Incorporating imperfect detection into joint models of communites: A response to Warton et al.

Trends in Ecology and Evolution
Steven R. Beissinger1, Kelly J. Iknayan1, Elise F. Zipkin2, Robert M. Dorazio3, J. Andrew Royle4, and Marc Kéry5.
By: , and 

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Abstract

Warton et al. [1] 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.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Incorporating imperfect detection into joint models of communites: A response to Warton et al.
Series title Trends in Ecology and Evolution
DOI 10.1016/j.tree.2016.07.009
Volume 31
Issue 10
Year Published 2016
Language English
Publisher Elsevier
Publisher location Amsterdam
Contributing office(s) Patuxent Wildlife Research Center
Description 2 p.
First page 736
Last page 737