Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 1 of 34
Extinction risk and conservation of the
world’s sharks and rays
Nicholas K Dulvy1,2*, Sarah L Fowler3, John A Musick4, Rachel D Cavanagh5, Peter
M Kyne6, Lucy R Harrison1,2, John K Carlson7, Lindsay NK Davidson1,2, Sonja V
Fordham8, Malcolm P Francis9, Caroline M Pollock10, Colin A Simpfendorfer11,12,
George H Burgess13, Kent E Carpenter14,15, Leonard JV Compagno16, David A
Ebert17, Claudine Gibson3, Michelle R Heupel18, Suzanne R Livingstone19,
Jonnell C Sanciangco14,15, John D Stevens20, Sarah Valenti3, William T White20
1IUCN Species Survival Commission Shark Specialist Group, Department of Biological
Sciences, Simon Fraser University, Burnaby, Canada; 2Earth to Ocean Research Group,
Department of Biological Sciences, Simon Fraser University, Burnaby, Canada; 3IUCN
Species Survival Commission Shark Specialist Group, NatureBureau International,
Newbury, United Kingdom; 4Virginia Institute of Marine Science, College of William and
Mary, Gloucester Point, United States; 5British Antarctic Survey, Natural Environment
Research Council, Cambridge, United Kingdom; 6Research Institute for the Environment
and Livelihoods, Charles Darwin University, Darwin, Australia; 7Southeast Fisheries Science
Center, NOAA/National Marine Fisheries Service, Panama City, United States; 8Shark
Advocates International, The Ocean Foundation, Washington, DC, United States;
9National Institute of Water and Atmospheric Research, Wellington, New Zealand; 10Global
Species Programme, International Union for the Conservation of Nature, Cambridge,
United Kingdom; 11Centre for Sustainable Tropical Fisheries and Aquaculture, James Cook
University, Townsville, Australia; 12School of Earth and Environmental Sciences, James
Cook University, Townsville, Australia; 13Florida Program for Shark Research, Florida
Museum of Natural History, University of Florida, Gainsville, United States; 14IUCN Species
Programme Species Survival Commission, Old Dominion University, Norfolk, United
States; 15Conservation International Global Marine Species Assessment, Old Dominion
University, Norfolk, United States; 16Shark Research Center, Iziko, South African Museum,
Cape Town, South Africa; 17Paciﬁc Shark Research Center, Moss Landing Marine
Laboratories, Moss Landing, United States; 18School of Earth and Environmental Sciences,
Australian Institute of Marine Science, Townsville, Australia; 19Global Marine Species
Assessment, Biodiversity Assessment Unit, IUCN Species Programme, Conservation
International, Arlington, United States; 20Marine and Atmospheric Research,
Commonwealth Scientiﬁc and Industrial Research Organisation, Hobart, Australia
Abstract The rapid expansion of human activities threatens ocean-wide biodiversity. Numerous
marine animal populations have declined, yet it remains unclear whether these trends are symptomatic
of a chronic accumulation of global marine extinction risk. We present the ﬁrst systematic analysis of
threat for a globally distributed lineage of 1,041 chondrichthyan ﬁshes—sharks, rays, and chimaeras.
We estimate that one-quarter are threatened according to IUCN Red List criteria due to overﬁshing
(targeted and incidental). Large-bodied, shallow-water species are at greatest risk and ﬁve out of the
seven most threatened families are rays. Overall chondrichthyan extinction risk is substantially higher
than for most other vertebrates, and only one-third of species are considered safe. Population depletion
has occurred throughout the world’s ice-free waters, but is particularly prevalent in the Indo-Paciﬁc
Biodiversity Triangle and Mediterranean Sea. Improved management of ﬁsheries and trade is
urgently needed to avoid extinctions and promote population recovery.
*For correspondence: dulvy@
Competing interests: The
authors declare that no
competing interests exist.
Funding: See page 27
Received: 29 January 2013
Accepted: 05 December 2013
Published: 21 January 2014
Reviewing editor: Ian T Baldwin,
Max Planck Institute for Chemical
This is an open-access article,
free of all copyright, and may be
freely reproduced, distributed,
transmitted, modiﬁed, built
upon, or otherwise used by
anyone for any lawful purpose.
The work is made available under
the Creative Commons CC0
public domain dedication.
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 2 of 34
Populations and species are the building blocks of the communities and ecosystems that sustain humanity
through a wide range of services (Mace et al., 2005; Díaz et al., 2006). There is increasing evidence that
human impacts over the past 10 millennia have profoundly and permanently altered biodiversity on land,
especially of vertebrates (Schipper et al., 2008; Hoffmann et al., 2010). The oceans encompass some of
the earth’s largest habitats and longest evolutionary history, and there is mounting concern for the increas-
ing human inﬂuence on marine biodiversity that has occurred over the past 500 years (Jackson, 2010). So
far our knowledge of ocean biodiversity change is derived mainly from retrospective analyses usually
limited to biased subsamples of diversity, such as: charismatic species, commercially-important ﬁsheries,
and coral reef ecosystems (Carpenter et al., 2008; Collette et al., 2011; McClenachan et al., 2012;
Ricard et al., 2012). Notwithstanding the limitations of these biased snapshots, the rapid expansion of
ﬁsheries and globalized trade are emerging as the principal drivers of coastal and ocean threat (Polidoro
et al., 2008; Anderson et al., 2011b; McClenachan et al., 2012). The extent and degree of the global
impact of ﬁsheries upon marine biodiversity, however, remains poorly understood and highly contentious.
Recent insights from ecosystem models and ﬁsheries stock assessments of mainly data-rich northern
hemisphere seas, suggest that the status of a few of the best-studied exploited species and ecosystems
may be improving (Worm et al., 2009). However, this view is based on only 295 populations of 147 ﬁsh
species and hence is far from representative of the majority of the world’s ﬁsheries and ﬁshed species,
especially in the tropics for which there are few data and often less management (Sadovy, 2005; Newton
et al., 2007; Branch et al., 2011; Costello et al., 2012; Ricard et al., 2012).
Overﬁshing and habitat degradation have profoundly altered populations of marine animals
(Hutchings, 2000; Lotze et al., 2006; Polidoro et al., 2012), especially sharks and rays (Stevens et al.,
2000; Simpfendorfer et al., 2002; Dudley and Simpfendorfer, 2006; Ferretti et al., 2010). It is not
clear, however, whether the population declines of globally distributed species are locally reversible or
symptomatic of an erosion of resilience and chronic accumulation of global marine extinction risk
(Jackson, 2010; Neubauer et al., 2013). In response, we evaluate the scale and intensity of overﬁshing
through a global systematic evaluation of the relative extinction risk for an entire lineage of exploited
marine ﬁshes—sharks, rays, and chimaeras (class Chondrichthyes)—using the Red List Categories and
Criteria of the International Union for the Conservation of Nature (IUCN). We go on to identify, (i) the life
eLife digest Ocean ecosystems are under pressure from overﬁshing, climate change, habitat
destruction and pollution. These pressures have led to documented declines of some ﬁshes in some
places, such as those living in coral reefs and on the high seas. However, it is not clear whether
these population declines are isolated one-off examples or, instead, if they are sufﬁciently
widespread to risk the extinction of large numbers of species.
Most ﬁshes have a skeleton that is made of bone, but sharks and rays have a skeleton that is
made of cartilage. A total of 1,041 species has such a skeleton and they are collectively known as
the Chondrichthyes. To ﬁnd out how well these ﬁsh are faring, Dulvy et al. worked with more than
300 scientists around the world to assess the conservation status of all 1,041 species.
Based on this, Dulvy et al. estimate that one in four of these species are threatened with extinction,
mainly as a result of overﬁshing. Moreover, just 389 species (37.4% of the total) are considered to be
safe, which is the lowest fraction of safe species among all vertebrate groups studied to date.
The largest sharks and rays are in the most peril, especially those living in shallow waters that are
accessible to ﬁsheries. A particular problem is the ‘ﬁn trade’: the ﬁns of sharks and shark-like rays
are a delicacy in some Asian countries, and more than half of the chondrichthyans that enter the ﬁn
trade are under threat. Whether targeted or caught by boats ﬁshing for other species, sharks and
rays are used to supply a market that is largely unmonitored and unregulated. Habitat degradation
and loss also pose considerable threats, particularly for freshwater sharks and rays.
Dulvy et al. identiﬁed three main hotspots where the biodiversity of sharks and rays was particularly
seriously threatened—the Indo-Paciﬁc Biodiversity Triangle, Red Sea, and the Mediterranean Sea—
and argue that national and international action is needed to protect them from overﬁshing.
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 3 of 34
history and ecological attributes of species (and taxonomic families) that render them prone to extinction,
and (ii) the geographic locations with the greatest number of species of high conservation concern.
Chondrichthyans make up one of the oldest and most ecologically diverse vertebrate lineages: they
arose at least 420 million years ago and rapidly radiated out to occupy the upper tiers of aquatic food
webs (Compagno, 1990; Kriwet et al., 2008). Today, this group is one of the most speciose lineages
of predators on earth that play important functional roles in the top-down control of coastal and oce-
anic ecosystem structure and function (Ferretti et al., 2010; Heithaus et al., 2012; Stevens et al.,
2000). Sharks and their relatives include some of the latest maturing and slowest reproducing of all
vertebrates, exhibiting the longest gestation periods and some of the highest levels of maternal
investment in the animal kingdom (Cortés, 2000). The extreme life histories of many chondrichthyans
result in very low population growth rates and weak density-dependent compensation in juvenile sur-
vival, rendering them intrinsically sensitive to elevated ﬁshing mortality (Musick, 1999b; Cortés, 2002;
García et al., 2008; Dulvy and Forrest, 2010).
Chondrichthyans are often caught as incidental, but are often retained as valuable bycatch of ﬁsh-
eries that focus on more productive teleost ﬁsh species, such as tunas or groundﬁshes (Stevens et al.,
2005). In many cases, ﬁshing pressure on chondrichthyans is increasing as teleost target species
become less accessible (due to depletion or management restrictions) and because of the high, and in
some cases rising, value of their meat, ﬁns, livers, and/or gill rakers (Fowler et al., 2002; Clarke et al.,
2006; Lack and Sant, 2009). Fins, in particular, have become one of the most valuable seafood
commodities: it is estimated that the ﬁns of between 26 and 73 million individuals, worth US$400-550
million, are traded each year (Clarke et al., 2007). The landings of sharks and rays, reported to the
Food and Agriculture Organization of the United Nations (FAO), increased steadily to a peak in 2003
and have declined by 20% since (Figure 1A). True total catch, however, is likely to be 3–4 times greater
than reported (Clarke et al., 2006; Worm et al., 2013). Most chondrichthyan catches are unregulated
and often misidentiﬁed, unrecorded, aggregated, or discarded at sea, resulting in a lack of species-
speciﬁc landings information (Barker and Schluessel, 2005; Clarke et al., 2006; Iglésias et al., 2010;
Bornatowski et al., 2013). Consequently, FAO could only be ‘hopeful’ that the catch decline is due to
improved management rather than being symptomatic of worldwide overﬁshing (FAO, 2010). The
reported chondrichthyan catch has been increasingly dominated by rays, which have made up greater
than half of reported taxonomically-differentiated landings for the past four decades (Figure 1B).
Chondrichthyan landings were worth US$1 billion at the peak catch in 2003, since then the value has
dropped to US$800 million as catch has declined (Musick and Musick, 2011). A main driver of shark
ﬁshing is the globalized trade to meet Asian demand for shark ﬁn soup, a traditional and usually
expensive Chinese dish. This particularly lucrative trade in ﬁns (not only from sharks, but also of shark-
like rays such as wedgeﬁshes and sawﬁshes) remains largely unregulated across the 86 countries and
territories that exported >9,500 mt of ﬁns to Hong Kong (a major ﬁn trade hub) in 2010 (Figure 1C).
Red List status of chondrichthyan species
Overall, we estimate that one-quarter of chondrichthyans are threatened worldwide, based on the observed
threat level of assessed species combined with a modeled estimate of the number of Data Deﬁcient spe-
cies that are likely to be threatened. Of the 1,041 assessed species, 181 (17.4%) are classiﬁed as threat-
ened: 25 (2.4%) are assessed as Critically Endangered (CR), 43 (4.1%) Endangered (EN), and 113 (10.9%)
Vulnerable (VU) (Table 1). A further 132 species (12.7%) are categorized as Near Threatened (NT).
Chondrichthyans have the lowest percentage (23.2%, n = 241 species) of Least Concern (LC) species of all
vertebrate groups, including the marine taxa assessed to date (Hoffmann et al., 2010). Almost half (46.8%,
n = 487) are Data Deﬁcient (DD) meaning that information is insufﬁcient to assess their status (Table 1).
