标题： 组合不确定性从样本到种群水平的快速扩展 显示英文标题

标题： Rapid Scaling of Compositional Uncertainty from Sample to Population Levels

摘要： 理解种群组成在生态学、进化、保护和资源管理的背景下都是至关重要的。 现代方法，如遗传种群鉴定（GSI），使用遗传数据来估计每个亚种群中个体的比例。 理想情况下，这些估计是通过混合分析获得的，这可以捕捉到抽样和遗传的不确定性。 然而，历史数据集通常依赖于仅考虑样本层面不确定性的个体分配方法，这限制了种群层面推断的有效性。 为了解决这个问题，我们提出了一种反向Dirichlet-多项式模型，并推导出多个方差估计器，以将不确定性从样本层面传播到种群层面。 我们将这个框架扩展到遗传标记-再捕获研究，通过模拟评估性能，并将我们的方法应用于估计Taku河中sockeye鲑鱼（Oncorhynchus nerka）的逃逸量。 显示英文摘要

摘要： Understanding population composition is essential across ecological, evolutionary, conservation, and resource management contexts. Modern methods such as genetic stock identification (GSI) estimate the proportion of individuals from each subpopulation using genetic data. Ideally, these estimates are obtained through mixture analysis, which captures both sampling and genetic uncertainty. However, historical datasets often rely on individual assignment methods that only account for sample-level uncertainty, limiting the validity of population-level inferences. To address this, we propose a reverse Dirichlet-multinomial model and derive multiple variance estimators to propagate uncertainty from the sample to the population level. We extend this framework to genetic mark-recapture studies, assess performance via simulation, and apply our method to estimate the escapement of Sockeye Salmon (Oncorhynchus nerka) in the Taku River.