ArticleEcology and evolution2026
Estimating Area of Occupancy From Incomplete Occurrence Records: Reproducible Workflow Illustrated on a Comprehensive Dataset of Georgian Snakes.
Article in Ecology and evolution, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Area of occupancy (AOO) is a widely used metric for assessing species' vulnerability. To standardize AOO estimation, the IUCN recommends using a fixed 2 × 2 km grid. However, for species represented by sparse and irregular records, this resolution can substantially underestimate AOO. Although spatial modeling offers a potential solution, model-based estimates may differ among taxa and studies depending on data structure, predictor choice, and modeling strategy. In this paper, we present a reproducible workflow that uses breakpoint analysis of record-accumulation curves to identify informative species-specific grid sizes and to estimate AOO from incomplete occurrence records. We applied this framework to 13 snake species found in Georgia, with diverse ecological characteristics within a topographically complex, irregularly sampled region. Repeated subsampling of the six best-represented species showed that accumulation-curve predictions were more accurate than raw occupied-cell counts in 96%-98% of comparisons. Nine of the 13 species showed significant breakpoint-derived scales between approximately 4 and 13 km rather than at the standard 2 km resolution. This workflow offers a practical way to explore scale dependence, sampling incompleteness, and sensitivity of AOO estimates derived from historical and citizen-science occurrence datasets. The workflow complements the standardized 2 × 2 km AOO used in formal IUCN Red List assessments.
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