Integrating graph-based connectivity metrics into species distribution models
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Species distribution models (SDMs) are commonly used in ecology to map the probability of species occurrence on the basis of predictive factors describing the physical environment. We propose an improvement on SDMs by using graph methods to quantify landscape connectivity. After (1) mapping the habitat suitable for a given species, this approach consists in (2) building a landscape graph, (3) computing patch-based connectivity metrics, (4) extrapolating the values of those metrics to any point of space, and (5) integrating those connectivity metrics into a predictive model of presence. For a given species, this method can be used to interpret the significance of the metrics in the models in terms of population structure. The method is illustrated here by the construction of an SDM for the European tree frog in the region of Franche-Comté (France). The results show that the connectivity metrics improve the explanatory power of the SDM and emphasize the important role of the habitat network.
KeywordsGraph theory Fragmented habitat Metapopulation Landscape connectivity Landscape metric Predictive model
The authors thank Timothy Keitt and two anonymous reviewers for their valuable comments. This research is funded by the French Ministry of Ecology, Energy, Sustainable Development and the Sea (ITTECOP program) as part of the Graphab project managed by the USR 3124 MSHE Ledoux. Computations were performed on the supercomputer facilities of the “Mésocentre de calcul de FrancheComté”. We thank Raanan Barzel and Christopher Sutcliffe for reviewing the English manuscript.
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