} 12 wn} JAydoiny : : : + + + + + + + + : . + + . + + . : : : + : : r e313d1))a BL 4BSUl), . . . . . . . . . . . . : + . . . + . . . . * . + . eo1seqe)ew e1uiduny . . . . . . . . . . . . . . . . . . . . . . . . . . euodsosy3Aua BLSA]BIlI1y : . : . . . . . . . . : . . . ie . . . . . . . . . . 1) )9z)ep BLsA]edld1y 9 : . . . . : + . + . : + + . . + + : : . c + + + 0 e31ue)Aaz eluapeysiiiy : a . A Q D a : O ° . i : é 5 3 . 0 . : 0 A a 0 3) 8 Ua} U0 ela) . . . . . . . . + . . . . . . . . . . . . . . . . . t4aupse6 snyjuedA}o1 a : Q a A “y . : 5 i. = A O 0 a . ny A a . a 1 Q c 891381Se BL) eppo! . : . 2 + . + + ie . . + A + . . . + : : . . + + ao 0 8) }asoquef sniuowll, 3 a . i . . . . . : + n : : : : : : : : 3 0 : . c : wn31416e).u ew6iysesja) B O . a . i. x fi f i. : 0 “ : c : $ ci < 0 A co ‘ cs 2 esojuawses eja2esjal ' : r c : ns . i . 5 + . : + + : oy A . : : : + + + oy @10)4 LAsed e1jeulwsal, A a 7 a S : O . a x s 8 Q a is a . a c 0 ‘ a . a : 8 )nqayo el jeulwias d a . : : . . . . . . : + + . : : + : . p G C + O : e314a}aq eLyeutwsay : : eunfue e1yeuiwsay : . . : . : . . . . . . . . . . . . : . . . . : . . e@aundund etsouyday : . : 7 . - c : . ' : jh 5 5 Q Fy 2 ' O Q : § S c BAe )4 euuase) + 3 * + + * 9 : + 2 D eo13e1se euuase| os : C 2 B s eo1pul snpul ewe} : . : . : . . . . . . A a . . . . . . : . . : . . . wns tue )Aaz wniBAzAS . . . . . . . . . . . . . + . . . “ . . . . ° . . . wnue i346im wni6AzAs og 9 r Q 3 3 4 3 0 ci 5 : 3 é 2 oi s A 5 a S f é o a wn3euiquna wn iBAZzASy : . . . . . . . . . . . : . . . . i. . . . : . . . : unpun3iqnu wnt BAzAs . . : : . 0 o 0 i wn3n)oAau wniBAZzAs, ; BD : + + 4 + + + + p : + 3 + > + + 3 ° 7 P + + + + wnue 1 saau wnt BAZASy . . . . . . . . . . + + + . . . . + . . . . . . . . wnyjueso tw wnt6AzAs, . . . . . . . . . . . . . . + . . + . . . . . . . . ynyew wni6AzAs,y . . . . . . . . . + . . . . . . . + . . . . . . + . wn) )Aydosst} wniGAzAs,y . . . . . . . . . . . . . . . . . + . . . . . . . . wn tyayds twey wniBAzAs . . . . . . . . . . . . . . . . . + . . . . . . . . apues6 wni6AzAs + + : : : tyuaupse6 wniBAzAsy 2 5 - + + + + + ® * : : : : : : : ; : + = + s wnw ty wniBAzASs, J + 5 : 7 wn31sput Ao wni6AzAs,y + ‘ ¢ : P tutuns wniBAzAs “+ + + + . “++ . . . . . . . . . . . . . . + . . + . . . . . . . . wnt }0}1psoo wniBAzAs, . . . . . . . . . . . . . . . . . + . . . . . . . . wnje) )AydoAueo wniBAzASy . . . . & + + + . . . . . . . . . . . . . . . i . . wnanbe wnt BAzAs 8 . : : + 2 + : + + : : : + . z + + ; 8 : + + + oqn)e wniBAZASy . & : Bp + + : ; + : + + - o : + ‘ : S 2 + + O 5 euysjnd $090 )duwAs . . . . . . . . . . . + . . . . . . . . . . . . . . 8) )Aydousew $090) dws . . Fs . : . . . . . . e)npidsiy $090 )GuAS, : . . . . . . . . . . . . . 4 : . és : . . . . : . : e}eauNd $090) GwASy ee ee ee ee SeS 25 £25 LIS 60S 80S 40S 90S SOS 10S 00S 66% 86% 167 IL 49% SS¥ ‘%L» LOY 00% 66£ 86£ BBE 69F SHE. 6zE satoads snus9 cet + . . . . . . . ° . ° . . . . . . . + + + + . . . . el }douso snydAz1z fr s R + : + . + : * i : : + : = . + , ; : . 2 + P : eosadeu snydAziZ» . . . . . . . . . . . . . . . . . . . . + + . . . . euel3lunew snydAz1z . . . . . . . . ° . . + . . . . . . . . ° . . . . . wnjepnes wn) Axoy3uez, . . . . + . . . . . + . . . + . + + . . . . . + . + @1)04tAued e1do Ax . . . . . . . . . . ° . . . . . . . . + . . . . . . suedtu6iu @1doyAX» . . . . + . + . + . . . + . + . + + . . . . . + . + ttuotdweys B1doyAXy . . . . + . . . + . + . . . . . . . . . . . + + . . wnJo)4turwed wn) )Aydoyjuex . . . . . . . . . . . . . . . . . . . . . . . . . . @1)0s13sn6ue BL YyBlImy ce x “ + + + + + + + 2 + + + + - . + . . -. . + + + + esjanbis3 BLWIOMy . . . . + + . + + . . . . . + . + + . . . . + . . . Byaylyssis B1aqu6n)) th» . . . + + . + . + . . . . + . . . + . . . . . . + . ejepidsnsiq ei pue )}puay 0 . J 65 ° a . 5 . . 5 5 3 a . 0 7 5 6 a 0 a 5 } 4 . SL] IQN}OA 8e}233e4 . : . . = . . 5 O . 5 3 . . * a O a c . . = o A 3 . 2381 ]05143 euns}ey . . . . . . . + . . . . . . . . . . . . . . . . . + eiptosid euns)e4 i = M + m + + + + + + : * + r + * + + + 2 a + + + + ewissi3)e KOLA 5 5 5 . 5 ° A . ° 5 5 . 6 . . O 5 O 5 a . a * 4 9 . eavoque e1uousan . . . . . . . . . . . . . + . . . . . + . . + . . . euejedsesapew o6e) Luda . . . + . + + + . . . . . . . . . + . . . . . . . . 12)que6 o6e) 1UaA 9 5 . 5 . . . . Q 5 O . Q . 5 O 0 . Q . a O 6 0 4 0 siuisge BD1JEAy . . . . . . + + . . . . . . . . . + . . . . . . . . 84351) )ed09 SLsa eA, . . . . + . + + + . . . . . . . + + . . . . . . + + eduesouayds BLJBANy . . . . . . + + . . . . . . + . . + . . . . + . . . 810} 1duedawas BLICANy 5 6 5 . 5 A + . . 5 = A a . 5 O O 5 O 5 5 5 . 5 a 5 wnueu e1uean . . . . . . . . . . . . . . . . . . . . . . . . . . edueso0u2ew eLueAn S2@S 92S £25 LLS 60S 80S 20S 90S SOS 10S 00S 66% 86% 26” Ily 9% £Sy 4%Ly 10% 00% 66£ 86£ BBE 69F EYE 62E saioeds snued x . 5 . . 6 . + + + + + + i; + + 5 ° 5 3 + + + = + Z + - + 2 + 6 x . « . 5 a . a a a + + + + + + . + $ + + + 5 x < 5 . . + + + + S + . 5 D 5 %. ° 5 a = . 5 . fe 5 9 fe + ° + + ie + z + a . 5 a 4 . £0£ £62 £92 £52 Le2 802 Loz 061 281 ect + . . . A . « . a . . 5 . . . . . . 5 5 5 . 5 . Q . 5 A 4 . . . 5 5 9 . 5 a . . O 5 As O o a rs c Q 5 8 5 5 . J 5 A . 5 : Q 4 5 c + + + + " + + + + + + + + + + + + . 5 . -. + + 5 + + 3 , + + F + + 3 7 9 + rf + + + + a . + + 5 . 5 5 ms O 5 a 5 5 5 G Q a a 5 5 . a 5 3 4 4 5 ns & . O - a O a 5 . . 5 5 . 5 5 a 5 . : . 0 a qd 5 . . 5 5 J ° 5 . + . ° 5 o 4 c “ 5 . 5 5 5 . . . 5 5 a 0 5 . c + + + + 2 + + + + + + + + + + + . + A . . . . . . . 5 5 5 5 . és . . . o ol . 5 : 0 o 5 . . 3 I c 5 a fe o a d a o a O 0 cs ci my . c me + + + M + + + + + + + + 2 + + . . 5 . : 5 a 5 ° 5 . . . 5 . * . . “ 5 . . ° 5 5 O . O : 5 “ O . 5 . 5 . 5 5 5 a : 5 5 . O O : % . 5 A . 5 . . ; . 9 a o a O c . 5 “ 5 . 0 9 5 5 * a O 5 . . c % 2 a 5 ° 3 5 . O 5 . D 5 “ O a 5 “ 5 . * . : ns , “ . : 5 O o + 5 5 . 5 . . 3 . . O 9 . “ Q 6 ° ° 5 5 5 . ° . 5 “ O : 5 . 5 o x 5 . 5 5 5 5 . . 5 G ci 0 as 5 O 98l BLL SZL Slt 79L Bet 62t O2b 22 69 S9 29 09 % Bf LE *Joquinu sap Aq pajousp awe s)scJ0.j *ys9}se us Xq pox oe solWopug STVWINV HO SdNOUD GALOATAS AO SAIOAdS AO LSIT Z xouuy ++ Sr wnJojaunp S13Stsj snjeBstaisy eo 1put uu Wwe @otpul e@joJ9s eunowsew xauas sn) ]@3uUa Si suauo)Aaz SN} 1posydewsay snpied yefjunw snutsun ewseds ejepnesisseso ed1uls @13N} $t))091461u eo lpul 143 tus snasnj snjeaut }qns wnJew)ed tpueAe) esourBbiqns snwixew 4Jo)oo1tun styeqnq eS 1pul Sixe saioads sn)eydasoi9y SaJayzopisoy Jazidiooy @)NILIIBALA snjn6bes) eae) sns eynjey S13Aqsaddy S13Aqsaid SNINXOpeIiedy snunxopesedg BuayjUed snoe1j}UNW snssin]aHW ewJsapebay siuew e2B08W, e437 snda X149SAH saysedioy saysaduay sn)nqueun4 sn)nqueun4 sn)nqueun4 S1)94 seyda)3 snAJag snyeqng eo00pueg SLXY S1WHHVU snua5 “+ + + £0£ 7+ “+ £62 “+ “+ “+ 8+ £92 £S2 2&2 802 Loz 061 28 981 BZL + e+, SLL £Ll 791 + g£l 62 02 + + “+ dL 69 s9 4] 09 99 gf D4 Ze 7 suadsa}nsseo SUaDULA soysuAysosyyAsa eotueael SN3tpul sisuaise}eq ely yHIL9 si suauo)Aao snisea suapua)ds soyouAysouzew laoew sisua)eybuaq sisejnes snotseqe)ew tuoj6utsso0y Joyootun ejeuso sndoosida snpion) Si suaulyouty20o suosjtune snp1sqay Bo ipul snoey3 ise sisuauls snysuAysoJo} yo snoijeise snutsassed snjeisys siqt 4a6t) tuow saudsajut ttAeu6 eaundund eauauld SLULS}e snjeuos02 snjnjnJ JayseBoue}ow @)npan6sanb snundtuaoyd S1y33e 8)N6)n6 e1ydi3 saisads snunso1g wnaeo dy wnaesig euBbAd0puag snyjueuoJpueg snunisdA9 siusoAg ededio1 yng snyjnang snaso9 snaso9 euldeso9 se.oes09 snysAsdo9 snyoAsdo9 BqUIN} 0D» 811890) 09 essldy B1utot) sajde)o20sAsy) stsdoso)y9 sisdosoyy9 seLuopl )49 sdeydos)ey) xAa) sndosjua) sndosjuad,y sn6 )nwiude9 St juewose) saptsojng snaynqng snwo}soysesjeg elueuasy 8)O0apsy eapsy eapsy sndy souas0esyjUuy snyjuy eButyuy seuy Ss tusounewy opa)y epneyy @1ULy3 LBay snua5 Set + : ; + 2 7 z : 4 : % © 2 + S 5 = + 2 2 3 = + R a 2 1} }Neusyssa) sdosay + + . a + S + + + : o + + B g < + + + + + : + + z eo1ue)Aaz ewie}ebay + + : + = = 2 2 D 4 P a - : + F RS * + p : ss * + : B @)) tdedtuqns ew.e) ebay ; + : . + + : + B + ss 5 3 3 + : 3 + : 3 : . B + . e)eydasewaey ewie}eboy + + ? + : + + + E % 7 + F ‘: + + + + + + * > . + + bs SUOJJ}LAB)} ewie ebay, O * . 5 O O rn O 5 a 6 5 0 O . 5 o “ « 5 0 . . ci . 0 eauuniq elutosny + + + + % + ‘ r + Y + + + c + + + + + + * + u + + + snul}}Auaq SN)NI1LIJOT, . c 5 . 6 O 9 . . ns O 5 5 O 5 < O O qc O 0 O 0 co a . 2381438 eunyau0} . . . . . . . . . + . . . + . . . . . . . . . . . . e@3e@)n39und eunyouo} 5 . © D 9 O 5 0 a O O 6 5 n O 0 a c a 9 . D . 9 6 6 esowt} esowly c . G O O 5 D b . ; O 5 a rs O 5 3 . AO = O D ny 7 o . sn3e}siJ9 sniuey . . . . . ° . . . . . . . . . . . . . . . . . . . . Si suauo)Aez ednjay . . . . . . . . . . . . . + . . . . . . . . . . . . Sisuauls snydAsqgox] . . . . . . . . . . . . . . . . . . . . . . . . . . snewoweuu ld snyoAsqox] . . . . . . . . . . . . . . . . + . + . . . . . . . sisuaAe}ew snjeeutj5] + + + + ‘ + 2 - & : + : : + + + + + + ‘ + 5 + + 5 Sisuaiseosebepew sazadisAy + + + + : + + . . % + + : ‘ + + + + + + . 4 5 + + : snoipul sajadisAy + + 4 + + + + + 5 + + + + 5 + % ‘ + + + + + 4 + + + eounze siwAyzodAy 5 ry ns . A a 5 ° a . . 5 . A 5 a . 5 O O a Q r 5 a a eo1jsna opunsth . . 5 : . O O . . . . 5 o O . G O 0 0 O 3 D : 5 o a e91unep opuns tH . . . . . . . . . . . . . + . . . . . . . . . . . . sndojuew ty sndojuewtH . + . . . . + . . . . . . . . . . . . . . . . . + . snjeoid sndiwey + + 7 + = 4 + za + + > % + % % * % + + + 4 + + + + = 8yeu0l0) aus0Jd way . + . . . . . . . + . + . . . . . + + + . . . . . . snjeises sajoedsey o O . . D . a . a O . : 5 Q s 5 . . o cj 5 6 ° ci 0 O snpul unjsetyeq . + . . . . . . . . . . . + . . . . . . . . . . . . Si suauJsAws uoAo} eH . . . . . . . . . . . . . . . . . . . . . . . . . . 4Jajyse6oona) $n}9081)]eH . + + . . + . . . . . . . . + . . + + . . . . . . . eso161)aJ eynses9 + + + ; a % : BD : * + ; : + + + + + + + 5 + 5 sAua6bo) 13d @)NIe19,y . A O 5 6 0 9 5 o 5 5 5 fe . 5 i O O . . 5 0 Q : Q . wnje ipes uniptone)9 . . fs : o Q O ° Q . : 5 a 3 O 6 - O e A . 7 a : 9 unjauoue}seo uniptone}]95 . : : . . . . . : . . : . . . : . . : . . f a : : : @91}0)1uU Yop! }9490)25 . . . . . . . . . . . . . . . . . + + . . . . . : . suoJjjlasauls KB )NIIEDy : Go . ns . . . . . . . . A . : . : . . 6 . A : : : t1zjaAeje)] SN) 18D» + + “+ + * + , + 2 = - 2 5 é 2 a + + ? + : 5 "4 + + + 83eI7e9)e91g X1tpsado] 25» . . . . . . . . . . . . . + . . . . . . . . . . . . ea9edo0}09s sAweuApn3 . . . . . . . . . . . . . . . . . . . . . . + . . . easis6 Xt4aqdowas3 . . . . . . . . . . . . . + . . . . . . . . + . . . elpewsajur e330163 . . . . . . . . . . . . . + . . . . . . . . + . . . @33907z4e6 8339463 . . . . . . . . . . . . . . . . . . . . . . + . . . eq)e 8310163 . . . . . . . . . . . . . + . . . . . . . . . . . . euy AsadAy e1jowng Gi a a . . . : 0 5 9 a R . 