DD chondrichthyans are found across all habitats, but particularly on continental shelves (38.4% of 482
species in this habitat) and deepwater slopes (57.6%, Table 2). Of the 487 DD species for which we
had sufﬁcient maximum body size (n = 396) and geographic distribution data (n = 378), we were able
to predict that at least a further 68 DD species are likely to be threatened (Table 3, Supplementary
ﬁle 1). Accounting for the uncertainty in threat levels due to the number of DD species, we estimate
that more than half face some elevated risk: at least one-quarter (n = 249; 24%) of chondrichthyans are
threatened and well over one-quarter are Near Threatened (Table 1). Only 37% are predicted to be
Least Concern (Table 1).
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 4 of 34
Drivers of threat
The main threats to chondrichthyans are overexploitation through targeted ﬁsheries and incidental
catches (bycatch), followed by habitat loss, persecution, and climate change. While one-third of threat-
ened sharks and rays are subject to targeted ﬁshing, some of the most threatened species (including
sawﬁshes and large-bodied skates) have declined due to incidental capture in ﬁsheries targeting other
species. Shark-like rays, especially sawﬁshes, wedgeﬁshes and guitarﬁshes, have some of the most valu-
able ﬁns and are highly threatened. Although the global ﬁn trade is widely recognized as a major driver
of shark and ray mortality, demand for meat, liver oil, and even gillrakers (of manta and other devil rays)
also poses substantial threats. Half of the 69 high-volume or high-value sharks and rays in the global
ﬁn trade are threatened (53.6%, n = 37), while low-value ﬁns often enter trade as well, even if meat
demand is the main ﬁshery driver (Supplementary ﬁle 2A). Coastal species are more exposed to the
combined threats of ﬁshing and habitat degradation than those offshore in pelagic and deepwater
ecosystems. In coastal, estuarine, and riverine habitats, four principal processes of habitat degradation
(residential and commercial development, mangrove destruction, river engineering, and pollution)
jeopardize nearly one-third of threatened sharks and rays (29.8%, n = 54 of 181, Supplementary ﬁle 2B).
The combined effects of overexploitation and habitat degradation are most acute in freshwater, where
over one-third (36.0%) of the 90 obligate and euryhaline freshwater chondrichthyans are threatened.
Figure 1. The trajectory and spatial pattern of chondrichthyan ﬁsheries catch landings and ﬁn exports. (A) The landed catch of chondrichthyans reported to
the Food and Agriculture Organization of the United Nations from 1950 to 2009 up to the peak in 2003 (black) and subsequent decline (red). (B) The rising
contribution of rays to the taxonomically-differentiated global reported landed catch: shark landings (light gray), ray landings (black), log ratio [rays/sharks],
(red). Log ratios >0 occur when more rays are landed than sharks. The peak catch of taxonomically-differentiated rays peaks at 289,353 tonnes in 2003.
(C) The main shark and ray ﬁshing nations are gray-shaded according to their percent share of the total average annual chondrichthyan landings reported to
FAO from 1999 to 2009. The relative share of shark and ray ﬁn trade exports to Hong Kong in 2010 are represented by ﬁn size. The taxonomically-differenti-
ated proportion excludes the ‘nei’ (not elsewhere included) and generic ‘sharks, rays, and chimaeras’ category.
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 5 of 34
Their plight is exacerbated by high habitat-speciﬁcity and restricted geographic ranges (Stevens et al.,
2005). Speciﬁcally, the degradation of coastal, estuarine and riverine habitats threatened 14% of sharks
and rays: through residential and commercial development (22 species, including river sharks Glyphis
spp.); mangrove destruction for shrimp farming in Southeast Asia (4 species, including Bleeker’s varie-
gated stingray Himantura undulata); dam construction and water control (8 species, including Mekong
freshwater stingray Dasyatis laosensis), and pollution (20 species). Many freshwater sharks and rays
suffer multiple threats and have narrow geographic distributions, for example the Endangered
Roughnose stingray (Pastinachus solocirostris) that is found only in Malaysian Borneo and Indonesia
(Kalimantan, Sumatra and Java). Population control of sharks, in particular due to their perceived risk
to people, ﬁshing gear, and other ﬁsheries has contributed to the threatened status of at least 12 species
(Supplementary ﬁle 2B). Sharks and rays are also threatened due to capture in shark control nets
(e.g. Dusky shark Carcharhinus obscurus), and persecution to minimise: damage to ﬁshing nets
(e.g. Green sawﬁsh Pristis zijsron); their predation on aquacultured molluscs (e.g. Estuary stingray
Dasyatis ﬂuviorum); interference with spearﬁshing activity (e.g. Grey nurse shark Carcharias taurus),
and the risk of shark attack (e.g. White shark Carcharodon carcharias). So far the threatened status of
only one species has been directly linked to climate change (New Caledonia catshark Aulohalaelurus
kanakorum, Supplementary ﬁle 2B). the climate-sensitivity of some sharks has been recognized (Chin
et al., 2010) and the status of shark and ray species will change rapidly in climate cul-de-sacs, such
as the Mediterranean Sea (Lasram et al., 2010).
Correlates and predictors of threat
Elevated extinction risk in sharks and rays is a function of exposure to ﬁshing mortality coupled with
their intrinsic life history and ecological sensitivity (Figures 2–6). Most threatened chondrichthyan
species are found in depths of less than 200 m, especially in the Atlantic and Indian Oceans, and the
Western Central Paciﬁc Ocean (79.6%, n = 144 of 181, Figure 2). Extinction risk is greater in larger-bodied
Table 1. Observed and predicted number and percent of chondrichthyan species in IUCN Red List categories
species number (%) CR EN VU NT LC DD
Skates and rays 539 (51.8) 107 (19.9) 14 (1.3) 28 (2.7) 65 (6.2) 62 (6.0) 114 (11.0) 256 (24.6)
Sharks 465 (44.7) 74 (15.9) 11 (1.1) 15 (1.4) 48 (4.6) 67 (6.4) 115 (11.0) 209 (20.1)
Chimaeras 37 (3.6) 0 0 0 0 3 (0.3) 12 (1.2) 22 (2.1)
All observed 1041 181 (17.4) 25 (2.4) 43 (4.1) 113 (10.9) 132 (12.7) 241 (23.2) 487 (46.8)
All predicted 249 (23.9) – – – 312 (29.9) 389 (37.4) 91 (8.7)
CR, Critically Endangered; EN, Endangered; VU, Vulnerable; NT, Near Threatened; LC, Least Concern; DD, Data Deﬁcient. Number threatened is the
sum total of the categories CR, EN and VU. Species number and number threatened are expressed as percentage of the taxon, whereas the percentage
of each species in IUCN categories is expressed relative to the total number of species.
Table 2. Number and percent of chondrichthyans in IUCN Red List categories by their main habitats
Habitat Species (%) Threatened (%) CR (%) EN (%) VU (%) NT (%) LC (%) DD (%)
Coastal and continental
482 (46.3) 127 (26.3) 20 (4.1) 26 (5.4) 81 (16.8) 73 (15.1) 97 (20.1) 185 (38.4)
39 (3.7) 17 (43.6) 0 3 (7.7) 14 (35.9) 13 (33.3) 5 (12.8) 4 (10.3)
Deepwater 479 (46.0) 25 (5.2) 2 (0.4) 6 (1.3) 17 (3.5) 45 (9.4) 133 (27.8) 276 (57.6)
Mesopelagic 8 (0.8) 0 0 0 0 0 4 (50.0) 4 (50.0)
33 (3.2) 12 (36.4) 3 (9.1) 8 (24.2) 1 (3.0) 1 (3.0) 2 (6.1) 18 (54.5)
Totals 1041 181 (17.4) 25 (2.4) 43 (4.1) 113 (10.9) 132 (12.7) 241 (23.2) 487 (46.8)
CR, Critically Endangered; EN, Endangered; VU, Vulnerable; NT, Near Threatened; LC, Least Concern; DD, Data Deﬁcient.
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 6 of 34
species found in shallower waters with narrower depth distributions, after accounting for phyloge-
netic non-independence (Figures 3 and 4). The traits with the greatest relative importance (>0.95) are
maximum body size, minimum depth, and depth range. In comparison, geographic range (measured
as Extent of Occurrence) has a much lower relative importance (0.79, Figure 3), and in the predictive
models it improved the variance explained by 2% and the prediction accuracy by 1% (Table 3). The prob-
ability that a species is threatened increases by 1.2% for each 10 cm increase in maximum body length,
and decreases by 10.3% for each 50 m deepening in the minimum depth limit of species. After ac-
counting for maximum body size and minimum depth, species with narrower depth ranges have a
1.2% greater threat risk per 100 m narrowing of depth range. There is no signiﬁcant interaction
between depth range and minimum depth limit. Geographic range, measured as the Extent of
Occurrence, varies over six orders of magnitude, between 354 km2 and 278 million km2 and is positively
correlated with body size (Spearman’s
= 0.58), and hence is only marginally positively related to
extinction risk over and above the effect of body size. Accounting for the body size and depth effects,
the threat risk increases by only 0.5% for each 1,000,000 km2 increase in geographic range (Table 4).
The explanatory and predictive power of our life history and geographic distribution models increased
with complexity, though geographic range size contributed relatively little additional explanatory
power and a high degree of uncertainty in the parameter estimate (Tables 3 and 4). The maximum
variance explained was 69% (Table 4) and the predictive models (without controlling for phylogeny)
explained 30% of the variance and prediction accuracy was 77% (Table 3).
By habitat, one-quarter of coastal and continental shelf chondrichthyans (26.3%, n = 127 of 482) and
almost half of neritic and epipelagic species (43.6%, n = 17 of 39) are threatened. Coastal and continental
shelf and pelagic species greater than 1 m total length have a more than 50% chance of being threatened,
compared to ∼12% risk for a similar-sized deepwater species (Figure 5). While deepwater chondrichthyans,
due to their slow growth and lower productivity, are intrinsically more sensitive to overﬁshing than their
shallow-water relatives (García et al., 2008; Simpfendorfer and Kyne, 2009) for a given body size they are
less threatened—largely because they are inaccessible to most ﬁsheries (Figure 5).
As a result of their high exposure to coastal shallow-water ﬁsheries and their large body size, sawﬁshes
(Pristidae) are the most threatened chondrichthyan family and arguably the most threatened family of
marine ﬁshes (Figure 6). Other highly threatened families include predominantly coastal and continental
shelf-dwelling rays (wedgeﬁshes, sleeper rays, stingrays, and guitarﬁshes), as well as angel sharks and
thresher sharks; ﬁve of the seven most threatened families are rays. Least threatened families are com-
prised of relatively small-bodied species occurring in mesopelagic and deepwater habitats (lanternsharks,
catsharks, softnose skates, shortnose chimaeras, and kiteﬁn sharks, Figure 6, Figure 6—source data 1).
Geographic hotspots of threat and conservation priority by habitat
Local species richness is greatest in tropical coastal seas, particularly along the Atlantic and Western
Paciﬁc shelves (Figure 7A). The greatest uncertainty, where the number of DD species is highest, is
centered on four areas: (1) Caribbean Sea and Western Central Atlantic Ocean, (2) Eastern Central
Atlantic Ocean, (3) Southwest Indian Ocean, and (4) the China Seas (Figure 7B). The megadiverse
China Seas face the triple jeopardy of high threat in shallow waters (Figure 7CD), high species richness
(Figure 7A), and a large number of threatened endemic species (Figure 8), combined with high risk
due to high uncertainty in status (large number of DD species, Figure 7B). Whereas the distribution of
Table 3. Summary of predictive Generalized Linear Models for life history and ecological correlates of IUCN status
1∼maximum length 2 −227.479 459 43.67 0.000 0.678 0.139
2∼ …+ minimum depth 3 −210.299 426.7 11.34 0.003 0.746 0.243
3∼ …+…+ depth range 4 −204.703 417.5 2.19 0.25 0.762 0.276
4∼ …+…+…+ geographic range 5 −202.578 415.3 0 0.748 0.772 0.298
Species were scored as threatened (CR, EN, VU) = 1 or Least Concern (LC) = 0 for n = 367 marine species. AICc is the Akaike information criterion
corrected for small sample sizes and ΔAIC is the change in AICc. The models are ordered by increasing complexity and decreasing AIC weight (largest
ΔAIC to lowest), coefﬁcient of determination (R2), and prediction accuracy (measured using Area Under the Curve, AUC).
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 7 of 34
Figure 2. IUCN Red List Threat status and the depth distribution of chondrichthyans in the FAO Fishing Areas of the
Atlantic, Indian and Paciﬁc Oceans, and Polar Seas. Each vertical line represents the depth range (surface-ward minimum
to the maximum reported depth) of each species and is colored according to threat status: CR (red), EN (orange), VU
(yellow), NT (pale green), LC (green), and DD (gray). Species are ordered left to right by increasing median depth. The
depth limit of the continental shelf is indicated by the horizontal gray line at 200 m. The Polar Seas include the following
FAO Fishing Areas: Antarctic–Atlantic (Area 48), Indian (Area 58), Paciﬁc (Area 88), and the Arctic Sea (Area 18).
