E XTENT OF OCCURRENCE ( CRITERIA A AND B)

Extent of occurrence is defined as "the area contained within the shortest continuous imaginary boundary which can be drawn to encompass all the known, inferred or projected sites of present occurrence of a taxon, excluding cases of vagrancy" (IUCN 2001, 2012b).

Extent of occurrence (EOO) is a parameter that measures the spatial spread of the areas currently occupied by the taxon. The intent behind this parameter is to measure the degree to which risks from threatening factors are spread spatially across the taxon’s geographical distribution. The theoretical basis for using EOO as a measure of risk spreading is the observation that many environmental variables and processes are spatially correlated, meaning that locations that are close to each other experience more similar (more correlated) conditions over time than locations that are far away from each other. These processes include both human threats (such as diseases, invasive species, oil spills, non-native predators, habitat loss to development, etc.) and natural processes (fluctuations in environmental variables such as droughts, heat waves, cold snaps, hurricanes and other weather events, as well as other disturbance events such as fires, floods, and volcanism). Higher correlation leads to higher overall extinction risk, so that, all other things being equal, a set of populations spread in a small area have higher extinction risk overall than a set of populations spread over a larger area.

EOO is not intended to be an estimate of the amount of occupied or potential habitat, or a general measure of the taxon’s range. Other, more restrictive definitions of “range” may be more appropriate for other purposes, such as for planning conservation actions. Valid use of the criteria requires that EOO is estimated in a way that is consistent with the thresholds set therein.

In thinking about the differences between EOO and AOO (area of occupancy; discussed in section 4.10), it may be helpful to compare species that have similar values for one of these spatial metrics and different values for the other. All else being equal, larger EOOs usually result in a higher degree of risk spreading (and hence a lower overall risk of extinction for the taxon) than smaller EOOs, depending on the relevant threats to the taxa. For example, a taxon with occurrences distributed over a large area is highly unlikely to be adversely affected across its entire range by a single fire because the spatial scale of a single occurrence of this threat is narrower than the spatial distribution of the taxon. Conversely, a narrowly distributed endemic taxon, with the same AOO as the taxon above, may be severely affected by a fire across its entire EOO because the spatial scale of the threat is larger than, or as large as, the EOO of the taxon.

In the case of migratory species, EOO should be based on the minimum of the breeding or non-breeding (wintering) areas, but not both, because such species are dependent on both areas, and the bulk of the population is found in only one of these areas at any time.

If EOO is less than AOO, EOO should be changed to make it equal to AOO to ensure consistency with the definition of AOO as an area within EOO.

"Extent of occurrence can often be measured by a minimum convex polygon (the smallest polygon in which no internal angle exceeds 180 degrees and which contains all the sites of occurrence)” (IUCN 2001, 2012b). The IUCN Red List Categories and Criteria state that EOO may exclude “discontinuities or disjunctions within the overall distribution of the taxa”.

However, for assessments of criterion B1, exclusion of areas forming discontinuities or disjunctions from estimates of EOO is strongly discouraged. Exclusions are not recommended for criterion B1, because disjunctions and outlying occurrences accurately reflect the extent to which a large range size reduces the chance that the entire population of the taxon will be affected by a single threatening process. The risks are spread by the existence of outlying or disjunct occurrences irrespective of whether the EOO encompasses significant areas of unsuitable habitat. Inappropriate exclusions of discontinuities or disjunctions within the overall distribution of a taxon will underestimate EOO for the purpose of assessing criterion B and consequently will underestimate the degree to which risk is spread spatially for the taxon.

When there are such discontinuities or disjunctions in a species distribution, the minimum convex polygon (also called the convex hull) yields a boundary with a very coarse level of resolution on its outer surface, resulting in a substantial overestimate of the range, particularly for irregularly shaped ranges (Ostro et al. 1999). The consequences of this bias vary, depending on whether the estimate of EOO is to be used for assessing the spatial thresholds in criterion B or whether it is to be used for estimating or inferring reductions (criterion A) or continuing declines (criteria B and C). The use of convex hulls is unlikely to bias the assessment of EOO thresholds under criterion B, because disjunctions and outlying occurrences often do contribute to the spatial spread of risk (see above). This is also true for "doughnut distributions" (e.g.

aquatic species confined to the margins of a lake) and elongated distributions (e.g., coastal species). In the case of species with linear elongated distributions, minimum convex polygon may lead to an overestimate of extinction risk. Nevertheless, given the paucity of practical methods applicable to all spatial distributions, and the need to estimate EOO consistently across taxa, minimum convex polygon remains a pragmatic measure of the spatial spread of risk.

However, the bias associated with estimates based on convex hulls, and their sensitivity to sampling effort, makes them less suitable as a method for comparing two or more temporal

estimates of EOO for assessing reductions or continuing declines. If outliers are detected at one time and not another, this could result in erroneous inferences about reductions or increases.

Therefore, a method such as the -hull (a generalization of a convex hull) is recommended for assessing reductions of continuing declines in EOO because it substantially reduces the biases that may result from the spatial arrangement of habitat (Burgman and Fox 2003). The -hull provides a more repeatable description of the external shape of a species’ range by breaking it into several discrete patches when it spans uninhabited regions. For -hulls the estimate of area and trend in area also converges on the correct value as sample size increases, unless other errors are large. This does not necessarily hold for convex hulls. Kernel estimators may be used for the same purpose but their application is more complex.

To estimate an -hull, the first step is to make a Delauney triangulation of the mapped points of occurrence (Figure 4.5). The triangulation is created by drawing lines joining the points, constrained so that no lines intersect between points. The outer surface of the Delauney triangulation is identical to the convex hull.

Figure 4.5. Illustration of -hull. The lines show the Delauney triangulation (the intersection points of the lines are the taxon’s occurrence locations). The sum of the areas of darker triangles is EOO based on the -hull. The two lighter coloured triangles that are part of the convex hull are excluded from the -hull.

The second step is to measure the lengths of all of the lines, and calculate the average line length. The third step is to delete all lines that are longer than a multiple () of the average line length. (This product of  and the average line length represents a “discontinuity distance”.) The value of  can be chosen with a required level of resolution in mind. The smaller the value of , the finer the resolution of the hull. Experience has shown that an  value of 2 is a good starting point for some species (however, the value to use for specific cases of assessing reductions in EOO should be based on a compromise between minimizing the potential bias associated with incomplete sampling of outlying occurrences and minimizing the departure from a convex hull).

This process results in the deletion of lines joining points that are relatively distant, and may subdivide the total range into more than one polygon. The final step is to calculate the extent of occurrence by summing the areas of all remaining triangles. When this exercise is repeated to estimate EOO from a second temporal sample of points (and hence assess change in EOO), the same discontinuity distance between points should be used as a threshold for deleting lines (rather than the same value of ). This will reduce bias due to variation in sampling effort between the two surveys and the bias due to changing average line length with more or fewer occurrences.

Extent of occurrence and area of occupancy are measures of the current distribution, i.e. they should not include areas where the species no longer exists. On the other hand, these measures should not only include the actually known sites, but also inferred or projected sites (see section 4.10.7). For instance, sites can be inferred from presence of known appropriate habitat, but where the species has not yet been searched for. In doing so, it will be important to judge to what extent the taxon has been looked for. Incorporating inferred sites results in a range of plausible values, which may give a range of plausible Red List Categories (see sections 3.1 on Data availability, inference and projection, and 3.2 on Uncertainty).