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.
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The source code used to undertake analyses and to prepare figures, in addition to the derived data underlying Fig. 2 maps (shark relative spatial density, longline-fishing effort and shark–longline-fishing overlap and FEI) and Fig. 3 plots (spatial overlap and FEI) is freely available on GitHub (https://github.com/GlobalSharkMovement/GlobalSpatialRisk).
Hays, G. C. et al. Key questions in marine megafauna movement ecology. Trends Ecol. Evol. 31, 463–475 (2016).
Lewison, R. L. et al. Global patterns of marine mammal, seabird, and sea turtle bycatch reveal taxa-specific and cumulative megafauna hotspots. Proc. Natl Acad. Sci. USA 111, 5271–5276 (2014).
McCauley, D. J. et al. Marine defaunation: animal loss in the global ocean. Science 347, 1255641 (2015).
Worm, B., Sandow, M., Oschlies, A., Lotze, H. K. & Myers, R. A. Global patterns of predator diversity in the open oceans. Science 309, 1365–1369 (2005).
Queiroz, N. et al. Ocean-wide tracking of pelagic sharks reveals extent of overlap with longline fishing hotspots. Proc. Natl Acad. Sci. USA 113, 1582–1587 (2016).
Scales, K. L. et al. Fisheries bycatch risk to marine megafauna is intensified in Lagrangian coherent structures. Proc. Natl Acad. Sci. USA 115, 7362–7367 (2018).
O’Connor, S., Ono, R. & Clarkson, C. Pelagic fishing at 42,000 years before the present and the maritime skills of modern humans. Science 334, 1117–1121 (2011).
Tickler, D., Meeuwig, J.J., Palomares, M.-L., Pauly, D., Zeller, D. Far from home: distance patterns of global fishing fleets. Sci. Adv. 4, eaar3279 (2018).
Lea, J. S. E. et al. Repeated, long-distance migrations by a philopatric predator targeting highly contrasting ecosystems. Sci. Rep. 5, 11202 (2015).
Guzman, H. M., Comez, C. G., Hearn, A. & Eckert, S. A. Longest recorded trans-Pacific migration of a whale shark (Rhincodon typus). Mar. Biodivers. Rec. 11, 8 (2018).
Block, B. A. et al. Tracking apex marine predator movements in a dynamic ocean. Nature 475, 86–90 (2011).
Worm, B. et al. Global catches, exploitation rates, and rebuilding options for sharks. Mar. Policy 40, 194–204 (2013).
Baum, J. K. et al. Collapse and conservation of shark populations in the Northwest Atlantic. Science 299, 389–392 (2003).
Ferretti, F., Worm, B., Britten, G. L., Heithaus, M. R. & Lotze, H. K. Patterns and ecosystem consequences of shark declines in the ocean. Ecol. Lett. 13, 1055–1071 (2010).
Oliver, S., Braccini, M., Newman, S. J. & Harvey, E. S. Global patterns in the bycatch of sharks and rays. Mar. Policy 54, 86–97 (2015).
Campana, S. E. Transboundary movements, unmonitored fishing mortality, and ineffective international fisheries management pose risks for pelagic sharks in the Northwest Atlantic. Can. J. Fish. Aquat. Sci. 73, 1599–1607 (2016).
International Commission for the Conservation of Atlantic Tunas (ICCAT). Report of the 2017 ICCAT Shortfin Mako Assessment Meeting (Madrid, Spain). https://www.iccat.int/en/assess.html (2017).
International Commission for the Conservation of Atlantic Tunas. Report of the Standing Committee on Research and Statistics (SCRS). doc. no. PLE 104/2017 https://www.iccat.int/en/scrs.html (2017).
Kroodsma, D.A. et al. Tracking the global footprint of fisheries. Science 359, 904–908 (2018).
Kroodsma, D.A. et al. Response to Comment on “Tracking the global footprint of fisheries”. Science 361, eaat7789 (2018).
McCauley, D. J. et al. Ending hide and seek at sea. Science 351, 1148–1150 (2016).
Shepperson, J. L. et al. A comparison of VMS and AIS data: the effect of data coverage and vessel position recording frequency on estimates of fishing footprints. ICES J. Mar. Sci. 75, 988–998 (2018).
Sala, E. et al. The economics of fishing the high seas. Sci. Adv. 4, eaat2504 (2018).
Scales, K. L. et al. On the front line: frontal zones as priority at-sea conservation areas for mobile marine vertebrates. J. Appl. Ecol. 51, 1575–1583 (2014).
