Capturing the diversity of fishing fleets and identifying distinct subgroups is essential for effectively directing research and management efforts. In this study, the German Baltic gillnet fleet was split into distinguishable groups of vessels by applying a stepwise approach. Monthly landing profiles were classified using clustering techniques and arranged as annual landing sequences, as the basis to form groups of vessels with distinct annual landing sequences using sequence analysis. Commercial landings from 1243 vessels across 11 years (2008–2018) resulted in 8031 annual landing sequences, which were clustered into eight groups, each with a characteristic, annually recurring, seasonal landing pattern (cod group, cod‐herring group, herring‐flounder group, herring group, freshwater fish group, pikeperch group, eel group and port group). The results highlight the heterogeneity of the fleet and a strong adaptation to regional and seasonal resource availability. Studying sequences of landings instead of isolated events in time provides insight into the interlinkage and succession of landings and can aid at classifying fishing fleets and better targeting groups of vessels of interest.

中文翻译：

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解开捕鱼船队的复杂性：使用序列分析基于商业着陆对可区分的船只类别进行分类

捕捉捕捞船队的多样性并确定不同的亚组，对于有效指导研究和管理工作至关重要。在这项研究中，德国波罗的海刺网舰队通过采用逐步进近的方法被分为不同的船群。使用聚类技术对月度着陆剖面进行分类，并按年度着陆顺序进行排列，以此为基础使用顺序分析形成具有不同年度着陆顺序的船只组。在11年（2008-2018）的1243艘船的商业降落中，产生了8031个年度降落序列，分为八类，每组都有一个特征性的，每年重复出现的季节性降落模式（鳕鱼组，鳕鱼鲱鱼组，鲱鱼比目鱼组，鲱鱼组，淡水鱼组，鲈鱼组，鳗鱼组和港口组）。结果强调了机队的异质性以及对区域和季节性资源可用性的强烈适应。通过研究降落顺序而不是及时地发现事件，可以深入了解降落的相互联系和连续性，并且可以帮助对捕鱼船队进行分类，并更好地将目标船只对准目标群体。