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