B1137
Title: Making more out of ecological community data: A conceptual framework and its implementation as models and software
Authors: Gleb Tikhonov - University of Helsinki (Finland) [presenting]
Otso Ovaskainen - University of Helsinki (Finland)
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, our 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 characteristics of their environment, with potential contingency on species traits and phylogenetic relationships. We capture biotic assembly structure by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalize the HMSC as a Bayesian joint species distribution model, melding hierarchical regression part that accounts for fixed effects, latent factor components, capturing species associations, and flexible generalized modelling techniques. We present an efficient full conditional Gibbs block sampler for full Bayesian estimation of model parameters and implement the framework as R- and Matlab-packages. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data.
Journal