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Species composition of the international shark fin trade assessed through a retail-market survey in Hong Kong


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The shark fin trade is a major driver of shark exploitation in fisheries all over the world, most of which are not managed on a species-specific basis. Species-specific trade information highlights taxa of particular concern and can be used to assess the efficacy of management measures and anticipate emerging threats. The species composition of the Hong Kong Special Administrative Region of China, one of the world's largest fin trading hubs, was partially assessed in 1999–2001. We randomly selected and genetically identified fin trimmings (n = 4,800), produced during fin processing, from the retail market of Hong Kong in 2014–2015 to assess contemporary species composition of the fin trade. We used nonparametric species estimators to determine that at least 76 species of sharks, batoids, and chimaeras supplied the fin trade and a Bayesian model to determine their relative proportion in the market. The diversity of traded species suggests species substitution could mask depletion of vulnerable species; one-third of identified species face serious risk of extinction. The Bayesian model suggested that 8 species each comprised >1% of the fin trimmings (34.1-64.2% for blue [Prionace glauca]; 0.2-1.2% for bull [Carcharhinus leucas] and shortfin mako [Isurus oxyrinchus]); thus, trade was skewed to a few globally distributed species. Several other coastal sharks, batoids, and chimaeras are in the trade but poorly managed. Fewer than 10 of the species we modeled have sustainably managed fisheries anywhere in their range, including the most common species in trade, the blue shark. Our study and approach serve as a baseline to track changes in composition of species in the fin trade over time to better understand patterns of exploitation and assess the effects of emerging management actions for these animals.
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Contributed Paper
Species composition of the international shark fin
trade assessed through a retail-market survey
in Hong Kong
Andrew T. Fields ,1Gunter A. Fischer,2StanleyK.H.Shea,
3Huarong Zhang,2
Debra L. Abercrombie,4Kevin A. Feldheim,5Elizabeth A. Babcock,6and Demian D. Chapman1,7
1Stony Brook University, Stony Brook, NY 11794, U.S.A.
2Kadoorie Farm and Botanic Garden, Tai Po, Hong Kong
3BLOOM Association Hong Kong, Suite 2405, 9 Queens Road, Central, Hong Kong
4Abercrombie and Fish, 14 Dayton Avenue, Port Jefferson Station, NY 11776, U.S.A.
5Pritzker Laboratory for Molecular Systematics and Evolution, The Field Museum, 1400 S. Lake Shore Drive, Chicago, IL 60605, U.S.A.
6Rosenstiel School of Marine and Atmospheric Science, University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33149, U.S.A.
7Department of Biological Sciences, Florida International University, 11200 SW 8th Street, Miami, FL 33199, U.S.A.
Abstract: The shark fin trade is a major driver of shark exploitation in fisheries all over the world, most
of which are not managed on a species-specific basis. Species-specific trade information highlights taxa of
particular concern and can be used to assess the efficacy of management measures and anticipate emerging
threats. The species composition of the Hong Kong Special Administrative Region of China, one of the world’s
largest fin trading hubs, was partially assessed in 1999–2001. We randomly selected and genetically identified
fin trimmings (n=4800), produced during fin processing, from the retail market of Hong Kong in 2014–
2015 to assess contemporary species composition of the fin trade. We used nonparametric species estimators
to determine that at least 76 species of sharks, batoids, and chimaeras supplied the fin trade and a Bayesian
model to determine their relative proportion in the market. The diversity of traded species suggests species
substitution could mask depletion of vulnerable species; one-third of identified species are threatened with
extinction. The Bayesian model suggested that 8 species each comprised >1% of the fin trimmings (34.1–64.2%
for blue [Prionace glauca], 0.2–1.2% for bull [Carcharhinus leucas] and shortfin mako [Isurus oxyrinchus]); thus,
trade was skewed to a few globally distributed species. Several other coastal sharks, batoids, and chimaeras
are in the trade but poorly managed. Fewer than 10 of the species we modeled have sustainably managed
fisheries anywhere in their range, and the most common species in trade, the blue shark, was not among
them. Our study and approach serve as a baseline to track changes in composition of species in the fin trade
over time to better understand patterns of exploitation and assess the effects of emerging management actions
for these animals.
Keywords: Asia, conservation, DNA, fisheries management, forensics, wildlife trade
on de Especies del Mercado Internacional de Aleta de Tibur´
on Evaluada por medio de un Censo de
Mercado al Menudeo en Hong Kong
Resumen: El mercado de aleta de tibur´
on es un importante conductor de la explotaci´
on de tiburones a
nivel mundial, la mayor´
ıa de los cuales no est´
an manejados a un nivel espec´
ıfico de especie. La informaci´
ıfica de especies en el mercado resalta taxones de preocupaci´
on particular y puede usarse para avaluar
Article Impact statement: One-third of species traded in the Hong Kong shark fin market are threatened with extinction and <10 modeled
have sustainably managed fisheries.
Paper submitted December 19, 2016; revised manuscript accepted August 18, 2017.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction
in any medium, provided the original work is properly cited.
Conservation Biology,Volume00,No.0,115
2017 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.
DOI: 10.1111/cobi.13043
2Shark Fin Trade
la eficiencia de las medidas de manejo y anticipar las amenazas emergentes. La composici´
on de especies en la
on Administrativa Especial de Hong Kong de la Rep´
ublica Popular China, uno de los puntos m´
as grandes
de venta de aletas, fue evaluada parcialmente entre 1999 y 2001. Seleccionamos al azar e identificamos
eticamente pedazos de aletas (n=4800) producidos durante el procesamiento de las aletas, en el mercado
al menudeo de Hong Kong entre 2014 y 2015 para evaluar la composici´
on contempor´
anea de especies dentro
del mercado de aletas. Utilizamos estimadores no-param´
etricos de especies para determinar que al menos 76
especies de tiburones, batoideos y quimeras suministraban al mercado de aletas y un modelo bayesiano para
determinar su proporci´
on relativa dentro del mercado. La diversidad de las especies en el mercado sugiere
que la sustituci´
on de especies podr´
ıa enmascarar la disminuci´
on de las especies vulnerables; un tercio de
las especies identificadas enfrentan riesgos severos de extinci´
on. El modelo bayesiano sugiri´
o que cada una
de ocho especies constituy´
o>1% de los pedazos de aletas (34.1-64.2% para el tibur´
on azul [Prionace glauca];
0.2-1.2% para el tibur´
on toro [Carcharhinus leucas] y el tibur´
on mako [Isurus oxyrinchus]); as´
ı, el mercado
estuvo sesgado a unas cuantas especies con distribuci´
on mundial. Muchos otros tiburones costeros, batoideos
y quimeras est´
an en el mercado pero con un manejo muy pobre. Menos de diez de las especies que modelamos
tienen pesquer´
ıas manejadas sustentablemente en cualquier parte de su extensi´
on, incluyendo a la especie
as com´
un en el mercado, el tibur´
on azul. Nuestro estudio y nuestra estrategia sirven como una l´
ınea de
base para rastrear los cambios en la composici´
on de las especies dentro del mercado de aletas a trav´
es del
tiempo para entender mejor los patrones de explotaci´
on y evaluar los efectos de las acciones de manejo
emergentes para estos animales.
Palabras Clave: ADN, Asia, ciencias forenses, conservaci´
on, manejo de pesquer´
ıas, mercado de vida silvestre
Fisheries-driven declines of many sharks around the
world have been linked to trade in their fins, used in
shark fin soup (e.g., Vannuccini 1999; Clarke et al. 2006a,
2006b; Dulvy et al. 2014). Despite the large volume of this
global trade, there have been few attempts to monitor it
on a species-specific basis (Rose 1996; Vannuccini 1999;
Fong & Anderson 2000; Clarke et al. 2006a, 2006b). Rel-
atively few fishing nations keep accurate species-specific
catch data for sharks and their relatives, so it is difficult
to assess the effect of fisheries supplying the fin trade
on shark populations and species (Dent & Clarke 2015).
Trade information can supplement or complement land-
ing information and may improve understanding and reg-
ulation of the species composition of fisheries (Eriksson
& Clarke 2015).
The trend in the annual import volume of fins in one
of the world’s largest hubs of the fin trade (Hong Kong
Special Administrative Region of China; HK) is similar to
the trend in global chondrichthian (sharks, batoids, and
chimaeras) landings reported to the UN Food and Agricul-
ture Organization (FAO), which peaked in 2003 and have
since declined approximately 20% (Davidson et al. 2015).
It is surprising that the fin trade has not declined even
more given the inherent vulnerability of this group (Dulvy
et al. 2014). This may be explained by geographical shifts
in sources, species substitution, or both. The geographic
sources of fins have changed somewhat over time (Eriks-
son & Clark 2015), but there is limited information on
possible shifts in species composition. Aggregated trade
data are difficult to interpret because they do not capture
variation in species-specific trends, which is important
for chondrichthians because of their varied life-history
traits, ecology, distributions, and conservation statuses
(Clarke 2004; Carrier et al. 2012).
