Population viability models for an endangered endemic subtropical butterfly: Effects of fire and hurricanes on population dynamics and risk of extinction
Population viability analyses for butterflies typically use metapopulation models, but for endemic species with no redundancy among subpopulations, we need to understand local population dynamics. However, little is known about the sensitivity of butterfly population dynamics and viability to disturbances such as fire and hurricanes. The Florida leafwing (Anaea troglodyta floridalis) is endemic to pine savannas of South Florida, and has declined to occupy a single locale following extirpation of a second remnant population following hurricane Wilma in 2005. In these disturbance prone pine savannas, tropical storms and fire maintain an open canopy. However, storm surge from strong hurricanes may flood coastal areas, contaminating groundwater and resulting in persistent negative effects.
We fit quadratic models to monthly butterfly count data (1999-2014) to estimate an annual population density index that represents density during peak abundance each year. Relative population growth rate was estimated using a time series of the population density index, and population dynamics parameters r0 and K were estimated by fitting relative growth rates to density independent and dependent models that include the effects of fire. We ask how sensitive is extinction risk to natural disturbance and explore the interaction between fire frequency and density dependence.
Stochastic simulations of population models projected 20 years into the future suggest that extinction risk is sensitive to carrying capacity and fire frequency. Although the density independent model had the highest relative likelihood, density dependent models produced population trajectories with behavior more congruent with data from the Anaea troglodyta floridalis population. Density dependent models provided a more optimistic outlook relative to density independent models (8% vs 66% probability of extinction in 20 years). The absence of fire increased sensitivity of relative growth rate to density, and the occurrence of fire buffered this sensitivity by increasing carrying capacity. Hurricanes did not occur frequent enough to test their effects statistically, but large hurricanes at their historic frequency were simulated to cause a string of years with poor growth. Large hurricanes increased extinction risk almost 30%.
Our work is one of very few population viability analyses that links butterfly population dynamics to stochastic disturbances. Extinction risk was most sensitive to the inclusion of density dependent dynamics. Broad confidence intervals around our estimates characterize the uncertainty in otherwise optimistic extinction probability estimates. Our simulations suggest that improving carrying capacity would provide the best buffer to extinction for this endangered endemic butterfly.