监测苏格兰海产大西洋鲑(Salmo salar L.)的月死亡率 II。分层动态线性模型

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-10-31 DOI:10.3389/fmars.2024.1483796
Carolina Merca, Annette Simone Boerlage, Anders Ringgaard Kristensen, Dan Børge Jensen
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引用次数: 0

摘要

鲑鱼养殖业的可持续性正受到死亡率上升的挑战。苏格兰开放源代码的三文鱼生产数据为开发全行业死亡率监测模型提供了可能,该模型对于识别和解决死亡率意外上升问题非常有价值,而无需不同公司之间的数据共享协议。本研究旨在利用这些数据开发一种分层动态线性模型(DLM),用于监测苏格兰海产大西洋鲑的月死亡率。与之前研究中开发的生产周期级 DLM 相比,我们评估了考虑数据中存在的分层结构(国家、地区和地点)是否会改善死亡率预测。我们的研究结果表明,分层 DLM 的效果优于生产周期级 DLM,证实了这种更复杂的建模方法的价值。尽管如此,分层模型与生产周期级 DLM 一样,在死亡率预测方面表现出一定的不确定性。当死亡率高于预期时,就会产生现场级警告,这可以鼓励生产者和检查人员进一步调查原因。在 2015 年至 2020 年期间,约有 25% 的生产周期和 50% 的地点至少遇到过一次警告,大多数警告发生在夏季和秋季。此外,分级模型还能监测多个级别的死亡率。作为监测系统的一部分,这些信息对各利益相关方都很有用,可以深入了解国家、地区和地点层面的死亡率趋势,从而从战略性资源管理中获益。对模型的改进建议包括利用更短的数据汇总周期,如每周一次,因为目前还没有开放源数据。
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Monitoring monthly mortality of maricultured Atlantic salmon (Salmo salar L.) in Scotland II. A hierarchical dynamic linear model
The sustainability of the salmon farming industry is being challenged by increased mortality rates. Scotland’s open-source salmon production data provides the possibility of developing an industry-wide mortality monitoring model, valuable for identifying and addressing unexpected increases in mortality without needing data sharing agreements across different companies. This study aimed to utilize these data to develop a hierarchical dynamic linear model (DLM) for monitoring monthly mortality of maricultured Atlantic salmon in Scotland. We evaluated whether considering the hierarchical structure present in the data (country, region, and site) would improve mortality predictions when compared to the production cycle level DLMs developed in a previous study. Our findings demonstrated that the hierarchical DLM outperformed the production cycle level DLMs, confirming the value of this more complex modelling approach. Nevertheless, the hierarchical model, like the production cycle level DLMs, exhibited some uncertainty in the mortality predictions. When mortality is higher than expected, site level warnings are generated, which can encourage producers and inspectors to further investigate the cause. Between 2015 and 2020, approximately 25% of the production cycles and 50% of the sites encountered at least one warning, with most warnings happening in the summer and autumn months. Additionally, the hierarchical model enabled monitoring mortality at multiple levels. This information is useful for various stakeholders as part of a monitoring system, offering insights into mortality trends at national, regional, and sites levels that may benefit from strategic resource management. Recommendations for model improvements include utilizing shorter data aggregation periods, such as weekly, which are not currently available as open-source data.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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