Incorporating Imperfect Detection into Joint Models of Communities: A Response to Warton Et Al.

Warton et al. [ 1 Warton D.I. et al. So many variables: joint modeling in community ecology. Trends Ecol. Evol. 2015; 30: 766-779 Abstract Full Text Full Text PDF PubMed Scopus (420) Google Scholar ] advance community ecology by describing a statistical framework that can jointly model abundances (or distributions) across many taxa to quantify how community properties respond to environmental variables. This framework specifies the effects of both measured and unmeasured (latent) variables on the abundance (or occurrence) of each species. Latent variables are random effects that capture the effects of both missing environmental predictors and correlations in parameter values among different species. As presented in Warton et al., however, the joint modeling framework fails to account for the common problem of detection or measurement errors that always accompany field sampling of abundance or occupancy, and are well known to obscure species- and community-level inferences. So Many Variables: Joint Modeling in Community EcologyWarton et al.Trends in Ecology & EvolutionOctober 27, 2015In BriefTechnological 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. Full-Text PDF