Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model

Cross-validation of species distribution models: removing spatial sorting bias and calibration... Species distribution models are usually evaluated with cross-validation. In this procedure evaluation statistics are computed from model predictions for sites of presence and absence that were not used to train (fit) the model. Using data for 226 species, from six regions, and two species distribution modeling algorithms (Bioclim and MaxEnt), I show that this procedure is highly sensitive to “spatial sorting bias”: the difference between the geographic distance from testing-presence to training-presence sites and the geographic distance from testing-absence (or testing-background) to training-presence sites. I propose the use of pairwise distance sampling to remove this bias, and the use of a null model that only considers the geographic distance to training sites to calibrate cross-validation results for remaining bias. Model evaluation results (AUC) were strongly inflated: the null model performed better than MaxEnt for 45% and better than Bioclim for 67% of the species. Spatial sorting bias and area under the receiver–operator curve (AUC) values increased when using partitioned presence data and random-absence data instead of independently obtained presence–absence testing data from systematic surveys. Pairwise distance sampling removed spatial sorting bias, yielding null models with an AUC close to 0.5, such that AUC was the same as null model calibrated AUC (cAUC). This adjustment strongly decreased AUC values and changed the ranking among species. Cross-validation results for different species are only comparable after removal of spatial sorting bias and/or calibration with an appropriate null model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecology Ecological Society of America

Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model

Ecology, Volume 93 (3) – Mar 1, 2012

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Publisher
Ecological Society of America
Copyright
Copyright © 2012 by the Ecological Society of America
Subject
Articles
ISSN
0012-9658
DOI
10.1890/11-0826.1
Publisher site
See Article on Publisher Site

Abstract

Species distribution models are usually evaluated with cross-validation. In this procedure evaluation statistics are computed from model predictions for sites of presence and absence that were not used to train (fit) the model. Using data for 226 species, from six regions, and two species distribution modeling algorithms (Bioclim and MaxEnt), I show that this procedure is highly sensitive to “spatial sorting bias”: the difference between the geographic distance from testing-presence to training-presence sites and the geographic distance from testing-absence (or testing-background) to training-presence sites. I propose the use of pairwise distance sampling to remove this bias, and the use of a null model that only considers the geographic distance to training sites to calibrate cross-validation results for remaining bias. Model evaluation results (AUC) were strongly inflated: the null model performed better than MaxEnt for 45% and better than Bioclim for 67% of the species. Spatial sorting bias and area under the receiver–operator curve (AUC) values increased when using partitioned presence data and random-absence data instead of independently obtained presence–absence testing data from systematic surveys. Pairwise distance sampling removed spatial sorting bias, yielding null models with an AUC close to 0.5, such that AUC was the same as null model calibrated AUC (cAUC). This adjustment strongly decreased AUC values and changed the ranking among species. Cross-validation results for different species are only comparable after removal of spatial sorting bias and/or calibration with an appropriate null model.

Journal

EcologyEcological Society of America

Published: Mar 1, 2012

Keywords: Key words : AUC ; Bioclim ; cross-validation ; MaxEnt ; model evaluation ; niche model ; pairwise distance sampling ; spatial autocorrelation ; spatial sorting bias ; species distribution model .

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