Estimating the time to extinction in an island population of song sparrows

We estimated and modelled how uncertainties in stochastic population dynamics and biases in parameter estimates affect the accuracy of the projections of a small island population of song sparrows which was enumerated every spring for 24 years. The estimate of the density regulation in a theta-logistic model (<(theta)over cap> = 1.09) suggests that the dynamics are nearly logistic, with specific growth rate (r) over cap(1) = 0.99 and carrying capacity (K) over cap = 41.54. The song sparrow population was strongly influenced by demographic (<(sigma)over cap>(2)(d) = 0.66) and. environmental (<(sigma)over cap>(2)(d) = 0.41) stochasticity. Bootstrap replicates of the different parameters revealed that the uncertainties in the estimates of the specific growth rate r(1) and the density regulation a were larger than the uncertainties in the environmental variance sigma(e)(2) and the carrying capacity K. We introduce the concept of the population prediction interval (PPI), which is a stochastic interval which includes the unknown population size with probability (1 - alpha). The width of the PPI increased rapidly with time because of uncertainties in the estimates of density regulation as well as demographic and environmental variance in the stochastic population dynamics. Accepting a 10% probability of extinction within 100 years, neglecting uncertainties in the parameters will lead to a 33% overestimation of the time it takes for the extinction barrier (population size X = I) to be included into the PPI. This study shows that ignoring uncertainties in population dynamics produces a substantial underestimation of the extinction risk.