Using joint species distribution models for evaluating how species‐to‐species associations depend on the environmental context

Abstract

Summary Joint species distribution models (JSDM) are increasingly used to analyse community ecology data. Recent progress with JSDMs has provided ecologists with new tools for estimating species associations (residual co‐occurrence patterns after accounting for environmental niches) from large data sets, as well as for increasing the predictive power of species distribution models (SDMs) by accounting for such associations. Yet, one critical limitation of JSDMs developed thus far is that they assume constant species associations. However, in real ecological communities, the direction and strength of interspecific interactions are likely to be different under different environmental conditions. In this paper, we overcome the shortcoming of present JSDMs by allowing species associations covary with measured environmental covariates. To estimate environmental‐dependent species associations, we utilize a latent variable structure, where the factor loadings are modelled as a linear regression to environmental covariates. We illustrate the performance of the statistical framework with both simulated and real data. Our results show that JSDMs perform substantially better in inferring environmental‐dependent species associations than single SDMs, especially with sparse data. Furthermore, JSDMs consistently overperform SDMs in terms of predictive power for generating predictions that account for environment‐dependent biotic associations. We implemented the statistical framework as a MATLAB package, which includes tools both for model parameterization as well as for post‐processing of results, particularly for addressing whether and how species associations depend on the environmental conditions. Our statistical framework provides a new tool for ecologists who wish to investigate from non‐manipulative observational community data the dependency of interspecific interactions on environmental context. Our method can be applied to answer the fundamental questions in community ecology about how species’ interactions shift in changing environmental conditions, as well as to predict future changes of species’ interactions in response to global change.

From individual to joint species distribution models: A comparison of model complexity and predictive performance

“…This particular result contradicts our initial hypothesis that the joint model would improve predictive performance, as similar studies have found that joint models generally improve the quality of fit and predictive performance (relative to individual models) for various communities (Clark et al, ; Harris, ; Ovaskainen, Abrego, et al, ; Taylor‐Rodríguez et al, ; Tikhonov et al, ). For example, Clark et al () and Taylor‐Rodríguez et al () show that joint models of forest communities improve predictive performance and the prediction of species richness over equivalent individual models (Guisan & Rahbek, ), with a similar study of plant communities confirming these findings (e.g.…”

“…For example, Clark et al () and Taylor‐Rodríguez et al () show that joint models of forest communities improve predictive performance and the prediction of species richness over equivalent individual models (Guisan & Rahbek, ), with a similar study of plant communities confirming these findings (e.g. Tikhonov et al, ). Studies applying joint models to freshwater communities suggest that shared responses to environmental conditions may be more important in determining community composition than residual correlations between taxa (Inoue, Stoeckl, & Geist, ; Pollock et al, ; Royan et al, ).…”

“…The residual correlations between taxa have been used to explore and test hypotheses of patterns between distributions of taxa arising due to biotic interactions or unknown explanatory variables, and to quantify associations between taxa under varying environmental conditions (e.g. Tikhonov, Abrego, Dunson, & Ovaskainen, ) or spatial scales (e.g. Ovaskainen, Abrego, Halme, & Dunson, ).…”

Comparing the prediction of joint species distribution models with respect to characteristics of sampling data

“…The different approaches might partially contribute to the divergence from our results, which suggested that sample sizes had minor influence on model predictability. Many studies have demonstrated that JSDMs consistently over-perform single-species SDMs implemented by GLMs (Hui et al 2013, Tikhonov et al 2017, Clark et al 2017, boosted regression trees, neutral network (Harris 2015), and spatial delta models (Thorson and Barnett 2017). As the marginal predictions usually involve extensive averaging among simulations, the predicted values tend to be stable (small standard error in Fig.…”

“…4, 5 and 6) but less precise for site-specific predictions. In addition, most JSDMs assume constant species associations over environmental gradients, space and time (Warton et al 2015a; but see Tikhonov et al 2017), which is not likely to be true due to adaptive behaviors of organisms. JSDMs are powerful tools for analyzing the structure and assembly processes of biotic communities.…”