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This article is a response to Murua et al.'s Matters Arising article in Nature, "Shark mortality cannot be assessed by fishery overlap alone," which arose from arising from N. Queiroz et al. Nature https://doi.org/10.1038/s41586-019-1444-4 (2019).

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... Shark populations are particularly vulnerable to overfishing on account of slow growth rates, late age at sexual maturity and relatively low fecundity, which makes them more prone to extinction risk than most other marine fishes (Dulvy et al., 2014). Large declines in global abundance of oceanic pelagic sharks driven by overfishing have occurred over the past half century (Pacoureau et al., 2021) as a result of substantial overlap of preferred shark habitats co-occurring with industrialised fisheries, within which fishing-induced mortality is higher where spatial overlap is greater (Queiroz et al., 2019(Queiroz et al., , 2021. ...
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Groups of basking sharks engaged in circling behaviour are rarely observed and their function remains enigmatic in the absence of detailed observations. Here, underwater and aerial video recordings of multiple circling groups of basking sharks during late summer (August and September, 2016–2021) in the eastern north Atlantic Ocean showed groups numbering between 6 and 23 non‐feeding individuals of both sexes. Sharks swam slowly in a rotating ‘torus’ (diameter range: 17–39 m) with individuals layered vertically from the surface to a maximum depth of 16 m. Within a torus, sharks engaged in close‐following, echelon, close flank‐approach or parallel swimming behaviours. Measured shark total body lengths were 5.4 – 9.5 m (mean LT, 7.3 m ± 0.9 S.D.; median 7.2 m, n = 27), overlapping known lengths of sexually mature males and females. Males possessed large claspers with abrasions that were also seen on female pectoral fins. Female body coloration was paler than that of males, similar to colour changes seen during courtship and mating in other shark species. Individuals associated with most other members rapidly (within minutes) indicating toroidal behaviours facilitate multiple interactions. Sharks interacted through fin‐fin and fin‐body contacts, rolling to expose ventral surfaces to following sharks, and breaching behaviour. Toruses formed in late summer when feeding aggregations in zooplankton‐rich thermal fronts switched to non‐feeding following and circling behaviours. Collectively, our observations explain a courtship function for toruses. This study highlights northeast Atlantic coastal waters as critical habitat supporting courtship reproductive behaviour of endangered basking sharks, the first such habitat identified for this species globally. This article is protected by copyright. All rights reserved.
... Ecologically important areas, such as large-scale frontal regions and oceanic seamounts, can be important space-use hotspots for pelagic sharks and severely bias species distributions in the open ocean (Morato et al., 2010;Queiroz et al., 2019). Thus, the implementation of Open Ocean Marine Protected Areas (OOMPAs) could be a particularly useful tool for tRFMOs to protect hotspot areas by reducing fisheries interactions (Worm et al., 2013;Queiroz et al., 2016Queiroz et al., , 2019Queiroz et al., , 2021). An additional measure would be to reduce overall fishing effort by managing nominal catches of blue sharks which are the main targeted species (Dinkel and Sánchez-Lizaso, 2020). ...
