A partnership with academia

Building knowledge for trade and development

Vi Digital Library - Text Preview

Guidelines for the Sustainable Management of Biotrade Products: Resource Assesment

Manual by Francisco Cuesta, María Teresa, 2013

Download original document

Guidelines for the design and implementation of resource assesment for biotrade wil-collected species. It includes their appraisal for BioTrade management, biological and socio-economic information on the managed species. The documents shows how to conduct field inventories to collect data on key population and demographic calculations. It gives guidelides on management, gives examples of good practices and ideas on how to improve monitoring.

U n i t e d n at i o n s C o n f e r e n C e o n t r a d e a n d d e v e l o p m e n t


Guidelines for the
Sustainable Management


of BioTrade Products:
ResouRce Assessment


Ph
ot


o
cr


ed
it:


fo
to


lia
©


k
on


ra
db


ak
, W


ON
G


SZ
E


FE
I




GUIDELINES FOR THE SUSTAINABLE
MANAGEMENT OF BIOTRADE PRODUCTS:


RESOURCE ASSESSMENT


New York and Geneva, 2013




Note
The material contained in this publication may be freely quoted or reprinted, but acknowledgement is requested,
together with a reference to the document number. A copy of the publication containing the quotation or reprint
should be sent to the UNCTAD Secretariat, at: Palais des Nations, 1211 Geneva 10, Switzerland.


The designations employed and the presentation of the material do not imply the expression of any position
whatsoever on the part of the United Nations Secretariat concerning the legal status of any country, territory, city
or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries, or regarding its economic
system or degree of development.


The views expressed in this publication are those of the authors and do not necessarily reflect the views of the
United Nations Secretariat.


For further information on UNCTAD’s BioTrade Initiative please consult the following website:
http://www.unctad.org/biotrade, or contact: biotrade@unctad.org.


Acknowledgements
Authors
Francisco Cuesta, Head of the Biodiversity Division of Consorcio para el Desarrollo Sostenible de la Ecoregión
Andina (Consortium for Sustainable Development of the Andean Ecoregion – CONDESAN), and María Teresa
Becerra, Coordinator for Environmental Area, General Secretariat of the Andean Community, prepared this
publication.


Contributions
This publication further benefited from information and inputs from other organizations. We extend our special
thanks to the Regional Programme Ecobona (Intercooperation – Cosude) and WWF-Nepal, specifically to Galo
Medina, Mario Larrea and Bryony Morgan for sharing information and reviewing the case studies of Caesalpinia
spinosa and Neopicrorhiza scrophulariiflora.


We would like to thank Elise Rebut, Bryony Morgan, Klaus Duerbeck, María Helena Cendales and Martha
Ortega for reviewing the publication. Thanks are also due to UNCTAD BioTrade, particularly Lorena Jaramillo and
Adrienne Stork. This publication was formatted by Rafe Dent and edited externally by Vivien Stone.


UNCTAD gratefully acknowledges the support of the Swiss State Secretariat for Economic Affairs (SECO) for this
publication.


UNCTAD/DITC/TED/2012/1
UNITED NATIONS PUBLICATION


Copyright © United Nations, 2013
All rights reserved




iii


Contents


Note ................................................................................................................................................... ii
Acknowledgements .............................................................................................................................. ii


I. INTRODUCTION ..................................................................................................... 1


A. Key concepts ............................................................................................................................................... 2
1. BioTrade: concepts and policy framework ............................................................................................. 2
2. Resource assessment ........................................................................................................................... 3
3. Population ecology................................................................................................................................ 3


II. GUIDELINES FOR DESIGN AND IMPLEMENTATION OF RESOURCE ASSESSMENTS FOR
BIOTRADE WILD-COLLECTED SPECIES ................................................................... 7


A. Introduction .................................................................................................................................................. 7


B. Stage 1. Species appraisal for BioTrade management .................................................................................. 9
1. Compilation of available biological and socio-economic information on the managed species ............... 9
2. Identification of information gaps and assessment of potential sustainable use ................................... 11


C. Stage 2. Assessment of demographic attributes of the managed population .............................................. 14
1. Field inventories to collect data on key population parameters ............................................................ 14
2. Data analysis and calculation of demographic parameters .................................................................. 16


D. Stage 3. Estimation of harvest rates and sustainable yield .......................................................................... 24
1. Analysis of population dynamics without harvesting ............................................................................ 25
2. Analysis of the implications of harvesting scenarios ............................................................................. 26


E. Stage 4. Management implications ............................................................................................................. 27
1. Identification of good practices............................................................................................................ 27
2. Improving monitoring systems ............................................................................................................. 27


Glossary ............................................................................................................................................. 31
References ......................................................................................................................................... 33


Contents






1I. Introduction


I. INTRODUCTION


In 1997, the United Nations Conference on Trade
and Development (UNCTAD) launched the BioTrade
Initiative with the primary goal of promoting trade
and investment in biological resources in support of
sustainable development, in line with the three objec-
tives of the Convention on Biological Diversity (CBD):
conservation of biodiversity, sustainable use and eq-
uitable sharing of its benefits. To achieve its objective
UNCTAD BioTrade Initiative has promoted the imple-
mentation of BioTrade Principles and Criteria through
the establishment of programmes and projects to
strengthen the capacity of organizations to enhance
the production of value-added products and services
derived from biodiversity for both domestic and inter-
national markets (UNCTAD 2007b).


BioTrade products are an extremely diverse group that
includes medicinal plants, natural ingredients, fruit,
oils, meat, honey, mushrooms, seafood, ecotourism
and many others derived from different ecosystems
(i.e. forests, mangroves, grasslands). These products
are key assets in the livelihoods and health of hun-
dreds of millions of people across the globe and have
been used by human populations for subsistence use
and trade over thousands of years (Vasquez and Gen-
try 1989, Ros-Tonen 2008).


Over the last decade, the market demand and the
interest in managing forests and other ecosystems
(i.e. alpine grasslands) for natural products has grown
tremendously, which has generated concern for the
ecological sustainability of the ecosystem resources
from which they are produced (Arnold and Pérez
2001, Olsen 2005, Widayati et al. 2010). The grow-
ing commercial trade of natural products, in particular
plant medicines and crafts, has resulted in the harvest
of increasing volumes from wild plant populations and
has therefore generated concern about overexploita-
tion (Arnold and Pérez 2001). Consequently, the es-
tablishment of good practices to support sustainable
management of wild-collected species becomes an
increasing need together with the development of
methodologies and tools to support management de-
cisions. Special care should be given to those species
that play key roles in ecosystem dynamics such as
palm trees (Montúfar 2011).


Sustainable management is built on the principle that
ecosystem management will meet current societal
needs without prejudice to future generations, or the


ecosystems’ capabilities to maintain their own health
(i.e. resilience). This concept embraces four funda-
mental standards:
• Ecosystemmanagementissociallyacceptableand


equitable;
• Theimpactisecologicallybenign;
• Theeconomicimpacttolocalcommunitiesisposi-


tive; and
• Increased commercial harvest of non-timber for-


est products (NTFPs) should add to the perceived
value of natural ecosystems, thereby increasing the
incentives to retain the habitat resource (Arnold and
Pérez 2001).


However, underscoring these considerations, there
lies a fundamental question: what are the ecological
consequences of native species harvest? Although it
is often assumed that harvest of wild species has little
or no ecological impact, extraction may alter biological
processes at many levels. For instance, harvest may
affect the physiology and vital rates (i.e mortality and
growth rates) of individuals, change demographic and
genetic patterns of populations and alter community-
and ecosystem-level processes (Balslev 2011, Liu et
al. 2011, and Montúfar et al. 2011).


Management of a wild species or an ecosystem re-
quires the implementation of in-situ or ex-situ actions
based on scientific or empirical knowledge that pro-
mote their conservation either by strict protection or
sustainable management alternatives (Primack 1994,
Salafsky et al. 2001).


Sustainable exploitation of renewable resources de-
pends on the existence of a reproductive surplus,
which is determined by population attributes such as
births, deaths and growth, which differ spatially and
temporally as environmental conditions vary (Hilborn
et al. 1995, and Robinson 1999). Therefore, under-
standing species biology and population dynamics is
essential to determine sustainable yields either by di-
rect experimentation, observation of natural systems
or deduction from biological understanding (Hilborn et
al. 1995).


With these considerations in mind, the purpose of this
document is to develop a set of practical guidelines
on conducting wild species resource assessments in
compliance with BioTrade Principles and Criteria (UNC-
TAD 2007a). This document will therefore emphasize
ecological concepts and methods to analyze species




2 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


population dynamics as a basis for the definition of
good collection practices, delineation of monitoring
criteria and other management considerations. Further,
modelling exercises are presented as a tool to assist
BioTrade organizations in analyzing population dynam-
ics as a way to establish a sustainable harvest rate
and evaluate different good collection practices and
management options. Additionally, the exercises pre-
sented help to evaluate the impacts of current or future
management practices on the species populations and
identify those underlying factors that drive population
dynamics and can be conceptualized as determinant
variables for the sustainable management of a species.


The present document complements the guidelines
developed by UNCTAD (2009) for the development
and implementation of management plans for wild-
collected plant species used by organizations working
with natural ingredients. The methodology for the de-
velopment and implementation of management plans
comprises five steps:
• Identificationofcollectionareasandcollectors;
• Assessmentofmanagedresources;
• Definitionofgoodpracticestobeimplemented;
• Definitionoffollow-upandmonitoringsystems;and
• Implementationofdocumentationsystems.


These guidelines will focus on the assessment of
managed resources (second step) by providing addi-
tional detail, key ecological concepts and methodolo-
gies for completing a resource assessment, and guid-
ance to incorporate findings into management plans
and monitoring systems. Primary emphasis is given to
guiding BioTrade organizations and other stakehold-
ers involved in resources management activities on
the analysis of species trade potential, comprehension
of relevant population ecology concepts and identifi-
cation of information gaps.


The guidelines will feature examples of applied re-
source assessments using specific case studies
for three traded species based on two information
sources: existing cases of UNCTAD BioTrade partners
and examples from scientific publications or project
reports. The population analysis and current harvest
rates for each species were based on raw or pro-
cessed field data plots. The case study species are
Caesalpinia spinosa, Mauritia flexuosa and Neopicro-
rhiza scrophulariiflora (for further information on the
case studies please visit: www.biotrade.org).


In the on-line web version these guidelines are orga-
nized into two main sections:


Section 1. Resource assessment guidelines which
present the steps needed for developing a resource
assessment on wild-traded species.


Section 2. Case studies, aimed at presenting the
application of the guidelines to three selected spe-
cies based on existing information (for further in-
formation on the case studies please visit: www.
biotrade.org).


A. Key concepts
1. BioTrade: concepts and policy framework


The term BioTrade includes those activities of collec-
tion/production, transformation and commercializa-
tion of goods and services derived from native biodi-
versity (genes, species and ecosystems) under criteria
of environmental, social and economic sustainability
(UNCTAD 2007b). BioTrade activities imply the appli-
cation of specific criteria to promote cost-effective ac-
tivities by stakeholders involved in collection and trade
of biodiversity-based products to assure the survival
of managed species and the conservation of their
habitats in the long term.


In establishing a business based on a wild-traded
species it is important to consider the following:
• Thedesignofasustainableproductionsystemac-


cording to the ecological traits of the species;
• Thedefinitionofgoodpracticesandamonitoring


programme based on an adaptive management
concept; and


• Anestimateoftheinvestmentrequiredtodesigna
management system that accomplishes BioTrade
sustainability criteria.


These fundamental decisions need to be based on
thorough biological knowledge about the species and
its potential markets which allow the analysis of risks
and of opportunities for commercial use.


BioTrade represents a innovative strategy to promote
biodiversity conservation and sustainable use based
on the following criteria:
1. The ecological impact of BioTrade products man-


agement is lower than conventional forest manage-
ment;


2. Income generation by BioTrade organizations in-
creases the perceived value of natural ecosystems,
as well as local commitment to conserve the natu-
ral habitats of managed species; and


3. BioTrade has a positive impact on rural livelihoods
income and promotes the preservation and valua-
tion of traditional knowledge.




3I. Introduction


The main international framework that regulates the
trade of BioTrade products is the Convention on Inter-
national Trade in Endangered Species of Wild Fauna
and Flora (CITES). It is an international agreement
between governments, which aims to ensure that in-
ternational trade in wild animals and plants does not
threaten their survival. Another relevant international
instrument for BioTrade organizations is the Conven-
tion on Biological Diversity (CBD) which is focused on
three main objectives: conservation, sustainable use
and equitable sharing of the benefits derived from the
use of biodiversity. This international framework rec-
ognizes trade as a positive incentive measure and
defines some approaches that encourage the imple-
mentation of good practices, which are applicable in
the case of trade in BioTrade products.


In the CBD context, the Addis Ababa Principles and
Guidelines (CBD 2004) provide a framework to as-
sist governments, resource managers, indigenous
and local communities, the private sector and other
stakeholders, in ensuring that their use of the different
components of biodiversity will not result in long-term
decline of biological diversity (Becerra 2009). Never-
theless, up to now, there are limited case studies avail-
able that can provide guidance on the implementation
of such principles (Perez and Byron 1999). However,
parties recognize that implementation depends on
many inter-related factors including the existence of
incentive measures, availability of information and
tools to implement sustainable management plans,
and the capacity to put into practice appropriate mon-
itoring systems.


Finally, it is important to take into consideration that
other international environmental agreements may
be relevant depending on the managed species, as
well as other non-environmental regulations which
also may have some influence in promoting the im-
plementation of practices or measures that in some
way or other affect the sustainable trade of BioTrade
products. This is the case of conservation agreements
such as the RAMSAR Convention on Wetlands that
provides the framework for national action and inter-
national cooperation for the conservation and wise
use of wetlands and their resources.


2. Resource assessment


A simple definition of a resource assessment is the
process by which resource managers estimate the fu-
ture production potential of a given product. For the
purpose of these guidelines, a resource assessment


are those activities needed to identify the sustainable
production potential of a managed species by un-
derstanding the population dynamics and appropri-
ate harvest rates and practices to assure sustainable
management (Wong 2000, Hall and Bawa 1993). Re-
source assessment provides information for identifying
information gaps that need to be filled as well as and
population attributes that need to be monitored in the
long term. This approach is focused on the managed-
population possibilities and considers those attributes
that directly affect the abundance of supply and the
potential for sustainable use (Hall and Bawa 1993).