Estimates indicate HK imports a substantial fraction
of the annual international trade in shark fins; these
fins are consumed locally or re-exported (FAO 2016a,
2016b). This enables multiple avenues for the collection
of species-specific information on international patterns
of chondrichthian harvest and trade. Clarke et al. (2006a,
2006b) estimated the total number of individual sharks
supplying fins for the trade globally and the proportional
contributions of a subset of commonly traded species
based on trader records from October 1999 to March
2001. Clarke et al. (2006a, 2006b) found that importers
auctioned about 20% of imported fins after sorting them
into approximately 30 trade categories, 11 of which
were verified genetically as concordant with a species or
species group. The 14 species in these groups comprised
about 46% of the auction volume for that year and a half.
No one has repeated this work or assessed the species
composition of the HK market beyond these species. We
sought to assess the contemporary species composition
of the HK fin market to assess what percentage of species
are threatened with extinction, and how assess have man-
agement measures and regional population declines have
affected the 14 species previously reported in the market.
Since the last examination of HK in 1999–2001, its
fin trade volume has dropped (likely 30–50% of global
trade instead of 44–59% [Clarke 2008]); however, it
remains a major importer and trades fins with an average
of 83 nations annually (Shea & To 2017). Although
the auctions described in Clarke et al. (2006a, 2006b)
remain inaccessible, HK has a large dried seafood
district (Sheung Wan and Sai Ying Pun), where imported
fins are sold by vendors supplied by local fin traders
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Volume 00, No. 0, 2017
Fields et al. 3
Table 1. Estimates of the number of chondrichthian species in trimmings from the Sheung Wan market, Hong Kong.
Model Estimate SE 95% CI
Homogeneous model (Chao & Lee 1992) 78.378 2.286 76.014–86.254
Chao1 (Chao 1984) 82.561 5.958 76.936–104.520
Chao1-bc (Chao et al. 2005) 81.110 4.976 76.509–99.728
iChao1 (Chiu et al. 2014) 83.851 4.313 78.581–96.872
ACEa(Chao & Lee 1992) 82.608 4.744 77.474–98.393
ACEa-1 (Chao & Lee 1992) 84.223 6.153 77.808–105.292
aabundance-based coverage estimator (ACE)
(i.e., those conducting the auctions). Retail market
surveys in HK therefore offer a means to assess the
species composition of the international fin trade.
Previously, the prohibitively high costs of purchasing
dried fins for genetic testing, the unwillingness of
retailers to donate fin samples, and the difficulty of
visually identifying processed fins to species in the
market precluded such surveys. We overcame these
obstacles by using trimmings produced when traders
remove skin, muscle, and basal cartilage during pro-
cessing (Supporting Information). Retailers collect and
sell these trimmings for relatively low prices, enabling
robust sampling through randomized purchasing. We
suggest that a modeled species composition of these fin
trimmings provides an index of the contemporary shark
fin trade in HK, which reflects global trade in shark fins
given the size of this market (Shea & To 2017). We esti-
mated the total number of species in the shark fin trade
based on a random survey of genetically identified fin
trimmings collected over 1 year and characterized traded
species in terms of taxonomy, habitat type, body size,
and conservation status. We also quantified the relative
amount of the most common species in fin trimmings
and determined whether the species Clarke et al. (2006a)
recorded still constitute the majority of traded species.
Sample Acquisition
We produced a list of all of the dried-seafood retail shops
that sell shark fins in the Sheung Wan and Sai Ying Pun
Districts of HK from January 2014 through February 2015.
We focused on these 2 districts because they are the
trading centers for dried seafood in the city and vendors
selling shark fin outside these areas yielded few stocked
trimmings. The list was initially produced by an exhaus-
tive walking tour of these 2 districts during which shops
selling shark fins were identified. This list was modified
every 6 months to allow for shops going in and out of busi-
ness and for shops that began or ceased selling fins during
the study. From February 2014 to February 2015, shops
were assigned a number, and 75 shops were randomly
selected without replacement from the complete shop
list every 2 weeks. A resident of HK visited these shops
in order of selection and sampled them by purchasing 2
bags of trimmings, which averaged 235 pieces per bag
(8–1861). If fin trimmings were not present, the next
shop was visited until 10 shops yielded 2 bags of fin
trimmings each (i.e., 20 bags collected from 10 shops).
All trimmings were individually numbered in each bag,
and 10 individual trimmings were randomly selected for
genetic analyses.
Genetic Identification of Fin Trimmings
Subsampled fin trimmings were washed with distilled
water and DNA was isolated under a laminar flow cab-
inet with Qiagen DNeasy kits (Qiagen, Valencia, Cal-
ifornia, U.S.A.). A mini-DNA barcoding approach for
identifying shark species from degraded samples (Fields
et al. 2015) was used to amplify and sequence ap-
proximately 120 base pairs from the 5end of the cy-
tochrome oxidase I (COI) gene. We used BLAST in Gen-
Bank ( and BOLD in the Barcode
of Life Data Systems ( to com-
pare our sequences to the databases. We considered the
species identifiable with this short COI fragment if BOLD
returned a 100% species-level match and the sequence’s
closest match in BLAST was unambiguous. A sequence
was considered unambiguous if it had a high homology
(>97%) of base calls of high quality (>Q20) and the
next species match was at least 2 base pairs different
(about 2%). If BOLD and BLAST searches did not yield
a conclusive species-level match, a second mini-barcode
located at the 3end of the COI barcoding region was
obtained via polymerase chain reaction (PCR) with 1
M13-tagged forward primer designed for this study and 2
M13-tagged universal fish barcoding reverse primers (de-
tails in Supporting Information). These sequences were
concatenated to the original sequence and used in BLAST
and BOLD searches with the above criteria.
Data Analyses
We created a rarefaction curve from these data in the R
package iNEXT (Chao et al. 2014) and estimated the total
number of species based on 3 different methods (Table 1)
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4Shark Fin Trade
(Hortal et al. 2006; Basualdo 2011; Gwinn et al. 2016) in
the ChaoSpecies function in the R package SpadeR (Chao
et al. 2016). iNext uses the different frequency of species
to estimate the Hill numbers (Hill 1973), which are used
to estimate the number of species at a given number
of samples (rarefaction curve). ChaoSpecies estimates
the species richness and its confidence interval through
multiple methods, including abundance-based coverage
estimator (ACE), Chao1, and iChao1, all of which use
the frequencies of rare species in the sample to infer the
number of undetected species and confidence intervals.
Traded species were characterized in terms of tax-
onomy (by family), habitat type, size at maturity, and
conservation status. Data used to assign species to cat-
egories were obtained from the International Union for
the Conservation of Nature (IUCN) Red List (IUCN 2016).
Habitat type was where the species completes most of
its life cycle: oceanic, off the continental shelf in sur-
face waters; coastal, over the continental shelf; and deep
benthic, off the shelf and close to the bottom. Size at
maturity was categorized as small, maturing at <100 cm
total length (TL), or large, >100 cm TL. Conservation
status for each species was drawn from IUCN (2016). We
counted the number of sustainable fisheries (i.e., operat-
ing under assessment-based catch limits) for each species
anywhere in its range (Simpfendorfer & Dulvy 2017).
We used a Poisson multinomial model (Baker 1994;
Shelton et al. 2012) to estimate species composition of
the fin trimmings and a Bayesian framework with nonin-
formative priors to estimate the parameters:
Yijkl Poisson(eθijkl),(1)
where Yijkl is the total number of fin trimmings of species
iinthesamplefromweekj, shop kand bag l,whichis
assumed to be Poisson distributed, with a mean equal
to eθijkl. These factors reflected differences over time
(week), supply chain (shop), and diversity within a shop
(bag). The mean is an exponent to ensure it is positive,
θijkl =λjkl +βi+δij +γik,(2)
where λjkl is a scaling term associated with the sample
size in samples j,k,l(Baker 1994), βiis the fixed effect
of species, δij is the random effect of week, and γik is the
random effect of shop with respect to species. Both δij
and γik are normally distributed random effects with a
value drawn from a normal distribution with mean zero
and variances σ2
δand σ2
γ, respectively. Because the pro-
portion of species in each sample (j,k,l) must sum to
1, the β,γ,andδparameters are all set to 0 for species
1. The proportion of each species in the trimmings is
estimated as the exponent of the species effect over the
sum of all the exponents of the species effects:
where Piis the proportion of species iin the trimmings
and eβiisthePoissonmeannumberofsamplesofapar-
ticular species observed at an average sampling event.
A Bayesian framework with noninformative priors was
used to estimate the parameters of this model with JAGS
(Lunn et al. 2013) in R (R2Jags package [Su & Yajima
2015]) in which a Markov chain Monte Carlo (MCMC)
algorithm estimates the posterior distribution of the pa-
rameters. The prior for λjkl was uniform between –100
and 100, as required to make the Poisson likelihood equiv-
alent to the multinomial (Lunn et al. 2013). The prior for
βwas normal with a mean of 0 (SD =1000). The prior
for the SDs for γand δwere uniform between 0.0001
and 100. In some model runs, σ2
δand σ2
γwere estimated
separately for each species and drawn from a lognor-
mal distribution with an estimated mean and variance.
