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BIODIVERSITY DATA MANAGEMENT
(Document 1)

DATA FLOW MODEL

in the context of the
Convention on Biological Diversity

WORLD CONSERVATION
MONITORING CENTRE

The mission of the
World Conservation Monitoring Centre is to provide
information on the status, security and
management of the Earth’s biological diversity.

BIODIVERSITY DATA MANAGEMENT
(Document 1)

DATA FLOW MODEL

in the context of the
Convention on Biological Diversity

United Nations Environment Programme

March 1995

ACKNOWLEDGEMENTS

This document is one of a series of four researched and compiled by the World
Conservation Monitoring Centre, Cambridge UK with 80% funding from the Global
Environment Facility (GEF) through the United Nations Environment Programme
(UNEP), Project GF/0301-94-40 (GF/0301-94-06). The need for the development of
a package of tools and materials to support national information management for the
Convention was identified and the project promulgated by Mark Collins (Director,
WCMC) and Robin Pellew (former Director of WCMC).

Principal authors were Ian Crain and Gwynneth Martin, incorporating a preliminary
data flow model by Claire Appleby. Many WCMC staff and consultants have
contributed and critically reviewed this complex document including Ian Barnes, Mark
Collins, Helen Corrigan, Harriet Gillett, Don Gordon, Jeremy Harrison, Martin ©
Jenkins, Gareth Lloyd, Richard Luxmore, Chris Magin, Jim Paine, and Jake
Reynolds. The document has benefited, as well, from review and comment from
NGOs, UNEP, and experts in a number of countries including those who participated
in a consultation meeting hosted by UNEP in Nairobi in October, 1994. Graphical
concepts were developed by Gwynneth Martin and Gareth Lloyd and executed by Ian
Kime of "Constructive Solutions". Document organisation, integration and input was
by Laura Battlebury. Ian Crain was the project manager and responsible for overall
design and editing.

Digitized by the Internet Archive
in 2010 with funding from
UNEP-WCMC, Cambridge

http://www.archive.org/details/dataflowmodelinc95wcmc

INTRODUCTION (3. 35 es ect ars mae gcd cll acl ie enicere 1
Dede Background ims Jee LEIS), ORE eae Re Foam toes op ase tl tole 1
1.2 Information Requirements of the CBD ..........-.---..-+--: 2
1.3. Approach to the Data Flow Model .............----+++-+-: 5
1.4 Conceptual Overview of National Activities forthe CBD ......... 7
Were Neosho its lg ea cling 6b cho.d Galois bod oO ola 0 46/9 nD o Bio 7
i422) Participants: qeiseaeee ss) Sie ecw. cee el ee ge Be: ie 9
1.5 Methodology and Symbolism ..............----------- 9
1.5.1 Data Flow Diagrams ................--+-+2e0e-- 9
S22) Data Models:iite wee tem. rehome eee Wee cc < joi -inc ie ve aerate 11
FIRST LEVELIPROGCESSES wig seen ede ee cnc a ee ile}e®
21) Conduct Country Study ery acts hc eek) oh) “oes eee) 13
2.2. Set Priorities and Prepare Action Plans .................. 14
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2: 4ulesEvaluateiResultsy .:\ie tates sot a oet etels.. 2 neal cme mayee 14
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216 “Biodiversity ‘Databases! .w") ee: &, A Peers. a SE Se 14
SECOND’ LEVEL PROCESSES #2568. 6 S23) 55008 .* eee 15
3al ~-Conduct:Country/Stidy 22 2k. Scan wsee <0 = memos) eomeionrs 15
3.1.1 Conduct Institutional Survey .................... 15
3.1.2 Identify Biciogical Diversity..................... 16
3.1.3 Identify Adverse Processes ..................... 16
3.1.4 Determine Economic Implications ................. 16
3.2 Set Priorities and Prepare Action Plans .................. 16
3.2.1 Establish Strategic Objectives .................... 16
3.2.2 Select Indicators and Establish Targets .............. 17
322-3) Develop Action Plansieas csr einen inne nee 17
393 4) Implement’Action Plans yas sees ee oe ile 18
3:3s1 Verify Informations yes Gee Sk & POSES Ae Bee eee 18
3.3.2 Collect Information and Fill Gaps ................. 18
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35374. ‘Enact Legislation. 2205 a)a)e is eye fens trees succeed oh ewes 19
35325) Perform Other, Actions|yee-ireate)) eee nn ene 19
3:4. ‘Evaluate; Resultsis: ences 2s Renee Sa ee ess Sia ae eee 19
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3.4.2 Assess Current Status vs Targets .................. 19
BAS. IREportito (SBD ier. sus) yierstes wicinone ech eis scene: Coenen tent mermen ete 20
DATASTORES . 2.214 wewlaiite ERAS Gr ho ee Eee a et 21
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Ara (Catalog of DatavHoldings 7): icin ee a ee OO. a 26

TABLE OF CONTENTS

4.5. Sectoral Information - Core Biodiversity Data .............. 2

45 i MOVETVIEW 2 per ho sacs CST ROR aeons! so Mile. ance Hone 27
4°52") Habitats’ les nis SMe Pe eI ae RO oe eee, 2 - 29
4:5-3ProtectedvAreas fv f pct tin otek yaa eget a cae ae ape 30
ASA SPECIES rice ee ne eee OE OEP CUED st aceon 31
4:5 5 Threats ek ake eae ee ee Un Se so Begala 34
4.5.6 Integrating Core Biodiversity Data ................. 36
REFERENCES einen eect yea A a Ue RE gu ies eee 39
LIST OF ANNEXES
ANALYSIS OF THE INFORMATION NEEDS OF THE CBD....... 41

LIST OF ACRONYMS & ABBREVIATIONS ................. 49

1 INTRODUCTION

1.1 Background

The Convention on Biological Diversity (CBD) was signed at the United Nations Conference
on Environment and Development in Rio de Janeiro in June 1992 by 154 nations and
subsequently came into force in November 1993. Article 7 of the Convention is concerned
with identification and monitoring activities to support Articles 8 to 10 (in-situ conservation,
ex-situ conservation and sustainable use of components of biological diversity). Contracting
parties are required to identify components of biological diversity important for its
conservation and sustainable use (Article 7a); to identify activities likely to have adverse
impacts (Article 7c); and to monitor the status of both components and threats (Articles 7b
and 7c). Specifically Article 7d identifies the requirement to:

"Maintain and organise, by any mechanism, data derived from identification and
monitoring activities”.

Having recognised this clearly identified need for management of data in support of national
planning related to biodiversity, the United Nations Environment Program (UNEP), in
collaboration with the World Conservation Monitoring Centre (WCMC), designed and
submitted to the Global Environment Facility (GEF), a project proposal entitled Biodiversity
Data Management Capacitation in Developing Countries and Networking Biodiversity
Information (BDM). This proposal was endorsed and subsequently a sub-project was
established between UNEP and WCMC for Development of Supporting Materials for
Biodiversity Data Management and Exchange.

The sub-project has produced an interlinked package of resource materials to assist in
national capacity building. There are four principal components of this package:

Document 1. Data Flow Model
(This Document)

Document 2. Guidelines for a National Institutional Survey
- to provide guidance to countries in conducting a survey and assessment of
the capacity of existing national institutions to support biodiversity
information management.

Document 3. Guidelines for Information Management
- to facilitate the development of capacity for information management and
exchange as required by the CBD.

Document 4. Resource Inventory
- the core output of the project; a collection of reference directories,
guidelines, and standards relating to biodiversity information management.

The Data Flow Model is intended to identify in a formal structure the relationships between

components of biodiversity data, from acquisition through to use in national strategy
development, planning, and monitoring for implementation of the CBD.

Data Flow Model - Document 1 1

1.2 Information Requirements of the CBD
The CBD has three main purposes:

© the conservation of biodiversity
@ encouraging the sustainable use of biodiversity
@ the equitable sharing of the benefits of the use of biodiversity.

The information required by a country to meet these objectives is wide ranging, going
beyond the normal boundaries of "conservation" or "environmental" information. A number
of Articles of the CBD require or imply the need for facilities for the management and
exchange of biodiversity information. Articles 7d, 12c, 13b, 15(7) and 16, each identify
information management and exchange requirements, and Article 17 explicitly indicates
"access to and transfer of technology among Contracting Parties are essential elements for i
the attainment of the objectives of this Convention". A clause-by-clause analysis of the ~
implied information requirements is given in Annex 1, and summarised below.

The CBD identifies three main categories of biodiversity information:
@ ecosystems and habitats
@ species and communities
@ described genomes and genes of social, scientific or economic importance.

To this basic list one must add:

@ the scientific and technical information required to measure, assess and take
decisions on appropriate action

@ bio-technology, its value and risks
© local knowledge of traditional uses and values of biological resources

@ interrelationships between biodiversity, human actions, laws and conditions,
economics and development.

