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An Integrated Approach for Predicting Fates of Reintroductions with Demographic Data from Multiple Populations

机译：利用来自多个人群的人口统计学数据预测重新引进的命运的综合方法

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We devised a novel approach to model reintroduced populations whereby demographic data collected from multiple sites are integrated into a Bayesian hierarchical model. Integrating data from multiple reintroductions allows more precise population-growth projections to be made, especially for populations for which data are sparse, and allows projections that account for random site-to-site variation to be made before new reintroductions are attempted. We used data from reintroductions of the North Island Robin(Petroica longipes), an endemic New Zealand passerine, to 10 sites where non-native mammalian predators are controlled. A comparison of candidate models that we based on deviance information criterion showed that rat-tracking rate (an index of rat density) was a useful predictor of robin fecundity and adult female survival, that landscape connectivity and a binary measure of whether sites were on a peninsula were useful predictors of apparent juvenile survival (probably due to differential dispersal away from reintroduction sites), and that there was unexplained random variation among sites in all demographic rates. We used the two best supported models to estimate the finite rate of increase (λ) for populations at each of the 10 sites, and for a proposed reintroduction site, under different levels of rat control. Only three of the reintroduction sites had λ distributions completely >1 for either model. At two sites, λ was expected to be >1 if rat-tracking rates were <5%. At the other five reintroduction sites, λ was predicted to be close to 1, and it was unclear whether growth was expected. Predictions of λ for the proposed reintroduction site were less precise than for other sites because distributions incorporated the full range of site-to-site random variation in vital rates. Our methods can be applied to any species for which postrelease data on demographic rates are available and potentially can be extended to model multiple species simultaneously.
机译：我们设计了一种新颖的方法来对重新引入的种群进行建模，从而将从多个站点收集的人口统计数据集成到贝叶斯层次模型中。整合来自多个重新引入的数据可以做出更精确的人口增长预测，尤其是对于数据稀疏的人群，并允许在尝试新的重新引入之前做出考虑站点之间随机变化的预测。我们使用了来自北岛罗宾（Petroica longipes）（一种地方性的新西兰雀形目）的再引入数据，控制了10个非本地哺乳动物捕食者的站点。我们根据偏差信息标准对候选模型进行的比较表明，大鼠追踪率（大鼠密度的指标）是知足的知更鸟繁殖力和成年雌性存活率的有用预测指标，其景观连通性和位点是否在一个水平上的二元测量值半岛是预测少年存活率的有用预测指标（可能是由于远离重新引入地点的差异性扩散），并且在所有人口统计比率的地点之间都存在无法解释的随机变化。我们使用两种最佳支持的模型来估计在不同水平的大鼠控制下，这10个地点中的每个地点以及拟议的再引入地点的种群的有限增加率（λ）。对于任何一个模型，只有三个重新引入位点的λ分布完全> 1。在两个位置，如果大鼠追踪率<5％，则λ预计> 1。在其他五个重新引入位点，λ预计接近1，目前尚不清楚是否预期增长。提议的重新引入位点的λ预测不如其他位点精确，这是因为分布纳入了生命周期内从位点到位点的随机变化的整个范围。我们的方法可以应用于任何具有人口统计信息的发布后数据的物种，并且可以扩展为同时为多个物种建模。

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