IUCN Red List Guidelines - The IUCN Red List of Threatened Species

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Red List Guidelines 16 at applying the criteria. In addition to the quality and completeness of the data (or lack of), there may be uncertainty in the data itself, which needs to be considered in a Red List assessment. Data uncertainty is discussed separately in section 3.2. The IUCN criteria use the terms Observed, Estimated, Projected, Inferred, and Suspected to refer to the quality of the information for specific criteria. For example, criterion A allows inferred or suspected reduction, whereas criterion C1 allows only estimated declines and criterion C2 specifies “observed, projected, or inferred” declines. These terms are defined as follows: Observed: information that is directly based on well-documented observations of all known individuals in the population. Estimated: information that is based on calculations that may include statistical assumptions about sampling, or biological assumptions about the relationship between an observed variable (e.g., an index of abundance) to the variable of interest (e.g., number of mature individuals). These assumptions should be stated and justified in the documentation. Estimation may also involve interpolation in time to calculate the variable of interest for a particular time step (e.g., a 10-year reduction based on observations or estimations of population size 5 and 15 years ago). For examples, see discussion under criterion A. Projected: same as “estimated”, but the variable of interest is extrapolated in time towards the future. Projected variables require a discussion of the method of extrapolation (e.g., justification of the statistical assumptions or the population model used) as well as the extrapolation of current or potential threats into the future, including their rates of change. Inferred: information that is based on indirect evidence, on variables that are indirectly related to the variable of interest, but in the same general type of units (e.g., number of individuals or area or number of subpopulations). Examples include population reduction (A1d) inferred from a change in catch statistics, continuing decline in number of mature individuals (C2) inferred from trade estimates, or continuing decline in area of occupancy (B1b(ii,iii), B2b(ii,iii)) inferred from rate of habitat loss. Inferred values rely on more assumptions than estimated values. For example, inferring reduction from catch statistics not only requires statistical assumptions (e.g., random sampling) and biological assumptions (about the relationship of the harvested section of the population to the total population), but also assumptions about trends in effort, efficiency, and spatial and temporal distribution of the harvest in relation to the population. Inference may also involve extrapolating an observed or estimated quantity from known subpopulations to calculate the same quantity for other subpopulations. Whether there are enough data to make such an inference will depend on how large the known subpopulations are as a proportion of the whole population, and the applicability of the threats and trends observed in the known subpopulations to the rest of the taxon. The method of extrapolating to unknown subpopulations depends on the criteria and on the type of data available for the known subpopulations. Further guidelines are given under specific criteria (e.g., see section 5.8 for extrapolating population reduction for criterion A assessments).
Red List Guidelines 17 Suspected: information that is based on circumstantial evidence, or on variables in different types of units, for example, % population reduction based on decline in habitat quality (A1c) or on incidence of a disease (A1e). For example, evidence of qualitative habitat loss can be used to infer that there is a qualitative (continuing) decline, whereas evidence of the amount of habitat loss can be used to suspect a population reduction at a particular rate. In general, a suspected population reduction can be based on any factor related to population abundance or distribution, including the effects of (or dependence on) other taxa, so long as the relevance of these factors can be reasonably supported. 3.2 Uncertainty The data used to evaluate taxa against the criteria are often obtained with considerable uncertainty. Uncertainty in the data should not be confused with a lack of data for certain parts of a species’ range or a lack of data for certain parameters. This problem is dealt with in section 3.1 (Data availability, inference and projection). Data uncertainty can arise from any one or all of the following three factors: natural variability, vagueness in the terms and definitions used in the criteria (semantic uncertainty), and measurement error (Akçakaya et al. 2000). The way in which uncertainty is handled can have a major influence on the results of an evaluation. Details of methods recommended for handling uncertainty are given below. 3.2.1 Types of uncertainty Natural variability results from the fact that species’ life histories and the environments in which they live change over time and space. The effect of this variation on the criteria is limited, because each parameter refers to a specific time or spatial scale. However, natural variability can be problematic, e.g. there is spatial variation in age-at-maturity for marine turtles, and a single estimate for these taxa needs to be calculated to best represent the naturally occurring range of values. Semantic uncertainty arises from vagueness in the definition of terms in the criteria or lack of consistency in different assessors’ usage of them. Despite attempts to make the definitions of the terms used in the criteria exact, in some cases this is not possible without the loss of generality. Measurement error is often the largest source of uncertainty; it arises from the lack of precise information about the quantities used in the criteria. This may be due to inaccuracies in estimating values or a lack of knowledge. Measurement error may be reduced or eliminated by acquiring additional data (Akçakaya et al. 2000; Burgman et al. 1999). Another source of measurement error is ‘estimation error’, i.e. sampling the wrong data or the consequences of estimating a quantity (e.g., natural mortality) based on a weak estimation method. This source of measurement error is not necessarily reduced by acquiring additional data. 3.2.2 Representing uncertainty Uncertainty may be represented by specifying a best estimate and a range of plausible values for a particular quantity. The best estimate can itself be a range, but in any case the best estimate should always be included in the range of plausible values. The plausible range may be established using various methods, for example based on confidence intervals, the opinion of a single expert, or the consensus view of a group of experts. The method used should be stated and justified in the assessment documentation.
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IUCN Red List Guidelines - The IUCN Red List of Threatened Species

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