The Report of the Open-ended Intergovernmental Meeting of Scientific Experts on Biological
Diversity - Second Session (UNEP/CBD/IC/2/11) provides in its annexes further indication
of the scope of the information and technology of biodiversity. For instance, Annex II lists
six major categories of technology "relevant to the identification, characterisation and
monitoring of ecosystems, species and genetic resources":

@ ecosystem evaluation technologies

ea ae ee rae ee
2 Data Flow Model - Document 1

© isolation, characterisation and classification technologies (for terrestrial,
marine and other aquatic organisms, for plants, animals microbes and genes,
and for indigenous and non-indigenous organisms)

@ key enabling technologies including, information technology, advanced
biochemical and molecular technologies, risk assessment etc).

These main headings were further subdivided into a large number of classes from
"biogeography", "ecosystem function" and "traditional knowledge" through to "abundance,
distribution and range" (of species) and "biotechnology".

The following paragraphs, adapted from the United Nations Environment Programme ~
(UNEP) Guidelines for Country Studies on Biological Diversity (1993), identify some of the
types of information which might be important, particularly for the initial assessment and
strategy development.

Biological

This is the primary focus of biodiversity conservation - the core data which includes the
requirements for species, ecosystems and genetic resources, covering issues ranging from
status and distribution of resources to functional relationships and the development of
tools to support the science.

Physical

Information on physical factors such as climate, topography and hydrology allows
biological data to be placed within a physical context, and also allows for the
development of predictive models (as the distribution of many species and vegetation
types can be predicted by a combination of physical characteristics). Physical factors can
also have a significant effect on potential use of resources, and on management options.

Socio-economic

The use and abuse of biological resources is essentially a function of socio-economic
factors. Important data might range from monitoring of forestry or fisheries practice, to
the impact of farming methods, or the distribution of population centres and transport
routes. Perhaps as significant is accessibility to natural resources, and the uses that local
peoples make of these resources. The latter often form an essential, but perhaps invisible
part of the local economy.

Costs and Benefits

In order for management of biodiversity to be efficient, it is necessary to know the true
value of biodiversity and the costs and benefits of management options. This needs to
cover questions such as the costs of managing protected area systems, the level of income
derived from tourism, and the value of indirect benefits such as watershed protection.
Methods for assessing some of these values are only just being developed, and further
dissemination of information on the methodologies will be required.

Data Flow Model - Document 1 3

Pressures and Threats i
Identifying and monitoring both potential and actual threats to biological diversity is an
important component of any information management programme aimed at improved
management of biological resources. However, the latter may need to look beyond
immediate physical causes and effects, to the underlying impact of human activities
(which links threats to socio-economic factors).

Sustainable Management

Conservation of biodiversity is about effective and sustainable resource management. To
assess that management, information will not only be required on the biodiversity itself,
its status and distribution, but also on current and past management activities, especially
on the use of biological resources. For example, information is likely to be required on
a range of factors concerning protected areas, plus on effective management regimes and —
technologies in a range of protected and unprotected habitats. i

Sources and Contacts

Information is also required on information models, standards, and technologies, and on
appropriate agencies and experts who can be contacted. This may include bibliographic
information on who has published what, where, basic information on names and
addresses of appropriately qualified experts, sources of information on reliable and
appropriate models, and metadatabases.

Interrelationships

The above paragraphs begin to illustrate the extent of the interrelationships between the
information that might be required in order to study and manage biodiversity more
effectively. It is essential that these interrelationships are kept clearly in mind when
planning information management strategies. Comprehensive forecasting of the effects
of these interrelationships is also necessary for efficient information sharing.

Another method of sub-dividing the information requirements of the CBD is the following
eight-point classification, which reflects the way in which national and international agencies
are organised to manage biodiversity information:

Conservation
Encompassing information on species, habitats, protected areas, biodiversity
indicators, wildlife, etc.

Genetic Resources
Encompassing agriculture, agricultural research, gene banks, use of genetic resources
for benefit of mankind, traditional use, genetic threats, etc.

Technology

Encompassing information on the technology of biodiversity monitoring and
assessment, such as data collection technology, computer systems and
telecommunications, remote sensing, geographic information systems, database
techniques and standards.

ae a ea rer
4 Data Flow Model - Document 1

Bio-technology
Encompassing a forum for interchange of information on research and application of

bio-technology.

Environmental Statistics/Economics
Encompassing resource utilisation, value of biodiversity, land use, industrial outputs,
equitable sharing of benefits, natural resource utilisation, trade, economics, etc.

Policy
Encompassing policy development, modelling, decision support systems and
technology, empowerment and public consultation techniques, etc.

Human Factors
Encompassing population, human health, social conditions, indigenous knowledge,
and their relationships to biodiversity.

Environmental Law
Encompassing environmental legislation, conventions, protocols, regulation,
standards, etc.

It should also be noted that Article 18(3) requires the establishment of "a clearing house
mechanism to promote and facilitate technical and scientific cooperation". This clearing
house mechanism is for exchange between countries, but it is clear that a proper response
to the CBD requires information exchange, integration and assimilation within each country,
as well as building of capacity, to effectively utilise the clearing house mechanism. This
document is intended to facilitate the development of such a national biodiversity information
system.

The more detailed levels of the data flow model presented in this document give emphasis
to the traditional areas of biodiversity information; that is conservation, genetic resources,
and environmental statistics/economics. However, later editions of the model are planned to
cover the other equally important areas.

1.3. Approach to the Data Flow Model

A distinction is often drawn between "data" and "information". Data normally refer to facts
which result from measurements or observations (such as wildlife counts or the chemistry
of a soil sample), whereas "information" is produced by analysing and interpreting data,
usually with the intent to communicate ideas and facilitate decision making. The
transformation of data into information may include processing the data using statistical
techniques, analysis through models, selection and abstraction, and often expert human
interpretation, for instance of the significance of patterns and trends.

An "information system" is essentially a structured set of processes (and associated people
and equipment) for converting data into information, and for presenting it in forms which
are useful for communication and decision making. Often the modern information~Xxystem
will utilise computers in some of the processes, and for storage, but this is by no means
necessary. The principles of information management remain the same whether or not

Data Flow Model - Document 1 5

computers are used - the need for data to flow from process to process, the need for well
defined processes (of analysis and integration), the need to store and maintain data (and
information), and the need to present or output the information in useful forms. Some or all
of the processes may be manual, requiring specialised knowledge and interpretation. In
considering a Data Flow Model for the CBD, no presumption is made on the extent and
nature of the use of computer technology. The Data Flow Model is intended to provide an
outline of the processes for information management under the Convention which is
independent of the extent to which computer systems are employed or which particular
hardware and software are adopted.

Strategy development and decision making in response to the CBD requires information
which is integrated and summarised to a very high level, the result being many stages of
processing removed from the original raw observations of the field scientist. A national —
information system supporting the CBD will be characterised by a series of summarising and ~
integrating steps. At the lower levels of the process, the outputs will require analysis and
interpretation by qualified specialists in sectoral institutions. However, as the information
becomes more refined, policy analysts and strategists will be required.

As noted and implied in the CBD and related documents (eg Country Study Guidelines, and
the reports of Expert Panels established to follow up the CBD), the range of potential
information types varies widely in biodiversity, with so many other types of information
necessary for its management and understanding. The information required includes numeric,
categoric, spatial and textual data occurring in a variety of forms and using a mixture of
different media. This illustrates the broad scope and complexity of the data to be collected,
exchanged and analyzed, and the potentially complex processes required to use it effectively.

It is also clear that the data derived from national monitoring activities depends upon the
specific threats identified in the country concerned. This means that no universally applicable
data requirements can be determined.

Consideration of these factors (breadth of information type, and country-to-country
variability) resulted in the decision to produce what is termed a "generic" data flow model.
This model has been developed through analysis of the processes that contracting parties are
expected to undertake to implement the CBD, and of the broad categories of data required
by these processes. An overview of the processes is given in Section 2 of this document, and
a further level of detail is described in Section 3. The various types of data used in these
Processes are discussed in Section 4 and data models are also suggested, again starting with
an overview and then expanding detail in selected areas. Applications of the models are also
illustrated in this section. The intent of this document is to provide, at the national level, a
‘sound basis for information management system designs that will:

© facilitate the presentation of biodiversity information to decision makers
@ have an underlying common structure
@ serve the goals of the CBD.

ScaURS acca ee ee see

6 Data Flow Model - Document 1

1.4 Conceptual Overview of National Activities for the CBD

1.4.1 Activities
The overall process for the implementation of the CBD within a country is depicted in Figure

1.1.
Information
from
Country Study

Set Strategic

Objectives
Define Action

Plans & Targets
Y

Implement

Action Plans

Evaluate
Results

Figure 1.1: Overview of National Activities in Support of the CBD

Each component of the figure is described below.

Information from Country Study

The purpose of the Country Study (UNEP, 1993), recommended as the first stage in
implementation of the CBD, is to provide information of various kinds to be used in the
formulation of national strategies and plans for the conservation of biodiversity. The data will
also provide a baseline for monitoring and assessment of the effect of measures taken (see
CBD Articles 6,7).