Figure 2. Continued on next page
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 8 of 34
threat in coastal and continental shelf chondrichthyans is similar to the overall threat pattern across
tropical and mid-latitudes, the spatial pattern of threat varies considerably for pelagic and deepwater
species. Threatened neritic and epipelagic oceanic sharks are distributed throughout the world’s
oceans, but there are also at least seven threat hotspots in coastal waters: (1) Gulf of California,
(2) southeast US continental shelf, (3) Patagonian Shelf, (4) West Africa and the western Mediterranean
Sea, (5) southeast South Africa, (6) Australia, and (7) the China Seas (Figure 7D). Hotspots of deepwater
threatened chondrichthyans occur in three areas where ﬁsheries penetrate deepest: (1) Southwest
Atlantic Ocean (southeast coast of South America), (2) Eastern Atlantic Ocean, spanning from Norway to
Namibia and into the Mediterranean Sea, and (3) southeast Australia (Figure 7E).
Hottest hotspots of threat and priority
Spatial conservation priority can be assigned using three criteria: (1) the greatest number of threatened
species (Figure 7A), (2) greater than expected threat (residuals of the relationship between total number
of species and total number of threatened species per cell, Figure 9), and (3) high irreplaceability—high
numbers of threatened endemic species (Figure 8). Most threatened marine chondrichthyans (n =
135 of 169) are distributed within, and are often
endemic to (n = 73), at least seven distinct threat
hotspots (e.g., for neritic and pelagic species
Figure 7D). With the notable exception of the US
and Australia, threat hotspots occur in the waters
of the most intensive shark and ray ﬁshing and
ﬁn-trading nations (Figure 1C). Accordingly
these regions should be afforded high scientiﬁc
and conservation priority (Table 5).
The greatest number of threatened species
coincides with the greatest richness (Figure 7A
vs 7C–E); by controlling for species richness we
can reveal the magnitude of threat in the pelagic
ocean and two coastal hotspots that have a
greater than expected level of threat: the Indo-
Paciﬁc Biodiversity Triangle and the Red Sea.
Throughout much of the pelagic ocean, threat is
greater than expected based on species richness
alone, species richness is low (n = 30) and a high
percentage (86%) are threatened (n = 16) or Near
Threatened (n = 10). Only four are of Least Concern
(Salmon shark Lamna ditropis, Goblin shark
Mitsukurina owstoni, Longnose pygmy Shark
Heteroscymnoides marleyi, and Largetooth
cookiecutter shark Isistius plutodus) (Figure 9). The
Indo-Paciﬁc Biodiversity Triangle, particularly the
Gulf of Thailand, and the islands of Sumatra, Java,
Borneo, and Sulawesi, is a hotspot of greatest
residual threat especially for coastal sharks and
rays with 76 threatened species (Figure 9). Indeed,
the Gulf of Thailand large marine ecosystem has
The following ﬁgure supplements are available for ﬁgure 2:
Figure supplement 1. Map of Food and Agriculture Organization of the United Nations Fishing Areas and their
codes: 18, Arctic Sea; 21, Atlantic, Northwest; 27, Atlantic, Northeast; 31, Atlantic, Western Central; 34, Atlantic,
Eastern Central; 37, Mediterranean and Black Sea; 41, Atlantic, Southwest; 47, Atlantic, Southeast; 48, Atlantic,
Antarctic; 51, Indian Ocean, Western; 57, Indian Ocean, Eastern; 58, Indian Ocean, Antarctic and Southern; 61,
Paciﬁc, Northwest; 67, Paciﬁc, Northeast; 71, Paciﬁc, Western Central; 77, Paciﬁc, Eastern Central; 81, Paciﬁc,
Southwest; 87, Paciﬁc, Southeast; and, 88, Paciﬁc, Antarctic.
Figure 2. Continued
Figure 3. Standardized effect sizes with 95% conﬁdence
intervals from the two best explanatory models of life
histories, geographic range and extinction risk in
chondrichthyans. The data were standardized by
subtracting the mean and dividing by one standard
deviation to allow for comparison among parameters.
The relative importance is calculated as the sum of the
Akaike weights of the models containing each variable.
Chondrichthyans were scored as threatened (CR, EN,
VU) = 1 or Least Concern (LC) = 0 for n = 367 marine
species. Threat status was modeled using General
Linear Mixed-effects Models, with size, depth and
geography treated as ﬁxed effects and taxonomy
hierarchy as a random effect to account for phyloge-
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 9 of 34
the highest threat density with 48 threatened
chondrichthyans in an area of 0.36 million km2.
The Red Sea residual threat hotspot has 29 threat-
ened pelagic and coastal species (Figure 9).
There are 15 irreplaceable marine hotspots that
harbor all 66 threatened endemic species (Figure 8;
Supplementary ﬁle 2C).
In a world of limited funding, conservation priori-
ties are often based on immediacy of extinction,
the value of biodiversity and conservation oppor-
tunity (Marris, 2007). In this study, we provide
the ﬁrst estimates of the threat status and hence
risk of extinction of chondrichthyans. Our system-
atic global assessment of the status of this lineage
that includes many iconic predators reveals a risky
combination of high threat (17% observed and
23.9% estimated), low safety (Least Concern, 23%
observed and >37% estimated), and high uncer-
tainty in their threat status (Data Deﬁcient, 46%
observed and 8.7% estimated). Over half of spe-
cies are predicted to be threatened or Near
Threatened (n = 561, 53.9%, Table 1). While no
species has been driven to global extinction—
as far as we know—at least 28 populations of
sawﬁshes, skates, and angel sharks are locally
or regionally extinct (Dulvy et al., 2003; Dulvy
and Forrest, 2010). Several shark species have
not been seen for many decades. The Critically
Endangered Pondicherry shark (Carcharhinus
hemiodon) is known only from 20 museum speci-
mens that were captured in the heavily-ﬁshed
inshore waters of Southeast Asia: it has not been
seen since 1979 (Cavanagh et al., 2003). The
now ironically-named and Critically Endangered
Common skate (Dipturus batis) and Common angel
shark (Squatina squatina) are regionally extinct
from much of their former geographic range in
European waters (Cavanagh and Gibson, 2007;
Gibson et al., 2008; Iglésias et al., 2010). The
Largetooth sawﬁsh (Pristis pristis) and Smalltooth
sawﬁsh (Pristis pectinata) are possibly extinct
throughout much of the Eastern Atlantic, particu-
larly in West Africa (Robillard and Séret, 2006;
Harrison and Dulvy, 2014).
Our analysis provides an unprecedented un-
derstanding of how many chondrichthyan species
are actually or likely to be threatened. A very high
percentage of species are DD (46%, 487 species);
that is one of the highest rates of Data Deﬁciency
of any taxon to date (Hoffmann et al., 2010). This
high level of uncertainty in status further elevates
risk and presents a key challenge for future assess-
ment efforts. We outline a ﬁrst step through our
estimation that 68 DD species are likely to be
Figure 4. Life history sensitivity, accessibility to ﬁsheries
and extinction risk. Probability that a species is
threatened due to the combination of intrinsic life
history sensitivity (maximum body size, cm total length,
TL) and accessibility to ﬁsheries which is represented as
minimum depth limit, depth range, and geographic
range size (Extent of Occurrence). The lines represent
the variation in body size-dependent risk for the upper
quartile, median, and lower quartile of each range
metric. The examplar species are all of similar maximum
body length and the difference in risk is largely due to
differences in geographic distribution. Chondrichthyans
were scored as threatened (CR, EN, VU) = 1 or Least
Concern (LC) = 0 for n = 366 marine species. The lines
are the best ﬁts from General Linear Mixed-effects
Models, with maximum body size and geographic
Figure 4. Continued on next page
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 10 of 34
threatened based on their life histories and distribu-
tion. Numerous studies have retrospectively
explained extinction risk, but few have made a
priori predictions of risk (Dulvy and Reynolds,
2002; Davidson et al., 2012). Across many taxa,
extinction risk has been shown to be a function of
an extrinsic driver or threat (Jennings et al.,
1998; Davies et al., 2006) and the corresponding
life history and ecological traits: large body size (low
intrinsic rate of population increase, high trophic level), small geographic range size, and ecological spe-
cialization. Maximum body size is an essential predictor of threat status, we presume because of the close
relationship between body size and the intrinsic rate of population increase in sharks and rays (Smith
et al., 1998; Frisk et al., 2001; Hutchings et al., 2012). Though we note that this proximate link may
be mediated ultimately through the time-related traits of growth and mortality (Barnett et al., 2013;
Juan-Jordá et al., 2013). Our novel contribution is to show that depth-related geographic traits are more
important for explaining risk than geographic range per se. The shallowness of species (minimum
depth limit) and the narrowness of their depth range are important risk factors (Figure 3). We hypoth-
esize that this is so because shallower species are more accessible to ﬁshing gears and those with
narrower depth ranges have lower likelihood that a proportion of the species distribution remains
beyond ﬁshing activity. For example, the Endangered Barndoor skate (Dipturus laevis) was elimi-
nated throughout much of its geographic range and depth distribution due to bycatch in trawl ﬁsh-
eries, yet may have rebounded because a previously unknown deepwater population component lay
beyond the reach of most ﬁsheries (Dulvy, 2000; Kulka et al., 2002; COSEWIC, 2010). We ﬁnd that
geographic range (measured as Extent of Occurrence) is largely unrelated to extinction risk. This is in
marked contrast to extinction risk patterns on land (Jones et al., 2003; Cardillo et al., 2005; Anderson
et al., 2011a) and in the marine fossil record (Harnik et al., 2012a, 2012b), where small geographic
range size is the principal correlate of extinction risk. We suggest that this is because ﬁshing activity is
now widespread throughout the world’s oceans (Swartz et al., 2010), and even species with the larg-
est ranges are exposed and often entirely encompassed by the footprint of ﬁshing activity. By contrast,
with a few exceptions (mainly eastern Atlantic slopes, Figure 7E), ﬁshing has a narrow depth pene-
tration and hence species found at greater depths can still ﬁnd refuge from exploitation (Morato et
al., 2006; Lam and Sadovy de Mitcheson, 2010).
The status of chondrichthyans is arguably among the worst reported for any major vertebrate lineage
considered thus far, apart from amphibians (Stuart et al., 2004; Hoffmann et al., 2010). The percentage
and absolute number of threatened amphibians is high (>30% are threatened), but a greater percentage
are Least Concern (38%), and uncertainty of status is lower (32% DD) than for chondrichthyans. Our
discovery of the high level of threat in freshwater chondrichthyans (36%) is consistent with the
emerging picture of the intense and unmanaged extinction risk faced by many freshwater and estuarine
species (Darwall et al., 2011).
Our threat estimate is comparable to other marine biodiversity status assessments, but our ﬁndings
caution that ‘global’ ﬁsheries assessments may be underestimating risk. The IUCN Global Marine Species
Assessment is not yet complete, but reveals varying threat levels among taxa and regions (Polidoro
et al., 2008, 2012). The only synoptic summary to-date focused on charismatic Indo-Paciﬁc coral reef
ecosystem species. Of the 1,568 IUCN-assessed marine vertebrates and invertebrates, 16% (range:
12–34% among families) were threatened (McClenachan et al., 2012). This is a conservative estimate
of marine threat level because although they may be more intrinsically sensitive to extinction drivers,
charismatic species are more likely to garner awareness of their status and support for monitoring and
conservation (McClenachan et al., 2012). The predicted level of chondrichthyan threat (>24%) is dis-
tinctly greater than that provided by global ﬁsheries risk assessments. These studies provide modeled
estimates of the percentage of collapsed bony ﬁsh (teleost) stocks in both data-poor unassessed ﬁsh-
eries (18%, Costello et al., 2012) and data-rich ﬁsheries (7–13%, Branch et al., 2011). This could be
because teleosts are generally more resilient than elasmobranchs (Hutchings et al., 2012), but in
addition we caution that analyses of biased geographic and taxonomic samples may be underesti-
mating risk of collapse in global ﬁsheries, particularly for species with less-resilient life histories.
Our work relies on consensus assessments by more than 300 scientists. However, given the
uncertainty in some of the underlying data that inform our understanding of threat status, such as
distribution traits treated as ﬁxed effects and taxonomy
hierarchy as a random effect to account for phylogenetic
non-independence. Each vertical line in each of the
‘rugs’ represents the maximum body size and Red List
status of each species: threatened (red) and LC (green).
