A null‐model for significance testing of presence‐only species distribution models

A null‐model for significance testing of presence‐only species distribution models Species’ distribution models (SDMs) attempt to predict the potential distribution of species by interpolating identified relationships between species’ presence/absence, or presence‐only data on one hand, and environmental predictors on the other hand, to a geographical area of interest. Currently, they are widely applied in biogeography, conservation biology, ecology, palaeo‐ecology, invasive species studies, and wildlife management ( Guisan and Zimmermann 2000 , Araújo and Pearson 2005 , Thuiller et al. 2005 , Peterson 2006 , Araújo and Guisan 2006 , Guisan et al. 2006 ). More recently, vast numbers of herbarium and natural history museum collections have become available ( Graham et al. 2004 ) and techniques to apply this special type of presence‐only data have been developed ( Hirzel et al. 2002 , Anderson et al. 2003 , Pearce and Boyce 2006 , Elith et al. 2006 , Phillips et al. 2006 ). Despite the widespread use of SDMs, several high‐priority research interests remain to be investigated ( Guisan and Thuiller 2005 , Araújo and Guisan 2006 ). One of these is the improvement of SDM validation, or the quantification of a model's predictive performance ( Araújo and Guisan 2006 ). The fact that the standard validation procedures http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecography Wiley

A null‐model for significance testing of presence‐only species distribution models

Ecography, Volume 30 (5) – Oct 1, 2007

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Publisher
Wiley
Copyright
Copyright © 2007 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0906-7590
eISSN
1600-0587
DOI
10.1111/j.2007.0906-7590.05041.x
Publisher site
See Article on Publisher Site

Abstract

Species’ distribution models (SDMs) attempt to predict the potential distribution of species by interpolating identified relationships between species’ presence/absence, or presence‐only data on one hand, and environmental predictors on the other hand, to a geographical area of interest. Currently, they are widely applied in biogeography, conservation biology, ecology, palaeo‐ecology, invasive species studies, and wildlife management ( Guisan and Zimmermann 2000 , Araújo and Pearson 2005 , Thuiller et al. 2005 , Peterson 2006 , Araújo and Guisan 2006 , Guisan et al. 2006 ). More recently, vast numbers of herbarium and natural history museum collections have become available ( Graham et al. 2004 ) and techniques to apply this special type of presence‐only data have been developed ( Hirzel et al. 2002 , Anderson et al. 2003 , Pearce and Boyce 2006 , Elith et al. 2006 , Phillips et al. 2006 ). Despite the widespread use of SDMs, several high‐priority research interests remain to be investigated ( Guisan and Thuiller 2005 , Araújo and Guisan 2006 ). One of these is the improvement of SDM validation, or the quantification of a model's predictive performance ( Araújo and Guisan 2006 ). The fact that the standard validation procedures

Journal

EcographyWiley

Published: Oct 1, 2007

References

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