Existing Biodiversity Information

Much information which can be used in the formulation of national strategies and action
plans for a country is undoubtedly already in existence within the country, in neighbouring
countries and/or with international agencies. The organisation and availability of the
information will vary considerably from country to country and this will influence how easily

Data Flow Model - Document 1 7

and effectively it can be used (see CBD Articles 6,7 and Document 2).

Set Strategic Objectives !
The setting of strategic objectives must start with the identification of the components of
biodiversity of importance to conservation in the country, then proceed with identification
of existing and potential threats, and evaluation of the economic implications of any
conservation measures. The objectives will be established at several levels of detail and
jurisdiction, and they will be integrated as far as possible into relevant policies and sectors
(see CBD Article 6a).

Define Action Plans and Targets

Based on the strategic objectives, priority areas for action will be defined and specific targets
set. The action plans should include an indication of how progress towards the targets should
be measured (see Measure Effects below and CBD Article 6a). Fa

Implement Action Plans
The defined action plans should be funded and implemented. This will involve multiple
organisations reflecting the different levels of strategic objectives noted above (see CBD

Articles 6-11).

Measure Effects

The success or failure of actions to meet defined targets should be measured. This may
require action after a specific time period, a monitoring programme on a more continuous
basis, or a combination of the two. It is important that the measurement process and targets
be discussed fully in the action plan (see CBD Article 7b).

Evaluate Results

The measured effects should be compared with defined targets, allowing an evaluation of the
actions to be made. As a result, it may be necessary to update the action plan, or go back
a step further and re-examine the strategic objectives. For example, further data may need
to be collected to supplement existing biodiversity information. There should be an iterative
cycle of planning, implementation, measurement, and evaluation (see CBD Articles 7b, 7d).

Report to CBD

The exact reporting requirements of the CBD have not yet been defined, but are soon to be
addressed by the Secretariat. Once the requirement is fully defined, its place in the overall
process will be apparent, permitting additional detail to be added to Figure 1.1 (see CBD
Article 26).

Some rectangles in Figure 1.1 represent information management "processes", and others
data collections or "datastores"; it is not necessarily clear which is which. In the terminology
of Section 1.3, there would seem to be six processes and two datastores, although clearly,
data are associated with "Evaluate Results" and "Measure Effects". The two datastores may
serve as information inputs or outputs or both. The arrows joining the four boxes in some
cases imply dataflow, in others some sort of action or sequence of events. This type of
diagram may give a good conceptual overview, but it is not consistent in meaning. For the
purpose of evolving a useful data flow model, to provide a more useful representation, it is

apa eo re ne polenta

8 Data Flow Model - Document 1

valuable to separate the "process" elements (what is done with the information) from the
information itself. This provides for a consistency of meaning within the diagrams and
independence from implementation methods (eg manual, computerised, or mixed). The
method chosen and symbolism used are defined in Section 1.5 below.

1.4.2 Participants

Because of the broad scope of information required to develop effective strategies, a wide
range of institutions and agencies are obliged to interact and participate in the activities
depicted in Figure 1.1. The participating institutions will include those concerned with both
research and policy in economic and social issues, as well as the environment and natural
resources. National and sub-national agencies might include those responsible for statistics,
health, education, economic development and planning, social development, science and
technology, tourism, industry, land tenure and management, and law, as well as renewable
and non-renewable resources, environment, museums, herbaria, national parks, heritage, and ~
wildlife.

Participants will also include national and international NGOs, educational institutions, the
corporate sector, multilateral and bi-lateral development agencies, scientific and social
councils. Ideally, these all work in partnership in an atmosphere of sharing (similar to the
concept of the international Clearing House Mechanism) to provide the necessary flow of
information required to fulfil the objectives of the CBD. A further parallel between the CBD
Clearing House Mechanism (WCMC, 1994) and national biodiversity information
management process, is the concept of a linked series of specialised institutions (which may
themselves be networks or "clearing houses") connected via a hub as depicted in Figure 1.2.

1.5 Methodology and Symbolism

1.5.1 Data Flow Diagrams

Following the methodology developed by Yourdon (1979), data flow diagrams are used to
illustrate the flow of data between processes. These are commonly used to analyse an
operation or system (eg the operation of a business) into elemental processes which are
clearly understood, and to define the data required in those processes. The conventions
adopted in this document are illustrated in Figure 1.3.

Operations may be expressed as a number of "processes" shown in rounded rectangles,
labelled with a single digit at the first level. Each process may be broken down into (sub)
processes which in turn may be split further, and so on, an extra decimal label being added
at each level. Data used in the overall operation may be in one of two types of "datastore" -
external, meaning generated outside the overall operation (depicted as a plain rectangle), or
-internal, implying that the data are generated by one of the defined processes (depicted by
an open-ended rectangle). —

The directional lines between processes and datastores indicate the direction of data flow. For
clarity, it is conventional that each data flow diagram should contain a limited number of
process boxes (4-6) and datastores.

Data Flow Model - Document 1 9

Statistics &

iN
TPB

ene

AX
K
St
7

Ces
oe
he

Figure 1.2: Conceptual View of a Cooperative Clearing House Network

Note that datastore is a generic term, and refers to any logically related collection of data.
The data or information held in a datastore may not be all physically in the same institution,
and may be in hard copy and/or electronic forms.

In this document, the overall operation is the implementation of the CBD within a country.
The content of the datastores depicted in the data flow diagrams in Sections 2 and 3 outline
the basic categories of information required. The detailed specification of the databases
required to store and process these data must be determined by individual countries on the
basis of their own particular needs and priorities, and the information management
capabilities in place. However, given the underlying nature of the data, plus the kind of
analyses and outputs frequently required, it is suggested that where computer systems are
employed, the most effective solution is a relational database management system (RDBMS)
linked to a Geographic Information System (GIS). The former allows extensive manipulation
-and reporting of non-spatial data, and the GIS extends these functions into the spatial domain.
allowing data sources to be integrated and analyzed to provide outputs in a variety of forms,
including graphs, tables and maps.

In the text, processes are identified in italics, and datastores in bold italics.

a
10 Data Flow Model - Document 1

Conduct Process

[: 2 | Catalog of Data Holdings Datastore (intemal)

Existing
informakon Datastore (extemal)
~<—— Data Flow

Figure 1.3: Symbolism Used in Data Flow Diagrams
(after Yourdon 1979)

1.5.2 Data Models

The content of the datastores is elaborated in Section 4. The discussion takes place within
the context of RDBMS/GIS technologies, and methodologies associated with these are used
in modelling the data. The basis is an "entity-relationship" approach (Chen, 1976), extended
to encompass spatial elements.

ot Entity (non-spatial)

Entity (spatial)

RELATIONSHIPS

one-to-one

many-to-many

Figure 1.4: Symbolism Used in Entity-Relationship Diagrams
(after Ashworth & Goodland, 1990)

Data Flow Model - Document 1 11

An "entity" is a item of interest whose attributes (properties) are being measured or recorded.
For instance, an institution might be an entity with attributes of location, name, year
established, mission, etc. The notation used is illustrated in Figure 1.4 and follows that of
Ashworth and Goodland (1990). Thus rectangles represent non-spatial entities, lozenges
represent spatial entities (points, vectors or polygons), and connecting lines show
relationships between entities. The latter comprise three types as illustrated.

In the text, entities are identified in bold.

It should be noted that data models are to some extent subjective. Thus two individuals may
produce distinct and valid models of the same data, reflecting the different objectives of their
applications. The models presented in this document are generic, since the intention is to
provide a framework which can be modified to meet specific situations.

12 Data Flow Model - Document 1

2 FIRST LEVEL PROCESSES

The first level data flow diagram is depicted in Figure 2.1. This level of analysis identifies
5 basic processes and one very general datastore. Each of these is elaborated below.

INFORMATION PROCESSES
(Datastores)

Evaluate
Results

CBD

Figure 2.1: CBD Data Flow Diagram, Level 1

2.1. Conduct Country Study

This process is well defined in the Guidelines for Country Studies on Biological Diversity
(UNEP, 1993), where it is indicated that the goal of the Country Study is to initiate a process
of improved biodiversity planning that will stimulate the action necessary at the national level
to implement the CBD. Specific objectives include the provision of an information base for
biodiversity planning and management through gathering and assessment of data required for
decision making. This includes information on population, economics, environment, and so
on, as well as biological datasets per se. Thus the Biodiversity Databases shown in the figure
depict layers of information from several sectors to be taken into account in biodiversity
management.

Data Flow Model - Document 1 13

2.2 Set Priorities and Prepare Action Plans

This is a combination of the setting of strategic objectives and defining action plans described
in Section 1.3. As noted the objectives are to be integrated with policies in relevant sectors.
For example, the general objective "increase the area of natural habitat under protection"
might contain "increase the area of protected forests" and/or "increase the area of protected
wetlands". The action plans specify targets which will be, as far as possible, quantifiable
results over specific time periods, for example "increase protected forest areas by 200 sq.km
over the next two years".

