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Use of taxonomy to delineate spatial extent of atlas data for species distribution models
Author(s) -
Niamir Aidin,
Skidmore Andrew K.,
Toxopeus Albertus G.,
Real Raimundo
Publication year - 2016
Publication title -
global ecology and biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.164
H-Index - 152
eISSN - 1466-8238
pISSN - 1466-822X
DOI - 10.1111/geb.12405
Subject(s) - atlas (anatomy) , species distribution , taxonomy (biology) , cartography , calibration , computer science , ecology , geography , biology , statistics , habitat , mathematics , paleontology
Abstract Aim The use of atlas data in combination with a variety of modelling approaches has become a common practice in species distribution studies. The spatial extent over which species distribution models ( SDMs ) should be fitted (i.e. the spatial extent) is often arbitrary and coincides with the extent of the atlases. In order to develop reliable SDMs using species atlas data, we propose an approach that incorporates the taxonomy of species and therefore delineates the spatial extent for SDMs . Location Mainland S pain. Methods We used atlas data to generate taxonomically delineated datasets for 365 terrestrial species. The presence records in the datasets were identical to those in the atlas, while the absence records were delimited to the presence of at least one species in the same family or order. We also generated two randomly delineated datasets that were the same size as the taxonomically delineated datasets. We assessed the predictive performance of the SDMs specifically by studying the model calibration ( M iller's statistic) and discrimination capacity (area under the curve of the receiver operating characteristic plot), along with the geographical similarity pattern of the predicted maps. Results The models that were trained using the taxonomically delineated datasets produced significantly improved models in terms of calibration, while their discrimination capacity was no greater than that of the models trained using the atlas dataset. The improvements to the calibration of the taxonomically delineated datasets were significantly greater than those with random absence sets. Main conclusion Delineating the spatial extent using taxonomical information leads to a significant improvement in the model performance of SDMs . This restriction can reduce the effect of environmental events beyond the species history during model parameterization, thus allowing the models obtained to more precisely depict the potential distribution of the species. We therefore recommend considering the delineation of spatial extent using species taxonomy when atlas data are employed in SDMs .