The model was also run with no random effects, only
a random effect of vendor, and only a random effect of
sampling event. The best model structure was chosen
based on the deviance information criterion (DIC) (Lunn
et al. 2013) (Supporting Information). The MCMC was
run with 2 chains for 500,000 iterations with a burn-in
of 10,000 and a thin of 100. The Gelman–Rubin diag-
nostic and effective sample size indicated adequate con-
vergence when the Gelman–Rubin diagnostic was <1.05,
and the effective sample size was >400 (Lunn et al. 2013).
Models were fitted to data that included all the species
that were at least 1% of the sample with the exception of
Dalatias licha (species found at 1 vendor and therefore
had the potential to not converge well in the model). We
used the DIC to determine which model best predicted
the species composition. After model selection, the per-
cent cutoff was adjusted downward for as long as the
model continued to converge. Species below the cutoff
were grouped, and unidentified samples were placed in
a separate category. The proportion of each species in
the trimmings was conservatively estimated using the
final model output; no assumptions were made about
the unidentified component.
Evaluation of Survey Design for Quantifying Species
We suggest our estimated species composition of the fin
trimmings reflects the broader species composition in
the HK fin trade. This is based on the assumption that
trimmings from a representative sample of imported
fins enter the retail market and processing practices and
times are the same for all species. Given the covert nature
of the shark fin trade, some of these assumptions are
challenging to evaluate directly. Informal conversations
with fin traders in HK and Guangzhou indicated fin
trimmings sold in HK originate from processing facilities
in Hong Kong and Guangzhou. Fins are trimmed of basal
muscle, skin, and cartilage, likely just after a fin arrives
at a processing center, because these tissues reduce the
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Fields et al. 5
value of the fin due to their odor (Dent & Clarke 2015).
Stockpiling of trimmings is unlikely because they are of
relatively low value and perishable. They are used in a
cheap version of soup or as a soup base. To determine
whether fins from different species are trimmed in a
similar way, we measured the length of a subsample
of (n=1787) genetically identified fin trimmings to
characterize their size distribution. The size distribution
of sampled fin trimmings was wide, so we binned them
into 3 categories: small, <41.75 mm (n=349); medium,
41.75–83.5 mm (n=521), and large >83.5 mm (n=
917). We reran the Bayesian model described previously
for all species that were >2% of each size class. A
chi-square test was run on a contingency table to test
the hypothesis that the size-at-maturity categories (large
and small) and the trimming sizes (small, medium, and
large) were independent variables.
We visited 334 of 373 retail shops and randomly pur-
chased 480 bags of fin trimmings from 92 retail ven-
dors (24.7% of all retailers). Individual fin trimmings
(n=4800) were randomly selected from these bags,
and 82.15% of them were identified to at least the genus
level (Table 2). The remaining trimmings failed to amplify
after repeated attempts.
We identified 59 species and another 17 groups from
16 families in 8 orders that consisted of sharks, batoids,
and chimaeras that were either only identifiable to genus
with the barcode available or that consisted of an unre-
solved species complex (Table 2 & Fig. 1). The rarefac-
tion curve of this sampling reached a plateau (Supporting
Information). We used the minimum and maximum con-
fidence intervals from the combined species richness es-
timates to determine that an additional 0 to 29 taxa occur
in the Hong Kong market. Traded species live in a wide
variety of habitats, but three-quarters primarily inhabit
coastal areas (Fig. 1b). Traded species included a simi-
lar proportion of small- and large-bodied taxa (Fig. 1c).
Nearly one-third of species recorded in trade were vul-
nerable (VU) or endangered (EN) (IUCN 2012) (Fig. 1d).
When the Poisson multinomial model was applied to
species found in at least 1% of the trimmings sampled,
the best model included a fixed effect of species, random
effects of shop and week, and variation between species
in the variance of the random effects (Supporting Infor-
mation). This model converged with data for species that
were at least 0.4% of the sampled trimmings, including
D. licha, a category for unidentified samples, and species
<0.4% grouped together (Supporting Information). The
model with a 0.4% cutoff served as our final model for
estimating the species composition of the market. The
species composition estimated by the models (Table 3)
was sometimes quite different from the raw species com-
Figure 1. Composition of the identified species by (a
and e) family, (b and f) primary habitat type, (c and
g) relative size at maturity, and (d, h) conservation
status (DD, data deficient; LC, least concern; NT, near
threatened; VU, vulnerable; EN, endangered [IUCN
2012]) for both the (a–d) fin trimmings by species
groups (n=76) from the Sheung Wan market, and the
(e–h) modeled fin trimmings by frequency (other,
groups of species that come from more than 1 threat
category, including the unidentified group in the
model). Species categorized as VU, EN, or CR are
considered at risk of extinction (IUCN 2012). Small
bodied means species that mature at 100 cm total
length or less. See text for habitat definitions.
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6Shark Fin Trade
Table 2. Species or species groups sampled from the Sheung Wan and Sai Ying Pun fin market, Hong Kong; fin trimmings identified to species; and the conservation status of each species.
Order Scientific name Common name IUCNa
of samples
Carcharhiniformes Prionace glauca Blue Shark NT large S oceanic 1632 34.00
Carcharhiniformes Carcharhinus falciformis Silky Shark NT large S oceanic 483 10.06
Carcharhiniformes C. limbatus,
C. amblyrhynchoides,
C. leiodon, C. tilstoni
Blacktip, Graceful,
Smoothtooth blacktip,
Australian blacktip Sharks
NT large S coastal 198 4.13
Carcharhiniformes Sphyrna lewini Scalloped hammerhead
EN II large S coastal 196 4.08
Carcharhiniformes Sphyrna zygaena Smooth hammerhead Shark VU II large S coastal 165 3.44
Lamniformes Isurus oxyrinchus Shortfin mako Shark VU large S oceanic 133 2.77
Carcharhiniformes Carcharhinus spp. Requiem Sharks S 114 2.35
Carcharhiniformes Carcharhinus leucas Bull Shark NT large S coastal 87 1.81
Carcharhiniformes Rhizoprionodon acutus Milk Shark LC small S coastal 66 1.38
Carcharhiniformes Carcharhinus brevipinna Spinner Shark NT large S coastal 55 1.15
Carcharhiniformes Carcharhinus amboinensis Pigeye Shark DD large S coastal 54 1.13
Squaliformes Dalatias licha Kitefin Shark NT large S deep benthic 53 1.10
Carcharhiniformes Carcharhinus sorrah Spot-tail Shark NT large S coastal 50 1.04
Carcharhiniformes Carcharhinus longimanus Oceanic whitetip Shark VU II large S oceanic 48 1.00
Carcharhiniformes Carcharhinus
Dusky/Galapagos Shark VU/NT large S coastal 42 0.88
Carcharhiniformes Sphyrna mokarran Great hammerhead Shark EN II large S coastal 41 0.85
Lamniformes Alopias superciliosus Bigeye thresher Shark VU II large S oceanic 37 0.77
Carcharhiniformes Negaprion acutidens Sicklefin lemon Shark VU large S coastal 29 0.60
Chimaeriformes Callorhinchus spp. Plough-nose Chimaeras NA small C 27 0.56
Rajiformes Rhynchobatus australiae
White-spotted guitarfish
VU large B coastal 26 0.54
Carcharhiniformes Rhizoprionodon taylori Australian sharpnose Shark LC small S coastal 24 0.50
Carcharhiniformes Carcharhinus limbatus Blacktip Shark NT large S coastal 21 0.44
Orectolobiformes Chiloscyllium spp. Bamboo Sharks small S coastal 20 0.42
Lamniformes Alopias pelagicus Pelagic thresher Shark VU II large S oceanic 19 0.40
Squaliformes Centrophorus spp. Gulper Sharks small S deep benthic 19 0.40
Carcharhiniformes Galeorhinus galeus Soupfin Shark VU large S oceanic 19 0.40
Lamniformes Lamna ditropis Salmon Shark LC large S oceanic 17 0.35
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Fields et al. 7
Table 2. Continued.
Order Scientific name Common name IUCNa
of samples
Carcharhiniformes Mustelus spp. Smoothhound Shark small S 17 0.35
Carcharhiniformes Rhizoprionodon
sharpnose Shark
LC small S coastal 17 0.35
Carcharhiniformes Galeocerdo cuvier Tiger Shark NT large S oceanic 16 0.33
Carcharhiniformes Carcharhinus
Grey reef Shark NT large S coastal 15 0.31
Carcharhiniformes Carcharhinus cf.
