BookPDF Available

Biodiversity Monitoring for Natural Resource Management. An Introductory Manual.


Abstract and Figures

The monitoring of biodiversity is in increasing demand in the international development sector, as the key role of biodiversity in securing livelihoods through the provision of basic goods and ecosystem services is more and more acknowledged. Many professionals working towards sustainable management of natural resources and nature conservation are confronted with the task of biodiversity monitoring yet have a background distant from biodiversity sciences. The internet offers an overwhelming amount of information, filtering and gleaning which can be a time-consuming way of getting a grip of the topic. A new manual titled “Biodiversity Monitoring for Natural Resource Management ― An Introductory Manual” addresses this issue and provides succinct practical guidance for planning biodiversity monitoring. It addresses some of the principal questions, issues and pit-falls in biodiversity monitoring, and offers selected references and download links to further reading. The manual, produced by the SNRD Asia, is available online a
Pressure-State-Benefit-Response (PSBR) model of biodiversity indicator types (modified after Sparks et al. 2011). Indicators include, e.g., pressures: socio-economic drivers of land-use change and resulting direct pressures (habitat conversion, degradation, fragmentation), invasive species, extractive activities, climate change, pollution, including also potential pressures (threats) (compare Fig. 4); state: species population sizes/ ranges, species richness, community composition, forest carbon stocks, habitat extent and condition, etc.; benefits: provisioning (e.g. food, raw materials, energy, medicine), regulating (e.g. climate regulation, water purification, crop pollination), supporting (maintaining ecosystem functioning, e.g. nutrient cycling), cultural ecosystem services (e.g. spiritual, recreational), as level of services provided (e.g. volume of water or wood), a monetary or derived value (e.g. jobs in forestry), etc.; responses: e.g. resource investment into sustainability, law enforcement/management efforts, laws and policy. Note that single indicators may match more than one category; e.g. forest cover can be indicative of pressure, state, response and benefits. Because they are challenging to assess, indicators of pressure, benefit and response are often based on existing information (e.g. from national statistics offices). Benefit indicators help highlight and communicate the value of biodiversity and are of increasing practical use in incentivisation schemes. However, they tend to be more challenging than indicators from the other categories. A yet more complex variant of the PBR concept is the Driver-Pressure-State-Impact-Response (DPSIR) model (
Content may be subject to copyright.
Biodiversity Monitoring For
Natural Resource Management
An Introductory Manual
Published by
Sector Network Rural Development (SNRD)
Implemented by
Deutsche Gesellschaft für
Internationale Zusammenarbeit (GIZ) GmbH
Registered offices
Bonn and Eschborn, Germany
Deutsche Gesellschaft für
Internationale Zusammenarbeit (GIZ) GmbH
P.O.Box 6091
Road 90, House 10/A, Gulshan 2
Dhaka 1212,
Gerrit Qualitz / GIZ Bangladesh
Florian A. Werner & Umberto Gallo-Orsi
Edited by
Delany Environmental, Opheusden, The Netherlands
Graphic Design and Layout concept
RedOrange Ltd., Dhaka, Bangladesh
Recommended Citation
Werner, Florian A. & Gallo-Orsi, Umberto. 2016. Biodiversity Monitoring for Natural Resource Management. An
Introductory Manual. GIZ, Eschborn and Bonn, Germany.
Photo copyrights
P. 1: NASA/Norman Kuring (Baja California from space); p. 1 (mud crab), 15, 21, 28: GIZ/Ranak Martin; p. 5: NASA/
Jeff Schmaltz; p. 8: GIZ/Laos; p. 12: GIZ/Joerg Boethling; p. 13; USAID/Drik/ Wahid Adnan; p. 19: NASA/Robert
Simmon; p. 27: Nikolay Petkov; other images by Florian Werner.
Date of publication: May 2016
This manual is a product of the Sector Network Rural Development (SNRD) Asia of the Deutsche Gesellschaft
für Internationale Zusammenarbeit (GIZ) GmbH). Support was provided through the GIZ project ‘Sustainable
Management of Biodiversity, South Caucasus’, the GIZ Biodiversity and Climate Change Adaptation Portfolio
(BCCAP), Bangladesh, and the SNRD Working Group Biodiversity. Valuable comments were provided by Stefan
Bepler, Urs Hintermann, Ismet Khaeruddin, Mirjam de Koning, Yannick Kühl Isabel Renner and Klaus Schmitt. For
any comments or questions please refer to Florian Werner (florianwerner (at)
1.1. Scope of this Document 5
1.2. Defining Biodiversity Monitoring 5
1.3. Why and When Monitor Biodiversity? 6
1.4. International Biodiversity Monitoring Commitments 6
2.1. Considering Different Indicator Categories 8
Box 1. Indicator Categories for Adaptive Management 10
2.2. What Makes a Good Indicator? 11
3.1. Stakeholder Engagement 12
3.2. Opportunities and Challenges in Participatory Biodiversity Monitoring 12
Opportunities in Participatory Biodiversity Monitoring 13
Challenges and Limitations of Participatory Biodiversity Monitoring 13
3.3. More Partners? 14
Citizen science 14
Academia 14
Private sector 14
4.1. Types of Monitoring 15
4.2. Forms of Data Acquisition 16
4.3. Study Design and Survey Methodology 16
Box 2. Checklist for Biodiversity Monitoring 17
4.4. Managing Raw Data 18
4.5. Data Analysis and Interpretation 19
4.6. How to Make the Best Use of Results? 20
Securing and sharing data 20
Feeding results back into management 20
Sharing results through publication 21
Adaptive Management and Opportunistic Monitoring 24
Participatory Monitoring 24
General Standard References for Monitoring 24
Selection of Monitoring Indicators 25
Study Design and Data Analysis 26
Survey Methods for Specific Groups of Organisms 26
Software for Data Management and Analysis 27
Appendix 1. Study Design in Field Monitoring 29
Appendix 2. Dealing with Variability in Random Sampling 30
Appendix 3. Organisms as Indicators 32
ANSAB Asia Network for Sustainable Agriculture and Bioresources
BBOP Business and Biodiversity Offset Programme
BM Biodiversity Monitoring
CBD Convention on Biological Diversity
CITES Convention on International Trade in Endangered Species of Wild Fauna and Flora
CMS Convention on the Conservation of Migratory Species of Wild Animals
COP Conference of Parties
EMAN Ecological Monitoring and Assessment Network
ENV Education for Nature Vietnam
FAO The Food and Agriculture Organization of the United Nations
GBIF Global Biodiversity Information Facility
GIZ Gesellschaft für Internationale Zusammenarbeit
ITPGRFA International Treaty on Plant Genetic Resources for Food and Agriculture
IUCN International Union for the Conservation of Nature
LIDAR Light Detection And Ranging
M&E Monitoring and Evaluation
METT Management Effectiveness Tracking Tool
NGO Non-Governmental Organisation
NBSAP National Biodiversity Strategy and Actions Plan
NTFP Non-timber forest product
OECD Organisation for Economic Cooperation and Development
PA Protected Area
PPP Public-Private Partnerships
PSR Pressure-State-Response
PSBR Pressure-State-Benefit-Response
REDD+ Reducing Emissions from Deforestation and Forest Degradation
SBIA Social and Biodiversity Impact Assessment
SMART Sensitive, Specific, Measurable, Achievable, Time-bound
SMART Spatial Monitoring and Reporting Tool
SNRD Sector Network Rural Development
TEEB The Economics of Ecosystems and Biodiversity
TRA Threat Reduction Assessment
UNEP United Nations Environment Programme
UNFCCC UN Framework Convention on Climate Change
WHC World Heritage Convention
Biodiversity Monitoring For Natural Resource Management
1.1. Scope of this Document
The monitoring of biological diversity (biodiversity) is
in increasing demand in the international development
sector, as the key role of biodiversity in securing
livelihoods through the provision of basic goods and
ecosystem services is increasingly acknowledged.
Meanwhile, the rapid global growth of conservation
schemes designed to incentivise communities and
other local stakeholders to effectively conserve
natural resources has placed new importance on the
role of biodiversity monitoring in assessing whether
agreements and targets linked to payments are being
met (Danielsen et al. 2014).
Practitioners in the sectors of sustainable forestry,
agriculture, fisheries and conservation who are
confronted with the task of biodiversity monitoring
often do not have a background affinity with biodiversity
sciences. The internet offers them an overwhelming
amount of information, the filtering and gleaning of
which can be a time-consuming way of getting a grip
of the topic. This brief introduction to biodiversity
monitoring aims to provide practical guidance
for professionals working towards sustainable
management of natural resources, especially in
developing countries. It addresses some of the
principal questions, issues and pitfalls in biodiversity
monitoring and offers carefully selected references
for further reading.
1.2. Defining Biodiversity Monitoring
Biodiversity has long been a buzz-word throughout
a range of fields, and notions of what biodiversity
actually means diverge widely. While some use the
term very narrowly to refer to single species or groups
of species of outstanding conservation concern
or economic significance, others (including most
ecologists) relate biodiversity to a much more general
and comprehensive context. This is relevant, since
diverging definitions often give rise to misconceptions
and misunderstandings. Setting clear definitions of
biodiversity and of related key terms is therefore an
important first step in ensuring that stakeholders
efficiently find common ground regarding the objectives
and concepts of their biodiversity monitoring. To date,
biodiversity is generally defined broadly as including
three dimensions:
diversity within species (genes),
between species and of ecosystems
(CBD, Art. 2),
including domesticated plants and animals.
Monitoring, defined as the collection and analysis of
repeated observations or measurements to evaluate
changes in conditions and progress towards meeting
a management objective (Elzinga et al. 2001), can
be applied to all three dimensions of biodiversity.
However, especially the monitoring of genes has some
practical limitations. For example, the measuring
of alleles (alternative forms of genes) requires
sophisticated and expensive detection systems. It
is also now widely recognised that it is not simply
numbers of alleles but how they combine in forming
multi-locus genotypes that determines effective
genetic diversity. Ecosystems are often non-discrete
Biodiversity Monitoring For Natural Resource Management
(with continuous transitional zones, ‘ecotones’); hence
their definition, number and area are arbitrary to some
extent (Boyle 2001). Biological species are somewhat
more palpable: they are natural and (mostly) intuitive
units that constitute the elementary building blocks of
ecosystems and can be assessed relatively easily in
the field. Traditionally, measures of species diversity
and ecosystem structure are the principal dimensions
of biodiversity as such (biodiversity ‘
’) assessed
by many biodiversity monitoring projects, and are also
covered here in some more depth (see Appendices).
Yet, as outlined below, monitoring programmes
should not only consider quantifying biodiversity state
but should also include related drivers, pressures, and
also managerial, governance and policy responses.
