Taxonomy-based hierarchical analysis of natural mortality: polar and sub-polar phocid seals

Knowledge of life‐history parameters is frequently lacking in many species and populations, often because they are cryptic or logistically challenging to study, but also because life‐history parameters can be difficult to estimate with adequate precision. We suggest using hierarchical Bayesian analysis (HBA) to analyze variation in life‐history parameters among related species, with prior variance components representing shared taxonomy, phenotypic plasticity, and observation error. We develop such a framework to analyze U‐shaped natural mortality patterns typical of mammalian life history from a variety of sparse datasets. Using 39 datasets from seals in the family Phocidae, we analyzed 16 models with different formulations for natural morality, specifically the amount of taxonomic and data‐level variance components (subfamily, species, study, and dataset levels) included in mortality hazard parameters. The highest‐ranked model according to DIC included subfamily‐, species‐, and datase...诸多物种种群的生活史参数往往仍未得到充分认知，其成因通常包括两类：一是研究对象较为隐蔽，或研究开展存在后勤层面的实际困难；二是生活史参数的精准估算本身颇具挑战。我们提出采用分层贝叶斯分析（hierarchical Bayesian analysis, HBA），用于分析近缘物种种群间生活史参数的变异特征，该方法的先验方差组分可涵盖共享分类学关联、表型可塑性与观测误差。我们构建了一套分析框架，用以处理各类稀疏数据集所呈现的、哺乳动物生活史典型的U型自然死亡率模式。我们利用来自海豹科（Phocidae）海豹类的39份数据集，针对16种不同形式的自然死亡率模型开展分析，这些模型在死亡风险参数所纳入的分类学与数据层级方差组分（即亚科、物种、研究与数据集层级）上存在差异。根据偏差信息准则（DIC）排名最高的模型纳入了亚科、物种及数据集[原文此处截断]