6 D Q n ‘ “ 6 “ ws D “ a O pauee e)nong . . . . + . . . rs . . . . By . . . . . . . . . . . . asua)e6uaq unidouig g 2 Q : * : D o = ° = B 0 0 9 : : ° a 0 D Q D ‘ sno.uo0)Aao snasipesed snunioig . . . . . . - . . . . . . . . . . . . . . . . . . . snaeydoona) snunsoig £0£ £62 £92 £52 22 B02 102 061 ZBL 9BL BLb SZL 2b Y9L BEL 621 O2L 22 69 S9 29 09 Be LE 42 satoads snua9 £0¢ + + + “+ £62 £92 £S2 yh x4 802 Loe 061 281 ++ ++ 981 8Zl + 84+ SLL £2b t+ tee eee eet + 91 gf 9¢eT + + 3 + : + + + + . O 5 5 5 . é 5 ‘ 5 5 . . 6 5 . a 5 ° A . as 6 a re 4 5 & és . 5 0 5 . 5 . ° 5 6 5 fe 5 O ms ns a 5 o A 5 5 . 5 . . 5 a 5 . . . . 5 5 As 5 O 5 ° 5 O A 8 O 5 4s 25 . 2 . 5 + + + + + . + i + "2 + + + + = 2 . 5 5 Fs 5 5 n 5 + + + + + + + 2 5 5 . i . i O o 5 . 0 . D . 5 a O 5 nl A . = ° i . 5 9 5 % A G ql + G + + = + + + 5 5 5 3 O O O a . ° ms 5 D © 5 O ° A ° . ° = ° * + e 3 + r . + + a A 5 5 5 D 5 ry 6et Oeb 22 69 G9 29 09 4 gf 2 + 8 92 Sisua)eybueq sdaoiuje ®@)oaune snJjaj3iue jaw $n}0a3n) Jajpeo tuawesy etujedna aedosy3)e9 89138A)AS $1]e120s ejeusoul Lipjalyssoy @}0ue,enbs snuiddi) 14d eundyseuq snydo}040}y49 snuen Sisuaz yesveyew snpijiu Sisjsou1ubew snipeq SLIJSOIIPLILA sn)eydas0ysuAd Ja6iu St] ]OILISNy snoumue } 4 snawoweuu td wn) ) 1des09sny stsuadd! )1yd snjeqsiso sntsojns snusoyzuex xe@J09139AU 891U0)Aaz e@1ua}0) eo1jeise e)eydas0ona) epipsos Ing jnw St4}Ss0U1}e) easaulo eolwesse snutddt) yd Styejuatso satodeds B)nJe43soy 8491 20doyy eunpidiyy snjouousAg snjououshg snjouousAq @)n9e331Sd 2@)n9e8331Sq @)N98331Sdy elULdd e@luldd eLULdd SNULYJOJeUOd SIJBLAN]d sna20)d ®33!d Sndtd Saplorid Saplorid sndo3so) Aud sndodso} Aud SN] tpoud snaeydosiuaeyd snaeydosiuaeld, X@J090198] 8d X8J090198] 8d $N}09049 1 dad SNJOI0491 ad WNBUIO} ]Aad» snuede ad OARd Snwo}0Y4340 sn)01s0 XBJOILIAN @1ulseq9aN @tulse3oaN BLULJG3I9N e143 9AW edes1osnhy edeo1osnw edeodtosnw ®@))19830W e4jsesiW sdouow sdosay snua5 . : * + . a « n 5 . . . + + , + a + = . . . . . Q ° . . . *s Fy ° . . . . . . . . . . . . . . . . “ 5 . . . . ° 5 ° . . . . D Fj . . . . . ¥ . x . . A > . . + + + + > + + ye 5 . . 5 . = + + - + + . . . . . . + + + + = + . . . . . . . . . . . . . . . . . + . . ° . . ° . + . . . . . . i . . . . . . . - . + . . Let “+ “+ “++ “+ + Si yeusayig snsoJod eunytpod snue ipuey tuopsaf ejeuso snjeulseo esadse 2 salsads | satoads 40}OD1SI3A snjeydasot) sidajot) sStsuauo)Aa. $a}0)]e9 e3eu0b is} Sisuauo)Aad Sisuauo)Ae. esougad ed Stsuauo}Aao 84330) 1ds snotput sdoda 4aje319Sns suadsayni S1utjse soona)odAy e)001e8)6 euopeduwod @}9UL91q snastu6 tsipeued snue isadipuod 113 )Neusydsa) xauas e2 1uues6039) Sisuautyo sisuajediu snjeyssio B@)aay4o sijequoi e3ed1 nN} S1yde)aspueq sn)Apos049 sidsewau), sidsewau9 sidseweu) eajadosAuy9 sidseosa9, esoydojes99, esoyudojesa, esoydojesa9, $3}0)e89 $2}0]89, $3}0]89, $9}0]89, $9}0]e89 eB1og e610g stydoue)eg, $3111d38 sdosa3soz sdoia}soZ, @494}007, sniauea edndn X1UUNL Saplopuniy saptopun, eBulsl eBuls) uoJad] uoJadt snyo01 auoydisduay stusoposyday enoo90e) SNUINIS» X143S et yadoydasys snjaezids snjeezids stusayids 8331S Saplo)o0o1xes oe i _ —emeeeeea £0£ £62 £92 £52 LE2 802 toe O6L 28 981 SZ SLL £Lb 791 gel 62L o2L Zl 29 09 9 gf Ze 92 saioeds snua5 + £0£ £62 £92 £S2 L£2 802 Loz 06L 28 98L 87eT ++ SZ SLL £Zt 791 gEl 62L 021 Zl 69 s9 4) 09 94 + snjnseu wnuedwh 3045 tw snoutysoona) $139A)AydoueAd t4yayjuan6 S1tsuauo0)Aao e3euso St4By0uN ) t1u99s6 e3e6nss09 snsout3n)6 $n}91}Soue}aw 113488 ]34 ta]esoyn3e | Satoads 40,eA)es Sisua)e6ueq snjeydasoub 143 sue6a)a eue t4a9et yuod e@3e39und snse)ow snsosow tuuewba tm snjeaut)qns tuojung e.seynoew ejeuises SLAaIq snjejnos snjeiu3s st suaueqoide tsueb xB) 18) a@yeudAy snje)noew 13 )Neuayssa) snssaidap snjuanJsaa nd snjnseu S13Sls} sn}e)}0aul }opnes gf 2 92 satsads SN3NB) tds» SNINE) Ids Snyney td e6Az0pi290 sAsydouuen, sAsydouuen, 8) Ayos3 LW sajauouW! Sa}IauOUW II, S8J29UuOUWL 1, St ydoAyyyoy ojng Oj}Ndy OjNGs ojng snuesea snuesea SNINSBJ9WI dy opn}sa) BUEIIS edoiy uoy3Ad seAjd $19dA49030, uopoB! 10% BISSON, BANQeHy eAnqew eAngew sn)eydas01JAq uoposA } SNOULISEHUB Ty snouldseyxue yy snouloseyue yy ayeudAy sn)Azoep way Sn] A32ep tweH Sn)A}2ep Way» siydoAuq siydoAsq siyde)aspuag Siyde)aspuag snua) ++ £0£ £62 £92 “+ ++ £52 £2 802 “+ “+ toe 061 281 “+ 981 8ZL “+ ++ sZt £Ll 91 gf 62cT + “+ 62L 02 69 s9 29 09 4 sf + + t+ 44+ 2 92 tswepe snjetosey ii WwNU Ld!) a4 epues6 BIt Ny snquadns sn}e1qe}oasou snsedsoud xLusoyd ewo}sewaey ew613soj0u @1d) 1M S140] }149}0A sniuodiuep SN}O}21A 2A93313 B1uaejona)d snjeisejouBiu snsojuawe) i} Si }esuop snje)noewiq SL )ewsayy isee)yuof Stsuauo)Aao euestyjed S1]ejuUatso enyoeb ejeubis 1 Jaus9aM snw} ey ydoun sda taauq snuepsewyos snje)noew sndossew JaBionus St) esodua3 $t)19e46 eoe1jueune St) !geliea satoads 8) 114095 Bul Jeyoy SNABIVy SNABIYy SNABIY» SNABIYy SNABOVy SISN110H BINISLYISy esoqsey, e10qsey, Bsoqsey snijund SNLjUuNdy SNLJUNdy SN13UNdy snijund sntjund SNL3UNdy sAy iyo }eydasopiday sAyjy91 )eydas0pideq, BIIED, O1uegy euueyd, euuey) @1)U0)98y sn) 1ay90)dy St} Iqosoyjuesy S3HSI4 eusajydowo) BWI8P]941y snsoydoseyy snuoydoseyy snuoydoseyy, euey Buby, BUueYy snjne} id snua9 “+ £0¢ £62 £92 £S2 £2 802 Loz O6L 281 + + 98L SZL SLL elt 991 SEL ++ 621 o2L OeT 69 s9 29 “t+ + + + + “++ 09 97 gf 2 92 snddisiw Bul oq esstJau uopadues uosop uouwewebe BUL]aAe e@ayj}u0le aqesay ejaseuseyd 3409 styeyona aoe luwi) ejewey snddisAsyo ea)6e styeyona sieyeo siey @13A}9 tyaujatu ayjuesAd euowod 3)e82049 sapiooyd uoydof euiq)e e8joune sniuaysa e epiwesdd 16u1Lwno 4ayaynd Bsoylplsy 1u0}]em 1sauuLys ej3essnoapiwas eotpul tuaupse6 @39npnse x1)e3s snoiue)Aa. aeawoppaq satoeds seuwt }odAq seuwt yodAy eutydni unt ydei9 wntydes9 untydei9 e1yey3n3 eryeyang ewaung @30)dn3 ea0)dn3 setjad sneueg sneueg sneueg sneueg $1}0]09 $130]09 BosYyd0II 19 ese) 149 etsoyye) ei) tsdoje9 81) 1sdoje9 e1)tsdo3e9 eveyepuig eunaueydosiy, seiddy staydeuy euesiqy $31744311Nn8 @S0}NOLy @SO]NIJOLy siyoey 2yUB 18) Lda B41dS061 104 B41dSOBL 10, @393a)dn3 @39a)dn3 @3929)dn3, 8393 )dn3 GUI, snsoydo}9A), @)) 140% snua) TeT £0¢ £62 £92 £S¢ £2 802 Loe 061 28L + + BL 821 S/L £21 91 gf 62L + +e ee eee . 021 69 99 (4) 09 94 gf 2 92 e@91u0)Aa9 e0J/a e91ue}Aao eua}ay @B]OLA eauew ety $a31}3e euew)e 403994 981y90)03sS14e ejue)eyd B@LA)AS 403SauUWA }od sa Ajod seua)}ay sna ]owap outdo snpew seyAy e.ujzed seweyze ew tpayd 8p2) auasAd auue | Jew snumAje st4s90ud eutu snyo4e) 14d snaouA} saizads ewiy3d, e]NPUulA e1sayen saploss e1uLysjal eyedey S198Id S198dd SL99Id snsopA}od snJopA}0d ejueyeud souay3Jed 01) 1deq 01) 1deq O1j}idedg o1ytded o1jided euaets30SI0 s13dan Sisa]eoAW epaseminw Si} 1ue)}oaW St}1Ue]9W seiXx] seixy BNXO7 StjLuswty e1sojdaq ew) 18) bap! snua5 A 5 7 n 6 ci a x 5 es A 5 . ° & . A 5 5 5 A . Os ets + + . . . o A 5 a 9 5 5 . o a o 6 Q x 5 d 5 5 . ° 4 5 + + + + + + + 5 : a + + : : + + + + . 5 . ° 5 . . . 5 4 . O . . . . 5 O + z + + + + 5 ci ; 5 % 5 6 O ° 9 . . 5 . 2 a 5 5 + + + + + + 5 ° 5 . . . . 5 . . 5 Q D . . . : a 5 . o as 9 “ . . 5 0 a O + + + G + + + . . : A 5 . . . . 9 ° a ; “ . 5 . . . és 5 5 a o . . . 5 . + + + + - + . . . 5 + + + + + . . 5 5 . + . . O 5 . . . 5 5 + + + + + . 5 + . A . . . . 5 + + a + + 5 . 5 . 5 > + + + Z 5 5 C i 5 m a : = 5 a + + + + 3 + 3 + 5 a A “a 6 5 5 3 a . n o A a a + + + + a A F . 2 ‘ f A + + + + . A : - 3 is 0 “ A A : x + i + + H 3 % : + + + + + = 2 + 5 a a n 5 3 . . 5 a 0 . 5 . a d e A a A O A . n . A i a a 5 6 5 + + + + + + + + : F. " R + + + + 2 = x + + “ + + 5 a ns a . . 3 5 . 5 . A a “ 7 5 . A . a . A O 5 bs * . fy 5 5 5 s . . 5 O 6 3 a . + + + + Ga m 2 ks + + + + + + + + 5 A 5 o me O 6 : + a . . o *y re . 5 a a a o . a . ms + + + + + + + 5 rs rs zs G . a 5 a ry “s a a re 6 0 5 . . . 0 x 0 5 . a o 4 o 5 A Gi + a és és 5 * 5 . + + + + + + =. z 6 A “ ns D 6 o 5 A a a a D O Q a easaulo Stutjje sn}eu0J09 snjnjnu 4a} seBoue jaw @ )npanBuanb snunotuaoyd S1y33e 2@)N6 )n6 e1ydi3 wnJojauwnp SL¥S14 snjeBstAtsy eo tpul Buu LWW eo tpul e@j049S eunowsew xauas sn) )a3ua Stsuauo)Aaz sn} tposydewsay snpied ye funw snuisin ewseds ejepnesisseso ediuls @13n) St))ootuBiu Bo tpul ty} 1Ws snosnj snjeaut)}qns wnsew)ed (puede) esoutBiqns snwixew 4o0)o91LuUN styeqnq Botpul Sixe Bapuy sndy soJaz0oes4y uy snyjuy e6utyuy seuy siusounewy Op93)V epne|y e1uty3 Lbay snyeydasousy sasayyoplusy Jajyidiooy saula B)NILIIBALA sn)n6es) CHEST sns eynjey S1}Aqsaddy StjAqsaug SNINXOPEIEdy snunxopesed eyayqued snoe13unW snssJn|aw ewJsapeban siueq CEY-B]:]" oy e43n7 snda} X149SAH sajsaduay sajzsadiay sn)nqueun4 sn)nqueun4 snjnqueun4 Styed seyda)3 snaJag snjeqng 83091 pueg SIxy —_—— ee ee ee EE eee Ses 2S £2S LLS 60S 80S 20S 90S SOS LOS 00S 66% 867 26% 1249 999 £59 LY 10% 007 66£ 86£ BBE 69 EYE 62k satoads rr “+ + + + . . . . + + . . + . . + + 7 + . . + . . . . + + . . a 5 6 A O 0 5 5 a 5 . 7 + ~ + + + + 4 + + + + + 5 5 5 . ° 5 . . : a . 5 O 5 5 - . B . 5 . . . . 5 . . 5 i a 5 0 . 4 A 5 . + + + . 5 5 a a . . ° 7 A mn 9 x is 5 ° . . O 5 ° A ° = Q 5 . . o 5 . A 5 6 . 5 5 . ae 6 5 + + i + bs . « . iS é % . c ‘ 5 eet . + . . . + . . . . . = + + + + . . . . . . . . . . + . . + . . . + . . . . . + . . 7. . . + . . . 4 ° 5 A . ° + . + 5 5 . . . A, . + i 5 . 5 5 . 5 . es 5 . . + + + a + + + + . A . . 5 3 5 a 5 5 x . . . . 5 + + ql + + “ + + . . . . a . . 5 . ny - + 5 O 5 5 . + . . © . . : s A 3 % a . . < 5 * . 6 o . ‘ . O = . . * “ . o . Q O O . a + . 5 O O i 0 Oi 5 5 5 . 7 o A 0 A A O D 5 rn 5 a o 5 . o a O x ‘ Bs . a < . . 5 o . x ny 6 $ . D c 4 . < c co ¥ D O o . . . - c O a e . 5 . a 5 a < . . 5 = a 5 5 ft 5 5 ea2edo)}09s easis6 @lpawsazut 8)}9z1e6 eqje Buy AsadAy eauae sua) ebueq snd1iuo)}Aaa snasipesed snaeydoona) suadsa)niaes SUIIULA soyouAysosy3Asa eotueael SNI1pul sSisuaise)eq ety AIL Stsuauo)Aao SN1JBA suapua)ds soyouAysousew laoew Sisua)eybuaq stuejnes snolseqe)ew tuo}Butss03 Jo)ootun ejeuso sndodsida snpion) Si suautyoutys0o suoJjtune snptagAy Botpul snoey3 tsa Stsuauls snyosuAysos0} 49 snoijzeise snutJassed snjelsjs siqt 4361) 1uow saidsaqut ttAeu6 eaundund sAweuApn3 Xt Jajdowes3 8339063 33063 @339163 e@Ljowng @)nong wnidourg snunsoig snunioig snunioig wnae21 dy wnaesig euBAd0Jpuag sny}ueuoJpuag snuntisdA9 siusoA) eded131)n9 snynon9 SNAJOD SNAJOD eutoeso9 se19es09 snyoAsdo9 snyoAsdo9 BQUIN} OD» @1 890) 09 @SS1Dy e1ut1stg sajde}oo0sksy9 Stsdoso)y9 sisdosoyy9 setuopt)49 sdeydos)ey) xAag sndoijua) sndojjuady sn6 }nwiude9 si juewoose) sapisojng sna)nqng snuwo}soysesjeg elueuasy @)O0apiy eapsy sss ').:):':2°080 SS. ee ee ee a a ee Ses 92S £2S tbS 60S 80S 20S 90S SOS LOS 00S 66% 86% 16% 1249 999 $59 LY 10 009 66£ 86£ BBE 69F EYE 62k satoads snua) vet . . . . . * : . . 3 . . : . 5 - : . . + 2 2 ° . z 5 810130) e1U1s8399N in A . 5 . . . . . . . . . . . . . . * . : . . : . e913e1S8e B1uLue320N . . . . . . ; qj . A . * G . . e 5 5 . . 5 . . . . . 