Other approaches are wider and seek to identify prod-
ucts and describe an ideal development process that
in some cases starts with the selection of species
and progresses through market research, resource
inventory, participatory assessments, determination
of sustainable harvest practices and intensities, man-
agement planning and monitoring (e.g. Peters 1994,
1996, and Stockdale 2005).


Whichever approach is taken in cases where sustain-
able yield or a harvest rate need to be defined, popula-
tion dynamics knowledge of the species is a primary
requisite. A simple inventory of the resource is not
enough for making decisions. Managers need proper
information of what is happening to the population
and the activities they need to implement to ensure a
sustainable management.


In cases where a BioTrade organization manages more
than one species with several products in various ar-
eas, resource assessments need to be carried out for
each species involved in each collection area. General
information of species biology can be applicable, but
productivity and demography vary according to envi-
ronmental conditions and degree of disturbance.


3. Population ecology


A population is a group of individuals of the same kind
living in the same place at the same time. The size of
a population in relation to the area it occupies is its
density. Population and individuals are distributed in
some kind of pattern over the landscape. Some are
uniformly distributed, some are randomly distributed,
but most are clumped in aggregations (Smith and
Smith 2008).


Individuals making up the population may be divided
into three ecological stages: pre-reproductive, repro-
ductive and post-reproductive. The distribution of in-
dividuals within each group influences considerably
the birth rate, mortality rate and population growth. A




4 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


Box 1. Exponential growth and carrying capacity concepts


Exponential growth


The population growth of a species in a newly colonized habitat will start exponentially. This is an example of positive
feedback. The more individuals there are in a population the faster they will breed. The growth curve looks like this (often
called the J-shaped curve):


The exponential growth curve can be modelled by the equation:


Where, r is the rate of increase of a species, dN the variation in the size of the population over the course of time (dT).
Of course the higher N becomes the bigger the increase in the population becomes. This leads to exponential growth.


Real examples of exponential growth


Alien species, which often become pest species, show this growth pattern. Demographic explosion of feral goats (Capra
hircus), rats (Rattus spp.) and feral cats (Felis catus) are well documented (Campbell et al. 2004). When a new species
is introduced accidentally or deliberately into a new environment it has no natural predators or diseases to keep it under
control. Another example of this is the bird Sturnus vulgaris, that was introduced into the United States at the end of the
19th century (160 of these birds were released in New York). By 1942 they had spread as far as California, with an esti-
mated population of between 140 and 200 million, making it one of the commonest species of bird in the world.


The carrying capacity


One of Darwin’s important observations was that a population never continues to grow exponentially forever. There is a
resistance from the environment as the food supply or nesting sites decrease (i.e. competition increases) and the numbers
of predators and pathogens increase. This resistance results from negative feedback. This leads to the classic s-shaped
or sigmoid population curve (below):


The population dynamic in this case is controlled by a component that will slow down the population growth as it reaches
a certain point, the carrying capacity of the environment (K). The equation is called the logistic equation:


Whilst N<K then r will be positive and the population will increase in size.
When N=K then r will be zero and the population growth will stop.
Should N>K then r will become negative and the population will decrease.


Source: Adapted from Smith and Smith (2008), Robinson and Bodmer (1999), Getz and Haight (1989), and Odum and
Barrett (2005).




5I. Introduction


large number of young about to enter the reproductive
stage suggests a potentially increasing population,
whereas a high proportion in the post-reproductive
age classes suggest a zero or declining population
growth. Population tends toward a stable age distri-
bution, in which the proportion of individuals in each
age class remains the same, as long as growth contin-
ues at a constant rate. When deaths equal births and
the proportion of individuals in the population remains
constant, the population has arrived at a stationary
age distribution (Odum and Barrett 2005).


Population size is influenced by the number of individ-
uals added to the group by births and immigration and
by the number leaving by death and emigration. The
difference between the two determines the growth
and mortality rates of populations. Mortality, concen-
trated in the young and the old, is often the greatest
reducer of populations (Smith and Smith 2008).


In an unlimited environment, populations expand geo-
metrically or exponentially, described by a J-shaped
curve (Box 1). Such growth may occur when a popu-
lation is introduced in an unfilled habitat. Nevertheless,
because resources are limited, geometric growth can-
not be sustained indefinitely. Population growth even-
tually slows and arrives at some point of equilibrium
with the environment, called the carrying capacity.
However, natural populations rarely maintain a stable
level, but rather fluctuate about some mean (Smith
and Smith 2008, Odum and Barrett 2005)


Mortality and its complement, survivorship, are two key
parameters for comparing demographic trends within
a population and among populations living under dif-
ferent environmental conditions, as well as for com-
paring survivorship among various species. In general,
mortality rates, graphically portrayed as curves, as-
sume a J shape, whereas survivorship curves fall into
one of three major types: type I, in which the survival
of young is low; type II, in which mortality and thus
survivorship, is constant through all ages; and type III,
in which individuals tend to live out their physiological
lifespans. Survivorship curves follow similar patterns in
both plants and animals.


The sustainable exploitation of renewable resources de-
pends on the existence of a reproductive surplus, which
is determined by the balance between births, deaths
and somatic growth (Hilborn et al. 1995). In the case of
wild-traded species, the definition of a sustainable yield
(i.e. harvest rate) and collection practices guidelines
must be based on an analysis of population dynamics.


The key to protecting and managing wild species is to
have a good understanding of the ecology of the spe-
cies, its distinctive characteristics (natural history), the
status of its populations and the dynamic processes
that affect population size and distribution (Primack
2004, Olmsted and Alvarez-Buylla 1995). Manage-
ment decisions should try to answer as many ques-
tions as possible with respect of the aforementioned
variables (Primack 2004).


Population dynamics helps resource managers to
analyse the effect of harvest regimes on the state of a
population through time, by means of contrasting the
balance between births, deaths and other fundamen-
tal ecological aspects of the traded species. These
analyses are essential to help formulate management
practices and establish sustainable harvest regimes.


4. Adaptive management


Trade in wild species has an increasing relevance con-
sidering the diversity of species and the value that it
represents to local economies of developing countries
(Burgener and Walter 2007). Several authors highlight
the fact that management criteria for natural resourc-
es are influenced by market pressures and demands,
which often affect the sustainability of subsistence
systems and promote overexploitation, local extinc-
tion of species and concentration on a few products
with high market potential (Arnold and Pérez 2001,
Bennett and Robinson 2000, Wilkie and Godoy 1996).


However, information regarding population dynamics
and basic ecological data for the majority of traded
wild species is incomplete or completely lacking,
especially in the Tropics (Primack 1994). This reality
constrains resource managers or BioTrade organiza-
tions in establishing a scientifically sound harvest rate,
which in turns hampers the possibility of determining
the sustainability of their activities without further in-
vestigation and monitoring.


Yet, management decisions may have to be made be-
fore relevant information is available or while it is being
gathered (Primack 2004, Hilborn et al. 1995). In this
scenario, the adaptive management framework allows
designing production systems that involve monitoring
practices and applied research activities that may pro-
vide resources managers with important information
to adjust management activities and assure their sus-
tainable use in the long term.


Therefore, an adaptive management approach repre-
sents a key strategy for sustainable management of




6 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


BioTrade species. According to Walters and Hilborn
(1978), adaptive management refers to all situations
where the best action for a certain system cannot be
defined a priori. It allows for the establishment of se-
quential evaluations of the sustainability of the natural
system and the subsequent modification of manage-
ment actions to assure the desired changes in the
state of the system.


In practice, adaptive management is based on the
systematic analysis of information, applied to a spe-
cific context in order to improve natural resource
management with a long-term perspective (Walters
and Holling 1990, Wilhere 2002). In this framework,
the use of experimental and quantitative models pro-
vides a tool to analyze the dynamic of production sys-
tems, as well as management risks and costs (Wal-
ters and Hilborn 1978, Hilborn et al. 1995, Schreiber
et al. 2004).


The adaptive management approach is based on the
design and implementation of a management plan for
each resource species that is continuously adapted
to the outcomes of well designed “experiments” that
allow stakeholders to collect data systematically and
use those data to take decisions about best alterna-
tives of management and conservation of the man-
aged resources or areas (Blumstein 2007) .


According to Schreiber et al. (2004) the adaptive man-
agement framework structures management process-
es in a series of seven stages with the whole cycle
being repeated through time (Figure 1). It is expected
that BioTrade organizations could apply and adjust
these stages in the development of their management
plans, according to the methodology proposed by
UNCTAD (2009) in the Guidelines for the Development
and Implementation of Management Plans for Wild-
collected Plant Species used by Organizations Work-
ing with Natural Ingredients.


In an adaptive management process, the resource
assessment provides critical information to model al-
ternative management options, and based on these
outcomes, identify good practices to be implemented,
information gaps and define key variables to be moni-
tored to adjust management activities.


According to Schreiber et al. (2004), the process of
identification and definition of management objectives
is a fundamental element of adaptive management
programmes. As presented in Figure 1, in the con-
text of species sustainable management a resource
assessment contributes directly to steps 2, 3 and 4
relating to information analysis, the definition of man-
agement goals (e.g. harvest rates, monitoring needs,
good practices) and the identification of sustainable
use options.


Figure 1. Adaptive management cycle applied to the sustainable management of BioTrade species


Step 1
Management


objectives
definition


Step 7
Monitoring and


evaluation


Step 5
Strengthening institutional


framework (value chain
responsibilities and agreements)


Step 4
Identification of


management options


Step 6
Implementation of


production activities


Step 2
Model existing knowledge


(analysis of existing information)


Step 3
Establishment of management


goals (What? How?)


Source: Adapted from Schreiber et al. (2004).




7II. Guidelines for design and implementation of resource assessments for BioTrade wild-collected species


II. GUIDELINES FOR DESIGN AND IMPLEMENTATION OF RESOURCE
ASSESSMENTS FOR BIOTRADE WILD-COLLECTED SPECIES


1. Introduction


Prior to conducting a resource assessment and in ac-
cordance with adaptive management principles (see
Figure 2), BioTrade organizations and resource man-
agers should have previously identified management
objectives for the selected species, collection areas
and collectors that may be involved in the species
management activities.


In this context the BioTrade organization should com-
pile the following information:
• Spatialinformationofpotentialcollectionareasthat


helps to assess the landscape context and the
ecosystems contained; the thematic resolution of
the information is subject to the size of the collec-
tion areas. In many cases information from an area
30 by 30 metres should be enough;


• Analysis of the quality (i.e. landscapematrix) and
conservation status (i.e. rapid ecological assess-
ments) of the collection areas;


• Taxonomic identification of species. It is recom-
mended to get professional advice from a herba-
rium;


• Local uses and harvest regimes. This should in-
clude the amount of the resource harvested (quan-
tities and periodicity), techniques and practices
applied for retrieving the product(s), the number
and gender of collectors and the communities to
which they belong, land tenure rights, incomes de-
rived from species management, among others.
This information can be collected using different
participatory techniques such as participatory rural
appraisal – PRA (Chambers 1994); and


• Localregulationsapplicabletothemanagementof
wild species (e.g. exclusion areas, temporal restric-
tions).


Preparation of a resource assessment should promote
community participation, integration of local and sci-
entific knowledge, local authorities’ involvement and
implementation of cost-efficient production systems.


The majority of natural products in the world are har-
vested by people from rural communities, of whom
a significant proportion are indigenous people (Stock-
dale 2005). In a resource assessment, community
participation has meanings at two levels. Local popu-
lations contribute with their experience and knowl-
edge on resource management and participate in


decision-making processes related to improvement
of management practices and market access. Tasks
such as the compilation of existing information about
species biology, management practices, markets and
other relevant issues can be done in close collabora-
tion with local communities involved in the production
system. The long-standing ties between natural prod-
ucts and communities mean that continued species
use is often also linked to the maintenance of rural
livelihoods (Stockdale 2005).


The relevance and applicability of a resource assess-
ment depends on information quality and availability;
for this reason a BioTrade organization should get ex-
pert advice from a qualified biologist, ecologist and/or
biodiversity science specialist, in charge of support-
ing resource managers throughout the processes of
producing the baseline information, which includes
the generation of harvesting scenarios (i.e. dynamic
populations models) and the design and implementa-
tion of an appropriate monitoring system.


The BioTrade organization, with the support of a spe-
cialist on species management, should guarantee a
good data management system in order to provide
accurate and scientifically verifiable results to improve
knowledge about the species, evaluate the imple-
mentation of management activities and adjust them
accordingly. The scientific expert should also directly
engage with the local communities, local authorities
and other relevant stakeholders during any participa-
tory assessment exercises.


In compliance with applicable local or national regula-
tions related to the management of resources, local
authorities should be involved in the elaboration of a
resource assessment. In this way, they are able to pro-
vide specific guidelines and recommendations for the
elaboration of management plans as well as specific
information required by national regulations.


Cost analysis is relevant before starting a resource as-
sessment. Analysis should take into account the exist-
ing information on the species, previous experiences
in wild collection activities and access to markets.
Cost of a resource assessment preparation is closely
related to the quantity and quality of information in
place, access to the collection areas and involvement
of relevant stakeholders. In this context costs should
consider field trips, stakeholders’ participation (e.g.




8 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


workshops, surveys, visits) and the development of
specific field research to collect relevant information
regarding local population, ecosystems status, or any
other relevant aspects.


The elements of a resource assessment


A resource assessment entails the following stages:
1. Species appraisal for BioTrade management
The first stage is the compilation and analysis of


existing knowledge (including traditional and sci-
entific information available) on the selected spe-
cies and its population’s ecological characteristics.
Based on this information, a BioTrade organization
will then be able to evaluate the species potential
for BioTrade schemes under sustainable use and
identify information gaps.


2. Assessment of demographic attributes of the
managed population


This stage involves the establishment of an ecologi-
cal baseline for the harvested population. This as-


sessment entails the estimation of the population
size and other key population ecology variables
such as population density, spatial distribution and
age structure among others.


3. Estimation of harvest rates and sustainable
yield


The objectives of this stage are twofold. The first
looks to analyze the species’ population dynam-
ics. The second aims to evaluate the implications
of harvest regimes on the population dynamics as
a way to identify a suitable harvest rate.


