How to make more out of community data? A conceptual framework and its implementation as models and software. (20th March 2017)

• Record Type: Journal Article
• Authors: Ovaskainen, Otso; Tikhonov, Gleb; Norberg, Anna; Guillaume Blanchet, F.; Duan, Leo; Dunson, David; Roslin, Tomas; Abrego, Nerea
• Editors: Chave, Jerome
• Is Part Of: Ecology letters. Volume 20:Number 5(2017)
• Page Start: 561
• Page End: 576
• Publication Date: 2017-03-20
• Subjects: Assembly process -- biotic filtering -- community distribution -- community modelling -- community similarity -- environmental filtering -- functional trait -- joint species distribution model -- metacommunity -- phylogenetic signal; Ecology -- Periodicals; 577
• DOI: 10.1111/ele.12757
• Languages: English
• ISSNs: 1461-023X

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.