Open AccessMind the gap: Can downscaling Area of Occupancy overcome sampling gaps when assessing IUCN Red List status?
Author(s) -
Marsh Charles J.,
Gavish Yoni,
Kunin William E.,
Brummitt Neil A.
Publication year - 2019
Publication title -
diversity and distributions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.918
H-Index - 118
eISSN - 1472-4642
pISSN - 1366-9516
DOI - 10.1111/ddi.12983
Subject(s) - occupancy , downscaling , iucn red list , sampling (signal processing) , range (aeronautics) , environmental science , raw data , statistics , ecology , species distribution , sampling bias , computer science , physical geography , geography , sample size determination , climate change , mathematics , biology , habitat , materials science , filter (signal processing) , composite material , computer vision
Aim The 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. Methods We 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? Results Downscaling 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 conclusions Occupancy 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.