4. Management implications
The final stage is to draw up conclusions and rec-


ommendations on good practices to be imple-
mented in order to assure a trade system founded
on a sustainable basis that is able to produce raw
materials in the quantities and qualities needed.


Chapter II contains four stages that provide a detailed
explanation of tools and analysis necessary to carry
out all stages of a resource assessment.


Figure 2. Elements needed to carry out a resource assessment and identify the contribution of this process to the
design of a management plan.


Identification of collection
areas and collectors


Assessment of managed
resources


Definition of good practices
to be implemented


Definition of follow-up and
monitoring systems


Implementation of
documentation systems


(monitoring)


Management plan
(UNCTAD 2009)


Species appraisal for
BioTrade management


Assessment of demographic
attributes of the managed population


Estimation of harvest
rates and sustainable yield


Management implications


Resource assessment
elements


Harvest rates and good
practices definition


Identification of variables influencing
managed species population


dynamics and productivity that need
to be monitored


Field data collection protocols
Data analysis protocols


Resource assessment input
for a management plan


Source: UNCTAD (2009).




9II. Guidelines for design and implementation of resource assessments for BioTrade wild-collected species


1. Compilation of available biological and socio-
economic information on the managed
species


The quality and relevance of the management de-
cisions for any species, including the definition of a
harvest rate, relies on the available information on the
population ecology of the species in question. This
type of information can be obtained from three major
sources: scientific papers (i.e. peer-reviewed journals),
published literature from research centers and non-
governmental organizations (NGOs) available on the
web, and grey literature (i.e. unpublished reports and
fieldwork data sheets).


An important source of information comes from tradi-
tional knowledge and scientific experts. In this sense,
it is recommended to BioTrade organizations to gather
in-situ information (i.e. management regimes, produc-
tivity) by rural appraisal methodologies such as focus
groups, interviews and questionnaires (Chambers
1994). At the same time, researchers at universities,
botanical gardens or research centers could be also


interviewed. A considerable amount of information
can be gleaned from unpublished reports written by
scientists, government agencies and conservation or-
ganizations (Primack 2004).


Information on the following parameters needs to be
compiled in order to complete a resource assess-
ment. This information will allow BioTrade organiza-
tions to understand the population dynamics and the
impact of current management practices, as well as
identify those aspects that determine management
conditions.


a) Management conditions and socio-economic
considerations


Before gathering specific information on the biology
of the species, it is recommended to understand the
current management practices and the social and
economic conditions associated with the manage-
ment of the species. It is important to involve local
communities and collectors in order to have a good
understanding of current practices, the quantities
commercialized and the price of raw materials, and
production costs, among other parameters that affect
species management and production sustainability in
the long term.


1. Products derived from the use of the species
and parts used


Management practices have a differential impact
on the population, which is directly related to the
living part of the individual that is collected (i.e.
leaves, roots, bark). Some of these management
practices require the extraction of the individual
from the population generating a direct impact on
the population. Others require the harvest of specific


Objectives:
• Appraisaloftheavailableinformationonthespe-


cies to be managed.
• Evaluateopportunitiesandalternatives forsus-


tainable management of the species.


Expected outcomes:
• Compilationofexisting informationon thebiol-


ogy of the species, habitats and current condi-
tions.


• Informationgapsidentified.
• Assessmentofthespeciespotentialforsustain-


able use.


Development:
Baseline establishment includes the collection of
key relevant information on the BioTrade species,
an assessment of their potential use and field data
collection to develop the in-situ baseline.


A. Compilation of available biological and socio-
economic information on the managed species.


B. Identification of information gaps and assess-
ment of potential sustainable use.


A. Stage 1. Species appraisal for
BioTrade management


Information to be gathered by BioTrade orga-
nizations
• Management conditions and socio-economic


considerations
• Speciesbiology
• Populationdemography
• Habitatinformationandecosystemcharacteris-


tics


Analysis of existing information allows BioTrade Or-
ganizations to understand the biological and eco-
logical characteristics of the managed species, as
well as to identify information gaps that need to be
fulfilled through field data collection and monitoring
schemes.




10 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


parts from the individuals such as fruits or leaves.
In these cases the management practices have a
different effect on the population, altering other at-
tributes such as the germination rate.


2. Traditional use
Information on traditional practices, management


regimes and current harvest rates is needed. This
information is important in the experimental design
and field protocols for establishing good manage-
ment practices in order to control its potential ef-
fects on the population demography.


3. Yield and harvest rate
Information on harvested quantities of specific


products (e.g. flowers, fruits, barks, roots and
seeds) and the capacity of production per individual
allows quantifying the potential yield of a population,
and thus, allows the establishment of a harvest rate
needed to supply demand but under the maximum
sustainable yield of the given species. In the case
of species that have been harvested traditionally or
commercially for a considerable period of time, it is
important to carry out interviews to obtain additional
information from the resources users in order to un-
derstand the possible impact of these practices.


4. Relevant socio-economic issues affecting re-
source management


Where a formal supply system of raw materials is
introduced, discussions with providers regarding
the estimated production and supply capacities are
recommended (UNCTAD 2009). Economic aspects
include information on prices and current produc-
tion costs including the investments needed to
implement good management practices.


b) Species biology


Information on the ecological traits of the species al-
lows BioTrade companies to identify physiological and
ecological limitations to the population growth and
consider the options of managing these to optimize
production and resource management (Guillot and
Becerra 2003). Reproduction strategies are impor-
tant variables that control population dynamics. In this
context it is recommended to gather information on
the following aspects:
1. Natural propagation strategies: seed dispersal, pol-


lination, sexual reproduction;
2. Reproductive biology: number and duration of re-


production events (annual, biannual, perennial).
This is especially important when the collected
parts are fruits, leaves or seeds as well as in the


case of annual species that have a unique repro-
ductive event;


3. Fecundity: offspring number and size in each repro-
ductive event;


4. Male to female ratio in the case of bisexual or dioe-
cious species;


5. Age of first reproduction and lifespan;
6. Information on species vulnerability and resilience


to anthropic disturbance; and
7. Interspecific interactions (parasitism, herbivory, pol-


lination).


c) Population demography


The main variables that describe the status and popu-
lation dynamics are the following: population size,
population density and population structure. For this
reason it should be a priority to collect good quantita-
tive data on these variables, either by published sci-
entific studies or through specific field studies imple-
mented as part of the baseline phase in the resource
assessment process (Guillot and Becerra 2003)


1. Density
Population size generally refers to the number of


individuals present in the population. Density refers
to the number of individuals in a given area. For
ecologists density is usually a more useful mea-
sure. This is because density is standardized per
unit area, and, therefore, can be correlated with
environmental factors or used to compare different
populations.


2. Population structure
Distribution of various age/size groups in a popu-


lation permits the analysis of how the population
is growing, the reproductive capabilities and likeli-
hood of the population in the mid- and long term.
In this context it is important to have information on
the distribution of the population according to their
age classes (e.g. saplings/infants, juveniles, adults)
and the characteristics of each such as:


a. Longevity/life expectation;
b. Growth time at each stage (transition time


among age classes);
c. Mortality rate; and
d. Density at each population stage.


d) Habitat information and ecosystem character-
istics


Habitat quality, ecosystems fragmentation and an-
thropic disturbance regimes such as timber extrac-
tion, fires, cattle grazing or hunting that might have




11II. Guidelines for design and implementation of resource assessments for BioTrade wild-collected species


considerable direct impacts on the target species and
its populations, need to be collected. In order to iden-
tify those variables that could affect sustainable man-
agement it is recommended to get general information
related to the following aspects:
1. Relevant habitat characteristics for the manage-


ment of the species;
2. Climate variability (seasonal, successional);
3. Existing habitat management regimes (disturbance,


conservation status, other uses); and


4. Status of fragmentation/connectivity of the land-
scape matrix.


2. Identification of information gaps and ass-
essment of potential sustainable use


Screening of BioTrade species via a multi-criteria
analysis helps to identify information gaps and as-
sess species potential for harvest and trade on a sus-
tainable basis considering the ecosystem/adaptive
management scheme. To identify information gaps a


Table 1. Information gap analysis on collection areas and managed resources of Caesalpinia spinosa


Variable
Information
availability


Source
Information gathering


tools


Management practices


Parts used and management
practices at local level


Available Field studies Ecobona
Regional Programme


Participatory assessment
(local knowledge)


Collection practices Available Field studies Ecobona
Regional Programme


Participatory assessment


Current harvest rates Available Field studies Ecobona
Regional Programme


Participatory assessment


Prices and trade flows Available Field studies Ecobona
Regional Programme


Participatory methods,
specific market studies


Selling seasons Available Field studies Ecobona
Regional Programme


Participatory assessment


Land tenure rights Available Field studies Ecobona
Regional Programme


Participatory assessment


Species biology and population demography


Reproduction strategies Incomplete Secondary sources Scientific research
Density Available Field studies Ecobona


Regional Programme
Field studies (inventories)


Age classes and demography Available Field studies Ecobona
Regional Programme


Field studies (inventories)


Germination/reproduction rates Non-existent Field studies (inventories)
Longevity/mortality rates Incomplete Secondary source Field studies (inventories)
Seasonality Incomplete Secondary source Secondary information


and field studies


Habitats and ecosystems


Ecosystems and habitats involved Available Field studies Ecobona
Regional Programme


Participatory assessment


Climate characteristics Available Field studies Ecobona
Regional Programme


Existing meteorological
information and reports


Landscape matrix characteristics Available Field studies Ecobona
Regional Programme


Mapping participatory
assessment


Habitat characteristics and
conservation status


Available Field studies Ecobona
Regional Programme


Field studies (habitat
analysis)


Source: Adapted from Becerra (2009), data from Larrea (2011).




12 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


checklist can be prepared to summarize the informa-
tion obtained and the tools used to get it (see Table
1). Once gaps have been identified the collection of
key additional information can be prioritized. The rel-
evance of this information for making management
decisions can then be analysed (Becerra 2009).


Based on the information gathered the BioTrade orga-
nization can use a multi-criteria assessment to carry
out a preliminary assessment to evaluate the feasibility
of a species for harvest and trade under sustainable
conditions (see Table 2). The assessment is based on
a qualification index aimed at evaluating the potential
sensitivity of the species to be collected and traded
based on the life traits, the population attributes and
the ecosystem characteristics of the species. When a
species has a low or medium score, resource manag-
ers should include specific management practices to
guarantee sustainable use.


Table 2 presents an example of the use of this multi-
criteria assessment to evaluate the potential of sus-
tainable use of Caesalpinia spinosa, Mauritia flexuosa
and Neopicrorhiza scrophulariiflora. All three species
are likely to be used for BioTrade purposes but with


specific considerations and best management prac-
tices formulated for each. However, in the case of
Mauritia flexuosa, which presents the lowest score in
the life history attributes, this should suggest to a Bio-
Trade organization that variables such as germination
rate and dispersal mechanisms require special atten-
tion and could have a high influence in the population
dynamics. On the other hand, the score of Caesalpinia
spinosa, shows that a BioTrade organization should
pay special consideration to ecosystem variables, as
the existing information indicates it does not have the
expected quality to assure sustainable production (for
further information on the case studies please visit:
www.biotrade.org).


The multi-criteria matrix is a very practical and useful
tool for BioTrade organizations. The matrix helps to
screen the potential use of a species under sustain-
able conditions. Further, the matrix identifies the key
variables that need to be considered in the design of
the production system. Where insufficient information
is available to screen a species on the matrix, it is re-
commended to contact experts for further guidance.


Table 2. Multi-criteria matrix to assess potential sustainable use. Data relate to the four study cases discussed in these
guidelines (for further information on the case studies please visit: www.biotrade.org)


a. Life history traits


Variable Options Scores Caesalpinia spinosa
Mauritia
flexuosa


Neopicrorhiza
scrophu-
lariiflora


Part of the plant har-
vested


Entire individuals, bark,
shots, roots


2


4 2 2Latex, flowers, pollen,
seeds


4


Leaves, fruits 6


Dispersion system
(spores, seeds)


Biotic specialist (frugivo-
res: birds, mammals)


2


6 2 6
Biotic generalist (small
mammals)


4


Abiotic (ramets, wind
cross-pollination)


6


Seed germination rate
Low 2


6 4 2Medium 4
High 6




13II. Guidelines for design and implementation of resource assessments for BioTrade wild-collected species


b. Population attributes


Variable Options Scores Caesalpinia spinosa
Mauritia
flexuosa


Neopicrorhiza
scrophu-
lariiflora


Birth rate
High 2


6 4 6Medium 4
Low 6


Juveniles mortality rate
High 2


2 4 6Medium 4
Low 6


Age-size class of first
reproduction


Late 2
4 6 6Mid 4


Early 6


Ecological strategy
Mature habitats 2


4 2 4Secondary forest 4
Colonizers 6


Population structure
Constrictive pyramid 2


2 2 4Stationary pyramid 4
Expansive pyramid 6


Population density
Low 2


6 4 4Medium 4
High 6


Population spatial dis-
tribution


Random 2
6 4 4Aggregated 4


Homogeneous 6


c. Ecosystem attributes


Variable Options Scores Caesalpinia spinosa
Mauritia
flexuosa


Neopicrorhiza
scrophu-
lariiflora


Landscape context


Small isolated patches
(highly fragmented) 2


4


6


2 6 4Long and connected
patches (fragmented)
Matrix (not fragmented)


Carrying capacity Low 2
2 6 4Medium 4


High 6
Habitat integrity Highly disturbed 2


2 6 4Average disturbance 4
Low disturbance 6


d. Total score


Variable Options Scores Caesalpinia spinosa
Mauritia
flexuosa


Neopicrorhiza
scrophu-
lariiflora


Score


Low (poor management
aptitude) 26–38


52 52 56
Medium (to be used
under specific manage-
ment considerations)


38–64


High (High potential of
in-situ sustainable use) 65–78


Source: Adapted from Becerra (2009).




14 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


Objectives:
• Toestablishanecologicalbaseline for thehar-


vested population by assessing the population
size and its conservation status in the manage-
ment area.


• Identifyandfillinexistinginformationgapstoes-
tablish a sustainable harvest rate.