1.3. Why and When Monitor Biodiversity?
Besides the obvious provisioning services (e.g. food,
medicine), biodiversity provides a wealth of regulating
(e.g. climate regulation, crop pollination), cultural,
and supporting services (maintaining ecosystem
functioning; Kumar 2010). Although many of these
ecosystem services are not tied to individual species,
and many ecosystems may appear richer in species
than necessary to sustain ecosystem functioning,
individual species do count. Species-rich ecosystems
have higher productivity and stability, are more
resistant to invasive species and more resilient to global
climate change and to natural disasters (e.g. Peterson
et al. 1998, Gamfeldt et al. 2013). Importantly, they
have higher potential for ecosystem-based adaptation
to climate change, which can mitigate immense
economic losses. Moreover, the impact of biodiversity
loss is believed to be gradual only to a certain critical
threshold of ecosystem stress (the ‘tipping point’) at
which ecosystem functions and services collapse.
Monitoring measures of biodiversity and related
parameters allows the detection, quantification and
forecasting of trends in the state of biodiversity, and to
measure compliance with standards and effectiveness
of management. It also allows the improvement
of understanding of causal relationships between
human actions and biodiversity. By allowing informed
decision-making, monitoring provides a fundamental
basis for effective management and governance of
1.4. International Biodiversity Monitoring
The Convention on Biodiversity (CBD) has set a
number of targets approved by the states that have
signed the convention (Contracting Parties). E.g., at the
CBD’s 11th Conference of Parties (COP) in Hyderabad
(2012), donor countries agreed to double the total
biodiversity-related international financial resource
flows by 2015 and pledged to maintain or increase
these levels until 2020. CBD Art. 6 requires states to
develop National Biodiversity Strategies and Actions
Plans (NBSAPs) that outline how they intend to fulfil
the objectives of the Convention in the light of specific
national circumstances. Based on the CBD 2011-2020
Strategic Plan for Biodiversity, which includes the 20
so-called Aichi Biodiversity Targets, each Contracting
Party is committed to monitoring and reviewing the
implementation of its NBSAP, making use of the
set of indicators developed for the Aichi Biodiversity
Targets, and to reporting to the COP. Any NBSAP
should therefore include actions aimed at measuring
the progress toward the Aichi targets. Commitments
Biodiversity Monitoring For Natural Resource Management
pledged in other international conventions likewise
demand or suggest national-level biodiversity
monitoring, including:
Convention on Wetlands (Ramsar Convention):
identification and monitoring of wetlands of
international importance based on clear criteria;
Convention on International Trade in Endangered
Species of Wild Fauna and Flora (CITES):
monitoring of international wildlife trade;
Convention on the Conservation of Migratory
Species of Wild Animals (CMS or ‘Bonn
Convention’): monitoring of populations and trends
of selected migratory species;
International Treaty on Plant Genetic Resources
for Food and Agriculture (ITPGRFA): monitoring
of genetic impoverishment in agricultural plants
(backed by an internatonal database: Global
Information System);
UN Framework Convention on Climate Change
(UNFCCC: most notably regarding forest
biodiversity co-benefits under REDD+);
World Heritage Convention (WHC): identification
and monitoring of sites of outstanding cultural and
natural value.
Most of these conventions have developed specific
monitoring guidelines and protocols (e.g. for Ramsar
wetlands). For further information, see the CBD
website or Latham et al. (2014).
Biodiversity monitoring schemes, often developed
following commitments made at international level,
are ultimately obligations of national governments.
This is because biodiversity monitoring aims to inform
decision making by agencies of national and local
governments. Data gathering is often coordinated by
the national statistical agency, with the collaboration
of the Ministry of Environment and the input of other
governmental agencies (forestry, fishery, agriculture,
land use planning, etc.). Alongside governmental
bodies, national and international NGOs and research
institutions often produce indicators or gather raw
data. Developing a biodiversity monitoring scheme
therefore often requires good knowledge of a large
number of stakeholders and the capacity to engage
and cooperate with them.
Biodiversity Monitoring For Natural Resource Management
An indicator is commonly defined as a measure based
on verifiable data that conveys information about
more than itself. In very general terms, the appeal of
biodiversity indicators is thus to provide information
of broader relevance to biodiversity in a technically
and financially feasible fashion. Indicators are
routinely linked to specific criteria, especially in forest
management (‘criteria & indicators’). Criteria define
the range of management targets and the essential
elements or principles of management. Each criterion
thus relates to a key element of management success,
and tying indicators to specific criteria helps set
up a comprehensive and efficient (targeted) set of
Any discussion on biodiversity monitoring needs to
begin with the question
what are the ultimate goals
of our monitoring?’
. The choice of suitable indicators
is made only
agreeing on clear and specific
monitoring objectives among key stakeholders.
2.1. Considering Different Indicator
Indicators of biodiversity
can be categorised
usefully into functional, structural or compositional
and according to their organisational levels (Fig.
1). However, the classical approach of monitoring
biodiversity itself (its
, e.g. species abundance or
composition, habitat quality or quantity) alone is rarely
sufficient to inform management or policy decisions.
This is partly because such indicators of state, while
documenting changes in biodiversity, rarely yield
useful information about the driving factors behind
these trends. Monitoring the state of biodiversity is
also a costly and long-term task. Given the assumed
causal relationship between management targets,
specific pressures (or threats, stressors) and the
management responses (interventions) designed to
mitigate these pressures, the monitoring of pressures
and responses allows measurement of progress
towards management goals over shorter periods,
albeit with lower confidence (Rao et al. 2009; Fig. 2).
Biodiversity Monitoring For Natural Resource Management
A useful and widely applied conceptual framework
for selecting biodiversity indictors that considers the
shortcomings of these approaches is the Pressure-
State-Response (PSR) model (OECD 1994). The core
message of the PSR framework is that monitoring
programmes should never monitor conservation
targets in isolation, but should always include the
Fig. 2. Trade-offs in costs, time and level of confidence when monitoring project interventions (management responses),
pressures or management targets (biodiversity state) themselves. Indirect pressures here refer to ultimate socioeconomic
drivers (e.g. population growth) behind direct (proximate) pressures (e.g. overfishing). Source: modified after Rao et al. 2009.
Fig.1. Compositional, structural, and functional components of biodiversity state, each encompassing multiple levels of
organisation. This conceptual framework facilitates the selection of state indicators. Structural parameters can often be
resource-efficient; e.g. the amount of deadwood of a forest is easy to measure, tends to be well correlated with many
biodiversity measures and therefore can serve as a good, simple proxy for ecosystem health. Functional aspects tend to be
important for an understanding of processes and cause-effect relationships, and play an important role in basic research
and validation monitoring, but less so in implementation and effectiveness monitoring. Source: redrawn after Noss 1990.
positive and negative influences on those targets
(Richards & Panfil 2011), thus combining indicators
of pressures, state and responses wherever feasible.
An additional category of biodiversity indicators worth
considering are indicators of benefits (CBD 2011; Box
Biodiversity Monitoring For Natural Resource Management
Box 1. Indicator Categories for Adaptive Management
Management strategies and plans are invariably based on assumptions (risks, threats, opportunities,
cause-and-effect relationships) that may or may not be correct under certain circumstances, and which
change over time. Adaptive management facilitates tackling these inherent uncertainties through an
iterative process of informed questioning and readjustment of management strategies based on the
results of monitoring. The straightforward concept of adaptive management is thus a regular cycle of
planning, implementing and monitoring, ensuring that management is continuously and effectively
improved by learning. Closely tied to this concept is the PSR/PSBR framework, which assists in
maximising the practical value of indicator sets (Fig. 3).
Fig.3. Pressure-State-Benefit-Response (PSBR) model of biodiversity indicator types (modified after Sparks et
al. 2011). Indicators include, e.g.,
: socio-economic drivers of land-use change and resulting direct
pressures (habitat conversion, degradation, fragmentation), invasive species, extractive activities, climate
change, pollution, including also
pressures (threats) (compare Fig. 4);
: species population sizes/
ranges, species richness, community composition, forest carbon stocks, habitat extent and condition, etc.;
: provisioning (e.g. food, raw materials, energy, medicine), regulating (e.g. climate regulation, water
purification, crop pollination), supporting (maintaining ecosystem functioning, e.g. nutrient cycling), cultural
ecosystem services (e.g. spiritual, recreational), as level of services provided (e.g. volume of water or wood), a
monetary or derived value (e.g. jobs in forestry), etc.;
: e.g. resource investment into sustainability, law
enforcement/management efforts, laws and policy.
Note that single indicators may match more than one category; e.g. forest cover can be indicative of pressure,
state, response and benefits. Because they are challenging to assess, indicators of pressure, benefit and response
are often based on existing information (e.g. from national statistics offices). Benefit indicators help highlight and
communicate the value of biodiversity and are of increasing practical use in incentivisation schemes. However,
they tend to be more challenging than indicators from the other categories. A yet more complex variant of the
PBR concept is the Driver-Pressure-State-Impact-Response (DPSIR) model (
Biodiversity Monitoring For Natural Resource Management
2.2. What Makes a Good Indicator?
For delivering useful results, biodiversity monitoring
needs to be tailored for specific objectives. It is
therefore critical to define monitoring objectives
clearly at an early planning stage.
The SMART criteria are crucial in the selection of
indicators. A good biodiversity indicator should be:
Sensitive and Specific for the environmental
condition (state), pressure or response under
question. Sensitivity refers to rapid detectability of
fine changes;
Measureable, if possible quantitatively, so as to
allow a measure of confidence in results;
Achievable with the resources at hand, and
economic (cost-efficient); see also Appendix 3
specifically respective species monitoring;
Relevant to agreed monitoring goals, natural
resource management and policy; any monitoring
scheme should further provide clear linkages to
the NBSAP and its objectives (see example in Fig.
Time-bound, because results must be accessible
within a defined time frame and yield information
on changes over time.
Further practical considerations include:
Choice of indicators responsive to both positive
and negative changes;
Choice of multiple indicators whenever possible.
Natural systems are complex and even a carefully
chosen indicator may fluctuate unpredictably; e.g.
a species population due to disease or extreme
climatic events (Richards & Panfil 2011) or may
be affected by factors outside the monitoring area
(e.g. migratory species, water quality in a shared
Intuitiveness. Is the indicator easily enough
understood to be effectively communicated to
local stakeholders and decision-makers? Does it
relate to something that people can use or does it
have emotional value?
Information availability. Historical data may serve
as a valuable baseline (e.g. land-use change,
distribution or abundance of species), while
present-day data (e.g. socio-economic indices
from national statistics) can complement many
monitoring schemes;
Sustainability. Can the monitoring scheme be
institutionalised (i.e. included in the duties of
government agencies) in order to ensure its long-
term implementation?
Fig.4. Common pressures on tropical forest biodiversity. This figure shows temporal trends (means and 95% confidence
intervals) in selected pressures inside (green circles) and outside (red squares) of 59 tropical forest reserves worldwide.
Trends are displayed as a relative index based on a semi-quantitative questionnaire (Laurance et al. 2012). Threat Reduction
Assessment (TRA; Margoluis & Salafsky 2001) offers a well-established tool for designing studies involving pressure
indicators; the Management Effectiveness Tracking Tool (METT; Stolton et al. 2007) provides another, even broader tool for
an assessment of indicators relevant to conservation management through questionnaires.