2.3. Implement Action Plans

This is as described in Section 1.3, and represents the totality of all the actions taken by the
institutions involved in implementing the CBD in a particular country, including measurement
and additional data collection.

2.4 Evaluate Results
This is as described in Section 1.3. Note that this process includes the measurement of effects

which may lead to revision of data collection plans.

2.5 Report to CBD
This is as described in Section 1.3.

2.6 Biodiversity Databases

It is unlikely that all the information required for biodiversity planning will be integrated into
a single database at one site. For example, a plant species database may be maintained by
the national herbarium, whereas data on protected areas may be managed by the country’s
national parks agency. In addition, it is clear that other information sources are required,
such as baseline data for the country (eg infrastructure), physical environment data (eg soils,
hydrology, geology and climate), socio-economic data (eg demographics, health, local use
of resources, and land-ownership), all of which will be maintained by the agency with the
relevant mandate (the custodian). The Biodiversity Databases shown in Figure 2.1 therefore
represent the total collection of data required for all of the processes involved in the
implementation of the CBD. All of the top level processes shown use the data; the processes
of conducting the country study, setting priorities, implementing plans and evaluating results
will add data to the overall store. In the next section, the processes are broken down into
components and the very general datastore, Biodiversity Databases, is further sub-divided.

an a Sa Re a i ee
14 Data Flow Model - Document 1

3 SECOND LEVEL PROCESSES

This section contains the level 2 data flow diagrams for each of the five processes outlined
above (using the methodology and symbolism described in Section 1.5). In each case, the
data flow diagram is followed by an expansion of the second level processes including a
description of related datastores.

3.1 Conduct Country Study
The first level 2 data flow diagram is Conduct Country Study (see Figure 3.1). Each of its
processes is described in subsequent paragraphs.

Conduct ial Institutions

Institutional Preliminary Catalog o'
Survey 12 Toe Data Holdings

Identify :
Fislogea | 3 | Catalog of Data Holdings

Diversity

Existing
Sectoral

Information

Identify —
Adverse 4 | Human Activity & Impacts

Processes

Determine 5 | Economic Values
Economic

Implications

3.1.1 Conduct Institutional Survey
The Guidelines for Country Studies on Biological Diversity (UNEP, 1993) explicitly identify
the need for an initial assessment of the country’s capacity for conservation and sustainable
use of biodiversity. The Guidelines suggest that the information required for this includes
institutional capacities, human resource capabilities, available technological facilities and
-information resources in place, and that an institutional survey would be undertaken to
acquire such knowledge. These suggestions are expanded in considerable detail in Guidelines
for a National Institutional Survey (Document 2), one of the other components of this project
(see 1.1). The latter include suggested methods of carrying out such a survey and details a
the type of data to be collected.

Data Flow Model - Document 1 15

The two outputs of the Conduct Institutional Survey process are related metadatabases. The
first contains institutional information (see Section 4.3) such as staff skills and technological
facilities (institutional capacity). The second catalogues the datasets (information resources)
held by the institutions (see Section 4.4). This metadatabase is labelled "preliminary", since
the institutional survey process involves only a cursory review of the datasets.

3.1.2 Identify Biological Diversity

This process ties directly to Article 7(a) of the CBD, ie the target is identification of
components of biodiversity important for its conservation and sustainable use. The
Convention does not include any definitive lists in this regard and items are to be defined as
appropriate for the individual country. An indicative list in Annex I of the Convention (see
Section 4.5) provides some insight into the nature of the required data. Clearly this process
will involve examination of existing data, including those held by international agencies. The
Preliminary Catalog of Data Holdings will provide pointers to relevant datasets in national ~
institutions. As the content of the datasets is used in subsequent planning processes, catalog
entries can be confirmed and detail added where necessary, producing the Catalog of Data
Holdings. This datastore may be used identify significant gaps in data resources.

3.1.3 Identify Adverse Processes

Again this process ties directly to an article of the Convention, namely Article 7(c). Direct
threats to biodiversity (ie adverse processes) include deforestation, drainage of wetlands,
emission of pollutants, urbanisation and the spread of invasive introduced species. Indirect
threats are less well known, but should nevertheless be considered. However, as in Section
3.1.2, these may differ greatly from one country to another. The identification of adverse
processes may involve integrating and interpreting data from a wide range of institutions. The
data may already suggest specific threats, or may indicate reductions in biological resources
for unconfirmed reasons.

3.1.4 Determine Economic Implications

The process of determining economic implications is documented in Section C of the
Technical Annex to the Country Study Guidelines (UNEP, 1993). This process includes
estimating the economic value of the benefits resulting from the sustainable use of
biodiversity, and quantification of the costs of current and proposed conservation actions.

3.2 Set Priorities and Prepare Action Plans
The level 2 data flow diagram for this process is illustrated in Figure 3.2.

3.2.1 Establish Strategic Objectives
The process of establishing strategic objectives in support of the CBD involves consultation
- amongst key institutions to identify the principal objectives in the context of primary threats,
economic values and the capacity of institutions to support actions for biodiversity. It should
make use of the four datastores resulting from the Conduct Country Study process, and
involve analysis and interpretation of a range of existing sectoral data sources. Priorities
should also identified in the process, in terms of human activities and impacts, and economic
values. The output datastore will contain narrative descriptions of the strategic objectives.
Attention is drawn to the paper National Biodiversity Strategies (UNEP/WRI, 1994).

16 Data Flow Model - Document 1

[+] Institutions

| 3 Catalog of Data Holdings
| | 4 Human Activity & Impacts
5 [ Economic Values

Establish
Strategic
Objectives

Existing
Sectoral
Information

Select
Indicators

& Establish
Targets

Develop
Action
Plans

Figure 3.2: Set Priorities and Prepare Action Plans (DFD - Level 2)

3.2.2 Select Indicators and Establish Targets

In order to measure the results of actions, appropriate indicators should be chosen along with
target values and critical thresholds. Indicators should be quantitative where possible, such
as "to have protected areas totalling 5% of each ecosystem in the country by the year 2010".
Consideration should be given to the paper Biodiversity Indicators for Policy Makers
(WRI/IUCN, 1993). The process makes use of defined Objectives, as well as other inputs
such as the Catalog of Data Holdings.

The output datastore Targets includes the definitions of selected indicators, methodologies
for estimating them, and specific target levels. It may comprise of a mixture of textual and
Numeric data.

3.2.3 Develop Action Plans

The planning process must include the estimation of the costs of proposed actions, and define
the institutions responsible for each task. This process therefore attempts to reconcile the
Targets identified in Process 2.2 with available institutional capacity (see Institutions). The
output Actions lists tasks, responsible institutions, required legislation and regulation, data
collection and monitoring plans, and associated costs and timetables. Although the output is

Data Flow Model - Document 1 ‘17

mainly textual in nature, it might benefit from organisation under an automated project
Management system.

3.3 Implement Action Plans a my at
The level 2 data flow diagram for this process is illustrated in Figure 3.3.

Sectoral Information

| 2 | Catalog of Data Holdings

[10] Laws/Regulations

os
8
re}

Actions
Figure 3.3: Implement Action Plans (DFD - Level 2)

3.3.1 Verify Information

The process to Implement Action Plans is assigned to a range of institutions, and commences
with a process of review and verification of existing data sources. This contributes towards
a distributed collection of selected and verified Sectoral Information. The selection should
reflect the information needs of the CBD as defined by the selection of indicators and targets.

3.3.2 Collect Information and Fill Gaps

Depending on the extent of the information gaps identified in the Catalog of Data Holdings,
this process may be a dominant or minor component of the overall implementation process.
The additional data also contributes to building up the national collection of up-to-date
sectoral information needed for estimating key indicators. The Sectoral Information
datastores represent the main biodiversity information resource of the country. These

a
18 Data Flow Model - Document 1

datastores may be extensive and held under the custodianship of a number of separate
institutions. They may occur in several forms including quantitative, textual, and spatial.

3.3.3 Monitor Change

The objective of implementing action plans is to achieve positive change. Thus new
information should be collected regularly, as defined in Actions, to keep Sectoral
Information datastores up to date. This may involve long-term site monitoring programmes
to record plant and wildlife populations, or regular habitat monitoring schemes using aerial
photography or remote sensing.

3.3.4 Enact Legislation
One of the primary tools for implementing action plans is the enactment or amendment of
legislation, regulations and policies (eg to create protected areas, to encourage or restrict
certain human activities, to limit industrial wastes). This results in a datastore of laws, ~
regulations and policies which is largely textual in nature, except for quantitative tables or,
for example, standards reflecting regulatory limits.

3.3.5 Perform Other Actions
Although legislation and regulation are important actions, a range of other activities are
desirable including:

institutional strengthening

human resource development and training
monitoring and enforcement of regulation
biodiversity research

operational actions to reduce threats.

