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Our second workshop on a newly developed, alternative approach to the segmentation of fishing fleets was held from 28th to 30th of March 2022. Due to travel restrictions caused by the global Covid-19 pandemic, the workshop took place online. Cisco Webex provided the online platform. The workshop was hosted by the Thünen Institute for Sea, Fisheries...
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... detailed description of the statistical background of the alternative fleet segmentation approach can be found in the report of the first fleet segmentation workshop. For an overview, please see figure 1. This section will focus on the amendments made to the approach prior to the workshop. ...
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... following clustering analysis was carried out via fleetSegmentation() -package and with the 2020 catch data by Finnish trawlers ( ) and vessels which used passive gears (n ). The average vessel lengths in catch data were 25 and 6 meters for trawlers and vessels using passive gears, respectively (Figure 1). ...
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... & Sprat cluster, which contains 5 relatively large vessels targeting on herring and sprat (initial cluster 5, figure 10). ...
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... cluster holding 6 vessels with length less than 20 meter. These vessels fish a quite of a number of different species, none of which seems to surpass the others (initial clusters 1,2,3,6,7 , figure 11). ...
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... started our experiments by looking the stock shares and assemblage shares over the seven clusters. The initial results were found quite promising, as we can see ( figure 14) that there is relatively clear distinctive specie representation among the clusters. We utilized cluster_assemblages_MDS() and clustering_MDS() functions to gain more information of the cluster candidates. ...
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... utilized cluster_assemblages_MDS() and clustering_MDS() functions to gain more information of the cluster candidates. From the left hand side (of figure 16) can be seen that cluster 2 stands out from the crowd, but clusters 5,6,7 and 1,3,4 appear to form relatively similar groups. The same phenomena can be seen from the right hand side (figure 16)). ...
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... we looked at the properties of the clusters in terms of the vessel length, cluster size and annual catches ( figure 18). From the annual catch point of view, it is interesting to see that the biggest share of catch is landed by cluster 2 ( figure 18) and the vessels in this cluster are the longest. ...
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... we looked at the properties of the clusters in terms of the vessel length, cluster size and annual catches ( figure 18). From the annual catch point of view, it is interesting to see that the biggest share of catch is landed by cluster 2 ( figure 18) and the vessels in this cluster are the longest. This notable amount of catch is natural, as the vessels in cluster 2 are targeting herring, and herring forms undoubtedly the largest share of annual landings among Finnish fisheries. ...
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... cluster holding in total of 84 medium size coastal vessels (initial cluster 2, figure 19), which use passive gears and target their fishing heavily on herring. ...
Citations
... Clustering of catch data : To arrive at a set of métiers for the Icelandic fleet, we performed a clustering analysis on the described data using the clustering methods of Sulanke and Berkenhagen ( 2022 ) to identify métiers (Baranowska et al. 2024 ). However, our methods deviate slightly from previously published methods by using Ward's method for hierarchical clustering (Ward 1963 ) and an Euclidean distance matrix. ...
... From here on, we will call these clusters métiers. While doing so, we used a set of algorithms to calculate the optimal number of clusters that summarized the data used in this analysis well, relying on the average silhouettes and Mantel test (Sulanke and Berkenhagen 2022 ). Due to computational memory constraints, we ran these algorithms for the optimal number of clusters for each year of the data separately. ...
... Due to computational memory constraints, we ran these algorithms for the optimal number of clusters for each year of the data separately. In combination with relying on these algorithms, we also studied the clustering tree and the data visually to arrive at a good number of clusters (Sulanke and Berkenhagen, 2022 ). ...
Marine ecosystem-based management requires the understanding of species interactions and what species are harvested together. This study combines two major questions: the first regarding what drives the probability that a métier (species assemblages, with spatial distribution and seasonality) will be observed as catch, and the second regarding the level of control fishers have over this catch mix. To address these questions, we analysed highly resolved logbook records of an Arctic and sub-Arctic industrial demersal fishery operating in Icelandic waters. The study employs a multi-class random forest model to identify predictors of métier occurrence and consistency of predictions using a dataset of > 100 000 hauls over 4 years (20 16-20 19). The overall accuracy of the random forest model is 69-70%, indicating moderate predictability of catch mix based on known en vironmental, vessel, and compan y characteristics. We find that habitat-related variables (depth and temperature) are most important to predict catch mix. Still, company, trip, and vessel characteristics are also very important (e.g. vessel and trip length, distance to port). Beyond these more traditional bio-economic variables, important predictors include variables related to harvesting strategies, such as quota di ver sity and a vessel's mobility. These findings contribute to a fuller picture of fisher decision-making in mixed fisheries.
... The analysis followed the methods used to identify métiers in the Iceland bottom trawl fishery (Baranowska et al. 2024;Oostdijk et al. in review). Briefly, we performed a clustering analysis following a previously reported clustering protocol (Sulanke et al. 2022) to identify métiers. As preliminary analyses defined many clusters based on single hauls, we used Ward's method for hierarchical clustering (Oksanen et al. 2022;Ward 1963). ...
While Iceland has an advanced and well-developed fisheries management system most species are managed individually, that is without consideration for their role in and interactions with the whole ecosystem or with the fact that fisheries target multiple species in different areas and at different times of the year. But long-term sustainability of these economically and culturally important resources requires consideration of a broader system. End-to-end ecosystem models provide one way to account for complex interactions and evaluate how changes in one species influences other species. Here we continue parameterization of the Iceland Atlantis end-to-end ecosystem model to include multispecies fisheries for the most important groundfish fleets in Iceland. A métier analysis was performed to determine how the bottom trawl fleet targets species spatially and temporally. The Iceland Atlantis model was then parameterized with historic fishing effort to include five different bottom trawl fleets and one longline fleet which target five of the most important commercial species in Iceland: Atlantic cod (Gadus morhua), haddock (Melanogrammus aeglefinus), saithe (Pollachius virens), redfish (Sebastes sp.), and Greenland halibut (Reinhardtius hippoglossoides). The updated model will serve as basis from which management decisions can be evaluated in the context of other harvested species as well as the ecosystem as a whole.
The appropriate segmentation of fishing fleets is controversially discussed in fisheries research and management and a variety of approaches has been introduced. The present approach, developed in a pilot study funded by the European Commission – Data Collection Framework (DCF), introduces a standardized multivariate approach for characterizing fisheries fleet segments by hierarchical agglomerative cluster analysis (HAC) of their catch composition. We chose data from 2021 of the Romanian fishing fleet as the basis of our analysis. Statistical analyses were performed using the program RStudio V3.6.1 by running the fleet segmentation package script. The specific indices, tests, and visual validation methods of the package were applied to determine the optimal number of clusters. The procedure was finalized by a post-hoc validation of the clustering result to identify the actual fleet segments. From the basic data, 6 fleet segments for Vessels using active and passive gears (PMP) were highlighted, representing 52 boats, where it was noted that fishing at Rapa whelk (RPW) prevailed with 91.94% of the total catches on the segment, respective 5 fleet segments for Vessels using passive gears (PG) only for vessels <12m, representing 78 vessels where the main catches or recorded at European anchovy (ANE) 25.16%, turbot (TUR) 13.08%, horse mackerel (HMM) 8.75%, thus 130 vessels from two classes of different gears in total. We detected mixed fishing, especially on various assemblages of demersal and pelagic fish, as well as target fishing on demersal and pelagic fish, Rapa whelk, and mussels. For a better understanding of the approach, further research is needed.
