2 research outputs found

    Global spatial risk assessment of sharks under the footprint of fisheries

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    Effective ocean management and the conservation of highly migratory species depend on resolving the overlap between animal movements and distributions, and fishing effort. However, this information is lacking at a global scale. Here we show, using a big-data approach that combines satellite-tracked movements of pelagic sharks and global fishing fleets, that 24% of the mean monthly space used by sharks falls under the footprint of pelagic longline fisheries. Space-use hotspots of commercially valuable sharks and of internationally protected species had the highest overlap with longlines (up to 76% and 64%, respectively), and were also associated with significant increases in fishing effort. We conclude that pelagic sharks have limited spatial refuge from current levels of fishing effort in marine areas beyond national jurisdictions (the high seas). Our results demonstrate an urgent need for conservation and management measures at high-seas hotspots of shark space use, and highlight the potential of simultaneous satellite surveillance of megafauna and fishers as a tool for near-real-time, dynamic management

    Reply to: Shark mortality cannot be assessed by fishery overlap alone

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    [Extract] Our previously published paper1 provided global fine-scale spatiotemporal estimates (1° × 1°; monthly) of overlap and fishing exposure risk (FEI) between satellite-tracked shark space use and automatic identification system (AIS) longline fishing effort. We did not assess shark mortality directly, but in addition to replying to the Comment by Murua et al.2, we confirm—using regression analysis of spatially matched data—that fishing-induced pelagic shark mortality (catch per unit effort (CPUE)) is greater where FEI is higher. We focused on assessing shark horizontal spatiotemporal overlap and exposure risk with fisheries because spatial overlap is a major driver of fishing capture susceptibility and previous shark ecological risk assessments (ERAs) assumed a homogenous shark density within species-range distributions3,4,5 or used coarse-scale modelled occurrence data, rather than more ecologically realistic risk estimates in heterogeneous habitats that were selected by sharks over time. Furthermore, our shark spatial exposure risk implicitly accounts for other susceptibility factors with equal or similar probabilities to those commonly used in shark ERAs3,5
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