Figure 4. Continued
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 11 of 34
ﬁsheries catch landings data, it is worth consid-
ering whether these uncertainties mean our
assessments are downplaying the true risk.
While there are methods of propagating uncer-
tainty through the IUCN Red List Assessments
(Akcakaya et al., 2000), in our experience this
approach was uninformative for even the best-
studied species, because it generated conﬁdence
intervals that spanned all IUCN Categories. Instead
it is worth considering whether our estimates of
threat are consistent with independent quantita-
tive estimates of status. The Mediterranean Red
List Assessment workshop in 2005 prompted
subsequent quantitative analyses of catch landings,
research trawl surveys, and sightings data.
Quantitative trends could be estimated for ﬁve
species suggesting they had declined by 96% to
>99.9% relative to their former abundance sug-
gesting they would meet the highest IUCN Threat
category of Critically Endangered (Ferretti et al.,
2008). By comparison the earlier IUCN regional
assessment for these species, while suggesting
they were all threatened, was more conservative
for two of the ﬁve species: Hammerhead sharks
(Sphyrna spp.)—Critically Endangered, Porbeagle
shark (Lamna nasus)—Critically Endangered,
Shortﬁn mako (Isurus oxyrinchus)—Critically
Endangered, Blue shark (Prionace glauca)—
Vulnerable, and Thresher shark (Alopias
We can also make a complementary comparison
to a recent analysis of the status of 112 shark and
ray ﬁsheries (Costello et al., 2012). The median
biomass relative to the biomass at Maximum
Sustainable Yield (B/BMSY) of these 112 shark and
ray ﬁsheries was 0.37, making them the most
overﬁshed groups of any of the world’s unassessed
ﬁsheries. Assuming BMSY occurs at 0.3 to 0.5 of
unexploited biomass then the median biomass of
shark and ray ﬁsheries had declined by between
81% and 89% by 2009. These biomass declines
would be sufﬁcient to qualify all of these 112
shark and ray ﬁsheries for the Endangered IUCN
category if they occurred within a three-generation
time span. By comparison our results are consider-
ably more conservative. Empirical analyses show
that an IUCN threatened category listing is trig-
gered only once teleost ﬁshes (with far higher den-
sity-dependent compensation) have been ﬁshed
down to below BMSY (Dulvy et al., 2005; Porszt et
al., 2012). Hence, our ﬁndings are consistent
with only around one-quarter of chondrichthyan
species having been ﬁshed down below the BMSY
target reference point. While there may be con-
cern that expert assessments may overstate
declines and threat, it is more likely that our con-
Figure 5. Life history, habitat, and extinction risk in
chondrichthyans. IUCN Red List status as a function of
maximum body size (total length, TL cm) and accessibility
to ﬁsheries in marine chondrichthyans in three main
habitats: coastal and continental shelf <200 m
(‘Continental shelf’); neritic and oceanic pelagic <200 m
(‘Pelagic’); and, deepwater >200 m (‘Deepwater’),
n = 367 (threatened n = 148; Least Concern n = 219).
The upper and lower ‘rug’ represents the maximum
Figure 5. Continued on next page
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 12 of 34
servative consensus-based approach has under-
stated declines and risk in sharks and rays.
For marine species, predicting absolute risk of
extinction remains highly uncertain because, even
with adequate evidence of severe decline, in many
instances the absolute population size remains
large (Mace, 2004). There remains considerable un-
certainty as to the relationship between census and
effective population size (Reynolds et al., 2005). Therefore, Red List categorization of chondrichthyans
should be interpreted as a comparative measure of relative extinction risk, in recognition that unmanaged
steep declines, even of large populations, may ultimately lead to ecosystem perturbations and eventually
biological extinction. The Red List serves to raise red ﬂags calling for conservation action, sooner rather
than later, while there is a still chance of recovery and of forestalling permanent biodiversity loss.
Despite more than two decades of rising awareness of chondrichthyan population declines and
collapses, there is still no global mechanism to ensure ﬁnancing, implementation and enforcement of
chondrichthyan ﬁshery management plans that is likely to rebuild populations to levels where they
would no longer be threatened (Lack and Sant, 2009; Techera and Klein, 2011). This management
shortfall is particularly problematic given the large geographic range of many species. Threat increased
only slightly when geographic range is measured as the Extent of Occurrence; however, geographic
range becomes increasingly important when it is measured as the number of countries (legal jurisdic-
tions) spanned by each species. The proportion of species that are threatened increases markedly with
geographic size measured by number of Exclusive Economic Zones (EEZs) spanned; one-quarter of
threatened species span the EEZs of 18 or more countries (Figure 10). Hence, their large geographic
ranges do not confer safety, but instead exacerbates risk because sharks and rays require coherent,
effective international management.
With a few exceptions (e.g., Australia and USA), many governments still lack the resources, expertise,
and political will necessary to effectively conserve the vast majority of shark and rays, and indeed many
body size and Red List status of each species: threat-
ened (upper rugs) and Least Concern (lower rugs). The
lines are best ﬁt using Generalized Linear Mixed-effects
Models with 95% conﬁdence intervals (Table 9).
Figure 5. Continued
Figure 6. Evolutionary uniqueness and taxonomic conservation priorities. Threat among marine chondrichthyan families varies with life history sensitivity
(maximum length) and exposure to ﬁsheries (depth distribution). (A) Proportion of threatened data sufﬁcient species and the richness of each taxonomic
family. Colored bands indicate the signiﬁcance levels of a one-tailed binomial test at p=0.05, 0.01, and 0.001. Those families with signiﬁcantly greater
(or lower) than expected threat levels at p<0.05 against a null expectation that extinction risk is equal across families (35.6%). (B) The most and least
threatened taxonomic families. (C) Average life history sensitivity and accessibility to ﬁsheries of 56 chondrichthyan families. Signiﬁcantly greater
(or lower) risk than expected is shown in red (green).
The following source data are available for ﬁgure 6:
Source data 1. Number and IUCN Red List status of chondrichthyan species in IUCN Red List categories by family (alphabetically within each order).
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 13 of 34
other exploited organisms (Veitch et al., 2012). More than 50 sharks are included in Annex I (Highly
Migratory Species) of the 1982 Law of the Sea Convention, implemented on the high seas under the
1992 Fish Stocks Agreement, but currently only a handful enjoy species-speciﬁc protections under the
world’s Regional Fishery Management Organizations (Table 6), and many of these have yet to be
implemented domestically. The Migratory Sharks Memorandum of Understanding (MoU) adopted by
the Parties to the Convention on Migratory Species (CMS) so far only covers seven sharks, yet there
may be more than 150 chondrichthyans that regularly migrate across national boundaries (Fowler,
2012). To date, only one of the United Nations Environment Programme’s Regional Seas Conventions,
the Barcelona Convention for the Conservation of the Mediterranean Sea, includes chondrichthyan
ﬁshes and only a few of its Parties have taken concrete domestic action to implement these listings.
Despite two decades of effort, only ten sharks and rays had been listed by the Convention on
International Trade in Endangered Species (CITES) up to 2013 (Vincent et al., 2014). A further seven
species of shark and ray were listed by CITES in 2013—the next challenge is to ensure effective imple-
mentation of these trade regulations (Mundy-Taylor and Crook, 2013). OSPAR (the Convention for
the Protection of the marine Environment of the North-East Atlantic) lists many threatened shark and
ray species, but its remit excludes ﬁsheries issues. Many chondrichthyans qualify for listing under CITES,
CMS, and various regional seas conventions, and should be formally considered for such action as a
complement to action by Regional Fisheries Management Organizations (RFMOs) (Table 6).
Bans on ‘ﬁnning’ (slicing off a shark’s ﬁns and discarding the body at sea) are the most widespread
shark conservation measures. While these prohibitions, particularly those that require ﬁns to remain
attached through landing, can enhance monitoring and compliance, they have not signiﬁcantly
reduced shark mortality or risk to threatened species (Clarke et al., 2013). Steep declines and the high
threat levels in migratory oceanic pelagic sharks suggest raising the priority of improved management
of catch and trade through concerted actions by national governments working through RFMOs
as well as CITES, and CMS (Table 7).
A high proportion of catch landings come from nations with a large number of threatened chon-
drichthyans and less-than-comprehensive chondrichthyan ﬁshery management plans. Future research
is required to down-scale these global Red List assessments and analyses to provide country-by-country
diagnoses of the link between speciﬁc ﬁsheries and speciﬁc threats to populations of more broadly
distributed species (Wallace et al., 2010). Such information could be used to focus ﬁsheries management
and conservation interventions that are tailored to speciﬁc problems. There is no systematic global
monitoring of shark and ray populations and the national ﬁsheries catch landings statistics provide
invaluable data for tracking ﬁsheries trends in unmanaged ﬁsheries (Newton et al., 2007; Worm
et al., 2013). However, the surveillance power of such data could be greatly improved if collected at
greater taxonomic resolution. While there have been continual improvements, catches are under-
reported (Clarke et al., 2006), and for those that are reported only around one-third is reported at the
Table 4. Summary of explanatory Generalized Linear Mixed-effect Models of the life history and
geographic distributional correlates of IUCN status
∼ maximum length 5 −197.06 404.3 28.31 0.000 0.32 0.58
∼ …+ minimum
6 −187.013 386.3 10.29 0.005 0.48 0.65
∼ …+…+ depth
7 −182.139 378.6 2.62 0.212 0.49 0.66
8 −179.785 376.0 0 0.784 0.69 0.80
Species were scored as threatened (CR, EN, VU) = 1 or Least Concern (LC) = 0 for n = 367 marine species. AICc is
the Akaike information criterion corrected for small sample sizes; ΔAIC is the change in AICc. The models are
ordered by increasing complexity and decreasing AIC weight (largest ΔAIC to lowest). R2GLMM(m) is the marginal
R2 of the ﬁxed effects only and R2GLMM(c) is the conditional R2 of the ﬁxed and random effects.
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 14 of 34
species level (Fischer et al., 2012). To comple-
ment improved catch landings data, we recom-
mend the development of repeat regional
assessments of the Red List Status of chondrich-
thyans to provide an early warning of adverse
changes in status and to detect and monitor the
success of management initiatives and inter-
ventions. Aggregate Red List Threat indices
for chondrichthyans, like those available for
mammals, birds, amphibians, and hard corals
(Carpenter et al., 2008) would provide one
of the few global scale indicators of progress
toward international biodiversity goals (Walpole
et al., 2009; Butchart et al., 2010).
Our global status assessment of sharks and
rays reveals the principal causes and severity of
global marine biodiversity loss, and the threat
level they face exposes a serious shortfall in the
conservation management of commercially-
exploited aquatic species (McClenachan et al.,
2012). Chondrichthyans have slipped through
the jurisdictional cracks of traditional national and
international management authorities. Rather
than accept that many chondrichthyans will inevi-
tably be driven to economic, ecological, or bio-
logical extinction, we warn that dramatic changes
in the enforcement and implementation of the
conservation and management of threatened
chondrichthyans are urgently needed to ensure
a healthy future for these iconic ﬁshes and the
ecosystems they support.
IUCN Red List Assessment process
and data collection
We applied the Red List Categories and Criteria
developed by the International Union for
Conservation of Nature (IUCN) (IUCN, 2004) to
1,041 species at 17 workshops involving more
than 300 experts who incorporated all available
information on distribution, catch, abundance,
population trends, habitat use, life histories,
threats, and conservation measures.
Some 105 chondrichthyan ﬁsh species had been
assessed and published in the 2000 Red List of
Threatened Species prior to the initiation of the
Global Shark Red List Assessment (GSRLA). These
assessments were undertaken by correspondence
and through discussions at four workshops
(1996—London, UK, and Brisbane, Australia;
1997—Noumea, New Caledonia, and 1999—
Pennsylvania, USA). These assessments applied earlier versions of the IUCN Red List Criteria and, where
possible, were subsequently reviewed and updated according to version 3.1 Categories and Criteria
during the GSRLA. The IUCN Shark Specialist Group (SSG) subsequently held a series of 13 regional and
thematic Red List workshops in nine countries around the world (Table 8). Prior to the workshops, each
Figure 7. Global patterns of marine chondrichthyan
diversity, threat and knowledge. (A) Total chondrichthyan
richness, (B) the number of Data Deﬁcient and threat
by major habitat: (C) coastal and continental shelf
(<200 m depth), (D) neritic and epipelagic (<200 m
depth), and (E) deepwater slope and abyssal plain
(>200 m) habitats. Numbers expressed as the total
number of species in each 23,322 km2 cell. The
numbers are hotspots refereed to in the text.
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 15 of 34
participant was asked to select species for assessment based on their expertise and research areas.
