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Do joint species distribution models reliably detect interspecific interactions from co‐occurrence data in homogenous environments?

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Whether species interactions influence species response to environment and species ranges has always been a central question in ecology. Joint species distribution models (JSDMs) simultaneously model the species–environment relationships of multiple species and the residual correlation between these species. These residual correlations are assumed to depict whether species co‐occur less or more often than expected by the modelled species–environment relationships, which could ultimately be attributed to species interactions, or hidden environmental information. Here, we propose to specifically test the capacity of JSDMs to detect species interactions from co‐occurrence data, at different scales of data aggregation. Using a recently published point‐process model, we simulated equilibrium co‐occurrence patterns of species pairs by varying the strength and type of interactions (e.g. competition, predator–prey, mutualism) as well as the prevalence of the interacting species in homogeneous environments (assuming the environment does not influence the species responses and co‐occurrence). Then, we fitted JSDMs without environmental predictors, and compared the estimated residual correlations against the known interaction coefficients. JSDMs detected competition and mutualism well, but failed with predator–prey interactions. For the latter, JSDMs predicted both negative and positive residual correlations for these kinds of interactions, depending on the prevalence of the interacting species. Interestingly, the estimated residual correlation was strongly influenced by species’ prevalence and can thus not be translated to interaction strength. At increasingly coarser data resolution, the signals of negative and positive interactions became indiscernible by JSDMs, but – reassuringly – were rarely confounded. The underlying point‐process model simulates the consequences rather than the mechanisms of interspecific interactions, and thus is better at corroborating rather than discrediting JSDMs. Nevertheless, our simple theoretical exercise pinpoints important limitations of JSDMs. In conclusion, we caution against interpreting residual correlations from JSDMs as interaction strength and against comparing these across different species and communities.
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Keywords: residual correlation; scale dependence; species covariance

Document Type: Research Article

Publication date: November 1, 2018

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