8) eydasoona} @143a39AW . . . ‘ . . . . A A . . Q . . . . fs . . . . . . . . epipsos edeotosnwy . . : . . . + . . . . . . . . . . . . . . . ep : . . in} 3nw edeotosnw 0 é . . Q . : 5 . 0 : . . . . ‘ . . G . . . . . A . $143S0413e) edeo1osnw . . q . . 5 . . . . . . . . ' a . . . 5 5 : . . . . eavaulo 8))10830W . . . . . . . . . fi . . . . . 5 : . . rs . . . . . a eolwesse COCHIN" a x : D “ : ° : : . . . . hn . ie * s . . . a . q . . snuiddt 14d sdosay . iS . . . 0 . . . . . . . . . . . A . A « a . . . . $1]83Ua1J0 sdouap . . . . . Ay . . . . . . . + + + . . . + + + + . . . 13 )Neuayosa) sdoiay 2 + + + D : : : : + : ; 2 : + + : : + + + + : : : : e91ue)Aaz ewe) ebay 4 2 : : + : R + + + + ¥ + : : : : + : : + + + + + 2 8) )tdedtuqns ewie)ebow + i + + , + i ; 2 bd ro + « . + . + 2 + + + + . 3 z e)eydasewaey ewie ebay 2 5 + + + + + + + + + + + + - ¢ + + . ~ . Z + + + v SUOJ}LAB)S ewie)e6an, . . . . . . . 5 . . . ° . 5 . . . . 5 . . . 5 . . 4 eauuniq elulosny ° ; ; + + + + + + + PB + : + : : + a . ‘ + + + + snult))Asaq SN]NILIOTy . . . . ° . . . . . . . A . . . . . * . . . . . . . 838143s eunysu07 . . . : . . . . . . . . . . 5 . ° A . . . . . . . . ®38)N39uNd eunyou07 . 5 . . . = . . . . . . . . 5 . . . Q . . . . . 6 . BSOU! } BSOULT . . . . . q . . . ° . * . . 5 a . . . . . . * 5 . a snje3siso sniueq . . . . . . . . . . . . . . . . . . . . . © . . . . St suauo)Aaz ednjey ry . . . . . . 5 . 5 . . . . . . . A O d a : . . . . sisuauls snyoAugox | . + . . . . . . . . . . . . . . . . . . . . . . . . snawoweuu ld snydAsqox | . . . . . . . . . . + . . . . . . + . . . . . + + . sisuaAe)ew snjeeutjs] o ‘ + + + 2 + + : - + p + + + : + + 5 : : + + + : Sisualseosebepew sajadisAy 4 : : + + + + + + + + + + : : : + + : : : : + + + snoiput sayadisAy . © + o + o ° = + 5 + ‘ + ° : : + + + + + + + + + + eaunze s 1wAy,odAH + . . . . . . . . . . . . . . . . + . . + + . . . . ed13snJ opunJ ty + . . . . . . . . . . . . . . . . . . . . . . . . . eotsnep opuns tH . . . . . . . . . . . . ° c a : . ci A . . . . . . . sndojuew!y sndojuew! q . . . . + . . + . . ey + . e . + . + . + . . . . . . snjeoid sndiwey © ) Q . 9 . o G : + a : s : + + : s + + + : Y G g 83eU0J09 aus0Jdiway ‘ p 3 e + % a : + : ; + : ? 5 : : + , E $ : + : + q snjeise} sajoedsex + . . . . . . . . . . . . . . . . . . . . + . . . . snput 4njsetyeH . . . . . . . . . . . . . . . . . . . . + . . . . . stsuausAws uoAo eH . . . . . . . . . . . . . . . . . . . . . + . . . . 4a}se6oona) sn}99e1]eH . : . . . . . . . + . . . . re . e . . . : . + . : . eso161)ja4 eynoses9 . : : : 4 : . : “ . . A a : A, . es + : : : : + ° + : sAuabo} 13d @)NIBID, . . . . . ° . . . . . . . . . . . . . . . . . . . . wnjetpes wnipione)5) . . . . . . . . . . . . . . . . . . . . . . . . . . wnjauoue}sed wnipione)5) * . . . . . . . . . . : . . . . . . . . + . . . . . @9130)1uU uop! }3490)29 . . . . . . . . . . . . . . . . . . . . . . . . . . suoJ}tasaulo XB )NIIED,y * . 6 OD 0 6 O D 5 t O . 0 G 6 O O n ; a a 7 e . . . t133aAeje)] Sn} 189» 9 ? ‘ 2 + ° + % : . : : : : ; 5 + + ; : : s . + + : 83e8189)891g Xt psado) ] 89» Ses 92S £25 LIS 60S 80S 20S 90S SOS 10S 00S 667 9867 267 IL7 9% £59 Ly 107 O07 66£ 86£ BBE 69f LYE 62k saioeds snus) + + . ses 92S £es bbs 60S 80S 20S 90S sos Los “+ + + 00s 669 869 169 tL 999 £S4 SE€T 99 + + + 107 ++ tee eet “+++ + 007 "+444 + 66£ OF I GOO oO “+ 8 "+ + + + 86£ get 69£ £%£ 62£ snue | 4a91puod 113 )Neuayosa) xauas e91uues6039} Sisuaulyd sisuayediu snjeyssio @)aay49 siyequosy e3e1 ny sisua)eybueq sdadiuje @)0aune snuajzotue jew $n}0a3n) 4ajyeo tyawesy e14sjedna aedosy3 89 @9138A)AsS $1)e@190s ejeusout Lipyetyssoy @)04e}enbs snuiddt) 14d eunAyseuq snydo}010)49 snuen sisua}jeseyeu snp tu Si4}sos1ubew snipeq SLIPSOIIPLILA snjeydas0yssAd JaBbiu $1) ]O919SsNy snaue | 4 snawoweuu i> wn) )1des0osny stsuadd, ) 14d snjejsiso snisojns snusoyjuex x8J09139AU e91U0)Aez saioads stusoposydas ens099e) SNUINIS» X149S @1}adojdas4s snjaezids snjaezids stusa}ids 8331S Sap1o}OoiKes 2@]N3e413Ss0y ®)4ya190doy4y eunpidiyy snyouousAg snjouousAg snjyouousAg @)n98331Sd @)n2e331Sd @)N98331Sdy etuldd eluldd @luldd SNULYsJOEWOd SL]BLAN|d sna30)d B3I3Id snoid Saplorid Seplortd sndooso) Aud sndodso) Aud SN) tPoud snaeydooiuaeyd snaeydosiuaeyd, x8J090498 Ud X81090198 ed SNJ09049 1 dad $N}09049 1 Jad WNBUIO} ad» snuede}ad OAed SMuwO030Y4IIO snjolso X8J09139AN e1uLse ION snua) . . + . . . + . . + . . . . . . . . Ses 92S £2S . + . + . . . . . . + + . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . + . . . . . . . . . . . . . . . . . + . . . ° . . . . . . . * . . . . . . . . . . + + LLS 60S 80S 20S 90S SOS . . . ry . . . . . + . . . . . . . . . . . . . . . . + . . . . + & . . . . . . . . . . . . . . . . . . + . + . . ey + . . . + . . . . . . . . . . . . . LOS 00S 667 86% 26% 9ET . . . « . 5 . O 5 5 rs fs + 0 5 . + 3 > + 7 O 9 A . . rs as + " e + . 5 5 . . . . 129 997 £57 LY . . . . . . . . < A 5 + 3 Fi < 5 6 . O . O c 0 C1 . + . 5 cA O 5 O 3 es 6 a O f . 0 0 A 5 fy a 5 5 ci + C " + + = + . . 5 . 5 ° 0 J 0 Ql O o e 5 Q oO . “ O O A ° 6 a . fe . 6 0 5 O A . O O . 7 a n o 0 = + + + + . . r a . . Q 5 a es a + + + + > 9 = P . n 5 5 ° 5 7 . 5 . 5 * a a O O 6 a o A . 5 2 o 5 . 5 5 bs 0 . 7 . . a a a 0 5 ° ° 0 A . 5 9 5 Q 6 5 3 “5 * a 5 a 107 007 66£ 86£ SBE 69E LYE 62k tsue6 we} 1e) @yeudAy snje}noew 1} jNeuayssa) snssaidap snjuanJaa nd snjnseu SL9St42 $N}@}0auUI }Opned St ]eusa;1q snsoJod euny ipod snue ipuey tuopsal e3eusoO snjeuised esadse 2 satoads | saioads JO}OI1S4BA sn)eydasot } sida}ol) S\isuauo)Aao $a}0)89 e3eu06143 stsuauo)A90 sSisuauo}Aao esoiqad)ed sisuauo)Aao @49}0)1ds Sno tpul sdoda 4ajej1osns suadsayni Slutjje soona)odAy e)o97e)6 euopeduwod BJ9U19Iq snastu6 tsipesed satoads snautosexue, snoutdse4uey, ayeudAy sn)Az2ep Way snjA}2ep wey SN}A}IEP LWAH » stydoAug siydoAug siyde)aupuag Siyde)aspuag styde)aupuag sn)Apos049 sidsewau), sidsewau9 sidsewaug eajadosAsy9 sidseosa9, esoydojesa9y eyoydojese), eyoydojesaa, $a}0)e9 $9}0)89» $9}0]89, $3]0)89, $3}0)89 e6 10g e610g siydoue)egy $3111d38 sdoia3soz sdoJa}so7z, 84943007, sni}eueA edndn Lun saplopsni, sapltopsny eBuls) eBulst uoJast uoJad) snyo01 auoydisdual snua9 Ses 92S £25 LLS 60S 80S 20S 90S SOS LOS 00S 66% 864 164 Let 1249 9799 £Sy LY + “+ + + sdaotaauq snuepsewyos snjejnoew sndoussew Jabionso $1) esodwa3 $1)19e46 e081 }uUeune St) !qelser snjnseu wnuedwA3oJ9 tw snout Yysoona) $139A)AydoueAd t4uayjuanb S\isuau0}Aa2 ejeuso Stseysouut } 11ua8s6 e3e6nss05 snsout3n)6 $Nj91}soue }ow $13Jee)]94 ta]es0xNIe | satoads JOJBA}eS stsua)e6uaq snyeydasou6 143 sueba)a eue tuaoe1yuod e3e39und snue ow snsosow tuusw6a im snjeaut)qns tuoz4Nq e.yeynoew ejeurses S1Aasq snjejnos snjelsjs Si suaueqosde saloads Buse dowo, CWI9P 241» snsoydoseyy snsoydoseyy snsoydoseyyy euey eued, CI snyney id SINC) Ld» SN3N@]LUdy snjne] td e6Az0pi330 sAsydouuen, sAsydouueny 8 )Ayous tw sa}2au0UWw I) $9} 99UOUWI Ty Sa}IIUOWWL Ty StydoAyayot» ojng O}NBs OjNBs ojng snueie, snuesea sninseJawtJ i» opnysel eueiis edoiy uoyzAd seAjd $13dAu9030, UOpoB ! 10% @LSSONy BANGE Wy eAngew eAngew snyeydas014A7 uoposA} snouLdsexue ly i hh i NN ee eee 107 009 66£ 86 SBE 69F EYE 62E snua5 Ses %2S £25 + + + + 5 Z i. G . A 5 5 . 5 5 5 5 . . 6 * A D . . 5 A a 5 A J a i Q 5 5 . . . . . . 5 . Q 3 . . O 5 5 . . . a . . . . . nm . O O a s i. O O A es O . . A + . . 5 5 5 . + . ° 5 5 . ° 5 ° 5 O 5 5 . . . . ° . . . . . . x . : . . Q A 5 tLS 60S 80S 20S 90S SOS . . . . . . . . ms + + . . . + + + . . . + . . . . + LOS 00S 667 86% 26% BET . + . . . + A . . + . . . . . + . . + + + . . . . . . . Ay . . . 1249 999 £59 19 a 5 O O . 5 & . 5 . a A 5 A, 0 O . . m és 5 5 5 O 6 A 5 rs 5 B 6 5 . . 5 aK . 5 a é 5 9 Q a . 5 - O D O “ “a 6 5 ° a O 6 A i D . 5 a . O + + 3 + + 3 z + 0 . Q . 0 a O 7 . O 5 5 5 a a O ° 5 O a “ 7 6 6 O a 3 : O . 5 * a O b . 5 . . G . . a . 7 7 C Fe Q 6 } o A } O . . . . Q A 5 6 O 107 00% 66£ 86£ BBE 69£ LYE 62k BSO}1s149 1uo}]eM L4yauulys ejessndapiues e31pul tsaupseb ej onpnoe X17]83S sna.iue}Aao aeawoppaq Iswepe snje19sezi43 wnutstyey apuel6 BoLyny snqJadns snjeiqe)}oasos snsadsoid xLuaoyd ewo}sewsey ew613sojou @31d) 1M St40}}14938A sniuodiuep SN}JO2ILA eAa3313 eiuaejouna)d snjeisejou6iu snsojuawe } Ly S1)essop snje)noewiq St pewsay3 tsee),u0f Stsuauo)Aao euesiyzed styejuaiso enyoeb ejeubis 14auJaM snw)eyzydoun saigeds Ue 18) 1 Udy @s1dso6l 04 e41dso6! }0, 839a)dn3 83939 )dn3 @39a)dn3, @39a)dn3 BUR» snuoydo)2A9~ 8) ) 140d, 2) 14004 BauOppegy ewodo }ny~ ewodo ny, euLzeysy SNABIVy SNACIVy SNABIVy SNABIVy SNABIVy SISNT1ON BJNISLYISy BJOGSEYy BJOGSEYy eyoqsey snijund snijundy SNLIUNdy SNi3undy snijund snijund SNLQUNd» sAyjyot yeydasopiday sAyjyat jeydasopidat, BJIEDy OLUBQ, BUUBYDy euuey) @1}U0)98y sn) 1ays0)dy $13 1qos0yueoy S3HSI4 snua) 6e€T sewey3e epaseniny + . . . . * . . . ewipayd Sijiuejay + : 2 s : a : ; : + + y + 2 + e : epe) St} 1uUe]oW . . . . . . - . . . . . . . . . . . . . . . . . . . auauAd sex] 3 5 . D . c . a Q . a O A A 6 : é a : O S : auue | sew seix] . . . . . . . . . . . . . . . . . . . . . . . . . . smuuAje BUNXO7 . . . . . . . . . . . * . . . . . . : . . . . . . . S1490ud St},uaWwL + + 0 0 0 p eutu e1sojyday . . . . . . . . . . . . . . . . . . . . . . . . . . snyose) 14d ew) ex + : : . . : . + c c 0 : + + O p snaouA} eapl . . . . . . . . . . . . . . . . . . . : . . . . . : snddisiw seu! }odAy A . A fi 3 . a n 5 a : 6 5 : c\ es a . : ~ : . . Bul ]0q seuw }odAy + ‘ : 9 essisau eurydny rs . . . . . . . . . . . . . uopadues unt ydeuy . . . . . . . + . + . x . . . . . . . : . . . . . . uosop uni ydeuy 5 . . A r is a a : 6 é qc . 3 A, 5 . ay uouwewebe untude5 . . o 7 ° : O : 0 . r " 0 6 a eul)aaa e1]843n3 . Q : 0 0 Q a 9 O 9 A r F a 9 a eayjuoze e1yeyang A : : é a ii 6 . 3 3 ' A 3 a 6 0 6 o d 6 5 A 3 . & aqesay ewaung . a . . . . . . . . . . . . . . . . . . . . . . . . ejaueuaeyd e3a0)dn3 + + . . . . ° . . . . 5 . . . re . A + A A . . . . . au02 8a0)dn3 . as . . . . . + . . 0 siseyone setjeq 0 + * . . : . . : 38 Luwt } sneueg . . . . . : 0 ejewey sneueg + . . . . . . . . . . . . . . . . . . . . . . . . . snddisAsyo sneueg + + : : . . . . . . . . . . . . . . . . . + . . . . ea)6e sneueg . qj . é 0 a 8 0 6 A 5 . a Q . é : ' 9 fe ci b J o 0 . si seyona $190}09 : 5 Q ; . s . . O . 7 : . 6 0 Q Q 9 : fs a o : a 6 0 s1e]e9 $130}09 . Q 5 : 6 . a 9 9 ; 0 : 0 6 : 0 0 : 6 é . a F P siey3 B04yd04419 A c : d 0 o i . 4 2 : é : : A : g . e13A)9 ese) 149 6 5 O : 0 Fi Q 0 : 5 O . 5 g . a . . 0 ‘ O : . : : t4yaujatu e@1soyjza9 . Q 7 fb . 6 . 0 0 D 3 6 5 5 dg é : 6 : b 6 q Q 3 P ayjuesdd et) 1sdoje9 x 0 . : 5 0 . : 6 J a : o O : i a . Q . f 0 > 0 : Buowod 81) 1sdo3e9 K 2 5 : . : : a]e209 et) 1sdoje9 . . . . . . . . . . 2 . . . . . . . . . . . . . . . saptooyd eveyepuig + p : : + : : : = + + : + uoydo f eunaueydosiy, A A, 5 O . s . . G G ¢ . a o Q f . 