Abstract
Decades of overexploitation have devastated shark populations, leaving considerable doubt as to their ecological status1,2. Yet much of what is known about sharks has been inferred from catch records in industrial fisheries, whereas far less information is available about sharks that live in coastal habitats3. Here we address this knowledge gap using data from more than 15,000 standardized baited remote underwater video stations that were deployed on 371 reefs in 58 nations to estimate the conservation status of reef sharks globally. Our results reveal the profound impact that fishing has had on reef shark populations: we observed no sharks on almost 20% of the surveyed reefs. Reef sharks were almost completely absent from reefs in several nations, and shark depletion was strongly related to socio-economic conditions such as the size and proximity of the nearest market, poor governance and the density of the human population. However, opportunities for the conservation of reef sharks remain: shark sanctuaries, closed areas, catch limits and an absence of gillnets and longlines were associated with a substantially higher relative abundance of reef sharks. These results reveal several policy pathways for the restoration and management of reef shark populations, from direct top-down management of fishing to indirect improvement of governance conditions. Reef shark populations will only have a high chance of recovery by engaging key socio-economic aspects of tropical fisheries.
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Data availability
Data used to reproduce the analysis—except for geolocations—can be accessed at https://github.com/mamacneil/FinPrint.
Code availability
Code used to reproduce the analysis can be accessed at https://github.com/mamacneil/FinPrint.
Change history
27 August 2020
A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-2692-z
References
Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. eLife 3, e00590 (2014).
Letessier, T. B. et al. Remote reefs and seamounts are the last refuges for marine predators across the Indo-Pacific. PLoS Biol. 17, e3000366 (2019).
Roff, G., Brown, C. J., Priest, M. A. & Mumby, P. J. Decline of coastal apex shark populations over the past half century. Commun. Biol. 1, 223 (2018).
Heithaus, M. R., Frid, A., Wirsing, A. J. & Worm, B. Predicting ecological consequences of marine top predator declines. Trends Ecol. Evol. 23, 202–210 (2008).
Cisneros-Montemayor, A. M., Barnes-Mauthe, M., Al-Abdulrazzak, D., Navarro-Holm, E. & Sumaila, U. R. Global economic value of shark ecotourism: implications for conservation. Oryx 47, 381–388 (2013).
Dent, F. & Clarke, S. State of the Global Market for Shark Products. FAO Fisheries and Aquaculture Technical Paper 590 (FAO, 2015).
Ward-Paige, C., Mills Flemming, J. & Lotze, H. K. Overestimating fish counts by non-instantaneous visual censuses: consequences for population and community descriptions. PLoS ONE 5, e11722 (2010).
Sandin, S. A. et al. Baselines and degradation of coral reefs in the Northern Line Islands. PLoS ONE 3, e1548 (2008).
Nadon, M. O. et al. Re-creating missing population baselines for Pacific reef sharks. Conserv. Biol. 26, 493–503 (2012).
Graham, N. A. J., Spalding, M. D. & Sheppard, C. R. C. Reef shark declines in remote atolls highlight the need for multi-faceted conservation action. Aquat. Conserv. 20, 543–548 (2010).
Gove, J. M. et al. Near-island biological hotspots in barren ocean basins. Nat. Commun. 7, 10581 (2016).
McClanahan, T. R. et al. Global baselines and benchmarks for fish biomass: comparing remote reefs and fisheries closures. Mar. Ecol. Prog. Ser. 612, 167–192 (2019).
Cinner, J. E. et al. Bright spots among the world’s coral reefs. Nature 535, 416–419 (2016).
Harvey, E. S., Santana-Garcon, J., Goetze, J. S., Saunders, B. & Cappo, M. in Shark Research: Emerging Technologies and Applications for the Field and Laboratory (eds Carrier, J. C., Heithaus, M. R. & Simpfendorfer, C. A.) Ch. 7 (CRC Press, 2018).
Jackson, J. B. C. What was natural in the coastal oceans? Proc. Natl Acad. Sci. USA 98, 5411–5418 (2001).