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An Ecological Risk Assessment (ERA; also known as Productivity and Susceptibility Analysis, PSA) was conducted on sixteen species (15 sharks and 1 ray) or 20 stocks of pelagic elasmobranchs to assess their vulnerability to pelagic longline fisheries in the Atlantic Ocean. This was a quantitative assessment consisting of a risk analysis to evaluate the biological productivity of these stocks and a susceptibility analysis to assess their propensity to capture and mortality in pelagic longline fisheries. The risk analysis estimated productivity (maximum rate of increase, r) using a stochastic life table/Leslie matrix approach that incorporated uncertainty in age at maturity, lifespan, and both age-specific natural mortality and fecundity. Susceptibility to the fishery was calculated as the product of four components, which were also computed quantitatively: availability of the species to the fleet, encounterability of the gear given the species vertical distribution, gear selectivity, and post-capture mortality. Information from observer programs by ten ICCAT nations was used to derive fleet-specific susceptibility values. Three metrics were used to calculate vulnerability (Euclidean distance, a multiplicative index, and the arithmetic mean of the productivity and susceptibility ranks). The five stocks with the lowest productivity were the bigeye thresher (Alopias superciliosus), sandbar (Carcharhinus plumbeus), longfin mako (Isurus paucus), night (Carcharhinus signatus), and South Atlantic silky shark (Carcharhinus falciformis). The highest susceptibility values corresponded to shortfin mako (Isurus oxyrinchus), North and South Atlantic blue sharks (Prionace glauca), porbeagle (Lamna nasus), and bigeye thresher. Based on the arithmetic mean vulnerability index, which did not show preferential correlation with the productivity or susceptibility indices, the bigeye thresher, longfin and shortfin makos, porbeagle, and night sharks were the most vulnerable stocks. In contrast, North and South Atlantic scalloped hammerheads (Sphyrna lewini), smooth hammerhead (Sphyrna zygaena), and North and South Atlantic pelagic stingray (Pteroplatytrygon violacea) had the lowest vulnerabilities. RÉSUMÉ Une évaluation des risques écologiques (ERA, connue comme une analyse de productivité et de susceptibilité, PSA) a été réalisée sur 16 espèces (15 requins et une raie) ou 20 stocks d'élasmobranches pélagiques en vue d'évaluer leur vulnérabilité face aux pêcheries palangrières pélagiques dans l'océan Atlantique. Il s'agissait d'une évaluation quantitative consistant en une analyse des risques en vue d'évaluer la productivité biologique de ces stocks et une analyse de susceptibilité en vue d'évaluer leur propension à la capture et à la mortalité dans le cadre des pêcheries palangrières pélagiques. L'analyse des risques estimait la productivité (taux maximum 2637 d'augmentation, r) à l'aide d'une table de survie stochastique/approche de matrice de Leslie qui incorporait l'incertitude dans l'âge à la maturité, la durée de vie, et la mortalité naturelle et la fécondité spécifiques à l'âge. La susceptibilité à la pêcherie a été calculée comme le produit de quatre composantes, qui ont également été calculées quantitativement : disponibilité de l'espèce pour la flottille, probabilité de rencontre de l'engin compte tenu de la distribution verticale de l'espèce, sélectivité de l'engin et mortalité après la capture. On a utilité l'information provenant de programmes d'observateurs de 10 pays de l'ICCAT afin d'obtenir les valeurs de susceptibilité spécifiques aux flottilles. Trois métriques ont été employées pour calculer la vulnérabilité (distance euclidienne, un indice multiplicatif et la moyenne arithmétique des classements de la productivité et de la susceptibilité). Les cinq stocks présentant la productivité la plus basse étaient le renard à gros yeux (Alopias superciliosus), le requin gris (Carcharhinus plumbeus), la petite taupe (Isurus paucus), le requin de nuit (Carcharhinus signatus) et le requin soyeux de l'Atlantique Sud (Carcharhinus falciformis). Le requin-taupe bleu (Isurus oxyrinchus), le requin peau bleue de l'Atlantique Nord et de l'Atlantique Sud (Prionace glauca), le requin-taupe commun (Lamna nasus) et le renard à gros yeux ont présenté les valeurs de susceptibilité les plus élevées. Sur la base de la moyenne arithmétique de l'indice de vulnérabilité, qui n'a pas dégagé de corrélation préférentielle avec les indices de productivité ou de susceptibilité, le renard à gros yeux, la petite taupe, le requin-taupe bleu, le requin-taupe commun et le requin