A statistical forecasting approach to metapopulation viability analysis
Conservation of at-risk species is aided by reliable forecasts of the consequences of environmental change and management actions on population viability. Forecasts from conventional population viability analysis (PVA) are made using a two-step procedure in which parameters are estimated, or elicited from expert opinion, and then plugged into a stochastic population model without accounting for parameter uncertainty. Recently-developed statistical PVAs differ because forecasts are made conditional on models that are fitted to empirical data. The statistical forecasting approach allows for uncertainty about parameters, but it has rarely been applied in metapopulation contexts where spatially-explicit inference is needed about colonization and extinction dynamics and other forms of stochasticity that influence metapopulation viability. We conducted a statistical metapopulation viability analysis (MPVA) using 11 years of data on the federally-threatened Chiricahua leopard frog to forecast responses to landscape heterogeneity, drought, environmental stochasticity, and management. We evaluated several future environmental scenarios and pond restoration options designed to reduce extinction risk. Forecasts over a 50-yr time horizon indicated that metapopulation extinction risk was <8% for all scenarios, but uncertainty was high. Without pond restoration, extinction risk is forecasted to be 5.6% (95% CI: 0?60%) by year 2060. Restoring six ponds by increasing hydroperiod reduced extinction risk to 1.0% (0 ? 11%) in year 2060. We found little evidence that drought influences metapopulation viability when managers have the ability to maintain ponds that hold water throughout the year and are free of invasive species. Our study illustrates the utility of the spatially explicit statistical forecasting approach to MPVA in conservation planning efforts.
|Outlet/Publisher:||Ecological Applications 2020:e02038|
ARMI Organizational Units:Rocky Mountains, Northern - Biology
Southwest, Arizona - Biology