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    Author(s): Troy M. Hegel; Samuel A. Cushman; Jeffrey Evans; Falk Huettmann
    Date: 2010
    Source: In: Cushman, Samuel A.; Huettmann, Falk, eds. Spatial complexity, informatics, and wildlife conservation. New York: Springer. p. 273-311.
    Publication Series: Book Chapter
    Station: Rocky Mountain Research Station
    PDF: View PDF  (307.67 KB)

    Description

    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).

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    Citation

    Hegel, Troy M.; Cushman, Samuel A.; Evans, Jeffrey; Huettmann, Falk. 2010. Current state of the art for statistical modeling of species distributions [Chapter 16]. In: Cushman, Samuel A.; Huettmann, Falk, eds. Spatial complexity, informatics, and wildlife conservation. New York: Springer. p. 273-311.

    Keywords

    statistical modeling, species distributions

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