Many national institutions may be involved in this process, which may be broken down into
a large number of sub-processes depending on specific national strategies and priorities. Such
sub-division is beyond the scope of this document.

3.4 Evaluate Results
The level 2 data flow diagram for this process is illustrated in Figure 3.4.

3.4.1 Estimate Indicators

Indicators are estimated (or calculated where possible) on the basis of data held in the
Sectoral Information datastore. This results in a further datastore of key numeric Indicator
Values.

- 3.4.2 Assess Current Status vs Targets

In this process, the indicators and other results are compared to their original targets. This
may involve simple numeric comparison, but more commonly, analytical assessments of
progress towards targets. In addition, the effectiveness of legislation and regulation should
also be assessed. The result of the analysis is a datastore of Assessments containing both
quantitative and textual (explanatory) material.

Data Flow Model - Document 1 19

Sectoral Information

14 | Indicator Values

Assess
Current
Status vs.
Targets

12 Assessments

Figure 3.4: Evaluate Results (DFD - Level 2)

3.5 Report to CBD
The reporting requirements of the CBD (see Article 26) have not yet been fully defined. For

this reason a detailed breakdown of the Report to CBD process cannot be given. However,
a suggested outline of the process is shown in Figure 3.5.

Sectoral Information

Indicator Values

Extract
Reporting
Elements

12 | Assessments
CBD Reporting Elements

CBD
|_ Report i —>] repre [r4]oo Report
: Requirements : to CBD

Figure 3.5: Report to CBD (DFD - Level 2)

20 Data Flow Model - Document 1

4 DATASTORES

4.1. Overview of Datastores

The analysis of the CBD process depicted in Sections 2 and 3 indicate the presence of
fourteen "datastores". As previously indicated, these datastores are conceptual, representing
logical groupings of information required by or produced by the processes; no assumption
is made on how and where the data may be kept. Datastores do not equate to physical
datasets, data holdings or institutions; the information required for a datastore may reside in
a number of institutions and derive from a range of disciplines. Note especially that datastore
9, Sectoral Information, represents the major national repository of scientific data relevant
to biodiversity, and for this reason is depicted as multiple datastores. This section examines
datastores from the perspective of data structure and content. The fourteen datastores in total
represent the "Biodiversity Databases" identified in the level 1 data flow diagram (Figure
2.1). Each is briefly outlined, following which selected datastores are expanded in more ©
detail in Sections 4.2 to 4.5. This initial pass at a data flow model focuses on core scientific
biodiversity data. Subsequent documents are planned to incorporate other relevant domains.

1 Institutions

This datastore keeps the information about the institutional strengths and biodiversity
information analysis and management capacity of the country. The custodian of this
information would normally be a lead agency in the implementation of the CBD, and
commonly would be implemented as a metadatabase. Wide distribution or ease of access
by all other agencies is important. The process of compiling this datastore is the subject
of Document 2 of this series. An important function of this datastore is to connect to the
reservoir of biodiversity technology and the associated enabling technology (survey and
monitoring techniques such as remote sensing), and to sources of expertise for
institutional strengthening. Datastores on technology are currently beyond the scope of
this model, but could be linked in to this key datastore, the structure of which is
elaborated in Section 4.3.

2 Preliminary Catalog of Data Holdings

3 Catalog of Data Holdings
These two datastores could be implemented as a single evolving metadatabase. This
would identify who has what data of relevance to the CBD, a key element in finding the
required information for analysis as well as identifying information gaps. The structures
of these datastores are elaborated in Section 4.4.

4 Human Activity and Impacts

This datastore is multi-sectoral and covers the driving forces which influence, positively
and negatively, the conservation and sustainable use of biodiversity. It should include
databanks on industrial and agricultural activities and outputs, population and social
factors, land tenure, landuse change, etc. This encompasses much of what is often
referred to as "environmental statistics". The scope of this document does not allow for
the elaboration of the structure of this large and complex datastore. However, suitable
data structures for environmental statistics are well established, with standard frameworks
available from organisations such as the UN Statistical Office and the OECD (see
Resource Inventory, Document 4, Section 5.9).

Data Flow Model - Document 1 21

5 Economic Values
An assessment of the economic value of biological resources is extremely important in
fostering their sustainable use and ensuring equitable sharing of benefits. Some models
for organising this information have been proposed, but none are universally accepted.
Implementation of such a datastore is likely to depend on national accounting and
statistical systems, and will vary between countries as a result. Some guidance on
assessing economic values and structuring the information is provided in the UNEP
Guidelines for Country Studies (UNEP, 1993).

6 Objectives
This datastore contains national biodiversity objectives at the strategic level. These would
normally be framed in general terms, eg "to sustainably harvest forest products while
maintaining biodiversity". This datastore would normally be implemented in narrative
form (electronic or manual) with a simple structure, such as a sectoral sub-division on ~
the lines of the governmental program delivery structure.

7 Targets
This datastore identifies measurable results sought in particular time frames. Although

targets might be framed in narrative terms, ideally they would be connected to
quantitative indicators. Implementation should therefore be integrated with the Indicator
Values datastore, which is elaborated in Section 4.2.

8 Actions

This datastore comprises the information on proposed and ongoing actions designed to
achieve specific targets. This includes information on field projects, biodiversity research
activities, biotechnology acquisition plans and projects, and planned and implemented
policies and programmes (including those aimed at equitable sharing of benefits,
sustainable use and conservation). This is not a scientific datastore, but rather an
information communication and referral tool. Implementation in the form of documented
"Action Plans" and bibliographic referral is most appropriate. The structure might
logically parallel that of datastores 6 and 7. Guidance for organising the information of
datastores 6 through 8 can be found in National Biodiversity Strategies (UNEP/WRI,
1994).

9 Sectoral Databases
As outlined in Section 1.2, the range of information which relevant to the CBD is vast.
These sectoral databases refer to the observational scientific data, traditionally gathered,
collected and managed in sectors such as marine science, soil science, agriculture,
wildlife, botany, zoology, forestry, genetic resources, and so on. The way in which these
sectors are divided varies from country to country, depending on the scientific and
administrative structure. Two main classes occur within this group:

© Core Biodiversity Data
Those data which relate directly to plants, animals, their habitats and
ecosystems, and related genetic resources.

a
22 Data Flow Model - Document 1

@ Natural Resource Base Data
Those data which define the resource base for biodiversity, including, soil,
geology, land capability and use, climate, physiography, hydrology, and
aquatic resources.

Potential data structures for core biodiversity data are elaborated in Section 4.5. The
management of natural resource base data is relatively mature (compared to
biological/ecological information). Thus conventions, classification systems, and standard
approaches have been defined in many areas such as soil science, geology, climate, and
oceanography. It is beyond the scope of this document to provide further detail of these
data management conventions. However, reference is made to the work of the
International Council of Scientific Unions (ICSU) CODATA program, to the practices
and standards of the various sectoral scientific unions (such as, the International Society

for Soil Science), and to Document 4, Sections 4 and 5. ;

10 Laws/Regulations

This datastore maintains information on the laws, regulations, policies, etc, which govern
the use and conservation of biological diversity, including related areas which effect
commercialisation, economics and benefit sharing. The structure of such a datastore is
best implemented as a simple index or metadatabase providing assistance in locating
relevant documents. This would be similar to any bibliographic or document management
system, such as the one employed by the IUCN Environmental Law Centre.

Indicator Values

Indicators improve communication by quantifying and simplifying information. They can
provide policy and decision makers with essential information on the status and trends
in biodiversity conservation, and can help evaluate effectiveness of conservation efforts
in relation to explicit management objectives or targets. They can be applied at scales
ranging from community level (to guide resource managers) up to national and
international levels, and can provide a framework for the collection and reporting of
information at all these scales. There is a continuous need for comparison of indicators,
and where possible a common approach to their selection, measurement and reporting
should be introduced. This also applies to the setting of baselines and targets. The
Indicator Values datastore is closely tied to that of Targets, and is elaborated in Section
4.2.

12 Assessments

Assessments are the results of analytic comparison of existing conditions (derived from
monitoring) with identified targets. These normally take the form of narrative reports
with quantitative tables of calculated indicator values and other summary data. This
datastore could be implemented as a document management system similar to that
advocated for datastore 5, 6, 7 and 11. Many countries may choose to integrate the
Targets and Assessments datastores to implement the desired feedback loop of the CBD
process. Quantitative tables included in the assessments could be integrated with national
"State-of-the-Environment" reporting systems where these exist, using the data structures
recommended by UNEP or Organisation for Economic Cooperation and Development
(OECD).

Data Flow Model - Document 1 23

13 CBD Reporting Elements

14 CBD Report
The CBD reporting elements consist largely of information extracted from other

datastores, especially from the Sectoral Information, Actions, and quantitative component
of the Assessments datastore. As the reporting requirements for the CBD are not yet fully
defined, it is not appropriate to explore a data structure for these datastores. However,
the quantitative component of the Reporting Elements datastore is likely to be maintained
as a set of relational database tables linked to narrative assessments in the form of text-
based files (for guidance on this approach see Document 3, Section 3.6).

