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Combining data‐derived priors with postrelease monitoring data to predict persistence of reintroduced populations

机译:将数据派生的先验数据与发布后的监测数据相结合,以预测重新引入种群的持久性

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Monitoring is an essential part of reintroduction programs, but many years of data may be needed to obtain reliable population projections. This duration can potentially be reduced by incorporating prior information on expected vital rates (survival and fecundity) when making inferences from monitoring data. The prior distributions for these parameters can be derived from data for previous reintroductions, but it is important to account for site‐to‐site variation. We evaluated whether such informative priors improved our ability to estimate the finite rate of increase (λ) of the North Island robin ( Petroica longipes ) population reintroduced to Tawharanui Regional Park, New Zealand. We assessed how precision improved with each year of postrelease data added, comparing models that used informative or uninformative priors. The population grew from about 22 to 80 individuals from 2007 to 2016, with λ estimated to be 1.23 if density dependence was included in the model and 1.13 otherwise. Under either model, 7?years of data were required before the lower 95% credible limit for λ was 1, giving confidence that the population would persist. The informative priors did not reduce this requirement. Data‐derived priors are useful before reintroduction because they allow λ to be estimated in advance. However, in the case examined here, the value of the priors was overwhelmed once site‐specific monitoring data became available. The Bayesian method presented is logical for reintroduced populations. It allows prior information (used to inform prerelease decisions) to be integrated with postrelease monitoring. This makes full use of the data for ongoing management decisions. However, if the priors properly account for site‐to‐site variation, they may have little predictive value compared with the site‐specific data. This value will depend on the degree of site‐to‐site variation as well as the quality of the data.
机译:监测是重新引入计划的重要组成部分,但可能需要多年的数据才能获得可靠的人口预测。通过从监视数据进行推断时,可以通过合并有关预期生命率(生存率和生殖力)的先前信息来潜在地缩短此持续时间。这些参数的先前分布可以从先前重新引入的数据中得出,但是考虑站点之间的差异非常重要。我们评估了这种信息性先验是否提高了我们估计重新引入新西兰Tawharanui区域公园的北岛知更鸟(Petroica longipes)种群的有限增长率(λ)的能力。我们比较了使用信息性或非信息性先验的模型后,每年发布后添加的数据如何提高精度。从2007年到2016年,人口从大约22个人增加到80个人,如果在模型中包括密度依赖性,则λ估计为1.23,否则为1.13。在这两种模式下,在λ的下限95%可信下限大于1之前,都需要7年的数据,从而确信该种群将持续存在。内容丰富的先验并没有减少这一要求。在重新引入之前,基于数据的先验是有用的,因为它们允许预先估计λ。但是,在这里检查的情况下,一旦获得特定于站点的监视数据,先验价值将不堪重负。提出的贝叶斯方法对于重新引入种群是合乎逻辑的。它允许将先前的信息(用于通知发布前的决策)与发布后的监视集成在一起。这将充分利用数据进行正在进行的管理决策。但是,如果先验正确地说明了站点之间的差异,则与站点特定的数据相比,它们可能没有什么预测价值。该值取决于站点间差异的程度以及数据的质量。

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