Expected outcomes:
• Relevant biological and demographic informa-


tion completed.
• Speciesdemographic informationthat includes


population density, age-size structure, transition
time among classes, germination rate (fecundity)
and seedlings mortality.


Development:
To start the analysis of population dynamics and
arrive at a harvest rate it is important to improve
the basic demographic information of the managed
species available to the resource assessment.


A. Field inventories to collect data on key popula-
tion parameters.


B. Data analysis and calculation of demographic
parameters.


B. Stage 2. Assessment of demographic
attributes of the managed population


One of the most fundamental problems faced by com-
munity and population ecologists is that of measuring
population sizes and distributions. They are necessary
for impact assessments (measuring the effects of dis-
turbance) and restoration ecology (restoring ecologi-
cal systems) as well as to set harvest limits on com-
mercial and game species (e.g. fish, deer).


The need to assess community structure has gener-
ated a number of quantitative field methods as well
as an appreciation of which methodology works best
in any given situation. These methods have been
designed to generate reliable estimates of the abun-
dance and distribution of each species within a com-
munity. This information makes it possible to compare
species or groups of species within a community or to
contrast species composition and abundance among
communities.


Furthermore, measures of species abundances with-
in a community taken at one point in time provide a
baseline against which future measures of species


abundances within that community can be compared.
This type of information allows resource managers to
evaluate population changes over time, as a response
to specific management activities (e.g. BioTrade).


In most cases it is either difficult or simply not possible
to census all of the individuals in the target area. The
only way around this problem is to estimate population
size using some form of sampling technique. There
are numerous types of sampling techniques. Some
are designed for specific types of organisms (e.g.
plants vs. mobile animals). As well there are numer-
ous ways of arriving at estimates from each sampling
technique. All of these procedures have advantages
and disadvantages. In general, the accuracy of an es-
timate depends on: the number of samples taken, the
method of collecting the samples and the proportion
of the total population sampled.


Cunningham (1994, 1996) proposed a method with
the focus of being a protocol to collect available knowl-
edge, local as well as scientific, about a resource spe-
cies and which does not itself generate any new data.
The compiled information is used to identify species,
resources or sites that may be vulnerable to overex-
plotation. A standard summary sheet is prepared and
the information collected evaluated according to a set
of criteria of sustainability drawn from ecology, eco-
nomics and social sciences. On the basis of the ass-
sessment, each species is classified into one of eight
management categories. Appropiate management
recommendations are given to each category. Yet this
rapid vulnerability assessment does not includes any
protocols for inventory as the method is intended to
be a rapid first assessment of the species. However,
the assessment is only as good as the information
available, which is often lacking.


This section provides information on the main quanti-
tative field methods best suited to study natural com-
munities and how to apply them, in order to establish
the baseline for the species of interest.


1. Field inventories to collect data on key
population parameters


Once a BioTrade organization has identified the prin-
cipal information gaps, those variables that need
more attention and have to be collected through field
inventories can be pinpointed. In order to begin field
sampling, the BioTrade organization needs to select
the area to be inventoried taking into account that all
managed habitats are well represented and the field
techniques to be applied are appropriated for the




15II. Guidelines for design and implementation of resource assessments for BioTrade wild-collected species


wild-collected species. The information collected for


the baseline and completed through the field invento-


ries will also provide key information for the population


modelling exercise and the establishment of a prelimi-


nary harvest rate.


Field inventories are similar to experiments in the


sense that they are carried out to test hypotheses,


which are further used to take management actions.


In case of management activities the hypotheses are


related to the effectiveness or efficiency of implemen-


tation of management practices in order to reach a


sustainable harvest rate.


a) Why sample?


Sampling methods are invaluable for numerous bio-
logical investigations. Such methods are used to de-
termine the structure of a natural community. It would
be extremely time consuming, for example, to count
and measure every individual of each species within
a community in order to determine the abundances
and distributions of each species within the commu-
nity. Sampling methods enable us to estimate reliable
information by use of samples. However, it is critical
that the samples be taken without bias and in suf-
ficiently large number so that the resulting data can
be summarized to give valid estimates of the desired


Box 2. Sampling methodologies


1. The area-sample method (quadrats or plots)


Area-sample methods are best suited for plant communities (e.g. mushrooms, herbs, forest trees) and sessile or seden-
tary animals (e.g. sponges).


Quadrats: When using quadrat samples it is important to determine the appropriate quadrat size to use on the basis of
the size and density of the individuals within the population being sampled. Quadrats must be large enough to contain
a number of individuals, but small enough that the individuals present can be separated, counted and measured. For
example, quadrat sizes for herbaceous vegetation might be 1 m2, while for shrubs 10–20 m2 and 100 m2 for forest trees.


Quadrat shape is also important as it affects the ease of establishing quadrats and the efficiency of sampling. For exam-
ple, circular “quadrats” are more easily established than square quadrats; and elongated rectangular “quadrats” furnish
more variety of species than an equal number of square quadrats of the same area. This latter relationship holds because
a rectangle encompasses more environmental variety due to environmental gradients (e.g. slopes, soil-moisture variation)
than a square of the same area. However, because rectangular quadrats have more perimeter than square quadrats of
the same area, accuracy tends to decline as quadrats become more elongated due to the “edge effect” (Barbour et al.
1999). The best quadrat size and shape depends on the application, for this reason it is recommended to define this with
the advice of a specialist.


Plots: Plots are use to sample count individuals and sample habitat characteristics. A subset of plots is used with the
assumption that it is a representative area. Selecting the area and the best number or location of plots requires insight
into patterns of distribution of the species across the landscape, the type of vegetation (forest, grassland) and the life form
growth in the case of plants.


2. The distance-sample method


Distance-sample method (e.g. transects) are more frequently used to sample species or community variation along envi-
ronmental gradients such as different types of ecosystems along an altitudinal gradient.


Transects: A transect is a straight line or series of straight-line segments laid out in the area to be sampled. Transects
are used widely to obtain systematic samples of spatially distributed populations (e.g. plants, insects). In this cases plots
placed along transects are the actual sample units. Each transect is treated as an independent observation. The estima-
tion of the abundance of biological populations (such as terrestrial mammal species) can be achieved using a number of
different types of transect methods, such as strip transects, line transects, belt transects, point transects and curved line
transects. As in the case of the area-sample method, the size and number of transects to set up depends on the ecologi-
cal and biological attributes of the species and the type of ecosystem where the collection area occurs.


Point sampling: A set of points is established throughout the population and measurements are taken from each point.
Points are spaced widely enough to negate members of the population being sampled more than once. This method
requires the use of mathematical formulas to estimate population size and is more frequently used for avian surveys.


Source: Adapted from Smith and Smith (2008), Robinson and Bodmer (1999), Getz and Haight (1989), Odum and Barrett (2005).




16 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


parameters, in this case population structure, den-
sity and other population parameters needed for the
establishment of the sustainable harvest rate of the
traded species.


b) Sampling methods


The specific sampling method used to assess com-
munity structure depends on the nature of the organ-
isms, including the variables listed above, and the
community to be sampled. In the context of these
guidelines, the sampling techniques to be used in a
resource assessment are defined according to the
population attributes of the species of interest such
as their spatial distribution and their life growth form
in the case of plants. Furthermore, to analyse popula-
tion dynamics it is recommended to identify a meth-
odology that allows documenting species density and
population structure as a standard basis.


With these considerations in mind, the application of
two quantitative field methods best suited to study
communities of sessile or sedentary animals and most
types of vegetation are recommended:
• Area-samplemethod(quadratsorplots);and
• Distance-samplemethods(e.g.transects).


Box 2 refers to the most common sampling tech-
niques that can be applied to collect basic information
to analyse managed population characteristics.


Survey design relies on the objectives of the field
study, for example estimation of population size, re-
cruitment, species composition, annual production of
fruits, etc. Based on this information sample size is
defined in order to have the best representation of the
managed population.


Sample size refers to the number of independent
sample units (e.g. quadrants). Definition of sample
size is referred to as the number of replicates (e.g. 5
transects of 100 m) and subsamples as the number of
observations in a sampling unit (e.g. 5 plots per tran-
sect) (Figure 3). The sample size should be big enough
to have a high likelihood of detecting a true difference
between two groups that is statistically significant.


Sampling size depends on the distribution of the man-
aged species in the landscape. Some species are
located in several habitats in the landscape, so it is
advisable to set sampling plots in areas with similar
characteristics. In the case of managed species, ob-
servations on non-managed areas (control areas) are
especially important in the design of field studies. Ob-
servations from randomly selected control areas can
be compared with observations associated with man-
aged populations to identify effects derived from man-
agement practices. Table 3 contains an example of a
survey design checklist.


2. Data analysis and calculation of demographic
parameters


Information derived from sampling plots and collect-
ed information should be analysed and organized in
a way that allows BioTrade organizations to evaluate
and define the biological and ecological characteris-
tics of the managed species.


a) Part of the plant harvested and collection
practices


Information on the type of management and parts of
the plants or animals being used allow organizations
to consider management implications and collection
practices (Figure 4).


Figure 3. Example of a sampling design for Caesalpinia spinosa including three sample units (transect) and five
subsample units (plots)


Transcect (100mx20m) Plot
20m2


Transcect (100mx20m) Plot
20m2


Transcect (100mx20m) Plot
20m2


Sampled individuals


Source: Adapted from Primack (2004).




17II. Guidelines for design and implementation of resource assessments for BioTrade wild-collected species


Table 3. Survey design checklist for Caesalpinia spinosa


Question Example
What is the survey objective? Evaluate density and population parameters of Caesalpinia


spinosa
To what population we want to make infer-
ences?


Community of Perucho (Imbabura, Ecuador)


What is the best sampling method? Transects
What will be the sampling unit? Plots
What is the estimated area to be sampled? 50 hectares
What sample design is the best? 5 managed forest fragments, 2 control forest fragments


5 transects of 100 m x 20 m in each forest fragments
5 plots of 20 m2 per transect


What are the variables to be measured? Number of individuals (all individuals identified as Caesalpinia
spinosa)
Population size distribution for each individual based on
diameter size at breast height
Reproductive condition
Evidence of mortality causes (diseases, seeds or fruit preda-
tion, herbivores, cattle grazing, etc.)


Frequency (specify sampling frequency in case
information is needed for monitoring)


Every year before extraction season


Source: Adapted from Bookhout (1996), data from Larrea (2008).


Figure 4. Examples of part of the species harvested and collection practices of three species


Caesalpinia spinosa
(seeds fruits and pods)


Neopicrorhiza
scrophrulariiflora


Rhizomes (ramets)


Mauritia flexuosa
(fruits)


• Seed production occurs once a year in a asynchrony pattern
lasting four to five months at the end of the rainy season (July till
November). Pods (and seeds) are collected from the soil or
directly from the tree avoiding the collection of non-mature pods.
On each collection almost all pods are collected (Larrea 2011).


• In the national parks plants are harvested only for local use, the
main users being specialists known as amchi trained in the
Tibetan medical system.


• In the buffer zone the species is collected for trade. The
harvesting approach of commercial collectors is destructive.


• Because of the height of adult individuals, female palms are cut
down to retrieve the fruits




18 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


b) Population density and structure (size-age
classes)


To analyze population dynamics it is important to un-
derstand the composition of the population in terms
of its age structure (i.e. number of individuals that
comprise the population per each age/sex class) and
its density (number of individuals per area unit). This
will aid a BioTrade organization in evaluating whether
the population has a good proportion of individuals
from which the traded parts will be collected (fruits,
seeds, barks, etc.), as well as whether the availabil-
ity of saplings and young individuals will be capable
of sustaining such production (harvest rate) in the fu-
ture. Normally in a healthy population the proportion
of seedling, saplings and young individuals is greater
than that for adult individuals, however, this condition
can vary depending on the species and the manage-
ment conditions.


1. Total density calculation
Densities are calculated measuring the number of in-
dividuals of each plot per plot area (individuals/area).
Average and standard deviation values of density
from several plots provide an estimation of the total
population density for that area (Table 4). In subtropi-
cal ecosystems, trees can be aged approximately by
counting annual growth rings and then correlate the
measures with standing individuals – their height and
trunk diameter at breast height (DBH). In this way indi-
viduals from a managed area can be grouped in size-
age classes. In tropical ecosystems without four sea-
sons where tree rings do not work properly, individuals
are classified using height and DBH measures as for
the tree species used in the study cases (for further
information on the case studies please visit: www.bio-
trade.org). Height measurements can be substituted
in the case of herbaceous plants, small understorey
palms or woody shrubs.


Densities can be monitored by sampling the same
transects and plots each production season or annu-
ally. Systematic data collection allows resource man-
agers to evaluate differences in production by season,
habitat conditions that could affect population growth
or analyse impact of collection practices or interven-
tion activities.


2. Densities by age/sex classes
Populations may be divided into three ecological pe-
riods: pre-reproductive, reproductive and post-repro-
ductive. It is important to revise species biology to
check those characteristics that allow the identification
of individuals at these stages. During the field study it
is important to get support from local managers or a
botanist to identify seeds, seedlings and young indi-
viduals that frequently are not easily identifiable.


This concept is important to adjust the size-age class-
es’ classification of any given population by defining
those characteristics that could be used as indicative
of the age of the individuals (e.g. size, height, pres-
ence of flowers, other characteristics to distinguish
young and adult individuals). In the case of plants this
is particularly important taking in to account that in-
dividuals with no flower production during flowering
season are either too young or too old.


Sex classes are defined in the case of animals and di-
oecious plants. Here it is important to get information
on physical characteristics to distinguish females and


c) Reproduction biology


The dynamics of a natural population are highly deter-
mined by species reproduction strategies. As shown
in the case studies, different strategies have different
implications for the management of the populations
(Table 6). (For further information on the case stud-
ies please visit: www.biotrade.org). In many cases this


Table 4: Example of a matrix of calculation of total density based on information derived from sampling units


Number of sampled individuals
Transect 1 Transect 2 Transect 3


Plot 1 23 39 25
Plot 2 20 28 28
Plot 3 10 36 25
Plot 4 19 12 32
Plot 5 30 25 30
Individuals/20 m2 20.4 28 28
Individuals/20 m2 25.4


Total density = 1.27 individuals/m2


Source: Adapted from Bookhout (1996), data from Larrea (2008).