4.2 Indicator Values

Indicators are determined by the specific issue under consideration, the target users of the
indicator, its spatial and temporal scope, data availability and the framework available for
analysis. They should be relevant to policy, should be well founded in technical and scientific :
terms, and should be measurable. They may have a range of components and may draw on
data contained in several datastores such as economic values, human impacts and sectoral
databases (eg protected areas, habitats, physical features). Indicators are "information"
derived by analysing primary data, even though they may be presented in tabular, map, or
graphical form. Their value derives from their ability to place data in the context of agreed
baselines and targets.

Many groups have developed indicators for environmental, social and economic monitoring,
and new indicators are continually being developed for application at sectoral, national and
even global scales. The World Bank, Organisation for Economic Cooperation and
Development (OECD), and the World Resources Institute (WRI) are but a few of these.
WCMC is currently testing a number of indicators of forest condition in a series of case
study sites in tropical regions.

Indicator Paes /
iti alculation
Definition Mathod

Indicator
Value

Figure 4.1: Indicator Values Data Model

ee EE non OREM Oe a IEP cI
24 Data Flow Model - Document 1

However, there continue to be problems in the development of consistent measurable
biodiversity indicators. These mainly stem from a lack of appropriate primary data, but are
also subject to debate over definitions, inadequate comparability of baselines and goals. As
a result there is no universally accepted data structure typifying a datastore of Indicator
Values.

Figure 4.1 suggests a simplified structure for such a datastore, with three entities: the
indicator value (specific instances of the measurement or estimation of an indicator at a
particular time and place); the indicator definition (which may be both descriptive and
quantitative and carry attributes such as the desired level of the indicator, or its critical
threshold); and estimation/calculation method (there may be more than one acceptable
estimation method for a given indicator).

4.3 Institutions

An Institution is defined as a recognisable organisation that maintains or uses information
of relevance to the CBD. The resulting metadatabase contains information on the strengths,
capacities and data holdings of each institution in the country and, if relevant, region. An
example of an Institution metadata entry is given below:

Name: World Conservation Monitoring Centre
Acronym: WCMC

Type: Non-governmental

Theme: Information Services

Keywords: biodiversity; conservation; information
Postal_Address: 219 Huntingdon Road

Postal_Code: CB3 ODL

City: Cambridge

Country: United Kingdom

Contact_Person: Jo Taylor

Contact_Status:

Information Officer

Telephone: 44-1223-277314

Fax: 44-1223-277136

Email: Internet: info@wcmc.org.uk

Update_Date: 1994-09-01

Mission: To provide research, information and technical services so that

decisions affecting the conservation and sustainable use of
biological resources may be based on the best available scientific
information.

- A few sample field definitions are given below to give a flavour of the institutional metadata
(the full Metadata Data Dictionary is defined in Document 2, Annex 6).

Name:

Definition Official name of the institution.

Format Maximum 50 characters.

Status Mandatory.

Example World Conservation Monitoring Centre.

Data Flow Model - Document 1 25

Type:

Definition The organisation type, selected from one of the following: governmental; non-
governmental; commercial; academic; inter-governmental; United Nations.

Format Maximum 20 characters.

Status Mandatory.

Example Non-governmental.

Theme:
Definition The primary function of the organisation, selected from one of the following:

research; consultancy; information services; campaigning. The selection of a
primary function keyword is not intended to wholly define the scope of the
organisation. Detailed description of the function of the organisation can be
expanded on in the "Mission" section.

Format Maximum 30 characters.
Status Mandatory.
Example Information Services.

A data model for this metadatabase is shown in Figure 4.2.

Linked
Institutions

Technical
Resources

Human
Resources

Figure 4.2: Institutions Data Model

4.4 Catalog of Data Holdings

- The Dataset is defined as a collection of data and accompanying documentation maintained
at an Institution. A collection of data refers to one or a series of Data Members which relate
to a specific theme or geographic region. Sample definitions of Dataset metadata items are:

Name: ;

Definition The name given to the dataset or activity being described. The title should be
descriptive enough to allow the reader to make a reasonable decision as to
whether the data may be of interest.

a a
26 Data Flow Model - Document 1

Format Maximum 50 characters.
Status Mandatory.
Example /.frican Protected Areas GIS.

Theme:

Definition This is the theme or parameter being measured by the dataset. The keyword
entered is the most general, and should, if possible, be taken from the
standard terminology lists.

Format Use INFOTERRA terminology list.
Maximum 31 characters.
Status Mandatory.

Example Terrestrial ecosystems.

A data model for this metadatabase is shown in Figure 4.3.

Dataset

Figure 4.3: Catalog of Data Holdings Data Model

4.5 Sectoral Information - Core Biodiversity Data
4.5.1 Overview
Annex I of the CBD contains the following indicative list of categories for identification an
monitoring:
"1. Ecosystems and habitats: containing high diversity, large numbers of
endemic or threatened species, or wilderness; required by migratory

Data Flow Model - Document 1 27

species; of social, economic, cultural or scientific importance, or, which
are representative, unique or associated with key evolutionary or other
biological processes.

74, Species and communities which are: threatened; wild relatives of
domesticated or cultivated species; of medicinal, agricultural or other
economic value; or social, scientific or cultural importance; or importance
for research into the conservation and sustainable use of biological
diversity, such as indicator species.

3. Described genomes and genes of Social, scientific or economic
importance."

Based on this list (particularly items 1 and 2) and given the ways in which biodiversity data
are currently organised by the key agencies, the core data may be considered as relating to Bc
four primary entities: habitats, protected areas, species and threats. These four entities
represent the first level entity-relationship (E-R) model for biodiversity data a the datastore
Sectoral Information.The relationships between these core biodiversity entities are shown
in Figure 4.4.

Protected
Areas

Threats

Figure 4.4: Core Biodiversity Data Entities

The figure shows many-to-many relationships between all entities, ie:

a species may be subject to many threats

a threat may apply to many species

a species may be in several protected areas
a protected area may harbour many species.

The entities shown are at a high level (Level 1) and each can be further broken down (Level
2). These are elaborated in turn in E-R diagrams in subsequent sections. Appropriate models
for genetic resources (item 3 above) have not been developed in this document and are
planned for future research. Some work has been done on establishing effective ways of
managing agricultural germplasm information at the International Plant Genetic Resources
Institute (IPGRI) in Rome, and there are close links to the way in which species information

28 Data Flow Model - Document 1

in general are organised (see Section 4.5.3).

4.5.2 Habitats .
Figure 4.5 shows a structure which could be used for handling data relating to habitats.
There are three entities:

@ area is a spatial entity defining the geographic location of the habitat
@ habitats has the basic attributes of the polygon, eg identifier, type, etc.

e habitat type gives more detail of the meaning of "type", eg type, description, etc.

Habitat

Figure 4.5: Habitats Data Model

There is a one-to-one relationship between area and habitats, and a one-to-many relationship
between habitat type and habitats.

This structure is simple and can be implemented using elementary geographic information
systems and data management tools. These allow produciion of maps showing the current
geographical distribution of the various habitat types, with tables giving the area and, for
example, percentage coverage of the total area of the country by a specific habitat type. This
type of output is useful in summarising data for decision makers for planning purposes.

The structure is easily extended to cope with habitat monitoring, by maintaining a sequence
of date-stamped editions describing the situation with respect to habitat at different points in
time. By comparing these, maps and tables showing change (either decrease or increase) in
the various habitat classes can be produced.

The principal problem in dealing with habitat data is not one of complexity in data structure
at this level, but the absence of an internationally accepted habitat or ecosystem classification
at an appropriate scale for national biodiversity planning. Again, the varying requirements
of different countries means that a widely applicable classification is difficult to conceive.
However the data management structure suggested here is independent of this problem.

Data Flow Model - Document 1 - 29

This basic structure is used in the Tropical Forests Database developed by WCMC. The
source data have been derived from a variety of sources including satellite imagery, existing
databases, maps, survey data, and so on, and harmonised into standardised broad forest
categories. For example, the four major categories of forest are lowland rain forest, montane
rain forest, inland swamp forest and mangrove. The forest polygons are held in an Arc/INFO
coverage with linked database tables, echoing the structure illustrated in Figure 4.5.

4.5.3 Protected Areas
Figure 4.6 shows a structure which could be used for handling data relating to protected

areas.

National
Management
Objectives &
Legislation

Management
Objectives
for P.A.

Biotic &
Abiotic
Components

Protected
Areas

Budgets /
Staffing

Ownership

Protection
Measures /
Effectiveness

Area

Figure 4.6: Protected Areas Data Model

30
Data Flow Model - Document 1

The entities and relationships are:

@ protected area is the basic entity containing primary attributes such as name,
year established, size, designation, description, etc.

