Identification of Shark Species Composition and …†Guy Harvey Research Institute, Nova Southeastern University Oceanographic Center, 8000 North Ocean Drive, Dania Beach, FL 33004,

Identification of Shark Species Composition andProportion in the Hong Kong Shark Fin Market Basedon Molecular Genetics and Trade Records

SHELLEY C. CLARKE,∗ JENNIFER E. MAGNUSSEN,† DEBRA L. ABERCROMBIE,†MURDOCH K. MCALLISTER,‡ AND MAHMOOD S. SHIVJI†∗∗∗Joint Institute for Marine and Atmospheric Research, c/o National Research Institute of Far Seas Fisheries, 5-7-1 Orido-Shimizu,

Shizuoka 474-0022, Japan

†Guy Harvey Research Institute, Nova Southeastern University Oceanographic Center, 8000 North Ocean Drive, Dania Beach,

FL 33004, U.S.A.

‡Renewable Resources Assessment Group, Imperial College London, Royal School of Mines Building, Prince Consort Road,

London SW7 2AZ, United Kingdom

Abstract: The burgeoning and largely unregulated trade in shark fins represents one of the most seriousthreats to shark populations worldwide. In Hong Kong, the world’s largest shark fin market, fins are classifiedby traders into Chinese-name categories on the basis of market value, but the relationship between marketcategory and shark species is unclear, preventing identification of species that are the most heavily traded.To delineate these relationships, we designed a sampling strategy for collecting statistically sufficient numbersof fins from traders and categories under conditions of limited market access because of heightened tradersensitivities. Based on information from traders and morphological inspection, we hypothesized matchesbetween market names and shark taxa for fins within 11 common trade categories. These hypotheses were testedusing DNA-based species identification techniques to determine the concordance between market category andspecies. Only 14 species made up approximately 40% of the auctioned fin weight. The proportion of samplesconfirming the hypothesized match, or concordance, varied from 0.64 to 1 across the market categories. Weincorporated the concordance information and available market auction records for these categories intostochastic models to estimate the contribution of each taxon by weight to the fin trade. Auctioned fin weightwas dominated by the blue shark ( Prionace glauca), which was 17% of the overall market. Other taxa, includingthe shortfin mako ( Isurus oxyrinchus), silky (Carcharhinus falciformis), sandbar (C. obscurus), bull (C. leucas),hammerhead (Sphyrna spp.), and thresher (Alopias spp.), were at least 2–6% of the trade. Our approach tomarketplace monitoring of wildlife products is particularly applicable to situations in which quantitative dataat the source of resource extraction are sparse and large-scale genetic testing is limited by budgetary or othermarket access constraints.

Key Words: DNA forensics, marketplace monitoring, marketplace sampling, seafood, wildlife

Identificacion de la Composicion y Proporcion de Especies de Tiburon en el Mercado de Aletas de Tiburon en

Hong Kong Con Base en Genetica Molecular y Registros Comerciales

Resumen: El floreciente y en gran parte no regulado comercio de aletas de tiburon representa una de lasmas serias amenazas para las poblaciones de tiburones en todo el mundo. En Hong Kong, el mayor mercadode aletas de tiburon en el mundo, los comerciantes clasifican a las aletas en categorıas con nombres en Chinocon base en el valor de mercado, pero la relacion entre la categorıa de mercado y las especies de tiburon noes clara, lo que limita la identificacion de especies que son sujetas de mayor comercio. Para delinear estas

∗∗Address correspondence to M. Shivji, email [email protected] submitted September 4, 2004; revised manuscript April 12, 2005.

201

Conservation Biology Volume 20, No. 1, 201–211C©2006 Society for Conservation Biology

DOI: 10.1111/j.1523-1739.2006.00247.x

202 Species in Shark Fin Trade Clarke et al.

relaciones, disenamos una estrategia para recolectar con comerciantes un numero estadısticamente suficientede aletas bajo condiciones de mercado de acceso limitado debido a la intensificacion de las sensibilidades delos comerciantes. Con base en informacion de los comerciantes y en inspeccion morfologica, hicimos hipotesissobre la correspondencia entre los nombres comerciales y los taxa de tiburones para aletas dentro de 11 cate-gorıas comerciales comunes. Probamos las hipotesis utilizando tecnicas de identificacion de especies con baseen ADN para determinar la concordancia entre la categorıa de mercado y la especie. Solo 14 especies abar-caron aproximadamente 40% del peso de las aletas a la venta. La proporcion de muestras que confirmaron lahipotesis de correspondencia, o concordancia, vario de 0.64 a 1 en las categorıas de mercado. Incorporamosla informacion de concordancia y los registros de venta disponibles en modelos estocasticos para estimar lacontribucion, segun el peso, de cada taxon al comercio de aletas. El peso de aletas a la venta fue dominadopor Prionace glauca, que comprendio 17% del total del mercado. Otros taxa, incluyendo a Isurus oxyrinchus,Carcharhinus falciformis, C. obscurus, C. leucas, Sphyrna spp. y Alopias spp., comprendieron por lo menos entre2 y 6% del comercio. Nuestro metodo para monitoreo del mercado de productos de vida silvestre es partic-ularmente aplicable a situaciones de escasez de datos cuantitativos obtenidos en el sitio de extraccion delrecurso y cuando el analisis genetico a gran escala esta limitado por restricciones presupuestarias o de accesoal mercado.

