In an NSF funded project with M. Antolin, CSU, K. Gage, CDC, and P. Stapp Cal State-Fullerton, We have developed a novel modeling approach to understanding epizootic outbreaks of plague in wild rodent hosts (Webb et al. 2006 PNAS).  This approach includes three possible routes of transmission in the same model and uses sensitivity analysis to determine their relative importance.  We developed an ODE model and its stochastic realization that is completely parameterized using data from the literature and field studies in the black-tailed prairie dog (Cynomys ludovicianus).  Results of the model are qualitatively and quantitatively consistent with independent data from the field sites of my colleagues.  While vector transmission via “blocked” fleas is the dominant paradigm in the literature, our model clearly predicts that this form of transmission cannot drive epizootics in prairie dogs.  Rather, transmission via a short-term reservoir is required for epizootic dynamics.  Our model predictions of the residence time of the short-term reservoir narrows the candidate infectious reservoirs to three possibilities.  We are currently extending our modeling project to incorporate the classical metapopulation structure found at the landscape level at study sites in Northern Colorado and to incorporate the role of the evolution of resistance in the system. Find out more on the project web site.

In another disease project, we are investigating the seasonal prevalence of rabies in bats with R. Bowen, CSU.  This work includes a statistical analysis of the seasonal prevalence of rabies positive submissions to the CDC and developing a more mechanistic, dynamical systems model of rabies in bat.

The response of biodiversity to a changing environment very likely constrains the way ecosystem function responds to environmental change.  The main goal of this project is to incorporate the dynamic response of functional diversity into our understanding of how diversity impacts the stability and resilience responses of ecosystems to environmental change.  Among candidate theoretical approaches for achieving this goal, trait-based modeling approaches are particularly well suited, given their inherent structure for simultaneously describing the relationship between functional diversity and ecosystem function and the response of functional diversity to temporal and spatial variability in the environment.  The most well-developed of these trait-based approaches (Norberg et al. PNAS 2001) aggregates inter- and intra-specific variability in functional traits into a biomass distribution of the functional trait.  Using a framework similar to quantitative genetics, environmental drivers produce changes in the trait distribution which in turn translate into changes in ecosystem function.  A weakness of current versions of trait-based approaches is that they deal with only a single trait and environmental driver, while real ecosystems are characterized by organisms with multiple, correlated traits responding to multiple, correlated environmental drivers.  Together with my collaborators (J. Norberg, Stockholm Univ., V. Savage, Harvard), we are extending Norberg et al.’s analytic trait-based framework to multiple, correlated traits and environmental drivers.  Because of the promise of trait-based approaches in addressing long-term ecosystem responses, we are also testing the predictive ability of trait-based approaches using long-term biodiversity studies conducted at multiple LTERs.  We are currently supported by grants from NSF and the Swedish National Research Council.