Whitecheek Shark NT small S coastal 15 0.31
Carcharhiniformes Mustelus punctulatus Blackspotted smoothhound
DD small S coastal 14 0.29
Carcharhiniformes Carcharhinus brachyurus Bronze whaler Shark NT large S coastal 13 0.27
Carcharhiniformes Mustelus mosis Arabian smoothhound
DD small S coastal 12 0.25
Carcharhiniformes Carcharhinus
Bignose/Sandbar Shark DD/VU large S coastal 11 0.23
Carcharhiniformes Mustelus mustelus Common smoothhound
VU small S coastal 10 0.21
Carcharhiniformes Carcharhinus acronotus Blacknose Shark NT small S coastal 9 0.19
Carcharhiniformes Rhizoprionodon spp. Sharpnose Sharks small S coastal 9 0.19
Chimaeriformes Hydrolagus
Dark ghostshark LC small C deep benthic 7 0.15
Chimaeriformes Hydrolagus spp. Other Chimaeras small C deep benthic 7 0.15
Carcharhiniformes Rhizoprionodon longurio Pacific sharpnose Shark DD small S coastal 7 0.15
Carcharhiniformes Carcharhinus
Graceful Shark NT large S coastal 0 0.13
Carcharhiniformes Carcharhinus isodon Finetooth Shark LC small S coastal 6 0.13
Carcharhiniformes Carcharhinus macloti Hardnose Shark NT small S coastal 6 0.13
Lamniformes Lamna nasus Porbeagle VU II large S oceanic 6 0.13
Carcharhiniformes Carcharhinus
Silvertip Shark NT large S coastal 5 0.10
Carcharhiniformes Hemipristis elongata Snaggletooth Shark VU small S coastal 5 0.10
Carcharhiniformes Negaprion brevirostris Lemon Shark NT large S coastal 5 0.10
Squaliformes Deania profundorum Arrowhead dogfish Shark LC small S deep benthic 4 0.08
Lamniformes Isurus paucus Longfin mako Shark VU large S oceanic 4 0.08
Carcharhiniformes Lamiopsis temminckii Broadfin Shark EN small S coastal 4 0.08
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Volume 00, No. 0, 2017
8Shark Fin Trade
Table 2. Continued.
Order Scientific name Common name IUCNa
of samples
Rajiformes Rhynchobatus cf. laevis VU large B coastal 4 0.08
Carcharhiniformes Scoliodon laticaudus Spadenose Shark NT small S coastal 4 0.08
Carcharhiniformes Loxodon spp. small S coastal 3 0.06
Carcharhiniformes Mustelus canis Smooth dogfish NT small S coastal 3 0.06
Carcharhiniformes Sphyrna tiburo Bonnethead Shark LC small S coastal 3 0.06
Lamniformes Alopias vulpinus Common thresher Shark VU II large S oceanic 2 0.04
Carcharhiniformes Carcharhinus
Spinner Shark/Bronze
whaler Shark
large S coastal 2 0.04
Carcharhiniformes Carcharhinus
Blacktip reef Shark NT small S coastal 2 0.04
Carcharhiniformes Carcharhinus porosus Smalltail Shark DD small S coastal 2 0.04
Carcharhiniformes Glyphis spp. River Shark EN large S riverine/ coastal 2 0.04
Carcharhiniformes Hemigaleus australiensis Australian weasel Shark LC small S coastal 2 0.04
Carcharhiniformes Loxodon macrorhinus Sliteye Shark LC small S coastal 2 0.04
Rajiformes Rhynchobatus djiddensis Giant guitarfish VU large B coastal 2 0.04
Chimaeriformes Callorhinchus
Elephantfish NA small C deep benthic 1 0.02
Lamniformes Carcharias taurus Sand tiger Shark VU large S coastal 1 0.02
Squaliformes Deania spp. Deepwater dogfish Sharks small S deep benthic 1 0.02
Carcharhiniformes Eusphyra blochii Winghead Shark EN large S coastal 1 0.02
Hexanchiformes Hexanchus griseus Bluntnose sixgill Shark NT large S deep benthic 1 0.02
Carcharhiniformes Mustelus californicus Grey smoothound Shark LC small S coastal 1 0.02
Carcharhiniformes Mustelus henlei Brown smoothhound Shark LC small S coastal 1 0.02
Carcharhiniformes Mustelus lunulatus Sicklefin smoothound Shark LC small S coastal 1 0.02
Squatiniformes Squatina californica Pacific angel shark NT small S coastal 1 0.02
Orectolobiformes Stegostoma fasciatum Zebra Shark EN large S coastal 1 0.02
Carcharhiniformes Triaenodon obesus Whitetip reef shark NT small S coastal 1 0.02
aInternational Union for Conservation of Nature status: DD, data deficient; LC, least concern; NT, near threatened; VU, vulnerable; EN, endangered. Species categorized as VU, EN, or critically
endangered (CR) are considered at risk of extinction (IUCN 2012).
bConvention on International Trade in Endangered Species Appendix.
cSmall bodied defined as species that mature at 100 cm total length or less.
dTaxonomic classification group: S, shark; B, batoid; C, chimaera.
eHabitat where species complete most of their life cycle: oceanic, off the continental shelf in surface waters; coastal, over the continental shelf; deep benthic, off the shelf and close to the bottom.
Conservation Biology
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Fields et al. 9
Table 3. Estimated mean and 95% CI of the number of fin trimmings in the 2014–2015 Hong Kong market by species from the deviance information criterion best Poisson multinomial model and
the rank order of species by abundance from 1999 to 2001 and 2014 to 2015 for species occurring in both this study and Clarke et al. (2006a).
Species or species group
2014 Mean %
(95% CI)
Rank order
Rank order
AppendixbRetention bansc
Number of sustainably
managed fisheriesdRange Habitat
Blue shark, Prionace
49.0 (34.1–64.2) 1 1 NT none global oceanic
Silky shark, Carcharhinus
4.6 (2.1–8.7) 2 3 NT II IATTC, ICCAT,
none global oceanic
Scalloped hammerhead
shark, Sphyrna lewinif,g
2.0 (0.9–3.8) 3 2 EN II ICCAT none global coastal
Smooth hammerhead shark,
Sphyrna zygaenaf,g
1.7 (0.7–3.3) 4 2 VU II ICCAT none global coastal
Shortfin mako shark, Isurus
0.6 (0.2–1.2) 5 4 VU none global oceanic
Bull shark, Carcharhinus
0.6 (0.2–1.2) 5 7 NT none global coastal
Oceanic whitetip shark,
0.3 (0.1–0.6) 7 8 VU II IATTC, ICCAT,
none global oceanic
Great hammerhead shark,
Sphyrna mokarranf
0.3 (0.1–0.7) 7 9 EN II ICCAT none global coastal
Dusky shark, Carcharhinus
0.3 (0.1–0.7)h7 10 VU U.S.A. (Atlantic) none global coastal
Thresher sharks, Alopias
vulpinus &A. pelagicuse
0.1 (0.0–0.2) 10 6 VU II IOTC one (U.S.A.) global/regional oceanic
Bigeye Thresher shark,
Alopias superciliosuse
0.1 (0.0–0.3) 10 6 VU II ICCAT, IOTC,
U.S.A. (Atlantic)
none global oceanic
Tiger shark, Galeocerdo
0.3hNR 11 NT none global oceanic
Sandbar shark,
Carcharhinus plumbeus
0.3iNR 5 VU U.S.A. (Atlantic) none global coastal
Blacktip shark complexj2.4 (1.1–4.6) NT 2 (U.S.A., Australia) regional coastal
Spinner shark,
0.6 (0.3–1.2) NT One (Australia) global coastal
Java shark, Carcharhinus
0.4 (0.1–0.8) DD 1 (Australia) regional coastal
Spot-tail shark,
Carcharhinus sorrah
0.3 (0.1–0.6) NT 2 (Australia) regional coastal
Smoothhound sharks,
Mustelus spp.
0.3 (0.1–0.6) 4 (U.S.A.) regional coastal
Sharpnose sharks,
Rhizoprionodon spp.
0.1 (0.0–0.3) 1 regional coastal
Milk shark,
Rhizoprionodon acutus
0.3 (0.1–0.6) LC 0 regional coastal
Wedgefish, Rhynchobatus
0.1 (0.0–0.2) VU India, Philippines
(one species)
0 regional coastal
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Volume 00, No. 0, 2017
10 Shark Fin Trade
Table 3. Continued.
Species or species group
2014 Mean %
(95% CI)
Rank order
Rank order
AppendixbRetention bansc
Number of sustainably
managed fisheriesdRange Habitat
Porbeaglefand Salmon
sharks, Lamna spp.,
0.1 (0.0–0.2) VU/LC II ICCAT 0 global/regional oceanic
Sicklefin Lemon shark,
Negaprion acutidens
0.0 (0.0–0.1) VU 0 regional coastal
Plough-nose chimaeras,
Callorhinchus spp.
0.0 (0.0–0.1) LC 2 (Australia, New
regional coastal
Kitefin shark, Dalatias
0.0 (0.0–0.1) NT 0 deep benthic coastal
Australian sharpnose shark,
Rhizoprionodon taylori
0.0 (0.0–0.1) LC 0 regional coastal
aThe International Union for Conservation of Nature Red List status.
bConvention on International Trade in Endangered Species Appendix.
cRegions with retention bans in regional fisheries management organizations or by individual nations.
dNumber of sustainably managed fisheries documented for the species by Simpfendorfer and Dulvy (2017).
eSpecies listed on CITES in October 2017.
fSpecies listed on CITES in September 2014.
gSpecies combined in Clarke et al. (2006a).
hIncludes Carcharhinus altimus within C. plumbeus and C. galapagensis within C. obscurus because these species cannot be differentiated with our methods.
iThis is the nonmodeled percentage of 2014–2015 sample; it was too infrequent to add to our model.
jIncludes Carcharhinus limbatus,C. tilstoni,C. leiodon,andC. amblyrhynchoides.