@ area is a spatial entity defining the geographic location of the protected area

®@ socio-economic values (such as tourism) may be associated with a protected
area

@ the land (of the protected area) may be owned (ownership entity) by one or
more agencies (or individuals)

® management objectives will be set for the protected area (and these may
relate in turn to national management objectives)

@ the protected area will be assigned budget/staffing for its operation

@ protection measures are established for a protected area and the
effectiveness recorded.

The spatial element in this remains simple, but there are more entities than in the case of
habitats and there is variation in the nature of the entities. For example, items in budget and
ownership are fairly apparent, but the attributes to be included in economic values may be
less so. The form of the entities could imply that more capability is required in the data
management tools.

With a protected areas database of this form, maps and reports could be produced:

showing the various areas under protection

summarising the current management objectives

giving budgetary roll-ups of expenditures on protected areas
totalling costs and benefits of tourism (an economic value)
highlighting interactions with surrounding land use.

There is a well established database of Protected Areas in use at WCMC. Although it
includes the elements mentioned above (among others), the purpose is to provide a source
of information on the world’s protected areas, rather than to provide a mechanism to assist
in managing protected areas which is left to experts in the countries concerned. This
inevitably leads to a difference in perspective. However, some outputs similar to those above
can be produced from the WCMC system, which is implemented using the FoxPro relational
database management system (RBDMS) and Atlas-GIS mapping package (see Document 4,
Section 3.2).

4.5.4 Species
Figure 4.7 shows a structure which could be used for handling data relating to plant and

animal species.

Data Flow Model - Document 1 31

Altemate
Names

Economic
Value

Taxonomic
Heirarchy

Protection
Measures /
Effectiveness

Geographic
Distribution

Legislation /
Regulation

Figure 4.7: Species Data Model

The following points should be noted:

@ the central entity species/taxa contains the basic attributes of the plant or animal,
eg name, and this may be at the species or taxa level

@ any species/taxa may have alternate names, both common and scientific
@ more than one collection may relate to any species/taxa

@ the taxonomic hierarchy, shown as one entity for the sake of simplifying the
figure, is composed of multiple entities

@ any species/taxa may have protection measures applied to it
© the protection measures in turn may relate to legislation/regulation

@ all species/taxa are located geographically (geographic distribution)

32 Data Flow Model - Document 1

e the geographic distribution may be located as one or more specific areas (or
points)

@ any species/taxa may have an economic value attached to it (which may be
expressed as several entities).

Again the spatial element remains simple and the complexity is in the non-spatial attributes
(especially as several of the entities are likely to expand into multiple entities) and
correspondingly more complex data management tools may be required to implement such
a database. In fact the form of the model is likely to be influenced by the facilities available
in the computer system used.

The type of analyses which could be generated include:

@ lists of species, grouped for example by family, with maps showing their
distribution

@ lists of endemic species and associated distribution maps
© lists of economically significant species, identifying the type of value

Again, comparison of the results of similar analyses conducted over a period of time, permits
the monitoring of species distribution and numbers.

This is a very simplified view, and additional linkages to threats, protected areas, trade in
species, etc will be needed. Figure 4.8 shows, for purposes of example, the main files in the
database used to manage plant information at WCMC and other establishments.
This structure echoes the data model given above, for example:

@ the names table is the central species/taxa entity

@ the genera, families, orders, subclasses tables give the taxonomic hierarchy

@ the distributions table links to WCMC areas (Biodiversity Reporting Units)
providing an index to geographic location

© the distributions table also contains a conservation status item which may link to
laws (c.f. protection measures and legislation).

- Note that the E-R diagram in Figure 4.8 showing the general relationships between the
principal data files in the database is diagrammatic, but not strictly correct.

Data Flow Model - Document 1 33

aa ie sl

aa
Data Source Data IVCN Staus
Locations Sources Categories

Figure 4.8: Main Elements of WCMC Plant Database ("BG-BASE")

Note also that the conceptual E-R diagram of Figure 4.7 contains no parallel to the "data
sources" and "data sources location" table. These are linked to many other tables and provide
a mechanism for documenting the source of the various items of data. This is valuable
information when dealing with biodiversity data of all kinds, and arises not so much from
the structure of the information itself, but from the requirements for managing it.

This particular implementation uses a system called BG-BASE, which has been developed
using the Advanced Revelation, a relational type of database management system (RBDMS)
which allows for variable field lengths and multi-value fields.

4.5.5 Threats

-The data which are needed to describe and analyse threats, and the structures needed to
effectively manage them, depend heavily on the nature of the threat. For example if the
threat to a species originates from trade, then details of the traffic and trade in related
commodities would be relevant; if the threat is due to loss of habitat, then rates of land use
change and related spatial information must be recorded. Different data structures are
required in each case. Threats deriving from widely distributed phenomena such as climate
change or long-range transport of pollutants, present different challenges again for data
organisation.

RN EN
34 Data Flow Model - Document 1

While the Country Studies Guidelines (UNEP, 1993) outline three major classes of threat:
External Socio-Economic Factors, Direct Threats: Local Impact, and Direct Threats: Global
Impact - in designing a national program of information management on threats, emphasis
should be placed mainly on the proximate (or "direct") threats of local origin. Socio-
economic factors should be considered as causal factors, rather than as threats per se.

This distinction is not always clear. There is considerable interrelation between causal
factors, threats to species, threats to habitats, human activities, mitigation measures, etc.
"Threats" to one species may result from measures aimed at conserving another. It is a
complex situation without as yet a great deal of standardisation of concepts and approaches.
Listed in UNEP (1993) are seven major categories of human-induced threats with very many
sub-classes, and a number of other ways of categorising threats are available.

From the perspective of threats to species, a primary breakdown would distinguish between ~
threats to the habitat of the species (such as loss, fragmentation and degradation of habitat
quality), and threats to the species itself (such as harvesting, hunting, introduction of
competitive species). The E-R diagram which follows (Figure 4.9) applies to the organisation
of information on threats which are internal to the country. Different structures will be
required to deal with external threats such as climate change.

CAUSAL
FACTORS

THREAT
DESCRIPTION

ACTIVITIES

IMPACT
ASSESSMENT

REMEDIAL
ACTIONS

Figure 4.9: Threats Data Model

Referring to Figure 4.9:

© The central entity threat description contains data such as threat category (preferably
following the standard IUCN classification), intensity and duration, a narrative
description of the threat, and pointers to relevant references.

Data Flow Model - Document 1 35

© the activities entity records the quantitative information related to the threat, for
example how much, how many, where, etc. The exact structure of this will be highly
dependent on the nature of the threat. For instance if the threat was "road building”,
activities might include the length, nature and position of roads, associated support
facilities, construction timetables, and so on. If "hunting" was the threat, then records
of annual take and the nature and number of hunting parties might be relevant. Many
activities are likely to be associated with each threat and vice versa.

© The causal factors entity identifies and describes the primary driving forces which
generate threats. For example, in the case of "road building", causal factors might
be mining and tourism. Socio-economic and other human factors (see suggested list
in UNEP, 1993) would also be listed here. Many causal factors may relate to each
threat and vice versa. Similarly, causal factors may generate many specific activities
which need monitoring. :

© The remedial actions entity includes data on the feasibility of actions to reduce the
threat, as well as the costs and benefits involved.

© The impact assessment entity contains estimates of the likely effects of the threats,
both ecological and economic, and suggests their potential for reversal.

This data model breaks down the Threats entity of the first level model of Figure 4.4, but
is still aimed at a general and generic level, and is thus provided as an example. Expanding
the model to include threats to habitats will require at the very least the addition of a spatial
entity to reflect the geographic extent of the threat. Other additions which should be
considered are linkages to information on institutions and their capabilities and roles in
remedial actions, traditional uses of the threatened habitats, economic benefits of resource
utilisation, international conventions and treaties, and so on.

Expanding or modifying the model to deal effectively with non-proximate threats, and more
general causal factors (such as marine pollution driven by industrial development, and
ultimately by population pressure) requires the introduction of additional entities such as
"ultimate driving forces", and "regional (or collective) threats" which would hold information
which is not specific to a species or habitat.

The consideration and classification of threats is at an early stage (see Document 4, Section
5.9), so no example can be given of an operational database based on this model. At the
present time, threats are normally described in narrative form in species or protected areas
information systems.

4.5.6 Integrating Core Biodiversity Data

Existing (computerised) databases tend not to attempt to hold all biodiversity information in
a single structure. The focus in any one implementation tends to concern a particular entity
(or small group of entities), with investment in detailed data about that entity. This may be
due to the sectoral mandate of the agency implementing the database, lack of data, or because
the user requirements are successfully achieved with that limited information. This is not to
say that relationships to other entities are necessarily ignored. For instance, both the

36 Data Flow Model - Document 1

examples of species and protected areas databases given above contain data w threats.
Arguably this is minimal within the databases, but users could establish links to other
(perhaps non-computerised) databases containing further details if desired.

