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Regional métier definition: A comparative investigation of statistical methods using a workflow applied to the international otter trawl fisheries in the North Sea

机译:区域管理员定义:使用适用于北海国际水獭拖网渔业的工作流程的统计方法的比较研究

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摘要

The European Common Fisheries Policy recognizes the importance of accounting for heterogeneity in fishing practices, and métier-based sampling is now at the core of the EU Data Collection Framework. The implementation of such an approach would require Member States to agree on the standard regional métier definitions and on practical rules to categorize logbook records into métiers. Several alternative approaches have been used in the past to categorize landings profiles, but no consensus has yet emerged. A generic open-source workflow is developed to test and compare a selection of methods, including principal components analysis (PCA), hierarchical agglomerative clustering (HAC), K-means, and Clustering LARge Applications (CLARA), and to provide simple allocation rules. This workflow is applied to a unique regional dataset consisting of bottom-trawl logbooks of five North Sea countries. No method proved to be infallible, but combining PCA with either CLARA or HAC performed best. For 2008, a hierarchical classification with 14 species assemblages is proposed. Discriminant analysis proved more robust than simple ordination methods for allocating a new logbook record into an existing métier. The whole approach is directly operational and could contribute to defining more objective and consistent métiers across European fisheries
机译:欧洲共同渔业政策认识到在捕捞活动中考虑到异质性的重要性,而基于métier的抽样现已成为欧盟数据收集框架的核心。要实施这种方法,将要求会员国就标准的区域等级定义和实际规则达成一致,以将日志记录分类为等级。过去曾使用几种替代方法对着陆情况进行分类,但尚未达成共识。开发了一个通用的开源工作流来测试和比较选择的方法,包括主成分分析(PCA),层次聚集聚类(HAC),K-means和聚类大型应用程序(CLARA),并提供简单的分配规则。此工作流应用于由五个北海国家的底拖网日志组成的唯一区域数据集。没有一种方法被证明是绝对可靠的,但是将PCA与CLARA或HAC组合使用效果最佳。对于2008年,提出了具有14个物种集合的分级分类。事实证明,判别分析比简单的排序方法更强大,可以将新的日志记录分配到现有的Métier中。整个方法是直接可操作的,可有助于在整个欧洲渔业中定义更多客观一致的方法

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