6 o : a 9 A c 0 0 euiqye seiddy . . . . . . . . . . . e . . . . ° . . . . . e . . . 8j0Une Siaydeuy + . . . . . . . . . . . + . . . snisayoa euesiqy $3114u311Na . . . . . . . . . . . . . . . . . . . . . . . . . . eyepiwesdAd @S0]NIOLy . . . . . . . . . . . . . . . . . . . . . . . + . . t6uiwns @SO}NIIOLy + + E 5 4ayajnd siyoey i ee ee Se ee ee eee S@S 92S £25 11S 60S 80S LOS 90S SOS 10S 00S 66% 86% 16 ILy 9% §£S» 41> LO” 00% 66£ B6f BBE 69 EYE 62E satoads snua9 Ses 92S £2es LLS 60S 80S 20S 90S sos “+ bos 00s 667 969 469 129 79% £S9 Ovt 919 + “+ “+ + “+ Lov 007 66£ 86£ 88t 69£ £7f 6ek e91u0)Aa9 ChYET) e91ue)Aao eua yay 3e)OLA eauew eyiudt $a317)3e euew)e 403I9y 981490]03S14e ejue)eud @LA)AS JoysauwAjod $a A}od seua ay sna owep outso snpew seyAy e.ujed saioads ewiy3d, B\NPULA e1sayeA saploi e1utysjal eyedey sloadd S199dd S199Id snsopA}0d SNJOpA Od ejueyeud souay3Jed o1j)idedg o1)!deg o1jided o1jided o1jided euael430S10 S13d0N s1isa)eoAW snua9 Tvl TWD €S8T epuyunptTutHA SozTeAyL Ttoedd Two wA}Teuuey sso *ueqga eTrosytzfsoddo sorddsorq ULH erebuAtped 6865 TwS bL6Tt BAB3 307 ULH STSexX8TTINpeq €98¢S TWD 9L6T eAfTouuey be at3) BMe{30N S889 Ssat umouxun Tu eAyTouuey TpS9 TWD eAyTeuuey S6e9 *ueqd e3noe sorddsotq TWD STTRD puy{zabea z6SD TWD eTTeD put {zabea beoz IoD €98L oquoTOD 1BeU uosnbiag ‘us Tw. wuemerequeg Z189 NWu 8S8T eanuebequy sezteMyL 98974 Nwa €S8T eanuebequy sazTeEMyL 9897dd Two wAtuepeATyeN ZES9 “Se TOH Tareyooy uoTAsemeH Two epeyereqey ¢za9 zest BpUuByOMOTTIN ueutiL ‘u's TWD epuexeTebomnyw $819 Two BpusyunpTUuTH guemrejsoy otssz Tyo eAtuepeATHEN 0659 BAe: | €set 93 3eyezE5 sozTeauL S€sedo UW wA}TBIEeG ZhO9 OTIDE “OOH UBMTEL “C °3eND UT suemMIaysoy .~zOUTAXe ATQeqoid. Iwu L98T ‘roy websey sesedo ULW weinpeusyey €78S *qkn earny-ognz weruebng rw eferequts suumzezs0y €pLez Iwu ufezeyuts sueu1ej90% ZIL9Z Iwa efezeyuts suemzejs0y 09ZLz Tw ‘iow webyeu eeyremyL LL9€dd Iw BPUBAISATTS 889 Tw ‘roy webpey soz TeAyL €9bdD Iw BpuBxTeATTS 9809 MIN zeet wurebt Tom ueuTizy curs Un wmupedtq 9129 Tw *z0y unpsed eedeTTeR iva wfezequts €€LS MIN S98T Ioy yBABIOH soyFemy, s00bdd ULH wrtebeAtpeqd 9865 * 14H eatny wruebng (eAFTouuey) Two eAyTauuey 9999 Tw. BpoezTyUBN UuoyduTYyzIOM (u)ZPTD ULH BucsutTeM 1079 ULn BpueyeTeumey O9T9 Tw LSet STIRS r¥veN seqtemyuL spsedd ULH wATTe19eq £209 *324W eaqeT6 eruebng ULW BUSUFTSM 96T9 UW STSxSTeUTA P06S UL eTexuBbektTO 168s Tw €S8T @TTeD rB8U soqztemuL 1449749 UH BItnusen ¢€98s *oeuysnjeurb2zeWe-opnesd snd1e5ewes TS 1980 “397d SITTERS sezseayL 6€€Edd Two BjoyeansoqTL Ty) 9981 eindeuzey seyTemyL 6€€Edd Two wpueyetebemny 7819 ZU°b BIOTA *Aey Tal TON NWy Ozer BrBynTey uocoW 6€EEdD Two ektuepekyyUN 98959 *oeuy @3ea0go snd1ie5.ewes suoTzeArasqgo “38Tq = eax AA 1TR907 30398T109 *ONH 387d AQy{BWOCT =OONH | “wed sefoeds MaTASBY UOT ABATeBUOD TBUOTIEN QONIAOUd NYFHLNOS NI SLINVTd AGOOM 4O SHIOddS FAVA AO SGUODAA ALITVIOT MAN € xouuy cot Iva €68T Bqrmeiey uesmTtiL *u's ULH wuvdt Qe TOW BAe: | 9S8T eurebeteo zeupreS [4:T- te) din STaxsTeyTtA Tw BMB330¥ TS BzOYNTOGGTL Two BpueyeTBbomny TwS BpuexXUNPTUTHA Two Bue TBMeL Two ernqueT en Iwu 986T eCezreyutg eXtinsekec Two eftuapeAtyeNn Two eAtTouuey Iva 6L6T eCereyuts suBurazsoy PLOLZ Two eAtTTouuey TWD 6S8T BuUNnpTUuTH sezyfemyL zeavedd ULW eaepedtq ULW ekrTe18g TWD Sset BuNpTUTH saqzTeMyL zapedo ULH ektTe190g ULW erebeXtpagq Tw nj 7euTeputy rIwa LL6T eT ebrytuwerey zeqng 90S Two BuBMETequEeg UIN «SL6T emepedtq sseM £9ET ULW euedtTyTmMoqnT ey ToD €68T BT Temesstay 39TMor T Two BmMeT Taq MIN LGBT Bqeinjen gseytemyL ssszdo MIN emepedtq Ivy €S8T BTebuoy saqyTemyL ssszdo UW epueyoTeumey TIN 888T eiopeusT Lset ‘roy uBTeYeWw awa efo Buco NW €S8T BuuequTg NOW dN eunyny 10d apel eAtreuuTW Tauprey5 = sgo0tdo lod eAtTITbtItTpaw Twa =SL6T BTebT TOM s3eM vOET Two ektuapedTyeN Twa =0zert ereqnTey uoow LLsdo Two elf Tauuey Tw ZL6T BUnPTUTH zewe15 S69E TWD TL6T eXtTouuey zeCton SL6 MIN eTebektpag Twu 2ozat BrBqn Tey uoon MIN aTSxBTTNpeg TWD PL6r eunpTutTH wnpebuesrTL Le9 Tw €98T eunpTtuTH saqremyL 6L9€dd Tu é ereqnTey soytemyL 619€dd MLW emepedtgq Iwa efereyuts Two n33eUuTepuTH iwa @TBUTTTS sgeM 9SPL bye) BTopemog Iwa eTeUT TTS azeften €0P Two ekyuapeATyeEN Two eAtTouuey 6T6T BIOANTBIOMERITL é Two BABT TEC Two 18st eqedo usutIE ULW emepedtq Two sest eandeujey seqyremyL 089zdd UIW BpueyoTeumey Tw €S8T eaindeuzey saqtemyL 089zdd ULW euedt We TOW IWd 886T eCereyuts eXtinsekec TWD oLé6t epuex,unptutTHy efktainseker TWO PL6t apuexyunpyUuTH guewzazs0y Two apuex,unpTutTY guoTzeAIesqo “3sTq BAX AyyTROOT 10zDeTTOD *ONH *4sTd AqTTBD0T 9€6S bI6S T069 €6L9 €819 0999 9799 ST99 86S9 T9s9 8LE9 8229 ovo9 9209 c6Ls 9089 sts9 STb9 €Te9 6S29 8PT9 9€L9 ozcs c8s9 8bS9 €86S 9L6S boss “s7g9 6TTL zs89 89L9 L6s9 999 6879 8079 “stg s909 oes9 ONH *qny *qny *equEs “TOTA -ydng *qodes *qodes *qodes “weg umoTput eusorodes unotqzdrtTe wnt TAyds2zAg unzpuezued umzAdorzates e3e6rzTa eoer0UuTY Truoow uo,019 Trucow eonypen Trsezremy3 wnrtnbeped unzo,~yroned wnrnbeped satoeds €bT TWD €98T STTeS aeupie) Cfo) WWH BMBYSIY ZIOL @3sTio erurdreser) awa eLcéet efezeyuts puTizebeg 88 Tw ssst “roy unpsed SO; FeMYL €bbedd ULW epueyeTeumuey Sprl9 Twa 8 €S8T B8S9M9H seqyrTemyL Ebbpedo wu eCezeyuts LSS BJReuUND BTTTUTPEn MON é eATTA ON uoquszouL MON S8BT eTebey usuTIy, Ive 188f wvandeujzey re09uU ueutay, iva €98T pueyezTmerey sezTemyL sTttedd MIN SS8T Bjexyuernbueg saz 7enyuL L6TTtdd Nw cset eanuebequy sozTemyL L6ttdd Two Buemerequeq £089 GPSl sUuTeTd WueYydeTg Zeuprey L6Ttdo ULW BTebntnzny ggp9 *qnu ejewre edubrueredg un BpuexTTued peed 2861 efereyuts eAtineeder wa BPUBXTSeATTS 8909 euzerg 9ST awd 261 ‘eTebnTeW0qG BATTS 8zr ULW epueyetTeuey 9519 “qnu unjepnes wnTAxoyjuez Tw c8st B30 UE usutTIyL Twa =9S6T ‘roy uwebtey sezTemyL 91974. ULW etebedtpaq Z16S TWO e€S8r eueBTt Tom saqTemyL 9L9z%dD UW eueAtTzeTNW 0S6S uUOUUY eoruerdez esntTitn MIN PpS6r erebereqey eXtinsetecr ESE NWX p86T sseutepTTM Ped ekyinsedkec Sz8z sofey 068z Ivd pe6rt ‘prozbuoT ektineekec oeez IW emepedtq p2z9 aqva@ 06st eTeqndey ueuTiT ULIN eyXYeTedy-eATuapueg spas NWY SSBI eanuebequy seqTeMyuL otsz Two eAtuapeATyeN p69 qnu erodsoryzdre eTsdTeotiaL df} eouts uoTzeTTOo ATuO Iv’ €S8f eaindeuzey eeu sozyTeMyL 9OEE Two sinqueTeN pI99 qnu unpraqeos eusordes Un emepedtq 9979 bpes suoTJBATaSqO *48TqC TBAex AAT TBOOT IozDaTToo *ONH *48Ta AATTROOT ~=—ONH sured gatoeds Annex 4 NEW LOCALITY RECORDS OF ENDEMIC ANIMAL SPECIES IN SOUTHERN PROVINCE Scientific name New localities Birds Centropus chlorohynchus Cissa omata Columba toringtoni Dicaum vixcens Gracula ptilogenys Muscicarpa sordida Rhalnicophaens pyrrhocephalus Sturnus senex Zosterops ceylonensis Reptiles Calotes ceylonensis Calotes liolepis Calotes nigrolabis Calotes liocephalus Ceratophora aspera Ceratophora sp. 01 Ceratophora sp. 02 Cnemaspis podihuna Lankascincus gansi Lankascincus fallax Lyriocephalus scutatus Cercapsis carinata Balanopsis ceylonensis Nakiyadeniya Panilkanda, Sinharaja (Beverly), Sinharaja (Kosmulla), Kurulugala Dellawa Dellawa, Horagala-Paragala, Panilkanda, Kalubowitiyana, Kurulugala, Habarakada, Kombala- Kottawa, Malambure, Nakiyadeniya, Tawalama, Tiboruwakota, Auwegalakanda Dandeniya-Aparekka, Horagala-Paragala, Mulatiyana, Panilkanda, Viharekele, Rammalakanda, Darukulkanda, Habarakada, Haycock, Malambure, Nakiyadeniya, Hindeinattu Sinharaja (Beverly), Sinharaja (Kosmulla), Panilkanda Dellawa, Sinharaja (Beverly), Ruhuna Block 4 Diyadawa, Sinharaja (Beverly), Dellawa, Panilkanda, Kurulugala, Kanneliya, Nakiyadeniya Panilkanda Kataragama, Ruhuna Blocks 1-4 Dediyagala, Beraliya (Akuressa), Rammalakanda, Panilkanda Panilkanda Diyadawa Viharekele, Mulatiyana, Dediyagala, Beraliya (Akuressa), Rammalakanda, Dellawa, Silverkanda Kurulugala. Silverkanda Silverkanda Ruhuna Block 4 Haycock, Habarakada, Kandawattegoda, Kombala-Kottawe, Tiboruwakota Ruhuna Blocks 1-2 Dandeniya-Aparekka, Kirinde Mahayayakele, Viharekele, Mulatiyana, Dediyagala, Beraliya (Akuressa), Dellawa, Panilkanda, Sinharaja (Kosmulla), Haycock, Kombala-Kottawa, Nakiyadeniya, Polgahakanda, Tawalama Mulatiyana Rammalakanda 144 Amphibians Nannophrys ceylonensis Dellawa, Haycock, Polgahakanda Nannophrys guntheri Kanneliya, Dellawa Philantus microtympanum Rammalakanda Limnonectes corrugata Rammalakanda Theldesma schmardanus Rammalakanda Fishes Channa orientalis Rammalakanda Danio pathirana Mulatiyana, Kanumuldeniya Garra ceylonensis Rarnmalakanda Lepdocephalichthys jonklaasi Kottawa Rasbora wilpita Kanneliya SS ———aoaOaOaasSoanaea wnnonOomno 145 aust gael by dileraplott recoeai Creraulen? rograyenamed eeitayndle 1 ry ae et ~ Bat SO | een Mapa eewrew A 1 * ptule tobe a pele eae td 2 ' : fs , HYG here | brevet - tae ' M ae sith i i. \ t ‘i % tae . y Wg t . Se ARCS MRS i bmi IRE: Le FTL ap ot) Ee ‘ PART D FUTURE STRATEGY AND PROGRAMME ON ARBORS Gis LOST GF 1. FUTURE STRATEGY AND PROGRAMME TO COMPLETE THE NCR 1.1 INTRODUCTION The purpose of this part of the document is to review progress achieved by the NCR to date, estimate the amount of time required to finish the fieldwork and identify a strategy which will enable the NCR to be completed within the overall time frame of the Environmental Management in Forestry Developments Project. It is concerned solely with the biological diversity component of the NCR. The soil conservation and hydrology assessment is a much more rapid procedure, being essentially a desk study supplemented with some field checking. This is evident from the assessment of Southern Province which took a total of four months, including at least one month to develop the methodology (Gunawardena, 1993). It is anticipated that the soil conservation and hydrology component will take a further one year to complete, which is well within the time frame of the NCR. 1.2. REVIEW OF PROGRESS Field surveys of all remaining natural forests (> = 50 ha) in Galle, Matara and Hambantota districts were completed by the end of 1992. This fieldwork took a total period of 15 months. An additional three months was spent identifying plant and, to a lesser extent, animal specimens collected from the field. Whereas it was Originally estimated that Galle and Matara districts would take some eight months to survey (Green, 1991), they have actually taken about one year. Reference to Table 1 (Part C) shows that a total of 367 plots were sampled in 83 days of fieldwork in the wet zone, and 107 plots in 23 days in the dry zone. This represents rates of 4.4 and 4.7 plots per day in the wet and dry zones, respectively, which is considerably lower than the original estimate of up to 8 plots per day for the wet zone (Green, 1991). Field trips were usually of seven days duration, with five days spent in the field and two days travelling to and from the study area. In practice, two field trips were completed