International Biometric Society
STATISTICAL MODELLING OF SPECIES COMMUNITIES
Otso Ovaskainen
Department of Biosciences, University of Helsinki
A central aim in community ecology is to understand the factors that determine the identities and
abundances of species found at any given locality. Central concepts in this area of research include
the ideas of regional and local species pools, environmental filtering and biotic assembly rules. Typical
datasets in community ecology involve a matrix describing the presence-absences (or abundances) of
a group of species at different sites, some environmental and geographical characteristics of those
sites, and possibly information on the ecological traits and phylogenetic relationships of the species.
Empirical ecologists routinely analyze such data with different ordination methods. While such
methods continue to be valuable in providing intuitive and illustrative summaries of the data, their
results typically remain somewhat descriptive and are not mechanistically rooted to the concepts listed
above. More recently, there has been an increasing interest in developing alternative approaches,
many of which derive from research on single-species distribution models. I will present one of such
approach, built in the standard framework of hierarchical generalized linear models. The model
captures environmental filtering at the community-level model by measuring the amount of variation
and covariation in the responses of individual species to various characteristics of their environment.
The selection of the local species pool from the regional species pool involves both deterministic (e.g.
systematic differences in dispersal abilities) and stochastic (e.g. randomness in the realized dispersal
patterns) processes. Biotic assembly rules are reflected in the model with the help of an association
matrix, which models positive or negative co-occurrence patterns not explained by the responses of
the species to their environment. I use a sparse Bayesian factor approach to enable model
parameterization with data on species-rich communities and thus with high-dimensional association
matrices. I illustrate the performance of the approach both with simulated and real data, and make
links to more traditional approaches in community ecology by discussing the relations between the
present modelling approach and ordinations.
International Biometric Conference, Florence, ITALY, 6 – 11 July 2014