So Many Variables: Joint Modeling in Community Ecology

Abstract

Technological advances have enabled a new class of multivariate models for ecology, with the potential now to specify a statistical model for abundances jointly across many taxa, to simultaneously explore interactions across taxa and the response of abundance to environmental variables. Joint models can be used for several purposes of interest to ecologists, including estimating patterns of residual correlation across taxa, ordination, multivariate inference about environmental effects and environment-by-trait interactions, accounting for missing predictors, and improving predictions in situations where one can leverage knowledge of some species to predict others. We demonstrate this by example and discuss recent computation tools and future directions. Many ecological questions require the joint analysis of abundances collected simultaneously across many taxonomic groups, and, if organisms are identified using modern tools such as metabarcoding, their number can be in the thousands.While historically such data have been analyzed using ad hoc algorithms, it is now possible to fully specify joint statistical models for abundance using multivariate extensions of generalized linear mixed models.These modern 'joint modeling' approaches allow the study of correlation patterns across taxa, at the same time as studying environmental response, to tease the two apart.Latent variable models are an especially exciting tool that has recently been used for ordination as well as for studying the factors driving co-occurrence.