Improving Area of Occupancy Estimates for Parapatric Species Using Distribution Models and Support Vector Machines

نویسندگان

چکیده

As geographic range estimates for the IUCN Red List guide conservation actions, accuracy and ecological realism are crucial. IUCN's extent of occurrence (EOO) is general region including species' range, while area occupancy (AOO) subset EOO occupied by species. Data-poor species with incomplete sampling present particular difficulties, but distribution models (SDMs) can be used to predict suitable areas. Nevertheless, SDMs typically employ abiotic variables (i.e., climate) do not explicitly account biotic interactions that impose constraints. We sought improve data-poor, parapatric masking out areas under inferred competitive exclusion. did so two South American spiny pocket mice: Heteromys australis (Least Concern) teleus (Vulnerable due especially poor sampling), whose ranges appear restricted competition. For both species, we estimated using AOO four approaches: grid cells, SDM prediction, this prediction masked approximations each congener. made masks support vector machines (SVMs) fit data types: coordinates alone; along predictions suitability. Given uncertainty in calculating low-data lower upper bounds AOO, only make recommendations H. as its full known was considered. The SVM approaches (especially second one) had classification error more ecologically realistic delineations contact zone. teleus, bound (a strongly biased underestimate) corresponded Endangered (occupied cells), (other approaches) led Near Threatened. currently lack determine true within post-processed recommend an updated listing include these AOO. This study advances methods estimating highlights need better ways produce unbiased bounds. More generally, post-processing hold promise improving other uses biogeography conservation.

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ژورنال

عنوان ژورنال: Bulletin of The Ecological Society of America

سال: 2021

ISSN: ['0012-9623', '2327-6096']

DOI: https://doi.org/10.1002/bes2.1813