de nuit étaient les stocks les plus vulnérables. En revanche, le requin-marteau halicorne de l'Atlantique Nord et de l'Atlantique Sud (Sphyrna lewini), le requin-marteau commun (Sphyrna zygaena) ainsi que la pastenague violette de l'Atlantique Nord et de l'Atlantique Sud (Pteroplatytrygon violacea) présentaient les niveaux de vulnérabilité les plus faibles. RESUMEN Se llevó a cabo una evaluación del riesgo ecológico (ERA, también conocida como análisis de productividad y susceptibilidad, PSA) sobre dieciséis especies (15 tiburones y 1 raya) o 20 stocks de elasmobranquios pelágicos para evaluar su vulnerabilidad a las pesquerías de palangre pelágico en el océano Atlántico. Fue una evaluación cuantitativa que consistía en un análisis de riesgo para evaluar la productividad biológica de estos stocks y un análisis de susceptibilidad para evaluar su propensión a la captura y la mortalidad en las pesquerías de palangre pelágico. El análisis de riesgo estimó la productividad (tasa máxima de incremento, r) utilizando un tabla vital estocástica/enfoque de matriz de Leslie que incorporaba la incertidumbre en la edad de madurez, el ciclo vital y la mortalidad natural y fecundidad específicas de la edad. La susceptibilidad a la pesquería se calculó como el producto de cuatro componentes, que fueron calculados también cuantitativamente: disponibilidad de las especies para la flota, probabilidad de encuentro con el arte teniendo en cuenta la distribución vertical de la especie, la selectividad del arte y la mortalidad posterior a la captura. Se utilizó la informaicón de los programas de observadores de diez naciones de ICCAT para derivar los valores de susceptibilidad específicos de la flota. Se utilizaron tres tipos de mediciones para calcular la vulnerabilidad (distancia euclidiana, un índice multiplicativo y una media aritmética de las clasificaciones de productividad y susceptibilidad). Los cinco stocks con la productividad más baja fueron zorro ojón (Alopias superciliosus), tiburón trozo (Carcharhinus plumbeus), marrajo carite (Isurus paucus), tiburón de noche (Carcharhinus signatus) y tiburón jaquetón del Sur (Carcharhinus falciformis). Los valores más elevados de susceptibilidad correspondieron al marrajo dientuso (Isurus oxyrinchus), tintorera del Atlántico norte y sur (Prionace glauca), marrajo sardinero (Lamna nasus) y zorro ojón. Basándose en la media aritmética del índice de vulnerabilidad, que no mostraba una correlación preferencial con los índices de productividad o susceptibilidad, los stocks de zorro ojón, marrajo carite, marrajo dientuso, el marrajo sardinero y tiburón de noche eran los más vulnerables. Por el contrario, la cornuda común del Atlántico norte y sur (Sphyrna lewini), la cornuda cruz (Sphyrna zygaena) y la raya pelágica del Atlántico norte y del Atlántico sur (Pteroplatytrygon violacea) presentaban los niveles más bajos de vulnerabilidad.
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Estimated declines in shark and ray populations worldwide have raised major, widespread concern about the impacts of global fisheries on elasmobranchs. The mechanisms causing elasmobranch mortality during fisheries’ capture are not fully understood, but we must gain greater clarity on this topic for fisheries managers to develop effective conservation plans to mitigate further population declines. To evaluate how two important factors, respiratory mode and fishing gear type, impact elasmobranch survival, we compiled publicly available data sources on the immediate mortality percentages of 83 species and post-release mortality percentages of 40 species. Using Bayesian models, we found that sharks and rays captured in longlines had significantly lower immediate mortality than those caught in trawls or gillnets. Our models also predicted the mean total discard mortality (combined immediate and post-release mortality) percentages of obligate ram-ventilating elasmobranchs caught in longline, gillnet and trawl gear types to be 49.8, 79.0 and 84.2%, respectively. In contrast, total discard mortality percentages of stationary-respiring species were significantly lower (longline capture mean = 7.2%, gillnet capture mean = 25.3%, trawl capture mean = 41.9%). Our global meta-analysis provides the first quantified demonstration of how mortality is affected by these two factors across a broad range of species. Our results and approach can be applied to data-deficient elasmobranchs and fisheries to identify species that are likely to experience high rates of mortality due to respiratory mode and/or fishing methods used, so that appropriate mitigation measures can be prioritized and investigated.