Where possible, experts carried out research and preparatory work in advance, thus enabling more syn-
thesis to be achieved during each workshop. SSG Red List-trained personnel facilitated discussion and
consensus sessions, and coordinated the production of global Red List Assessments for species in each
region. For species that had previously been assessed, participants provided updated information and
assisted in revised assessments. Experts completed assessments for some wide-ranging, globally distrib-
uted species over the course of several workshops. In total, 302 national, regional, and international
experts from 64 countries participated in the GSRLA workshops and the production of assessments. All
Red List Assessments were based on the collective knowledge and pooled data from dedicated experts
Figure 9. Spatial variation in the relative extinction risk of marine chondrichthyans. Residuals of the relationship
between total number of data sufﬁcient chondrichthyans and total number of threatened species per cell, where
positive values (orange to red) represent cells with higher threat than expected for their richness alone.
Figure 8. Irreplaceability hotspots of the endemic threatened marine chondrichthyans. Endemics were deﬁned as
species with an Extent of Occurrence of <500,000 km2 (n = 66). Irreplaceable cells with the greatest number of
small range species are shown in red, with blue cells showing areas of lower, but still signiﬁcant irreplaceability.
Irreplaceability is the sum of the inverse of the geographic range sizes of all threatened endemic species in the cell.
A value of 0.1 means that on average a single cell represents one tenth of the global range of all the species
present in the cell. The numbers are hotspots referred to in the text.
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 16 of 34
Table 5. Scientiﬁc and conservation priority according to threat, knowledge and endemicity by FAO Fishing Area
FAO Fishing Area
species (% of
total, n = 181)
species (% of
total, n = 487)
Number of endemic
endemics) Threatened endemic species
(1) Indian, Eastern 67 (37.0) 69 (14.2) 58 (5) Atelomycterus baliensis, Himantura ﬂuviatilis, Zearaja maugeana, Trygonorrhina melaleuca,
(2) Paciﬁc, Western Central 76 (42.0) 81 (16.6) 51 (14) Glyphis glyphis, Aulohalaelurus kanakorum, Hemitriakis leucoperiptera, Brachaelurus
colcloughi, Hemiscyllium hallstromi, H. strahani, Himantura hortlei, H. lobistoma, Pastinachus
solocirostris, Aptychotrema timorensis, Rhinobatos jimbaranensis, Rhynchobatus sp. nov. A,
Rhynchobatus springeri, Urolophus javanicus
(3) Paciﬁc, Northwest 48 (26.5) 116 (23.8) 80 (6) Benthobatis yangi, Narke japonica, Raja pulchra, Squatina formosa, S. japonica, S. nebulosa
(4) Indian, Western 61 (33.7) 104 (21.4) 62 (8) Carcharhinus leiodon, Haploblepharus kistnasamyi, H. favus, H. punctatus, Pseudoginglymostoma
brevicaudatum, Electrolux addisoni, Dipturus crosnieri, Okamejei pita
(5) Atlantic, Western Central 32 (17.7) 81 (16.6) 62 (4) Diplobatis colombiensis, D. guamachensis, D. ommata, D. pictus
(6) Paciﬁc, Southwest 34 (18.8) 49 (10.1) 28
(7) Atlantic, Southwest 52 (28.7) 52 (10.7) 37 (19) Galeus mincaronei, Schroederichthys saurisqualus, Mustelus fasciatus, M. schmitti, Atlantoraja
castelnaui, A. cyclophora, A. platana, Rioraja agassizii, Sympterygia acuta, Benthobatis kreffti,
Dipturus mennii, Gurgesiella dorsalifera, Rhinobatos horkelii, Zapteryx brevirostris, Rhinoptera
brasiliensis, Squatina argentina, S. guggenheim, S. occulta, S. punctata
(8) Atlantic, Southeast 37 (20.4) 51 (10.5) 13
(9) Atlantic, Eastern Central 42 (23.2) 44 (9.0) 6
(10) Paciﬁc, Southeast 26 (14.4) 67 (13.8) 32 (3) Mustelus whitneyi, Triakis acutipinna, T. maculata
(11) Paciﬁc, Eastern Central 20 (11.0) 52 (10.7) 19 (2) Urotrygon reticulata, U. simulatrix
(12) Atlantic, Northeast 33 (18.2) 23 (4.7) 8
(13) Atlantic, Northwest 22 (12.2) 17 (3.5) 3 (1) Malacoraja senta
(14) Mediterranean & Black Sea 34 (18.8) 16 (3.3) 3 (1) Leucoraja melitensis
(15) Paciﬁc, Northeast 9 (5.0) 11 (2.3) 0
(16) Indian, Antarctic 1 (0.6) 4 (0.8) 2
(17) Atlantic, Antarctic 1 (0.6) 4 (0.8) 2
(18) Paciﬁc, Antarctic 0 3 (0.6) 0
(19) Arctic Sea 0 0 0
Endemics were deﬁned as those species found only within a single FAO Fishing Area. FAO Fishing Areas were ranked according to greatest species richness, percent threatened species, percent Data
Deﬁcient species, number of endemic species and number of threatened endemic species.
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 17 of 34
Figure 10. Elevated threat in chondrichthyans with
the largest geographic ranges, spanning the
greatest number of national jurisdictions. Frequency
distribution of number of jurisdictions spanned by all
chondrichthyans (black, n = 1,041) and threatened
species only (red, n = 174), for (A) country EEZs, and
(B) the overrepresentation of threatened species
spanning a large number of country EEZs, shown by
the log ratio of proportion of threatened species
over the proportion of all species. The proportion of
threatened species is greater than the proportion of
all species where the log ratio = 0, which corre-
sponds to range spans of 16 and more countries.
across the world, ensuring global consultation
and consensus to achieve the best assessment for
each species with the knowledge and resources
available (‘Acknowledgements’). Any species
assessments not completed during the workshops
were ﬁnalized through subsequent correspondence
The SSG evaluated the status of all described
chondrichthyan species that are considered to
be taxonomically valid up to August 2011 (see
“Systematics, missing species and species cov-
erage” below). Experts compiled peer-reviewed
Red List documentation for each species, including
data on: systematics, population trends, geo-
graphic range, habitat preferences, ecology, life
history, threats, and conservation measures. The
SSG assessed all species using the IUCN Red List
Categories and Criteria version 3.1 (IUCN, 2001).
The categories and their standard abbreviations
are: Critically Endangered (CR), Endangered (EN),
Vulnerable (VU), Near Threatened (NT), Least
Concern (LC), and Data Deﬁcient (DD). Experts
further coded each species according to the
IUCN Habitats, Threats and Conservation Actions
Authority Files, enabling analysis of their habitat
preferences, major threats and conservation action
requirements. SSG Program staff entered all data
into the main data ﬁelds in the IUCN Species
Information Service Data Entry Module (SIS DEM)
and subsequently transferred these data into
the IUCN Species Information Service (SIS) in
Systematics, missing species and
The SSG collated data on order, family, genus,
species, taxonomic authority, commonly-used
synonyms, English common names, other common
names, and taxonomic notes (where relevant).
For taxonomic consistency throughout the spe-
cies assessments, the SSG followed Leonard J V
Compagno’s 2005 Global Checklist of Living
Chondrichthyan Fishes (Compagno, 2005), only deviating from this where there was extensive
opposing consensus with a clear and justiﬁable alternative, as adjudicated by the IUCN SSG’s Vice
Chairs of Taxonomy, David E Ebert and William T White.
Keeping pace with the total number of chondrichthyans is a challenging task, especially given the
need to balance immediacy against taxonomic stability. One-third of all species have been described
in the past thirty years. Scientists have described a new chondrichthyan species, on average, almost
every 2–3 weeks since the 1970s (Last, 2007; White and Last, 2012). Since Leonard V J Compagno
completed the global checklist in 2005, scientists have recognized an additional ∼140 species (mostly
new) living chondrichthyan species. This increase in the rate of chondrichthyan descriptions in recent
years is primarily associated with the lead up to the publication of a revised treatment of the entire
chondrichthyan fauna of Australia (Last and Stevens, 2009), requiring formal descriptions of previ-
ously undescribed taxa. In particular, three CSIRO special publications published in 2008 included
descriptions of 70 previously undescribed species worldwide (Last et al., 2008a, 2008b, 2008c). The
number of new species described in 2006, 2007 and 2008 was 21, 23, and 81, respectively, with all but
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 18 of 34
nine occurring in the Indo–West Paciﬁc. Additional nominal species of chondrichthyans are also
included following resurrection of previously unrecognized species such as the resurrection of
Pastinachus atrus for the Indo–Australian region, previously considered a synonym of P. sephen (Last
and Stevens, 1994). Scientists excluded some nominal species of dubious taxonomic validity from
this assessment. Thus, the total number of chondrichthyan species referred to in this paper (1,041)
does not include all recent new or resurrected species, which require future work for their inclusion in
Many more as yet undescribed chondrichthyan species exist. The chondrichthyan faunas in several
parts of the world (e.g., the northern Indian Ocean) are poorly known and a large number of species
are likely to represent complexes of several distinct species that require taxonomic resolution, for
example some dogﬁshes, skates, eagle rays, and stingrays (Iglésias et al., 2010; White and Last,
2012). Many areas of the Indian and Paciﬁc Oceans are largely unexplored and, given the level of
micro-endemism documented for a number of chondrichthyan groups, it is likely that many more
species will be discovered in the future (Last, 2007; Naylor et al., 2012). For example, recent surveys
of Indonesian ﬁsh markets revealed more than 20 new species of sharks out of the approximately 130
recorded in total (White et al., 2006; Last, 2007; Ward et al., 2008).
SSG experts created a shapeﬁle of the geographic
distribution for each chondrichthyan species with
GIS software using the standard mapping protocol
for marine species devised by the IUCN GMSA
team (http://sci.odu.edu/gmsa/). The map shows
the Extent of Occurrence of the species cut to
one of several standardized basemaps depending
on the ecology of the species (i.e., coastal and
continental shelf, pelagic and deepwater). The
distribution maps for sharks are based on orig-
inal maps provided by the FAO and Leonard JV
Compagno. Maps for some of the batoids were
originally provided by John McEachran. New maps
for recently described species were drafted
where necessary. The original maps were updated,
corrected, or veriﬁed by experts at the Red List
workshops or out-of-session assessors and SSG
staff and then sent to the GMSA team who mod-
iﬁed the shapeﬁles and matched them to the dis-
tributional text within the assessment.
Occurrence and habitat preference
SSG assessors assigned countries of occurrence
from the ‘geographic range’ section of the Red
List documentation and classiﬁed species to the
FAO Fishing Areas (http://www.iucnredlist.org/
technical-documents/data-organization) in which
they occur (Figure 2—ﬁgure supplement 1).
Each species was coded according to the IUCN
Habitats Authority File (http://www.iucnredlist.
habitats-classiﬁcation-scheme-ver3). These cate-
gorizations are poorly developed and often irrel-
evant for coastal and offshore marine animals.
For the purposes of analysis presented here we
assigned chondrichthyans to ﬁve unique habi-
tat-lifestyle combinations (coastal and continental
shelf, pelagic, meso- and bathypelagic, deepwater,
Table 6. Progress toward regional and
international RFMO management measures
for sharks and rays
1. Bans on ‘ﬁnning’ (the removal of a shark’s ﬁns and
discarding the carcass at sea) through most RFMOs
(Fowler and Séret, 2010);
2. North East Atlantic Fisheries Commission (NEAFC)
bans on directed ﬁshing for species not actually
targeted within the relevant area (Spiny dogﬁsh
[Squalus acanthias], Basking shark [Cetorhinus
maximus], Porbeagle shark [Lamna nasus]) (NEAFC,
3. Convention on the Conservation of Antarctic Marine
Living Resources bans on ‘directed’ ﬁshing for skates
and sharks and bycatch limits for skates (CCMLR,
4. A Northwest Atlantic Fisheries Organization (NAFO)
skate quota (note: this has consistently been set higher
than the level advised by scientists since its
establishment in 2004) (NAFO, 2011);
5. International Commission for the Conservation of
Atlantic Tunas (ICCAT) bans on retention,
transshipment, storage, landing, and sale of Bigeye
Thresher (Alopias superciliosus), and Oceanic whitetip
shark (Carcharhinus longimanus), and partial bans
(developing countries excepted under certain
circumstances) on retention, transshipment, storage,
landing, and sale of most hammerheads (Sphyrna spp.),
and retention, transshipment, storage, and landing (but
not sale) of Silky shark (Carcharhinus falciformis) (Kyne
et al., 2012);
6. An Inter-American Tropical Tuna Commission (IATTC)
ban on retention, transshipment, storage, landing, and
sale of Oceanic whitetip sharks (IATTC, 2011);
7. An Indian Ocean Tuna Commission (IOTC) ban on
retention, transshipment, storage, landing, and sale of
thresher sharks-with exceptionally low compliance and
reportedly low effectiveness (IOTC, 2011); and,
8. A Western and Central Paciﬁc Fisheries Commission
ban on retention, transshipment, storage, and landing
(but not sale) of Oceanic whitetip sharks (Clarke et al.,
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 19 of 34
and freshwater) mainly according to depth distri-
bution and, to a lesser degree, position in the
water column. The pelagic group includes both
neritic (pelagic on the continental shelf) and epi-
pelagic oceanic (pelagic in the upper 200 m of
water over open ocean) species. Species habitats
were classiﬁed based on the ﬁndings from the
workshops combined with a review of the pri-
mary literature, FAO ﬁsheries guides and ﬁeld
guides (Cavanagh et al., 2003; Cavanagh and
Gibson, 2007; Cavanagh et al., 2008; Gibson
et al., 2008; Camhi et al., 2009; Kyne et al.,
2012). Species habitat classiﬁcations tended to
be similar across families, but for some species
the depth distributions often spanned more than
one depth category and for these species habitat
was assigned according to the predominant loca-
tion of each species throughout the majority of its
life cycle (Compagno, 1990). This issue was mainly
conﬁned to coastal and continental shelf species
that exhibited distributions extending down the
continental slopes (e.g., some Dasyatis, Mustelus,
Rhinobatos, Scyliorhinus, Squalus, and Squatina).