Fields, A. T. et al. Species composition of the international shark fin trade assessed through a retail-market survey in Hong Kong. Conserv. Biol. 32, 376–389 (2018).
Curtis, T. H. et al. Seasonal distribution and historic trends in abundance of white sharks, Carcharodon carcharias, in the western North Atlantic Ocean. PLoS ONE 9, e99240 (2014).
Kohler, N. E. & Turner, P. A. Shark tagging: a review of conventional methods and studies. Environ. Biol. Fishes 60, 191–224 (2001).
Byrne, M. E. et al. Satellite telemetry reveals higher fishing mortality rates than previously estimated, suggesting overfishing of an apex marine predator. Proc. R. Soc. Lond. B 284, 20170658 (2017).
O’Leary, B. C. et al. Addressing criticisms of large-scale marine protected areas. Bioscience 68, 359–370 (2018).
Hays, G. C. et al. Translating marine animal tracking data into conservation policy and management. Trends Ecol. Evol. 34, 459–473 (2019).
Sequeira, A. M. M. et al. Convergence of marine megafauna movement patterns in coastal and open oceans. Proc. Natl Acad. Sci. USA 115, 3072–3077 (2018).
Wilson, R. P., Ducamp, J.-J., Rees, W. G., Culik, B. M. & Niekamp, K. in Wildlife Telemetry (eds Priede, I. G. & Swift, S. M.) 131–134 (Ellis Horwood, Chichester, 1992).
Delong, R. L., Stewart, B. S. & Hill, R. D. Documenting migrations of northern elephant seals using day length. Mar. Mamm. Sci. 8, 155–159 (1992).
Lam, C., Nielsen, A. & Sibert, J. Improving light and temperature based geolocation by unscented Kalman filtering. Fish. Res. 91, 15–25 (2008).
Johnson, D. S., London, J. M., Lea, M.-A. & Durban, J. W. Continuous-time correlated random walk model for animal telemetry data. Ecology 89, 1208–1215 (2008).
Sippel, T., Holdsworth, J., Dennis, T. & Montgomery, J. Investigating behaviour and population dynamics of striped marlin (Kajikia audax) from the southwest Pacific Ocean with satellite tags. PLoS ONE 6, e21087 (2011).
Jonsen, I. D., Flemming, J. M. & Myers, A. E. Robust state-space modeling of animal movement data. Ecology 86, 2874–2880 (2005).
Lunn, D. J., Thomas, A., Best, N. & Spiegelhalter, D. WinBUGS – a Bayesian modelling framework: 472 concepts, structure, and extensibility. Stat. Comput. 10, 325–337 (2000).
Ward-Paige, C. A., Britten, G. L., Bethea, D. M. & Carlson, J. K. Characterizing and predicting essential habitat features for juvenile coastal sharks. Mar. Ecol. (Berl.) 36, 419–431 (2014).
Queiroz, N. et al. Convergent foraging tactics of marine predators with different feeding strategies across heterogeneous ocean environments. Front. Mar. Sci. 4, 239 (2017).
Fisher, R., Wilson, S. K., Sin, T. M., Lee, A. C. & Langlois, T. J. A simple function for full-subsets multiple regression in ecology with R. Ecol. Evol. 8, 6104–6113 (2018).
Burnham, K. P. & Anderson, D. R. Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004).
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Series B Stat. Methodol. 73, 3–36 (2011).
Teo, S. L. H. et al. Validation of geolocation estimates based on light level and sea surface temperature from electronic tags. Mar. Ecol. Prog. Ser. 283, 81–98 (2004).
Nielsen, A., Bigelow, K. A., Musyl, M. K. & Sibert, J. R. Improving light-based geolocation by including sea surface temperature. Fish. Oceanogr. 15, 314–325 (2006).
Sims, D. W. in Sharks and Their Relatives II – Biodiversity, Adaptive Physiology and Conservation (eds Carrier, J. C. et al.) 351–392 (CRC, Boca Raton, 2010).
Amoroso, R. O., Parma, A. M., Pitcher, C. R., McConnaughey, R. A. & Jennings, S. Comment on “Tracking the global footprint of fisheries”. Science 361, eaat6713 (2018).
Lowther, A. D., Lydersen, C., Fedak, M. A., Lovell, P. & Kovacs, K. M. The Argos–CLS Kalman filter: error structures and state-space modelling relative to Fastloc GPS data. PLoS ONE 10, e0124754 (2015).
