School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, 85721, USA.
Desert Tortoise Recovery Office, U. S. Fish and Wildlife Service, Ventura, California, 93003, USA.
Ecol Appl. 2018 Dec;28(8):2055-2065. doi: 10.1002/eap.1794. Epub 2018 Sep 24.
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 (ϕ ), juvenile survival (ϕ ), 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, P ). 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 (0.94-1.03), which reflected spatial variation in (0.85-0.95), (0.70-0.89), and (0.07-0.13). Recruitment data were too sparse for spatially explicit estimates; therefore, we used a range-wide estimate ( = 0.32 1-yr-old females per female per year). Spatial patterns in demographic rates were complex, but , , and tended to be lower and higher in the northwestern portion of the range. Spatial patterns in P varied with local abundance. For local abundances >500, P was near zero (<0.05) across most of the range after 100 yr; as abundances decreased, however, P approached one in the northwestern portion of the range and remained low elsewhere. When local abundances were <50, western and southern populations were vulnerable (P > 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.
许多控制动物种群生存力的过程在空间上存在差异，但明确考虑空间变异的种群生存力分析 (PVA) 却很少见。我们开发了一种 PVA 模型，该模型将自相关纳入局部人口统计信息分析中，以便在较大的空间范围内相对精细的空间尺度上产生人口统计和生存力的空间显式估计。我们使用来自多个位置的捕获-再捕获数据的分层、空间、自回归模型来获得成体存活率 (φ)、幼体存活率 (φ) 和幼体到成体过渡率 (ψ) 的空间显式估计，以及来自多个位置的补充数据的空间自回归模型来获得补充 (R) 的空间显式估计。我们将分阶段种群模型中局部人口统计率的估计值结合起来，以估计种群变化率 (λ)，然后使用 λ 的估计值 (及其不确定性) 来预测当地丰度的变化，并产生生存力的空间显式估计值 (灭绝概率，P)。我们将该模型应用于在亚利桑那州整个地理范围内收集的索诺兰沙漠龟 (Gopherus morafkai) 的人口统计数据。 (0.94-1.03) 存在适度的空间变化，这反映了 (0.85-0.95)、 (0.70-0.89) 和 (0.07-0.13) 的空间变化。补充数据过于稀疏，无法进行空间显式估计；因此，我们使用了一个范围广泛的估计值 (= 0.32 1 岁以下雌性/每年每只雌性)。人口统计率的空间模式很复杂，但在范围的西北部， 、 和 往往较低， 较高。P 的空间模式随当地丰度而变化。对于当地丰度 >500，在 100 年后，范围的大部分地区的 P 接近零 (<0.05)；然而，随着丰度的降低，P 在范围的西北部接近 1，而在其他地方则保持较低水平。当地丰度 <50 时，西部和南部种群处于脆弱状态 (P >0.25)。这种 PVA 方法有可能揭示人口统计和生存力的空间模式，从而为多个空间尺度的保护和管理提供信息，深入了解种群生态学中的规模相关研究，并提高对景观水平现象的基本生态知识。