We caution that some of the heterogeneity in
depth distribution or unusually large distributions
may reﬂect taxonomic uncertainty and the exist-
ence of species complexes (White and Last,
2012). We deﬁned the deep sea as beyond the
continental and insular shelf edge at depths greater
than or equal to 200 m. Coastal and continental
shelf includes predominantly demersal species
(those spending most time dwelling on or near
the seabed), and excluded neritic chondrichthy-
ans. Pelagic species included macrooceanic and
tachypelagic ocean-crossing epipelagic sharks
with circumglobal distributions as well as sharks
suspected of ocean-crossing because they exhibit
circumglobal but disjunct distributions, for example
Galapagos shark (Carcharhinus galapagensis).
Our classiﬁcation resulted in a total of 33 obli-
gate freshwater and 1,008 marine and euryhaline
chondrichthyans of which 482 species were found
predominantly in coastal and continental shelf, 39
in pelagic, 479 in deepwater, and eight in meso-
and bathypelagic habitats. To evaluate whether
the geographic patterns of threat are robust to
alternate unique or multiple habitat classiﬁcations
we considered two alternate classiﬁcation schemes,
one where species were classiﬁed into a single
habitat and another where species were classiﬁed
in one or more habitats. The alternate unique
classiﬁcation scheme yielded 42 pelagic (Camhi
et al., 2009), and 452 deepwater chondrichthyans
(Kyne and Simpfendorfer, 2007), leaving 517
coastal and continental shelf and 33 obligate
freshwater species (totaling 1,044, based on an
Table 7. Management recommendations: the
following actions would contribute to rebuilding
threatened chondrichthyan populations and
properly managing associated ﬁsheries
Fishing nations and regional ﬁsheries management
organizations (RFMOs) are urged to:
1. Implement, as a matter of priority, scientiﬁc advice
for protecting habitat and/or preventing overﬁshing of
2. Draft and implement Plans of Action pursuant to
the International Plan Of Action (IPOA–Sharks), which
include, wherever possible, binding, science-based
management measures for chondrichthyans and their
3. Signiﬁcantly increase observer coverage,
monitoring, and enforcement in ﬁsheries taking
4. Require the collection and accessibility of
species-speciﬁc chondrichthyan ﬁsheries data, including
discards, and penalize non-compliance;
5. Conduct population assessments for
6. Implement and enforce chondrichthyan ﬁshing
limits in accordance with scientiﬁc advice; when
sustainable catch levels are uncertain, set limits based
on the precautionary approach;
7. Strictly protect chondrichthyans deemed
exceptionally vulnerable through Ecological Risk
Assessments and those classiﬁed by IUCN as Critically
Endangered or Endangered;
8. Prohibit the removal of shark ﬁns while onboard
ﬁshing vessels and thereby require the landing of sharks
with ﬁns naturally attached; and,
9. Promote research on gear modiﬁcations, ﬁshing
methods, and habitat identiﬁcation aimed at mitigating
chondrichthyan bycatch and discard mortality.
National governments are urged to:
10. Propose and work to secure RFMO management
measures based on scientiﬁc advice and the
11. Promptly and accurately report species-speciﬁc
chondrichthyan landings to relevant national and
12. Take unilateral action to implement domestic
management for ﬁsheries taking chondrichthyans,
including precautionary limits and/or protective status
where necessary, particularly for species classiﬁed by
IUCN as Vulnerable, Endangered or Critically
Endangered, and encourage similar actions by other
13. Adopt bilateral ﬁshery management agreements
for shared chondrichthyan populations;
14. Ensure active membership in Convention on
International Trade in Endangered Species (CITES),
Convention for the Conservation of Migratory
Species (CMS), RFMOs, and other relevant regional
and international agreements;
15. Fully implement and enforce CITES
chondrichthyan listings based on solid non-detriment
ﬁndings, if trade in listed species is allowed;
Table 7. Continued on next page
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 20 of 34
older taxonomic scheme). When species were
classiﬁed in more than one habitat this resulted in
513 species in the coastal and continental shelf,
564 in deepwater, 54 in pelagic, and 13 meso-
and bathypelagic habitats. We found the geo-
graphic pattern of threat was robust to the choice
of habitat classiﬁcation scheme, and we present
only the unique classiﬁcation (482 coastal and
continental shelf, 39 pelagic, 479 deepwater hab-
SSG assessors coded each species according to
the IUCN Major threat Authority File (http://www.
We coded threats that appear to have an important impact, but did not describe their relative impor-
tance for each species.
The term ‘bycatch’ and its usage in the IUCN Major threat Authority File do not capture the
complexity and values of chondrichthyan ﬁsheries. Some chondrichthyans termed ‘bycatch’ are actually
caught as ‘incidental or secondary catch’ as they are used to a similar extent as the target species or
are sometimes highly valued or at least welcome when the target species is absent. ‘Unwanted bycatch’
refers to cases where the chondrichthyans are not used and ﬁshers would prefer to avoid catching
them (Clarke, S personal communication, Sasama Consulting, Shizuoka, Japan). If the levels of unwanted
bycatch are severe enough, chondrichthyans can be actively persecuted to avoid negative and costly
gear interactions—such as caused the near extirpation of the British Columbian population of Basking
shark (Cetorhinus maximus) (Wallace and Gisborne, 2006).
Red List Assessment
We assigned a Red List Assessment category for each species based on the information above using
the revised 2001 IUCN Red List Categories and Criteria (version 3.1; http://www.iucnredlist.org/technical-
documents/categories-and-criteria). We provided a rationale for each assessment justifying the classi-
ﬁcation along with a description of the relevant criteria used in the designation. Data ﬁelds also present
the reason for any change in Red List categories from previous assessments (i.e., genuine change in
status of species, new information on the species available, incorrect data used in previous assessments,
change in taxonomy, or previously incorrect criteria assigned to species); the current population trend
(i.e., increasing, decreasing, stable, unknown); date of assessment; names of assessors and evaluators
(effectively the peer-reviewers); and any notes relevant to the Red List category. The Red List docu-
mentation for each species assessment is supported by references to the primary and secondary literature
cited in the text.
Data entry, review, correction, and consistency checking
Draft regional Red List Assessments and supporting data were collated and peer-reviewed during the
workshops and through subsequent correspondence to produce the global assessment for each
species. At least one member of the SSG Red List team was present at each of the workshops to facilitate
a consistent approach throughout the data collection, review and evaluation process. Once experts
had produced draft assessments, SSG staff circulated summaries (comprised of rationales, Red List
Categories and Criteria) to the entire SSG network for comment. As the workshops took place over a
>10-year period, some species assessments were reviewed and updated at subsequent workshops or
by correspondence. Each assessment received a minimum of two independent evaluations as a part of
the peer-review process, either during or subsequent to the consensus sessions (a process involving
65 specialists and experts across 23 participating countries) prior to entry into the database and
submission to the IUCN Red List Unit.
SSG Red List-trained personnel undertook further checks of all assessments to ensure consistent
application of the Red List Categories and Criteria to each species, and the then SSG Co-chair Sarah
L Fowler, thoroughly reviewed every assessment produced from 1996 to 2009. Following the data
review and evaluation process, all species assessments were entered in the Species Information Service
database and checked again by SSG Red List Unit staff. IUCN Red List Program staff made the ﬁnal
16. Propose and support the listing of additional
threatened chondrichthyan species under CITES and
CMS and other relevant wildlife conventions;
17. Collaborate on regional agreements and the CMS
migratory shark Memorandum of Understanding (CMS,
2010), with a focus on securing concrete conservation
18. Strictly enforce chondrichthyan ﬁshing and
protection measures and impose meaningful penalties
Table 7. Continued
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 21 of 34
check prior to the acceptance of assessments in the Red List database and publication of assessments
and data online (http://www.iucnredlist.org/).
Subpopulation and regional assessments
We included only global species assessments in this analysis. In many cases, subpopulation and
regional assessments were developed for species before a global assessment could be made. For very
wide-ranging species, such as the oceanic pelagic sharks, a separate workshop was held to combine
these subpopulation or regional assessments (Table 8). A numerical value was assigned to each threat
category in each region where the species was assessed, and where possible these values were then
averaged to calculate a global threat category (Gärdenfors et al., 2001). Hence, the Red List categories
of some species may differ regionally; for example, porbeagle shark (Lamna nasus) is classiﬁed as VU
globally, but CR in the Northeast Atlantic and Mediterranean Sea. Often population trends were not
available across the full distribution of a species. In these cases, the degree to which the qualifying
threshold was met was modiﬁed according to the degree of certainty with which the trend could be
extrapolated across the full geographic range of a species. The calculation of the overall Red List cat-
egory for globally distributed species is challenging, particularly when a combination of two or more
of the following issues occurs: (1) trend data are available only for a part of the geographic range;
(2) regional trend data or stock assessments are highly uncertain; (3) the species is data-poor in some
other regions; (4) the species is subject to some form of management in other regions; and, (5) the
species is moderately productive (Dulvy et al., 2008). This situation is typiﬁed by the Blue shark
(Prionace glauca) that faces all of these issues. The best abundance trend data come from the
Atlantic Ocean, but the different time series available occasionally yield conﬂicting results; surveys
of some parts of the Atlantic exhibit declines of 53–80% in less than three generations (Dulvy et al.,
2008; Gibson et al., 2008), while a 2008 stock assessment conducted for the International Commission
for the Conservation of Atlantic Tuna (ICCAT) indicate, albeit with substantial uncertainty, that the
North Atlantic Blue shark population biomass is still larger than that required to generate Maximum
Sustainable Yield (BMSY) (Gibson et al., 2008). The Blue shark is one of the most productive of the
oceanic pelagic sharks, maturing at 4–6 years of age with an annual rate of population increase of
∼28% per year and an approximate BMSY at ∼42% of virgin biomass, B0 (Cortés, 2008; Simpfendorfer
et al., 2008). While the available data may support the regional listing of the Atlantic population
of this species in a threatened category, the assessors could not extrapolate this to the global
distribution because the species may be subject to lower ﬁshing mortality in other regions. Hence
the Blue shark was listed as NT globally. Further details on this issue and additional data require-
ments to improve the assessment and conservation of such species are considered elsewhere
(Gibson et al., 2008; Camhi et al., 2009).
Red Listing marine ﬁshes
We assessed most threatened chondrichthyans (81%, n = 148 of 181) using the Red List popula-
tion reduction over time Criterion A. Only one of the threatened species, the Skate (Dipturus) was
assessed under the higher decline thresholds of the A1 criterion, where ‘population reduction in
the past, where the causes are clearly reversible AND understood AND have ceased’. The remaining
threatened species were assessed using the IUCN geographic range Criterion B (n = 29) or the
small population size and decline Criterion C (n = 4: Borneo shark Carcharhinus borneensis,
Colclough’s shark Brachaelurus colcloughi, Northern river shark Glyphis garricki, and Speartooth
shark Glyphis glyphis). The Criterion A decline assessments were based on statistical analyses and
critical review of a tapestry of local catch per unit effort trajectories, ﬁsheries landings trajectories
(often at lower taxonomic resolution), combined with an understanding of ﬁsheries selectivity and
We assessed most chondrichthyans using the Red List criterion based on population reduction
over time (Criterion A). The original thresholds triggering a threatened categorization were
Criterion A1: VU 20%; EN 50%; and CR 80% decline over the greater of the past (A1) or future (A2)
10 years or three generations (IUCN Categories and Criteria version 2.3). IUCN raised these
thresholds in 2001 to VU, ≥30%; EN, ≥50%; and CR, ≥80% decline over the greater of 10 years or
three generations in the past (A2), future (A3) and ongoing (A4), and changed A1 to a reduction
over the past 10 yrs or 3 generations of VU ≥50%; EN ≥70%; CR ≥90%, where the causes of reduction
are understood AND have ceased AND are reversible. This was in response to concerns that the
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 22 of 34
original thresholds were too low for managed populations that are being deliberately ﬁshed down
to MSY (typically assumed to be 50% of virgin biomass under Schaeffer logistic population growth)
(Reynolds et al., 2005). This revision was designed to improve consistency between ﬁsheries limit
reference points and IUCN thresholds reducing the likelihood of false alarms—where a sustainably
exploited species incorrectly triggers a threat listing (Dulvy et al., 2005; Porszt et al., 2012).
