Correlation and process in species distribution models: Bridging a dichotomy

Carsten F. Dormann, Stanislaus J. Schymanski, Juliano Cabral, Isabelle Chuine, Catherine Graham, Florian Hartig, Michael Kearney, Xavier Morin, Christine Römermann, Boris Schröder, Alexander Singer

Research output: Contribution to journalArticlepeer-review

483 Scopus citations


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.

Original languageEnglish
Pages (from-to)2119-2131
Number of pages13
JournalJournal of Biogeography
Issue number12
StatePublished - Dec 2012


  • Hypothesis generation
  • Mechanistic model
  • Parameterization
  • Process-based model
  • SDM
  • Species distribution model
  • Uncertainty
  • Validation


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