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This report is strictly confidential. It presents estimates of average daily Foreign Fishing Vessel (FFV) numbers for thirteen months of surveillance data. Data was provided by Border Protection Command (BPC) in the Australian Customs Service on all surveillance flights from June 2006 to June 2007. Apprehension data for the same period was provided by the Australian Fishing Management Authority (AFMA). The first three months of the project, after receiving surveillance data in January 2008, was spent cleaning and preparing the data for analysis using the methodology developed by Salini et al. (2007). This included the calculation of flight swath areas using a modified analysis method (utilising Oracle Spatial) described in Section 4, which dramatically reduced data processing time. Further methodological development was undertaken, namely developing a new estimator (nQ), which was hoped to decrease processing an analysis time. The data was processed in order to calculate: • the area covered for all surveillance flights overlapping Zones 1, 6 and 7 and most of Zone 2, and the areas immediately outside the Australian Exclusive Economic Zone (AEEZ) from those Zones, • the exact location of each legal and illegal foreign fishing vessel (FFV) sighted during those flights. Legal FFVs are those located in regions outside the AEEZ plus some regions inside the AEEZ where either fishing is allowed in general, or where particular types of FFVs are allowed. Illegal FFVs are those located in regions inside the AEEZ which do not have any such allowance. The processed data were organised into months according to the commencement time (in UTC time) of each flight, and the flight in which each FFV was sighted. This data was used as input to a process which provided estimates of the daily number of legal and illegal FFVs in each of these Zones and the individual regions within them. Six estimators are presented, including results for a new ‘quick’ estimator nQ . Although nQ performed reasonably well in comparison to the preferred estimator nC recommended by Salini et al. (2007), it tended to slightly overestimate when the density of FFVs was low and produce more variable results when FFV numbers were high. Key assumptions made in the analysis include: • the swath widths provided by BPC for Type3 vessels for each aircraft and each sea state are representative, • the detection rate within that swath width is 100%, • the reduced swath width for the smaller Type2 vessels (typically, a 15% narrower swath) does not have a significant impact on the estimates, since only 16% of all vessels sighted are not in the Type3 class, • there are few, if any, vessels at grid points which are not surveyed within a given month. Estimates of daily numbers of legal and illegal FFVs are provided for thirteen months from June 2006 to June 2007. Detailed estimates by month and region are provided in Appendix B but the key summary tables for illegal FFVs are shown in Table 6.4. We have also implemented the algorithm of calculating the new estimator, nD, which was not reported previously. This is reassuring that it produced nearly identical results to nC, indicating that our recommendation of using nC or nD is probably reasonable. We concentrated on reporting results using nC to estimate the daily number of FFVs present in a region for easier comparison with the results of Salini et al. (2007) and shows longer-term trends in FFV numbers and apprehensions. The analyses for the 13 months of surveillance data provide a useful temporal comparison between similar times of the year. The results may be summarised as follows: • the daily numbers of illegal FFVs in the study region was highest between August and October both in the present study and the preceding 18 month study period of Salini et al. (2007). This is likely to be due to more favourable weather conditions for smaller vessels travelling from Indonesia, particularly to Zones 6 and 7. • the daily numbers of illegal FFVs increased slightly overall during the first four months of the study to around 18 FFVs, after which time FFV numbers steadily declined to about 3 FFVs per day in June 2007. However, to place these results in context, FFV numbers had declined from about 60 FFVs per day in September 2005 to about 15 FFVs at the start of the present study in June 2006. Results for particular Zones can be summarised as follows (see Section 6.3.1): • The number of FFVs in Zone 1 was generally low, averaging around 2.5 FFVs per day but reaching 5-6 FFVs between January and March 2007. • For Zone 2, estimated daily FFVs were extremely low, with none seen in the last 8 months of the 13 month study period. • For Zone 6, the number of FFVs was highest in September 2006 (13-15), which was similar to FFV numbers in the previous 18 month study. However, after September 2006 numbers dramatically declined to less than one vessel in June 2007. It is important to note that the period of decline coincides with a seasonal decline in FFVs observed in previous years. However, the magnitude of decline clearly indicates a reduction in FFV numbers. • For Zone 7, the number of FFVs showed a significant and consistent decline from around 10 FFVs per day in June 2006 to less than 1 vessel per day in June 2007. To place these results in context with the results of Salini et al. (2007), FFV numbers declined from a peak of around 42 per day in January 2006. A number of assumptions are required to make any estimate of FFV fleet size. Some attempt is made at this in Section 7, but there is little information on which to base good estimates of either “residence time” per visit or numbers of visits per month by each vessel. A plausible range of values for these is used and leads to a coarse estimate of FFV fleet size in the range between 15 and 180 depending on the assumptions used. This demonstrates a significant reduction in fleet size estimated to be between 55 and 600 in 2005-2006. These assumptions