In a context of global changes, and amidst the perpetual modification of community structure undergone by most natural ecosystems, it is more important than ever to understand how species interactions vary through space and time. The integration of biogeography and network theory will yield important results and further our understanding of species interactions. It has, however, been hampered so far by the difficulty to quantify variation among interaction networks. Here, we propose a general framework to study the dissimilarity of species interaction networks over time, space or environments, allowing both the use of quantitative and qualitative data. We decompose network dissimilarity into interactions and species turnover components, so that it is immediately comparable to common measures of β-diversity. We emphasise that scaling up β-diversity of community composition to the β-diversity of interactions requires only a small methodological step, which we foresee will help empiricists adopt this method. We illustrate the framework with a large dataset of hosts and parasites interactions and highlight other possible usages. We discuss a research agenda towards a biogeographical theory of species interactions.

MacArthur and Wilson's Theory of Island Biogeography (TIB) is among the most well-known process-based explanations for the distribution of species richness. It helps understand the species-area relationship, a fundamental pattern in ecology and an essential tool for conservation. The classic TIB does not, however, account for the complex structure of ecological systems. We extend the TIB to take into account trophic interactions and derive a species-specific model for occurrence probability. We find that the properties of the regional food web influence the species-area relationship, and that, in return, immigration and extinction dynamics affect local food web properties. We compare the accuracy of the classic TIB to our trophic TIB to predict community composition of real food webs and find strong support for our trophic extension of the TIB. Our approach provides a parsimonious explanation to species distributions and open new perspectives to integrate the complexity of ecological interactions into simple species distribution models.