19II. Guidelines for design and implementation of resource assessments for BioTrade wild-collected species


type of information is obtained from secondary sourc-
es such as scientific reports or local knowledge.


1. Growth time
Life expectancy and growth time of individuals in each
age-size class are important to analyse in population
dynamics and determining the possibilities of sustain-
able production in the long term.


Growth time information could be derived from sec-
ondary information from scientific studies on the spe-
cies biology or traditional local knowledge. In the case
of rapid growth species, such as annual herbs, a field
study could be carried out to monitor the individual’s
growth, measuring the size of individuals in sampling
plots.


When information is not available for the managed
species, it is recommended to take into account in-
formation on similar species and use some referential
values that could be adjusted in the monitoring pro-
gramme under the adaptive management framework.
Table 7 presents examples of growth time information
available for three species and helps to understand
how age classes are defined. More details are found
in the case studies (for further information on the case
studies please visit: www.biotrade.org).


d) Germination, natality and mortality rates


This information can be gathered by scientific literature
if available. If not, permanent plots should be estab-
lished as part of a monitoring programme aimed at


Table 5. Average densities and age classes for Caesalpinia spinosa from two sites in the Ecuadorian Andes


Age class Characteristics
No. individuals
(average plots)


Population
(%)


Seedlings ≤ 1 cm diameter at the base 151 76
Saplings 1–2 cm 13.5 6.82


Young adults
> 2–6 cm (from this size har-


vest starts)
11 5.56


Adults > 6–20 cm 25 7.07
Elders > 20 cm 9 4.55
Total density 209 100
Estimated total density: 0.36–0.63 trees per m2 (mean = 0.5; standard deviation = 0.19)


Source: Adapted from Bookhout (1996), data from Larrea (2008).


Table 6. Information on the reproduction biology for Caesalpinia spinosa, Mauritia flexuosa and Neopicrorhiza
scrophrulariiflora based on secondary sources


Species Sexuality Management implications


Caesalpinia spinosa


Perennial, produces flowers
and seeds every year. Sexual
reproduction occurs through cross-
pollination by insects (i.e. wasps,
honey bees).


In this case it is important to identify the insects
that are responsible for the pollination of plants
and assure that management practices do not
affect pollination.


Mauritia flexuosa


Sexual reproduction system.
Palm dioecious (male and female
individuals are differentiated).
Evidence of density dependence is
reported.


Because of the dioecious condition and density
dependence, management is strongly influenced
by the females’ dynamics (i.e. growth and
reproduction characteristics) and the density of
the population and the effects of this variable on
germination rates.


Neopicrorhiza
scrophrulariiflora


Vegetative growth (ramets) and
sexual reproduction.


Reproduction by ramets makes it difficult to
identify individuals and population analysis
is frequently based on the rate of ramet
production. Management practices should take
into account this condition.


Source: Larrea (2008), Holm et al. (2008) and Ghimire et al. (2005)




20 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


the occurrence of one individual reduces the likelihood
of finding another individual nearby. In this case the in-
dividuals tend to be spread out as far from each other
as possible. In a clumped dispersion, the occurrence
of one individual increases the likelihood of finding an-
other individual nearby. In this case, individuals tend to
form groups (or clumps).


Ecologists are often interested in the spatial distribu-
tion of populations because it provides information
about the social behaviour and/or ecological require-
ments of the species. For example, some plants occur
in clumped distributions because they propagate by
rhizomes (underground shoots) or because seed dis-
persal is limited. Clumped distributions in plants may
also occur because of slight variations in soil chemis-
try or moisture content. Many animals exhibit rather
uniform distributions because they are territorial (es-
pecially birds), expelling all intruders from their territo-
ries. Random distributions are also common, but their
precise cause is more difficult to explain.


Unfortunately, it is often difficult to visually assess the
precise spatial distribution of a population. Further-
more, it is often useful to obtain some number (quan-
titative measure) that describes spatial distribution in
order to compare different populations. For this rea-
son, there are a variety of statistical procedures that
are used to describe spatial distributions.


1. How to measure distribution/dispersion:
Generally two types of methods are used: quad-
rat methods and distance methods (transects). The
quadrat method (variance/mean ratio method) is
based on the Poisson probability distribution whereas
the distance method (Clark-Evans method) relies on
the distance to the nearest neighbour measure, which
measures the probability that a circle of radius (r) is
not empty.


The variance/mean ratio method focuses mainly on
determining whether a species fits a randomly spaced
distribution, but can also be used as evidence for ei-
ther an even or clumped distribution. In the variance/
mean ratio method, data are collected from several
random samples of a given population. In this analy-
sis, it is imperative that data from at least 50 sample
plots are considered. The number of individuals pres-
ent in each sample is compared with the expected
counts in the case of random distribution. The ex-
pected distribution can be found using Poisson dis-
tribution. If the variance/mean ratio is equal to 1, the
population is found to be randomly distributed. If it is


Table 7. Transition time required of individuals from
populations of the three case studies species
from lower size-age class to higher size-age
class


Caesalpinia spinosa (Larrea 2008)


Age class
Transition time


(years)
Seedlings 1
Saplings 2
Young adults 4
Adults 15
Elders 30
Natural dead 10
Total life expectancy 62


Mauritia flexuosa (Holm et al. 2008)


Age class
Transition time


(years)
Seedlings 1
Young juveniles 5
Juvenile 1 8
Juvenile 2 5
Old juvenile 10
Adult 1 17
Adult 2 (elders) 14
Total life expectancy 60


Neopicrorhiza scrophrulariiflora (Ghimire et al. 2005)


Age class
Transition time


(years)
Saplings 1
Young 1.5
Adult 1 3
Adult 2 8
Total life expectancy 13.5


Sources: Larrea (2008), Holm et al. (2008) and


Ghimire et al. (2005) .


filling information gaps not identified during the es-
tablishment of the baseline. This requires marking a
statistically representative number of individuals from
adults to seedlings and to monitor over time (Table 8).


e) Distribution/dispersion


Basically, there are three possible types of spatial dis-
tribution or dispersion of individuals (see Figure 5). In a
random dispersion, the locations of all individuals are
independent of each other. In a uniform dispersion,




21II. Guidelines for design and implementation of resource assessments for BioTrade wild-collected species


Table 8. Considerations to identify key variables used to determine germination and mortality rates


Species Germination rate Mortality rate


Caesalpinia spinosa


The dynamic population model was
built under the assumption of a
germination rate of 0.9 per cent of
all seeds being produced annually.


Age class Mortality
Seedlings 0.91
Saplings 0.19
Young adults 0.08
Adults -0.27
Elders 0.36


Mauritia flexuosa


The effect of density means that
populations with a higher number
of individuals present a lower
growth rate. Seedling survival and
growth decreases as density of the
entire palm population increases.
Consequently, in this case, female
population size and population
growth are the key variables that
determine the number of new
individuals that become part of the
population.


Mortality rate was estimated based on the
number of individuals contained in each size
class that pass to the following size class at the
next time interval. For example, if there were 260
saplings and 247 individuals became juveniles, it
was assumed that 13 individuals died over one
year (time a sapling takes to become juvenile).
These figures give a mortality rate of 22 per cent
(0.22).


Neopicrorhiza
scrophrulariiflora


Based on the data provide by
Ghimire et al. (2005), the dynamic
population model was built on the
assumption of a recruitment rate of
2.3 new individuals each year over
the 1 m2 surveyed.


The assumption was made that immature
individuals have a mortality rate of 11.4 per cent
and other size-age classes 9 per cent. More
specific information is needed.


Source: Larrea (2008), Holm et al. (2008) and Ghimire et al. (2005).


Figure 5. Examples of types of population’s special distributions


Random Clumped Uniform




22 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


Box 3. Species distribution models (SDMs) – an important tool to estimate population size and its distribution at the
habitat level


Building ecological niche models to predict species ranges has been one of the main focuses over the last 20 years in
landscape ecology and conservation biology. Special concern has been devoted to develop species ranges in poorly
known regions where only presence points are available (Guisan and Thuiller 2005, Marmion et al. 2009, and Pearson et
al. 2006). Several analytical approaches have been applied to these challenges, varying from simple sets of rules based
on overlays of environmental and species occurrences data creating a so-called “environmental envelop” (Krabbe et al.
1998) to sophisticated multivariate analyses such as Mahalanobis distance (Cuesta et al. 2003) or logistic regression
(Loiselle et al. 2003).


In the management of larger areas, where inventory plots would not be sufficient to analyse population densities and
distribution, field data could provide information for species niche modelling. In the context of a resource assessment,
habitat models improve substantially the ability to predict species occurrence and to locate new populations for initiation
for management purposes under sustainable harvest regimes. For example, the odds of finding American ginseng in
Shenandoah National Park, United States of America, based on the habitat model were 12.3 times greater than random
searches, and this helped the Fish and Wildlife Service to evaluate population estimates of this traded species (Van Manen
et al. 2005).


The increase in applications of species distribution models is based on the growth in the availability of remotely sensed
data and development of geographical information systems (GIS) techniques integrated with novel statistical methods
(Guisan and Zimmermann 2000). Carefully generated predictive models can effectively contribute to the insufficient field
survey and museum data (Muñoz et al. 2005, Guisan et al. 2006, and Rodriguez et al. 2007) and occasionally even
provide a more useful basis for biodiversity assessments than existing published range and national ecosystem maps
(Bustamante and Seoane 2004).


In this context, different studies confirm that among the different modelling techniques, the maximum entropy algorithm
of the Maxent platform (Phillips et al. 2006, Phillips and Dudík 2008) is one of the best suited for this type of exercises.
The Maxent algorithm is a machine learning technique used to fit the geographical distribution of a species based on a
set of presence-only data points, and a set of environmental descriptors (Elith et al. 2010, Phillips et al. 2006). Maxent has
been tested extensively and has been found to outperform most of the “common” niche modelling techniques, such as
Bioclim (Busby 1991), Domain (Carpenter et al. 1993), generalized additive and generalized linear models (Austin 2002),
genetic algorithm for rule-set production (GARP) (Stockwell 1999), while performing similar to other novel approaches and
machine learning-based techniques both under current and future conditions (Costa et al. 2010, Fitzpatrick and Hargrove
2009, Hijmans and Graham 2006, Loiselle et al. 2008, Phillips and Dudik 2008, VanDerWal et al. 2009, and Veloz 2009).


With these considerations in mind, for the purpose of these guidelines we propose the use of Maxent to define the habi-
tat suitability for any given species of interest in the BioTrade programme, and based on the model outputs define on a
more sound base the collection area and from there quantify the size of the population. The use of this tool could support
national authorities and resources users in the analysis of potential management areas, as well as with the identification
inventory methods to use according to information available and monitoring needs..


significantly greater than 1, the population is found to
be a clumped distribution. Finally, if the ratio is sig-
nificantly less than 1, the population is found to be
evenly distributed. Typical statistical tests used to find
the significance of the variance/mean ratio include the
t-test and chi squared.


In the Clark-Evans nearest neighbour method, the
distance of an individual to its nearest neighbor is re-
corded for each individual in the sample. For two indi-
vidual that are each the other’s nearest neighbor, the
distance is recorded twice, once for each individual.


To receive accurate results, it is suggested that the
number of distance measurements should be at least
50. The average distance between nearest neighbors
is compared with the expected distance in the case
of random distribution to give the ratio:


If this ratio is equal to 1, then the population is ran-
domly dispersed. If the ratio is significantly greater than
1, the population is evenly dispersed. Lastly, if the ratio


Sources: Guisan and Thuiller (2005), Marmion et al. (2009), Pearson et al. (2006), Krabbe et al. (1998), Cuesta et al. (2003),
Loiselle et al. (2003), Van Manen et al. (2005), Guisan and Zimmermann (2000), Muñoz et al. (2005), Guisan et al.
(2006), Rodriguez et al. (2007), Bustamante and Seoane (2004), Phillips and Dudík (2008), Elith et al. (2010), Phillips
et al. (2006), Busby (1991), Carpenter et al. (1993), Austin (2002), Stockwell (1999), Costa et al. (2010), Fitzpatrick
and Hargrove (2009), Hijmans and Graham (2006), Loiselle et al. (2008), VanDerWal et al. (2009), and Veloz (2009)




23II. Guidelines for design and implementation of resource assessments for BioTrade wild-collected species


is significantly less than 1, the population is clumped.
Statistical tests (such as the t-test, chi squared, etc.)
can then be used to determine whether the ratio is
significantly different from 1.


However, many researchers believe that species dis-
tribution models based on statistical analysis, without
including ecological models and theories, are too in-
complete for prediction. Instead of conclusions based
on presence or absence data, probabilities that con-
vey the likelihood a species will occupy a given area


are preferred because these models include an esti-
mate of confidence in the likelihood of the species be-
ing present or absent. Additionally, they are also more
valuable than data collected based on simple pres-
ence or absence because models based on probabil-
ity allow the formation of spatial maps that indicate
how likely a species is to be found in a particular area.
Similar areas can then be compared to see how likely
it is that a species will occur there also; this leads to
a relationship between habitat suitability and species
occurrence (see Box 3).




24 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


The reproductive surplus differs spatially and tempo-
rally as environmental conditions vary, and even in the
absence of exploitation, change is the rule and con-
stancy is the exception (Hilborn et al. 1995). Sustain-
able yields may be estimated by direct experimenta-
tion, observation of natural systems or deduction from
biological understanding (Hilborn et al. 1995, Daly
1990).


Information for estimating harvest rates could derive
from harvesters or scientists. Harvesters have empiri-
cal data on resources availability (yield) and production
trends to analyse whether a population is increasing
or decreasing. Models are then used to calculate sus-
tainable yields for the resource (Stockdale 2005).


Most of the models developed to define harvest rates
depend either on secondary data or observations over
only short time periods. Given the complexity of tropi-
cal ecosystems, such models are very much a “first
look” at the problem and much research has to be
done (Wong 2000). The development of methods for
determining the optimal model on which to base har-
vesting decisions is very much something “science
can contribute” to BioTrade organizations. There is
therefore a need to evaluate both the theory and ex-


Objectives:
• Analyzepopulationdynamicsandharvestimpli-


cations.
• Identifyasuitableharvestrateaccordingtospe-


cies population dynamics.


