University of Pittsburgh Department of Biological Sciences presents:
Fall 2020 Seminar Series
Dr. Thorsten Wiegand
Helmholtz Centre for Environmental Research - UFZ
Multivariate spatiotemporal point processes, individual-based simulation models and the dynamics of species-rich plant communities
Thorsten Wiegand, Ecological Modelling, Helmholtz Center for Environmental Research - UFZ, Leipzig, Germany, and German Centre for Integrative Biodiversity Research (iDiv), Leipzig, Germany
Assessing the relative importance of processes that determine the spatial distribution of species and the assembly and dynamics of species rich plant communities is one of the major challenges in ecology. I argue that we can advance in this question by adopting a spatially explicit perspective that allows using the incredible information that is buried in fully mapped forest dynamic plots of the CTFS-ForestGEO network. This can be done by taking advantage of recent advances in three fields: individual-based simulation models, spatial point process theory, and inference for stochastic simulation models. First, the individual-based model, which can be viewed as a multivariate spatiotemporal point process, mimics the most important biological processes and produces a time series of multivariate point patterns (with tree species and possible tree size being marks) of the same type as the CTFS census data sets. Second, a variety of summary functions of the multivariate point patterns is estimated to capture the main features of the observed and simulated data. They include summary functions of uni- and bivariate patterns, but also novel summary functions that characterize spatiotemporal structures in species and functional diversity. Examples for such functions are the individual species-area relationship or the phylogenetic mark correlation function. Finally, statistical inference for stochastic simulation models is used to fit the individual-based model to the multiple summary functions. This step involves aggregation of simulated and observed data via summary statistics, likelihood approximation based on the summary statistics, and efficient sampling. I show examples of this approach from tropical forest communities. This spatially-explicit approach moves previous ecological theory towards a dynamic spatial theory of biodiversity and demonstrates the value of spatial data to identify ecological processes. This opens up new avenues to evaluate the consequences of additional process for community assembly and dynamics.
Monday, Septemeber 21, 2020
Virtual- Zoom Meeting
11:00 A.M. EST
Host: Dr. Justin Kitzes