Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non-manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data-driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R-and Matlab-packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data. We illustrate the use of this framework through a series of diverse ecological examples.
Joint Species Distribution Modelling (JSDM) is becoming an increasingly popular statistical method for analyzing data in community ecology. JSDM allow the integration of community ecology data with data on environmental covariates, species traits, phylogenetic relationships, and the spatiotemporal context of the study, providing predictive insights into community assembly processes from non-manipulative observational data of species communities. Hierarchical Modelling of Species Communities (HMSC) is a general and flexible framework for fitting JSDMs, yet its full range of functionality has remained restricted to Matlab users only. To make HMSC accessible to the wider community of ecologists, we introduce HMSC-R 3.0, a userfriendly R implementation of the framework described in Ovaskainen et al (Ecology Letters, 20 (5), 561-576, 2017) and further extended in several later publications. We illustrate the use of the package by providing a series of five vignettes that apply HMSC-R 3.0 to simulated and real data. HMSC-R applications to simulated data involve single-species models, models of small communities, and models of large species communities. They demonstrate the estimation of species responses to environmental covariates and how these depend on species traits, as well as the estimation of residual species associations. They further demonstrate how HMSC-R can be applied to normally distributed data, count data, and presence-absence data. The real data consist of bird counts in a spatio-temporally structured dataset, environmental covariates, species traits and phylogenetic relationships. The vignettes demonstrate how to construct and fit many kinds of models, how to examine MCMC convergence, how to examine the explanatory and predictive powers of the models, how to assess parameter estimates, and how to make predictions. The package, along with the extended vignettes, makes JSDM fitting and post-processing easily accessible to ecologists familiar with R.
Joint Species Distribution Modelling (JSDM) is becoming an increasingly popular statistical method for analysing data in community ecology. Hierarchical Modelling of Species Communities (HMSC) is a general and flexible framework for fitting JSDMs. HMSC allows the integration of community ecology data with data on environmental covariates, species traits, phylogenetic relationships and the spatio‐temporal context of the study, providing predictive insights into community assembly processes from non‐manipulative observational data of species communities. The full range of functionality of HMSC has remained restricted to Matlab users only. To make HMSC accessible to the wider community of ecologists, we introduce Hmsc 3.0, a user‐friendly r implementation. We illustrate the use of the package by applying Hmsc 3.0 to a range of case studies on real and simulated data. The real data consist of bird counts in a spatio‐temporally structured dataset, environmental covariates, species traits and phylogenetic relationships. Vignettes on simulated data involve single‐species models, models of small communities, models of large species communities and models for large spatial data. We demonstrate the estimation of species responses to environmental covariates and how these depend on species traits, as well as the estimation of residual species associations. We demonstrate how to construct and fit models with different types of random effects, how to examine MCMC convergence, how to examine the explanatory and predictive powers of the models, how to assess parameter estimates and how to make predictions. We further demonstrate how Hmsc 3.0 can be applied to normally distributed data, count data and presence–absence data. The package, along with the extended vignettes, makes JSDM fitting and post‐processing easily accessible to ecologists familiar with r.
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.
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