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APPLYING THE IUCN RED LIST CATEGORIES IN A FOREST SETTING
The present version is not expected to be updated in the foreseeable future. A comprehensive
set of guidelines (IUCN 2005) and a documentation format have also been produced. Evaluated
species must now follow a submission system, involving the completion of a four-page
information sheet with a 15-page annex to capture information on habitat, threat, conservation
measures, use and trade. Forms are submitted to the Red List Secretariat and evaluated by the
appropriate Red List Authority. Depending on the approval of the assessment the species will
be published in the next edition of the IUCN Red List of Threatened Species TM .
A QUANTITATIVE ASSESSMENT WHERE FEW
QUANTITATIVE DATA EXIST
All numerical data, as well as less quantitative information, are uncertain to some extent and
most of the difficulty of using the red list categories is related to uncertainty of various kinds
(Akçakaya et al. 2000). Estimating population sizes and declines for individual species depends,
at best, on the use of statistical distributions that are subject to environmental influences, intra
and inter-population variation, or, at worse, on circumstantial information, inferences from
related taxa or trends in the species’ habitat.
The way in which uncertainty within the data is handled has a significant influence on the
outcome of the assessment. Perversely, the more data available on a species the greater the
number of options available to carry out the categorization, and as a consequence additional
uncertainties creep into the assessment and the need for detail in the guidelines increases. An
illustration of this paradox is the category ‘data deficient’, which is intended for both species
that are “well-studied, with biology well known, but where appropriate data on abundance
and/or distribution are lacking”; and for species known from type specimens for which there
are no available data at all.
Data uncertainty is recognized to be a result of either measurement error or natural variation
or semantic vagueness (Akçakaya et al. 2000)—the latter being the payback for designing a
system that has to limit explicitness in order to conserve its general applicability. The authors
of the guidelines and criteria make a considerable effort to describe how assessors deal with
data paucity and uncertainty. Specific methods for dealing with different forms of uncertainty
are developed using fuzzy numbers (Akçakaya et al, 2000). Assessors are suggested to provide
range values and best estimates and describe the means through which these were attained—
through confidence limits or expert opinion etc. They are also advised to be explicit about
their attitude to risk and dispute, both of which influence the interpretation of data and the
management of uncertainty. The qualification of individual species under a range of categories
to reflect data uncertainty is acceptable—although only one category will be published in a
red listing.
Fuzzy numbers are most effective when datasets are relatively rich and measurement error is
the greatest constraint. Where data are poor, the assessor is faced with the quandary of using
estimation, inference and even suspicion in what appears to be a well-defined quantitative
framework. In these cases, where qualitative data are used to answer a quantitative question
the possibilities for interpretational and semantic errors become more significant. For example,
a ‘subpopulation’, which is used in criteria B and C, is defined by rates of genetic exchange
(“typically one successful migrant individual per year or less”). Taking tree species as an
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