Jaiteh, V. F. et al. Higher abundance of marine predators and changes in fishers’ behavior following spatial protection within the world’s biggest shark fishery. Front. Mar. Sci. 3, 43 (2016).
Sadovy de Mitcheson, Y. et al. Out of control means off the menu: the case for ceasing consumption of luxury products from highly vulnerable species when international trade cannot be adequately controlled; shark fin as a case study. Mar. Policy 98, 115–120 (2018).
Brunnschweiler, J. M. The Shark Reef Marine Reserve: a marine tourism project in Fiji involving local communities. J. Sustain. Tour. 18, 29–42 (2010).
Haider, L. J., Boonstra, W. J., Peterson, G. D. & Schlüter, M. Traps and sustainable development in rural areas: a review. World Dev. 101, 311–321 (2018).
Ward-Paige, C. A. A global overview of shark sanctuary regulations and their impact on shark fisheries. Mar. Policy 82, 87–97 (2017).
Smart, J. J. et al. Effects of including misidentified sharks in life history analyses: a case study on the grey reef shark Carcharhinus amblyrhynchos from Papua New Guinea. PLoS ONE 11, e0153116 (2016).
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).
Booth, H., Squires, D. & Milner-Gulland, E. J. The neglected complexities of shark fisheries, and priorities for holistic risk-based management. Ocean Coast. Manage. 182, 104994 (2019).
Edgar, G. J. et al. Global conservation outcomes depend on marine protected areas with five key features. Nature 506, 216–220 (2014).
Chapman, D. D., Pikitch, E. K., Babcock, E. & Shivji, M. S. Marine reserve design and evaluation using automated acoustic telemetry: a case-study involving coral reef-associated sharks in the Mesoamerican Caribbean. Mar. Technol. Soc. J. 39, 42–55 (2005).
MacKeracher, T., Diedrich, A. & Simpfendorfer, C. A. Sharks, rays and marine protected areas: a critical evaluation of current perspectives. Fish Fish. 20, 255–267 (2019).
MacNeil, M. A. et al. Recovery potential of the world’s coral reef fishes. Nature 520, 341–344 (2015).
Cinner, J. E. et al. Linking social and ecological systems to sustain coral reef fisheries. Curr. Biol. 19, 206–212 (2009).
Cappo, M., Speare, P. & De’ath, G. Comparison of baited remote underwater video stations (BRUVS) and prawn (shrimp) trawls for assessments of fish biodiversity in inter-reefal areas of the Great Barrier Reef Marine Park. J. Exp. Mar. Biol. Ecol. 302, 123–152 (2004).
Willis, T. J., Millar, R. B. & Babcock, R. C. Detection of spatial variability in relative density of fishes: comparison of visual census, angling, and baited underwater video. Mar. Ecol. Prog. Ser. 198, 249–260 (2000).
Schobernd, Z. H., Bacheler, N. M. & Conn, P. B. Examining the utility of alternative video monitoring metrics for indexing reef fish abundance. Can. J. Fish. Aquat. Sci. 71, 464–471 (2014).
Conn, P. B. An Evaluation and Power Analysis of Fishery Independent Reef Fish Sampling in the Gulf of Mexico and US South Atlantic (National Oceanic and Atmospheric Administration, 2011).
Cinner, J. E. et al. Comanagement of coral reef social–ecological systems. Proc. Natl Acad. Sci. USA 109, 5219–5222 (2012).
Cinner, J. E. et al. Gravity of human impacts mediates coral reef conservation gains. Proc. Natl Acad. Sci. USA 115, E6116–E6125 (2018).
MacNeil, M. A. & Connolly, S. R. in Ecology of Fishes on Coral Reefs (ed. Mora, C.) 116–126 (Cambridge Univ. Press, 2015).
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall/CRC, 2018).
Polunin, N. V. C. & Roberts, C. M. Greater biomass and value of target coral-reef fishes in two small Caribbean marine reserves. Mar. Ecol. Prog. Ser. 100, 167–176 (1993).
Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).
