Cluster analysis methods applied to daily vessel location data to identify cooperative fishing among tuna purse-seiners

Clustering of disaggregated fisheries data reveals functional longline fleets across the Pacific

“…[43][44][45] Indeed, socioeconomic factors rather than environmental ones may be primarily responsible for observed patterns of fishing activity. 26,46 Because fleet-specific differences in target and non-target species interactions and catch efficiency may have substantial impacts on the population dynamics of pelagic resources, 47 identifying and accounting for diversity of fishing activity should be a priority for research and management. 48 Scientific understanding of pelagic fishing fleets and resource dynamics is rapidly improving, yet to date limited efforts have been made to integrate vessel movement data with catch and effort records, 44,49,50 particularly at the spatial scales relevant to high seas, pelagic longline fishing operations.…”

Fuel consumption and air emissions in one of the world’s largest commercial fisheries

“…For instance, this has been observed in the cases where there are large concentrations of tuna and all purse seiners were found in the same concentrated fishing grounds (Fonteneau et al, 2008). Further work is required to study how collaborative fishing may affect fishing success, the relationship between fuel consumption and catch measured by the FUI and more generally the use of purse seine catch rates as indices of tuna abundance (Lennert-Cody et al, 2020).…”

“…At short time scales, fishing tactics depend on the technical skills and different sources of information available to the skipper, e.g., location of oceanographic features and acoustic estimates of tuna abundance around GPS-tracked FADs (Baidai et al, 2020;Gaertner et al, 1999). In addition, cooperative fishing is an essential component of purse seine fishing as FAD position information can be shared between vessels and some skippers work in groups (Lennert-Cody et al, 2020;Snouck-Hurgronje et al, 2018). Cooperative behaviour may significantly reduce searching time and associated fuel costs, and this could explain the nonlinear pattern observed for the number of FSC sets in our model, i.e., the initial reduction in fuel consumed when the number of FSCs increased from 0 to 60.…”