Palabras Clave: ADN forense, mariscos, monitor, muestreo, vida silvestre

Introduction

Advances in molecular biology over the past decade havecreated a variety of powerful conservation tools, includ-ing the ability to identify the species or population originof wildlife products, enabling new types of trade monito-ring and analysis. The often secretive nature of the wildlifetrade and the prohibitively high cost of purchasing sam-ples, however, may constrain sample access and acqui-sition and thus limit application of genetic technology.Under these circumstances, the appropriate choice ofstudy objectives and the sampling design become criti-cal aspects of any research program aimed at surveyingmarkets to monitor and quantify wildlife trade.

Many researchers have applied genetic techniques todetermine whether particular species, populations, or in-dividuals are present in wildlife trade (e.g., Malik et al.1997; Birstein et al. 1998; Hoelzel 2001; Dalebout et al.2002). These researchers, however, were concerned pri-marily with demonstrating that the proposed techniqueis effective for forensic identification of individual mar-ketplace products and did not attempt to characterizethe composition and proportion of species in the tradeas a whole. Marketplace sampling and characterizationof wildlife products are particularly valuable when thesources of extraction (e.g., landing ports for fish or hunt-ing camps for terrestrial mammals) are diffuse and lackstandardized, species-specific records. In such cases mon-itoring of species parts in trade, particularly in large ware-houses at the center of global supply chains, may be themost effective means of obtaining data necessary for off-take assessment and management.

Increasing awareness of the vulnerability of sharkspecies to exploitation (Camhi et al. 1998; Castro et al.1999) and a documented proliferation of finning (i.e., re-moval of fins and discarding of the carcass at sea) in some

fisheries (Camhi 1999) have contributed to growing con-cerns that the fin trade may be driving shark catches tounsustainable levels. Policy responses to these concerns,including the Food and Agriculture Organization’s Inter-national Plan of Action for Sharks (Food and AgricultureOrganization 1998) and the listing of three shark speciesin the appendices of the Convention on InternationalTrade in Endangered Species of Wild Fauna and Flora(CITES), have been hampered by a lack of species-specificcatch and trade data for most fisheries (Clarke 2004a).Shark resource management based on an amalgam ofspecies is suboptimal because it is likely to exaggerate theconservation concern for some species (e.g., sharks withhigh fecundities and low age of maturity) while poten-tially understating the vulnerability of less prolific sharkstocks (Smith et al. 1998; Cortes 2002).

Hong Kong is the world’s largest shark fin market, rep-resenting at least 50% of the global trade (Fong & Ander-son 2002; Clarke 2004b). Previous studies of the HongKong fin trade describe fin types with primarily Englishcommon names for sharks (Parry-Jones 1996; Vannuccini1999; Fong & Anderson 2000) and rely solely on traders’knowledge of these English names, with no means of inde-pendently verifying the actual species origin. Traders use30–45 market categories of fins (Yeung et al. 2000), butthe Chinese names of these categories do not correspondto the Chinese taxonomic names of shark species (Huang1994). Instead Chinese market categories for shark finsappear to be organized primarily by the quality of fin raysproduced and secondarily by distinguishing features ofdried fins because often traders have never observed thewhole animal.

Our recent analysis of Hong Kong shark fin auctiondata produced minimum estimates of quantities (metrictonnes [mt]) of shark fins traded in 11 market categories.We used statistical models to account for missing records

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Clarke et al. Species in Shark Fin Trade 203

and simulate a complete dataset (Clarke et al. 2004). The11 studied market categories comprised 46% of the auc-tioned shark fins, with the remainder auctioned underother trade names or in unspecified categories. Over-all, 18% of the auctioned fins were labeled as ya jian,which on the basis of trader information and morpholog-ical examination were hypothesized to originate from theblue shark (Prionace glauca). Several other categorieshypothesized to derive from other coastal and pelagicsharks comprised 3–5% each of the estimated auctionedfin quantity (Clarke et al. 2004). Although this study pro-vides the first quantitative information on trade composi-tion by market category, its usefulness is limited becauseChinese market categories could not be related conclu-sively to shark taxa and thus extraction rates for individualspecies could not be assessed.

Here we investigated, using molecular genetic identi-fication, concordances between the most common Chi-nese market categories and species in the Hong Kongshark fin market. These concordance results were de-signed to be modeled in conjunction with estimates ofthe traded weight of shark fins in each market category(Clarke et al. 2004) to better understand the compositionand contribution by species to the fin trade. Another ofour objectives was to devise effective market samplingstrategies for animal parts under conditions fraught withsampling constraints and to guide other applications ofmolecular diagnostic methods to wildlife trade monitor-ing faced with similar access limitations.

