OOS 4-5
Spatio-temporal N-mixture models for predicting metapopulation dynamics

Monday, August 10, 2015: 2:50 PM
316, Baltimore Convention Center
Paige Howell, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, USA
Richard Chandler, , University of Georgia, ,
Erin Muths, , USGS Fort Collins Science Center, , CO, USA
Blake Hossack, Northern Rocky Mountain Science Center, USGS, Missoula, MT, USA
Brent H. Sigafus, , U.S. Geological Survey, Tucson, AZ, USA
Background/Question/Methods

A central tenet of metapopulation biology is that colonization is determined by distances among sites and subpopulation-specific demographic rates. However, most statistical models of metapopulation dynamics include proxies such as “distance to nearest neighbor” which fail to account for the actual migrant exchange process that determines colonization. Recently developed spatial occupancy models have made it possible to account for distance effects, but these models greatly simplify the problem by focusing on site occupancy rather than abundance. Our objective was to develop an abundance-based spatially-explicit metapopulation model to predict metapopulation viability using simple count data. To demonstrate, we applied the model to data from a multi-year study of the federally-threatened Chiricahua leopard frog (Lithobates chiricahuensis) in the Buenos Aires National Wildlife Refuge (BANWR), Arizona. 

Results/Conclusions

Our results indicate that pond hydroperiod and distance among sites influenced abundance, extinction, and colonization dynamics. Metapopulation viability is estimated to be high over a 100 year time-horizon assuming that current conditions (site dispersion, hydroperiod, and invasive predator control) persist. However, the loss of a few predator-free wetlands with permanent water would greatly increase metapopulation extinction risk. Our study extends existing statistical models of metapopulation dynamics by incorporating abundance effects and the spatial configuration of sites in the network.