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Cited by 683

The DOI matches its Crossref record; 109 of the 109 references it cites were found in a registry or an index.

What do we gain from simplicity versus complexity in species distribution models?

Open access
Merow, Cory, Smith, Mathew J., Edwards, Thomas C., Guisan, Antoine, McMahon, Sean M., Normand, Signe, Thuiller, Wilfried, Wüest, Rafael O., Zimmermann, Niklaus E., Elith, Jane·2014·Ecography
DOI 10.1111/ecog.00845
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Abstract

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence–environment relationships using statistical and machine‐learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree‐based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence–environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building ‘under fit’ models, having insufficient flexibility to describe observed occurrence–environment relationships, we risk misunderstanding the factors shaping species distributions. By building ‘over fit’ models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

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Metadata

Authors
Merow, C., Smith, M. J., Edwards, T. C., Guisan, A., McMahon, S. M., Normand, S., Thuiller, W., Wüest, R. O., Zimmermann, N. E., & Elith, J.
Venue
Ecography
Publisher
Wiley
Published
2014-09-16
Volume
37
Issue
12
Pages
1267-1281
ISSN
  • 0906-7590 (ISSN-L)
  • 1600-0587
DOI
10.1111/ecog.00845
OpenAlex
W2129435498
Cited by
683
References
109
FWCI
22.16
Type
journal article
Language
en
Open access
hybrid

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