Methods

Genetic Identification

The recent development of diagnostic DNA sequencetests with species-specific PCR primers for sharks be-lieved to be common in the shark fin trade has provideda new tool for studying this trade. Detailed methodolo-gies for these diagnostic tests are provided in Pank etal. (2001), Shivji et al. (2002), Chapman et al. (2003),Abercrombie (2004), Nielsen (2004), and Abercrombieet al. (2005), and are overviewed briefly here. The tech-nique relies on DNA sequence differences among sharkspecies in the nuclear ribosomal DNA ITS2 locus for thedevelopment of species-specific primers. Each primer isa short, synthetic, single-stranded piece of DNA designedto recognize (anneal to) and PCR amplify only DNA froma single shark species (e.g., a primer designed for short-fin mako shark will amplify only DNA from fins of thatspecies), producing a species-specific DNA band (ampli-con). Each primer we used here was tested extensivelyagainst its target species from worldwide sources to en-sure its global diagnostic utility (i.e., absence of false nega-tives) and approximately 72 nontarget species, includingvery closely related species (congeners) and those be-

lieved to be common in the fin trade, to ensure speciesspecificity (i.e., absence of false positives).

Sequences and diagnostic validation data for thespecies-specific primers used in this study are avail-able from the following sources: shortfin mako (Isu-rus oxyrinchus), longfin mako (I. paucus), silky (Car-charhinus falciformis), dusky (C. obscurus), and bluesharks in Shivji et al. (2002); common (Alopias vulpi-nus), bigeye (A. supercilious), and pelagic thresher (A.pelagicus) sharks in Abercrombie (2004); tiger (Galeo-cerdo cuvier), bull (C. leucas), and spinner (C. brevip-inna) sharks in Nielsen (2004); and scalloped (Sphyrnalewini), smooth (S. zygaena), and great hammerhead (S.mokarran) sharks in Abercrombie et al. (2005). The sand-bar shark (C. plumbeus) primer sequence is in Pank etal. (2001; additional validation data from M. Henning &M.S.S., unpublished data).

All primers are highly reliable in their diagnostic ac-curacy (above references), with the following caveatsfor two primers. The dusky primer cross-amplifies twoof its congeners, Galapagos shark (C. galapagensis) andoceanic whitetip shark (C. longimanus). Because fins ofthe oceanic whitetip shark are unmistakably distinct inappearance (Castro 1993), however, we used the duskyprimer to indirectly confirm morphological identificationof these fins. The uncertainty introduced when using thedusky primer is thus limited to the inability to distinguishfins from dusky and Galapagos sharks only. The bull sharkprimer faintly cross-amplifies its congener, the Caribbeanreef shark (C. perezi). Because cross-amplification of theCaribbean reef shark by the bull shark primer is ineffi-cient, however, consistently producing only a very faintamplicon, discrimination between these two species hasnot been problematic (Nielsen 2004). Nevertheless, a sec-ondary testing of all fins identified as originating frombull shark with an extensively validated Caribbean reefshark primer (M. Henning & M.S.S., unpublished data)confirmed identification of bull shark fins.

Sampling Constraints in the Hong Kong Shark Fin Market

Approximately 50 retail or wholesale establishments inthe dried seafood business district of Hong Kong (Sai YinPun) deal in shark fin products. Of this number, about16 wholesalers take turns hosting daily shark fin auctionsand at least an equal number import large quantities offins and then export them to mainland China for process-ing without auctioning. Traders receive fins from at least85 countries and territories (Clarke & Mosqueira 2002)in poorly sorted shipments; because of on-site space lim-itations they immediately re-sort the fins into market cat-egories and promptly auction or sell them. Our samplingopportunities were limited to these re-sorting periods be-cause traders refused us access to the fins at other times.Limits on sampling during re-sorting periods arose from




the patchy nature of the shipments (i.e., during each sam-pling episode fins tended to be clustered in a few marketcategories from a particular source country) and the needto minimize sampling time to avoid delaying re-sorting op-erations. Furthermore, random sampling of the completerange of fins on hand was not feasible because of thelogistics of moving large sacks in cramped warehouses.Given these restrictions, and based on the results of initialenquiries at more than 30 trading houses, instead of ran-dom sampling we tested samples of particular shark finmarket categories across as wide a range of traders andsource countries as possible.

Table 1. A priori hypothesized matches (x→x′) between traders’ market categories and shark taxa for fins targeted in the sampling.

Number ofHypothesized samples required

Market predominant shark Assignedcategory taxa within market Taxonomic a priori CVp = CVp =(x)∗ category (x′) uncertainty value of p 0.1 0.05

Ya jian blue (Prionaceglauca)

visually distinct; low probability ofmixing with other species

0.95 6 22

Qing lian shortfin mako(Isurusoxyrinchus)

some traders mentioned infrequentmixing with the less abundantlongfin mako (I. paucus)

0.80 25 100

Wu yang silky (Carcharhinusfalciformis)

observed presence of silvertip sharkfins (C. albimarginatus); potentialfor mixing with other carcharhinidsespecially Galapagos shark (C.galapagensis)

0.70 43 172

Hai hu dusky (C. obscurus) visually similar to bai qing and ruansha but high value of these finscauses traders to sort themaccurately

0.90 12 45

Bai qing sandbar (C.plumbeus)

visually similar to hai hu and ruan shabut high value of these fins causestraders to sort them accurately

0.90 12 45

Ruan sha tiger (Galeocerdocuvier)

visually similar to hai hu and bai qingbut high value of these fins causestraders to sort them accurately

0.90 12 45

Chun chi smooth andscallopedhammerhead(Sphyrnazygaena and S.lewini)

traders state this category contains allhammerheads (Sphyrna andEusphyra spp., 7 species in total)other than great (S. mokarran);hammerhead species not listed aspredominant taxa are believed to beconsiderably less abundant

0.60 67 267

Gu pian great hammerhead(S. mokarran)

high value suggests sorting is accuratebut category may inadvertentlycontain other light-coloredhammerheads (Sphyrna spp.)

