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Environmental modelling results
Estimated relationships between mean monthly relative density of all sharks (top) and fishing effort of all AIS-equipped fishing vessels (middle) and longline-fishing vessels only (bottom), with all environmental variables in the highest-ranked model (model 1) of the GAM tested. The third column shows the interaction results between the two variables described in the first and second columns. Asterisks indicate the significance level for each smooth term included in the GAM; ***P < 0.001.

Environmental modelling results Estimated relationships between mean monthly relative density of all sharks (top) and fishing effort of all AIS-equipped fishing vessels (middle) and longline-fishing vessels only (bottom), with all environmental variables in the highest-ranked model (model 1) of the GAM tested. The third column shows the interaction results between the two variables described in the first and second columns. Asterisks indicate the significance level for each smooth term included in the GAM; ***P < 0.001.

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... For example, in North Atlantic blue sharks and shortfin mako have on average 76% and 62% of their monthly space use, respectively, overlapped by longline fishing effort 1,2 , with greater fishinginduced mortality of pelagic sharks where spatial overlap intensity was greater 49 . Importantly, even though logbook data and VMS/AIS locations did not overlap temporally, fishing hotspots tend to remain constant over the years 2,49,50,98 . For example, the fishing hotspot for the Spanish fleet in the eastern South Pacific remained relatively stable, between 2010 and 2019 ( Fig. 3a, b,c). ...
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... Vol:. (1234567890) concentration of studies, particularly those for movement ecology, occurred around North America and Australia, potentially reflecting variation in research funding (i.e., a pattern that is also noted in large collaborative tagging studies such as Queiroz et al. (2019) and Andrzejaczek et al. (2022)). In contrast, fisheries science studies exhibited a broader geographical distribution (Figure 2). ...
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... While there is a growing knowledge base of information on how threats like overfishing, habitat loss, and climate change-induced shifts in prey availability impact shark species in shallow coastal and open ocean regions, there is comparatively limited information available from the deep sea (Field et al., 2009;Queiroz et al., 2019). Additionally, the extensive exploitation of oil, gas, and fisheries biomass presents substantial, and potentially increasing, challenges to deep-sea environments (Howell et al., 2020). ...
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... Given their wide spatial ranges and regular migrations, they are subject to inconsistent levels of management and a myriad of threats (Block et al., 2011;Dunn et al., 2016;Harrison et al., 2018;Pinsky et al., 2018;Santos et al., 2024). Understanding HMS movement patterns in response to changing oceanographic conditions and adapting their protective measures appropriately are research priorities (Hazen et al., 2013;Queiroz et al., 2019). In addition to the direct conservation benefits of HMS populations, adequate management of marine species remains ecologically beneficial to the ecosystems they inhabit and economically imperative for fisheries and ecotourism (Hammerschlag et al., 2019). ...
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... Similar competition for resources has been documented where silky sharks and oceanic whitetip sharks (Carcharhinus longimanus) co-occur with tuna species, which are simultaneously targeted by commercial fishers (Tolotti et al. 2020;Young and Carlson 2020). Larger scale studies have also documented a high degree of overlap between longline fishing activity and a range of pelagic shark species (Queiroz et al. 2019). ...
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Fisher-shark conflict is occurring at Lord Howe Island, Australia due to high levels of Galapagos shark (Carcharhinus galapagensis) depredation (where sharks consume hooked fish) and bycatch. Depredation causes costly loss of target catch and fishing gear and increased mortality of target species, and sharks can be injured or killed when bycaught. This study applied acoustic telemetry and vessel tracking from 2018 to 2021 to identify; (1) how the movements of 30 tagged sharks and activity of six fishing vessels overlapped, and (2) where key ‘hotspots’ of overlap occurred. Fisher surveys were also conducted to collect information about mitigating shark interactions. Residency index analysis indicated that three sharks tagged at a fish waste dumping site had markedly higher residency. Core home ranges of sharks overlapped with higher fishing activity at four ‘hotspots’. Statistical modelling indicated positive linear effects of fishing activity and bathymetric complexity on shark detections and tagged sharks were present for 13% of the total time that vessels were fishing close to acoustic receivers. Spatio-temporal overlaps between shark movements and fishing activity could potentially have occurred because sharks learned to associate fishing vessels with food (i.e. hooked fish) and because fishers and sharks utilise highly productive shelf edge areas, however more research is needed to investigate these relationships. Fishers reported that rotating fishing areas and reducing time at each location, fishing deeper than 100 m, and using electric reels and lures instead of bait, reduced bycatch and depredation. The integrated approach used here identified practical methods for reducing fisher-shark conflict, improving socio-economic outcomes for fishers and conservation prospects for this unique shark population.