each month. The NCR is proceeding more slowly than originally expected because much more fieldwork has been necessary than anticipated in the Project Document! where it is noted (p. 68) that "A certain amount of field work will be necessary but the Conservation Review must be completed by the end of March 1994." This is due to the following reasons: e Existing information on the distribution of Sri Lanka’s flora and fauna is inadequate, apart from the avifauna for which data have been collected at the level of the national grid (10 x 10 kn”). At best information is patchy, even for some of the better studied forests, such as Sinharaja and Ruhuna (Yala), where surveys have not been comprehensive but restricted to particular parts. Most forests, however, have never even been inventoried. e In order to identify the most important forests for conservation, each forest needs to be sampled using a standard, systematic methodology. Biological diversity is considered to be one of the most important attributes of forests (p. 64 of Project Document), and this requires a high investment in terms of field work, particularly in the species-rich wet zone where a large number of samples are required to adequately assess species diversity. e A considerable amount of time is required to identify plant specimens collected from the field. No provisions are made for this in the Project Document. e As discussed in Part C (Section 1) of this document, it was considered necessary to re-survey the 30 ‘Environmental Management in Forestry Developments. Project Document. Mission Report SRL/89/012. December 1989. 147 forests covered by the ACR in order to be able to compare these sites with those of the NCR using the same standard methodology. This decision is proving to be justified: comparisons between the results of the ACR and NCR show that more species are being consistently recorded in the NCR (see Part C, Section 2.6). Despite the relatively slow progress of the NCR, the gradsect’ method designed to systematically inventory biological diversity is extremely rapid and cost effective when compared to traditional biological survey methods (see Part A, Section 2.2.1). 1.3. IDENTIFICATION OF REMAINING NATURAL FORESTS TO SURVEY The New 1:500,000 Scale Forest Map of Sri Lanka, produced by the GoSL/UK ODA Forest & Land Use Mapping Project in 1992, provides the basis for identifying areas of remaining natural forest (including mangrove). Copies of this map were provided to the Project reproduced at a scale of 1:100,000. Forests were selected for inclusion in the NCR on the basis of the following criteria: e any legally designated reserve’ containing closed canopy natural forest, as defined in the new forest map, irrespective of its size, and e any other state forest (including proposed reserves) with at least 100 ha‘ of closed canopy natural forest. Boundaries and the names of forest reserves, together with conservation areas under the jurisdiction of the Department of Wildlife Conservation, have been marked on this map at a scale of 1:100,000. This enables those forests which are notified under either the Forest Ordinance, National Heritage Wilderness Areas Act, or Fauna & Flora Protection Ordinance to be distinguished from proposed reserves and other state forests that lack any legal protection. Gradsects have been marked on the new forest map, using the 1:63,630 series of topographic maps to align them along altitudinal gradients (i.e. at right angles to contours). In the case of extensive forests, Landsat Thematic Mapper false-colour images (scale 1:50,000) were used to differentiate between community types and ensure that each is sampled. A comprehensive list of all legally designated reserves is given in Annex 1. Those with closed canopy forest are indicated for inclusion in the NCR in the status column, together with any other state forests meeting the above criteria. Based on a sampling rate of 5 plots per day in the wet zone and 6 plots per day in the dry zone, the number of plots and amount of time required to survey each forest has been estimated. The sampling rates used for these estimates are slightly higher than those achieved to date, but they should be attainable in view of the ever increasing field experience of the national consultants. Table 1 provides a summary of the legal status of forests with respect to their inclusion in the NCR. It should be noted that many legally designated reserves have been excluded from the NCR, some having been converted to plantations or other forms of land use, while a few proposed reserves are below the 100 ha criterion and others (mostly sanctuaries) have never supported forest. 2A gradsect is a gradient-directed transect. 3The term legally designated reserve denotes any forest reserve or national heritage wilderness area administered by the Forest Department, and any national reserve or sanctuary administered by the Department of Wildlife Conservation. “A threshold of 50 ha was applied to Galle, Matara and Hambantota districts at the outset of the NCR. but this was later raised to 100 ha to expedite the survey. 148 Table 1 Number of reserves included and excluded from the NCR, classified by their legal status. NHWA" FR’ PR’ OSF* NP* SNR* NR* s* INCLUDED IN NCR To survey 1 43 87 139 7 2 1 17 Surveyed 0 19 6 13 4 1 0 4 Subtotal 1 62 93 152 11 3 1 21 EXCLUDED FROM NCR Non forest 0 n/a n/a n/a 2 0 0 20 Converted: plantation n/a 31 33 p/a n/a p/a n/a p/a Converted: scrub 0 5 3 n/a 0 10) 0 0 Converted: other 0 45 62 n/a 0 0 0 1 Forest < 100 ha n/a n/a 9 n/a n/a p/a n/a n/a Subtotal 0 81 107 n/a 2 0 0 21 POLITICALLY INACCESSIBLE TO NCR Subtotal 0 34 17 bu 11 0 1 16 TOTALS 1 177 217 152 24 3 2 57 National heritage wilderness areas are notified under the National Heritage Wilderness Areas Act, 1988. Only Sinharaja has been notified as such to date. Forest reserves are notified under the Forest Ordinance, 1907 (amended 1966). The Ordinance also provides for other state forests. Proposed reserves are other state forests proposed for reserved forest status. National parks, strict natural reserves, nature reserves and sanctuaries are notified under the Fauna and Flora Protection Ordinance, 1937 (amended 1970). = + 1.3.1 Completion of wet zone Based on the estimates in Annex 1, it is calculated that the remaining districts of the wet zone (comprising lowland rain forest, submontane and montane forest) will take 187 days to survey. This equates to 38 field trips, each of seven days duration, with five days spent in the field and two days travelling. With two field trips completed per month, the total amount of time required to finish surveying the wet zone will be about 20 months (including one month for contingencies). A breakdown for each district is given in Table 2. It is estimated that an additional four months will be required for identification of plant and animal specimens collected from the field, making a total of two more years to finish the wet zone. The wet zone districts, which comprise 23% of the country, will have taken a total of 3.5 years to survey, including identification of plant and animal specimens. Their completion can be anticipated in March 1995. This is 12 months after the National Conservation Review is scheduled to be completed for the entire country. 1.3.2 Completion of dry zone* Dry zone forests can be surveyed more rapidly than those of the wet zone because their biological diversity is much lower. However, they are more extensive than wet zone forests. The amount of time required to survey 5For present purposes the intermediate zone is treated as part of the dry zone because most districts of the intermediate zone also straddle the dry zone. 149 Table 2 Summary of estimates of the number of transects and plots required to sample species diversity within natural forests for each district in the wet and dry zones, together with the number of days of fieldwork District Original Present No No Length No No area area” forests trans. trans. plots days (ha) (ha) (km) SURVEYS COMPLETED: WET ZONE (9.91-12.92) Galle 39952 36997 19 32 82 286 41 Matara 13539 12464 16 24 55 224 29 Totals 53491 49461 35 56 137 510 70 SURVEYS COMPLETED: DRY ZONE (9.91-12.92 Hambantota 49844 49553 12 10 18 60 12 B Totals 49844 49553 12 10 18 60 TO SURVEY: WET ZONE Colombo 3355 3284 4 5 8 36 7 Gampaha 170 103 1 1 1 5 1 Kalutara 22114 19646 11 14 30 114 22 Kandy 80888 80849 12 23 37 162 32 Kegalla 26877 26869 10 13 33 107 21 Nuwara Eliya 37960 34054 14 21 39 153 30 Ratnapura 45083 42458 54 67 85 375 74 Totals 216448 207265 106 144 233 952 187 TO SURVEY: DRY ZONE Anuradhapura* 93329 91650 25 35 87 231 38 Badulla 68203 68043 12 21 36 122 22 Kurunegala 29098 26346 17 27 39 114 21 Matale 41726 41726 36 45 65 246 48 Monaragala 107023 106952 32 42 111 266 48 Polonnaruwa 73320 71564 14 20 80 158 26 Puttalam 69015 62002 10 12 30 68 16 Totals 4817161 468286 146 202 448 1205 219 “Accounts for lands released subsequent to their notification as forest reserves or their designation as proposed reserves. “Excludes forests north of Anuradhapura City (8° 21’ N) that lie in politically sensitive areas. 150 politically accessible districts in the dry zone is estimated to be 219 days (Table 2). This equates to 44 field trips spread over a period of 22 months (two trips per month). These districts constitute 48% of the country. It is estimated that an additional two months will be required for identification of plant and animal specimens collected from the field, making a total of two years to complete the dry zone. It is unlikely that it will be possible to survey forests in the Eastern and Norther provinces (i.e. Amparai, Batticaloa, Jaffna, Kilinochchi, Mannar, Mullaittivu, Trincomalee and Vavuniya districts, which cover 29% of the country) in the foreseeable future due to the prevailing political situation. 1.4 FUTURE STRATEGY The most appropriate strategy to adopt to ensure that the NCR is completed within the overall time frame of the Environmental Management in Forestry Developments Project (1991-96) and without any break in continuity is considered to be as follows: e Extend the contracts of the two national consultants (botanist and zoologist) from 30 to 42 months (ending in March 1995) to enable them to complete the wet zone. e Place a second team of national consultants in the field to survey the dry zone over a two-year period during 1993-95. e Engage the services of the international consultants responsible for the NCR and Database Development, respectively, to cover the period 1993-96 as appropriate. Additional funds will be required in order to implement this strategy, and a proposal for a financial extension to the Project has been drafted®. This proposal is due to be presented to the Ministry of Lands, Irrigation & Mahaweli Development for endorsement and submission to UNDP. It is anticipated that the proposal will be considered at the forthcoming tripartite meeting in June 1993. It is imperative that funding be secured in the immediate future to pre-empt any break in continuity, particularly with respect to the fieldwork. 