We thank all who were involved in fieldwork and data collection (full details are given in the Supplementary Information). Data analysis was funded in part by the Marine Biological Association (MBA) and the UK Natural Environment Research Council (NERC) (NE/R00997X/1) (to D.W.S.) with additional research support from the Save Our Seas Foundation and the NERC Oceans 2025 Strategic Research Programme, in which D.W.S. was a principal investigator. D.W.S. was supported by an MBA Senior Research Fellowship, N.Q. by European Regional Development Fund (FEDER) via the Programa Operacional Competitividade e Internacionalização (COMPETE), National Funds via Fundação para a Ciência e a Tecnologia (FCT) under PTDC/MAR/100345/2008 and COMPETE FCOMP-01-0124-FEDER-010580 (to N.Q. and D.W.S.), and Norte Portugal Regional Operational Programme (NORTE 2020) under the PORTUGAL 2020 Partnership Agreement through the European Regional Development Fund (ERDF) in project MarInfo (NORTE-01-0145-FEDER-000031). Additional support was provided by an FCT Investigator Fellowship IF/01611/2013 (N.Q.), FCT Doctoral Fellowship PD/BD/52603/2014 (M.V.), PTDC/MAR-BIO/4458/2012, Xunta de Galicia - Isabel Barreto Program 2009-2012 (G.M.), Australian Research Council (ARC) grant DE170100841 and operational funds from the Australian Institute for Marine Science (AIMS) (both to A.M.M.S.). We thank Stanford University, the Tagging of Pacific Predators programme and Global Fishing Watch for making data freely available. We thank M. Dando for creating the shark images. This research contributes to the Global Shark Movement Project (GSMP).
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Peer review information Nature thanks Julia Baum, Brendan Godley, Ian Jonsen and David Kroodsma for their contribution to the peer review of this work.
Extended data figures and tables
Extended Data Fig. 1 The locations of shark tag deployment sites in relation to shark space-use hotspots, ocean currents, physical features and fishing areas.
a, Red circles denote the locations in which satellite transmitters were attached and sharks released, and blue squares in the eastern Pacific Ocean denote the annual median deployment locations of tags by the TOPP program11. Shark space-use hotspots are shown as the 75th (blue dotted lines) and 90th percentiles (red dotted lines) of the mean monthly relative density of estimated shark locations within 1° × 1° grid cells given in Fig. 2a. b, c, Schematic maps of major ocean currents (b) and physical features overlaid on FAO fishing areas (c). Coloured arrows in b denote the thermal regime of currents, with warmer colours indicating higher water temperatures. CGFZ, Charlie Gibbs Fracture Zone; GBR, Great Barrier Reef; PNG, Papua New Guinea.
a, Distribution of the fishing effort of AIS-tracked vessels (mean annual days spent per grid cell) between 2012 and 2016 (Methods). b, Distribution of the mean monthly overlap and level of the fishing effort for all vessels (in days) to which sharks were exposed in overlapping areas, for all species, within 1° × 1° grid cells (Methods). Spatial overlap hotspots were defined as 1° × 1° grid cells with ≥75th percentile of mean FEI. Note that the overlap pattern of sharks and all mapped AIS-equipped fishing vessels is similar to that determined for sharks and longline-fishing vessels in Fig. 2c. c, Distribution of the fishing effort of AIS-equipped purse-seine vessels, using mean annual days spent per grid cell between 2012 and 2016 (Methods). d, Distribution of the mean monthly overlap and level of fishing effort of purse-seine vessel (in days) to which sharks were exposed in overlapping areas, for all species, within 1° × 1° grid cells (Methods). Spatial overlap hotspots were defined as 1° × 1° grid cells with ≥75th percentile of mean FEI.
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.
Extended Data Fig. 4 Effect of grid-cell size on risk-exposure patterns of sharks to longline fisheries.
a–d, North Atlantic Ocean (a), east Pacific Ocean (b), southwest Indian Ocean (c) and Oceania (d). Note that, regardless of the grid-cell size at which the individual-species mean spatial overlap and FEI were calculated, the species that occur in the highest-(red) and the lowest-risk zones (green) remain notably conserved, which indicates a general pattern that is not dependent on the scale at which these data were analysed. Shark-species identification codes corresponding to marker colours are given in Fig. 3. In addition, for the North Atlantic (a), hammerhead sharks (Sphyrna spp.) are represented by a black circle and oceanic whitetip sharks (C. longimanus) by a light blue circle; for the eastern Pacific Ocean (b), the salmon shark (L. ditropis) is represented by a light orange circle. Error bars are ± 1 s.d. An additional comparison of 2° × 2° with 1° × 1° grid-cell size is given in Supplementary Fig. 4.
