在存在生存和捕获异质性的情况下,建模配对释放-恢复数据,并应用于标记的幼年鲑鱼

K. Newman
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引用次数: 42

摘要

多项模型的结果已成为分析动物释放-恢复数据的标准方法。提出了伪似然模型和贝叶斯非线性层次模型。这两种方法都可以在一定程度上解释生存和捕获概率的异质性,而不是协变量所解释的。伪似然方法允许恢复期特定的过分散。分层方法将存活率和捕获率视为固定效应和随机效应的总和。标准和替代方法应用于一组配对释放-恢复鲑鱼数据。有标记的幼年奇努克鲑鱼(Oncorhynchus tshawytscha)被放生,其中一些在淡水中恢复为幼体,另一些在海水中恢复为成年。兴趣集中在模拟淡水存活率作为生物和水文协变量的函数。在乘积多项式公式下,大多数协变量具有统计学显著性。相比之下,在伪似然和分层公式下,系数的标准误差要大得多,其中伪似然标准误差要大5 ~ 8倍,且具有统计学显著性的系数较少。协变量包括水温、水流量和供人类使用的出口水量,在所有公式中都很重要,具有重要的管理意义。在交叉验证中使用的训练子集的估计系数方面,层次模型相当稳定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling paired release-recovery data in the presence of survival and capture heterogeneity with application to marked juvenile salmon
Products of multinomial models have been the standard approach to analysing animal release-recovery data. Two alternatives, a pseudo-likelihood model and a Bayesian nonlinear hierarchical model, are developed. Both approaches can to some degree account for heterogeneity in survival and capture probabilities over and above that accounted for by covariates. The pseudo-likelihood approach allows for recovery period specific overdispersion. The hierarchical approach treats survival and capture rates as a sum of fixed and random effects. The standard and alternative approaches were applied to a set of paired release-recovery salmon data. Marked juvenile chinook salmon (Oncorhynchus tshawytscha) were released, with some recovered in freshwater as juveniles and others in marine waters as adults. Interest centered on modelling freshwater survival rates as a function of biological and hydrological covariates. Under the product multinomial formulation, most covariates were statistically significant. In contrast, under the pseudo-likelihood and hierarchical formulations, the standard errors for the coefficients were considerably larger, with pseudo-likelihood standard errors five to eight times larger, and fewer coefficients were statistically significant. Covariates, significant under all formulations, with important management implications included water temperature, water flow and amount of water exported for human use. The hierarchical model was considerably more stable with regard to estimated coefficients of training subsets used in a cross-validation.
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