Expected outcomes:
• Scenarios to analyze implications of harvest


rates on managed population dynamics.
• Suitableharvestratesidentified.


Development:
Harvest rate estimation is developed based on
analysis of species and population data. Dynamic
population models are used as a tool to analyse
information and generate management scenarios.


A. Analysis of population dynamics without har-
vesting.


B. Analysis of the implications of harvesting sce-
narios.


C. Stage 3. Estimation of harvest rates
and sustainable yield


perience of wild-collected species harvesting in order
to derive sound proposals for the analysis of sustain-
ability which can be applied by Biotrade organizations,
from national to local levels.


On the other hand, is important to consider that in
many cases, BioTrade organizations lack sufficient
specialized skills and access to modelling tools. Yet,
based on local knowledge and on the information
gathered during the baseline establishment, organiza-
tions are capable of setting up a preliminary differen-
tial harvest rate in different management areas. The
sustainability of these thresholds will be evaluated with
permanent plots under the monitoring programme
that has to be set up as part of the management plan.
Within the permanent plots marked trees can be eval-
uated as proposed by Peters (1994) in the periodic
harvest adjustment methodology together with key
population variables such as seedlings and sampling
dynamics, age structure and density.


In this context, system dynamics is a tool that has
been applied to natural resources in order to simu-
late population growth and the potential effects of
disturbance, markets, economy and other variables.
System dynamics is an approach to understand the
behaviour of complex systems over time. It deals with
internal feedback loops, stocks, flows and time delays
that affect the behaviour of the entire system.


Simulation models help to visualize and understand
the type of information required for a resource assess-
ment, the applicability of population models for the
design of a robust resource assessment, and high-
light the information gaps and the limitations inherent
in the majority of traded species due to fragmented
or limited knowledge on the ecology of the species.
Yet, model outputs and their ecological interpreta-
tion are extremely useful in the sense of providing a
baseline to produce more refined and sophisticated
population models when more information is available
or produced by the monitoring programme associated
with the management plan. Finally, the outputs of the
models serve as a basis to test if the harvest rate pro-
posed is sustainable over time and the definition of
priority variables that have to be monitored on a long-
term basis.


To guide decisions on harvest rates and management
practices, a first step in defining harvest rates is to
establish the current harvest regimes by local resource
users. Such data relate to management conditions
and socio-economic issues. Examples of the consid-




25II. Guidelines for design and implementation of resource assessments for BioTrade wild-collected species


erations to take into account for the three case studies
are presented in the Box 4, and more information is
presented in Section 2 (For further information on the
case studies please visit: www.biotrade.org).


This guide provides an overview of the key ecological
aspects to consider in defining specific harvest rates
for the three case studies:
• Caesalpinia spinosa;
• Mauritia flexuosa; and
• Neopicrorhiza scrophulariiflora


1. Analysis of population dynamics without
harvesting


Knowing about population dynamics is very important
when estimating sustainable the harvesting limit, as a


Box 4. Considerations to estimate harvest rates for each case study species


Caesalpinia spinosa Mauritia flexuosa
Locally people harvest almost 100 per cent of the total seed
production (Larrea 2008). Yet, different harvest rates were
used in order to create different scenarios and evaluate the in-
fluence harvest rate has on population viability of the species.


Considering a harvest system oriented to extract adult
females from the population, four harvesting scenarios
were generated: 10, 25, 50 and 75 per cent of the
adult female population (Holm et al. 2008). The scenar-
ios consider a density dependence conditions, which
translates into an increased growth rate (λ = 2.366) due
to a lower resource competition a therefore a higher
germination rate per each female palm.


Neopicrorhiza scrophrulariiflora
Based on information provided by Ghimire et al. (2005) the model was built considering different harvesting scenarios:
0, 25, 50, 75, and 100 per cent of adult population as a way to evaluate the influence harvest rate has on population
viability of the species.


harvesting limit estimated at current population lev-
els will be unsustainable if the population is in decline
(Stockdale 2005).


Population dynamics analysis allows the identifica-
tion of key variables that influence the population’s
density in the long term. For example in the case of
Caesalpinia spinosa (Box 5) the seedling mortality rate
was identified as a determinant variable. In this case,
analyses were made using different values of seedling
mortality rates to identify how this variable influences
densities in the long term. As an important conclusion
for management, the analyses allow the identification
of practices to assure low seedling mortality rates to
assure sustainable management.


Box 5. Population dynamics of Caesalpinia spinosa


A dynamic system model using Vensim (version 5.11) was built in order to model the population dynamics of Caesalpinia
spinosa for a timespan of 100 years in a 400 m2 plot. According to this model, the current population presents a continu-
ous decline if all the environmental conditions remain the same. Yet, the population is quite sensitive to the parameter
“seedling mortality rate”. With high mortality rates (0.90 and 0.80) the populations grew in both cases during the first
years reaching a maximum of 400 and 310 individuals during years 6 and 4 respectively. After that, populations decline for
both scenarios reaching 7 and 13 individuals at the end of the 100-year period. On the contrary, if lower rates of seedling
mortality are applied (0.75 and 0.70), then a different outcome is obtained. After 20 years, with a seedling mortality rate
of 0.70, the population is 4 and 5 times bigger than the 0.90 and 0.80 scenarios. Further, when a mortality rate of 0.70 is
applied, the population after 100 years is a bit smaller than at the start, reaching 161 individuals.


The behavior of the different segments of the population in these scenarios differs. The older trees increase their popula-
tion size for all the scenarios during the first 30 years reaching a size 10 times bigger. On the other hand, the saplings
show a dramatic decrease in all scenarios after an initial exponential growth during the first 3 years. After less than 10
years the number of saplings is below the starting value. After 50 years this segment of the population is extinct except
in the 0.70 seedlings mortality rate scenario. This implies that the current population status is highly affected by past and
present human impacts and important management actions are required such as working to decrease the mortality rate
of seedlings.


Sources: Adapted from Larrea (2008), Ghimire et al. (2005), and Holm et al. (2008).




26 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


2. Analysis of the implications of harvesting
scenarios


In population ecology and economics maximum sus-
tainable yield (MSY) is theoretically, the largest yield
(or catch) that can be taken from the stock of a spe-
cies over an indefinite period. This concept has been
developed based on the principles that harvest rates
should equal regeneration rates (Daly 1990). However,
there is currently a need for a review of the definition
and methodology of determining sustainable yield due
to difficulties in its quantitative application. Taking into
account these methodological difficulties, the use of
simulations to analyse harvesting scenarios is a tool to
understand the possible effects of harvesting on the
population and define a harvest rate that could be ad-
justed by monitoring activities.


Once the current population dynamics are under-
stood, population behaviour is simulated using differ-
ent scenarios. Scenarios can be defined according
harvest rates used and varying relevant population
characteristics. For example, in the case of Caesalpin-


Box 6. Harvesting scenarios for Caesalpinia spinosa


Harvesting scenarios were generated: 100, 80, 75 and 50 per cent of the fruit (pods) production and considering seedling
mortality rates of 0.90 and 0.70.


Scenario set 1: Simulations for the four harvest rates tested, using seedling mortality rate of 0.90, population show an
exponential growth during the first 5 to 6 years; after this point, the species declines very fast and ending in a population
of less than 12 individuals for all cases.


Scenario set 2: When a seedling mortality rate of 0.70 is applied, the modelled results are very different, showing a bet-
ter response of the population to the harvested regimes even for the 100 per cent harvesting scenario in which the total
population at the end of the simulation is 20 individuals of which all belong to the elder age class.


A regime with a harvest rate of 50 per cent and 0.70 seedling mortality rate shows a good response by the population
at the end of the 100-year period with a total population of 325 individuals (195 elders, 56 adults, 33 young adults, 21
sapling and 20 seedlings), resembling a constrictive pyramid shape, suggesting that if the population is well managed and
recovers from historical human impacts, it can be sustainably managed in its natural habitat.


According to these results resource managers should consider a harvest rate of 50 per cent and guarantee the imple-
mentation of practices to safeguard seedling survival. Densities and mortality at each age class need to be monitored as
well as the effectiveness of the good practices implemented. Further details are presented in the Section 2. (For further
information on the case studies please visit: www.biotrade.org).


ia spinosa, simulations were made based on different
harvest rates (50, 75, 80 and 100 per cent) and varia-
tions in seedling mortality (90 and 70 per cent), tak-
ing into account that this was identified as a sensitive
parameter. Box 6 presents the results of the scenarios
for Caesalpinia spinosa.


In general, the first scenario set shows that under the
current conditions of the remnant population of Cae-
salpinia spinosa in Ecuador, the total population will
decline after 10 years. We can conclude that this spe-
cies cannot be managed on sustainable basis without
specific environmental good practices guidelines such
as habitat enrichment, silviculture and monitoring of
seedlings (Becerra 2009). Yet, if the remnant habitats
are preserved and human activities are set aside, es-
pecially timber and cattle grazing, the species might
recover and will present a better population structure
and the high mortality rate of seedlings will decrease.
As the second scenario set portrays, with a lower
mortality rate of seedlings, the population is capable
of resisting high fruits extraction regimes.




27II. Guidelines for design and implementation of resource assessments for BioTrade wild-collected species


Objectives:
• Identifygoodmanagementpracticesthatshould


be included in the management plan.
• Recognize information gaps and variables that


need to be included in the monitoring system of
the management plan


Expected outcomes:
• Good practices identified and included in the


management plan.
• Keyvariablestobemonitoredidentified


Development:
Based on results from the population dynamics
and harvest rate scenarios, BioTrade organizations
identify key variables that influence the population’s
density in the long term, information gaps and vari-
ables that determine the species productivity. In
this context, the resource assessment provides
specific information to analyse best practices and
propose a set of variables to be monitored in or-
der to improve the population dynamics knowledge
and, consequently, the productive system.


A. Identification of good practices.
B. Improving monitoring systems.


D. Stage 4. Management implications


1. Identification of good practices


According to WHO (2003) good collection practices
are those that maintain the basic conditions of man-
aged populations and the quality of raw materials in
the long term as well as the survival of wild popula-
tions and their corresponding habitat.


Table 9. Main facts affecting Caesalpinia spinosa production and good practices suggested (for further information on
the case studies please visit: www.biotrade.org)


Main factors affecting population dynamics Good practices suggested


Caesalpinia spinosa population presents high
seedling mortality rate caused by cattle trampling
and grazing. This affects the population growth in
the future and therefore the production of seeds for
trade.
High regimes of seeds extraction represent the
reduction of seeds available in natural habitats to
generate new individuals affecting population growth
and genetic diversity in the mid-term.


• Implement practices to reduce cattle grazing in
Caesalpinia spinosa forest remnants.


• Enrichment of natural habitats by planting seed-
lings and juvenile individuals if possible.


• Regulate seed harvest rates and identify a per-
centage of seeds that should not be harvested in
order to increase germination rates.


• Create a seed bank to monitor germination rates
and produce seedlings for habitat enrichment
and keep a good representation of species ge-
netic diversity.


UNCTAD (2009) recommends that good collection
practices should be implemented, taking into consid-
eration three key factors:
• Thedirectmanagementofspecies;
• Managementofimpactonhabitat;and
• Interaction of actorsmanaging the species along


the value chain.


Based on the results of the population dynamics anal-
yses, a BioTrade organization will be able to identify
those good practices related to the direct species
management of the species such as habitat improve-
ment to assure juveniles survival, identify techniques
to improve germination rates or improve collection
practices to diminish the impact of collection on indi-
vidual survival.


Good collection practices can be defined based on the
identification of such variables that have a high effect
on the population dynamics and the yield. For exam-
ple, in the case of Caesalpinia spinosa cattle grazing
affects directly the survival of seedlings and this has a
direct impact on population growth. In this case, good
practices should be oriented to implement specific
practices such as reduction in cattle grazing, monitor-
ing seedling mortality, proper extraction techniques
that do not affect seedlings and agronomic practices
to increase seedlings survival. Table 9 lists good man-
agement practices which consider the impact of har-
vest rates on population dynamics and the long-term
sustainability of the Caesalpinia spinosa population.


2. Improving monitoring systems


Monitoring is an integral part of management; it is a
process that commences with a baseline survey, ide-
ally undertaken before any interventions take place




28 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


and continuing at frequent intervals with the data used
to revise management prescriptions as necessary.
Without a quantitative and biometrically rigorous in-
ventory it is not possible to say with any confidence
that changes in the resource base are occurring
(Wong 2000).


From an ecological standpoint, one of the most es-
sential ingredients required to achieve a sustainable
level of resource use is information, information about
the density and distribution of resources within the
forest, information about the population structure and
productivity of the resources, and information about
the ecological impact of differing harvest levels. These
are the main themes a monitoring programme should
focus on (Peters 1996).


According to Peters (1994), the impact of harvesting
needs to be evaluated across the entire life cycle of an
exploited species as long-term productivity depends
on continued recruitment of new individuals, as well
as the productivity of the adults. In this context, if an
organization identifies those key variables that have a
direct impact on the population density and the pro-
ductivity, it could define a good monitoring system that
would allow it to improve its management system pro-
gressively.


However, there is no specific methodology for re-
source monitoring and most of the techniques and
methodology discussed above can be used within the
context of a monitoring scheme for NTFP extraction.
An important biometric issue in the design of monitor-
ing activities is the consideration of the “power” of the
design (Wong 2000). This is the programme’s ability
to distinguish trends from random errors in the esti-
mates. Yet, getting sampling errors low requires large
numbers of plots and is therefore costly. Cost is an
important issue for routine monitoring and as a con-
sequence there is much interest in the use of simple,
easy and cheap indirect indicators of resource condi-
tion.


Indirect indicators of NTFP stocks (e.g. market sur-
veys, harvest levels, basal area sweeps) will be an
appropriate basis for making management decisions
(Abbot and Guijt 1998, Cunningham 1996). For ex-
ample, through market surveys Vasquez and Gen-
try (1989) were able to alert conservationists to the
advent of destructive harvesting. Note that different
stakeholders will have different perceptions of change
and therefore will consider different indicators to be
appropriate (Abbot and Guijt 1998).