Biodiversity Monitoring For Natural Resource Management
3.1. Stakeholder Engagement
Early identification and thorough consultation of
relevant stakeholders from international down to local
level is critical to ensure the success of monitoring
initiatives. This helps ensure that misunderstandings
are avoided and support is maximised, allows common
monitoring goals and objectives to be defined,
suitable indicators to be subsequently selected,
and methodologies to be optimised. Stakeholder
involvement is also vital for securing the sustainability
of monitoring arrangements (e.g. long-term financing,
institutionalisation). Relevant stakeholders include all
entities and people that can provide the necessary
capacity, data and technical expertise and those
affected by or benefiting from monitoring activities
and outcomes.
Objectives and indicators need to be tailored to
the stakeholders’ needs. In practice, however,
stakeholders tend to ask for too much. It is therefore
important to question initial ideas and wishes, e.g.
‘What are you going to do with this information?
Might it instead be sufficient to have only …?’
result models and product matrices helps identify
real needs and limit costs. Fewer but meaningful and
well-assessed parameters will be more informative
than numerous but poorly assessed indicators.
Stakeholder consultation is an iterative process that
can often require a lot of mediation to find practical
3.2. Opportunities and Challenges in
Participatory Biodiversity Monitoring
The term ‘participatory monitoring’ is usually applied
to monitoring activities that involve local people (Evans
& Guariguata 2008). While the involvement of local
expertise from governmental institutions or NGOs for
biodiversity monitoring is already common practice,
the involvement of local communities remains widely
Opportunities in Participatory Biodiversity
Irrespective of their educational background,
committed local people can collect high-quality data
at low cost (see Danielsen et al. 2014). Including local
communities, residents and institutions in biodiversity
monitoring can bring about a number of important
benefits such as:
Raising awareness, reflection and sense of
ownership of their biodiversity among local
communities and other local stakeholders;
Increasing conservation support among local
communities and other local stakeholders;
Improving local livelihoods through additional
Biodiversity Monitoring For Natural Resource Management
Blending of extensive traditional indigenous
knowledge with scientific rigour;
Promoting local human capacities;
Making labour-intensive monitoring feasible
and affordable (e.g. when continuous field data
collection is necessary). Participatory monitoring
approaches can reduce costs immensely (see e.g.
Holck 2008);
Improving sustainability of monitoring schemes
through low running costs and permanent local
Challenges and limitations of Participatory
Biodiversity Monitoring
Many indicators and sampling protocols require
extensively trained professionals, and costly and
delicate equipment; training locals for such tasks
may not be realistic;
Higher likelihood of limited, variable (between
persons) or uncertain data quality compared to that
collected professionally; one measure for cross-
checking data quality in participatory biodiversity
monitoring is to have different individuals or teams
occasionally repeat the collection of each others
Reliably identifying suitably prepared, self-
motivated and dependable personnel is critical
and may be challenging;
Initial investments for training may be high
and may bring about dependence on long-term
commitment of staff;
Results obtained may more easily be influenced by
expectations or desired outcomes;
Rotation and work time splitting of local staff can
be detrimental to monitoring results, yet tensions
frequently arise when only a few community
members benefit from employment.
How much local participation is feasible or ideal
depends strongly on project contexts. However, while
well-trained, non-specialist locals may often be a
good choice to actually gather data, it is wise to have at
least one trained scientist overseeing the monitoring
(Pitman 2011).
Biodiversity Monitoring For Natural Resource Management
3.3. More Partners?
Citizen science
‘Citizen science’, the involvement of
the general public into research, is a participatory
approach to data collection that offers many new
opportunities through web-based tools (Bonney et
al. 2014). Voluntary contributions by members of the
public may range from opportunistic reporting to full-
time volunteering, and often also involves non-locals.
Besides providing valuable information at lowest
possible cost, the involvement of citizens is also
an effective outreach and education tool. Good
examples include e.g. ebird, an online database that
taps into the immense expertise and activities of the
birdwatching community; the Vietnamese NGO ENV
employs a web based reporting system that facilitates
citizens’ reporting of wildlife crimes via smart
phones; Australia’s Great Barrier Reef National Park
asks visitors to help take and upload imagery taken
from fixed points which then serve for monitoring
mangroves. Refer also to the Cornell Lab for more
Linking with individual scientists and/or universities
or other research institutions is an underused
opportunity for increasing the cost-efficiency,
outcomes and impact of a survey.
Assuming good quality and documentation
of collected plant and animal material, many
international taxonomic experts will readily help
identify voucher specimens (plant or animal samples
collected for identification and reference); many of
them will be happy to advise further on preparation
and documentation of specimens or on preliminary
species recognition in the field.
National and international research scientists in
ecology, remote sensing or related fields may often
be available for helping design and advise during the
study, or to recruit and supervise thesis students from
their own institution or elsewhere. Their involvement
can greatly increase impact through professional
planning, analysis and publication of results in
scientific journals. Often interested in long time series
they can help ensure further sustainability by providing
momentum and resources for continuing monitoring.
Well-supervised thesis students can provide
substantial help with coordination, data collection
and analysis at comparably low costs (coverage of
expenses commonly sufficing for Bachelor or MSc
students). National thesis students offer excellent
opportunities for developing professional in-country
capacity. Carefully selected, they will help ensure
success through high motivation and skills and a very
real interest in high quality results.
Scientists employed by research institutions will often
charge comparably low fees, if any, given the prospect
of obtaining and getting to publish interesting results.
On the down-side, planning and implementing a
collaboration with academic partners will often require
more preparatory time than the classical consultancy
(e.g. permit requirements for specimen shipment,
availability of thesis students). Identifying possible
academic partners at an early stage of planning is
therefore vital for successful coordination.
Private sector
Private companies are becoming more and more
interested in contributing to biodiversity conservation
and monitoring. This is the result of a growing
appreciation of three main reasons: the economic value
of biodiversity and its ecosystem services; improved
national legislation requirements for economic
development planning; and a more prominent and
widespread appreciation of biodiversity in society.
As a result, Public-Private Partnerships (PPP) offer
attractive opportunities for sustainable financing
of biodiversity monitoring. Notably the Business
and Biodiversity Offset Programme (BBOP) offers
an opportunity for both biodiversity monitoring and
conservation in the context of economic development
projects. It provides a useful framework (‘mitigation
hierarchy’) and detailed best-practice guidelines
and standards for biodiversity-friendly economic
development projects.
The establishment of Conservation Trust Funds,
often created by cooperation between governmental
and private contributions is also increasingly popular
and effective in delivering important resources for
biodiversity conservation and monitoring in all parts
of the world (see e.g. World Bank 2013). Refer also to
the CBD’s Global Platform on Buiness and Biodiversity
for a variety of approaches and best practices.
Biodiversity Monitoring For Natural Resource Management
4.1. Types of Monitoring
Conceptually, it is useful to distinguish between
four different categories of biodiversity monitoring
according to their objectives:
Surveillance monitoring: focuses on detecting
long-term changes of biodiversity (number of
species, species composition, etc.). Surveillance
monitoring programmes must be representative
for the area under consideration (single habitat,
landscape, region or country) and tend to cover
a broad range of species or species groups and
functional types (both mobile and sessile species,
herbivores and carnivores, etc.). In general,
surveillance monitoring aims at detecting changes
in state parameters but is not hypothesis-driven or
management-oriented, is poor at resolving cause-
and-effect relationships and relatively costly. It is
sometimes referred to as ‘omnibus’ monitoring
for the large (and vague) variety of possible
applications, such as: providing data for strategic
planning, reporting and information sharing
in compliance with legislation or international
agreements (e.g. CBD); generic early warning (e.g.
response to climate change); documenting and/or
modelling global change effects on biodiversity
(e.g. for climate change adaptation, conservation
planning); gathering of baseline information (e.g.
for projecting trends). The Swiss Biodiversity
Monitoring Programme is a classic example of
surveillance monitoring;
Implementation monitoring (‘operational
monitoring’): used to evaluate the compliance of
management activities against a management
plan, guidelines or agreed standards (e.g. following
of stakeholder engagement procedures for REDD+
certification; reporting to line agencies, donors or
certifying bodies);
Effectiveness monitoring (‘impact monitoring’):
focuses on measuring the effects of interventions
on management targets (e.g. impact of the
construction of a motorway, effects of incentives
to farmers, effects of groundwater management).
Typical applications include the monitoring of
management effectiveness (e.g. law enforcement
or sustainable resource extraction in protected
areas) and related compliance- or results-based
incentive schemes (e.g. through evaluation
and rewarding of staff or communities; REDD+
Biodiversity Monitoring For Natural Resource Management
Validation monitoring: used to determine
whether the accomplishment of specific objectives
was actually a consequence of management
activities. This is the most challenging category of
monitoring because it involves establishing causal
relationships between some management action(s)
and an environmental response (Lindenmayer &
Franklin 2002). Validation monitoring therefore
needs to include a wider range of indicators and
may not always be feasible.
Importantly, the latter three monitoring categories
are complementary, applied (’targeted’) monitoring
types which should ideally be combined for adaptive
management. While combining surveillance
monitoring with these applied types of monitoring is
theoretically possible, they generally exclude each
other for methodological and financial reasons.
One-off biodiversity assessments share many
methodological similarities and challenges even
though they are not monitoring in the sense of
data collection. Such assessments may
provide valuable baseline information for future
monitoring programmes, e.g. gathering information
on biodiversity patterns for environmental impact
assessment (EIA), land-use and conservation planning
(e.g. gap analysis or zoning for protected areas).
4.2. Forms of Data Acquisition
Three broad categories of data can be distinguished
according to the method of their collection:
Systematically collected data: a) remotely sensed
(aerial photography, satellite imagery, radar,
LIDAR, etc.) or b) field-collected data (e.g. plots,
questionnaires) gathered systematically following
a rigid study design so as to maximise their value.
See section 4.3 for more information.
Opportunistically collected data: consequent
recording of observations made during routine
work can create valuable information where
extensive inspecting or patrolling is carried out
by project staff or collaborators (e.g. in forestry or
protected area management), especially regarding
parameters that are difficult or tedious to record
in systematic fashion (e.g. records of selected
animals, human presence, traps or other illegal
activities). Data are typically processed into simple
indices to make up for the lack of systematising
of sampling efforts (e.g. incidents per person
day or per km patrolled). This is a cost-efficient
option with high potential for sustainability, and
is frequently a most valuable addition, if not a
starting point for monitoring programmes.
Third-party data: provided by state agencies (e.g.
national offices of statistics), international bodies
(e.g. FAO, IUCN), initiatives (e.g. globalforestwatch),
NGOs or others, and which can be used as is or
processed to serve as indicators.
