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

• Published in: Ecology letters Vol. 20; no. 5; pp. 561 - 576
• Main Authors: Ovaskainen, Otso, Tikhonov, Gleb, Norberg, Anna, Guillaume Blanchet, F., Duan, Leo, Dunson, David, Roslin, Tomas, Abrego, Nerea, Chave, Jerome
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01-05-2017
• Subjects: Assembly; Assembly process; Bayes Theorem; Bayesian analysis; Biodiversity; biotic filtering; Communities; community distribution; Community ecology; community modelling; community similarity; Data processing; Ecological monitoring; Ecology; Ecosystem; Ekologi; environmental filtering; Filtration; functional trait; Integration; joint species distribution model; Mathematical models; metacommunity; Models, Theoretical; phylogenetic signal; Phylogeny; Software; Spatial distribution; Species; functional trait; biotic filtering; joint species distribution model; Assembly process; community modelling; community similarity; environmental filtering; metacommunity; community distribution; phylogenetic signal
• ISSN: 1461-023X; 1461-0248; 1461-0248
• DOI: 10.1111/ele.12757

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.