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首页> 外文期刊>ICES Journal of Marine Science >A method for the identification and characterization of clusters of schools along the transect lines of fisheries-acoustic surveys
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A method for the identification and characterization of clusters of schools along the transect lines of fisheries-acoustic surveys

机译：沿渔业声学调查的横断面线识别和表征学校集群的方法

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The school-aggregation pattern (schools and clusters of schools) is presumed to play a significant role in determining pelagic fish-stock catchability. However, its analysis has seldom been undertaken because it requires field-behavioural data that is seldom available. Such information can now be obtained by analysing school-based data of fisheries-acoustic surveys. This paper proposes a method for doing so. The method allows for the identification of clusters of schools and the estimation of their parameters along one-dimensional, acoustic-survey transect lines. It is based on a spatial point-process approach that considers schools as point events occurring along the track sailed by a ship. More precisely, it is based on defining a maximum distance between schools in a cluster. This distance is chosen to optimize various criteria and in particular that of homogeneity concerning school location inside the clusters and school number per km. The algorithm is described and applied to a series of acoustic surveys carried out in the Bay of Biscay. The pertinence of the clusters obtained by the algorithm is evaluated by analysing which component of the spatial distribution of the schools corresponds to those clusters. This involves considering all the distances between school events and performing simulations of cluster point processes. The school clusters obtained by the proposed algorithm represent a small-range structure of a few kilometres when a longer-range structure of tens of kilometres was also present in the data.
机译：假定学校聚集模式（学校和学校群）在确定中上层鱼类种群可捕性方面起重要作用。但是，由于需要很少的现场行为数据，因此很少进行分析。现在可以通过分析基于学校的渔业声学调查数据来获得此类信息。本文提出了一种这样做的方法。该方法可以识别学校集群，并沿一维声学调查样线估算其参数。它基于空间点过程方法，该方法将学校视为沿着船航行的轨迹发生的点事件。更准确地说，它基于定义群集中学校之间的最大距离。选择该距离以优化各种标准，尤其是关于集群内部学校位置和每公里学校数量的同质性标准。描述了该算法并将其应用于在比斯开湾进行的一系列声学勘测中。通过分析学校的空间分布的哪一部分与那些聚类相对应，可以评估通过算法获得的聚类的相关性。这涉及考虑学校活动之间的所有距离并执行聚类点过程的模拟。通过提出的算法获得的学校集群代表了几公里的小范围结构，而数据中还存在数十公里的长范围结构。

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