Empirical testing shows that this has worked and demonstrates that a species exploited at ﬁshing
mortality rates consistent with achieving MSY (FMSY) would lead to decline rates that would be
unlikely to be steep enough to trigger a threat categorization under these new thresholds (Dulvy
et al., 2005).
It is incontrovertible that a species that has declined by 80% over the qualifying time period is
at a greater relative risk of extinction than another that declined by 40% (in the same period).
Regardless, there may be a wide gap in the population decline trajectory between the point at
which overﬁshing occurs and the point where the absolute risk of extinction becomes a real con-
cern (Musick, 1999a). In addition, ﬁsheries scientists have expressed concern that decline criteria
designed for assessing the extinction risk of a highly productive species may be inappropriate for
species with low productivity and less resilience (Musick, 1999a), although this was addressed
with the use of generation times to rescale decline rates to make productivity comparable (Reynolds
et al., 2005; Mace et al., 2008). In response to concerns that IUCN decline thresholds are too low
and risk false alarms, the American Fisheries Society (AFS) developed alternate decline criteria
(Musick, 1999a) to classify North American marine ﬁsh populations (Musick et al., 2000). This
approach only categorizes species that have undergone declines of 70–99% over the greater of
three generations or 10 years. Nonetheless, most of the species so listed by AFS also appear on
the relevant IUCN Specialist Group lists and vice versa, although the risk categories are slightly
different. The reason for the concordance is that in most instances the decline had far exceeded
50% over the appropriate timeframe long before it was detected. Consequently, SSG scientists
generally agreed in assigning threat categories to species that had undergone large declines, but
many were reluctant to assign a VU classiﬁcation to species that were perceived to be at or near
50% virgin population levels and presumably near BMSY
. In practice, the latter were usually classiﬁed as
NT unless other circumstances (highly uncertain data, combined with widespread unregulated
ﬁsheries) dictated a higher level of threat according to the precautionary principle.
Table 8. The locations, dates, number of participants and the number of countries represented at
each of the SSG Red List workshops, along with unique totals
Red List workshop Location Date Participants Countries
Australia and Oceania Queensland, Australia March 2003 26 5
South America Manaus, Brazil June 2003 25 8
Sub-equatorial Africa Durban, South Africa September 2003 28 9
Mediterranean San Marino October 2003 29 15
Deep sea sharks Otago Peninsula,
November 2003 32 11
North and Central
Florida, USA June 2004 55 13
Batoids (skates and
Cape Town, South Africa September 2004 24 11
Expert Panel Review Newbury, UK March 2005 12 5
Northeast Atlantic Peterborough, UK February 2006 25 9
West Africa Dakar, Senegal June 2006 25 12
Expert Panel Review Newbury, UK July 2006 9 12
Pelagic sharks Oxford, UK February 2007 18 11
Batangas, Philippines June/July 2007 23 13
Totals 227 57
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 23 of 34
Modeling correlates of threat
Vulnerability to population decline or extinction is a function of the combination of the degree to
which intrinsic features of a species’ behavior, life history and ecology (sensitivity) may reduce the
capacity of a species to withstand an extrinsic threat or pressure (exposure). We tested the degree to
which intrinsic life histories and extrinsic ﬁshing activity inﬂuenced the probability that a chondrichthyan
species was threatened. Threat category was modeled as a binomial response variable; with LC
species assigned a score of 0, and VU, EN & CR species assigned a 1. We used maximum body length
(cm), geographic range size (Extent of Occurrence, km2), and depth range (maximum–minimum depth,
m) as indices of intrinsic sensitivity, and minimum depth (m) and mean depth (maximum–minimum
depth/2) as a measure of exposure to ﬁshing activity. All variables were standardized to z-scores by
subtracting the mean and dividing by the standard deviation to minimize collinearity (variance inﬂation
factors were less than 2). Mean depth was not included in model evaluation as it was computed from,
and hence, correlated to minimum depth (Spearman’s
= 0.52). We ﬁtted Generalized Linear Mixed-
effect Models with binomial error and a logit link to model the probability of a species being threatened,
using taxonomic structure as a nested random effect (e.g., order/family/genus) to account for phylo-
genetic non-independence. The probability of a species i being threatened was assumed to be binomially
distributed with a mean pi, such that the linear predictor of pi was:
0 , , , ,
log = ,
i j i j i k i k
β β β
i,k are the ﬁtted coefﬁcients for life history or geographic range traits j and k, and Xi,j and
Xi,k are the trait values of j and k for species i (Tables 4 and 9). The effect of a one standard deviation
increase in the coefﬁcient of interest was computed as:
0 1 0 1
1/ 1 exp 1/ 1 ( ,)exp * 2
β β β β
─+ + + +
following (Gelman and Hill, 2006). Models were ﬁtted using the lmer function in the R package lme4
(Bates et al., 2011). The amount of variance explained by the ﬁxed effects only and the combined
ﬁxed and random effects of the binomial GLMM models was calculated as the marginal R2GLMM(m)
and conditional R2GLMM(c), respectively, using the methods described by Nagakawa and Schlielzeth
Estimating the proportion of potentially threatened DD species
We predicted the number of Data Deﬁcient species that are potentially threatened based on the
maximum body size and geographic distribution traits (Table 3; Supplementary ﬁle 1). Speciﬁcally,
based on the explanatory models described above, all variables were log10 transformed and we
ﬁtted generalized linear models of increasing complexity assuming a binomial error and logit link
(Equation 2; Table 3). Model performance was evaluated using Receiver Operating Characteristics
by comparing the predicted probability that the species was threatened p(THR) against the true
observed status (Least Concern = 0, and threatened [VU, EN & CR] = 1) (Sing et al., 2005; Porszt
et al., 2012). The prediction accuracy was calculated as the Area Under the Curve (AUC) of the
relationship between false positive rates and true positive rates, where a false positive is a model
prediction of ≥0.5 and true observed status is 0 (or <0.5 and 1) and a true positive is a prediction of
≥0.5 and true observed status is 1 (or <0.5 and 0). True and false positive rates, and accuracy (AUC)
were calculated using the R package ROCR (Sing et al., 2005). The probability that a DD species
was threatened p(THR)DD was predicted based on the available life history and distributional traits.
DD species with p(THR)DD ≥ 0.5 were classiﬁed as threatened and <0.5 as Least Concern. This
optimum classiﬁcation threshold was conﬁrmed by comparing accuracy across the full range of possible
thresholds (from 0 to 1). We ﬁtted models using the gls function and calculated pseudo-R2 using the
With these models we can estimate the number and proportion of species in each category
(Table 1). We estimated that 68 of 396 DD species are potentially threatened, and hence the
remainder (396–68 = 328) is likely to be either Least Concern or Near Threatened. Assuming these
species are distributed between these categories according to the observed ratio of NT:LC species of
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 24 of 34
0.5477 this results in a total of 312 (29.9%) Near Threatened species (132 known + 180 estimated)
and 389 (37.4%) Least Concern species (241 known +148 estimated). After apportioning the DD
species among threatened (68), NT (312), and LC (389), only 91 (8.7%; 487–396) are likely to be
Data Deﬁcient (Table 1).
The SSG and the GMSA created ArcGIS distribution maps as polygons describing the geographical
range of each chondrichthyan depending on the individual species’ point location and depth informa-
tion. Pelagic species distribution maps were digitized by hand from the original map sources. For spatial
analyses, we merged all species maps into a single shapeﬁle. We mapped species using a hexagonal grid
composed of individual units (cells) that retain their shape and area (∼23,322 km2) throughout the globe.
Speciﬁcally, we used the geodesic discrete global grid system, deﬁned on an icosahedron and projected
to the sphere using the inverse Icosahedral Snyder Equal Area (ISEA) (Sahr et al., 2003). A row of cells
near longitude 180°E/W was excluded, as these interfered with the spatial analyses (Hoffmann et al.,
2010). Because of the way the marine species range maps are buffered, the map polygons are likely
to extrapolate beyond known distributions, especially for any shallow-water, coastal species, hence
not only will range size itself likely be an overestimate, but so will the number of hexagons.
We excluded obligate freshwater species from the ﬁnal analysis as their distribution maps have yet
to be completed. The maps of the numbers of threatened species represent the sum of species that
have been globally assessed as threatened, in IUCN Red List categories VU, EN or CR, existing in each
∼23,322 km2 cell. We caution that this should not be interpreted to mean that species existing within
that grid cell are necessarily threatened in this speciﬁc location, rather that this location included species
that are threatened, on average, throughout their Extent of Occurrence. The number of threatened
species was positively related to the species richness of cells (F1, 14,846 = 1.5 e5, p<0.001, r2 = 0.91). To
remove this ﬁrst-order effect and reveal those cells with greater and lower than expected extinction
risk, we calculated the residuals of a linear regression of the number of threatened species on the
number of non-DD species (referred to as data sufﬁcient species). Cells with positive residuals were
mapped to show areas of greater than expected extinction risk compared to cells with equal or
negative residuals. Hexagonal cell information was converted to point features and smoothed across
neighboring cells using ordinary kriging using a spherical model in the Spatial Analyst package of ArcView.
Such smoothing can occasionally lead to contouring artefacts, such as the yellow wedge west of southern
Africa in Figure 7D, and we caution against over-interpreting marginal categorization changes.
We identiﬁed hotspots of threatened endemic chondrichthyans to guide conservation priorities. To
describe the potential cost of losing unique chondrichthyan faunas, we calculated irreplaceability
scores for each cell. Irreplaceability scores were calculated for each species as the reciprocal of its area
of occupancy measured as the number of cells occupied. For example, for a species with an Extent of
Occurrence spanning 100 hexagons, each hexagon in its range would have an irreplaceability 1/100 or
0.01 in each of the 100 hexagons of its Extent of Occurrence. The irreplaceability of each cell was
calculated by averaging log10 transformed irreplaceability scores of each species in each cell. Averaging
irreplaceability scores controls for varying species richness across cells. We calculated irreplaceability
both for all chondrichthyans and for threatened species only. Irreplaceability was also calculated using
only endemic threatened species, whereby endemicity was deﬁned as species having an Extent of
Occurrence of <50,000, 100,000, 250,000 or 500,000 km2. Different deﬁnitions of endemicity gave
similar patterns of irreplaceability and we present the results of only the largest-scale deﬁnition of
endemicity. Hence the irreplaceability of threatened species and particularly the threatened endemic
chondrichthyans represents those locations or ‘hotspots’ (Myers et al., 2000) at greatest risk of losing
the most unique chondrichthyan biodiversity.
Fisheries catch landings and shark ﬁn exports to Hong Kong
We extracted chondrichthyan landings reported to FAO by 146 countries and territories from a total
of 128 countries (as some chondrichthyan ﬁshing nations are overseas territories, unincorporated
territories, or British Crown Dependencies) from FishStat (FAO, 2011). We categorized landings into
153 groupings, comprised of 128 species-speciﬁc categories (e.g., angular roughshark, piked dogﬁsh,
porbeagle, Patagonian skate, plownose chimaera, small-eyed ray, etc) and 25 broader nei (nei = not
elsewhere included) groupings (e.g., such as various sharks nei, threshers sharks nei, ratﬁshes nei, raja
rays nei). For each country, all chondrichthyan landings in metric tonnes (t) were averaged over the decade
2000–2009. Landings reported as ‘<0.5’ were assigned a value of 0.5 t. Missing data reported as ‘.’ were
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 25 of 34
Table 9. Continued on next page
Table 9. Parameter estimates for General Linear Mixed-effects Models testing the probability that a
species is threatened p(THR) given either categorical habitat class or continuous measure of depth
distribution and maximum size
(A) Habitat category
p(THR) = maximum length + habitat category, random effect = Order/Family/Genus
Fixed effects Standardized coefﬁcient Standard error p-value
Intercept (Coastal and
0.27 0.33 0.4
Deepwater −2.01 0.39 <0.001
Pelagic −0.46 0.94 0.62
Maximum length 2.59 0.69 <0.001
marginal R2GLMM(m) of ﬁxed effects only = 0.40.
conditional R2GLMM(c) of ﬁxed and random effects = 0.60.