1.5 8 FUTURE PROGRAMME 1.5.1 Fieldwork Fieldwork is continuing on a district-by-district basis, with priority given to the wet zone, ahead of the dry zone, whenever weather conditions permit. The Forest Department attaches great importance to the survey of wet zone forests, and the present moratorium on logging in the wet zone awaits review in the light of the results of the NCR. There are 14 forests in the wet zone, covering a total area of 259 km’, that have been earmarked for conservation by the Forest Department in the wake of the ACR. Management plans are due to be prepared for these forests, beginning in mid-1993. Of these 14, eight lie in Souther Province and have already been surveyed by the NCR team. The six outstanding sites are Kalugala in Kalutara District and Bambarabotuwa, Delwela, Gilimale-Eratne, Madampe and Nahiti Mukalana (treated as one site), and Messana in Ratnapura District. It has been agreed that these should be surveyed by May 1993 in order that survey data can be made available for management planning purposes. ©Proposal for a financial extension for the Environmental Management Component of the Environmental Management in Forestry Developments Project. Draft. March 1993. 18 pp. 151 Thus, forests should be surveyed in the following order of priority: - 1. six outstanding wet zone forests designated for conservation, 2. remaining wet zone forests, and 3. dry zone forests. All forests to be surveyed are indicated in Annex 1 and identified on the set of 1:100,000 maps held by the Project, as previously discussed (Section 1.3). In order to ensure that the NCR is completed within the times estimated (see Table 2), it is essential that two field trips of at least seven days duration each be conducted every month (i.e. alternate weeks should be spent in the field), and that at least 5 plots be sampled for each day spent in the field in the wet zone and six plots in the case of the dry zone. 1.5.2 Identification of specimens There is seldom adequate time between field trips to identify specimens collected on previous trips, particularly plants, and unidentified material quickly accumulates. The backlog of over 1,000 plant specimens collected up to December 1992 took about three months to identify. From time to time it will be necessary to halt fieldwork in order to catch up on identification work. It is suggested that this be done each time a district is completed, both to avoid the accumulation of large numbers of specimens and to make the fieldwork more efficient by through familiarisation with previously unidentifiable species. 1.5.3 Database management The ACF/Database Management & Conservation Review is overall responsible for the operation and maintenance of EIMS (Environmental Information Management System), including the entry and checking of data, and regular backing up of the system. The plant (PltDat.dbf) and animal (AniDat.dbf) data files currently hold some 23,000 and 7,000 records, respectively. They are already large databases and will become very much larger, by almost an order of magnitude. Considerable care must be taken to ensure that these (and other) databases are properly maintained. The normal routine for entering fresh data should be as follows: il. Enter field data into a file having the name of the transect (e.g. Plt4]1.dbf, Ani52.dbf etc). This should be done in the \EIMS\CR\DATENT subdirectory. 2. Check the data for duplicate entries, incorrect species codes, or incorrect gradsect and plot numbers using the appropriate programmes. 3. Print out the records and check them against those in the original field form. Make further corrections as necessary and file a hard copy. 4. Append the transect file to the master data file (Pltdat.dbf or AniDat.dbf). 5. Zip’ the transect file to ensure that it is not mistakenly appended to the master file a second time. The transect file should not be deleted in case it is ever necessary to refer to this original data file. 7Zipping is a procedure for compressing files using PKWARE software. Not only is it useful for storing data, but also for protecting data from the possibility of corruption. 152 1.5.4 Taxonomic lists A draft check-list of animal species has been generated from the taxonomic subsystem of EIMS and is almost ready for publication. The Forest Department should secure the necessary funds to publish this under the GoSL/UK ODA Forestry Research & Information Project. A check-list of woody plants is in preparation. Taxonomic names need to checked, updated as necessary (authorities for many species need to be added) and referenced; and the endemic and threatened status of species require further verification. This is an important task which should be completed during 1993. REFERENCES Green, M.J.B. (1991). Conservation Review Pzogress Report (August - October 1991). TUCN/EMD Report No. 7. 24 pp. Gunawardena, E.R.N. (1993). A report on the importance of natural forests in Galle, Matara and Hambantota districts for soil conservation and hydrology. IUCN/EMD Report No. 15. 19 pp. Legg, C. and Jewell, N. (1992). A new 1:500,000 scale forest map of Sn Lanka. Forest and Land Use Mapping Project, Forest Department, Colombo. 12 pp. 153 Annex 1 LIST OF SITES REVIEWED FOR THE NCR Below is a comprehensive list of all forest reserves, proposed reserves and national heritage wilderness areas administered by the Forest Department, and national reserves and sanctuaries administered by the Department of Wildlife Conservation for all districts except those in the Eastern and Norther provinces. Also listed are all other state forests (OSFs) having a closed canopy area of at least 100 ha. The status of each forest with respect to its inclusion or exclusion from the NCR is given in the status column. Forests were selected for inclusion in the national survey on the basis of the criteria given in Section 1.3, and have been marked on the new forest map of Sri Lanka (Legg and Jewell, 1992), reproduced at a scale of 1:100,000. Estimates of the number of plots are based on 4 plots per 1 km of transect in the wet zone and 2-4 plots in the dry zone. The absolute minimum number of plots sampled per forest is five. The number of days of fieldwork required to sample species diversity within each forest is based on the revised sampling estimate of five plots per day in the wet zone and six plots per day in the dry zone, except in the case of Galle and Matara districts which are based on the original estimate of 8 plots per day. Approximate geographic coordinates of OSFs are given to enable them to be located on the 1:63,360 topographic series of maps. This is not necessary in the case of legally designated reserves because they are clearly marked on these maps. FOREST AREA CONSERVATION REVIEW LOCATION No Name Desig- Notified Present Status’ No Leng No No District(s) Lat. Long. nation (ha) (ha) tran tran plot day WET ZONE DISTRICTS COLOMBO 36 Bellanwila-Attidiya s 60.0 60.0 E/N 0 0 0 0 COL 6 50 719 54 110 Getamarawa-Dunkolahena PR 129.7 129.7 EC 0 0 0 0 COL - - 146 Indikada Mukalana PR 786.1 747.5 I/F 2 2 10 2 COL - 170 Kananpella FR 295.2 263.5 I/F 1 1 5 1 COL - - 222 Labugama-Kalatuwana FR 2150.1 2150.1 V/F 1 4 16 3 COLKAL RAT - 285 Miryagalla FR 123.7 123.1 I/F 1 1 5 1 COL 419 Sri Jayawardenapura Bird S 449.2 449.2 E/N 0 0 0 0 COL - Totals 3355.1 3284.2 1=4 5 8 36 7 GALLE 8 Ambalangoda-Hikkaduwa Rocky Islets S 1.3 1.3 E/N 0 0 0 0 GAL 6 9 80 8 509 Auwegalakanda OSF 250.0 250.0 S/F 2 3 8 1 GAL : = 511 Bambarawana OSF 248.0 248.0 S/F 1 2 8 1 GAL - - 37 Beraliya (Akuressa) PR 1859.9 1645.5 S/F 3 6 2« 3 GAL MTR - = 38 Beraliya (Kudagala) PR 4241.1 2571.8 S/F 1 5 16 2 GAL - - 62 Darakulkanda PR 457.6 141.7 S/F 3 3 8 1 GAL 65 Dediyagals FR 3789.9 3789.9 S/F 1 6 24 3 GAL MTR 69 Dellawa PR 2034.0 2236.3 S/F 3 12. 40 5 GAL MTR - : 120 Habarakada PR 209.6 209.6 1129 0 0 0 0 GAL - = 135 Hikkaduwa Marine Ss 44.5 44.5 E/N 0 0 0 0 GAL 6 8 80 8 508 Hindeinatm OSF 200.0 200.0 S/F 2 3 8 1 GAL - 507 Homadola OSF 300.0 300.0 W750 0 0 0 GAL 137 Honduwa Island Ss 8.0 8.0 E/N 0 0 0 0 GAL c 173 Kandawattegoda PR 404.7 358.6 I/F 1 2 8 1 GAL = 175 Kanneliya FR 6114.4 6024.5 S/F 2 10 32 4 GAL - = 193 Kelunkanda FR 249.0 196.3 EC 0 0 0 0 GAL - = 208 Kombala-Kottawa PR 2289.7 1624.6 I/F 2 5 16 2 GAL - - 253 Malambure FR 1012.3 929.8 S/F 2 5 16 2 GAL - 2 303 Nakiyadeniya PR 2292.1 2235.5 S/F 2 5 16 2 GAL - = 328 Olabedda FR 153.6 73.0 EC 0 0 0 0 GAL - 2 No Name Desig- Notified Present Stams’ No Leng No No District(s) Lat. Long nation (ha) (ba) tran tran plot day 340 Panagoda PR 266.3 266.3 D/F 1 2 8 1 GAL - 354 Parapuduwa Nun’s Island s 189.8 189.8 EN 0 0 0 0 GAL - 369 Polgahakanda FR 862.3 577.4 253 0 0 0 0 GAL - 370 Polgahawila PR 304.7 286.6 UF 1 1 4 1 GAL 372 Polhunnawa FR 193.0 193.0 E/C 0 0 0 0 GAL - 414 Sinharaja NHWAI11187.0 11187.0 U/F* 4 10 42 10 GAL MTR RAT - 428 Tawalama PR 167.5 167.5 D/F 1 1 4 1 GAL - 505 Tawalama OSF 1000.0 1000.0 S/F 0 0 0 0 GAL - 430 Tetwatta s 1424.5 1424.5 DM 0 0 0 0 GAL - 506 Tiboruwakota OSF 600.0 600.0 UF 1 2 8 1 GAL - 445 Uragaha PR 1567.3 1567.3 D/F )) 0 0 0 GAL - 510 Yakdehikanda OSF 100.0 100.0 UF 1 2 8 1 GAL - 490 Yakkatrwa FR 296.2 296.2 EC 0 0 0 0 GAL - Totals 39952.7 36997.1 S=19 32 82 286 41 GAMPAHA 5 Alawala-Ataudakanda PR 352.8 352.8 BC 0 0 0) 0 GAM - 26 Bajjangoda PR 175.9 175.9 EP 0 0 0 0 GAM - 54 Dambukanda PR 41.7 41.7 EC 0 0 0 0 GAM - 125 Halpankanda PR 159.3 158.5 EP 0 0 0 0 GAM - 139 Horagolla Ss 13.4 13.4 E/N 0 0 0 0 GAM 8.1 80 51 181 Karagahatenna PR 55.4 55.4 EC 0 0 0 0 GAM - 189 Kebalawita PR 114.9 114.9 EP 0 0 0 0 GAM - 212 Kotakanda PR 254.8 242.7 EP 0 0 0 0 GAM - 247 Mahakanda PR 170.6 103.0 UF 1 1 5 1 GAM - 252 Maimbulkande-Nittambuwa Ss 21.8 21.8 E/N 0 0 0 0 GAM 88 80 5 284 Mirigamkanda PR 139.3 139.2 EC 0 0 0 0 GAM - 286 Mitirigala FR 511.5 353.7 B/C 0 0 0 0 GAM - 457 + Walbotalekanda PR 41.7 41.7 E/P 0 0 0 0 GAM : 477 Wilikulakanda PR 352.2 310.0 E/C 0 0 0 0 GAM - Totals 170.6 103.0 I=1 1 1 5 1 KALUTARA 20 Badagama PR 24.7 24.7 BC 0 0 0 0 KAL - 516 Boralugoda OSF 100.0 100.0 /F 1 1 5 1 KAL 25 80 17 42 Botale PR 276.1 276.1 EC 0 0 0 0 KAL - 70 Detmella Yatagampitiya PR 2033.7 1413.3 V/F 1 1 5 1 KAL - 76 Diwalakada PR 281.1 144.3 EC 0 0 0 0 KAL - 129 Haycock FR 362.0 362.0 S/F 0 0 0 0 KAL - 147 Ingiriya FR 407.0 282.6 UVF 1 1 5 1 KAL - 162 Kaharagala PR 31.8 31.8 EC 0 0 0 0 KAL - 166 Kalugala PR 4630.1 4288.0 I/F 2 5 15 3 KAL - 200 Kirigala Mukalana PR 18.8 18.8 E/F 0 0 0 0 KAL - 215 Kudaganga FR 141.3 137.4 EC 0 0 0 0 KAL - 221 Kurana Madakada PR 1391.2 1161.4 l/F 2 3 15 3 KAL - 224 = Latpandura PR 42.1 42.1 BC 0 0 (1) 0 KAL - 244 Mahagama FR 368.7 227.1 EC 0 0 0 0 KAL - 269 Meegahatenna PR 282.8 277.4 VF 1 1 5 1 KAL - 289 Morapitiya-Runakanda PR 7012.5 6732.5 l/F 2 6 18 3 KAL - 297 Nahalla PR 35.1 35.1 EC 0 0 0 0 KAL - 315 Neluketiya Mukalana PR 2625.2 2384.4 /F 1 5 16 3 KAL - 363 Pelawatta FR 110.0 110.0 EC 0) 0 0 0 KAL - 367 Plenda West PR 145.3 145.3 BC 0 0 0 0 KAL - 368 Polawattakanda FR 29.4 0.3 EC 0 0 0 0 KAL - 390 Ranwarngalakanda PR 192.1 192.1 VF 1 1 5 1 KAL - 512 Vellihallure OSF 425.0 425.0 VF 1 2 10 2 KAL 26 80 8 454 Wagawatta PR 143.3 113.0 EC 0 0 0 0 KAL - 486 Yagirala PR 34.1 34.1 EC 0 0 0 0 KAL - 487 Yagirala FR 3014.7 2390.2 I/F 1 4 15 3 KAL - Totals 22114.3 19646.9 I=11 14 30 114 22 KANDY 25 Bahirawakanda OSF 3.2 3.2 ™m 0 0 0 0 KAN - 45 Campbell’s Land FR 292.6 292.6 VF 1 1 5 1 KAN MTL - 79 Dotalugala PR 1871.7 1871.7 522. 