Extended Data Fig. 5 Relationship between shark landings by fisheries in the North Atlantic Ocean, and shark density–longline FEI.
Plot showing shark landings from the North Atlantic Ocean (mean, 2007−2016), extracted from the FAO total-capture production database, was dependent upon the longline-fishing effort in the North Atlantic Ocean, as estimated with the individual-species FEI (70% of sharks tracked, 2007–2017; AIS data, 2012−2016) (Methods). Using linear regression, we tested the null hypothesis (H0) that β = 0 after normalizing landings (in metric tonnes) by log-transformation and for median FEI per species. Regression analysis gave the equation: log(landings) = 1.364 + 8,732 FEI, with a regression coefficient (b) standard error of 3,369. We computed r2 = 0.45, F = 6.72 and F0.05(1),1,7 = 5.59, therefore rejecting H0 at the 5% level of significance with P < 0.05.
Extended Data Fig. 6 Relative density and spatial overlap distributions for the five most data-rich individual shark species that occur in multiple oceans.
a–e, Mean monthly relative density of shark species (left) tracked in 2002–2017 in comparison with species-mean FEI per grid cell (right) for the 5 most data-rich species or taxa groups that occur in multiple oceans: blue shark (a), shortfin mako shark (b), tiger shark (c), whale shark (d) and white shark (e). Red boxes denote areas shown in Fig. 3. Shark images created by M. Dando.
Extended Data Fig. 7 Relative density and spatial overlap distributions for the sixth to tenth most data-rich individual shark species that occur in multiple oceans.
Mean monthly relative density of shark species (left) tracked in 2002–2017 in comparison with species-mean FEI per grid cell (right) for the sixth to tenth most data-rich species or taxa groups that occur in multiple oceans: oceanic whitetip shark (a), porbeagle shark (b), silky shark (c), bull shark (d) and hammerhead sharks (d) (comprising the scalloped hammerhead shark, the great hammerhead shark and the smooth hammerhead shark). Shark images created by M. Dando. CLO, Carcharhinus longimanis; SPH, Sphyrna spp.
Mean monthly spatial density was calculated for each two-year period across species. We used consecutive two-year groups to reduce gaps in coverage. Note that there were broad-scale shark tracks in the east Pacific Ocean in all eight two-year periods (2002–2003 to 2016–2017), in the North Atlantic Ocean between 2006–2007 and 2016–2017, in the southwest Indian Ocean in 2010–2011 to 2014–2015, and in Oceania between 2004–2005 and 2014–2015. This indicates that temporal consistency of shark tracks was present within the ocean regions studied, which suggests that the spatial hotspots that we identified are more likely to be persistent between years.
Extended Data Fig. 9 Risk-exposure patterns of sharks from longline fisheries between 2012 and 2016.
a–d, North Atlantic Ocean (a), east Pacific Ocean (b), southwest Indian Ocean (c) and Oceania (d). Note that species patterns of exposure to risk in highest-risk (red) and lowest-risk (green) zones in the years 2012−2016, which matched shark density data with longline-fishing effort data from AIS-equipped vessels directly, were very similar to patterns found for shark density (2002−2017) and longline-fishing effort data from AIS-equipped vessels (species-mean FEI) (2012−2016) (shown in Fig. 3), which indicates that there was no important effect of temporally mismatched datasets on the results. LDI, L. ditropis.
Extended Data Fig. 10 Seasonal shifts in sharks, longline-fishing vessels and patterns of overlap with fishing effort.
a–h, Mean quarterly relative spatial density of sharks (left), longline-fishing effort (in days) (middle) and mean FEI per grid cell (the fishing effort to which sharks were exposed in overlapped areas) (right) for blue sharks in the North Atlantic Ocean in December–February (a), March–May (b), June–August (c) and September–November (d), and for shortfin mako sharks in the North Atlantic Ocean in December–February (e), March–May (f), June–August (g) and September–November (h).
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Queiroz, N., Humphries, N.E., Couto, A. et al. Global spatial risk assessment of sharks under the footprint of fisheries. Nature 572, 461–466 (2019). https://doi.org/10.1038/s41586-019-1444-4
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