4.3. Study Design and Survey
Project conditions are invariably too unique to allow
existing study designs to be adopted without major
adjustments. Sound study planning is therefore an
important investment towards resource efficiency and
success (see also Box 2). As self-evident as this sounds,
innumerable biodiversity monitoring initiatives have
underachieved or failed due to poorly adapted or
flawed study designs. Changing a sampling strategy
the onset of monitoring activities comes at a high
cost (data comparability, resources). Moreover, many
pitfalls of study design such as pseudoreplication,
sampling bias or undersampling will go unnoticed
Biodiversity Monitoring For Natural Resource Management
Box 2. Checklist for Biodiversity Monitoring
Checklist for planning of a biodiversity monitoring cycle (not strictly sequential, and numerous steps
are worth repeating).
1. Identify and involve stakeholders, define common terms and needs;
2. Conceptualise the project context in terms of result chains/impact models of system response to
pressures and management;
3. Agree common and specific monitoring objectives and priorities with key stakeholders, define spatial
and temporal scope of activities;
4. Define
and management-oriented
hypotheses where possible to assure effective and
efficient resource use (see e.g. Nichols & Williams 2006);
5. Consider involvement of local and external (e.g. academic) partners, and paths towards institutionalisation;
6. Screen for existing data and indicators that can be built upon;
7. Decide on and prioritise suitable indicators. Consider inherent trade-offs in breadth, precision, confidence
and cost for setting priorities. Focusing on fewer parameters may yield more valuable results;
8. Choose methodology that, based on previous studies and experience, ensures accuracy, quality and
efficiency. Note in particular sample sizes used and the variability in results as a guide for choosing
appropriate replication (Coe 2008). Consider confounding factors and, in particular, which environmental
factors may add substantial variability and how this can be minimised or estimated;
9. Assess how and to what extent participatory approaches can be included;
10. For draft sampling design and analysis strategy, consider pilot sampling for testing feasibility. If relying on
random sampling make sure you have enough statistical power to ensure that the sampling methodology
is practical and effective (consider power analysis);
11. Develop M&E and reporting systems: clarify financial, logistical and technical responsibilities for the
entire duration of proposed monitoring;
12. Approximate costs vs. budget: clarify financial, logistical and technical responsibilities for the entire
duration of envisioned monitoring. This is often challenging due to the low priority given to monitoring
and the need for monitoring to extend far beyond typical budget cycles. Assess whether the objectives
are achievable or whether the scope of the proposed monitoring scheme should be reduced. Develop a
detailed monitoring plan including a detailed, illustrated description of methods fit to serve as a manual;
13. Critically re-evaluate sampling methods early after onset of survey and modify where necessary. Later
changes will often affect comparability of sampling runs and can be very costly;
14. Periodically collect, archive, analyse and interpret data, entering and quality-checking data as early as
15. Work towards institutionalisation of the programme;
16. Customise results and channel them to specific target audiences;
17. Evaluate sampling strategy and indicator performance and consider refinement (back to top).
Biodiversity Monitoring For Natural Resource Management
until it is too late to fix them―typically during data
analysis. This is particularly tricky where only
random sampling of small subsamples is possible
(typical field monitoring; e.g. plot- or interview-
based surveys) rather than wall-to-wall monitoring
(e.g. remote sensing). Some relevant considerations
are summarised in Appendix 1
(Study design in field
monitoring). Furthermore, most ecosystems and
project settings are highly complex, and strong data
noise (scatter) due to resulting natural variability
in space and time poses particular challenges to
biodiversity monitoring. Options for mitigating such
variability are discussed in Appendix 2
(Dealing with
variability in random sampling)
Widely used sampling protocols (describing an exact
methodology used to collect data) have many benefits.
They are tried and tested, are likely to allow sound data
analysis, and increase the value of data by facilitating
comparison with other studies. However, the limited
consensus on survey methodology for many types of
biodiversity indicators also shows that a single method
often cannot be ideally suitable for any situation, and
that modifications may often be advisable.
Generally, the likelihood of success will be highest
monitoring schemes, which, while
often elaborate and tricky to design, will be robust
and require only limited resources and technical
capacities to collect and analyse data. E.g., a rough
index obtained with an easily replicable method not
highly dependent on the observer’s individual skills
may often outperform elaborate assessments.
References on methodologies for specific types
and groups of indicators are given under Further
Crucial to sustained success is to elaborate and keep
available a
, unambiguous documentation
of sampling protocols so as to ensure that methods
remain truly replicable and as independent of staff
continuity as possible.
The workload following data collection is easily
underestimated. The time and resources required for
data entry, management and analysis typically equal
or exceed those involved in field collection of data
(Gibbs et al. 1999).
4.4. Managing Raw Data
Sound data management is much more costly than
often acknowledged. E.g., Lindenmayer & Franklin
(2002) recommend that as much as 20-25% of the
budget for long-term monitoring programmes should
be allocated to data management.
Spreadsheets are familiar and handy tools for
managing tabulated data. However, biodiversity
data tend to be complex and, for analysis, often
require compilation into sheets that quickly become
excessively large and unhandy. More importantly,
spreadsheet data can be entirely corrupted by a single
sorting error and such errors often go unnoticed. This
becomes likely when data are entered or handled by
Biodiversity Monitoring For Natural Resource Management
several or inexperienced staff, a hazard avoidable
by using database software. Ready access by
various project staff is an important reason for user-
friendliness being a key characteristic of database
software. Manual data entry may nevertheless easily
turn into a bottleneck. Reducing the workload of data
entry is often achievable though sound customisation
of a database (e.g. dropdown lists for quick, error-
free entry) or by, as far as possible, replacing data
recording on field forms by tablets, smartphones or
GPS units from which data only needs to be imported
(see 6.2 ‘Software for Data Managment and Analysis’).
Important early steps in data management are
rigorous quality control (detecting and correcting
erroneous data points, dealing with missing data) and
thorough documentation (when/where/how exactly
were data recorded and by whom). The experience of
the BDM Switzerland programme suggests that as
much as 10% of budget should be allocated for quality
Digital photos of organisms, signs or habitats can be
of great value for documentation and identification,
assuming that they are well-labelled (coded) and
thus readily searchable. Short-coding of images may
include information on taxonomy, specimen collection
number and/or reference to sampling unit (e.g. plot
identity). Software such as Picasa facilitates the use
of image databases with rapid search options and
custom-size thumbnail views.
4.5. Data Analysis and Interpretation
Data analysis needs to be considered during study
planning and
the beginning of sampling to
avoid design flaws and maximise resource-efficiency.
Critical questions include:
Which analysis methods are suitable to obtain
meaningful, reliable results for the smallest
possible effort?
How many sampling units need to be monitored,
and where? Frequency of sampling?
Who conducts analyses and interprets and writes
up results?
How are results to be shared (e.g. publicly via
informal reports or following scientific standards)?
The use of statistical methods is worth careful
consideration, since statistics provide a number of
important advantages such as:
providing a reliable estimate of error probability
(statistical ’significance’; determining the
likelihood of a trend being real or only coincidental).
For indicators based on random samples (i.e. small
sub-areas or sub-populations sampled rather than
edge-to-edge, as e.g. in remote sensing) statistical
significance can provide evidence for patterns or
trends (e.g. in animal populations along transects)
and a truly solid and objective basis for decision-
allowing detection of subtle trends or trends that
otherwise remain masked (concealed) by other
helping ensure that management decisions are
based on correct assumptions;
providing evidence on conservation trends for
incentivisation, certification or legal measures;
helping convince decision-makers and the
general public;
allowing the sharing of widely acceptable results
with communities of scientists and practitioners;
ultimately, optimising the usefulness and
efficiency of limited resources for monitoring.
Biodiversity Monitoring For Natural Resource Management
The main message of this section is that study design
and data analysis have immense influence on the
success of any biodiversity monitoring project and
must not be neglected. Sound study design and data
analysis are the best possible investment towards both
resource efficiency and quality of outputs. Because
the topic is complex it will pay to seek advice from a
trained research ecologist for planning of monitoring
schemes, supervision of collection and entry of
data, help with data analysis, and interpretation and
presentation of results. It should be borne in mind,
however, that professional scientists often bring their
own biases to monitoring projects. Ornithologists will
favour birds whereas GIS experts will prefer remote
sensing (Pitman 2011). One of the most important
challenges for project coordinators is to consider
and mediate these biases among stakeholders and
technical staff, and to minimise their effects on project
4.6. How to Make the Best Use of
Securing and sharing data
Raw, systematic biodiversity data are of lasting
value and should be made freely and permanently
available wherever possible. They allow a wealth of
future analyses and serve as invaluable historical
baselines for future monitoring efforts (Magurran et
al. 2010). Given regular data back-up, stand-alone
database systems (e.g. BIOTA) allow for professional
data management and analysis but often cannot
ensure permanence of data beyond the duration
of a project cycle, nor their distribution among
potential users. Web-based international biodiversity
database networks offer free, searchable and reliably
permanent data storage for species records (registers
of a species in time and space) or simple checklists,
Biodiversity Monitoring For Natural Resource Management
e.g. GBIF. More complex ecological data (e.g. raw
survey data on bird diversity ordered by sampling
units and dates) can be permanently stored and made
visibly available via the database PREDICTS, the data
repository DataOne or other services. Adding detailed
metadata (information that helps understand and use
data; e.g. information on project setting, methods
used) is key for preserving the value of data.
Feeding results back into management
Periodic review of indicator trends is essential for
adaptive management. Challenges may often lie in the
limited resources available for regularly (e.g. monthly,
yearly) summarising and reviewing results in reports.
Visualising results through accessible, simple graphs
(e.g. bar charts, scatter plots) or maps facilitates ready,
time-efficient interpretation of trends by decision-
makers, while limitations in man power and technical
capacity for producing such periodical summaries
can often be compensated through automatisation.
The software platform R has noteworthy potential
for automating analysis and reporting.The Spatial
Monitoring and Reporting Tool SMART can likewise be
a useful software programme for many applications.
Monitoring itself also needs to be subjected to the
adaptive management approach. Hence its progress,
methods and the system in place need to be critically
assessed on a regular basis through monitoring and
evaluation in order to ensure that the data are providing
useful and reliable information in an efficient manner.
Sharing results through publication
Monitoring results must be shared regularly with major
stakeholders. The weight, circulation and sustained
impact of reports can be boosted greatly by adhering
to some minimum standards for scientific publishing,
allowing outcomes to be cited and built upon by others
in particular: clear specification of authors, publication
year and publishing body, detailed description of
methods used (for ’replicability’), wide and permanent
availabiliy. At this point, again, the involvement of
scientists can pay off for more ambitious monitoring
projects through dissemination of major results in
peer-reviewed scientific journals. Doing this will
boost the acceptability and usefulness of monitoring
outcomes, and thus expand and perpetuate the impact
of a biodiversity monitoring project.
Because scientific publications are very technical, they
invariably make poor advocacy materials. Refining
results for decision-makers and the general public
is a distinct process for which people other than
scientists tend to be better suited. For these target
groups, results also need to be communicated
through entirely different channels (e.g. hardcopy and
executive reports, leaflets, educational materials,
press notes, public meetings, websites).
Biodiversity Monitoring For Natural Resource Management
Beatty J.M., McDonald L.E., Westcott F.M. & Perrin
C.J. 2006. Guidelines for Sampling Benthic
Invertebrates in British Columbia Streams.
