Inferring species interactions in ecological communities: a comparison of methods at different levels of complexity. Issue 8 (31st March 2015) - Carrara, Francesco; Giometto, Andrea; Seymour, Mathew; Rinaldo, Andrea; Altermatt, Florian
Summary: Natural communities commonly contain many different species and functional groups, and multiple types of species interactions act simultaneously, such as competition, predation, commensalism or mutualism. However, experimental and theoretical investigations have generally been limited by focusing on one type of interaction at a time or by a lack of a common methodological and conceptual approach to measure species interactions. We compared four methods to measure and express species interactions. These approaches are, with increasing degree of model complexity, an extinction‐based model, a relative yield model and two generalized Lotka‐Volterra (LV) models. All four approaches have been individually applied in different fields of community ecology, but rarely integrated. We provide an overview of the definitions, assumptions and data needed for the specific methods and apply them to empirical data by experimentally deriving the interaction matrices among 11 protist and rotifer species, belonging to three functional groups. Furthermore, we compare their advantages and limitations to predict multispecies community dynamics and ecosystem functioning. The relative yield method is, in terms of final biomass production, the best method in predicting the 11‐species community dynamics from the pairwise competition experiments. The LV model, which is considering equilibrium among the species, suffers from experimental constraints given the strict equilibrium assumption, and this may be rarely satisfied in ecological communities. We show how simulations of a LV stochastic community model, derived from an empirical interaction matrix, can be used to predict multispecies community dynamics across multiple functional groups. Our work unites available tools to measure species interactions under one framework. This improves our ability to make management‐oriented predictions of species coexistence/extinction and to compare ecosystem processes across study systems.