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

Abstract

1267 complexity in the occurrence-environment relationships that they fit. Capturing the appropriate amount of complexity for particular study objectives is challenging. 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. As such, determining a suitable amount of complexity to include in SDMs is crucial for biological applications. Because traditional model selection is challenging when comparing models from different SDM modeling approaches (e.g. those in Table 1), we argue that researchers must constrain model complexity based on attributes of the data and study objectives and an understanding of how these interact with the underlying bio- Species distribution models (SDMs), also known as ecological niche models or habitat selection models, are widely used in ecology, evolutionary biology, and conservation (Elith and Leathwick 2009, Franklin 2010, Zimmermann et al. 2010, Peterson et al. 2011, Svenning et al. 2011, Guisan et al. 2013. SDMs can provide insights into generalities and idiosyncrasies of the drivers of complex patterns of species' geographic distributions. SDMs are built using a variety of statistical methods -e.g. generalized linear/additive models, tree-based models, maximum entropy -which span a range of 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 process...

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