Ministry of Environment, Victoria. Available here.
Bonney R., Shirk J.L., Phillips T.B., Wiggins A., Ballard
H.L., Miller-Rushing A.J. & Parrish J.K. 2014. Next
steps for citizen science. Science 343: 1436-1437.
Available here.
Boyle T.J.B. 2001. Interventions to enhance the
conservation of biodiversity. Pp. 82–101 in Evans K.
(ed.): The Forests Handbook, Volume 2: Applying
Forest Science for Sustainable Management.
Blackwell, Oxford.
CBD. 2011. Recommendation adopted by the
Subsidiary Body on Scientific, Technical and
Technological Advice at its fifteenth meeting XV/1.
Indicator framework for the Strategic Plan for
Biodiversity 2011-2020 and the Aichi Biodiversity
Targets. UNEP/CBD, Montreal. Available here.
Coe R. 2008. Designing ecological and biodiversity
sampling strategies. Working paper no. 66, World
Agroforestry Centre, Nairobi. Available here.
Danielsen F., Jensen P.M., Burgess N.D. Altamirano R.,
Alviola P.A., Andrianandrasana H., Brashares J.S.,
Burton A.C., Coronado I., Corpuz N., Enghoff M.,
Fjeldså J., Funder M., Holt S., Hübertz H., Jensen
A.E., Lewis R., Massao J., Mendoza M.M., Ngaga
Y., Pipper C.B., Poulsen M.K., Rueda R.M., Sam
M.K., Skielboe T., Sørensen M. & Young R. 2014.
Multicountry assessment of tropical resource
monitoring by local communities. Bioscience 64:
236–251. Available here.
Evans K. & Guariguata M.R. 2008. Participatory
monitoring in tropical forest management: a
review of tools, concepts and lessons learned.
CIFOR, Bogor. Available here.
Elzinga C.L., Salzer D.W., Willoughby J.W. & Gibbs J.P.
2001. Monitoring plant and animal populations. A
handbook for field biologists. Blackwell, Malden.
Available here.
Gamfeldt L., Snäll T., Bagchi R., Jonsson M., Gustafsson
L., Kjellander P., Ruiz-Jaen M.C., Fröberg M.,
Stendahl J., Philipson C.D., Mikusiński G.,
Andersson E., Westerlund B., Andrén H., Moberg
F., Moen J. & Bengtsson J. 2013. Higher levels of
multiple ecosystem services are found in forests
with more tree species. Nature Communications.
DOI: 10.1038/ncomms2328. Available here.
Gardner T.A., Barlow J., Araujo I.S., Ávila-Pires T.C.,
Bonaldo A.B., Costa J.E., Esposito M.C., Ferreira,
L.V., Hawes J., Hernandez M.M., Hoogmoed M.S.,
Leite R.N., Lo-Man-Hung N.F., Malcolm J.R.,
Martins M.B., Mestre L.A.M., Miranda-Santos R.,
Overal W.L., Parry L., Peters S.L., Ribeiro-Junior
M.A., da Silva M.N.F., da Silva Motta S. & Peres
C.A. 2008. The cost-effectiveness of biodiversity
surveys in tropical forests. Ecology Letters 11:
139–150. Available here.
Gibbs J.P., Snell H.L. & Causton C.E. 1999. Effective
monitoring for adaptive wildlife management:
lessons from the Galápagos islands. Journal of
Wildlife Management 63: 1055–1065. Available
Holck M.H. 2008. Participatory forest monitoring:
an assessment of the accuracy of simple cost–
effective methods. Biodiversity Conservation 17:
2023–2036. Available here.
Imai N., Tanaka A., Samejima H., Sugau J.P., Pereira
J.T., Titin J., Kurniawan Y. & Kitayama K. 2014.
Tree community composition as an indicator in
biodiversity monitoring of REDD+. Forest Ecology
and Management 313: 169–179. Available here.
Kessler M., Abrahamczyk S., Bos M., Buchori D., Putra
D.D., Gradstein S.R., Höhn P., Kluge J., Orend
F., Pitopang R., Saleh S., Schulze C.H., Sporn
S.G., Steffan-Dewenter I., Tjitrosoedirdjo S.S. &
Tscharntke T. 2011. Cost-effectiveness of plant and
animal biodiversity indicators in tropical forest and
agroforest habitats. Journal of Applied Ecology 48:
330–339. Available here.
Kindt R. & Coe R. 2005. Tree diversity analysis. A manual
and software for common statistical methods for
ecological and biodiversity studies. Nairobi: World
Agroforestry Centre (ICRAF). Available here.
Biodiversity Monitoring For Natural Resource Management
Kumar P. (ed.). 2010. The Economics of Ecosystems
and Biodiversity (TEEB). Ecological and Economic
Foundations. Earthscan, London. Available here.
Latham J., Trivedi M., Amin R. & D’Arcy L. (eds.). 2014.
Biodiversity Monitoring for REDD+: A Sourcebook
of Why, What and How to Monitor. ZSL, London.
Available here.
Laurance W.F., Camargo J.L.C., Luizão R.C.C.,
Laurance S.G., Pimm S.L., Bruna E.M., Stouffer
P.C., Williamson G.B., Benítez-Malvido J.,
Vasconcelos H.L., Van Houtan K.S., Zartman C.E.,
Boyle S.A., Didham R.K., Andrade A. & Lovejoy T.E.
2011. The fate of Amazonian forest fragments: A
32-year investigation. Biological Conservation144:
56–67. Available here.
Laurance W.F., Useche D.C., Rendeiro J., Kalka M.,
Bradshaw C.J.A., Sloan S.P., Laurance S.G.,
Campbell M. et al. 2012. Averting biodiversity
collapse in tropical forest protected areas. Nature
489: 290–294. Available here.
Lindenmayer D.B. & Franklin J.F. 2002. Conserving
Forest Biodiversity - A Comprehensive Multiscaled
Approach. Island Press, Washington. Available
Magurran A.E. Baillie S.R., Buckland S.T., Dick J.M.,
Elston D.A., Scott E.M., Smith R.I., Somerfield
P.J. & Watt A.D. 2010. Long-term datasets in
biodiversity research and monitoring. Trends in
Ecology and Evolution 25: 574–582. Available here.
Margoluis R. & Salafsky N. 2001. Is our project
succeeding? A guide to threat reduction
assessment for conservation. Biodiversity Support
Program, Washington, DC. Available here.
Mellin C., Delean S., Caley J., Edgar G., Meekan M. ,
Meekan M., Pitcher R., Przeslawski R., Williams
A. & Bradshaw C. 2011. Effectiveness of biological
surrogates for predicting patterns of marine
biodiversity: aglobalmeta-analysis. PLoS ONE:
e20141. Available here.
Nichols J.D. & Williams B.K. 2006. Monitoring for
conservation. Trends in Ecology and Evolution 21:
668–673. Available here.
Noss R.F. 1990. Indicators for monitoring biodiversity:
a hierarchical approach. Conservation Biology 4:
354–364. Available here.
OECD. 1994. Environmental indicators. OECD core
sets. Organisation for Economic Cooperation and
Development, Paris. Available here.
Peterson G., Allen C.R. & Holling C.S. 1998. Ecological
resilience, biodiversity and scale. Ecosystems 1:
6–18. Available here.
Pitman N. 2011. Social and Biodiversity Impact
Assessment (SBIA) manual for REDD+ projects:
Part 3 – Biodiversity impact assessment toolbox.
Forest Trends, Climate, Community & Biodiversity
Alliance, Rainforest Alliance and Fauna & Flora
International, Washington, DC. Available here.
Rao M., Stokes E.J. & Johnson A. 2009. Monitoring for
management of protected areas – an overview.
WCS and NUL, Vientiane. Available here.
Richards M. & Panfil S.N. 2011. Social and Biodiversity
Impact Assessment (SBIA) Manual for REDD+
Projects: Part 1 – Core guidance for project
proponents. Version 2. Climate, Community &
Biodiversity Alliance, Forest Trends, Fauna & Flora
International, and Rainforest Alliance. Washington,
DC. Available here.
Sparks T.H. Butchard S.H.M., Balmford A., Bennun
L., Stanwell-Smith D., Walpole M., Bates N.R.,
Bomhard B., Buchanan G.M., Chenery A.M., Collen
B., Csirke J., Diaz R.J., Dulvy N.K., Fitzgerald C.,
Kapos V., Mayaux P., Tierney M., Waycott M, Wood
L. & Green R.E. 2011. Linked indicator sets for
addressing biodiversity loss. Oryx 45: 411–419.
Available here.
Stolton S., Hockings M., Dudley N., MacKinnon K.,
Whitten T. & Leverington F. 2007. Reporting
progress in protected areas. A site -level
Management Effectiveness Tracking Tool: Second
edition. WWF, Gland. Available here.
Ward D.F. & Larivière M.-C. 2004. Terrestrial
invertebrate surveys and rapid biodiversity
assessment in New Zealand: lessons from
Australia. New Zealand Journal of Ecology 28:
151–159. Available here.
World Bank. 2013. Expanding financing for biodiversity
conservation. Experiences from Latin America
and the Caribbean.World Bank, Washington, DC.
Available here.
Biodiversity Monitoring For Natural Resource Management
Adaptive Management and Opportunistic
Conservation Measures Partnership. 2013.
Open standards for the practice of conservation.
Authoritative, compact manual outlining the open
standards for adaptive management in conservation.
Available here.
Margoluis R., Stern C., Swaminathan V., Brown M.,
Johnson A., Placci G., Salafsky N. & Tilders I. 2013.
Results chains: a tool for conservation action design,
management, and evaluation. Ecology and Society 18:
Conceptual paper describing results chains as an
important tool for helping teams clearly specify their
theory of change in adaptive management.
Nyberg B. 1999. An Introductory guide to adaptive
management. British Columbia Forest Service,
Enjoyable compact manual on adaptive
Available here.
Schmitt K. 2006. Ranger-based data collection. A
reference guide and training manual for protected area
staff in Cambodia. Ministry of Environment, Phnom
Practical introduction to opportunistic data
collection in protected area management.
Stokes E.J. 2010. Improving effectiveness of protection
efforts in tiger source sites: Developing a framework
for law enforcement monitoring using MIST. Integrative
Zoology 5: 363–377.
Concise paper on opportunistic,
software-supported law enforcement monitoring.
Available here.
Participatory Monitoring
ANSAB. 2010. Participatory biodiversity monitoring
in community forests. ANSAB, Kathmandu.
step-by-step guidance to community involvement in
biodiversity monitoring.
Available here.
Corrigan C. & Hay-Edie T. 2013. A toolkit to
support conservation by indigenous peoples and
local communities: building capacity and sharing
knowledge for indigenous peoples and community
conserved territories and areas (ICCAs). UNEP-
WCMC, Cambridge.
A wealth of useful references
including short descriptions and links.
Available here.
Evans K. & Guariguata M.R. 2008. Participatory
monitoring in tropical forest management: a review
of tools, concepts and lessons learned. CIFOR, Bogor.
