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

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…

• NS
  National Science Foundation

  Awards: 1137366, FP7/2007-2013
• SN
  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

  Awards: FP7/2007-2013, 125240
• DF
  Danmarks Frie Forskningsfond
• UG
  U.S. Geological Survey