can be refined as outlined under Recommendation 6 and would lead to more precise estimates. Based on the analyses of surveillance and apprehension data from June 2006 – June 2007 inclusive, we make the following recommendations for improving surveillance data gathering: Recommendation 1: We recommend that data for the months since June 2007 be analysed using the methods in this report, to provide estimates for at least one more year to confirm the decline in FFVs is real and not due to artefacts such as seasonal incursions. Recommendation 2: We recommend that BPC adopt the methods outlined in this report (using estimator nC or nD) into their normal practice. The new ‘quick’ estimator, nQ, is useful for obtaining ‘ball park’ estimates of FFV numbers, but should not be used for accurately estimating FFV numbers. Recommendation 3: We recommend that a further analysis of the current database be undertaken to provide empirical estimates of detection probabilities as a function of distance from the aircraft's track, in order to validate or refine the swath widths currently in use. Recommendation 4: We recommend that BPC routinely collect the time and position of the aircraft at the point where any FFV sighting takes place. Recommendation 5: The data field recording “Type of vessel” needs to be redefined to provide an unambiguous and clearer distinction between the different types of fishing vessels. Recommendation 6: More effort needs to be given to the estimation of “residence time” and “number of visits per month” in order to provide reliable estimates of the size of the fishing fleet involved. The extent to which apprehension effort may influence the residence time should also be explored. Recommendation 7: We recommend carrying out statistical evaluation of all the estimators and providing “hard” evidence on their performance, in particular which one is the most appropriate for the given dataset which has a biased nature of sampling because flight path is not random in the surveillance area. This will allow us to access to which extent the quasi-Poisson assumption is valid which is vital for our calculation of the standard errors. The study should include bias, variance, and mean square errors (MSE). This would require high statistical skills and extensive simulation studies. Recommendation 8: We recommend that a further study be undertaken to estimate the size of the catch of shark species from these illegal FFVs in order to improve the quality of stock assessment of sharks in northern Australia, and to improve current ecosystem models that can predict the ecological effects of IUU on the broader ecosystem.
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Juvenile silky sharks Carcharhinus falciformis comprise the largest component of the incidental elasmobranch catch taken in tropical tuna purse seine fisheries. During a chartered cruise on board a tuna purse seine vessel conducting typical fishing operations we investigated the post-release survival and rates of interaction with fishing gear of incidentally captured silky sharks using a combination of satellite linked pop-up tags and blood chemistry analysis. To identify trends in survival probability and the point in the fishing interaction when sharks sustain the injuries that lead to mortality, sharks were sampled during every stage of the fishing procedure. The total mortality rates of silky sharks captured in purse seine gear was found to exceed 84%. We found survival to precipitously decline once the silky sharks had been confined in the sack portion of the net just prior to loading. Additionally, shark interactions recorded by the scientists were markedly higher than those recorded by vessel officers and the fishery observer. Future efforts to reduce the impact of purse seine fishing on silky shark populations should be focused on avoidance or releasing sharks while they are still free swimming.
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Marine defaunation, or human-caused animal loss in the oceans, emerged forcefully only hundreds of years ago, whereas terrestrial defaunation has been occurring far longer. Though humans have caused few global marine extinctions, we have profoundly affected marine wildlife, altering the functioning and provisioning of services in every ocean. Current ocean trends, coupled with terrestrial defaunation lessons, suggest that marine defaunation rates will rapidly intensify as human use of the oceans industrializes. Though protected areas are a powerful tool to harness ocean productivity, especially when designed with future climate in mind, additional management strategies will be required. Overall, habitat degradation is likely to intensify as a major driver of marine wildlife loss. Proactive intervention can avert a marine defaunation disaster of the magnitude observed on land. Copyright © 2015, American Association for the Advancement of Science.
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We introduce a new method that uses generalized linear mixed models to infer the depth distribution of pelagic fishes. It uses existing data from research surveys and observers on commercial vessels to estimate changes in catchability when longline fishing gear is lengthened to access deeper water. We infer the depth distribution of catchability for 37 fish species that are caught on pelagic longlines in the Pacific Ocean. We show how the estimates of catchability can be used to correct abundance indices for variations in longline depth. Our method facilitates the inclusion of data from early surveys in the time series of commercial catch rates used to estimate abundance. It also resolves inconsistencies in the time series caused by a rapid switch to deep longlining in the 1970s. The catchability distribution does not always match depth preferences derived from tracking studies. Therefore, depth preferences from tracking studies should not be used to correct abundance indices without additional information on feeding behavior.
