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Correlation and process in species distribution models: bridging a dichotomy

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Within the field of species distribution modelling an apparent dichotomy exists between process‐based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlative–process spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the process–correlation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process‐based approaches to species distribution modelling lags far behind more correlative (process‐implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process‐explicit species distribution models and how they may complement current approaches to study species distributions.

Document Type: Research Article


Affiliations: 1: Free Floater Group Biodiversity, Macroecology and Conservation Biogeography, 37077 Göttingen, Germany 2: Equipe BIOFLUX, Centre d’Ecologie Fonctionnelle et Evolutive–CNRS, 34293 Montpellier Cedex 05, France 3: Department of Ecology and Evolution, Stony Brook, NY 11794-5245, USA 4: Helmholtz Centre for Environmental Research – UFZ, Department Ecological Modelling, 04318 Leipzig, Germany 5: Department of Zoology, The University of Melbourne, Melbourne, Vic. 3010, Australia 6: ETH Zürich, Forest Ecology, Institut für Terrestrische Ökosysteme, 8092 Zürich, Switzerland

Publication date: December 1, 2012


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