The mortality of Atlantic salmon is one of the main challenges to achieving its sustainable production. This sector benefits from generating many data, some of which are collated in a standardized way, on a monthly basis at site level, and are accessible to the public. This continuously updated resource might provide opportunities to monitor mortality and prompt producers and inspectors to further investigate when mortality is higher than expected. This study aimed to use the available open-source data to develop production cycle level dynamic linear models (DLMs) for monitoring monthly mortality of maricultured Atlantic salmon in Scotland. To achieve this, several production cycle level DLMs were created: one univariate DLM that includes just mortality; and various multivariate DLMs that include mortality and different combinations of environmental variables. While environmental information is not collated in a standardized way across all sites, open-source remote-sensed satellite resources provide continuous, standardized estimates. By combining environmental and mortality data, we seek to investigate whether adding environmental variables enhanced the estimates of mortality, and if so, which variables were most informative in this respect. The multivariate model performed better than the univariate DLM (P = .004), with salinity as the only significant contributor out of 12 environmental variables. Both models exhibited uncertainty related to the mortality estimates. Warnings were generated when any observation fell above the 95% credible interval. Approximately 30% of production cycles and more than 50% of sites experienced at least one warning between 2015 and 2020. Occurrences of these warnings were non-uniformly distributed across space and time, with the majority happening in the summer and autumn months. Recommendations for model improvement include employing shorter time periods for data aggregation, such as weekly instead of on a monthly basis. Furthermore, developing a model that takes hierarchical relationships into account could offer a promising approach.

中文翻译：

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监测苏格兰海水养殖大西洋鲑鱼 (Salmo salar L.) 的月死亡率 I. 生产周期水平的动态线性模型


大西洋鲑鱼的死亡率是实现其可持续生产的主要挑战之一。该部门受益于生成许多数据，其中一些数据以标准化方式每月在站点级别进行整理，并且可供公众访问。这种不断更新的资源可能会提供监测死亡率的机会，并促使生产者和检查员在死亡率高于预期时进一步调查。本研究旨在利用现有的开源数据开发生产周期水平动态线性模型 (DLM)，用于监测苏格兰海水养殖大西洋鲑鱼的每月死亡率。为了实现这一目标，创建了多个生产周期级别的 DLM：一个仅包含死亡率的单变量 DLM；以及各种多变量 DLM，包括死亡率和环境变量的不同组合。虽然所有站点的环境信息并未以标准化方式进行整理，但开源遥感卫星资源提供了连续、标准化的估计。通过结合环境和死亡率数据，我们试图调查添加环境变量是否可以提高死亡率的估计值，如果是的话，哪些变量在这方面提供的信息最多。多变量模型的表现优于单变量 DLM (P = .004)，盐度是 12 个环境变量中唯一重要的影响因素。两种模型都表现出与死亡率估计相关的不确定性。当任何观察结果低于 95% 可信区间时，就会生成警告。 2015 年至 2020 年间，大约 30% 的生产周期和超过 50% 的工厂至少经历过一次警告。 这些警告的出现在空间和时间上分布不均匀，大多数发生在夏季和秋季。模型改进的建议包括采用更短的时间段进行数据聚合，例如每周而不是每月。此外，开发一个考虑层次关系的模型可以提供一种有前途的方法。