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.

Estimation of intra- and interspecific interactions from time-series on species-rich communities is challenging due to the high number of potentially interacting species pairs. The previously proposed sparse interactions model overcomes this challenge by assuming that most species pairs do not interact. We propose an alternative model that does not assume that any of the interactions are necessarily zero, but summarizes the influences of individual species by a small number of community-level drivers. The community-level drivers are defined as linear combinations of species abundances, and they may thus represent e.g. the total abundance of all species or the relative proportions of different functional groups. We show with simulated and real data how our approach can be used to compare different hypotheses on community structure. In an empirical example using aquatic microorganisms, the community-level drivers model clearly outperformed the sparse interactions model in predicting independent validation data.

The ongoing global change and the increased interest in macroecological processes call for the analysis of spatially extensive data on species communities to understand and forecast distributional changes of biodiversity. Recently developed joint species distribution models can deal with numerous species efficiently, while explicitly accounting for spatial structure in the data. However, their applicability is generally limited to relatively small spatial data sets because of their severe computational scaling as the number of spatial locations increases. In this work, we propose a practical alleviation of this scalability constraint for joint species modeling by exploiting two spatial-statistics techniques that facilitate the analysis of large spatial data sets: Gaussian predictive process and nearest-neighbor Gaussian process. We devised an efficient Gibbs posterior sampling algorithm for Bayesian model fitting that allows us to analyze community data sets consisting of hundreds of species sampled from up to hundreds of thousands of spatial units. The performance of these methods is demonstrated using an extensive plant data set of 30,955 spatial units as a case study. We provide an implementation of the presented methods as an extension to the hierarchical modeling of species communities framework.

Summary Understanding of the ecological factors that shape intraspecific variation of insect microbiota in natural populations is relatively poor. In Lepidopteran caterpillars, microbiota is assumed to be mainly composed of transient bacterial symbionts acquired from the host plant. We sampled Glanville fritillary (Melitaea cinxia) caterpillars from natural populations to describe their gut microbiome and to identify potential ecological factors that determine its structure. Our results demonstrate high variability of microbiota composition even among caterpillars that shared the same host plant individual and most likely the same genetic background. We observed that the caterpillars harboured microbial classes that varied among individuals and alternated between two distinct communities (one composed of mainly Enterobacteriaceae and another with more variable microbiota community). Even though the general structure of the microbiota was not attributed to the measured ecological factors, we found that phylogenetically similar microbiota showed corresponding responses to the sex and the parasitoid infection of the caterpillar and to those of the host plant's microbial and chemical composition. Our results indicate high among‐individual variability in the microbiota of the M. cinxia caterpillar and contradict previous findings that the host plant is the major driver of the microbiota communities of insect herbivores.

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