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首页> 外文期刊>ICES Journal of Marine Science >A Linear Mixed Model With Temporal Covariance Structures In Modelling Catch Per Unit Effort Of Baltic Herring
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A Linear Mixed Model With Temporal Covariance Structures In Modelling Catch Per Unit Effort Of Baltic Herring

机译：在波罗的海鲱每单位努力量捕捞量建模中具有时间协方差结构的线性混合模型

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摘要

Changes in the structure and attributes of a fleet over time will break down the proportionality of catch per unit effort (cpue) and stock biomass. Moreover, logbook data from commercial fisheries are hierarchical and autocorrelated. Such features not only complicate the analysis of cpue data but also seriously limit the application of a generalized linear model approach, which nevertheless is applied commonly. We demonstrate a linear mixed model application for a large hierarchical dataset containing autocorrelated observations. In the analysis, the key idea is to explore the properties of the error term of the model. We modified the residual covariance matrix, allowing the introduction of assumed fisher behaviour, influencing the catch rate. Fisher behaviour consists of accumulated knowledge and learning processes from their earlier area- and time-specific catch rates. Also, we investigated the effects of vessel-specific parameters by introducing random intercepts and slopes in the model. A model with the autoregressive moving average residual covariance matrix structure was superior over the block-diagonal and autoregressive (AR1) structure for the data, having a time-dependent correlation among trawl hauls. The results address the benefits of statistically advanced methods in obtaining precise and unbiased estimates from cpue data, to be used further in stock assessment. Fisheries agencies are encouraged to monitor the relevant vessel and gear attributes, including engine power and gear size, and the deployment practices of the gear.
机译：船队的结构和属性随时间的变化将破坏单位捕捞量（cpue）和生物量库存的比例。而且，来自商业渔业的日志数据是分层的并且是自相关的。这些特征不仅使对cpue数据的分析变得复杂，而且严重限制了通用线性模型方法的应用，尽管这种方法仍然普遍使用。我们为包含自相关观测值的大型分层数据集演示了线性混合模型应用程序。在分析中，关键思想是探索模型误差项的属性。我们修改了残差协方差矩阵，允许引入假定的渔民行为，从而影响捕获率。费舍尔的行为包括从其早期地区和特定时间捕获率中积累的知识和学习过程。此外，我们通过在模型中引入随机截距和斜率来研究特定于船只的参数的影响。具有自回归移动平均残差协方差矩阵结构的模型的数据优于块对角和自回归（AR1）结构，拖网运输之间具有时间相关的关系。结果解决了统计上先进的方法从cpue数据中获得精确且无偏估计的好处，并将进一步用于库存评估。鼓励渔业机构监测有关船只和渔具的属性，包括发动机功率和渔具尺寸以及渔具的部署做法。

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