A network approach for inferring species associations from co-occurrence data

by broad-scale models, then stacking independent species distribution models to predict species assemblages (sensu Guisan and Rahbek 2011, Calabrese et al. 2014) will provide misleading predictions of fine-scale community assembly. Thus, a better understanding of species associations across scales could improve predictions of the dynamics of local community composition in changing environments. The goal of this paper is to improve the tools needed to detect interspecific associations from co-occurrence data. We first briefly describe the development of co-occurrence methods and then draw from different lines of research to present a more complete and flexible general framework for inferring species associations that overcomes multiple challenges faced by previous approaches.

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