Practical introduction to key concepts and issues
around participatory monitoring. 50 pp.
Available here.
Monitoring Matters.
Website offering many useful
scientific references on participatory biodiversity
monitoring for download.
Website here.
Participatory Monitoring and Management
A platform for participatory monitoring.
Website here.
General Standard References for Monitoring
BDM Coordination Office. 2014: Swiss Biodiversity
Monitoring BDM. Description of methods and
indicators. Environmental Studies no. 1410. Federal
Office for the Environment, Bern.
A comprehensive
description of the exemplary Swiss national
surveillance monitoring programme. This and many
related publications can be found
Eymann J., Degreef J., Häusler C., Monje J.C., Samyn
Y. & Vanden D. (eds.). 2010. Manual on field recording
techniques and protocols for all taxa biodiversity
inventories. ABC Taxa Vol. 8.
A broad range of up-to-
date standard techniques and protocols for monitoring
Biodiversity Monitoring For Natural Resource Management
a large variety of organism groups (e.g. all classes of
vertebrates, invertebrates, plants, fungi) in a broad
variety of habitats, plus useful chapters on specimen
preservation and data management.
Available here.
Gardner T.A. 2010. Monitoring forest biodiversity.
Earthscan, London.
Comprehensive 390-
pp. monograph on biodiversity monitoring for
conservation, tailored to a scientific and advanced
conservation practitioners’ readership, with a focus
on the monitoring of species assemblages for forest
management. Introduces a wealth of relevant concepts
and provides ample references.
Hill D., Fasham M., Tucker G., Shewry M. & Shaw
P. (eds.). 2005. Handbook of biodiversity methods
and monitoring: Survey, evaluation and monitoring.
Cambridge University Press, Cambridge.
covering planning of monitoring projects, habitat
monitoring and survey techniques for a wide range of
organism groups in 580 pp. Since the book is tailored
for the UK, some of the proposed methods will not
be ideally suited for tropical environments, but many
other sections (notably planning) are of very general
Latham J., Trivedi M., Amin R. & D’Arcy L. (eds.). 2014.
Biodiversity monitoring for REDD+: A sourcebook
of why, what and how to monitor. ZSL, London.
Providing a practical introduction to key concepts
and cornerstones of biodiversity monitoring such as
relevant international initiatives, agreements, indicator
selection, and monitoring methods with emphasis on
vertebrates and remote-sensing, and many useful
Available here.
Newton A. 2007. Forest ecology and conservation:
a handbook of techniques. Oxford University Press,
Exceptionally broad introduction to a wide
range of techniques (both remotely sensed and field
data) for assessing forest extent, condition, structure,
composition and dynamics at different scales.
Particularly useful regarding structural and functional
Richards M. & Panfil S.N. 2011. Social and Biodiversity
Impact Assessment (SBIA) Manual for REDD+ Projects:
Part 1 – Core Guidance for Project Proponents. Version
2. Climate, Community & Biodiversity Alliance, Forest
Trends, Fauna & Flora International, and Rainforest
Alliance. Washington, DC.
Excellent starting point for
planning biodiversity monitoring in tropical forests;
also offering good guidance on study planning and
indicator selection following the Open Standards for
the Practice of Conservation.
Available here.
Sutherland W.J. (ed.). 2006. Ecological census
techniques: A handbook. Cambridge University Press,
Outlining methods for surveying all major
groups of vertebrates, invertebrates (both aquatic and
terrestrial) and plants in compact, practically oriented
chapters, plus general chapters on study design and
environmental variables. Excellent starting point for
biological monitoring.
Available here.
Biodiversity Monitoring For Natural Resource Management
Selection of Monitoring Indicators
Biodiversity Indicator Partnership. 2011. Guidance
for national biodiversity indicator development and
use. UNEP World Conservation Monitoring Centre,
An authoritative, concise manual on
indicator selection and use, offering close step-by-
step guidance for selecting and monitoring national
biodiversity indicators that is likewise helpful for
smaller-scale monitoring.
Many other
useful resources via the BIP
CIFOR. 2009. Criteria & indicator toolbox. Guidelines
for developing, testing and selecting criteria and
indicators for sustainable forest management. CIFOR,
Bogor. Available here.
Pereira H.M., Ferrier S., Walters M., Geller G.N.,
Jongman R.H.G., Bruford M.W., Brummitt N., Butchart
S.H.M., Cardoso A.C., Coops N.C., Dulloo E., Faith D.P.,
Freyhof J., Gregory R.D., Heip C., Höft R., Hurtt G.,
Jetz W., Karp D.S., McGeoch M.A., Obura D., Onoda Y.,
Pettorelli N., Reyers B., Sayre R., Scharlemann J.P.W.,
Stuart S.N., Turak E., Walpole M., Wegmann M. 2013.
Essential biodiversity variables. Science 339: 277–
Still roughly hewn classification of elementary
biodiversity state indicators by the Group on Earth
Observations Biodiversity Observation Network (GEO-
BON), with more detailed indicators to follow soon.
Available here.
Pomeroy R.S., Parks J.E. & Watson L.M. 2004. How is
your MPA doing? A Guidebook of Natural and Social
Indicators for Evaluating Marine Protected Area
Management Effectiveness. IUCN, Gland.
conceptual and practical guidance for planning
and conducting monitoring in adaptive resource
management examplified for the case marine
protected areas, yet of much more general applicability.
Includes thorough treatment of a broad array of useful
indicators (organized into biophysical, socio-economic
and governance indicators) to choose from. 232 pp.
Available here.
Study Design and Data Analysis
Elzinga C.L., Salzer D.W. & Willoughby J.W. 2001.
Measuring and monitoring plant populations. Bureau
of Land Management, Denver.
Excellent, freely available
and accessible tome on biodiversity monitoring, giving
practical advice on the whole cycle of biodiversity
monitoring, from rationale over study planning and
design, data collection and analysis to feeding results
back into management. The geographical context
(Colorado) is peculiar, so that field methods will not
be complete or ideal for some settings.
Available here.
Feinsinger P. 2001. Designing field studies for
biodiversity conservation. Island Press, Washington,
Engaging, clear, practically oriented and succinct
book on designing, planning and implementing
biodiversity surveys, with an emphasis on the
fundamentals of study design.
Available here
Fowler J., Cohen L. & Jarvis P. 1998. Practical
statistics for field biology. Wiley, London.
Focuses on
study design, principles and fundamental methods
of statistical analysis and the presentation of results.
Among the most clearly-written and encouraging
introductions to this scary topic, the book focuses on
the fundamentals and ends where many statistics
books for advanced scholars begin (ANOVA).
Horning N., Robinson J.A., Sterling E.J., Turner W.
& Spector S. 2010. Remote sensing for ecology and
conservation: A handbook of techniques. Oxford
University Press, Oxford.
Sound, broad introduction to
remote sensing for biodiversity monitoring. 470 pp.
Biodiversity Monitoring For Natural Resource Management
Survey Methods for Specific Groups of
Dodd K. (ed.). 2009. Ecology and conservation of
amphibians. A handbook of techniques. Oxford
University Press, Oxford.
Comprehensive volume
thoroughly covering the broad range of methods for
monitoring amphibians across all life stages and
Gerwing J.J., Schnitzer S.A., Burnham R.J., Bongers
F., Chave J., DeWalt S.J., Ewango C.E.N., Foster
R., Kenfack D., Martínez-Ramos M., Parren M.,
Parthasarathy N., Pérez-Salicrup D.R., Putz F.E.,
Thomas D.W. 2006. A standard protocol for liana
censuses. Biotropica 38: 256–261.
guidelines for liana sampling.
Available here.
See also
Schnitzer S.A. et al. 2008. Supplemental protocol for
liana censuses. Forest Ecology and Management 255:
Available here.
Lawesson J.E. (ed.). 2000. A concept for vegetation
studies and monitoring in the Nordic countries.
TemaNord Vol. 517. Nordic Council of Ministers,
Very useful despite its focus on cold
temperate environments, offering excellent chapters
on a variety of topics such as study design, sampling
methods, data treatment, analysis and interpretation
for remotely sensed as well as field-collected data.
Available here.
McDiarmid R.W., Foster M.S., Guyer C., Gibbons J.W. &
Chernoff N. (eds.). 2012. Reptile biodiversity: Standard
methods for inventory and monitoring. University
of California Press, Berkeley.
Comprehensive tome
stretching from study design over survey techniques
to data analysis.
New T.R. 1998. Invertebrate surveys for conservation.
Oxford University Press, Oxford.
A 240-pp.-summary
of techniques for surveying both terrestrial, freshwater
and marine invertebrates.
Roberts-Pichette P. & Gillespie L. 1999. Terrestrial
vegetation biodiversity monitoring protocols.
Ecological Monitoring and Assessment Network
(EMAN) Occasional Paper Series Report No. 9.
Burlington, Canada.
Clear guidance for monitoring
vegetation in plots or transects structured as four
independent sections (1. trees, 2. shrubs/treelets and
3. herb layer in plots, and 4. vegetation transects).
Designed for Canada but likewise useful elsewhere;
incl. good illustrations.
Available here.
Sutherland W., Newton I. & Green R.E. (eds.). 2004. Bird
ecology and conservation: A handbook of techniques.
Oxford University Press, Oxford.
Authoritative book
on bird survey techniques, but also a host of related
Software for Data Management and Analysis
Free, well-tested database system for
professional management of complex survey data (by
sampling units or lot batches), voucher specimens,
etc. based on 4D engine. Allows for reference to
photos, batch print-out of specimen labels and
generous customisation. Comes with a detailed
Available here;
see also
for a list of many
alternative database software options.
Freely available, user-friendly software
for estimating (extrapolating) species richness,
calculating biodiversity indices (alpha diversity, species
turnover), individual- and sample-based rarefaction
for comparing different-sized samples.
Available here.
Biodiversity Monitoring For Natural Resource Management
Widely used and freely available software
for mark-recapture analysis of animal populations.
Available here.
Freely available software for adaptive (result-
based) management following the Open Standards for
the Practice in Conservation
Assists in defining project
scope, designing conceptual models and spatial
maps of project sites, prioritising threats, developing
objectives and actions, selecting monitoring indicators
and developing work plans and budgets.
Free, very compact and easy-to use package
with a wide range of statistical and graphical analysis
options for biodiversity data including standard
multivariate techniques but also some truly fancy
tricks (e.g. NPMANOVA). Customising and graphics
layouting options are limited.
Available here.
Affordable package for analysing multi-
species (multivariate) biodiversity data. Offers a wealth
of ordination techniques, multivariate significance
testing, indicator analysis. More user-friendly than R
while offering more options than PAST.
Available here.
This open-source software sticks out among
free GIS packages as being user-friendly and rapidly
growing in functionality and support. Many more
advanced analysis functions are available through
plug-ins for other packages such as SAGA, GRASS or
Available here.