0.85 18 71

Wu gu thresher (Alopiasspp., 3 species)

some traders mentioned infrequentmixing with longfin mako (I.paucus)

0.90 12 45

Sha qing bull (C. leucas) some traders mentioned infrequentmixing with another unknown shark,possibly pigeye (C. amboinensis)

0.80 25 100

Liu qiu oceanic whitetip (C.longimanus)

visually distinct; low probability ofmixing with other species

0.95 6 22

∗Trade names are romanized under the Pinyin (Putonghua) system. Chinese characters (complex form) are given to facilitate translationbetween Pinyin and other forms of Chinese romanization.

Sampling Program Design

We selected 11 market categories for study because theywere common in the trade and because we had diagnos-tic PCR primers for the species we hypothesized to bepresent in each category. Concordances between Chi-nese market names and taxa (Table 1) were hypothe-sized based on information from traders and were com-pared with published information on morphologies offins and whole sharks (Fisheries Agency of Japan 1999;Vannuccini 1999; Yeung et al. 2000; Froese & Pauly 2002).We avoided market categories representing mixed fin




lots such as those simply sorted by size because of lowvalue.

Given the highly sensitive nature of the fin trade and theaccompanying logistical constraints to market sampling,and to reduce the uncertainty in downstream estimationof the proportion of fins in the trade by individual species,we developed an explicit sampling strategy. By determin-ing the sampling effort required to estimate concordanceswithin a given confidence interval for each market cate-gory, we minimized sampling bias toward the most eas-ily sampled categories (i.e., most common or least valu-able products). Simultaneously, we prioritized samplingof those categories hypothesized to contain a mixtureof look-alike species’ fins over those expected to con-tain distinct products from a single species. Determiningsample sizes necessary per category in the absence of ex-isting information requires setting an a priori probability,p, that the hypothesized match between market categoryand taxonomic identity would hold for a given tested fin.In setting the a priori p we assumed that market cate-gories containing morphologically distinctive fins mostlikely derived from a single species and would thus havehigh p values (i.e., near 1). We correspondingly reducedthe p values for other categories according to the de-gree of taxonomic uncertainty as determined from traderinformation and preliminary market reconnaissance(Table 1).

The required number of samples, n, was determinedbased on the binomial model (sensu Zar 1999), with theformula

n = q

(CVp)2 p,

where p is the proportion correctly identified, q is theproportion incorrectly identified, and CVp is the coeffi-cient of variation in p, as given by CVp = sp/p, where sp

is the standard deviation of p. Using this formula, and thea priori p values, the requisite number of samples for twoCV levels for p (CVp = 0.05 and 0.10) was calculated foreach market category (Table 1).

The sampling program aimed to estimate, based on ge-netic analysis, the a posteriori probability p that a fin iden-tified by a trader as market category x is taxon x′ with alow CV for p (CVp < 0.10). The 11 market categories weanalyzed genetically were modeled earlier with Markovchain Monte Carlo methods to determine the percentageby weight of auctioned fins in each category on an annualbasis (Clarke et al. 2004). We extended this model basedon the results of the concordance testing such that in eachmodel iteration the stochastically determined weight offins in each market category was multiplied by a stochas-tic estimate of p, generated from data on the number ofsamples tested and the number of samples verifying thehypothesized match for each category. This allowed us todetermine the proportion of fins in the trade by species.

Individual warehouses were sampled between Novem-ber 2000 and February 2002. At each establishment,traders were shown a list of desired fin types (in Chinesecharacters) and asked to allow free-of-charge sampling ofsmall pieces of tissue from the edges of dried fins. Sam-ples were clipped with clean metal pincers or scissors,and laboratory genetic analyses avoided using cut edgeswhen extracting DNA in case of cross-sample contamina-tion introduced at the point of cutting. In the majorityof visits, not all fin types could be sampled because oflimited stocks. In addition, some traders were reluctantto allow sampling of high-value fins for fear of productdamage. Many traders did not understand the rationalefor replicate sampling within a category and this led tosample sizes of n = 1 during many visits. We used theinformation-theoretic evenness measure of Brillouin fornonrandom samples (Zar 1999) to assess the diversity ofsamples collected in each market category by trader andgeographic source region. Higher values for this statisticindicate that the samples were spread more evenly amongthe various trader or source regions.

Results

Implementation of the Sampling Design

We obtained records of 29% of all auctions held during an18-month period (October 1999–March 2001), includingnumerous records from all major trading houses. Detailsof the auction records and methodology for using existingrecords to simulate a complete set of records for the 18-month period are described in Clarke et al. (2004).

A total of 596 fin samples from the 11 target market cat-egories was collected. The number of collected samplesexceeded the target number for CVp = 0.10 for each mar-ket category, and for three categories ( ya jian, wu gu, andliu qiu) the number of collected samples also exceededthe target number for CVp = 0.05 (Tables 1 & 2).