0 0 0 0 KAN MTL - 100 Galaha PR 242.8 242.8 VF 1 1 5 1 KAN - 519 Gunuyalle OSF 175.0 175.0 l/F 1 1 5 1 KAN 20 80 58 518 Hopewell OSF 125.0 125.0 I/F 1 1 5 1 KAN 15 80 37 155 108 114 117 124 141 143 188 191 196 203 204 211 225 226 258 304 308 316 351 356 359 361 514 416 427 551 470 Name Netiyapana Paradeniya Paspolakanda Peak Wilderness Peak Wilderness Sembawatte Siyambalangamuwa Taranagala Usgala Welhella-Ketagille Welhella-Ketangilla Yalapitiya Totals 3900.0 2944.9 30000.0 48.6 250.0 566.6 104.0 104.0 42087.1 242.0 200.0 56.9 80888.0 182.1 518.0 24.8 42.9 300.0 172.3 103.2 275.0 28.3 85.6 42.5 17.4 156.4 55.8 73.0 40.5 36.4 12.2 113.3 1155.1 86.2 265.9 21.6 29.5 33.8 31.7 209.0 72.8 51.7 18.0 31.2 112.5 5665.7 22379.2 1200.0 63.7 28.3 700.0 134.2 128.8 43.3 26877.4 75.0 182.3 560.0 50.0 2578.2 89.3 1800.0 100.0 62.5 401.7 80849.3 181.7 514.0 24.8 41.0 300.0 169.5 103.2 275.0 28.3 85.6 42.5 17.4 156.3 55.8 72.4 18.1 19.4 12.2 113.3 1155.1 86.2 263.0 21.6 29.5 30.0 30.0 209.0 72.8 51.7 2.2 31.2 107.4 5665.7 22379.2 1200.0 63.3 28.3 700.0 134.2 114.6 43.3 26869.2 75.0 147.7 348.3 50.0 2447.7 89.1 1800.0 100.0 62.5 379.9 156 I/F coe rH co OoOHpwococcceceooHK oocooonococooooosoocoocor oor oo _ w ee ee) ~ Com cohHonHouunea wo s ovrocooooooooorsoonoon = eceoHmnoonaccosccooooocoo w Pe) Nee Borne we Oo Ss ZRo _ ownoawoonwnsd 162 - _ oooow _ N ~- cosotcoococo coco coc eoUNceccoNCco Coc COC COC oOSCSO S oon 107 _ anao caakod _ Com OMRON KF ON WwW w comp noonunoocoocooocoooHoocoowNrcocoscoscocoscooosoSoOoONnooNO oN N S FIFI PERIEFFIE ~ No Name Desig- Notified Present Stams’.No Leng No No District(s) Lat. Long nation (ba) (ba) tran tran plot day 201 Kirinda Mahayayakele FR 374.1 252.7 S/F 1 2 8 1 MTR - 214 Kudagalkanda FR 151.8 25.7 D/F 1 1 4 1 MTR - - 498 Kurutugala OSF 175.0 175.0 S/F 1 2 8 1 MTR - - 263 Masmullekele FR 805.4 618.0 S/F 1 4 16 2 MTR - - 293 Mulatiyana FR 3277.5 3148.9 S/F 4 10 40 4 MTR - - 329 Oliyagankele FR 488.6 486.0 S/F 1 1 8 1 MTR - - 343 Panilkanda FR 588.1 588.1 S/F 3 5 20 2 MTR - - 387 Rammalakanda PR 4.8 4.8 388 =O 0 0 0 MTR - - 499 Silverkanda OSF 1000.0 1000.0 S/F 2 4 16 2 MTR - - 453 Viharekele FR 825.1 625.1 S/F 1 3 12 2 MTR - 471 Welihena FR 333.1 296.8 S/F 1 3 12 2 MTR - - 474 Wellana FR 85.4 85.4 B/S 0 0 0 0 MTR - - Totals 13539.1 12464.2 S=16 24 55 224 29 NUWARA ELIYA 1 Agra-Bopats PR 9105.4 6933.6 I/F 2 8 22 4 NUW - - 9 Ambaliyadde PR 61.7 61.7 EP 0 0 0 0 NUW - - 40 Bogawantalawa PR 4289.7 4289.7 I/F 1 3 10 2 NUW - - 52 Conical Hill PR 1569.5 707.5 VF 2 3 10 2 NUW - - 53 Dambakele FR 71.2 71.2 UF 1 1 5 1 NUW - - 105 Galpalama PR 73.6 68.0 EC 0 0 0 0 NUW - - 106 Galway’s Land PR 56.7 56.7 1107 OO 0 0 0 NUW - - 107 Galway’s Land Ss 56.7 56.7 In 1 1 5 1 NUW - - 128 Harasbedda PR 364.2 364.2 VF 1 1 5 1 NUW - - 140 Horton Plains NP 3159.8 3159.8 UF 1 6 20 4 NUW 6 48.5 80 48 171 Kandapola Sita Eliya PR 109.6 97.9 E/F 0 0 0 0 NUW - - 172 Kandapola Sita Eliya FR 2721.2 2615.9 VF 1 1 5 1 NUW - - 197 Kikilimana PR 4868.4 4580.6 VF 3 4 16 3 NUW - - 248 Mahakudagala PR 1762.5 1638.7 VF 1 2 10 2 NUW - - 270 Meepilimana FR 981.8 711.5 UF 1 1 5 1 NUW - - 307 Nanu Oya FR 420.8 415.9 I/F 1 1 5 1 NUW - - 331 Ottery-Queenswood PR 52.6 52.6 EC 0 0 0 0 NUW - - 346 Pannala PR 1173.7 769.1 1/452. 0 0 0 0 NUW - - 358 Pattipola-Ambawela PR 1498.0 1480.3 I/F 2 2 #10 2 NUW - - 362 Pedro PR 6879.7 6757.0 UVF 3 5 25 5 NUW - - 378 Preston-Elsmere PR 60.7 60.7 E/F 0 0 0 0 NUW - - 383 Ragalla PR 268.1 268.1 l/F 1 1 5 1 NUW - - Totals 37960.3 34054.0 1=14 21 39 153 30 RATNAPURA 549 Abutwelawisahena OSF 800.0 800.0 VF 1 1 5 1 RAT 6 43 80 36 527 Angamana OSF 175.0 175.0 VF 1 1 5 1 RAT 6 41 80 26 530 Appalagala OSF 200.0 200.0 I/F 1 1 5 1 RAT 6 43 80 45 528 Asantanakanda OSF 800.0 800.0 I/F 1 2 10 2 RAT 6 38 80 36 19 Ayagama PR 661.7 214.3 I/F 1 1 5 1 RAT - - 28 Bambarabotuwa FR 5440.3 5440.3 I/F 3 3 15 3 RAT - - 41 Boranjamuwa OSF 70.8 70.8 oa 0 0 0 0 RAT - - 57 Dambuluwana FR 485.2 401.1 VF 1 1 5 1 RAT - - 68 Delgoda PR 998.0 $98.0 V/F 1 2 6 1 RAT - - 71 + Detwela PR 1560.9 1560.1 I/F 1 2 10 74 RAT - - 72 + Demanagammana PR 114.9 114.1 I/F 1 1 5 1 RAT - - 542 Digandala OSF 100.0 100.0 I/F 1 1 5 1 RAT 6 30 80 33 529 Dotalugala OSF 175.0 175.0 I/F 1 1 5 1 RAT 6 41 80 36 548 Dumbara OSF 100.0 100.0 1/F 1 1 5 1 RAT 6 41 80 16 91 Etabedda FR 91.1 70.8 EC 0 0 0 0 RAT - - 540 Galbokaya OSF 175.0 175.0 /F 1 1 6 1 RAT 6 26 80 45 538 Gallegodahinna OSF 200.0 200.0 l/F 1 2 6 1 RAT 6 34 80 46 534 Galleletota OSF 325.0 325.0 I/F 1 2 6 1 RAT 6 38 80 50 112 Gilimale-Eratne PR 5832.7 4838.8 VF 4 7 20 4 RAT - - 546 Gongala OSF 1600.0 1600.0 l/F 1 2 8 2 RAT 6 23 80 39 544 Gorangala OSF 400.0 400.0 I/F 2 2 10 2 RAT 6 29 80 23 545 Handapan Ella OSF 3600.0 3600.0 VF 1 3 10 2 RAT 6 26 80 36 543 Handuwelkanda OSF 150.0 150.0 V/F 1 1 5 1 RAT 6 30 80 25 536 Hapugala OSF 600.0 600.0 I/F 1 1 5 1 RAT 6 36 80 49 539 Hataramune OSF 200.0 200.0 I/F 1 2 5 1 RAT 6 34 80 47 130 Helapandeniya PR 136.0 21.4 E/F 0 0 0 0 RAT - - 134 Hidellana FR 48.6 48.6 EC 0 0 0 0 RAT - - 133 Hidellana-Weralupe PR 136.8 128.1 VF 1 1 5 1 RAT - - 153 Iriyagahahena PR 44.5 44.5 EC 0 0 0 0 RAT - - 154 Iriyagahahena Mukalana FR 74.5 44.1 I/F 1 1 5 1 RAT - - No Name Desig- Notified Present Status’ No Leng No No District(s) Lat. Long. i nation (ha) (ha) “tran tran plot day a ea 8 SSS SSS 541 Kabarakalapatana OSF 675.0 675.0 /F 1 2 #10 2 RAT 6 28 80 34 182 Karandana FR 77.8 77.8 BC 0 0 0 0 RAT - - 184 Karawita PR 1375.9 1211.8 UF 3 3 15 3 RAT - - 550 Kiribatgala OSF 300.0 300.0 UF 1 1 5 1 RAT 6 37 80 31 205 Kobahadunkanda PR 890.3 890.3 UF 1 1 5 1 RAT - - 531 Kudagoda OSF 650.0 650.0 UF 1 2 8 2 RAT 6 43 80 48 217 Kudumiriya PR 2144.8 2144.8 UF 1 2 #10 2 RAT - - 169 Kumburugamuwa FR 1523.2 1480.7 VF 1 1 5 1 RAT - - 535 Kuregala OSF 325.0 325.0 UF 2 2 6 1 RAT 6 38 80 52 233 Madampe PR 40.5 40.5 17234 «(0 0 0 0 RAT - = 234 Madampe FR 237.3 224.8 UF 1 1 6 1 RAT S 5 241 Magurugoda FR 275.4 241.0 UF 1 1 5 1 RAT - - 242 Magurugoda PR 45.7 24.7 E/F 0 0 0 0 RAT - : 272 Marakele PR 131.5 106.2 UF 1 1 5 1 RAT : - 273 Marakele FR 76.9 16.9 BC 0 0 0 0 RAT - - 262 Masimbula FR 20.2 20.2 EC 0 0 0 0 RAT - S 504 Masimbula PR 255.0 255.0 UF 1 1 5 1 RAT - = 274 Messana PR 724.4 433.8 I/F 1 2 10 2 RAT - - 288 Morahela FR 930.5 846.9 VF 1 1 5 1 RAT - 292 Mudunkotuwa PR 78.1 78.1 EC 0 0 0 0 RAT - - 533 Mulgama OSF 200.0 200.0 UF 1 1 5 1 RAT 6 39 80 49 294 Muwagankanda FR 164.8 132.1 I/F 1 1 5 1 RAT - - 298 Nahiti Mukalana FR 195.7 195.7 UF 1 1 5 1 RAT - - 537 Narangattahina OSF 250.0 250.0 VF 2 2 6 1 RAT 6 35 80 46 339 Pallepattu FR 680.9 657.9 BC 0 0 0 0 RAT - - 348 Pannala FR 129.9 129.0 I/F 1 1 5) 1 RAT - - 547 Paragala OSF 900.0 900.0 I/F 1 2 10 2 RAT 6 35 80 19 384 Rajawaka PR 2387.6 2387.6 I/F 3 3 15 3 RAT - - 386 Rammaliakanda PR 453.7 453.7 I/F 1 1 5 1 RAT - - 389 Ranwala PR 1117.7 867.5 I/F 1 1 6 1 RAT - - 391 Rathkarawwa PR 4050.5 4021.4 E/P 0 0 0 0 RAT - - 421 Talagahakanda FR 60.4 60.4 EC 0 0 0 0 RAT - - 532 Talawegoda OSF 450.0 450.0 l/F 1 1 5 1 RAT 6 39 80 47 424 Tandikele PR 370.3 290.2 EC 0 0 0 0 RAT - - 432 Tibbutukanda PR 233.9 233.9 I/F 1 1 5 1 RAT - - 443 Ulinduwewa FR 104.7 104.7 I/F 1 1 5 1 RAT - - 455 Walankanda FR 832.9 711.5 /F 1 1 5 1 RAT - - 456 Walawe Basin FR 3237.5 3229.7 1361 0 0 0 0 RAT - - 459 Waratalgoda PR 1889.9 1889.9 I/F 2 3 2 RAT - - 476 Wewelkandura PR 429.0 429.0 I/F 1 1 5 1 RAT - - Totals 45083.1 42458.8 1=54 67 85 375 74 158 Name Kahalla-Pallekele Katupotakanda Labunoruwa Likolawewa Lunu Oya Mahakanadarawa Wewa Manawewakanda Marasinhagama Medalassa Korale Medawachchiya Mihintale Mihintale Nuwaragam Padaviya Tank Padawiya Pahala Mawatawewa Puliyamkulam Puliyankulama Ranawekanda Ratmale Kanda Ritigala Tambaragalawewa Wedakanda Wilpatt Block 1 Wilpatm Block 3 Wilpattu Block 4 Yoda Ela Totals BADULLA 29 Bandarawela Bibilehela Ella Erabedda Enalapitiya Hakgala Haputale Judges Hill Karandekumbura Keeriyagolla Kithedallakanda Kohile Kotakitulakanda Madigala Maduru Oya Block 1 Migollegama Namunukula Ohbiya Pattipola Ravana Ella Rawanella Tangamalai PR PR OSF Ss OSF OSF PR OSF OSF OSF FR DRY ZONE DISTRICTS 440.2 29640.2 400.0 3500.7 150.0 800.0 7689.0 450.0 250.0 700.0 1021.8 25511.1 441.9 34.0 3397.7 21690.0 175.0 300.0 325.7 3647.4 0.0 325.0 100.0 175.0 2892.5 3308.2 999.6 2584.8 6475.0 97901.7 325.0 125.0 150.0 575.0 700.0 1528.2 350.0 5180.0 54953.2 22981.4 25252.9 2288.2 93329.2 15.4 610.0 52.2 1538.9 269.1 1141.6 141.3 10.9 72.8 125.0 100.0 12.1 60.7 1350.0 51469.4 141.2 279.3 1925.5 394.9 1932.0 331.8 131.5 384.0 28957.1 400.0 3500.7 150.0 800.0 7689.0 450.0 250.0 700.0 1021.8 25217.7 441.9 34.0 3292.5 21690.0 175.0 300.0 325.7 3647.4 0.0 325.0 100.0 175.0 2878.4 2462.9 999.6 2314.6 6475.0 97664.3 325.0 125.0 150.0 575.0 700.0 1528.2 350.0 5180.0 54953.2 22981.4 25252.9 1585.6 91650.1 12.6 606.3 52.2 1538.8 269.1 1141.6 141.1 10.7 72.8 125.0 100.0 12.1 60.7 1350.0 51469.4 1412 279.3 1769.1 393.3 1932.0 331.8 131.5 159 KH COCONNKE BeBe etn DC OHP COR HRB OCC OR HE NOOR ROHR RB RB OR RB ORNS 1=25 35 NON ONK OHNO CK K COCOCOOCOF OSCOOCOF SO _ NVROSCOCANUYNHNHNOCCHCONNHXSCOCONNERBOCOWNONNKWONNONSGO 00 _ NOnNnNOwWNOMWOCOSCKK COCCOONO CoO CS iN] ao w w APSOCONANYAARDASDCTCOCASCCAARSCSCSCAAGCCSOCAOGCOSCAAAINOAN AGA _ ~~ 231 Aocno-acznooounnooosooooanco KCOONeEP NKR ee Ke Kc ooeH coe eK OOo OoOKF He NnooKeuNnoerKeK oer Kr oreo 2222222222232 2552552222222222222222222% w oo —m=OCOF ONKOHNCOKF RK COOONOCOFK SCS 222 3 3 Fe) é BAD AMP POL BAD NUW CI0-0 00 00 5 0D 00 0D + OD OD OD ~) OO OD OO 17 20 10 20 55 19 29 17 31 38 32 36 27 48 16 15 Name Wedasitikanda Wirawila-Tissa Yala Totals Degadaturawa Dehelgamuwa Dewalakanda Digalla Dikkele Mukalana Doluwakanda