Free open-source platform rapidly growing in
popularity. An immense and fast-growing range of
statistical and graphical techniques can be downloaded
and installed as packages (e.g. SPACECAP for mark-
recapture analysis, MODIS for MODIS satellite imagery
acquisition and processing, odfWeave for automated
reporting). Commands are made through code, which
is why getting familiar with this software, on the
down-side, is significantly more arduous than with
traditional general statistical packages such as SPSS
offers a freely available, user-
friendly graphical user interface for R.
Available here.
SMART (‘Spatial Monitoring and Reporting Tool’).
opportunistic data collection and analysis towards
efficient site-based conservation management.
Recently developed based on the functionality of and
experiences with the software
Information System’) this free open-source software
combines a database with a GIS module as to allow
keeping track of pressure, state and response
indicators (threats, selected species, patrolling efforts).
The reporting module allows the 1-click generation of
summary analyses (maps, graphs, tables) as standard
format reports. A plug-in also integrates functionalities
of the software
, which facilitates field-
recording GPS-linked data using smartphones or
tablets. An application that permits centralised
management of core functions (such as data storage
or analysis, supervision‘SMART Connect’) is scheduled
to be available soon.
Available here.
Biodiversity Monitoring For Natural Resource Management
Appendix 1. Study Design in Field
Important considerations during planning for a field
study that (as usual) relies on random sampling
include (among others):
Carefully avoid pseudoreplication. Pseudo-
replication means that sampling units (e.g. survey
transects) are not spatially independent and
therefore not true replicates (but rather double-
counting of the same place). This happens when
sampling units are too closely spaced to each
other and leads to unreliable or even misleading
Sampling bias has many faces and is one of the
commonest major flaw in quantitative surveys
based on random sampling. Sampling bias leads
to unrepresentative survey results which do not
accurately represent the area, conditions or
populations under study. For example, if the exact
positions of sampling plots are selected by the field
team, difficult situations (e.g. dense underbrush,
difficult terrain, distant sectors) will often be
avoided and their underrepresentation will lead to
unrepresentative results. Randomising the layout
of sampling locations or even laying them out in a
systematic grid would be measures to avoid this
specific source of bias;
Replicability. In essence this means meticulously
defining and describing work procedures and
materials used to the point that someone entirely
unfamiliar with the project could repeat (replicate)
the study and get to the same results;
Edge effects. Be aware of and avoid proximity to
habitat edges (or ecotones) unless edge effects
are the subject of study. Edge effects can be
physical (e.g. climate) or biotic (e.g. biodiversity)
changes that can often be detected up to several
hundred metres into the forest interior (Laurance
et al. 2011). Related to this is the ‘spill-over effect’:
degraded habitats show higher biodiversity near
intact habitat because even many sensitive species
will visit or pass through (spill over) into unsuitable
habitats (‘source-sink dynamics’) where they often
cannot be distinguished from true (constantly
present and successfully reproducing) residents;
Species richness is strongly scale-dependent
(’scale effects’). As an example, a single square
metre of forest burned the previous year may easily
hold more plant species than intact forest, but this
picture changes drastically as the sampling area
is increased;
Defining a meaningful baseline or reference
(‘control’) as a point of reference for monitoring
results. E.g. it is very difficult to evaluate the effects
of overgrazing without having the vegetation in a
naturally grazed state for comparison. Controls
further help distinguish the effects of target
activities (e.g. selective timber harvest) from
effects of unrelated environmental variability
(e.g. climate). Bear in mind that smaller habitat
fragments cannot offer ideal controls (see
Laurance et al. 2011);
Defining hypotheses. The formulation of working
hypotheses (a priori predictions) on patterns,
trends or relationships of interest helps in the
elaboration of a suitable study design (including
adequate number and distribution of sampling
units) and methods of analysis. Later statistical
testing of these hypotheses allows the objective
assessment of whether and to what degree
monitoring results confirm expectations and thus,
ultimately, provide hard evidence for advising on
policy and management decisions;
Critically re-evaluating sampling strategy and
after beginning of field work,
and readjusting to match unforeseen challenges.
Back to main text .
Biodiversity Monitoring For Natural Resource Management
Appendix 2. Dealing with Variability in
Random Sampling
Most field-recorded indicators are work-intensive
and require random sampling of small subareas
or subpopulations. Variability is a particularly big
challenge in analyzing such data. E.g., the likelihood
of encountering a species in such a sub-sample (e.g.
plot, transect) strongly varies with a wealth of natural
(biotic and abiotic) and man-made parameters (e.g.
patterns in socioeconomics and resulting land-
use). This is the principle reason why the especially
high spatial complexity and biodiversity of many
tropical ecosystems poses challenges to ecological
monitoring. As a more specific example, the density of
fruit-eating birds is naturally highly variable in space
and time due to the perpetually changing patchiness
of fruiting trees. Limiting such unexplained variability
(‘data noise’) is key because it allows for more reliable
results at
sampling efforts and thus costs.
Measures worth considering in this regard include
Stratification should be considered whenever
the area of study is not homogenous, because
habitat heterogeneity will typically translate into
high variability in many response parameters.
Stratification can greatly reduce this uncontrolled
variability. This is done by distributing (and
analyzing) sampling units more strategically
according to pre-defined (separated) habitat types
(‘strata’; e.g. according to terrain or land-use). See
Kindt & Coe 2005;
Restricting sampling focusing on thorough
sampling of central parameters to obtain
meaningful results, despite limited resources (e.g.
by omitting other parameters, reducing sampling
area, taking absence-presence data rather than
abundances). See Fig. 5, Ward & Larivière 2004;
Recording of environmental parameters. Many
important environmental parameters such
as altitude, slope inclination and exposure,
topographic position (from ridge crest to valley
bottom) or vegetation structure (e.g. canopy height
and closure, tree basal area) can be measured
easily, and the resulting data are usually of great
value during analysis and interpretation of results
(e.g. allowing quantification of their effects or
explanation of variability). Robust, affordable data
loggers already allow automated recording of key
climate variables such as rainfall, temperature or
air humidity (e.g. Testo or Onset), which will often
prove invaluable, especially in the case of extreme
climate events;
Controlling temporal variation by taking into
account different seasons (e.g. dry vs. rainy
season) and day times, and/or controlling for
sampling time (e.g. by surveying different habitat
types simultaneously rather than sequentially);
Plot versus transect sampling. Thanks to their
compact shape, sampling plots are less affected
by spatial habitat variability than transects. The
strength of transects, on the other hand, lies
in maximising detection rates of low-density
parameters (e.g. in the monitoring of large
mammals or illegal activities, rapid biodiversity
Permanent survey units. Species are distributed
patchily due to environmental heterogeneity of
habitats and resources. Permanently marking
Fig. 5. The inherent trade-offs in any biodiversity monitoring
between sample detail (the number and resolution of
species and environmental measurements taken at a site),
sample quality (the number of sub-samples collected to
achieve satisfactory representation) and sample quantity
(replication across space). Source: redrawn after Gardner
Biodiversity Monitoring For Natural Resource Management
Back to main text .
and repeatedly surveying the same sampling units
(plots, transects, trap points) allows reduction of
this large source of ‘random’ variability in data
(‘noise’) and, in sessile (immobile) organisms
such as plants or corals, the fate of individuals
can be followed, further reducing noise. Similarly,
marking individuals of mobile species allows
much more precise estimates of population sizes,
densities and trends (‘mark & recapture’);
Pilot sampling and power analysis during study
design allow for a quantification of the actual
variability in the data and the determination of an
adequate number of sampling units, and thus avoid
both undersampling (monitoring too few sampling
units to obtain clear results) and oversampling
(spending more effort than necessary and
efficient). This measure will pay off especially for
more ambitious (costly) monitoring projects;
Quantitative (‘numerical’) data (counts, measures,
if unavoidable even estimates) should be collected
rather than qualitative data such as ‘categorical’
(e.g. classes) or ‘ordinal’ (e.g. rankings) because
they allow more powerful and meaningful
analyses; they are also less susceptible to personal
judgment (bias) and thus much more replicable
and comparable.
Biodiversity Monitoring For Natural Resource Management
Appendix 3. Organisms as Indicators
Because most ecosystems are overwhelmingly
complex, limited financial and human resources
usually require the reduction of biodiversity monitoring
to a very small subset of organisms (‘taxa’; usually
indivdual species or groups of related species).
Groups of organisms differ greatly in their sensitivity to
specific natural or man-made environmental pressures
(both among and within groups of organisms). Hence,
the indicative power and suitability of an organism
group depends strongly on the specific monitoring
objectives and project context. For example, while
the species richness or community composition of
lichens is an excellent indicator of air pollution it is
of little practical value to monitor forest degradation
and entirely useless for assessing hunting pressure.
Likewise, many hoofed mammals are sensitive to
opportunistic hunting, yet tend to be tolerant or even
appreciative of forest degradation. Careful selection
of suitable groups of organisms for the specific
monitoring objectives (‘indicator species’) is therefore
a critical step
monitoring objectives have been
agreed on among relevant stakeholders.
Some specific practical considerations for selecting
suitable indicator organisms include:
Taking into account population fluctuations and
generation lengths. Some species populations
naturally fluctuate much more strongly and
unpredictably in size than others (e.g. due
to predator-prey cycles, climate sensitivity);
moreover, species with long generation cycles may
respond too slowly to management intervention to
be ideal indicators.
In practice, the most reliable indicator may
often not be the most sensitive species or those
most threatened and/or of highest conservation
concern, but rather other species sensitive to the
given pressure (e.g. opportunistic hunting) that
are still reasonably common, and easy to identify
and quantify (see Gardner 2010, p.76 ff., for a
discussion on the use of threatened species).
Can the indicator be easily and reliably identified?
Species identification can be very challenging
and resource-intensive. Also consider that exact
species identification may not be necessary.
For many monitoring purposes, recognising
species as being distinct without actually naming
them (‘morphospecies’) is often sufficient when
many species are monitored. Even more coarse
identifications may suffice: the abundance of ‘all
ungulate species’ (typically comprising several
families) or ‘all primates’ lumped together can be
detailed enough for monitoring hunting pressure.
Coarse segregation of aquatic macroinvertebrates
into genera, families or even orders of organisms
is often sufficiently exact to monitor changes in
water quality (Beatty et al. 2006). Likewise, the
diversity of higher taxa (families and orders) has
been recognised as a good surrogate indicator
for overall marine diversity (Mellin et al. 2011).
An important consideration, however, is that
the composition of species assemblages or
communities is often much more sensitive an
indicator of environmental conditions than species
richness (e.g. Imai et al. 2014), which requires
higher identification resolution and efforts. See
also Ward & Larivière (2004) for a helpful review of
four commonly used approaches to reducing effort
when making rapid biological assessments.
