Studying survival in megafauna populations is a challenge. Survival can vary over time and can be altered by increasing pressures from human activities. Considering time variations in inter-annual survival or cumulative survival is necessary to evaluate conservation status and anticipate detrimental demographic changes before large declines in abundance materialize. Estimating survival is straightforward with mark-recapture methods but remains a challenge if individuals cannot be easily identified or recaptured. This is often the case for marine mammals because natural marks are insufficient to identify individual or because tagging raises insuperable logistical difficulties. Here, we propose to fill this methodological gap using age-at-death data with a flexible Bayesian linear modelling framework. We designed a simulation study with several scenarios of decline in survival and size of the data set. We also applied the approach to a real dataset on harbour porpoise (). We evaluated the ability of a model that includes both a trend and yearly random effects to estimate time variations in survivorship, age-specific survival, and hazard rates. We compared the performance of the model to that of simpler models. Our results show that the model is very effective in detecting a trend in survivorship, but can yield biased estimates of age-specific survival rates. We provide a pragmatic modelling framework to address time variations in a megafauna species survivorship using age-at-death data. This framework will enhance our ability to study populations that cannot be individually identified and to design proactive conservation management measures.

中文翻译：

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通过横截面抽样设计解决存活率的时间趋势：适用于巨型动物保护的建模框架

研究巨型动物种群的生存是一项挑战。生存率会随着时间的推移而变化，并且会因人类活动压力的增加而改变。考虑年际生存率或累积生存率的时间变化对于评估保护状况并在丰度大幅下降之前预测有害的人口变化是必要的。使用标记重新捕获方法来估计生存率很简单，但如果无法轻松识别或重新捕获个体，那么仍然是一个挑战。海洋哺乳动物通常就是这种情况，因为自然标记不足以识别个体，或者因为标记会带来难以克服的后勤困难。在这里，我们建议使用死亡年龄数据和灵活的贝叶斯线性建模框架来填补这一方法论空白。我们设计了一项模拟研究，其中包含几种生存率下降和数据集大小下降的场景。我们还将该方法应用于港湾鼠海豚的真实数据集 ()。我们评估了包含趋势和年度随机效应的模型估计生存率、特定年龄生存率和危险率的时间变化的能力。我们将该模型的性能与更简单的模型的性能进行了比较。我们的结果表明，该模型在检测生存趋势方面非常有效，但可能会产生对特定年龄生存率的有偏差的估计。我们提供了一个实用的建模框架，使用死亡年龄数据来解决巨型动物物种生存的时间变化。该框架将增强我们研究无法单独识别的种群和设计主动保护管理措施的能力。