A spatially explicit hierarchical model to characterize population viability

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...

调控动物种群生存力的诸多过程存在空间异质性（spatial heterogeneity），但明确考虑空间变异的种群生存力分析（Population Viability Analysis, PVA）却较为罕见。我们构建了一个PVA模型，该模型将自相关（autocorrelation）纳入局域种群统计信息分析，从而在大空间范围内的相对精细空间尺度上生成种群统计和生存力的空间显式估计（spatially explicit estimate）。我们针对多地点捕获-重捕获数据（capture-recapture data）采用分层空间自回归模型（hierarchical spatial autoregressive model），以获取成体存活率（Φad）、幼体存活率（Φjuv）及幼体到成体转换率（ψ）的空间显式估计；同时针对多地点补充量数据采用空间自回归模型（spatial autoregressive model），以获取补充量（R）的空间显式估计。我们将局域种群统计速率估计整合到阶段结构种群模型（stage-structured population model）中，以估算种群变化率（λ），随后利用λ的估计值（及其不确定性）预测...的变化。