[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122257-en":3,"doc-seo-122257-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122257,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Amending the European fishing fleet segmentation based on machine learning and multivariate statistics","Considering the critical issue of overexploited stocks due to overfishing, the EU’s Data Collection Framework (DCF) supports sustainable fisheries management through data collection and analysis. However, DCF segmentation relies heavily on technical vessel parameters, which may not reflect real fishing activities. An alternative segmentation approach is proposed using multivariate statistics coupled with machine learning for automation and validation against two decades of German fisheries data.","Fisheries Research 281 (2025) 107190  \nContents lists available at ScienceDirect  \nFisheries Research  \n[journal homepage: www.elsevier.com/locate/fishres](journal homepage: www.elsevier.com/locate/fishres)  \n| Amending the European fishing fleet segmentation based on machine learning and multivariate statistics\u003Cbr>E. Sulankea,*,1, V. Rubel b,1, J. Berkenhagen a, M. Bernreuthera, T. Stoeckb, S. Simonsaa Thünen Institute of Sea Fisheries, Bremerhaven, Germany\u003Cbr>b RPTU Rheinland-Pf¨alzische Universit¨at Kaiserslautern, Landau, Germany |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: Classification\u003Cbr>Data collection Fisheries\u003Cbr>Fisheries management Fleet economic data Random forest |  | Considering the critical issue of overexploited stocks due to overfishing, the EU’s Data Collection Framework (DCF) was established. Within the DCF, member states collect and analyze data relevant to sustainable fisheries management. To evaluate the status of fisheries, it is necessary to categorize fishing fleets into fleet segments. However, the current DCF segmentation is primarily based on technical vessel parameters, such as vessel length and predominant fishing gear, which often do not accurately represent the fishing activities of the vessels. To address this, we developed an alternative fleet segmentation approach that provides a more realistic overview of fishing activities. This approach utilizes multivariate statistics and is coupled with machine learning techniques for automatization. Applying this approach to two decades of German fisheries data resulted in a data set with fewer segments compared to the DCF approach, which represented the actual fishing strategies more closely. The comparison of biological stock health indicators calculated for both the current and the novel segmentation schemes revealed that the current scheme often misses signs of segments relying on overexploited stocks. The machine learning technique applied showed high classification accuracy, with misclassifications being rare and only occurring in segments with overlapping catch composition. Since machine learning enables almost perfect allocation to the revised segments, we expect a successful implementation of this protocol for future fleet segmentation. This approach is highly suitable for data collection and analysis procedures and can serve as a standard tool. Therefore, this novel approach can contribute to the improvement of fishing fleet analyses and policy advice for better fisheries management. |\n\n1. Introduction  \nFisheries are facing a period of global upheaval. Decades of industrialized fishing have extensively exploited the world’s oceans, leading many fish stocks to critical depletion levels or placing them in slow recovery phases (FAO, 2024). Moreover, the impacts of climate change on marine ecosystems are becoming increasingly evident, bearing considerable challenges for fisheries management (Miles, 2011). Changes such as rising sea temperatures, ocean acidification, and shifts in ocean circulation patterns, which are disrupting fish populations, altering habitats, and unsettling the balance of ecosystems (Brierley and Kingsford, 2009). Many commercially important fish species are migrating in response to new temperature regimes, causing noticeable distribution shifts on a global scale (Cheung et al., 2010).  \nEffective fisheries management plays a crucial role in addressing  \nthese emerging challenges by implementing adaptive measures in response to changing environmental conditions (Burden and Fujita, 2019). Key strategies include setting sustainable catch limits, modifying fishing gear, and adopting ecosystem-based approaches. By integrating both ecological and socio-economic considerations, fisheries management can develop balanced strategies that support the recovery and sustainability of fish stocks, protect marine environments, and safeguard the livelihoods of fishing communities (Frost and Anders","cbCais2Ko2bkaLWB","https://ap.wps.com/l/cbCais2Ko2bkaLWB","pdf",4923119,1,16,"English","en",105,"# Introduction\n## Motivation: overexploited stocks and management challenges\n## Role of data collection and DCF segmentation\n## Need for realistic fleet activity representation","[{\"question\":\"Why does the EU DCF fleet segmentation need improvement?\",\"answer\":\"The DCF segmentation mainly uses technical vessel parameters such as length and predominant gear, which often fail to represent the vessels’ actual fishing activities accurately.\"},{\"question\":\"What does the proposed alternative segmentation approach use?\",\"answer\":\"It combines multivariate statistics with machine learning to automate the segmentation and produce a more realistic overview of fishing activities.\"},{\"question\":\"How is the new approach evaluated and what are the main outcomes?\",\"answer\":\"Applied to two decades of German fisheries data, it yields fewer segments that better reflect actual fishing strategies, and machine learning shows high classification accuracy with rare misclassifications.\"}]","Amending the European fishing fleet segmentation based on machine learning and multivariate statistics | 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