Guidelines for a priori grouping of species in hierarchical community models

Abstract

Recent methodological advances permit the estimation of species richness and occurrences for rare species by linking species-level occurrence models at the community level. The value of such methods is underscored by the ability to examine the influence of landscape heterogeneity on species assemblages at large spatial scales. A salient advantage of community-level approaches is that parameter estimates for data-poor species are more precise as the estimation process “borrows” from data-rich species. However, this analytical benefit raises a question about the degree to which inferences are dependent on the implicit assumption of relatedness among species. Here, we assess the sensitivity of community/group-level metrics, and individual-level species inferences given various classification schemes for grouping species assemblages using multispecies occurrence models. We explore the implications of these groupings on parameter estimates for avian communities in two ecosystems: tropical forests in Puerto Rico and temperate forests in northeastern United States. We report on the classification performance and extent of variability in occurrence probabilities and species richness estimates that can be observed depending on the classification scheme used. We found estimates of species richness to be most precise and to have the best predictive performance when all of the data were grouped at a single community level. Community/group-level parameters appear to be heavily influenced by the grouping criteria, but were not driven strictly by total number of detections for species. We found different grouping schemes can provide an opportunity to identify unique assemblage responses that would not have been found if all of the species were analyzed together. We suggest three guidelines: (1) classification schemes should be determined based on study objectives; (2) model selection should be used to quantitatively compare different classification approaches; and (3) sensitivity of results to different classification approaches should be assessed. These guidelines should help researchers apply hierarchical community models in the most effective manner.

Assembly dynamics of a forest bird community depend on disturbance intensity and foraging guild

“…We allowed responses to vary by functional group, which prevented ubiquitous species from dominating the ‘mean’ treatment response (Pacifici et al . ). For example, three non‐leaf‐gleaner species, dark‐eyed junco, house wren and white‐crowned sparrow, were detected most frequently and we did not find evidence of a treatment effect for these three species.…”

“…To evaluate specific predictions about avian responses to herbicide treatments, we included a hyperprior in the model to distinguish leaf‐gleaner and non‐leaf‐gleaner guilds (Gelman & Hill ; Pacifici et al . ; Homyack et al . ).…”

Study of biological communities subject to imperfect detection: bias and precision of communityN‐mixture abundance models in small‐sample situations

“…In these models which estimate abundance of species (herein, community abundance or community N-mixture models), the occurrence (or occupancy) of a species is naturally a function of its local abundance (i.e., a species occurs if its local abundance is greater than zero), and community-level species richness and total abundance is obtained as a derived parameter. We can assume that a studied community is composed of multiple (functional) groups in which species may have similar parameters, which are summarized by group-level hyper-parameters (Sauer and Link 2002;Ruiz-Gutie´rrez et al 2010;Yamaura et al 2012;Chen et al 2013;Barnagaud et al 2014;Pacifici et al 2014).…”

Nest survival modelling using a multi‐species approach in forests managed for timber and biofuel feedstock

“…Each species‐level response is drawn from a prior, and then each species‐level prior is in turn drawn from a community‐level hyper‐prior. This is accomplished by specifying the hyper‐priors for each parameter as random variables to be estimated from a probability distribution among species within a community (or guild; LeSaffre & Lawson, ; Pacifici, Zipkin, Collazo, Irizarry, & DeWan, ). Concurrently fitting parameters that describe these relationships among, and for each species individually, then allow simultaneously modelling of coefficients that affect nest survival for each individual species within the bird community.…”