Dunkanda Elawaka Galgiriyakanda Galketiyagama Getadivula Gonagama Gorakadola Habilikanda Henegedaralanda Heraliyawala Irmminna Kadawatkele Kaduruwewa Kala Oya Kalugala Kalugalkanda BHA°QRAR o"x a i) “az A°° Osan be 3] g° aR 38 PR FR FR PR FR OSF OSF PR PR PR PR 8093.7 6216.0 414.4 1165.5 200.0 712.0 678.7 564.4 214.5 1003.6 300.0 2071.8 975.2 0.0 238.6 797.4 372.7 300.0 0.0 13.8 1698.1 50.0 13679.2 277.2 1343.4 4164.2 28904.7 49844.9 139.2 228.7 49.9 159.0 72.7 78.5 100.0 1050.0 1062.3 97.1 43.2 161.9 58.0 112.5 90.3 336.4 400.6 301.1 168.3 1182.5 40.5 581.5 457.7 191.9 180.9 731.7 13.8 25.8 283.3 120.2 4949.7 3365.0 153.0 7527.2 6216.0 414.4 1125.0 200.0 712.0 678.7 564.4 214.5 1003.6 300.0 2071.8 975.2 0.0 238.6 722.3 372.5 300.0 0.0 13.8 1406.7 50.0 13679.2 252.9 1343.4 4164.2 28904.7 49553.5 139.2 213.9 49.9 159.0 72.7 78.5 100.0 1050.0 1062.3 11.1 34.7 161.9 4.1 112.5 87.0 308.1 400.6 301.1 168.3 1182.5 40.5 581.5 235.1 191.1 180.9 729.6 13.8 25.8 267.1 120.2 4949.7 2705.9 152.9 160 oonooovrococr oer Oo oem omer Oo OoOewmooooKo onoorw oorw cococooeonorernococre cooowreoocooococo 36 oovrocooonoonor co oKonwoonoooonone _ oo onoonoce ooocofonnocoonocow~uncococococcnso ~ oonooonooanaonoonoanancoonwmocscacnc 3 _ eonconoounscoccoocooancoococoonoocoonewewoococscso SCONDTOHNGDGOH OHO OH OHM HBO OHHH COOH _ N orm oor oor ocococoocoonoer Kw co ooo ooonewoooooco 4 = o Zz 5 EEEEEPEPEEEEEEEEEEEEEER EEE: 40 81 10.5 81 4 80 5.5 80 25 81 18 81 3 80 16.5 81 29 81 3 80 28 80 12 11 49 165) 28 8k 614 571 620 618 574 628 Nakele Mukalana Nawagatta Nelawa Nelligalkanda Nettipolagama Neugalkanda Nikawekanda Nugampola Pallekele Pannagama Pannawa-Geppalawa Pansalhinna Panwewa Paragaharuppe Polgolla Polkatukanda Potuwewa Rambodagalla Sangappale Sawarangalawa Sundapola Talagomuwa Talpattekanda Tambutakanda Timbiriwewa Udapolakands Waulkele Welikumbura Weuda Mukalana Yakdessakanda Totals FR 1108.0 PR 211.7 FR 69.2 PR 0.2 s 492.1 FR 171.0 PR 102.8 FR 3462.2 FR 383.6 PR 314.0 FR 117.1 PR 2152.9 FR 124.6 PR 746.2 PR 235.9 FR 21.3 PR 319.3 PR 774.2 FR 123.1 FR 39.8 PR 62.7 FR 48.0 FR 50.0 FR 1.0 PR 376.0 PR 151.8 PR 339.9 FR 14513.8 PR 165.9 PR 316.5 PR 123.4 FR 241.7 FR 54.0 FR 53.6 FR 151.5 PR 241.6 PR 202.3 PR 4694.8 PR 6309.5 FR 306.9 FR 81.3 OSF 150.0 OSF 250.0 PR 1274.0 PR 63.9 FR 20.7 FR 80.9 FR 152.1 PR 1011.7 29098.1 OSF 450.0 OSF 450.0 OSF 250.0 OSF 250.0 PR 104.4 PR 870.1 OSF 325.0 OSF 325.0 OSF 600.0 OSF 600.0 OSF 1500.0 OSF 1500.0 OSF 750.0 OSF 750.0 OSF 650.0 OSF 650.0 OSF 950.0 OSF 950.0 BESssssssssgssg a fo) ee ee nS ee tt — Rocoooonnoconooooooccocofooewoocoocooococeecoecoeoecoonooooooco-°o w -) WBwWWwKWe ae BWwe eee NOe eee eceooocooooocooooooanaooocooooouns _ Noococcocooanococoonococoscccocococecenoon - _ —_ a SCOMMMAMMNSO MAMA AYH —_ =e Anoonnwn nNnocoooemwxHnO oom ooocooococoonocoowoocoeoccoccooscocoooKoocoeoooo-o 21 BEEEEEEEEEEEEEELEE NunNxANNY eoonnriInyNrnryrYyrnnr: No Name Desig- Notified Present Status’ No Leng No No District(s) Lat. Long. nation (ha) (ba) tran tran plot day EEE 559 Inamalawa OSF 600.0 600.0 UF 0 0 0 0 MTL 737 ~=«-80 613 Inamalawa OSF 600.0 600.0 vi44 0 0 0 0 MTL 737 ~~ 80 144 Inamaluwa PR 309.6 309.6 IP 2 4 12 2 MTL é : 145 Inamahuwa FR 1896.9 1863.6 EP 0 0 0 0 MTL < A 565 Makutussa OSF 325.0 325.0 UF i oie Ge i MTL 732 ~©80 619 Makuhssa OSF 325.0 325.0 VF 1 2 5 1 MTL 7 32 80 558 Masawa OSF 150.0 150.0 UF ese (ou a MTL 743 «80 612 Masawa OSF 150.0 150.0 UF it) 9G, oi MTL 743 ~©80 266 Medaulpota PR 2340.2. «2340.2 y460 0 0 0 0 MTL POL . 5 572 Menikdeniya OSF 450.0 450.0 UF i a fl MTL 745 80 626 Menikdeniya OSF 450.0 450.0 UF fe) ai) 95) oat MTL 745 80 319 Nikawebera PR 33.2 33.2 BC 0 0 0 0 MTL = . 561 Opelagala OSF 350.0 350.0 UF i a eS), A MTL 735 80 615 Opalagaia OSF 350.0 350.0 a Gy MTL 735 80 335 Pallegama-Himbiliyakeda PR 4547.2. 4547.2 VWF 2 3 10 2 MTL 2 : 364 Petwehera PR 240.0 240.0 BC 0 0 0 0 MTL ; : 365 Pelwehera FR 1925.9 1925.9 BC 0 0 0 0 MTL . E 376 Potawa PR 77.2 71.2 BC 0 0 0 0 MTL 2 2 573 Puswellagolia OSF 10000.0 10000.0 Er 4) 16 «20)°« 4 MTL 746 80 627 Puswellagolla OSF 9200.0 9200.0 VF 4 6 2% 5 MTL 746 © 80 562 Sacombe OSF 250.0 250.0 UF fh af MTL 737 ~=-80 616 Sacombe OSF 250.0 250.0 UF i et a5 al MTL 737 ~=« 80 563 Talabugahacla OSF 300.0 300.0 VF it aS oi MTL 738 80 617 Talabugehacla OSF 300.0 300.0 VE 1 a oT MTL 738 © ©80 570 Tottawelgola OSF 800.0 800.0 VF 1 1 5 1 MTL 7 50 80 624 Tottawelgola OSF 800.0 800.0 UF Ta ee ese aa MTL 750 80 466 Wegodapola PR 418.5 398.2 BC 0 0 0 0 MTL 5 e Totals 41726.9 41726.9 1=36 45 65 246 48 MONARAGALA 27 Bakinigahawela FR 200.3 200.3 VF rot SF Ff MON a z 605 Balanagala OSF 800.0 800.0 UF Tt) 3t_ Jao. — al MON 721 81 589 Begahapatana OSF 325.0 325.0 VE i, Aer ar MON 646 ~~ «81 593 Bolhindagala OSF 375.0 375.0 VF 2 8 G20 MON 627. 81 61 Daragoda FR 748.9 748.9 EP 0 0 0 0 MON : : 575 Dewagiriya OSF 600.0 600.0 Vege e240 10) 2 MON 632. 81 579 Diggala OSF 250.0 250.0 VF Wot 7 MON 650 81 586 Diggalahela OSF 200.0 200.0 VF ty wo Gat MON 657 81 580 Dummalahela OSF 125.0 125.0 VE 1 P.°G5 4 MON 6 58) 8 606 Dyabodahela OSF 1100.0 1100.0 VF Mme S, Gy a MON 725 8 97 Gal Oya Valley NP 25899.9 25899.9 it Se Wen 2 MON 711.5 81 98 Gal Oya Valley North-East S 12432.0 124320 UF 1 3. Ge i AMP MON 711.5 81 99 Gal Oya Valley South-West Ss 15281.0 15281.0 V/F 1 3 6 1 AMP MON 7 11.5 81 594 Golupitiyahela OSF 200.0 200.0 VF 1 of 6 «4 MON 71 ~~ 80 584 Guruhela OSF 275.0 275.0 UF 1 tT Ss 7 MON 6 52 81 187 Kataragama s 837.7 837.7 US 0 0 0 0 MON - : 585 Kitulhela OSF 450.0 450.0 VF it ts ue 4 MON 7) 0) ess 577 Korathalhinna OSF 1500.0 1500.0 UF a MON 647 81 582 Lolehela OSF 400.0 400.0 UF f f S @ MON 652 81 581 Monerakelle OSF 1650.0 1650.0 M9 9 § fG. 2 MON 6 54 81 591 Murutukanda OSF 800.0 800.0 I/F 2 3 11 2 MON 6 48 81 305 Namandiya FR 861.4 790.6 VF ho eg i MON = 2 595 Radaliwinnekota OSF 900.0 900.0 UF imi 6) GA MON 716 81 590 Randeniya OSF 300.0 300.0 UF Te (26s! | al MON 647 81 607 Rediketiya OSF 3900.0 3900.0 UF P FW DB MON 722 81 399 Ruhuna Block 2 NP 9931.0 9931.0 SF 0 0 0 0 MON : 2 400 Rubuna Block 3 NP 40775.4 4075.4 SF 0 0 0 0 MON - - 401 Ruhuma Block 4 NP 26417.7 26417.7 I/F* 1 6 12 2 MON - > 402 Ruhuna Block 5 NP 6656.2 6656.2 VF 1 16 6 3 MON 2 = 408 Senanayake Samudra Ss 9323.9 9323.9 E/N 0 0 0 0 MON 7 11.5 81 592 Sitarama OSF 800.0 800.0 VF 3 #7 #12 2 MON 623 81 438 Uda Walawe NP 30821.0 30821.0 SF 3 9 18 3 MON RAT 6 29.5 80 576 Ulgala OSF 1500.0 1500.0 I/F 1 6 12 2 MON 6 43 81 578 Ulgala (old) OSF 225.0 225.0 V/F 1 2 6 1 MON 6 44 81 583 Velihela OSF 200.0 200.0 VF 1 1 5 1 MON 6 53 81 604 Viyanahela OSF 900.0 900.0 I/F 1 3 10 2 MON gL AL 81 588 Wadinahela OSF 700.0 700.0 I/F 1 z4 6 1 MON 6 42 81 587 Westminster Abbey OSF 800.0 800.0 im 8 9 i 2 MON Pe ol 162 No Name 2 Desig- Notified Present Status’ No Leng No No District(s) Lat. Long. nation (ha) (ha) tran tran plot day Totals 107023.5 106952.7 1=32 42 111 266 48 POLONNARUWA 597 Badanagala OSF 200.0 200.0 VF 1 2 6 1 POL 7 45 81 6 95 Flood Plains NP 17350.7 17350.7 EN 0 0 0 0 POL 8 0.5 81 7 96 Gal Oya PR 9036.6 8897.4 I/F 1 3 12 2 POL - - 113. Giritale PR 1077.3 1063.1 EC 0 0 0 0 POL - - 598 Gunner’s Quoin OSF 450.0 450.0 UF 1 2 6 1 POL 7 52 81 8 596 Kudagala North OSF 475.0 475.0 UF 2 2 6 1 POL 7 41 81 6 601 Kumadiya Tulana OSF 400.0 400.0 UF 1 3 6 1 POL 8 #7 81 9 599 Mahamorakanda OSF 175.0 175.0 VF 1 2 6 1 POL 8 11 80 53 502 Medirigiriya Tulana OSF 8000.0 8000.0 UF* 2 9 18 3 POL 8 12 80 59 279 Minnertya PR 2444.3 828.0 UF 1 2 6 1 POL - - 280 Mimneriya-Giritale S 6693.5 6693.5 1/627 0 0 0 0 POL 8 15 80 53 281 Minnertya-Giritale Block 1 NR 7529.1 7529.1 VF 2 8 14 2 POL - - 602 Mutugalla Tulana OSF 425.0 425.0 VF 1 3 6 1 POL 7 59 81 12 600 Palliyagodelia Tulana OSF 9600.0 9600.0 VF 1 9 18 3 POL 8 12 81 9 374 Polonnaruwa Ss 1521.6 1521.6 EN 0 0 0 0 POL 7 56 81 0 396 Riverine Ss 824.1 824.1 E/N 0 0 0 0 AMP POL 7 43.5 80 595 410 Sigirtya Ss 5099.0 5099.0 VF 2 6 12 2 POL MTL - - 603 Simnakallu OSF 450.0 450.0 I/F 1 3 6 1 POL 8 4 81 12 417 Somawathiya Block 1 NP 21056.5 21056.5 P/F 0 0 0 0 POL TRI 8 11.5 81 6 418 Somawathiya Block 2 NP 16589.2 16589.2 P/F 0 0 0 0 POL TRI 8 11.5 81 6 436 Trikonamadu NR 25019.2 25019.2 P/F 0 0 0 0 POL BAT 8 12 81 175 460 Wasgomuwa Lot 1 NP 29036.0 29036.0 V/F 3 26 36 6 POL MTL 7 45.5 80 58 461 Wasgomuwa Lot 2 NP 4612.7 4612.7 1/460 0 0 0 0 POL 7 45.5 80 58 Totals 73320.0 71564.5 1=14 20 80 158 26 PUTTALAM 10 Ambanmukalana FR 1085.9 1004.8 I/F 1 3 6 1 PUT - - 15 Arachchikotrwa PR 0.8 0.8 EC 0 0 0 0 PUT - - 554 Aruakalu OSF 2100.0 2100.0 I/F 2 4 13 2 PUT 8 17 79 50 16) Attavillu PR 429.4 429.4 EC 0 0 0 0 PUT - - 17° Attavillu FR 9009.1 5179.4 I/F 1 2 5 1 PUT - - 31 Bar Reef Marine Ss 30670.0 30670.0 0 0 0 0 PUT - - 556 Chilaw Lake OSF 300.0 300.0 I/M 0 0 0 1 PUT 7 32 79 48 93 = Etaritiya PR 1558.0 1428.5 E/P 0 0 0 0 PUT - - 103 Galkuliya PR 4775.3 4127.8 EP 0 0 0 0 PUT - - 148 Ipolagama PR 4451.5. 4203.7 BC 0 0 0 0 PUT : 2 555 Kalu Aru OSF 600.0 600.0 I/M 0 0 0 2 PUT @ 17 79 51 259 Manuwangama-Nariyagama FR 537.6 244.2 EC 0 0 0 0 PUT - - 301 Nakele PR 80.9 80.9 EC 0 0 0 0 PUT - - 375 Pomperippu FR 7021.3. 7021.3 In oO 0 0 Oo PUT s 557 Puttalam Lagoon OSF 400.0 400.0 IM 0 0 0 1 PUT 8 8 79 50 381 Pyrendawa PR 125.5 110.6 EP 0 0 0 0 PUT - - 382 Pyrendawa FR 361.3 360.4 E/P 0 0 0 0 PUT - - 406 Sellankandal FR 4268.6 4265.8 I/F 2 3 6 1 PUT - - 407 Sellankandal PR 5526.0 4542.2 V/F 2 4 10 2 PUT - - 434 Tonigala PR 1486.8 937.3 E/P 0 0 0 0 PUT - - 444 Unaliya PR 1400.2 1096.7 EC 0 0 0 0 PUT - - 458 Wanniyagama PR 15596.6 14417.8 I/F 1 5 10 2 PUT - - 465 Weerakulicholai-Elavankulam PR 30128.9 29192.4 I/F 3 9 18 3 PUT - - 467 Weherabendikele PR 285.7 275.0 EC 0 0 0 0 PUT - - 479 Wilpatu Block 2 NP 7021.4 7021.4 P/F 0 0 0 0 PUT 8 33.5 80 1 482 Wilpattu Block 5 NP 21484.8 21484.8 P/F 0 0 0 0 PUT 8 33.5 80 1 484 Wilpotha PR 2665.3 2547.5 E/P 0 0 0 0 PUT - - Totals 69015.9 62002.8 I=10 12 30 68 16 E/F: Exchude from NCR (forest < 100 ha threshold) I/F: Inctuded in NCR, surveys ongoing (forest) E/N: Exchide from NCR (non forest) I/M: Include in NCR (mangrove) E/P: Exchude from NCR (plantation forest) I/?: Inchude ? (listed n FD Register but not mapped) E/S: Exchide from NCR (scrub) 1/364: Include as part of No. 364 D/F: Visited but too degraded to sample (forest) S/F: Sampled (forest) D/M: Visited but too degraded to sample (mangrove) S/M: Sampled (mangrove) P/F: Politically macceasible (forest) 163 ioe Se a > om. + Ta cs gee 24+ ioe @ oe es >> a here: ‘oko Ane SA ae. S835 7* ye cal a ie > a. =) es. cm a wey - me Wm as & = S£e nad HS. oO = “ees : ‘a= << Se Adon So Ker wom os ae. ined wars ; ; Bes eesines ea ‘= RB Zz. | = . i ee, BES 53- Je th a sae 58 sGebedhiiae SGerate & vata ass 5 8 , e #8 a 0 ae " & a 6 a r.. Ly | on hae Boe A a" ae 5