Modelling Species Diversity Through Species Level Hierarchical Modelling

Abstract

Understanding spatial patterns of species diversity and the distributions of individ-ual species is a consuming problem in biogeography and conservation. The Cape floristic region of South Africa is a global hot spot of diversity and endemism, and the Protea atlas project, with about 60 000 site records across the region, provides an extraordinarily rich data set to model patterns of biodiversity. Model development is focused spatially at the scale of 1-super-′ grid cells (about 37 000 cells total for the region). We report on results for 23 species of a flowering plant family known as "Proteaceae" (of about 330 in the Cape floristic region) for a defined subregion. Using a Bayesian framework, we developed a two-stage, spatially explicit, hierarchical logistic regression. Stage 1 models the "potential" probability of presence or absence for each species at each cell, given species attributes, grid cell (site level) environmental data with species level coefficients, and a spatial random effect. The second level of the hierarchy models the probability of observing each species in each cell given that it is present. Because the atlas data are not evenly distributed across the landscape, grid cells contain variable numbers of sampling localities. Thus this model takes the sampling intensity at each site into account by assuming that the total number of times that a particular species was observed within a site follows a binomial distribution. After assigning prior distributions to all quantities in the model, samples from the posterior distribution were obtained via Markov chain Monte Carlo methods. Results are mapped as the model-estimated probability of presence for each species across the domain. This provides an alternative to customary empirical 'range-of-occupancy' displays. Summing yields the predicted richness of species over the region. Summaries of the posterior for each environmental coefficient show which variables are most important in explaining the presence of species. Our initial results describe biogeographical patterns over the modelled region remarkably well. In particular, species local population size and mode of dispersal contribute significantly to predicting patterns, along with annual precipitation, the coefficient of variation in rainfall and elevation. Copyright 2005 Royal Statistical Society.

Detecting diversity: emerging methods to estimate species diversity

“…We will refer to these as MSOMs when used to estimate incidence-based measures of diversity [14,31,32]. Hierarchical diversity models have been developed without explicitly accounting for the detection process [33,34]. However, a great advantage of incorporating the detection process into community models is the ability to account explicitly for the effects of survey-, site-, species-, and individual-level factors affecting detectability (Figure 1) through the inclusion of one or multiple detection covariates [14,31].…”

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

“…As an alternative, one might analyze multiple or all species together in an analysis that stratifies by species, thereby accounting for the effects of species identity on parameters of abundance and detection (Alldredge et al 2007), perhaps treating them as random effects, so that some information is shared among them (Ke´ry and Royle 2008;Zipkin et al 2009). Dorazio and Royle (2005) and Dorazio et al (2006) proposed an approach of modeling a community as an ensemble of elemental species-level models from which community-level variables such as species richness or site similarity can naturally be derived (for a similar approach see also Gelfand et al 2005;Ovaskainen and Soininen 2011). Using a series of binary detection/nondetection data of all species detected in a community, their community occupancy model estimates binary occupancy (presence/absence) of individual species at each site while correcting for imperfect detection.…”

Erratum: Monitoring plant functional diversity from space

“…Combined with trait and phylogenetic data, and potentially other ecological information (such as typical stand density), hierarchical statistical models and downscaling techniques 58,59 may, with some uncertainty, allow the pinpointing of particular species and the make-up of communities. We hypothesize that such predictions will generally be much more effective at coarser levels of biological organization, such as higher-level clades or other wellcharacterized species groups that can be associated with the aggregate functions of the spectral signal of a pixel.…”