NR, not reported in the model output.
position (Table 2) because the model estimated a typical
species composition after accounting for random varia-
tion between vendors and sampling periods. This tended
to increase the importance of species found in a higher
fraction of samples and vendors relative to those that
were found rarely.
Although three-quarters of the traded species live in
coastal areas, >50% of the trade was from oceanic species
(Figs. 1b & 1f). A small number of species comprised
the majority of modeled fin trimmings (Fig. 2), particu-
larly the blue shark (Prionace glauca) (33.9–64.1%) and
silky shark (Carcharhinus falciformis) (2.1–8.7%). With
1 exception (sandbar shark [Carcharhinus plumbeus]),
the 14 species identified in the HK trade approximately
15 years earlier were among the most common species
in the modeled trimmings in 2014–2015 (Tables 2 & 3).
We found these globally distributed, large-bodied species
are now nearly all subject to management measures
(Table 3), and that only 1 species of the 14 (the common
thresher [Alopias vulpinus]) has a fishery that is managed
sustainably anywhere (Simpfendorfer & Dulvy 2017). The
taxa not previously reported in the market were primarily
range-restricted coastal species and a small number of
deep benthic taxa, most of which have relatively little
management (Table 3). Several of these taxa support
fisheries that are managed sustainably, but only in the
United States, Australia, and New Zealand (Simpfendorfer
& Dulvy 2017).
The Bayesian models for the fin trimmings we binned
into 3 categories converged adequately (Supporting In-
formation). The estimated species composition of the
modeled trimmings was not generally sensitive to the
sampled trimming size in that the estimated credible
intervals overlapped for every species. The rank or-
der of the most abundant large species was generally
the same regardless of whether the modeled trimmings
were small, medium, or large (Table 4). Nevertheless,
there was a significant relationship between the size of
the shark and the size of the sampled trimmings;
small species (e.g., sharpnose [Rhizoprionodon spp.],
smooth hounds [Mustelus spp.]) were more fre-
quent in small trimmings (χ2=12.4, df =2, p=
0.002) (Supporting Information). Trimmings from small
species were not frequent in medium and large cat-
egories (Table 4), but a similar number of species
and species groups were found in each size group
(large, 27; medium, 31; small, 30), although the trajec-
tory of the species richness curves varied (Supporting
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Volume 00, No. 0, 2017
Fields et al. 11
Figure 2. Estimated total contribution to fin trimmings of all species modeled and the cumulative curve of the
mean of samples identified (unidentified, samples failed to amplify after repeated attempts; other species, all
speciesorgenerathatmakeup<0.4% of the total sample; error bars, 95% CI; numbers in parentheses, species i
value from the model (Eq. (1)), which provides an association with the model output [Supporting Information]).
The cumulative curve is based on the assumption that the unidentified portion was not identified because of
degraded DNA and therefore the proportion from each species was recalculated after removing the unidentified
category. The insert zooms in on the less common species that are differentiated by the ivalue from the model.
Table 4. Mean estimatedacontribution to fin trimmings in the Sheung Wan market and the rank order of those point estimates within a fin-trimming
Rank order Contribution (%)
Species or group all large medium small all large medium small
Prionace glauca 1 1 1 1 35.07 (19.3–54) 47.87 (28.4–67.5) 36.69 (20.7–55.5) 27.3 (11.3–49.7)
2 2 2 3 5.43 (2.1–11.4) 4.45 (1.4–10.1) 7.53 (2.8–15.4) 4.75 (1.1–12.1)
Carcharhinus spp. 3 4 3 2 3.95 (1.5–8.4) 1.99 (0.6–4.9) 2.56 (0.8–5.9) 6.93 (1.9–16.4)
Sphyrna zygaena 4 5 4 5 2.41 (0.8–5.3) 1.8 (0.5–4.4) 2.55 (0.8–5.9) 2.91 (0.6–7.9)
C. limbatus, C. am-
blyrhynchoides, C.
leiodon, C. tilstoni
5 3 7 7 2.06 (0.7–4.6) 2.25 (0.7–5.4) 1.12 (0.3–2.9) 1.69 (0.3–5)
Sphyrna lewini 6 6 5 4 1.83 (0.6–4.1) 1.29 (0.3–3.3) 1.77 (0.5–4.3) 3.49 (0.8–9.2)
Mustelus spp. 7 NR 8 6 0.87 (0.3–2.1) 0.68 (0.1–1.9) 1.93 (0.3–5.6)
Isurus oxyrinchus 8 7 NR 10 0.42 (0.1–1.1) 0.65 (0.1–1.8) 0.37 (0–1.4)
Dalatias licha 9 NR 9 NR 0.07 (0–0.2) 0.18 (0–0.7)
Carcharhinus sorrah NR NR 6 8 1.15 (0.3–2.9) 0.76 (0.1–2.6)
Rhizoprionodon spp. NR NR NR 9 0.6 (0.1–2.1)
Unidentified 33.42 (23.1–44.1) 30.39 (18.7–43.7) 31.59 (21.2–43.2) 30.5 (18.8–44.5)
Other 14.48 (6.3–26.9) 9.3 (3.3–19.6) 14.18 (6.1–26.5) 18.77 (6.8–37.4)
aEstimated with the Poisson multinomial applied to the counts of trimmings in each size category (95% CI is in parentheses).
NR, not reported in the model output.
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Volume 00, No. 0, 2017
12 Shark Fin Trade
Overall Species Diversity and Characteristics of the
Contemporary Fin Trade
From the identified fin trimmings sampled, we estimated
there were at least 76 species in the fin trade, indicating a
high potential for species substitution (Eriksson & Clarke
2015). The trade focused on sharks, and the majority of
identified species (80% of the species) were from just
2 of 8 orders (Carcharhiniformes and Lamniformes). Al-
most 50% of species were from 1 family (Carcharhinidae)
(Fig. 1a). Although it is possible our primers failed to
amplify some more distantly related species, trials show
that they amplify species from at least 7 of the 8 extant
orders of sharks (Fields et al. 2015). These primers also
amplified the more distantly related batoids (at least 3
species) and chimaeras (at least 2 species) that are also
present in the fin trade.
It is commonly assumed overexploitation could cause
a collapse in the fin trade (Clarke 2014). Although fin
value tends to increase with fin size (Vannuccini 1999),
small-bodied sharks and chimaeras made up almost half
of the species in the HK trade (48%). This indicates that
consumers are willing to pay for diverse fin sizes and
morphologies. This willingness could facilitate substitu-
tion if supplies of particular species decline and enable
robust trade despite depletion of the most vulnerable
groups (Eriksson & Clarke 2015). One-third of the species
present in the HK trade were listed in threatened cate-
gories by IUCN.
Species Composition
Fin trimmings for a given species were not of uniform
size, and small species were more abundant in small
trimmings. This size and species composition suggests
processers make a variable number of cuts per fin; thus,
our Bayesian estimates of species composition of the trim-
mings is most likely to be proportional to the number of
individual sharks in the trade or a combination of this
and fin mass. Clarke et al.’s (2006a) survey of the Hong
Kong fin trade in 1999–2001 produced an estimate of
the partial species composition of the trade relative to
the traded weight of each species in the HK market.
The species-specific proportions we and Clarke et al.
(2006a) estimated are not directly comparable, and our
estimates should not be expressed as a direct proportion
of a species in trade. Instead, our metrics represent an
index of relative abundance. Comparisons of the rank
order of species between the periods 1999–2001 and
2014–2015 are likely valid because both studies enabled
an assessment of the relative importance of an overlap-
ping suite of common species to the global fin trade that
passes through HK.
Despite the high species diversity we found, our re-
sults suggest the contemporary fin trade is dominated
by a small number of species. Only 8 species or com-
plexes likely comprise >1% of the modeled trimmings
each: blue, silky, blacktip complex, scalloped hammer-
head, smooth hammerhead, shortfin mako, bull and spin-
ner sharks. Their contributions ranged from 34.1–64.2%
(blue) to 0.2–1.2% (bull and shortfin mako). Skewed
species composition is concordant with fin exports from
Indonesia, United Arab Emirates, and Taiwan, which are
important suppliers of HK, albeit each with its own
unique set of dominant species (Jabado et al. 2015;
Sembiring et al. 2015; Chuang et al. 2016). It is also
concordant with the 1999–2001 HK trade (Clarke et al.
