title: Predicting [corrected] extinction risk in spite of predator-prey oscillations.
abstract: Most population viability analyses (PVA) assume that the effects of species interactions are subsumed by population-level parameters. We examine how robust five commonly used PVA models are to violations of this assumption. We develop a stochastic, stage-structured predator-prey model and simulate prey population vital rates and abundance. We then use simulated data to parameterize and estimate risk for three demographic models (static projection matrix, stochastic projection matrix, stochastic vital rate matrix) and two time series models (diffusion approximation [DA], corrupted diffusion approximation [CDA]). Model bias is measured as the absolute deviation between estimated and observed quasi-extinction risk. Our results highlight three generalities about the application of single-species models to multi-species conservation problems. First, our collective model results suggest that most single-species PVA models overestimate extinction risk when species interactions cause periodic variation in abundance. Second, the DA model produces the most (conservatively) biased risk forecasts. Finally, the CDA model is the most robust PVA to population cycles caused by species interactions. CDA models produce virtually unbiased and relatively precise risk estimates even when populations cycle strongly. High performance of simple time series models like the CDA owes to their ability to effectively partition stochastic and deterministic sources of variation in population abundance.
authors: Faculty of Ecology, Evolution and Environmental Science, School of Life Sciences, Arizona State University, P.O. Box 874501, Tempe, Arizona 85287-4501, USA. John.L.Sabo@asu.edu
authors: John L
authors: JL
authors: Sabo
authors: Leah R
authors: LR
authors: Gerber
mesh_terms: D000374:Aggression; D000818:Animals; D001522:Behavior, Animal; D003710:Demography; D017753:Ecosystem; D004784:Environmental Monitoring; D053476:Extinction, Biological; D005544:Forecasting; D006801:Humans; D008954:Models, Biological; D011157:Population Dynamics; D018570:Risk Assessment; D013269:Stochastic Processes; D013997:Time Factors