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Management of shark fisheries in Northern Australia. FAO Fisheries Technical Paper No
  • J D Stevens
Stevens, J. D. Management of shark fisheries in Northern Australia. FAO Fisheries Technical Paper No. 378/2 http://www.fao.org/3/x2097e/X2097E20.htm#ch16 (FAO, 1999).
39 Agence de Recherche pour la Biodiversité à la Réunion (ARBRE)
  • Ephe-Cnrs-Upvd
  • French Papetoai
  • Polynesia
EPHE-CNRS-UPVD, Papetoai, French Polynesia. 39 Agence de Recherche pour la Biodiversité à la Réunion (ARBRE), Réunion, Marseille, France. 40 Institut de Recherche pour le
42 Save Our Seas Foundation-D'Arros Research Centre (SOSF-DRC)
  • Développement
  • Espace-Dev
  • Réunion
  • France Marseille
Développement, UMR 228 ESPACE-DEV, Réunion, Marseille, France. 42 Save Our Seas Foundation-D'Arros Research Centre (SOSF-DRC), Geneva, Switzerland. 43 South African Institute for Aquatic Biodiversity (SAIAB), Grahamstown, South Africa. 44 Department of Fisheries Evaluation, Fisheries Research Division, Instituto de Fomento Pesquero (IFOP),
45 School of Biological
  • Chile Valparaíso
Valparaíso, Chile. 45 School of Biological, Earth and Environmental Sciences, University College Cork, Cork, Ireland. 46 MaREI Centre, Environmental Research Institute, University College Cork, Cork, Ireland. 47 College of Science and Engineering, Flinders University,
51 Department of Fish and Wildlife Conservation
  • Crawley Western Australia
  • Australia Crawley
Crawley Western Australia, Crawley, Australia. 51 Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, USA. 52 OCEARCH, Park City, Utah, USA.
74 Cape Research and Diver Development
  • M D Columbia
Columbia, MD, USA. 71 Pelagios-Kakunja, La Paz, Mexico. 72 Mote Marine Laboratory, Center for Shark Research, Sarasota, FL, USA. 73 Biological Sciences, University of Windsor, Windsor, Ontario, Canada. 74 Cape Research and Diver Development, Simon's Town, South
77 Dyer Island Conservation Trust, Western Cape, South Africa. 78 Blue Wilderness Research Unit
  • Australia Australia
Australia, Australia. 77 Dyer Island Conservation Trust, Western Cape, South Africa. 78 Blue Wilderness Research Unit, Scottburgh, South Africa. 79 University of California Davis, Davis, CA, USA. 80 Cape Research Centre, South African National Parks, Steenberg, South Africa.
South Africa. 82 Institute for Communities and Wildlife in Africa
  • Shark Spotters
  • Fish Hoek
Shark Spotters, Fish Hoek, South Africa. 82 Institute for Communities and Wildlife in Africa, Department of Biological Sciences, University of Cape Town, Rondebosch, South Africa.
88 Pontificia Universidad Católica del Ecuador Sede Manabi, Portoviejo, Ecuador. 89 Marine Megafauna Foundation
  • Chatham Conservancy
Conservancy, Chatham, MA, USA. 87 Fisheries and Aquaculture Centre, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia. 88 Pontificia Universidad Católica del Ecuador Sede Manabi, Portoviejo, Ecuador. 89 Marine Megafauna Foundation, Truckee, CA, USA. 90 Conservation and Fisheries Department, Ascension Island Government, Georgetown, Ascension Island, UK. 91 Marine Conservation Society Seychelles, Victoria, Seychelles. 92 CORDIO, East Africa, Mombasa, Kenya. 93 Upwell, Monterey, CA, USA. 94
104 Instituto de Fisica Interdisciplinar y Sistemas Complejos
  • Spain Pasaia
Pasaia, Spain. 103 IKERBASQUE, Basque Foundation for Science, Bilbao, Spain. 104 Instituto de Fisica Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Cientificas, University of the Balearic Islands, Palma de Mallorca, Spain. 105 Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Tubney, UK. 106
109 Present address: The University of the Sunshine Coast
  • Southampton Southampton
Southampton, Southampton, UK. 107 Centre for Biological Sciences, University of Southampton, Southampton, UK. 108 Present address: Hatfield Marine Science Center, Oregon State University, Newport, OR, USA. 109 Present address: The University of the Sunshine Coast, Sippy Downs, Queensland, Australia. ✉ e-mail: dws@mba.ac.uk
International Commission for the Conservation of Atlantic Tunas
International Commission for the Conservation of Atlantic Tunas. Report of the 2018 ICCAT Intersessional Meeting of the Sharks Species Group (2018).