2006a, 2006b). It is remarkable given the sustained har-
vest over the last 15 years that large hammerheads, dusky,
threshers, and oceanic whitetip sharks all contributed
substantially to both surveys in HK (i.e., 1999–2001 and
2014–2015), despite being globally or regionally listed
as endangered or vulnerable for over a decade (IUCN
2016); there is evidence of large regional declines for at
least some of them (Hayes et al. 2009; Walsh & Clarke
2011; Clarke et al. 2013; Grubbs et al. 2016). Many of
these species have a global distribution, so it is possible
that shifts in geographic sources of fins or expansion of
fishing areas have enabled a relatively high-volume trade
to continue (Eriksson & Clarke 2015). The exception was
the sandbar shark, which was rarely encountered in the
2014–2015 trimmings sampled but common in auctioned
fins in 1999–2001 (Table 2). Two fisheries supplying large
volumes of sandbar shark existed along the Atlantic coast
of the United States and the coast of Western Australia
in 1999–2001, but they were subsequently subject to
large reductions in catch limits in response to popula-
tion declines (SEDAR 2010; McAuley & Rowland 2012).
These declines and regulatory measures may explain the
reduced abundance of this species in the contemporary
HK fin trade.
both surveys, consistent with very high landings of this
species between 1999–2001 and 2014–2015 (Davidson
et al. 2015; Eriksson & Clarke 2015). Clarke et al. (2006b)
suggested global blue shark landings were at or close to
maximum sustainable yield in 1999–2001, yet the global
proportion of shark landings identified as blue shark
and reported to the FAO nearly tripled from 2000 to
2013 (Davidson et al. 2015; Eriksson & Clarke 2015). Al-
though this increase could in part reflect improvements
in species-specific reporting over this period, it is also
plausible that the market contribution of this productive
species has increased (Davidson et al. 2015; Eriksson &
Clarke 2015). Blue sharks are not currently classified as
overfished, but the quality of the data used in assessments
is generally poor, and none of the regional fisheries man-
agement organizations (RMFOs) have imposed catch lim-
its for this species (Simpfendorfer & Dulvy 2017). Given
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Volume 00, No. 0, 2017
Fields et al. 13
the importance of this species in the fin trade, it is critical
that RMFOs develop assessment-based catch limits that
are closely monitored and enforced.
We identified additional species and species groups as
relatively common in trade that were outside of the scope
of Clarke et al.’s (2006b) 1999–2001 survey. We cate-
gorized most of them as oceanic; large or small coastal
sharks; coastal batoids; or chimaeras. Except for the chi-
maeras and oceanic species, most are poorly studied
and not managed for sustainability anywhere outside of
the United States, Australia, or New Zealand (Table 3)
(Simpfendorfer & Dulvy 2017). The oceanic species
(porbeagle and salmon shark) are very different. The
porbeagle is listed on CITES Appendix II and consid-
ered vulnerable to extinction, and the salmon shark is
at low risk of extinction but is largely unmanaged in
much of its North Pacific distribution (Stevens et al. 2006;
Goldman et al. 2009). The relatively commonly traded,
large coastal (including the blacktip shark species com-
plex, spinner, Java, and sicklefin lemon), and small
coastal sharks (spot-tail, sharpnoses, and smoothhounds)
often have restricted ranges (Musick et al. 2004) and
highly structured populations that make them vulnera-
ble to localized overexploitation and possibly regional
extirpation due to limited immigration (Chapman et al.
2015). Many of the small species are relatively productive
es 2016) and generally considered of least concern
(IUCN 2016). A few of the large and small coastal species
support sustainably managed fisheries (Simpfendorfer &
Dulvy 2017), and more potentially could, given their
productivity, if basic investments in fisheries manage-
ment were made. The coastal chimaeras tend to support
sustainably managed fisheries (Didier et al. 2012; IUCN
2016), whereas the coastal batoids in trade primarily orig-
inate from Rhinidae (wedgefishes), which are listed as at
high risk of extinction (IUCN 2016). Overall, the near
absence of sustainably managed fisheries for many of
the coastal sharks and batoids highlights the need for
a new focus on domestic coastal fisheries management
in many of the nations that supply fins to HK and more
stringent protection and trade regulation for some highly
vulnerable species (e.g., wedgefish).
Future Monitoring
Our results indicate species-aggregated data from the fin
trade kept by HK and FAO potentially mask key species-
specific trends that urgently need monitoring. The ma-
jority of species in trade are not as yet known to sup-
port sustainably managed fisheries outside the developed
world, and around one-third of the species we identified
are at serious risk of extinction (IUCN 2016). We suggest
monitoring fin trimmings in HK over time would reveal
trends in the relative abundance of species in trade. This
could enable robust testing of the hypothesis that the
contribution of more productive species (e.g., blue) is
increasing and obscuring declines in other species. Con-
tinued monitoring could also enable an assessment of the
effect of new species-specific management measures, in-
cluding CITES listings and RMFO zero-retention policies,
on the trade of threatened chondrichthian species.
We particularly thank F. Yang for continuous support
in the laboratory and K. Ho and the many volunteers
who have aided us in the field and in the laboratory. We
also thank the anonymous reviewers for their suggestions
and insights. This study was funded by the Pew Charita-
ble Trusts, The Pew Fellowship Program, and the Roe
Foundation (to D.D.C.).
Supporting Information
Additional methods (Appendix S1), images of fin trim-
ming and rarefaction curves (Appendix S2), and ad-
ditional tables reporting model parameters and the
contingency table (Appendix S3) are available as part
of the on-line article. The authors are solely responsi-
ble for the content and functionality of these materials.
Queries (other than absence of the material) should be
directed to the corresponding author.
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Conservation Biology
Volume 00, No. 0, 2017
... The most commonly encountered shark in this work was the blue shark. This shark was found in 8 of 14 bowls, and though not listed under CITES and classified as Near Threatened under the IUCN Red List of Threatened Species, blue sharks are one of the most commonly encountered sharks in the global fin trade and are traded extensively throughout Hong Kong and Singapore [9,12,48,49]. Scientific evidence suggests that this species is overexploited and should have its catch regulated to avoid population crashes [44,50]. Blue sharks collected from the Atlantic and Australian waters have been documented to contain high levels of mercury and selenium in muscle and liver tissue [25,51]. ...
... Soups in this study ranged in price from a minimum of USD 9.11 to a maximum of USD 53. 49. ...
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Shark fin soup, consumed by Asian communities throughout the world, is one of the principal drivers of the demand of shark fins. This near USD 1 billion global industry has contributed to a shark population declines of up to 70%. In an effort to arrest these declines, the trade in several species of sharks is regulated under the auspices of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Despite this legal framework, the dried fins of trade-regulated sharks are frequently sold in markets and consumed in shark fin soup. Shark fins found in soups break down into a fibrous mass of ceratotrichia, meaning that identifying the species of sharks in the soup becomes impossible by visual methods. In this paper, we use DNA barcoding to identify the species of sharks found in bowls of shark fin soup collected in Singapore. The most common species identified in our samples was the blue shark (Prionace glauca), a species listed as Near Threatened on the International Union for Conservation of Nature (IUCN) Red List with a decreasing population, on which scientific data suggests catch limits should be imposed. We identified four other shark species that are listed on CITES Appendix II, and in total ten species that are assessed as Critically Endangered, Endangered or Vulnerable under the IUCN Red List of Threatened Species. Globally, the blue shark has been shown to contain levels of mercury that frequently exceed safe dose limits. Given the prevalence of this species in the examined soups and the global nature of the fin trade, it is extremely likely that consumers of shark fin soup will be exposed to unsafe levels of this neurotoxin.
... The lack of local processing plants for fins also means that wedgefishes and giant guitarfishes imported from Indonesia had a higher likelihood of having their fins removed prior to arrival in Singapore, and fins are processed elsewhere. The fact that fins were removed from individuals regardless of their size was not surprising since there is now a burgeoning trade of small fins in Southeast Asia to supply demand for inexpensive shark fin soup [39,40]. Preference for wedgefish fins over those from other sharks in Kuching is also indicative of their high-quality fin needles and unique texture which can command higher prices from exporters to supply the Chinese market. ...
Wedgefishes (Rhinidae) and giant guitarfishes (Glaucostegidae) are amongst the most threatened marine taxa globally. Research was undertaken in Singapore, a globally significant trading hub for shark and ray products, between May 2019 (two months after they were proposed for listing on the Convention on International Trade in Endangered Species of Flora and Fauna (CITES)) and August 2019 (three months before listings entered into force). The study documents the composition of imports and landings, estimates the scale of the trade, describes the supply chain, and analyzes completeness of product labels through surveys in fishery ports and retail markets as well as informal interviews with traders. Of 590 individuals recorded at fishery ports, 215 from six species could be identified to the species-level. Rhynchobatus australiae was the most commonly encountered wedgefish species (66%) while only one species of giant guitarfish (Glaucostegus typus) was recorded. Individuals were primarily claimed to be imported from Indonesia and Malaysia. The high value of wedgefish fins was evident as a large proportion of individuals without fins (66%) were recorded. Businesses in Singapore were utilizing by-products of the fin trade which appeared to have a distinct supply chain. Traders noted declining supplies of wedgefishes and dried shark fins in recent years. Shark and ray products notably lacked information on species and country of origin on their labels. Findings here provide baseline data for determining the effectiveness of new trade controls and suggest that a multi-pronged approach with trade monitoring, additional traceability and labeling requirements, and enhanced fisheries management would conserve globally declining, wild populations.