The review of available literature suggests that there
are two general approaches to monitoring natural
products harvesting. These are monitoring the health
of residual populations which is forest-based (e.g.
methods proposed by Hall and Bawa 1993, Sheil and
van Heist 2000, Pilz and Molina 1996) and monitoring
the size and quality of the harvest (e.g. Wong 2000).
Ideally both approaches should be used in tandem
(such as for caiman, Velasco et al. 2003) and at the lo-
cal scale to permit the development of an understand-
ing of the interaction between resource availability,
harvest intensity and market values.


In the context of the preparation of a resource as-
sessment and applying the adaptive management
approach, the impact of such harvest rates and prac-
tices need to be monitored in order to adjust the pro-
duction system, either to increase productivity or re-
duce the impact on the managed populations. Results
presented by UNCTAD (2009) suggest that monitoring
systems should generate information at three levels:
• Impactonthemanagedresource;
• Biologyofthespecies;and
• Yield.


This would establish whether or not production ca-
pacity (in terms of quality and quantity) meets sustain-
ability criteria. Table10 presents examples of variables
that need to be part of a monitoring system for Cae-
salpinia spinosa including a point (d.) related to rel-
evant information gaps.


This example of Caesalpinia spinosa defines some vari-
ables related to the species biology and yield that could
be applicable to other wild-collected species such as:
• Populationgrowth (totaldensity, adult individuals/


area, young individuals/area);
• Germinationrate;
• Natalityandmortalityrates(individuals/area);and
• Production(biomass/area-biomass/individual).


Other variables, related to the impact of management
practices or fulfilment of information gaps, need to be
identified case by case. For example in the case of
Caesalpinia spinosa it is important to monitor cattle
grazing taking into account that this activity has a
specific impact on the young individuals and conse-
quently on population growth.


a) Periodic harvest adjustment


Harvest rate is a variable that is not monitored as such
taking into account that it depends on the decision
of the resource managers and is adjusted according




29II. Guidelines for design and implementation of resource assessments for BioTrade wild-collected species


to the results of the monitoring system. When several
collection areas are managed at the same time, it is
advisable to define different harvest rates and moni-
tor one or two areas without harvest. This condition
would allow the BioTrade organization to compare the
impact of different harvest rates on the managed pop-
ulation and adjust harvest rates applying an adaptive
management approach.


A method has been developed by Peters (1994, 1996)
that integrates harvesting impacts by monitoring the


Table 10. Suggested variables to be assessed under a monitoring programme for Caesalpinia spinosa


a. Impact on the managed resource
Current situation Variables to be monitored


Cattle grazing is having a negative impact on young
individuals’ survival. A consequence of this will be
a reduction of total population density in the future,
and therefore a reduction in production capacity.


In this context good practices could be monitored to
analyse the reduction of impact of cattle grazing on
the population:
• Practicestoreducecattlegrazing;
• Enrichmentofnaturalhabitat;
• Regulationofseedharvestrates;and
• Creationofaseedbank.


Seedling mortality by cattle grazing.


Forest area affected by cattle grazing.


Growth rate of seedling/juvenile individuals planted.
Percentage of seeds not collected.


Seedling density in natural habitats.


Seed germination rate in seed banks.


b. Biology of the species
Current situation Variables to be monitored


Production capacity of Caesalpinia spinosa depends
on the following aspects:
• Currentpopulationdensitytoassurethe


production of fruits;
• Germinationrate–collectionpracticesneed


to assure a good quantity of seeds that could
generate new individuals; and


• Mortalityofyoungindividualsneedstobe
reduced.


Total density – number of individuals of each age
class in sampling plots.


Germination rate – number of viable seeds produced
per kilogramme based on samples of different
Caesalpinia spinosa forest remnants.


Mortality rate of seedlings and young individuals
that, according to the model, have a higher mortality
rate.


c. Yield
Current situation Variables to be monitored


Fruits of Caesalpinia spinosa are collected for inter-
national markets.


Production of average of fruits per tree.


Production of fruits per area.


d. Information gaps
Current situation Variables to be monitored


There is no information on germination rates. Varia-
tion in germination rates affects directly the popu-
lation growth and the production capacity in the
mid-term.


Number of viable seeds produced per kilogramme
based on samples of different Caesalpinia spinosa
forest remnants.


health of regeneration. This method is based on the
establishment of a network of small permanent regen-
eration plots and visual appraisal of the conditions of
adult trees.


1. Regeneration survey
In these plots a total number of seedlings and sap-
lings from the selected species is recorded and clas-
sified into four size classes. These data represent the
threshold values by which sustainability is measured.
These plots are enumerated at five-year intervals. If at




30 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


a subsequent enumeration seedling or sapling density
drops below the threshold value the harvest intensity
is reduced and vice versa.


2. Harvest assessment


This entails visual appraisals of the conditions of adult
trees along with harvesting activities (Peters 1994).
During harvest activities, the health, flower and seed
abundance and harvesting impacts are recorded for
marked trees in yield plots. If specific problems are
identified, e.g. a drop in productivity, then adjustments
in harvest regimes are needed.


A methodology for using successive approximations
to arrive at a sustainable harvesting level would be


to firstly determine the magnitude and patterns of
year-to-year variability in productivity. This would re-
quire annual observations of fruit production over a
number of consecutive years and complementary
records of climate variable such as rainfall. These
data could provide the basis for forecast models of
fruit production (Wong 2000). Harvest levels could
be set in relation to either long-term yields, to main-
tain the population into the future, or a fraction of the
forecast annual yield etc. Perhaps more important
than being able to set a harvesting level is the ability
to forecast the current year’s harvest so people can
make considered choices as to whether to harvest
and make the necessary preparations (Belonogova
1988).


(For further information on the case studies please visit: www.biotrade.org)




31Glossary


Glossary
BioTrade initiatives: Business ventures in different


stages of development headed by economic
actors (communities, community-based asso-
ciations, small and medium-sized enterprises,
among others) that meet the BioTrade principles
and criteria (UNCTAD 2007a).


BioTrade products and services: BioTrade activi-
ties are generally oriented towards the produc-
tion, transformation and commercialization of
products derived from the sustainable use of bio-
logical resources, or to the provision of services
derived from such resources. BioTrade products
may include those coming from wild collection
or from cultivation practices. The latter refers to
products derived from cultivation of native spe-
cies (domesticated and wild varieties) through
activities such as agriculture or aquaculture. In
this case, cultivation is considered as a strategy
to assure the conservation of concerned species
and their ecosystems. Products derived from wild
collection include products such as fauna (e.g.
ornamental fish), fauna derivates (e.g. crocodile
leather or meat) and flora (e.g. medicinal plants)
(UNCTAD 2007a). Services include, for example,
carbon sequestration and ecotourism.


BioTrade: The term BioTrade refers to those activi-
ties of collection/production, transformation and
commercialization of goods and services derived
from native biodiversity (species and ecosys-
tems), under criteria of environmental, social and
economic sustainability.


Age distribution: The proportion of individuals in a
population in age classes. Typically this is dis-
played in a modified bar chart called an age pyra-
mid.


Age-specific fertility rate: The number of births per
individual within a specific age interval during a
specified time.


Age-specific mortality rate: The fraction of individu-
als in a population that die during a given age
interval. For example, if the probability of dying
between age 5 and 10 is 0.25 or 25 per cent,
that would be the mortality rate for that age class.


Crude birth rate: The number of individuals, per thou-
sand in the population, born during a time inter-
val. For example, crude birth rates for the human
are generally in the range from 10 per thousand
per year to 40 per thousand per year.


Crude death rate: The number of individuals, per
thousand in the population, dying during a time
interval. For example, crude death rates for the
human population generally range from 5 per
thousand per year to 25 per thousand per year.


Density dependence: A form of population growth in
which the birth rates and/or death rates per indi-
vidual depend on the size or density of a popula-
tion. This often results when individuals are com-
peting for some limiting resource.


Dependency ratio: The fraction of a population that is
“dependent” on the rest of the population. In the
human population, this has generally been con-
sidered to be the fraction under 15 years plus the
fraction over 65 years.


Doubling time: The time it would take a population to
double, given no changes in age-specific mortal-
ity or fertility rates. Any change in the fertility or
the mortality graphs changes the doubling time.
Demography represents doubling times as nega-
tive if the population is decreasing.


Finite rate of increase (lambda): A measure of the
rate of growth of a population. The amount that
the population must be multiplied by to give the
population size in the next time unit (assuming
the population is in stable age distribution)


Generation time: The average age at which a female
gives birth to offspring. This is equivalent to the
time that it takes for a population to increase by a
factor equal to the net reproductive rate.


Intrinsic rate of increase (r): A measure of the rate of
growth of a population. This is the instantaneous
rate of change (per individual per time interval),
assuming the population is in stable age distribu-
tion. It is equal to the natural log (ln) of the finite
rate of increase.


Mean life expectancy: How long an individual can be
expected to live, on average. This is influenced
only by the age-specific mortality graph.


Net reproductive rate (R0): The average number of
offspring an individual in a population will pro-
duce in his/her lifetime. Unlike the total fertility
rate, R0 depends on age specific mortality rates.


Population momentum: The tendency for a rapidly
growing population to keep on growing, even
after the implementation of policies designed to
halt population growth.




32 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


Sex ratio: The fraction of the population that is fe-


male. Technically, this value is not a “ratio”, but


this has become a common way of represent-


ing the gender distribution of a population. The


primary sex ratio is the proportion of births that


are female.


Stable age distribution: The age distribution which


the population will reach if allowed to progress


until there is no longer a change in the distribu-
tion.


Survivorship: The probability that an individual sur-
vives from age zero to a given age.


Total fertility rate (TFR): The total number of offspring
a female would have, on average, if she were to
live to the maximum age. (Compare with net re-
productive rate.)




33References


References


Abbot J and Guijt I (1998). Changing views on change: Participatory approaches to monitoring the environment.
International Institute for Environment and Development. London.


Arnold JEM and Pérez MR (2001). Can non-timber forest products match tropical forest conservation and devel-
opment objectives? Ecological Economics 39: 437–447.


Augspurger CK and Kelly CK (1984). Pathogen mortality of tropical tree seedlings: Experimental studies of the
effects of dispersal distance, seedling density, and light conditions. Oecologia 61: 211–217.


Austin MP (2002). Spatial prediction of species distribution: an interface between ecological theory and statistical
modelling. Ecological Modelling 157: 101–118.


Balslev H (2011). Palm Harvest Impacts in North-Western South America. The Botanical Review 77 (4).


Baniya A (2010). FairWild Implementation in a High Risk Species (Neopicrorhiza scrophulariiflora). A model case
study from Nepal Himalaya. WWF Nepal. Unpublished report.


Barbour MT, Gerritsen J, Snyder BD and Stribling JB (1999). Rapid Bioassessment Protocols for Use in Streams
and Wadeable Rivers: Periphyton, Benthic Macroinvertebrates and Fish, Second Edition. EPA 841-B-99-
002. United States Environmental Protection Agency, Office of Water. Washington, DC.


Becerra MT (2009). Aplicación del enfoque de manejo adaptativo en especies silvestres usadas en actividades
de comercio. Memoria para la obtención del Diplomado en Estudios Avanzados. Programa de doctorado:
Biodiversidad: Conservación y Gestión de las Especies y sus Hábitat. Departamento de Ciencias Ambien-
tales y Recursos Naturales. Instituto Universitario de Investigación: Centro Iberoamericano de la Biodivers-
idad (CIBIO) – Universidad de Alicante.


Belonogova TV (1988). Yield forecasting and optimization of berry harvesting in the forests of Southern Karelia,
USSR. Acta Botanica Fennica 136: 19–21.


Bennett EL and Robinson JG (2000). Hunting of wildlife in tropical forests, implications for biodiversity and forest
peoples. Environment Department Papers No. 76, World Bank. Washington, DC.


Blumstein DT (2007). Darwinian decision making: Putting adaptive into adaptive management. Conservation
Biology 21(2): 552–553.


Bodmer RE (1990). Responses of ungulates to seasonal inundations in the Amazon floodplain. Journal of Tropical
Ecology 6: 191–201.


Bookhout TA (1996). Research and management techniques for wildlife and habitats. National Biological Ser-
vices. Ohio Cooperative Fish and Wildlife Research Unit. The Ohio State University. Columbus, Ohio.


Burgener M and Walter S (2007). Trade measures – tools to promote the sustainable use of NWFP? An assess-
ment of trade related instruments influencing the international trade in non-wood forest products and as-
sociated management and livelihood strategies. FAO. Rome.


Busby JR (1991). Bioclim – a bioclimate analysis and prediction system. Plant Protection Quarterly 6: 8–9.


Bustamante J and Seoane J (2004). Predicting the distribution of four species of raptors (Accipitridae) in southern
Spain: Statistical models work better than existing maps. Journal of Biogeography 31: 295–306.


Campbell K, Donlan CJ, Cruz F, Carrion V (2004). Eradication of feral goats Capra hircus from Pinta Island, Ga-
lápagos, Ecuador. Oryx 38: 328–333.


Carpenter G, Gillison AN, Winter J (1993). DOMAIN: A flexible modelling procedure for mapping potential distri-
butions of plants and animals. Biodiversity and Conservation 2: 667–680.


Carrera L (2000). Aguaje (Mauritia flexuosa) a promising crop of the Peruvian Amazon. Acta Horti 531: 229–235.


Chambers R (1994). Participatory rural appraisal (PRA): Analysis of experience. World Development 22: 1253–
1268.




34 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


Convention on Biological Diversity (2004). Addis Ababa Principles and Guidelines. www.cbd.int/sustainable/ad-
dis.shtml.


Coomes OT, Barham BL and Takasaki Y (2004). Targeting conservation–development initiatives in tropical for-
ests: Insights from analyses of rain forest use and economic reliance among Amazonian peasants. Ecologi-
cal Economics 51: 47–64.


Coppen JJW (1995). Non-wood forest products 6: gums, resins and latexes of plant origin. FAO. Rome.


Costa G, Nogueira C, Machado R, Colli G (2010). Sampling bias and the use of ecological niche modeling in con-
servation planning: a field evaluation in a biodiversity hotspot. Biodiversity and Conservation 19: 883–899.


Cuesta F, Peralvo M and van Manen FT (2003). Andean Bear Habitat Use in the Oyacachi River Basin, Ecuador.
Ursus 14: 198–209.


Cunningham AB, Shanley P, Laird SA (2008). Health, habitats and medicinal plant use. In: Human health and
forests: a global overview of issues, practice and policy. Earthscan. London, UK.


