Abstract
AbstractAimThe Area of Occupancy (AOO) of a species is often utilized to assess extinction risk for determining IUCN Red List status. However, the recommended raw‐counts method of summing occupied grid cells likely reflects only sampling effort, as the majority of species have not been sampled across their entire range at the fine grains required by IUCN. More accurate measurements can be generated at coarser grains (so‐called atlas data) as false absences are reduced. If we fit the occupancy‐area relationship to these data, we can extrapolate the relationship down to estimate occupancy at finer grains. Numerous models have been proposed to carry out such occupancy downscaling, but have only been tested on a limited range of species.MethodsWe test the ability of downscaling models to recover fine grain AOO against the raw‐counts method for 28,900 virtual species with a wide range of prevalence and aggregation characteristics, subsampled to reflect common spatial biases in sampling effort. We address several questions for ensuring accurate downscaling: How to generate accurate atlas data? How far can we accurately extrapolate the occupancy‐area relationship given perfect data? Can occupancy downscaling overcome false absences at fine grain sizes? And how does sampling bias and coverage affect accuracy?ResultsDownscaling was more accurate than the raw‐counts method in all scenarios except where sampling coverage was very high and/or the sampling bias was positively related to the species distribution. However, if atlas data contained many false absences, then even downscaling under‐estimated actual occupancy.Main conclusionsOccupancy downscaling has the potential to be a useful tool for estimating AOO for IUCN Red List assessments, especially when sampling coverage is low and the currently recommended method is ineffective. However, its application should be tailored to the species’ characteristics, as well as the sampling coverage and bias of the species’ records.
Highlights
The geographic range size of a species is an important character‐ istic describing a species’ rarity, as it is correlated with species’ local and total abundances (Gaston, 1991; Gaston & Lawton, 1990) which require more detailed information to estimate
Range size can be quantified at two extremes (Gaston, 1994); the Extent of Occurrence (EOO) is the geographic range that encompasses all occurrences of a species, and the Area of Occupancy (AOO) is the total area within that range that is occupied
The finer the grain size, the greater the like‐ lihood that atlas data will be inaccurate as we increase the likeli‐ hood of false absences. We explored this by predicting AOO at the 1 × 1 grain size using atlas data generated at grains sizes of 4 × 4, 8 × 8, 16 × 16 and 32 × 32
Summary
The geographic range size of a species is an important character‐ istic describing a species’ rarity, as it is correlated with species’ local and total abundances (Gaston, 1991; Gaston & Lawton, 1990) which require more detailed information to estimate. Range size can be quantified at two extremes (Gaston, 1994); the Extent of Occurrence (EOO) is the geographic range that encompasses all occurrences of a species, and the Area of Occupancy (AOO) is the total area within that range that is occupied. The issue, is that AOO is intrinsically scale‐dependent (Kunin, 1998): a species will be seen to occupy different amounts of area if grids of different spatial “grain” are used. The finer the grain size used, the closer the correlation with total abundance, so that if a grain size is set to cover a single individual AOO will eventually equal population size (Kunin, 1998)
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