... Shark fins, however, as highly prized shark commodities, have been the subject of numerous studies aiming to describe the trade and market dynamics for shark fins and the species composition at different points in trade (e.g. Cardeñosa et al., 2020;Chuang et al., 2016;Clarke et al., 2006aClarke et al., , 2006bCripps et al., 2015;Fields et al., 2018). Shark fin soup is historically considered one of the most expensive seafood dishes and the average value of the global shark fin imports and exports has remained relatively high, with 286 and 216 million USD declared each year respectively (FAO, 2019a). ...
The past decade has seen a considerable rise in international concern regarding the conservation status of sharks and rays. The demand for highly prized shark commodities continues to fuel the international trade and gives fisheries incentive to use these resources, which have a low intrinsic capability to recover. Recognising the urgency for regulation, many countries voted to include more shark and ray species in the Appendices of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). However, the identification of fins in fisheries landings before they enter international trade is a major limitation for CITES compliance. This study reports the current performance of the iSharkFin system, a machine learning technology which aims to allow users to identify the species of a wet shark dorsal fin from its image. Photographs of 1147 wet dorsal fins from 39 shark species, collected in 12 countries, were used to train the algorithm over a four-year period. As new cohorts of images were used to test the performance of the learning algorithm, the accuracy of species assignments of known specimens was variable but did increase, reaching 85.3% and 59.1% at genus and species level respectively. The accuracy in predicting CITES-listed sharks versus unlisted sharks was 94.0% based on the 39 species currently represented in the baseline. Our results suggest that if supplied with high data inputs for specific fisheries assemblages and accompanied by user training, iSharkFin has promise for site-specific development as a rapid field identification tool in fisheries monitoring, and as a screening tool alongside traditional field morphology to detect potential CITES specimens for fisheries compliance and enforcement.
... All four species are listed in Barcelona Convention Annex II, and one was additionally listed in CITES appendix II and CMS: Appendix I & II. Protected species accounted only for the 3.6% of the samples, a considerably low percentage in comparison with the global trends, where unregulated elasmobranch meat landings and commercialization are considered more common (Appleyard, White, Vieira, & Sabub, 2018;Fields et al., 2018;Palumbi, Robinson, Van Houtan, & Jorgensen, 2018). As expected, all protected batoids species encountered in the Mediterranean, are listed as either Endangered or Critically Endangered in the Red List of threatened species of the IUCN for the Mediterranean . ...
Mislabeling of seafood products and marketing of protected species remains a worldwide issue despite the labeling regulations set at a local, European and International level. DNA barcoding has proven to be the most popular and accurate method of detection of fraudulent seafood products. This study investigated the batoid meat market of Greece, the mislabeling rates and the protected species occurrence. A total of 114 ray products were collected from fishmongers, open markets, supermarkets, and restaurants across eight Greek cities. The cytochrome oxidase subunit I (COI) gene was used to analyze samples, and the sequences were compared against genetic databases for species identification. At least 13 species across nine genera were identified. The results did not indicate significant differences in species utilization among cities, retailers, and labels. However, in the pairwise comparisons, Athens differed from all other locations and a similar trend was followed by the label “salachi”. Moderate mislabeling levels were recorded (13.5%), while 3.5% of the identified samples belonged to species with prohibitions on landings, confirming an ongoing market for protected species. Overall, 19.8% of the samples originated from species that are locally listed in threatened categories of the IUCN Red List of species.
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Globally, elasmobranch populations (sharks and rays) are declining due to increasing anthropogenic and climate pressures. Genetic connectivity between elasmobranch populations is crucial to ensure their persistence and sustain the ecological integrity of ecosystems. Genetic connectivity implies gene flow among discrete populations occurring via the dispersal of individuals outside their population of origin, followed by reproduction — a process that can be biased between sexes (i.e. sex-biased dispersal or SBD). In this thesis, I first examine the current knowledge of population structure and SBD in elasmobranchs, and the tools that are commonly used. Next, this thesis uses novel genomic approaches (kinship, nuclear single nucleotide polymorphisms, and mitochondrial genomes) to provide insights into the patterns of (i) population structure, (ii) sex-chromosome systems, and (iii) SBD in elasmobranchs. My thesis focuses on three shark species that allow the study of dispersal patterns based on life history, local ecology, population size and different seascape features: Northern River Shark, Glyphis garricki; School Shark, Galeorhinus galeus; and Bull Shark, Carcharhinus leucas. Overall, male-biased dispersal (MBD) was observed in 25 of the 50 studied species. Population structure was found at both broad (Bull Shark) and fine (Northern River Shark) spatial scales. I demonstrated that 19 out of the 21 studied elasmobranch species contain X and Y chromosomes using the R function I developed. Combined, the sex-linked markers and kinship data supported the evidence of MBD in the Northern River Shark and the Bull Shark. My final discussion synthesised the observed dispersal patterns and examines the potential ecological and evolutionary drivers for these patterns. I critically compared the genetic and analytical approaches for the detection of population structure and SBD. Finally, potential implications of these quantitative findings for management were highlighted.
Shark populations have declined by more than 70% over the past 50 years. These declines have largely been attributed to increases in fishing efforts. Despite increased public awareness surrounding the conservation of sharks, three-quarters of all oceanic shark species are currently considered at risk of extinction. Here, we use DNA barcoding to identify shark DNA found in pet food purchased within Singapore. We identified a number of sharks that have some degree of control over their trade exerted under the auspices of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), or through their classification as threatened by the International Union for Conservation of Nature (IUCN). The most commonly identified shark was the blue shark, Prionace glauca , a species that is not listed in CITES or classified as threatened by the IUCN, but one which scientific evidence suggests is overexploited and should have its catch regulated. The second most commonly encountered species was the CITES Appendix II listed silky shark, Carcharhinus falciformis. None of the products specifically listed shark as an ingredient, listing only generic terms, such as “ocean fish,” “white fish,” and “white bait.” The vague terminology used to describe pet food ingredients, and in some cases, the mislabeling of contents, prevents consumers – in this case, pet owners – from making informed and environmentally conscious decisions; consequently, pet owners and animal lovers may unwittingly be contributing to the overfishing of endangered sharks.
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The bull shark (Carcharhinus leucas Valenciennes, 1839) is a large, primarily coastally distributed shark famous for its ability to penetrate far into freshwater bodies in tropical, subtropical, and warm-temperate climates. It is a cosmopolitan species with a geographical range that includes the coastlines of all major ocean basins (Atlantic Ocean, Indian Ocean, Pacific Ocean). As a consequence, freshwater occurrences of C. leucas are possible everywhere inside its geographic range. Carcharhinus leucas is a fully euryhaline, amphidromous species and possibly the widest-ranging of all freshwater tolerating elasmobranchs. This species is found not only in river systems with sea access that are not interrupted by human impediments but in hypersaline lakes as well. Rivers and estuaries are believed to be important nursery grounds for C. leucas, as suggested by observations of pregnant females in estuaries and neonates with umbilical scars in rivers and river mouths. Due to the physical capability of this species to enter riverine systems, the documentation of its occurrence in fresh and brackish water is essential for future conservation plans, fishery inspections, and scientific studies that focus on the link between low salinity habitats, shark nurseries, and feeding areas. The author’s review of the available literature on C. leucas revealed the absence of a comprehensive overview of fresh and brackish water localities (rivers and associated lakes, estuaries) with C. leucas records. The purpose of this literature review is to provide a global list of rivers, river systems, lakes, estuaries, and lagoons with records and reports of this species, including a link to the used references as a base for regional, national, and international conservation strategies. Therefore, the objective of this work is to present lists of fresh and brackish water habitats with records of C. leucas as the result of an extensive literature review and analysis of databases. This survey also took into account estuaries and lagoons, regarding their function as important nursery grounds for C. leucas. The analysis of references included is not only from the scientific literature, but also includes semi-scientific references and the common press if reliable. The result of 415 global fresh and brackish water localities with evidence of C. leucas highlights the importance of these habitats for the reproduction of this species. Moreover, gaps in available distribution maps are critically discussed as well as interpretations and conclusions made regarding possible reasons for the distribution range of C. leucas, which can be interpreted as the result of geographic circumstances, but also as a result of the current state of knowledge about the distribution of this species. The results of the examination of available references were used to build a reliable and updated distribution map for C. leucas, which is also presented here.
