Novel Approaches for Process-Based Species Distribution Models
Friday, August 15, 2014: 8:00 AM-11:30 AM
307, Sacramento Convention Center
Margaret E. K. Evans, University of Arizona
Sydne Record, Harvard University; and
Sean McMahon, Smithsonian Tropical Research Institute
Brian J. Enquist, University of Arizona
Species distribution or niche models are now one of the most widely-used tools in large-scale ecology, conservation biology, and biogeography. Indeed, predicting species’ current and future geographic distributions is a central challenge in ecology, particularly in the light of climate and other global change factors. One of the most pressing issues is how to integrate ecological and physiological mechanisms into niche or distribution models. Many ecologists have called for the development of a better suite of process-based models to improve our understanding of species’ current range dynamics, and forecast their future distributions. Process-based range models look beyond correlations between species’ presence and environmental variables, towards dynamic influences on species’ geographic distributions. Such models should, in principle, have better predictive ability. Our goal is to gather together speakers tackling the challenge of process-based range modeling from a diversity of modeling frameworks and incorporating a diversity of processes. The processes under consideration include physiology, phenology, demography, dispersal, allometry, species interactions, and community dynamics. The organization of the session will follow this same hierarchy – beginning with models based on physiology, and ending with community-level models. We are targeting speakers who will be able to contribute case studies – novel modeling frameworks applied to real data – rather than theoretical or conceptual advancements. In addition to the particular models and organisms they will speak about, we will ask the speakers to address some of the key challenges posed by process-based range modeling, including i) the integration of different sources of data to gain better inference on important processes and parameters, ii) addressing sampling bias and spatial autocorrelation, iii) scaling from field sampling units (plots, etc.) to entire geographic ranges, and iv) validating model predictions.