Current State of the Art for Statistical Modelling of Species Distributions

Over the past decade the number of statistical modelling tools available to ecologists to model species' distributions has increased at a rapid pace (e.g. Elith et al. 2006; Austin 2007), as have the number of species distribution models (SDM) published in the literature (e.g. Scott et al. 2002). Ten years ago, basic logistic regression (Hosmer and Lemeshow 2000) was the most common analytical tool (Guisan and Zimmermann 2000), whereas ecologists today have at their disposal a much more diverse range of analytical approaches. Much of this is due to the increasing availability of software to implement these methods and the greater computational ability of hardware to run them. It is also due to ecologists discovering and implementing techniques from other scientific disciplines. Ecologists embarking on an analysis may find this range of options daunting and many tools unfamiliar, particularly as many of these approaches are not typically covered in introductory university statistics courses, let alone more advanced ones. This is unfortunate as many of these newer tools may be more useful and appropriate for a particular analysis depending upon its objective, or given the quantity and quality of data available (Guisan et al. 2007; Graham et al. 2008; Wisz et al. 2008). Many of these new tools represent a paradigm shift (Breiman 2001) in how ecologists approach data analysis. In fact, for a number of these approaches, referring to them as new is a misnomer since they have long been used in other fields and only recently have ecologists become increasingly aware of their usefulness (Hochachka et al. 2007; Olden et al. 2008).