Many of the processes that govern the viability of animal populations vary spatially, yet population viability analyses (PVAs) that account explicitly for spatial variation are rare. We develop a PVA model that incorporates autocorrelation into the analysis of local demographic information to produce spatially explicit estimates of demography and viability at relatively fine spatial scales across a large spatial extent. We use a hierarchical, spatial autoregressive model for capture-recapture data from multiple locations to obtain spatially explicit estimates of adult survival (Φad), juvenile survival (Φjuv), and juvenile-to-adult transition rates (ψ), and a spatial autoregressive model for recruitment data from multiple locations to obtain spatially explicit estimates of recruitment (R). We combine local estimates of demographic rates in stage-structured population models to estimate the rate of population change (λ), then use estimates of λ (and its uncertainty) to forecast changes in local abundance and produce spatially explicit estimates of viability (probability of extirpation, Pex). We apply the model to demographic data for the Sonoran desert tortoise (Gopherus morafkai) collected across its geographic range in Arizona. There was modest spatial variation in λ (0.94–1.03), which reflected spatial variation in Φad (0.85–0.95), Φjuv (0.70–0.89), and ψ (0.07–0.13). Recruitment data were too sparse for spatially explicit estimates, therefore we used a range-wide estimate (R = 0.32 one-year old females per female per year). Spatial patterns in demographic rates were complex, but Φad, Φjuv, and λ tended to be lower and ψ higher in the northwestern portion of the range. Spatial patterns in Pex varied with local abundance. For local abundances > 500, Pex was near zero (Pex approached one in the northwestern portion of the range and remained low elsewhere. When local abundances were Pex > 0.25). This approach to PVA offers the potential to reveal spatial patterns in demography and viability that can inform conservation and management at multiple spatial scales, provide insight into scale-related investigations in population ecology, and improve basic ecological knowledge of landscape-level phenomena.
|Date made available||2018|