Taxonomic or functional groups? Put simply,
taxonomic (or phylogenetic) groups are groups of
organisms that share a common ancestry. Because
species identification is often very challenging,
most studies have traditionally been limited to one
or few such taxonomic groups. The traditional use
of entire taxonomic groups for biodiversity surveys
today facilitates comparison of biodiversity among
studies and sites. While members of single
taxonomic groups often have the advantage
of being identifiable fairly reliably to species
level by a single expert, their members typically
diverge greatly in their ecological requirements
and thus rarely yield the strongest indicators for
certain environmental drivers. Functional groups
or ’guilds’ comprise all species in an ecosystem
that share certain central life history traits
irrespective of their common ancestry and genetic
relatedness. Such shared traits often invoke
exceptional sensitivity to certain environmental
parameters (more so than groups instead defined
by genetic relatedness), and functional groups can
therefore be powerful indicators. E.g., at the top of
the food chain the guild ’apex predators’ are highly
sensitive to a variety of human pressures, as large
frugivorous birds are to opportunistic bush meat
Biodiversity Monitoring For Natural Resource Management
Cost efficiency varies widely among taxonomic
or functional groups of organisms, related not
only to differrences in their indicative power but
also to great variability in types and amounts of
resources required for sampling and identifying
these organisms. E.g. dung beetles can offer an
easily monitored, highly cost-efficient proxy for
abundance and diversity of larger mammals and
thus a more cost-efficient indicator for hunting
pressure (Fig. 6, Fig. 7).
There is no ideal surrogate indicator for overall
biodiversity. The key objective of many biodiversity
Fig. 6. Relationship between survey costs and the
percentage of indicator species (as a proxy of indicative
power) among different groups of organisms in old-
growth rainforest and two agroforest habitats (high-
and low-shade cocoa, respectively) in Sulawesi,
Indonesia. Indicator species are given as the sum
of species with significant indicator value for one
of the three habitat types. Note that the different
groups of organisms are not entirely comparable
since sampling efforts and completeness were not
standardised. In this particular study, dung beetles
and birds show among the best cost-benefit ratios,
liverworts and canopy beetles the poorest. In practice,
the cost-efficiency of indicators is more complex.
E.g. while monitoring trees is rather costly it often
has many co-benefits (for differentiating vegetation
types, estimating carbon stocks, management of
sustainable resource use, etc.). Source: redrawn
after Kessler et al. 2011
Fig. 7. Total survey costs for 14 groups of organisms in
the Brazilian Amazon. In this study, dung beetles, birds
and blowflies (scavenger flies) showed the highest returns
in terms of indicative value for human alterations (or
lack thereof) relative to expenses, small mammals and
selected moths the lowest (not shown). Labour costs can
occur mostly in the field (e.g. birds) or the lab (e.g. moths).
Source: redrawn after Gardner et al. 2008.
monitoring initiatives is to quantify trends in
species richness of an area or habitat
type. Biodiversity ‘surrogates’, single groups of
organisms that reliably reflect general biodiversity,
are a necessity to make this practically feasible.
Unfortunately, ideal surrogates do not exist —
virtually every group of organisms responds in
an inherent and peculiar manner. Some groups
do, however, make better surrogates than others.
E.g. plants and birds are generally well-known,
feasible to monitor, and usually correlate well
with overall biodiversity.
Consider monitoring of ‘key species. Depending
on the specific objectives, monitoring may not
necessarily target species of maximum indicative
value but species with other outstanding
properties or values (‘key species’). Be aware
that the term ‘key species’ is not clearly defined
and can easily cause misunderstandings. Key
species are often defined based on the following
Biodiversity Monitoring For Natural Resource Management
concepts: Umbrella species are species with high
requirements regarding habitat size and quality,
the conservation of which almost consequentially
secures the conservation of the bulk of other
species sharing their habitat (e.g. tiger). Flagship
species are ambassadors of conservation
initiatives selected for being charismatic (to
attract support) and usually rather large in size
to promote the conservation of extensive areas
(e.g. tiger, panda). Keystone species are named
for their paramount importance for ecosystem
stability and/or functioning (e.g. top carnivores,
giant armadillo or fig trees). The related term
ecosystem engineers is commonly applied to
keystone animal species that strongly shape their
habitat, especially through mechanical action
(e.g. beaver building dams, elephants maintaining
forest clearings). The IUCN Red List is authoritative
regarding the national or global conservation
status of species (IUCN threat categories e.g.
being vulnerable, endangered or threatened
with extinction). Restricted-range species are
of particular conservation concern because their
naturally narrow geographical distributions make Back to main text .
them particularly prone to extinction. Restricted-
range species are often termed endemics (=
endemic species) where their range coincides
with geographical or political entities (e.g. limited
to a biogeographical region, country, a province,
mountain range or catchment).
Don’t get carried away. Simple approaches are
usually the most cost-effective, feasible and
sustainable and may be entirely sufficient. E.g.
a simple index of relative abundance for a given
target species based on opportunistic data (e.g.
catch per effort) may often suffice to replace
a resource-intensive setup that yields actual
(absolute) population size.
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
The rapid global growth of conservation schemes designed to incentivize local communities to conserve natural resources has placed new importance on biological monitoring to assess whether agreements and targets linked to payments are being met. To evaluate competence in natural resource monitoring, we compared data on status and trends collected independently by local-community members and trained scientists for 63 taxa and five types of resource use in 34 tropical forest sites across four countries over 2.5 years. We hypothesized that the results would vary according to differences in the education and value systems of the monitors. We found that, despite considerable differences in countries, cultures, and the types of natural resources monitored, the community members and the scientists produced similar results for the status of and trends in species and natural resources. Our findings highlight the potential value of locally based natural resource monitoring for conservation decisionmaking across developing countries. © 2014 The Author(s) 2014. Published by Oxford University Press on behalf of the American Institute of Biological Sciences. All rights reserved. For Permissions, please e-mail: [email protected] /* */
Full-text available
Around the globe, thousands of research projects are engaging millions of individuals—many of whom are not trained as scientists—in collecting, categorizing, transcribing, or analyzing scientific data. These projects, known as citizen science, cover a breadth of topics from microbiomes to native bees to water quality to galaxies. Most projects obtain or manage scientific information at scales or resolutions unattainable by individual researchers or research teams, whether enrolling thousands of individuals collecting data across several continents, enlisting small armies of participants in categorizing vast quantities of online data, or organizing small groups of volunteers to tackle local problems. (Article available "toll-free" at: )
Full-text available
This manual can be downloaded for free from URL Effective data analysis requires familiarity with basic concepts and an ability to use a set of standard tools, as well as creativity and imagination. Tree diversity analysis provides a solid practical foundation for training in statistical methods for ecological and biodiversity studies. This manual arose from training researchers to analyse tree diversity data collected on African farms, yet the statistical methods can be used for a wider range of organisms, for different hierarchical levels of biodiversity and for a variety of environments — making it an invaluable tool for scientists and students alike. Focusing on the analysis of species survey data, Tree diversity analysis provides a comprehensive review of the methods that are most often used in recent diversity and community ecology literature including: • Species accumulation curves for site-based and individual-based species accumulation, including a new technique for exact calculation of sitebased species accumulation. • Description of appropriate methods for investigating differences in diversity and evenness such as Rényi diversity profiles, including methods of rarefaction to the same sample size for different subsets of the data. • Modern regression methods of generalized linear models and generalized additive models that are often appropriate for investigating patterns of species occurrence and species counts. • Methods of ordination for investigating community structure and the influence of environmental characteristics, including recent methods such as distance-based redundancy analysis and constrained analysis of principal coordinates. The manual also introduces a powerful new software programme, BiodiversityR, that is capable of performing all the statistical analyses described in the book. The software is built using the free R language and environment for statistical computing, and several of its libraries such as the vegan community ecology package and the R-commander graphical user interface. The software is provided on CD. After publishing this manual, the BiodiversityR software was modified into a package that can be downloaded and installed from URL The vegan community ecology package can be downloaded from URL Installation guidelines for windows users are available from URL A tutorial for ensemble suitability modelling is available from URL
Full-text available
Successful monitoring underpins effective wildlife management insofar as monitoring serves to track the response of wildlife resources to management and to identify whether management should be continued or changed. Here we provide both general guidelines and specific examples for the design and implementation of effective monitoring programs for adaptive wildlife management based, in part, on lessons we have learned in the Galapagos Islands, where development of a comprehensive monitoring program for its wildlife is un­ derway. To be effective, wildlife monitoring programs should (l) be framed by well-articulated objectives that are closely linked to management goals; (2) measure a subset of informative indicators with sampling methods that permit unbiased and statistically powerful results while minimizing costs and logistical problems; (3) ensure program continuity despite the vagaries of change in personnel, technology, and program objectives; and (4) quickly make accessible appropriately analyzed information to a wide audience, particularly policymakers. Only through such an integrated process can the adaptive "loop" in wildlife management be closed and management practices and policies evolve in a manner ultimately beneficial to wildlife, both in Galapagos and elsewhere.
The growing need for baseline data against which efforts to reduce the rate of biodiversity loss can be judged highlights the importance of long-term datasets, some of which are as old as ecology itself. We review methods of evaluating change in biodiversity at the community level using these datasets, and contrast whole-community approaches with those that combine information from different species and habitats. As all communities experience temporal turnover, one of the biggest challenges is distinguishing change that can be attributed to external factors, such as anthropogenic activities, from underlying natural change. We also discuss methodologi-cal issues, such as false alerts and modifications in design, of which users of these data sets need to be aware.
While the primary purpose of REDD+ (Reducing Emissions from Deforestation and forest Degradation) is climate change mitigation, it may have enormous co-benefits for biodiversity conservation. Biodiversity monitoring is essential for REDD+ to safeguard biodiversity. However, methods of biodiversity monitoring have not yet been established, because there is, so far, no reliable metric of biodiversity. Tree assemblage can be a promising indicator for biodiversity monitoring in REDD+, because the sampling of trees is relatively easy and inexpensive, tree diversity often correlates with that of the other taxa, and patterns in remotely-sensed data of forest canopies often correlate with floristic patterns, implying a potential tool of large-scale monitoring of trees. However, it is still unclear (1) which metric (e.g., species richness and species composition) of tree species is more appropriate as an evaluation metric, and (2) how to reduce the cost of tree identification in inventory. We examined (1) the robustness of the two major evaluation metrics (species richness and species composition) as a surrogate of forest degradation, and (2) the cost-effectiveness of two different approaches (i.e., one is raising taxonomic level from species to genus and the other is raising lower limit dbh from 10cm to 20cm). We established fifty 20m-radius circular plots across primary to highly-degraded forests in each of the three logging concessions in Borneo. Stem density and species richness (the number of species per unit number of stems) of shade-tolerant and pioneer species were positively and negatively correlated, respectively, with increasing above-ground biomass (AGB). Total species richness sharply increased with increasing AGB, and became nearly asymptotic when AGB was >200Mgha−1. By contrast, community composition (nMDS axis 1 scores) varied linearly with AGB in all concessions, indicating that community composition (but not richness) is a sensitive and robust metric to evaluate the magnitude of forest degradation. Patterns in species composition corresponded to those in generic composition. Generic-level identification could save the efforts of inventory by 60%, compared with species-level identification. Monitoring generic composition of canopy trees in 50–60 plots in a logging concession will be the most adequate method to detect effects (either negative or positive) of management on biodiversity during a REDD+ project.