Code and data from: A hierarchical approach for estimating state-specific mortality and state transition in dispersing animals with incomplete death records

Unbiased mortality estimates are fundamental for testing ecological and evolutionary theory as well as for developing effective conservation actions. However, mortality estimates are often confounded by dispersal, especially in studies where dead-recovery is not possible. In such instances, missing individuals (i.e. individuals with unobserved time of death) may have died or permanently emigrated from a study area, making inferences about their fate difficult. Mortality before and during dispersal, as well as the decision to disperse, usually depend on a suite of individual, social, and environmental covariates, which in turn can be used to draw conclusions about the fate of missing individuals.Here, we propose a Bayesian hierarchical model that takes into account time-varying covariates to estimate transitions between life-history states and mortality in each state using mark-resighting data with missing individuals. Specifically, our framework estimates mortality rates in two states (..., ,

无偏死亡率估计是检验生态学与进化理论、制定有效保护措施的核心基础。然而，死亡率估计常受扩散过程的混杂干扰，尤其在无法开展死亡回收（dead-recovery）研究的场景中。在此类情形下，失踪个体（即死亡时间未被观测到的个体）可能已经死亡，或是永久迁出研究区域，这使得对其命运的推断极具挑战性。扩散发生前与扩散过程中的死亡率，以及扩散决策本身，通常取决于一系列个体层面、社会层面与环境层面的协变量，而这些协变量可用于推断失踪个体的最终命运。为此，我们提出一种贝叶斯分层模型（Bayesian hierarchical model），该模型能够纳入时变协变量，基于带有失踪个体的标记重捕（mark-resighting）数据，估计不同生活史状态间的转移情况以及各状态下的死亡率。具体而言，我们的框架可对两种状态下的死亡率进行估计（……）