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A spatially explicit hierarchical model to characterize population viability

机译:空间显式的分层模型,以表征种群可行性

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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_(ad)), juvenile survival (Φ_(juv)), and juvenile-to-adult transition rates (λ), 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 (k), 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_(ex)). 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 ^k (0.94-1.03), which reflected spatial variation in Φ_(ad) (0.85-0.95), Φ_(juv) (0.70-0.89), and λ (0.07-0.13). Recruitment data were too sparse for spatially explicit estimates; therefore, we used a range-wide estimate (R=0.32 1-yr-old females per female per year). Spatial patterns in demographic rates were complex, but Φ_(ad), Φ_(juv), and λ tended to be lower and λ higher in the northwestern portion of the range. Spatial patterns in P_(ex) varied with local abundance. For local abundances >500, P_(ex) was near zero (<0.05) across most of the range after 100 yr; as abundances decreased, however, P_(ex) approached one in the northwestern portion of the range and remained low elsewher
机译:许多管理动物人群的生存能力的过程在空间上变化,但占空间变化明确的人口活力分析(PVA)是罕见的。我们开发了一个PVA模型,该模型将自相关的内容结合到局部人口统计信息的分析中,以在较大的空间范围内在相对精细的空间尺度上产生空间显式估计的人口统计学和可行性。我们使用来自多个位置的捕获重新捕获数据的分层,空间,自回归模型,以获得成人存活的空间明确估计(φ_(Ad)),少年存活(φ_(j_(j_(juv))和少年到成人的过渡率( λ)以及来自多个位置的招聘数据的空间自回归模型,以获得招聘(r)的空间明确估计。我们将局部结构化人口模型中的人口率估计相结合,以估计人口变化率(k),那么使用λ(及其不确定性)的估计来预测局部丰度的变化,并产生空间显式估计的活力(灭绝概率,P_(前))。我们将模型应用于在亚利桑那州的地理范围内收集的Sonoran Desert龟(Gopherus Morafkai)的人口统计数据。 ^ K(0.94-1.03)有适度的空间变化,其反映了φ_(AD)(0.85-0.95),φ_(JUV)(0.70-0.89)和λ(0.07-0.13)的空间变化。招聘数据对于空间明确估计,稀疏了;因此,我们使用范围范围的估计(r = 0.32每年女性每年雌性的女性)。人口统计率的空间模式是复杂的,但φ_(AD),φ_(juv),λ倾向于较低,在该范围的西北部的λ更高。 P_(前)的空间模式随着局部丰富而变化。对于局部丰度> 500,P_(EX)在100年后的大部分范围内接近零(<0.05);然而,由于大量降低,P_(前)在范围的西北部接近一个,并且仍然很低

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