Integrating habitat-masked range maps with quantifications of prevalence to estimate area of occupancy in IUCN assessments

Abstract

Estimates of species geographic ranges constitute critical input for biodiversity assessments, including those for the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species. Area of occupancy (AOO) is one metric that IUCN uses to quantify a species' range, but data limitations typically lead to either under- or overestimates (and unnecessarily wide bounds of uncertainty). Fortunately, existing methods in which range maps and land-cover data are used to estimate the area currently holding habitat for a species can be extended to yield an unbiased range of plausible estimates for AOO. Doing so requires estimating the proportion of sites (currently containing habitat) that a species occupies within its range (i.e., prevalence). Multiplying a quantification of habitat area by prevalence yields an estimate of what the species inhabits (i.e., AOO). For species with intense sampling at many sites, presence-absence data sets or occupancy modeling allow calculation of prevalence. For other species, primary biodiversity data (records of a species' presence at a point in space and time) from citizen-science initiatives and research collections of natural history museums and herbaria could be used. In such cases, estimates of sample prevalence should be corrected by dividing by the species' detectability. To estimate detectability from these data sources, extensions of inventory-completeness analyses merit development. With investments to increase the quality and availability of online biodiversity data, consideration of prevalence should lead to tighter and more realistic bounds of AOO for many taxonomic groups and geographic regions. By leading to more realistic and representative characterizations of biodiversity, integrating maps of current habitat with estimates of prevalence should empower conservation practitioners and decision makers and thus guide actions and policy worldwide.

Keywords: AOO; IUCN Red List; Lista Roja UICN; amenazada; detección; detection; distribución geográfica; extinción; extinction; geographic distribution; occupied; ocupada; riesgo; risk; threatened.

FIGURE 1

Schematic hypothetical illustration of existing methods to estimate area of occupancy (AOO) for assessment for the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species following the approach explained in the IUCN (2022) guidelines (black dots, sites of known presence for a species; white dots, sites where sampling was conducted but did not detect the species due to its absence or inadequate sampling; grid, 2×2 km cells of standardized resolution for calculating AOO): (a) known and other sampled sites on a habitat‐masked range map (light gray) that indicates areas currently suitable for the species, (b) occupied‐cell method in which the species’ occupancy is assigned only to sites of known presence with current habitat (often leads to underestimates of AOO), (c) methods based on expert‐drawn or model‐based habitat‐masked range map that shows all areas currently suitable for the species across its distribution (Table 2) (often lead to overestimates of AOO, e.g., here at half the sampled sites in currently suitable areas, the species was not detected).

FIGURE 2

Illustration of the prevalence‐based conversion method to estimate the area of occupancy (AOO) for assessment for the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species following the approach explained in the IUCN (2022) guidelines (terms defined in Tables 1 & 2): part I, an overview of the process (dashed arrows, process repeated as necessary); part II, three options for calculating prevalence (feeds back to part I) (ovals and circles, necessary data inputs; hexagon, habitat‐masked range map [input to the process]; squares, quantitative estimates produced; option A, detectability equals 1; option B, detectability estimated in the process of calculating prevalence; option C, detectability estimated from primary biodiversity data and inventory completeness; part requires future development [red, C3]).

FIGURE 3

Hypothetical example for the forest‐restricted Ecuadorean spiny pocket mouse (Heteromys teleus) showing how the proposed prevalence‐based conversion method for calculating the area of occupancy (AOO) could improve assessment for the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species following IUCN (2022) guidelines: (a) continuous suitability range, showing stark gradients within the species’ range (warmer colors indicate higher suitability) and (b) binary habitat‐masked range map (green) after considering current forest cover (Kass et al., 2021a). Both maps are at the native resolution of environmental data (not 2×2 km as used for calculating AOO). Existing methods (Table 2) yield a wide range of implausible estimates for AOO, designating the species as either endangered or only near threatened. In contrast, the prevalence‐based conversion method could yield an unbiased estimate of the area occupied. For example, a hypothetical uniform value of 0.15 (90% CI 0.10−0.20) for prevalence across all currently suitable areas in the range of H. teleus would indicate the species is threatened (vulnerable). Quantifying prevalence stratified by suitability level would aosimprove estimates of AOO (most deforestation in the range of H. teleus corresponds to areas of low modeled suitability).