• search hit 3 of 3
Back to Result List

Correlation and process in species distribution models: bridging a dichotomy

  • 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 correlativeprocess 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 processcorrelation continuum as well as the domains of questions that each approach is addressing. Nonetheless,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 correlativeprocess 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 processcorrelation 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.show moreshow less

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author:Carsten F. Dormann, Stanislaus J. Schymanski, Juliano Sarmento CabralORCiD, Isabelle Chuine, Catherine Graham, Florian Hartig, Michael Kearney, Xavier Morin, Christine Römermann, Boris Schröder-EsselbachORCiDGND, Alexander Singer
DOI:https://doi.org/10.1111/j.1365-2699.2011.02659.x
ISSN:0305-0270 (print)
Parent Title (English):Journal of biogeography
Publisher:Wiley-Blackwell
Place of publication:Hoboken
Document Type:Review
Language:English
Year of first Publication:2012
Year of Completion:2012
Release Date:2017/03/26
Tag:Hypothesis generation; SDM; mechanistic model; parameterization; process-based model; species distribution model; uncertainty; validation
Volume:39
Issue:12
Pagenumber:13
First Page:2119
Last Page:2131
Funder:LOEWE- BiK-F Biodiversity and the Climate Research Centre Frankfurt; Helmholtz Association [VH-NG 247]; German Research Foundation DFG [DO 686/5-1]; Max Planck Society; DFG [RO 3842/1-1]; Biodiversity and Climate Research Centre (BiK-F), part of the LOEWE programme 'Landes-Offensive zur Entwicklung Wissenschaftlich-okonomischer Exzellenz' of Hesse's Ministry of Higher Education, Research and the Arts
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Erd- und Umweltwissenschaften
Peer Review:Referiert