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Effective ocean management and conservation of highly migratory species depends on resolving overlap between animal movements and distributions and fishing effort. Yet, this information is lacking at a global scale. Here we show, using a big-data approach combining 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 high-seas fishing effort. Results demonstrate an urgent need for conservation and management measures at high-seas shark hotspots and highlight the potential of simultaneous satellite surveillance of megafauna and fishers as a tool for near-real time, dynamic management.
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ARTICLE https://doi.org/10.1038/s41586-019-1444-4
Global spatial risk assessment of sharks
under the footprint of fisheries
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.
Industrialized fishing is a major source of mortality for large marine
animals (marine megafauna)
1–6
. Humans have hunted megafauna in
the open ocean for at least 42,000 years7, but international fishing fleets
that target large, epipelagic fishes did not spread into the high seas until
the 1950s8. Prior to this, the high seas constituted a spatial refuge that
was largely free from exploitation, as fishing pressure was concentrated
on continental shelves
3,8
. Pelagic sharks are among the widest-ranging
vertebrates, with some species exhibiting annual migrations on
the ocean-basin scale9, long term trans-ocean movements10 and/or
fine-scale site fidelity to preferred shelf and open ocean areas5,9,11.
These behaviours could cause extensive spatial overlap with different
fisheries that exploit regions ranging from coastal areas to the deep
ocean. On average, large pelagic sharks account for 52% of all of the
identified shark catch worldwide, from both shark-targeting fisheries
and as bycatch
12
. Regional declines in abundance of pelagic sharks have
previously been reported
13,14
, but it is unclear whether exposure to high
levels of fishing effort extends across ocean-wide population ranges and
overlaps areas of the high seas in which sharks are most abundant5,13.
The conservation of pelagic sharks—the management of which on
the high seas is currently limited
12,15,16
—would benefit greatly from a
clearer understanding of the spatial relationships between the habitats
of sharks and active fishing zones. However, obtaining unbiased esti-
mates of the distributions of sharks and fishing effort is complicated by
the fact that most data on pelagic sharks come from catch records and
other fishery-dependent sources4,15,16.
Here we provide a global estimate of the extent of overlap in the use
of space between sharks and industrial fisheries. This estimate is based
on analysis of the movements of pelagic sharks tagged with satellite
transmitters in the Atlantic, Indian and Pacific Oceans, together with
the movements of fishing vessels that are monitored globally by the
automatic identification system (AIS), which was developed as a vessel
safety and anti-collision system (Methods). Our study focuses on 23
species of large pelagic sharks that occupy oceanic and/or neritic hab-
itats, which span a broad distribution from cold–temperate to tropical
waters (Supplementary Table1). All of these species face some level
of fishing pressure from coastal, shelf and/or high-seas fisheries: the
International Union for the Conservation of Nature (IUCN) Red List
assesses almost two-thirds of these species as being endangered (26%)
or vulnerable (39%), and a further quarter as near-threatened (26%)
(Supplementary Table2). Although regional-fisheries management
organizations are tasked with the management of sharks in the high
seas, little or no management is in place for most species3,5,1218.
