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 used to reproduce the analysis—except for geolocations—can be accessed at https://github.com/mamacneil/FinPrint.
Code used to reproduce the analysis can be accessed at https://github.com/mamacneil/FinPrint.
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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.
The authors declare no competing interests.
Peer review information Nature thanks Nick Dulvy, Peter Mumby, David Sims and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
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.
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.
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.
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.
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.
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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