PARENT SESSION
Oral Session #56: Spatial Ecology: Pattern,movement,dispersal.
Presiding: G. Hartvigsen
Wednesday, August 7. 8:00 AM to 11:30 AM. Grand Ballroom West, Radisson.
Spatial patterns of species richness and geographic ranges in African Proteaceae: Bayesian hierarchical models.
Silander, John*,1, Schmidt, Alexandra1, Latimer, Andrew1, Wu, Shanshan1, Gelfand, Alan1, Rebelo, Tony2, Cowling, Richard3, 1 University of Connecticut, Storrs, CT2 National Botanical Institute, Kirstenbosch, Cape Town, South Africa3 University of Port Elizabeth, Port Elizabeth, South Africa
ABSTRACT- Understanding spatial patterns of species diversity and the distributions of individual species is a consuming problem in biogeography and conservation. The Cape Floristic Region of South Africa is a global hotspot of diversity and endemism, and the Protea Atlas Project, with some 60,000 site records across the region, provides an extraordinarily rich dataset to model biodiversity patterns. Model development focussed spatially at the one minute grid-cell scale (~40,000 cells total). We report on results for 23 species of Proteaceae (of ~370 total) for a defined subregion. Using a Bayesian framework, we developed a two stage, spatially explicit, hierarchical logistic regression. Stage one models the probability of presence/absence for each species at each cell, given species attributes, 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 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 MCMC methods. Results are mapped as the probability of presence for each species across the domain. Summing yields the predicted species richness over the region. Histograms of the posterior of each environmental coefficient, show which variables are most important in explaining species presence. Our initial results describe biogeographical patterns over the modeled region remarkably well. In particular, species local population size and dispersal mode contribute significantly to predicting patterns, along with annual precipitation, C.V. in rainfall, and elevation.
KEY WORDS: species diversity patterns, species distributions, spatially explicit models, Bayesian hierarchical models