Movement patterns of sharks and fishing vessels
The 11 shark species (or taxa groups) that accounted for 96% of the
1,804 satellite tags that were deployed are among the largest of shark
species: blue sharks (Prionace glauca); shortfin mako sharks (Isurus
oxyrinchus); tiger sharks (Galeocerdo cuvier); salmon sharks (Lamna
ditropis); whale sharks (Rhincodon typus); white sharks (Carcharodon
carcharias); oceanic whitetip sharks (Carcharhinus longimanus); por-
beagle sharks (Lamna nasus); silky sharks (Carcharhinus falciformis);
bull sharks (Carcharhinus leucas); and hammerhead sharks (Sphyrna
spp.) (Supplementary Tables35). Movement patterns indicated that
multiple species aggregated within the same major oceanographic
features (Fig.1), such as the Gulf Stream (blue sharks, shortfin mako
sharks, tiger sharks, white sharks and porbeagle sharks), the California
Current (blue sharks, shortfin mako sharks, white sharks and salmon
sharks) and the East Australian Current (blue sharks, shortfin mako
sharks, tiger sharks, white sharks and porbeagle sharks), (Extended
Data Fig.1; see ‘Supplementary results and discussion, section2.1’
intheSupplementary Information). The global relative density map
(Fig.2a) reveals distribution patterns of pelagic sharks and the locations
of space-use hotspots (defined here as areas with75th percentile of
weighted daily location density) (Methods). Major space-use hotspots
of tracked pelagic sharks in the Atlantic Ocean were in the Gulf Stream
and its western approaches, the Caribbean Sea, the Gulf of Mexico
and around oceanic islands such as the Azores (Fig.2a, Supplementary
Table6). In the Indian Ocean, space-use hotspots were evident in the
Agulhas Current, Mozambique Channel, the South Australian Basin
and northwest Australia, and in the Pacific Ocean, space-use hotspots
were in the California Current, Galapagos Islands and around New
Zealand. Although, as expected, tagging sites occurred in some space-
use hotspots (as tagging rates are inherently higher in hotspots), we also
identified space-use hotspots in which no tagging sites occurred in the
North Atlantic Ocean (outer Gulf Stream, Charlie Gibbs Fracture Zone,
western European shelf edge and the Bay of Biscay), the Indian Ocean
(southern Madagascar, the Crozet and Amsterdam Islands, and the
South Australian Basin) and the Pacific Ocean (Alaska Current, outer
California Current, the white shark ‘café’ area, halfway between Baja
California and Hawaii11, North Equatorial Current, Clipperton Island
A list of authors and their affiliations appears in the online version of the paper.
22 AUGUST 2019 | VOL 572 | NATURE | 461
Content courtesy of Springer Nature, terms of use apply. Rights reserved
... Global studies have emphasized the problems inherent in assessing the status of sharks, providing detailed descriptions of the difficulties on every level [1][2][3][4]13,26]. For a shark fishery to be sustainable, it must be possible to determine not only what the shark fishing mortality is, but also the mortality that will produce maximum sustainable yield (MSY), yet in the case of sharks those reference points are often not known or are extremely uncertain [4,5,20,41,80]. Most shark hunting nations still do not keep species-specific catch records [1,2,6,26,81], and recorded catches are known to be inaccurate. Catch data from artisanal fisheries are generally ignored, but in many regions, they are significant [82]. ...
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... For pelagic species of sharks, large MPAs and no-take zones that include the High Seas are required for effective protection because most are highly migratory. There is a particularly urgent need for conservation and management measures at high-seas and coastal hotspots of shark space [80,187]. Designation of such MPAs should take the high degree of spatial overlap between sharks and industrial fishing vessels into consideration, especially in those areas that attract fish, because of their favourable productivity and temperature profiles [187,188]. ...
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Kroodsma et al. (Reports, 23 February 2018, p. 904) mapped the global footprint of fisheries. Their estimates of footprint and resulting contrasts between the scale of fishing and agriculture are an artifact of the spatial scale of analysis. Reanalyses of their global (all vessels) and regional (trawling) data at higher resolution reduced footprint estimates by factors of >10 and >5, respectively.
Article
Understanding the distribution of fishing activity is fundamental to quantifying its impact on the seabed. Vessel monitoring system (VMS) data provides a means to understand the footprint (extent and intensity) of fishing activity. Automatic Identification System (AIS) data could offer a higher resolution alternative to VMS data, but differences in coverage and interpretation need to be better understood. VMS and AIS data were compared for individual scallop fishing vessels. There were substantial gaps in the AIS data coverage; AIS data only captured 26% of the time spent fishing compared to VMS data. The amount of missing data varied substantially between vessels (45-99% of each individuals' AIS data were missing). A cubic Hermite spline interpolation of VMS data provided the greatest similarity between VMS and AIS data. But the scale at which the data were analysed (size of the grid cells) had the greatest influence on estimates of fishing footprints. The present gaps in coverage of AIS may make it inappropriate for absolute estimates of fishing activity. VMS already provides a means of collecting more complete fishing position data, shielded from public view. Hence, there is an incentive to increase the VMS poll frequency to calculate more accurate fishing footprints. © International Council for the Exploration of the Sea 2017. All rights reserved.