Betancourt, M. & Girolami, M. in Current Trends in Bayesian Methodology with Applications (eds Upadhyay, S. K., Singh, U., Dey, D. K. & Loganathan, A.) 79–97 (Taylor & Francis, 2015).
Salvatier, J., Wiecki, T. V. & Fonnesbeck, C. Probabilistic programming in Python using PyMC3. PeerJ Comput. Sci. 2, e55 (2016).
Brooks, E. J., Sloman, K. A., Sims, D. W. & Danylchuk, A. J. Validating the use of baited remote underwater video surveys for assessing the diversity, distribution and abundance of sharks in the Bahamas. Endanger. Species Res. 13, 231–243 (2011).
Bakker, J. et al. Environmental DNA reveals tropical shark diversity in contrasting levels of anthropogenic impact. Sci. Rep. 7, 16886 (2017).
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).
Acknowledgements
Core funding for Global FinPrint was provided by the Paul G. Allen Family Foundation (to D. Chapman and M. Heithaus). M.A.M. was supported by the NSERC Canada Research Chairs Program. We thank our individual funders, whose contributions greatly enhanced the sampling coverage of the projects; all of the government permitting agencies that allowed us to work in their waters; the Global FinPrint volunteers from Stony Brook University, Florida International University, James Cook University, the Aquarium of the Pacific and Shedd Aquarium who watched the BRUVS footage; N. A. J. Graham, N. J. Barrowman and J. Zamborian-Mason for helpful comments on drafts of our manuscript and R. Steele for assistance with manuscript preparation.This is contribution #205 from the Coastlines and Oceans Division of the Institute of Environment at Florida International University.
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D. Chapman and M. Heithaus conceived the study with assistance from M. Heupel, C.A.S., M.M., E.H. and M.A.M. D. Chapman, M. Heithaus, M. Heupel, C.A.S., M.M. and E.H. directed fieldwork run by J.G., J.K., M.E.B., L.M.C.-R., C.W.S., K.I.F., J.V.-A., G.C. and C.S.S. Database management was by T. Gorham. M.A.M. and D. Chapman drafted the manuscript, with help from M. Heithaus, M. Heupel, C.A.S., J.E.M.C., M.M., E.H., J.G., J.K., M.E.B., L.M.C.-R., C.W.S., C.S.S., M.J.R., V.U. and T. Gorham. All other authors contributed equally, made substantial contributions to data collection, provided input and approved the text in the manuscript.
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Extended data figures and tables
Extended Data Fig. 1 Relationships between MaxN and mean count.
a, Relationships for 82 BRUVS sets across the range of MaxN values observed by Global FinPrint. Linear model estimates (median (95% uncertainty intervals of the highest posterior density)) are 1.56 (1.09–2.05) (intercept) and 3.03 (2.75–3.26) (slope). The red line is the highest posterior density model fit; grey lines are 100 realizations of possible model fits given random samples from the model posteriors, showing a tight relationship to the estimated red line. b, Bootstrap-based linear model estimates (using 1,000 repetitions) from samples of 5 BRUVS sets (with replacement) from the sample of 82 BRUVS sets in a, with estimates of 1.26 (0.29–2.67) (intercept) and 3.17 (1.89–6.49) (slope), using the OLS function of the scipy statsmodels package in Python. The wider variability in b is because lines were estimated from using five data points with replacement. c, Bootstrap-based power analysis results showing the number of observations required to have a 95% probability of the estimated linear slope being >0; probabilities on the y axis are the proportion of bootstrap replicates (with replacement) for which the 95% confidence intervals of the estimated slope do not overlap zero.
Extended Data Fig. 2 Expected probability of sharks being observed.
Expected probabilities for BRUVS sets from 58 national jurisdictions surveyed by Global FinPrint. The number of reefs surveyed is indicated in parentheses. Among the eight lowest-ranked nations there is estimated to be less than 0.1% chance of a shark being present any time a BRUVS is deployed, suggesting that sharks are functionally extinct on these survey reefs. Functional extinction in this study means that reef sharks are essentially absent from the ecosystem, and have little to no functional role in structuring the surrounding reef fish community through predation or fear-based effects43. Symbols are median (circles), 50% (wide horizontal lines) and 95% (thin horizontal lines) highest posterior density (credible) intervals. Note that although there are more reefs observed from jurisdictions such as Australia, the hierarchical model explicitly accounts for such imbalances.