Our ability to assess (1) the fidelity of nomenclatureacross the trading community and (2) the traders’ abilityto consistently classify fins imported from different partsof the world into coherent categories was determinedby the distribution of samples among different tradinghouses and source ocean basins or regions. The distri-bution by trader was skewed such that 372 of the 596samples (62%) were obtained from just two traders. Thisis a reflection of the sharp decline in trade-community co-operation following a shark conservation campaign con-ducted in Hong Kong in March 2001. Because many of thetraders were unable to be precise about the geographicorigin of the fins, we used general categories to assessthe distribution of samples across ocean basins or areas.The two heavily sampled traders specialized in importsfrom South America, and this is reflected in the fact that264 of 596 samples (44%) were said to have originated in




Table 2. Shark fin samples collected and their distribution within each market category by trader and geographic source (ocean basin/region)of fins.

Source ocean basinsTrader’s Traders Evenness or regions represented Evennessmarket Hypothesized Samples represented for traders (of 8 total, excluding for sourcescategory species match collected (n) (of 21 total) (Brillouin J)a unidentified)b (Brillouin J)a

Ya jian P. glauca 37 11 0.81 5 0.77Qing lian I. oxyrinchus 69 11 0.68 6 0.82Wu yang C. falciformis 110 13 0.71 5 0.58Hai hu C. obscurus 34 7 0.52 4 0.43Bai qing C. plumbeus 40 9 0.64 3 0.69Ruan sha G. cuvier 26 6 0.63 3 0.48Chun chi S. zygaena or S. lewini 94 13 0.68 7 0.60Gu pian S. mokarran 35 8 0.70 4 0.29Wu gu Alopias spp. 75 9 0.69 6 0.81Sha qing C. leucas 53 10 0.74 5 0.64Liu qiu C. longimanus 23 9 0.76 3 0.76

aThe evenness statistics (Brillouin J) (Zar 1999) represent only the equity of the distribution of samples among traders or sources that weresampled and are not a function of the total possible number of sampled traders or sources.bGeographic sources of fins cited by the traders providing samples were grouped into eight categories as follows: South America (Brazil,Ecuador); Eastern Atlantic/West Africa (Spain, Togo); Southeast Asia (Australia, Indonesia, Philippines); Indian Ocean (India, Sri Lanka,Maldives, Bangladesh); Southern Africa (South Africa, Angola); Central America (Gulf of Mexico, Costa Rica); South Pacific (Fiji); Middle East(may include fins from unspecified African countries).

South America. Nevertheless, one of the heavily sampledtraders also received shipments of fins from other loca-tions, and these other locations contributed 50% of thesamples from this trader.

Evenness of sampling diversity, based on the BrillouinJ statistic, across traders ranged from 0.52 to 0.81 (Ta-ble 2). The lowest value resulted for the category hai hu(n = 34), for which 56% of the samples were collectedfrom one trader, with another 29% from a second trader.The remaining samples were distributed among five othertraders. Evenness of sampling diversity across geographicsource regions ranged from 0.29 to 0.82 (Table 2), withthe lowest value for category gu pian (n = 35), for which89% of the samples were collected from one source re-gion, with the remaining samples distributed among threeother regions or an unidentified source.

Species Identification of Samples

All samples (n = 596) were initially tested with thespecies-specific primer representing the hypothesizedtaxonomic match (Table 1). Complete sample amplifica-tion failure (i.e., failure of the positive control ampliconto appear [Pank et al. 2001; Shivji et al. 2002]) occurredin <4% of the samples. If each amplifiable sample failedto amplify with its hypothesized species primer (a q fin),it was consecutively tested in a multiplex PCR format (de-tailed in Shivji et al. [2002]) against primers for each ofthe 16 species we addressed (i.e., all the species listedin Table 1 plus longfin mako and spinner) until a matchwas found (a qi sample) or until all the primers had beentried without achieving identification (a qu sample). The

a posteriori values of p, the proportion of samples vali-dating the hypothesized match, ranged from 0.64 for shaqing to 1.00 for liu qiu (Table 3). Calculation of a poste-riori estimates of the coefficient of variation of p (CVp)suggested that for the majority of market categories thesample size was sufficient to provide a robust estimateof the a posteriori value of p because all values of CVp

were <0.11 (Table 3). The results from further testing ofsamples that did not verify the hypothesized match aregiven in Table 4.

The results for some market categories, which were eas-ily distinguished from most other types of fins becauseof size, shape, or color, conformed closely to hypothe-sized identities. For example, none of the liu qiu finswas from any shark other than oceanic whitetip, and theonly ya jian that was not a blue shark fin was from abigeye thresher shark, which is similar in fin dimensions.Gu pian fins, recognizable because of their light colorand large size, mainly derived from great hammerheadsas expected, and four of the five gu pian fins derivingfrom other species were from the morphologically simi-lar, though smaller, scalloped hammerhead.

Some market categories validated the hypothesizedspecies matches at higher probabilities (i.e., were lessdiverse) than expected. We expected that a posteriori val-ues of p for chun chi, smooth or scalloped hammerhead(n = 94), concordance would be relatively low becauseof the potential presence of other hammerhead species(e.g., S. tudes, S. corona, S. media, and Eusphyra blochii)for which primers have not yet been developed, in thechun chi category. However, except for two samples iden-tified as deriving from the great hammerhead, one sample




Table 3. Results of genetic analysis of shark fin samples by market category.

