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Correlation and process in species distribution models: bridging a dichotomy
Author(s) -
Dormann Carsten F.,
Schymanski Stanislaus J.,
Cabral Juliano,
Chuine Isabelle,
Graham Catherine,
Hartig Florian,
Kearney Michael,
Morin Xavier,
Römermann Christine,
Schröder Boris,
Singer Alexander
Publication year - 2012
Publication title -
journal of biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 158
eISSN - 1365-2699
pISSN - 0305-0270
DOI - 10.1111/j.1365-2699.2011.02659.x
Subject(s) - correlative , computer science , process (computing) , econometrics , bridging (networking) , distribution (mathematics) , strengths and weaknesses , management science , statistical physics , epistemology , mathematics , economics , physics , computer network , mathematical analysis , philosophy , linguistics , operating system
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.