Cunningham AB (1994). Combining skills: participatory approaches in biodiversity conservation. Strelitzia 1: 149–
168. In: Huntley BJ, ed. Botanical diversity in southern Africa. National Botanical Institute. Pretoria.


Cunningham AB (1996). People, park and plant use: recommendations for multiple-use zones and development
alternatives around Bwindi-Impenetrable National Park, Uganda. People and Plants Working Paper 4: 1–58.
UNESCO. Paris.


Daly HE (1990). Toward some operational principles of sustainable development. Ecological Economics 2: 1–6.


De Franca LF, Reber G, Meireles A, Machado N and Brunner G (1999). Supercritical extraction of carotenoids
and lipids from buriti (Mauritia flexuosa), a fruit from the Amazon region. Journal of Supercritical Fluids 14:
247–256.


Denevan WM and Treacy J (1983). Young managed fallows at Brillo Nuevo. Advances in Economic Botany 5:
8–46.


Duke JA (1981). Caesalpinia spinosa. In: Handbook of Legumes of World Economic Importance. Plenum Press.
New York.


Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2010). A statistical explanation of MaxEnt for ecolo-
gists. Diversity and Distributions 17: 43–57.


Fitzpatrick M, Hargrove W (2009). The projection of species distribution models and the problem of non-analog
climate. Biodiversity and Conservation 18: 2255–2261.


Ford A (2009). Modeling the Environment, second edition. Island Press. Washington DC.


Getz WM and Haight RG (1989). Population harvesting. Princeton University Press. Princeton, New Jersey.


Ghimire SK, McKey D, Aumeeruddy-Thomas Y (2005). Conservation of Himalayan medicinal plants: Harvesting
patterns and ecology of two threatened species, Nardostachys grandiflora DC. and Neopicrorhiza scrophu-
lariiflora (Pennell) Hong. Biological Conservation 124: 463–475.


Guillot G and Becerra MT (2003). Lineamientos para el manejo sostenible de sistemas productivos in situ. In:
Becerra MT ed. Lineamientos para el manejo de sistemas de aprovechamiento de recursos naturales in
situ. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt. Bogotá, Colombia. Pages
164–183. (www.humboldt.org.co/download/andes/IAVH-00259.pdf).


Guisan A, Lehmann A, Ferrier S, Austin M, Overton JMC, Aspinall R, Hastie T (2006). Making better biogeo-
graphical predictions of species’ distributions. Journal of Applied Ecology 43: 386–392.


Guisan A, Thuiller W (2005). Predicting species distribution: offering more than simple habitat models. Ecology
Letters 8: 993–1009.


Guisan A, Zimmermann NE (2000). Predictive habitat distribution models in ecology. Ecological Modelling 135:
147–186.




35References


Hall P and Bawa K (1993). Methods to assess the impact of extraction of non-timber forest products on non-
timber tropical forest products on plant populations. Economic Botany 47: 234–247.


Henderson A, Galeano G and Bernal R (1995). Field guide to the palms of the Americas. Princeton University
Press. Princeton, New Jersey.


Hijmans RJ, Graham CH (2006). The ability of climate envelope models to predict the effect of climate change on
species distributions. Global Change Biology 12: 2272–2281.


Hilborn R, Walters CJ and Ludwig D (1995). Sustainable exploitation of renewable resources. Annual Review of
Ecology and Systematics 26: 45–67.


Holm JA (2007). Population dynamics of the Amazonian palm Mauritia flexuosa: Model development and simula-
tion analysis. Thesis for the degree of Master of Science. University of Florida.


Holm JA, Miller CJ and Cropper WP Jr (2008). Population Dynamics of the Dioecious Amazonian Palm Mauritia
flexuosa: Simulation Analysis of Sustainable Harvesting. Biotropica 40: 550–558.


Kahn F (1991). Palms as key swamp forest resources in Amazonia. Forest Ecology Management 38: 133–142.


Krabbe N et al. (1998). An atlas of avian diversity in the Ecuadorian Andes. Centre for Research on Cultural and
Biological Diversity of Andean Rainforests (DIVA), DIVA. Technical Report No. 4. Quito, Ecuador.


Larrea M (2008). Aspectos ambientales relevantes para la definición de criterios de manejo sustentable de la tara
(Caesalpinia spinosa) en cuatro sectores (Provincias de: Pichincha, Imbabura, Tungurahua/Chimborazo, y
Loja) aledaños a remanentes de matorral seco interandino. Documento de consultoría. Programa Regional
Ecobona. Quito.


Larrea M (2011). La tara, guarango o Tayo (Caesalpinia spinosa), en la región Andina: Criterius ambientales para
la sustentabilidad de su aprovechamiento y manejo en Bolivia, Ecuador y Perú. Programa Regional Eco-
bona. Quito.


Liu X-L, Qian Z-G, Liu F-H, Yang Y-W, Pu C-X (2011). Genetic diversity within and among populations of Neopi-
crorhiza scrophulariiflora (Scrophulariaceae) in China, an endangered medicinal plant. Biochemical System-
atics and Ecology 39: 297–301.


Loiselle BA, Howell CA, Graham CH, Goerck JM, Brooks T, Smith KJ and William PH (2003). Avoiding pitfalls of
using species distribution models in conservation planning. Conservation Biology 17: 1591–1600.


Loiselle BA, Jørgensen PM, Consiglio T, Jiménez I, Blake JG, Lohmann LG, Montiel OM (2008). Predicting spe-
cies distributions from herbarium collections: does climate bias in collection sampling influence model out-
comes? Journal of Biogeography 35: 105–116.


Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W (2009). Evaluation of consensus methods in predic-
tive species distribution modelling. Diversity and Distributions 15: 59–69.


Martinez-Ramos, M, Sarukhan J and Pinero D (1988). The demography of tropical trees in the context of gap
dynamics: The case of Astrocaryum mexicanum at Los Tuxtlas tropical rainforest. In: Davy J, Hutchings MJ
and Watkinson AR, eds Plant population ecology. Blackwell Publications. Oxford, UK.


Matos DM, Freckleton RP and Watkinson AR (1999). The role of density dependence in the population dynamics
of a tropical palm. Ecology 80: 2635–2650.


Montúfar R, Anthelme F, Pintaud J-C, Balslev H (2011). Disturbance and Resilience in Tropical American Palm
Populations and Communities. The Botanical Review 77 (4): 426–461.


Muñoz AR, Real R, Barbosa AM, Vargas JM (2005). Modelling the distribution of Bonelli’s eagle in Spain: implica-
tions for conservation planning. Diversity and Distributions 11: 477–486.


Odum E and Barrett G (2005). Fundamentals of ecology. Thomson Brooks/Cole.


Olmsted I, Alvarez-Buylla ER (1995). Sustainable Harvesting of Tropical Trees: Demography and Matrix Models of
Two Palm Species in Mexico. Ecological Applications 5: 484–500.




36 GUIDELINES FOR THE SUSTAINABLE MANAGEMENT OF BIOTRADE PRODUCTS: RESOURCE ASSESSMENT


Olsen CS, Larsen OH (2003). Alpine medicinal plant trade and Himalayan mountain livelihood strategies. Geo-
graphical Journal 169: 243–254.


Olsen CS (2005). Trade and conservation of Himalayan medicinal plants: Nardostachys grandiflora DC. and Neo-
picrorhiza scrophulariiflora (Pennell) Hong. Biological Conservation 125: 505–514.


Padoch C (1988). Aguaje (Mauritia flexuosa L. f.) in the economy of Iquitos, Peru. Advances in Economic Botany
6: 214–224.


Pearson RG, Thuiller W, Araújo MB, Martinez-Meyer E, Brotons L, McClean C, Miles L, Segurado P, Dawson
TP and Lees D (2006). Model-based uncertainty in species’ range prediction. Journal of Biogeography 33,
1704–1711.


Perez MR, Byron N (1999). A Methodology to Analyze Divergent Case Studies of Non-Timber Forest Products
and Their Development Potential. Forest Science 45: 1–14.


Peters CM (1994). Sustainable harvest of non-timber plant resources in tropical moist forest. An ecological
primer. Biodiversity Support Program. Washington, DC.


Peters CM (1996). The Ecology and Management of Non-timber Forest Resources. World Bank Technical Paper
No. 322. The World Bank, Washington, DC.


Peters CM, Balick MJ, Kahn F and Anderson AB (1989). Oligarchic forests of economic plants in Amazonia: uti-
lization and conservation of an important tropical resource. Conservation Biology 3: 341–349.


Phillips SJ, Anderson RP and Schapire RE (2006). Maximum entropy modeling of species geographic distribu-
tions. Ecological Modelling 190: 231–259.


Phillips SJ and Dudík M (2008). Modeling of species distributions with Maxent: new extensions and a compre-
hensive evaluation. Ecography 31: 161–175.


Pilz D, Molina R eds (1996). Managing forest ecosystems to conserve fungus diversity and sustain wild mush-
room harvests. General Technical Report PNW-GTR-371. Portland, Oregon. United States Department
of Agriculture, Forest Service, Pacific Northwest Research Station. http://www.fs.fed.us/pnw/pubs/pnw_
gtr371.pdf.


Ponce ME, Stauffer FW, Lourdes-Olivo M and Ponce MA (2000). Mauritia flexuosa L. f. (Arecaceae). A revision
of its use and conservation status in the Amazon Basin, with particular emphasis on Venezuela. Acta Bot.
Venezuelica 23: 19–46.


Primack RB (2004). Primer of Conservation Biology. Sinauer Associates. Sunderland, MA.


Robinson JG and Bodmer RE. (1999). Towards wildlife management in tropical forests. Wildlife Society. Bethes-
da, MD, USA.


Rodríguez JP, Brotons L, Bustamante J, Seoane J (2007). The application of predictive modelling of species
distribution to biodiversity conservation. Diversity and Distributions 13: 243–251.


Ros-Tonen MAF, van Andel T, Morsello C, Otsuki, K, Rosendo S and Scholz I (2008). Forest-related partnerships
in Brazilian Amazonia: there is more to sustainable forest management than reduced impact logging. Forest
Ecology and Management 256: 1482–1497.


Salafsky N, Margoluis R and Redford KH (2001). Adaptive management: a tool for conservation practitioners.
Biodiversity Support Program. Washington, DC.


Sarukhan J, Pinero D and Martínez-Ramos M (1985). Plant demography: A community-level interpretation. In:
White J, ed. Studies in plant demography. Academic Press. London, UK.


Schreiber ES, Bearlin A, Nicol S and Todd C (2004). Adaptive management: A synthesis of current understanding
and effective implementation. Ecological Management and Restoration 5: 177–182.


Sheil D and van Heist M (2000) Ecology for tropical forest management. International Forest Review 2: 261–270.


Smith TM and Smith RL (2008). Elements of Ecology. Pearson Benjamin Cummings.




37References


Stockdale M (2005). Steps to sustainable and community-based NTFP management. A manual written with
special reference to South and Southeast Asia. NTFP Exchange Programme for South and Southeast Asia.
Philippines.


Stockwell D (1999). The GARP modelling system: problems and solutions to automated spatial prediction. Inter-
national Journal of Geographical Information Science 13: 143–158.


UNCTAD (2007a). UNCTAD BioTrade Initiative: BioTrade Principles and Criteria. United Nations Conference on
Trade and Development. UNCTAD/DITC/TED/2007/4. http://unctad.org/en/docs/ditcted20074_en.pdf.


UNCTAD (2007b). UNCTAD BioTrade Implementation Strategy. United Nations Conference on Trade and Devel-
opment. UNCTAD/DITC/TED/2005/5.


UNCTAD (2009). Guidelines for the Development and Implementation of Management Plans for Wild-collected
Plant Species used by Organizations Working with Natural Ingredients. United Nations Conference on Trade
and Development. UNCTAD/DITC/TED/2007/8.


Van Manen FT, Young JA, Thatcher CA, Cass WB and Ulrey C (2005). Habitat Models to Assist Plant Protection
Efforts in Shenandoah National Park, Virginia, USA. Natural Areas Journal. 25: 339–350.


VanDerWal J, Shoo LP, Graham C, Williams SE (2009). Selecting pseudo-absence data for presence-only distri-
bution modeling: How far should you stray from what you know? Ecological Modelling 220: 589–594.


Vasquez R, Gentry AH (1989). Use and Misuse of Forest-harvested Fruits in the Iquitos Area. Conservation Biol-
ogy 3: 350–361.


Velasco AG, Colomine R de Sola and Villaroel G (2003). Effects of sustained harvests on wild populations of Cai-
man crocodilus crocodilus in Venezuela. Interciencia 28: 3–16.


Veloz SD (2009). Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche
models. Journal of Biogeography 36: 2290–2299.


Wagner A, Kriechbaum M, Koch MA (2008). Applied Vulnerability Assessment of Useful Plants: A case study of
Tibetan Medicinal Plants from Nepal. Botanische Jahrb 127: 359–387.


Walters CJ and Hilborn R (1978). Ecological optimization and adaptive management. Annual Review of Ecology
and Systematics 9: 157–188.


Walters CJ and Holling S (1990). Large-scale management experiments and learning by doing. Ecology 71:
2060–2068.


Widayati A, Jones S, Carlisle B (2010). Accessibility Factors and Conservation Forest Designation Affecting Rat-
tan Cane Harvesting in Lambusango Forest, Buton, Indonesia. Human Ecology 38: 731–746.


Wilhere G (2002). Adaptive management in habitat conservation plans. Conservation Biology 16: 20–29.


Wilkie DS and Godoy RA (1996). Trade, Indigenous Rain Forest Economies and Biological Diversity. In: Ruiz, M
and Arnold JEM, Current issues in non-timber forest products research. Centre for International Forestry
Research. Bogor, Indonesia.


Wong JLG (2000). The biometrics of non-timber forest product resource assessment: A review of current meth-
odology. Forest Research Programme of the United Kingdom Department for International Development.


WHO (2003). Guidelines on good agricultural and collection practices (GACP) for medicinal plants. World Health
Organization. Geneva.


Young J, van Manen F, Thatcher C (2011). Geographic Profiling to Assess the Risk of Rare Plant Poaching in
Natural Areas. Environmental Management 48: 577–587.




Login