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

Abstract

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, butreassuringly -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.

Testing species assemblage predictions from stacked and joint species distribution models

“…Joint species distribution models simultaneously estimate the species-environment relationship of multiple species and the residual correlation between those species that could be indicative of biotic interactions (Zurell, Pollock, & Thuiller, 2018), but also missing environmental information and other ecological processes. Similar to SDMs, JSDMs generate site-level occurrence probabilities of the different species, which have to be combined ("stacked") in order to yield predictions of local community composition.…”

“…Joint species distribution model (JSDM) estimates of environmental correlation (left) and residual correlation among tree species (right). Previous studies have shown that the signal of competitive interactions is more easily lost at increasingly coarser resolution than the signal of facilitative interactions(Araújo & Rozenfeld, 2014;Zurell et al, 2018). For ease of comparison, the same order was used for depicting residual correlation.…”

Measuring competitive impact: Joint‐species modelling of invaded plant communities

“…Even with these improvements to JSDMs, separating environmental responses from competitive impacts is challenging (Adler et al, ), suggesting we should apply JSDMs to systems where the primary environmental drivers of species abundances are well understood (Giannini, Chapman, Saraiva, Alves‐dos‐Santos, & Biesmeijer, ; Wisz et al, ; Zurell, Pollock, & Thuiller, ). In grasslands around the world, non‐native plant species often increase in dominance at higher fertility sites and when grazing is excluded (Seabloom et al, ).…”

Understanding ecological change across large spatial, temporal and taxonomic scales: integrating data and methods in light of theory

“…competition) influence physiological tolerances to abiotic factors (e.g. However, simple formulations of these models should be used with care as they have been found to capture some biotic interaction types unreliably (Zurell et al 2018). One way to begin tackling these challenges at large spatiotemporal scales is to use joint species distribution models (SDMs).…”