40 Agence de Recherche pour la Biodiversité à la Réunion (ARBRE)
  • Ephe-Cnrs-Upvd
  • French Papetoai
  • Polynesia
EPHE-CNRS-UPVD, Papetoai, French Polynesia. 40 Agence de Recherche pour la Biodiversité à la Réunion (ARBRE), Réunion, Marseille, France. 41 Institut de Recherche pour le
48 Department of Conservation
  • South Adelaide
  • Australia Australia
Adelaide, South Australia, Australia. 48 Department of Conservation, Auckland, New Zealand.
52 Department of Fish and Wildlife Conservation, Virginia Tech
  • Western Crawley
  • Australia Australia
Crawley, Western Australia, Australia. 52 Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, USA. 53 OCEARCH, Park City, UT, USA. 54 Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada. 55 National Institute of Water and Atmospheric Research, Wellington, New Zealand. 56 Beneath the Waves, Herndon, VA, USA. 57
63 Griffith Centre for Coastal Management
  • South Australia
Adelaide, South Australia, Australia. 61 Zoological Society of London, London, UK. 62 Galapagos Whale Shark Project, Puerto Ayora, Galapagos, Ecuador. 63 Griffith Centre for Coastal Management, Griffith University School of Engineering, Griffith University, Gold Coast, Queensland, Australia. 64 Saving the Blue, Cooper City, FL, USA. 65 Smithsonian Tropical Research Institute, Panama City, Panama. 66 Leonard and Jayne Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, FL, USA. 67 Galapagos Science Center, San Cristobal, Galapagos, Ecuador. 68 Universidad San Francisco de Quito, Quito, Ecuador. 69
83 Institute for Communities and Wildlife in Africa
  • London London
London, London, UK. 77 Centre for Sustainable Aquatic Ecosystems, Harry Butler Institute, Murdoch University, Perth, Western Australia, Australia. 78 Dyer Island Conservation Trust, Western Cape, South Africa. 79 Blue Wilderness Research Unit, Scottburgh, South Africa. 80 University of California Davis, Davis, CA, USA. 81 Cape Research Centre, South African National Parks, Steenberg, South Africa. 82 Shark Spotters, Fish Hoek, South Africa. 83 Institute for Communities and Wildlife in Africa, Department of Biological Sciences, University of Cape Town, Rondebosch, South Africa. 84 Western Cape Department of Agriculture, Veterinary Services, Elsenburg, South Africa. 85 Departamento de Biologia Marinha, Universidade Federal Fluminense (UFF), Niterói, Brazil. 86 Department of Zoology, University of Cambridge,
89 Pontificia Universidad Católica del Ecuador Sede Manabi, Portoviejo, Ecuador. 90 Marine Megafauna Foundation
  • U K Cambridge
  • Atlantic White Shark
  • Conservancy
  • M A Chatham
Cambridge, UK. 87 Atlantic White Shark Conservancy, Chatham, MA, USA. 88 Fisheries and Aquaculture Centre, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia. 89 Pontificia Universidad Católica del Ecuador Sede Manabi, Portoviejo, Ecuador. 90 Marine Megafauna Foundation, Truckee, CA, USA. 91 Conservation and Fisheries Department, Ascension Island Government, Georgetown, Ascension Island, UK.
95 Department of Zoology and Institute for Coastal and Marine Research
  • Kenya Mombasa
Mombasa, Kenya. 94 Upwell, Monterey, CA, USA. 95 Department of Zoology and Institute for Coastal and Marine Research, Nelson Mandela University, Port Elizabeth, South Africa.
110 Present address: The University of the Sunshine Coast
  • Southampton Southampton
Southampton, Southampton, UK. 109 Present address: Hatfield Marine Science Center, Oregon State University, Newport, OR, USA. 110 Present address: The University of the Sunshine Coast, Sippy Downs, Queensland, Australia. ✉ e-mail: dws@mba.ac.uk