How to make more out of community data? A conceptual framework and its implementation as models and software

TLDR

Community ecology seeks to understand the factors that determine the assembly and dynamics of species assemblages across spatial and temporal scales. The authors propose Hierarchical Modelling of Species Communities (HMSC) to integrate conceptual and statistical approaches in community ecology. HMSC models environmental filtering through species’ responses to environmental variables, captures biotic assembly via species‑to‑species association matrices, and is implemented as a hierarchical Bayesian joint species distribution model in R and Matlab packages for efficient analysis of large datasets. Using HMSC, ecologists can analyze diverse data types, such as spatially explicit and time‑series datasets, to gain insights into community structure.

Abstract

Abstract 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.