In the context of the CBD, some countries may wish to integrate all their biodiversity
information into a single information system at one site, such as a national environmental
information centre. For the reasons mentioned above, others may prefer to leave the
responsibility of data custodianship to several agencies, and implement an effective
coordinating mechanism. The latter could be the foundation of a phased approach leading to
integration in the longer term. Regardless of the mechanism, the work to produce standards
for classification schemes, agreed taxonomies, data transfer mechanisms, and high level
dataflow models, is needed even where custodianship is maintained on a sectoral basis, in
order to have a conceptually integrated biodiversity information management system for the
country. This integrated view will then permit the development of sound national strategies ~
and actions in response to the CBD.

Data Flow Model - Document 1 37

—_—_—_—_—_—____eee—e—eee"”:_OCO ee — ee

38 Data Flow Model - Document 1

5 REFERENCES
Ashworth, C. and Goodland, M. 1990. SSADM.: A Practical Approach. McGraw Hill.

Chen, P. 1976. The Entity-Relationship Model - Towards a Unified View of Data. ACM
Trans. on Database Systems. 1:9-36.

Yourdon, E. 1979. Structured Design: Fundamentals of a Discipline of Computer Program
and Systems Design. Prentice Hall.

UNEP 1993. Guidelines for Country Studies on Biological Diversity. United Nations
Environment Programme, Nairobi, Kenya.

UNEP/WRI 1994. National Biodiversity Strategies - Guidelines for Biodiversity Planning and ©
Profiles from Early Country Experience. World Resources Institute, Washington DC, in prep.

WCMC 1994. The Biodiversity Clearing House - Concept and Challenges. WCMC
Biodiversity Series No 2, World Conservation Press, Cambridge, UK, pp.34.

WRI/IUCN 1993. Biodiversity Indicators for Policy Makers. World Resources Institute,
Washington, DC, pp.42.

Eee en eee ee eee ee ee
Data Flow Model - Document 1 39

Data Flow Model - Document 1

ANNEX 1: ANALYSIS OF THE INFORMATION NEEDS OF THE CBD

[Article | Information Requirements __—(|_—Type"___
it

Objectives

2, None

Use of Terms

3. How "actions within the jurisdiction" are | Informative
Principle affecting the environment of other
jurisdictions.

Global and regional state-of-the- Scientific

environment information.

Interrelationships and effects of outputs Scientific

(pollution, wastes etc).

How trading and other economic activities | Economic

affect the environment of other nations.

ju Ama

National institutional strengths and Metadata
capabilities.

4.
|| Jurisdictional Scope
5)

Cooperation

Needs of Contracting Parties. Informative

Strengths and capabilities of "competent Metadata

international organisations”.

Informative
and metadata

Current sectoral and cross-sectoral plans
and strategies which may effect
conservation and sustainable use of
biodiversity.

6.
General Measures for
Conservation and

Sustainable Use

TTT eee

Data Flow Model - Document 1 41

Us [As identified in Annex I to the CBD]:
Identification and
Monitoring "]. Ecosystems and habitats: containing
high diversity, large numbers of endemic | Scientific
or threatened species, or wilderness;
required by migratory species; of social,
economic, cultural or scientific
importance; or, which are representative,
unique or associated with key evolutionary
or other biological processes.
Economic,
2. Species and communities which are: social
threatened; wild relatives of domesticated
or cultivated species; of medicinal, Scientific
agricultural or other economic value; or
social, scientific or cultural importance
for research into the conservation and
sustainable use of biological diversity,
such as indicator species; and

3. Described genomes and genes of
social, scientific or economic
importance."

A broad program of systematic data
collection on species, protected areas,
critical habitats, and ecosystems (these are
referred to as the "core biodiversity data"
and elaborated considerably in the body
of this document).

Human activities, including industrial
activities, agricultural practices, land use
etc.

Monitoring data on the state of the
environment, measured according to
Standard procedures in continuous time
series.

nee ee
42 Data Flow Model - Document 1

8. 8a toh Scientific
In-Situ Conservation As in Article 7, but with more specific
reference to protected areas and to the
status of ecosystems and species
populations.
Informative
8g and scientific
The effects of the introduction of "living
modified organisms" in other Scientific
jurisdictions, and monitoring data on local
effects.
Informative
8h
Information on alien species (presence,
sources). Informative

Eradication and control measures for alien | Informative
species.

8i Economic
Present uses of biodiversity. ;

8j
Innovations and practices of indigenous
and local communities.

Accrued benefits (especially economic) of
use of biodiversity and relative
contributions of local communities (to
enable fair sharing of benefits).

8k
Existing legislation and regulation on
protection of endangered species.

Effects of legislation and regulation in
other jurisdictions.

8l,m
Financial requirements of conservation
measures.

Data Flow Model - Document 1 43

9.
Ex-Situ Conservation

10.

Sustainable Use of
Components of
Biological Diversity

11.
Incentive Measures

12:
Research and Training

13.
Public Education and
Awareness

Capabilities and facilities of institutions
for research and ex-situ conservation.

Research results on effective methods of
re-introduction of species.

10a,b
As per Article 6.

10c,d

Customary use and traditional cultural
practices and how these can be used for
remedial action.

10e

Strengths and capabilities of private sector

organizations for the development of
methodologies.

Incentive measures found to be effective
in other jurisdictions.

Cost/effectiveness of incentive measures
employed.

Training and education needs and
priorities.

Available sources of training and
education.

Biodiversity research activities world-
wide.

Available materials suitable for public
awareness.

Successful awareness tools and activities.

Bibliographies, technology of networking
and information exchange tools.

Data Flow Model - Document 1

Metadata

Informative

Metadata

Informative

Economic

Informative
Metadata

Metadata

Informative

Metadata,
informative

14.

Impact Assessment and
Minimising Adverse
Impacts

15.
Access to Genetic
Resources

16.
Access to and Transfer
of Technology

17.
Exchange of
Information

18.
Technical and Scientific
Cooperation

Major projects which may have impact on
biodiversity.

Impact assessment methodologies.

Resources and population at risk in-
country and in neighbouring regions.

Nature, availability and location of
emergency response facilities.

Emergency response contingency plans
and strategies.

Pts 1-6

Systematic record of available genetic
resources (germplasm, plant and animal
genetic research results).

Environmental sound uses of genetic
resources.

Pt7

Benefits, commercial and otherwise, of
genetic research and resulting genetic
resources.

As in Article 15 but related to technology
innovation rather than genetic resources.

Bibliographies, directories, metadatabases
on research, technology, and available
data (world wide).

National institutional strengths and
capabilities.

Technical and scientific advances and
research programmes of Contracting
Parties.

Metadata

Informative

Scientific

Informative

Scientific

Scientific and
informative

Metadata

Data Flow Model - Document 1

19. [From Article 19]

Handling of "any available information the use and

Biotechnology and safety regulations required by that

Distribution of its Contracting Party in handling such

Benefits organisms, as well as any available
information on the potential adverse
impact of the specific organisms
concerned to the Contracting Party into
which those organisms are to be
introduced."

20. Financial resources available to support Economic
Financial Resources activities under the CBD.

Economic and social conditions within the | Social
developing countries.

Environmental conditions within the Scientific
developing countries.

2A: As per Article 20. Economic
Financial Mechanism

Terms and conditions of other relevant
Benet with Other | international conventions.
International
Conventions

23. None
Conference of the

Parties

24. None
Secretariat

Integrated information from all other Scientific,
eis Body on Articles. informative
Scientific, Technical
and Technological
Advice

26. Yet to be determined.
Reports

Procedural and administrative Articles
with little information management
requirement.

46 Data Flow Model - Document 1

“Types of Information:
Informative. Descriptive information about the issue. Usually in narrative form.
Legal. Regulations, legislation and other legal instruments.

Scientific. Measured or scientifically observed data, often in numeric or categoric form
in databases.

Economic. Information related to costs, expenditures, and other financial information,
usually in numeric form.

Social. Information on population, health, and other social measures, usually in
numeric form. ;

Data Flow Model - Document 1 47

Data Flow Model - Document 1

ANNEX 2: LIST OF ACRONYMS & ABBREVIATIONS

BDM Biodiversity Data Management

CBD Convention on Biological Diversity

DFD Data Flow Diagram

ERD Entity Relationship Diagram

GEF Global Environment Facility

GIS Geographic Information Systems

ICSU International Council of Scientific Unions

IUCN World Conservation Union

OECD Organisation for Economic Cooperation & Development
UNEP United Nations Environment Programme

WCMC World Conservation Monitoring Centre

WRI World Resources Institute

NB See also the index of acronyms and abbreviations in the Resource

Inventory (Document 4).

Data Flow Model - Document 1

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WORLD CONSERVATION
~ _ MONITORING CENTRE

- World Conservation Monitoring Centre
219 Huntingdon Road
_ Cambridge CB3 ODL
United Kingdom
Telephone +44 223 277314
Fax +44 223 277136

_ The World Conservation Monitoring Centre is a joint-venture between the thr ce
_ partners who developed the World Conservation Strategy and its successor coi ir
the Earth: TUCN-The World Conservation Union, UNEP- United Nations Environment og
Programme, and pe Wee Wide Fund for Nature.

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