Samples confirmed A posteriori estimateb

Trader’s Hypothesized genetic Samples Samples with as matching themarket category identification tested control failuresa hypothesized species p CVp sp

Ya jian P. glauca 37 1 35 0.97 0.028 0.027Qing lian I. oxyrinchus 69 2 57 0.85 0.051 0.044Wu yang C. falciformis 110 2 86 0.80 0.049 0.039Hai hu C. obscurus 34 0 29 0.85 0.071 0.061Bai qing C. plumbeus 40 6 25 0.74 0.103 0.076Ruan sha G. cuvier 26 1 21 0.84 0.087 0.073Chun chi S. zygaena or S. lewini 94 1 89 0.96 0.022 0.021Gu pian S. mokarran 35 0 30 0.86 0.069 0.059Wu gu Alopias spp. 75 7 50 0.74 0.073 0.054Sha qing C. leucas 53 3 32 0.64 0.106 0.068Liu qiu C. longimanus 23 0 23 1.00 0 0Total 596 23 477

aSamples with control failures are those for which no positive control amplification could be obtained after several trials.bThe proportion of samples validating the hypothesized match is the a posteriori p value (equal to the number of matching samples pernumber of samples tested minus control failures).

identified as pelagic thresher, and one sample that did notamplify with any primer, only scalloped and smooth ham-merhead fins were present.

The other category that was less species-diverse thanexpected was wu yang (n = 110). Amplification with thesilky shark species-specific primer was observed in 80%of the successfully tested samples. Most of the remain-ing amplifiable fins were identified as scalloped hammer-heads (n = 6) or shortfin makos (n = 4). Only one each ofthree other Carcharhinus congeners were present (Table4), suggesting that these are most likely present in onlysmall numbers in the wu yang category. The presenceof several shortfin makos in this category may be due tosampling error because some of the traders call shortfinmakos wu yang ( , i.e., same romanization but differ-ent Chinese characters). Identification of six scallopedhammerheads in the wu yang samples collected during

Table 4. Summary of shark fin samples not confirming the hypothesized match in each market category.

Market category qu samplesa qi samplesb Identity of qi samplesc

Ya jian 0 1 bigeye thresher (1)Qing lian 2 8 longfin mako (6), bigeye thresher (1),

scalloped hammerhead (1)Wu yang 9 13 scalloped hammerhead (6), shortfin mako (4),

sandbar (1), dusky or Galapagos (1), spinner (1)Hai hu 4 1 silky (1)Bai qing 8 1 dusky or Galapagos (1)Ruan sha 3 1 bull (1)Chun chi 1 3 great hammerhead (2), pelagic thresher (1)Gu pian 1 4 scalloped hammerhead (4)Wu gu 1 17 longfin mako (15), shortfin mako (1), blue (1)Sha qing 17 1 tiger (1)Liu qiu 0 0 not availableTotals 46 50

aSamples that did not amplify with any species-specific primer.bSamples that were found to amplify with another species-specific primer.cTrue species identity and number of samples corresponding to each species for qi samples.

two sampling events from two different traders is lesseasily explained.

Most of the remaining trade categories contained ahigher diversity of species than expected. The mixingof longfin mako fins with threshers in the wu gu cate-gory (22% of fins) and with shortfin makos in the qinglian category (9% of fins) was extensive. Of the qi sam-ples in the wu gu and qing lian categories, 88% and 75%,respectively, were longfin mako fins. All the high-valuecarcharhinid fins (i.e., hai hu, bai qing, and ruan sha)contained other carcharhinid species and several uniden-tifiable fins that did not amplify with any existing primer.The market category with the lowest a posteriori value ofp was sha qing, which was originally expected to showa strong concordance with the bull shark. Of the 50 shaqing samples successfully tested, 17 could not be iden-tified and may have derived from the morphologically




similar pigeye shark (C. amboinensis) for which no pri-mer is yet available.

Market Composition by Trade Category

Hong Kong auction records typically contained 19 majormarket categories describing auction lots. These 19 cate-gories in conjunction with alternative names for the samefin types, names for rare fin types, and combinations ofnames applied to mixed lots resulted in nearly 100 totalmarket categories. The stochastic model of auctioned finquantities, extended to incorporate genetic information,provided taxa-specific estimates of the composition, byweight, of the shark fin trade and confirmed that bluesharks comprised by far the largest distinct proportion(17.3% by weight) of fins auctioned in the Hong Kongmarket (Table 5).

Discussion

Evaluation of the Sampling Program

Our ability to draw sound conclusions regarding thespecies composition of the shark fin trade based on ourresults is a function of the robustness of the sampling de-sign, the extent of category-specific sorting and labelingby Hong Kong traders, and the species-diversity scopeof the diagnostic primer library. Each factor is discussedindividually and then integrated in the context of eachstudied market category in the following sections.

Sampling Design

Studies involving sampling of traded wildlife products cangain an even greater understanding of the trade if mer-chant records can be accessed and understood. Shortcom-ings of such records should then be explicitly addressed

Table 5. Means and 95% probability intervals for estimated proportion of the Hong Kong shark fin trade composed of each taxa.a

Proportion of trade by market categoryb Proportions of trade by taxac

Market category mean lower 95% P.I. upper 95% P.I. Tested taxon/taxa mean lower 95% P.I. upper 95% P.I.