Extended Data Fig. 3 Forrest plot of ZINB posterior distribution effect sizes for candidate models.
Left and centre columns, covariates present in the count component of the model. Right column, covariates present in the zero-inflation component of the model. The median (circles) and 95% (thin horizontal lines) highest posterior density (credible) intervals for four independent Markov chain Monte Carlo chains (left) are shown. Models with PSIS-LOO-based weights >0 include the ZINB full model (77% weight; squares), the ZINB full count model (16% weight; upwards pointing triangle) and ZINB partial null (7% weight; downwards pointing triangle); circles indicate model-weighted estimates used for inference. R-hat values for all parameters were between 1.01 and 1, suggesting no evidence that models failed to converge. Note the differences in the scale along the x axes.
Extended Data Fig. 4 Diagnostic plots for Hamiltonian Monte Carlo analyses.
a–d, Bivariate plots of posterior traces (red dots) of global zero slopes versus region-level zero variances for a centred parameterization (a); global count slopes versus region-level count variances for a centred parameterization (b); global zero slopes versus region-level zero variances for a non-centred parameterization (c); and global count slopes versus region-level count variances for a non-centred parameterization (d). Green dots highlight potentially divergent transitions in NUTS samples. a, b, A high number of clustered divergent transitions are clustered, indicative of pathological parameter space. c, d, These problems are eliminated using a non-centred, re-parameterization39 of the original full zero-inflated hierarchical model, which had 77% of posterior PSIS-LOO model weight.
Extended Data Fig. 5 Sensitivity plot for the effects of N(0, σ) or C(0, γ) prior standard deviations (σ) and scales (γ).
Selected posterior parameter estimates are shown for the full zero-inflated negative binomial model (77% of PSIS-LOO weight) of reef shark MaxN observations in Global Finprint. Symbols are median (circles), 50% (wide horizontal lines) and 95% (thin horizontal lines) highest posterior density (credible) intervals. Results show minor effects of prior specification on the global rate, with small changes to Cauchy prior scale values for HDI, VOICE and POP.
Extended Data Fig. 6 Posterior predictive distributions.
a, b, Distributions (blue) are shown for the full model, which had the majority (77%) of posterior model weight, of observed MaxN (vertical red lines) for 25 (of >15,000) randomly selected BRUVS sets from Global FinPrint (a) and the observed overall mean MaxN (vertical blue line) (b). Distributions that consistently overlap observed values are taken as evidence that the full ZINB model is consistent with the observed data. c, Frequency distribution of posterior predictive densities (box plots) and observed mean MaxN values (red dots) for 15,176 BRUVS sets observed as part of Global FinPrint. Note, in a, only 4.3% of observations were outside their 95% highest posterior predictive density, suggesting that there is no evidence that the full model is inconsistent with the observed data. Note, the plot in c was truncated at MaxN = 10 (representing >99% of observed MaxN) for clarity.
Extended Data Fig. 7 Expected relative abundance (MaxN) conditional on an excess zero not occurring.
Data are shown for BRUVS sets from 58 national jurisdictions surveyed by Global FinPrint. The number of reefs surveyed is indicated in parentheses. Symbols are median (circles), 50% (wide horizontal lines) and 95% (thin horizontal lines) highest posterior density (credible) intervals.
Supplementary information
Supplementary Tables
This file contains Supplementary Tables 1-3.
Supplementary Table 4
Abundance scores and conservation potential among 58 national jurisdictions surveyed by Global FinPrint.
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MacNeil, M.A., Chapman, D.D., Heupel, M. et al. Global status and conservation potential of reef sharks. Nature 583, 801–806 (2020). https://doi.org/10.1038/s41586-020-2519-y
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DOI: https://doi.org/10.1038/s41586-020-2519-y