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In recent decades, a combination of increasing demand and economic globalisation has created a global market for elasmobranch products, especially the highly prized shark fins for Asian markets. Morphological species identification, as well as traditional cytochrome c oxidase subunit I (COI) barcoding of shark fins and other products, become challenging when in a processed state (such as dried or bleached shark fins). Here a mini-barcoding multiplex assay was applied to determine the species of origin in case studies from southern Africa involving confiscated shark fins in different states of processing. This highlights that the illegal shark fin trade in southern Africa to a large extent comprises threatened species. Matching of sequences of the confiscated fins against public databases revealed several threatened species, including the CITES-listed species Carcharodon carcharias, Carcharhinus longimanus, Isurus oxyrinchus, Rhynchobatus djiddensis and Sphyrna lewini. The findings highlight the need for improved trade monitoring, such as to eliminate illegal trade in shark fins, which can in part be achieved through more widespread genetic sampling of internationally traded products. However, a major limitation to DNA barcoding in general lies in the lack of curated voucher specimens available on public databases. To facilitate the application of molecular methods in a more comprehensive evaluation of elasmobranch trade regionally, a concerted effort to create reliable curated sequence data is recommended.
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Identifying the geographical scale at which natural populations structure themselves is essential for conservation. One way to gauge this structure is by estimating local effective population size (Ne) and the associated measure of effective number of breeders (Nb), as the smaller and more isolated natural populations are, the smaller Ne and Nb they will present. However, as Ne and Nb are greatly influenced by demographic events and by both species’ behavior and biology, assessing the effectiveness of sample design is necessary to ensure the reliability of said estimates. Here, we first test the sample size effect on yearly Nb and generational Ne estimates from a lemon shark Negaprion brevirostris nursery in Bimini (The Bahamas) and subsequently compare these parameters to estimates of the minimal number of breeders based on pedigree reconstruction. We found that yearly estimates of Nb are positively correlated to annual variations in number of breeders estimated via pedigree reconstructions. Moreover, we measured that 30 individuals from a single cohort were sufficient to obtain reliable yearly estimates of Nb in Bimini’s lemon sharks. We then estimated generational Ne in 10 lemon shark nurseries across the Western Atlantic. Almost every nursery sampled represents an independent population on a generational time scale, with Ne rarely higher than 100 individuals. Our study reveals strong local population structure in lemon sharks, and thus their exposure to localized depletion or extirpation, suggesting that studies of coastal shark nursery areas could routinely estimate Ne and Nb to obtain management-relevant information on adult populations. .
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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.
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When identifying potential trophic cascades, it is important to clearly establish the trophic linkages between predators and prey with respect to temporal abundance, demographics, distribution, and diet. In the northwest Atlantic Ocean, the depletion of large coastal sharks was thought to trigger a trophic cascade whereby predation release resulted in increased cownose ray abundance, which then caused increased predation on and subsequent collapse of commercial bivalve stocks. These claims were used to justify the development of a predator-control fishery for cownose rays, the "Save the Bay, Eat a Ray" fishery, to reduce predation on commercial bivalves. A reexamination of data suggests declines in large coastal sharks did not coincide with purported rapid increases in cownose ray abundance. Likewise, the increase in cownose ray abundance did not coincide with declines in commercial bivalves. The lack of temporal correlations coupled with published diet data suggest the purported trophic cascade is lacking the empirical linkages required of a trophic cascade. Furthermore, the life history parameters of cownose rays suggest they have low reproductive potential and their populations are incapable of rapid increases. Hypothesized trophic cascades should be closely scrutinized as spurious conclusions may negatively influence conservation and management decisions.
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The increasing consumption of shark products, along with the shark's fishing vulnerabilities, has led to the decrease in certain shark populations. In this study we used a DNA barcoding method to identify the species of shark landings at fishing ports, shark fin products in retail stores, and shark fins detained by Taiwan customs. In total we identified 23, 24, and 14 species from 231 fishing landings, 316 fin products, and 113 detained shark fins, respectively. All the three sample sources were dominated by Prionace glauca, which accounted for more than 30% of the collected samples. Over 60% of the species identified in the fin products also appeared in the port landings, suggesting the domestic-dominance of shark fin products in Taiwan. However, international trade also contributes a certain proportion of the fin product markets, as four species identified from the shark fin products are not found in Taiwan's waters, and some domestic-available species were also found in the customs-detained sample. In addition to the species identification, we also found geographical differentiation in the cox1 gene of the common thresher sharks (Alopias vulpinus), the pelagic thresher shark (A. pelagicus), the smooth hammerhead shark (Sphyrna zygaena), and the scalloped hammerhead shark (S. lewini). This result might allow fishing authorities to more effectively trace the origins as well as enforce the management and conservation of these sharks.
Zoogeography is the study of patterns of distribution of animals on earth and the biological, geological and climatic processes that influence these patterns (Lieberman, 1999; Mooi and Gill, 2002). Historically, two major fields of scientific inquiry have developed relative to zoogeography: historical zoogeography and ecological zoogeography (Brown and Lomolino, 1998; Mooi and Gill, 2002). Historical zoogeography examines distributions of animals over large spatial scales, often at various taxonomic levels, and involves zoogeographic mechanisms over long temporal scales (Briggs, 1995). Ecological zoogeography focuses on short-term ecological and evolutionary processes that influence the distribution, abundance, and diversity of animals, usually at lower taxonomic levels and small spatial scales (MacArthur and Wilson, 1967). This chapter presents a review of the historical zoogeography of sharks (Selachii).
Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity. It also features a large number of worked examples and a wide range of applications from various disciplines. The book introduces regression models, techniques for criticism and comparison, and a wide range of modelling issues before going into the vital area of hierarchical models, one of the most common applications of Bayesian methods. It deals with essentials of modelling without getting bogged down in complexity. The book emphasises model criticism, model comparison, sensitivity analysis to alternative priors, and thoughtful choice of prior distributions—all those aspects of the "art" of modelling that are easily overlooked in more theoretical expositions. More pragmatic than ideological, the authors systematically work through the large range of "tricks" that reveal the real power of the BUGS software, for example, dealing with missing data, censoring, grouped data, prediction, ranking, parameter constraints, and so on. Many of the examples are biostatistical, but they do not require domain knowledge and are generalisable to a wide range of other application areas. Full code and data for examples, exercises, and some solutions can be found on the book’s website.
Shark fin has long been one of the most highly demanded 'luxury seafood' in the Chinese market. From the latest available data (1998–2013), 130 countries/territories around the world were recorded as exporting shark fin to Hong Kong. Spain, Taiwan, Indonesia, UAE, Singapore and Japan made up over 50% of all of Hong Kong's shark fin imports. Comparison of Hong Kong's import data with the exporting countries/territories' FAO declarations indicates that some countries/territories are potentially consistently underreporting shark fin exports. Since 2009 Vietnam had overtaken China as the most important destination of Hong Kong's shark fin re-exports, a change that warrants further investigation. Ocean transportation was also identified as the most important transportation mode for shark fin imports into and re-exports from Hong Kong. Given the importance of Hong Kong and based on findings from this study, suggestions are made for the Hong Kong Government to tighten controls to reduce illegal trades, and eliminate loopholes so that a more comprehensive statistical representation of the shark fin trade may be captured for future analysis.
Sharks, rays and chimeras (class Chondrichthyes; herein ‘sharks’) today face possibly the largest crisis of their 420 million year history. Tens of millions of sharks are caught and traded internationally each year, many populations are overfished to the point where global catch peaked in 2003, and a quarter of species have an elevated risk of extinction [1–3]. To some, the solution is to simply stop taking them from our oceans, or prohibit carriage, sale or trade in shark fins [4]. Approaches such as bans and alternative livelihoods for fishers (e.g. ecotourism) may play some role in controlling fishing mortality but will not solve this crisis because sharks are mostly taken as incidental catch and play an important role in food security [5–7]. Here, we show that moving to sustainable fishing is a feasible solution. In fact, approximately 9% of the current global catch of sharks, from at least 33 species with a wide range of life histories, is biologically sustainable, although not necessarily sufficiently managed.
The intrinsic rate of population increase (rmax) is a fundamental metric in ecology and evolution of immediate practical application in conservation and wildlife management. I examine the interpretation of rmax by revisiting the theory behind the density-independent and density-dependent paradigms. The criticism that density-independent approaches underestimate rmax per se, often expressed in the field of fisheries, is shown to be theoretically unfounded. The difficulty in estimating rmax is due to lack of knowledge on the depletion level of the population rather than theory. I reviewed a method commonly used to estimate extinction risk of marine and terrestrial populations and show that it has been used incorrectly. I also examined five other methods to calculate rmax, the Euler-Lotka equation, and four other methods derived from it. I used the same data inputs for a suite of 65 shark populations with a broad range of life histories as an example to show that the incorrectly used extinction risk method overestimates rmax. I compared the rmax values for sharks obtained with the incorrectly applied extinction risk method to published values for other vertebrate taxa to further show that this method generates implausible values for this group of predators. I advocate focusing on obtaining estimates of all required vital rates simultaneously when possible while taking into consideration the exploitation history of the population under study as a pragmatic way to provide plausible estimates of rmax. The Euler-Lotka equation and its derivations are recommended for different degrees of data availability, particularly for slow- and medium-growing populations, to provide sensible advice for conservation and management of living vertebrates in situations where a series of credible abundance estimates are not available as is often the case in marine systems. Methods that combine allometry and demography should also be further explored.