Ya jian 18.2 16.6 20.0 P. glauca 17.3 15.5 19.1Qing lian 3.2 2.8 3.6 I. oxyrinchus 2.7 2.3 3.1Wu yang 4.4 4.0 4.9 C. falciformis 3.5 3.1 4.0Hai hu 1.7 1.5 2.0 C. obscurus 1.4 1.2 1.7Bai qing 3.3 2.8 3.8 C. plumbeus 2.4 1.9 2.8Ruan sha 0.16 0.10 0.23 G. cuvier 0.13 0.08 0.19Chun chi 4.7 4.2 5.2 S. zygaena or S. lewini 4.4 3.9 4.9Gu pian 1.8 1.5 2.1 S. mokarran 1.5 1.2 1.8Wu gu 3.2 2.8 3.6 Alopias spp. 2.3 2.0 2.7Sha qing 3.5 3.0 4.0 C. leucas 2.2 1.8 2.6Liu qiu 1.9 1.6 2.1 C. longimanus 1.8 1.6 2.1

aThe possible presence of additional fins of these taxa within the 54% of the trade that could not be characterized implies that the estimatesbelow are minimums.bProportions by market category (and probability intervals, P.I.) are taken from model results presented in Clarke et al. (2004).cProportions by taxon/taxa were derived for this study by extending the model to adjust the market category for taxonomy (see text).

in the sampling design whenever possible. For example,we acknowledge that traders’ market categories are basedon fin value, which may have little connection with scien-tific taxonomy. This issue was addressed for the categoriesof interest through an a priori formulation of p (hypoth-esized market category-taxon concordance), which wasused to calculate the necessary number of samples.

Our ability to obtain the requisite number and distri-bution of samples across traders and geographic sourceregions was a key factor. Access constraints directed sam-pling toward testing concordances between market cat-egories and shark taxa, rather than random sampling ofthe trade as a whole. Target sample sizes were achieved(Table 3) by following the concordance testing frame-work developed, and reasonable sample distribution wasobtained including 6–13 of the 21 traders and three toseven of the eight geographic source regions, dependingon market category. Overall, given the covert nature ofthe trade and the sensitivities generated by ongoing con-servation campaigns, the implementation of the samplingprogram was highly successful.

Trader Sorting and Labeling

A large volume of fins (54% by weight) traded in unstud-ied, and often nonspecific, categories (Clarke et al. 2004)could not be characterized. This shortcoming could notbe overcome but was accounted for by limiting the studyto common and relatively distinct types of fins. These un-studied categories may contain additional fins of studiedtaxa, particularly when fin size is small, and the low valuedoes not warrant careful sorting; therefore, taxa-specificestimates from this study should be considered as mini-mum proportions in trade.

Another difficulty presented by the trade labeling sys-tem was the existence of cases where several taxa are con-tained in one market category (e.g., wu gu category used




for all three thresher species) and cases where many mar-ket categories may be used for one taxon (e.g., the longfinmako found within several categories). Those cases thatwere detected early in the study were addressed throughcareful definition of target categories and sampling re-quests to traders (e.g., clarifying whether the fin wassometimes called by another name).

Scope of the Primer Library

Although diagnostic primers for each shark species in theshark fin trade (i.e., at least 50 species) are desirable, thisis a large and ongoing task. In the interim, especially inlight of considerable concerns about the status of severalpopulations, shark resource management requires cur-rent information on the fin trade to assess questions ofsustainable use, and it is therefore necessary to pursuetrade studies despite the current taxonomically incom-plete primer library.

Estimates of Species Proportions in Trade

Combining results of the genetic analyses with thestochastic modeling of trade records allowed the contri-bution of individual species to the trade to be determined.Shortfin mako fins showed an 85% concordance with themarket category qing lian, which equates to a trade pro-portion for that species of approximately 2.7% (probabil-ity interval 2.3–3.1%). The actual percentage is likely tobe somewhat higher, given the presence of shortfin makofins in the wu yang (silky shark) trade category.

The wu gu category contained mostly fins from thethree thresher species (74%), resulting in these speciescollectively accounting for approximately 2.3% (2.0–2.7%) of the shark-fin trade. The actual proportion in tradeis also likely to be slightly higher given the discovery ofthresher fins, albeit at low frequency, in other categories(e.g., ya jian, qing lian, and chun chi) (Table 4). Thelongfin mako presented a key nomenclatural issue thataffected both the wu gu (thresher) and qing lian (short-fin mako) categories. Discussions with traders suggestedthat a minority of traders sort longfin mako fins into aseparate category (qing hua); most either combine themwith shortfin mako or thresher fins due to similarity ofappearance and market value.

Tiger sharks, assumed by traders and authors of pre-vious studies to be hai hu (Chinese for ocean tiger),were instead highly concordant with the ruan sha cate-gory, which comprised only 0.08 to 0.19% of the trade.Commonly traded carcharhinid sharks included the blue(17.3% [15.5%–19.1%]), silky (3.5% [3.1–4.0%]), sandbar(2.4% [1.9–2.8%]), and bull (2.2% [1.8–2.6%]) sharks. Thepresence of fins from these carcharhinid species in othermarket categories was noted (Table 4) and would tend toincrease these estimates.