ΔAIC without taxonomic inclusion = −18.7.
ΔAIC for differing threat metrics: binomial THR (CR + EN + VU + NT) = −165.7; categorical = −975.6.
(B) Minimum depth
p(THR) = maximum length + minimum depth, random effect = Order/Family/Genus
Fixed effects Standardized coefﬁcient Standard error p-value
Intercept −0.74 0.31 0.015
Minimum depth −2.73 0.78 <0.001
Maximum length 2.46 0.61 0.002
marginal R2GLMM(m) of ﬁxed effects only = 0.48.
conditional R2GLMM(c) of ﬁxed and random effects = 0.64.
ΔAIC without taxonomic inclusion = −12.9.
ΔAIC for differing threat metrics: binomial THR (CR + EN + VU + NT) = −153.4; categorical = −985.8.
(C) Maximum depth
p(THR) = maximum depth + maximum length, random effect = Order/Family/Genus
Fixed effects Standardized coefﬁcient Standard error p-value
Intercept −0.60 0.28 <0.001
Maximum depth −2.35 0.54 <0.001
Maximum length 3.03 0.63 <0.001
marginal R2GLMM(m) of ﬁxed effects only = 0.45.
conditional R2GLMM(c) of ﬁxed and random effects = 0.63.
ΔAIC without taxonomic inclusion = −17.2.
ΔAIC for differing threat metrics: binomial THR (CR + EN + VU + NT) = −156.7; categorical = −981.7.
(D) Depth range
P(THR) = median depth + maximum length, random effect = Order/Family/Genus
Fixed effects Standardized coefﬁcient Standard error p-value
Intercept −0.51 0.26 0.002
Depth range −1.82 0.50 <0.001
Maximum length 3.17 0.64 <0.001
marginal R2GLMM(m) of ﬁxed effects only = 0.42.
conditional R2GLMM(c) = 0.62.
ΔAIC without taxonomic inclusion = −22.3.
ΔAIC for differing threat metrics: binomial THR (CR + EN + VU + NT) = −158.7; categorical = −982.3.
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 26 of 34
assigned a zero. Total annual chondrichthyan landings are underestimated as data are not reported for
1,522 out of a total count of 13,990 entries in the dataset. Therefore, 11% of chondrichthyan landings
reported to the FAO over the 10-year period are ‘data unavailable, unobtainable’. We mapped FAO
chondrichthyan landings as the national percent share of the average total landings from 2000 to 2009.
For the analysis of landings over time we removed the aggregate category ‘sharks, rays, skates, etc’
and all nine of the FAO chimaera reporting categories. The ‘sharks, rays, skates, etc’ FAO reported
category comprised 15,684,456 tonnes of the reported catch from all countries during 1950–2009,
which is a total of 45% of the total reported catch for this time period. However, the proportion of
catch in this category has declined from around 50% of global catch to around 35%, presumably due to
better reporting of ray catch and as sharks have declined or come under stronger protection (Figure 1).
The nine chimaera categories make up a small fraction of the global catch, 249,404.5 tonnes from
1950 to 2009, representing 0.72% of the total catch.
Hong Kong has long served as one of the world’s largest entry ports for the global shark ﬁn trade.
While ﬁns are increasingly being exported to Mainland China where species-speciﬁc trade data is more
difﬁcult to obtain, each year (from 1996 to 2001) Hong Kong handled around half of all ﬁn imports
(Clarke et al., 2006). Data on shark ﬁn exports to Hong Kong were requested directly from the Hong
Kong Census and Statistics Department (Hong Kong Special Administrative Region Government,
2011). We mapped exports to Hong Kong as the proportion of the summed total weight of the four
categories of shark ﬁn exported to Hong Kong in 2010: (1) shark ﬁns (with or without skin), with cartilage,
dried, whether or not salted but not smoked (trade code: 3055950), (2) shark ﬁns (with or without skin),
without cartilage, dried, whether or not salted but not smoked (3055930), (3) shark ﬁns (with or without
skin), without cartilage, salted or in brine, but not dried, or smoked (3056940), and (4) shark ﬁns (with or
without skin), with cartilage, salted or in brine, but not dried or smoked (3056930). We could not correct
the difference in weight due to product type. To identify the threat classiﬁcation of the chondrichthyan
species in the ﬁn trade, we included records of the most numerous species used in the Hong Kong ﬁn
trade as well as those species with the most-valued ﬁns (Clarke et al., 2006, 2007; Clarke, 2008).
We thank the UN Food and Agriculture Organization and John McEachran for providing distribution
maps. We thank all SSG staff, interns and volunteers for logistical and technical support: Sarah
Ashworth, Gemma Couzens, Kendal Harrison, Adel Heenan, Catherine McCormack, Helen Meredith,
Kim O’Connor, Rachel Kay, Charlotte Walters, Lindsay MacFarlane, Lincoln Tasker, Helen Bates and
Rachel Walls. We thank Rowan Trebilco, Wendy Palen, Cheri McCarty, and Roger McManus for their
comments on the manuscript, and Statzbeerz and Shinichi Nagakawa for statistical advice. Opinions
expressed herein are of the authors only and do not imply endorsement by any agency or institution
associated with the authors.
Table 9. Continued
(E) Geographic range (Extent of Occurrence)
p(THR) = geographic range + maximum length, random effect = Order/Family/Genus
Fixed effects Standardized coefﬁcient Standard error p-value
Intercept −0.50 0.52 0.33
Geographic range 5.22 3.7 0.12
Maximum length 2.16 0.75 0.004
marginal R2GLMM(m) of ﬁxed effects only = 0.65.
conditional R2GLMM(c) = 0.81.
ΔAIC without taxonomic inclusion = −25.8.
ΔAIC for differing threat metrics: binomial THR (CR + EN + VU + NT) = −156.5; categorical = −982.9.
The improvement of model ﬁt by inclusion of phylogenetic random effect was calculated as the difference in AIC
(ΔAIC) between the GLMM (with phylogenetic random effect) and a GLM as ΔAIC = AIC(GLMM)-AIC(GLM). p(THR)
was binomially distributed assuming species that were CR, EN or VU were threatened (1) and LC species were not
(0). We present ΔAIC for two other threat classiﬁcations, assuming: THR also includes NT species, or THR was a
continuous categorical variable ranging from LC = 0 to CR = 5.
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 27 of 34
Assessing species for the IUCN Red List relies on the willingness of dedicated experts to contribute
and pool their collective knowledge, thus allowing the most reliable judgments of a species’ status
to be made. Without their enthusiastic commitment to species conservation, this work would not be
possible. We therefore thank all of the SSG members, invited national, regional and international experts
who have attended Regional, Generic and Expert Review SSG Red List workshops, and all experts
who have contributed data and their expertise by correspondence. A total of 209 SSG members and
invited experts participated in regional and thematic workshops and a total of 302 scientists and
experts were involved in the process of assessing and evaluating the species assessments. We express
our sincere thanks and gratitude to the following people who have contributed to the GSRLA since 1996.
We ask forgiveness for any names that may have been inadvertently omitted or misspelled.
Acuña E, Adams W, Affronte M, Aidar A, Alava M, Ali A, Amorim A, Anderson C, Arauz R, Arfelli C,
Baker J, Baker K, Baranes A, Barker A, Barnett L, Barratt P, Barwick M, Bates H, Batson P, Baum J, Bell
J, Bennett M, Bertozzi M, Bethea D, Bianchi I, Biscoito M, Bishop S, Bizzarro J, Blackwell R, Blasdale T,
Bonﬁl R, Bradaï MN, Brahim K, Branstetter S, Brash J, Bucal D, Cailliet G, Caldas JP, Camara L, Camhi M,
Capadan P, Capuli E, Carlisle A, Carocci F, Casper B, Castillo-Geniz L, Castro A, Charvet P, Chiaramonte G,
Chin A, Clark T, Clarke M, Clarke S, Cliff G, Clò S, Coelho R, Conrath C, Cook S, Cooke A, Correia J,
Cortés E, Couzens G, Cronin E, Crozier P, Dagit D, Davis C, de Carvalho M, Delgery C, Denham J,
Devine J, Dharmadi, Dicken M, Di Giácomo E, Diop M, Dipper F, Domingo A, Doumbouya F, Drioli M,
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Lamónaca A, Last P, Lea R, Lemine Ould S, Leandro L, Lessa R, Licandeo R, Lisney T, Litvinov F, Luer C,
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Marshall A, Marshall L, Martínez Ortíz J, Martins P, Massa A, Mazzoleni R, McAuley R, McCord M,
McCormack C, McEachran J, Medina E, Megalofonou P, Mejia-Falla P, Meliane I, Mendy A, Menni R,
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Nel R, Nolan C, Norman B, Notarbartolo di Sciara G, Oetinger M, Orlov A, Ormond C, Pasolini P,
Paul L, Pegado A, Pek Khiok A.L, Pérez M, Pérez-Jiménez J.C, Pheeha S, Phillips D, Pierce S, Piercy A,
Pillans R, Pinho M, Pinto de Almeida M, Pogonoski J, Pollard D, Pompert J, Quaranta K, Quijano S,
Rasolonjatovo H, Reardon M, Rey J, Rincón G, Rivera F, Robertson R, Robinson L, J.R, Romero M, Rosa
R, Ruίz C, Saine A, Salvador N, Samaniego B, San Martín J, Santana F, Santos Motta F, Sato K,
Schaaf-DaSilva J, Schembri T, Seisay M, Semesi S, Serena F, Séret B, Sharp R, Shepherd T, Sherrill-
Mix S, Siu S, Smale M, Smith M, Snelson, Jr, F, Soldo A, Soriano-Velásquez S, Sosa-Nishizaki O, Soto
J, Stehmann M, Stenberg C, Stewart A, Sulikowski J, Sundström L, Tanaka S, Taniuchi T, Tinti F, Tous
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Conservation International Sarah L Fowler
Packard Foundation Sarah L Fowler
Save Our Seas Foundation Nicholas K Dulvy
UK Department of Environment and Rural Affairs Sarah L Fowler
US State Department Nicholas K Dulvy,
Sarah L Fowler
US Department of Commerce Nicholas K Dulvy
Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 28 of 34
Marine Conservation Biology Institute Sarah L Fowler
Pew Marine Fellows Program Sarah L Fowler
Mohamed bin Zayed Species Conservation Foundation Nicholas K Dulvy
Zoological Society of London Nicholas K Dulvy
Canada Research Chairs Program Nicholas K Dulvy
Natural Environment Research Council, Canada Nicholas K Dulvy
Tom Haas and the New Hampshire Charitable Foundation Sarah L Fowler
Oak Foundation Sarah L Fowler
Future of Marine Animal Populations, Census of Marine Life Sarah L Fowler
IUCN Centre for Mediterranean Cooperation Sarah L Fowler
UK Joint Nature Conservation Committee Sarah L Fowler
National Marine Aquarium, Plymouth UK Sarah L Fowler
New England Aquarium Marine Conservation Fund Sarah L Fowler
The Deep, Hull, UK Sarah L Fowler
Blue Planet Aquarium, UK Sarah L Fowler
Chester Zoo, UK Nicholas K Dulvy,
Sarah L Fowler
Lenfest Ocean Program Sarah L Fowler
WildCRU, Wildlife Conservation Research Unit,
University of Oxford, UK
Sarah L Fowler
Institute for Ocean Conservation Science,
University of Miami
Sarah L Fowler
Flying Sharks Nicholas K Dulvy
The funders had no role in study design, data collection and interpretation, or the
decision to submit the work for publication.
NKD, SLF, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting
or revising the article, Contributed unpublished essential data or reagents; JAM, CG, Acquisition of
data, Drafting or revising the article; RDC, Conception and design, Acquisition of data, Analysis and
interpretation of data, Drafting or revising the article; PMK, LRH, SV, Acquisition of data, Analysis and
interpretation of data, Drafting or revising the article; JKC, LNKD, MPF, GHB, SRL, Analysis and inter-
pretation of data, Drafting or revising the article; SVF, JCS, JDS, Analysis and interpretation of data,
Drafting or revising the article, Contributed unpublished essential data or reagents; CMP, Acquisition
of data, Analysis and interpretation of data; CAS, Conception and design, Analysis and interpretation
of data, Drafting or revising the article; KEC, Analysis and interpretation of data, Contributed unpub-
lished essential data or reagents; LJVC, Acquisition of data, Drafting or revising the article, Contributed
unpublished essential data or reagents; DAE, MRH, WTW, Drafting or revising the article, Contributed
unpublished essential data or reagents
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Dulvy et al. eLife 2014;3:e00590. DOI: 10.7554/eLife.00590 29 of 34
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