Oceanic whitetip sharks, although circuitously identi-fied in the genetic testing, are highly distinct and showed

a 100% concordance with their hypothesized marketmatch. Given the ease of morphological identification ofthese fins by traders, the best estimate of oceanic whitetipsharks’ contribution to the trade (1.8% [1.6–2.1%]) islikely more accurate than other species because thesefins are less likely to be inadvertently sorted into othercategories.

The three hammerhead species (scalloped, smooth,and great) collectively formed a significant proportion ofthe trade (approximately 5.9%). The occurrence of somescalloped hammerhead fins in the gu pian (great hammer-head) category most likely resulted from the fact that bothscalloped and great hammerhead fins are similarly lightin color. Inadvertent introduction of large, light-coloredscalloped hammerhead fins into the gu pian category andsmall, light-colored great hammerhead fins into the chunchi category (Table 4) thus affects the traded weight es-timates for the various species of hammerheads in bothcategories.

The least reliable results were those for the dusky shark,which was estimated to contribute approximately 1.4%(1.2–1.7%) of the fins in the Hong Kong market. Althoughalready low, this figure most likely overestimates thisspecies’ proportion in trade because the dusky primerused does not distinguish between dusky and Galapagossharks.

Our results indicate that between 34% and 45% (95%probability interval), and possibly more (because of pres-ence in unstudied categories), of the Hong Kong shark finauction trade is composed of only 14 species (i.e., ninemostly single-species trade categories plus scalloped andsmooth hammerheads and the three species of thresh-ers). This finding may reflect the relative abundance ofthese species in global fisheries, preferential demand fortheir fins in this market, or a combination of these andother factors. Our results indicate that blue sharks form aparticularly large component of the market, a finding con-sistent with and potentially resulting from the fact thatblue sharks form a large proportion of the shark bycatchin pelagic longline fisheries targeting tuna and swordfish(Nakano & Seki 2003). Although this species is one ofthe most productive shark species (Cortes 2002), furtheranalyses are needed to determine whether the currentdemand exerted by the shark fin trade on this species issustainable. Other species comprise approximately 0.1 to4.4% each of the total trade volume, and, given the rangein biological productivity of these species (Cortes 2002),it is expected that some will be better able to withstandthe growing demands of the fin trade than others.

Present and Future Shark Fin Trade Characterization

Available information does not provide a definitive an-swer to the question of whether auction records are rep-resentative of the entire global trade. However, the HongKong market serves both Hong Kong and mainland China




(Clarke 2004a), and thus includes both high-end andbudget-conscious consumers with a wide spectrum oftastes. Furthermore, Hong Kong auctions represent 20%of all Hong Kong imports (Clarke et al. 2004), and HongKong is thought to handle more than 50% of the globalshark fin trade (Clarke 2004b). Therefore, although fur-ther studies are necessary to investigate other compo-nents of the global shark fin market, our conclusions arebased on what appears to be the largest trading channelin the world.

Development of diagnostic primers for the CITESAppendix II-listed basking (Cetorhinus maximus) andwhale (Rhincodon typus) sharks, as has been done forgreat white sharks (Carcharodon carcharias) (Chapmanet al. 2003), will allow products from these species tobe identified quickly, reliably, and at low cost. A largerchallenge for rare species in trade will arise from design-ing a sampling program. In addition to the usual issuesassociated with sampling coverage and statistical power,awareness of conservation concerns and regulatory pro-tections may lead traders to camouflage certain typesof fins through cryptic labeling, as has been observedfor basking (S.C., personal observation) and great white(Shivji et al. 2005) shark fins. Even without deliberate sub-terfuge, rare fins lacking distinctive characters and marketvalues may be mixed within voluminous, undifferentiatedmarket stocks and thus become nearly impossible to de-tect. A more practical approach to expanding the list ofspecies we examined is to identify additional distinctivemarket categories commonly used by traders and thendevelop primers that can identify some of the speciesexpected to be found in these categories. Molecular ge-netic testing of putative products derived from controlledspecies such as CITES-listed sharks is likely to remain lim-ited to specific enforcement cases (Clarke 2004a).

Addressing the critical questions of shark resource sus-tainability will require further work to link species com-position and proportion findings with estimates of thecatch weight or number of sharks represented by finsin the market. Trade-based estimates of weight or num-bers by taxon can then provide supporting informationto evaluate whether overexploitation is occurring and toelucidate the role of the shark fin trade in driving sharkcatches and disposition.

Acknowledgments

We are grateful to Imperial College London, the WildlifeConservation Society, the David and Lucile Packard Foun-dation, the Curtis and Edith Munson Foundation, the Ep-pley Foundation, the Hai Stiftung Foundation, the FloridaSea Grant Program (R/LR-B-54), and the Japan Society forPromotion of Science for their support of this research.Staff of the National Taiwan Ocean University, including

S. J. Joung and Y. Y. Liao, generously contributed their ex-pertise to the study. We thank E. J. Milner-Gulland, M. V.Ashley, and two anonymous reviewers for comments onprevious versions of this manuscript.

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Identification of Shark Species Composition and …†Guy Harvey Research Institute, Nova Southeastern University Oceanographic Center, 8000 North Ocean Drive, Dania Beach, FL 33004,