Despite growing concerns about overexploitation of sharks, lack of accurate, species-specific harvest data often hampers quantitative stock assessment. In such cases, trade studies can provide insights into exploitation unavailable from traditional monitoring. We applied Bayesian statistical methods to trade data in combination with genetic identification to estimate by species, the annual number of globally traded shark fins, the most commercially valuable product from a group of species often unrecorded in harvest statistics. Our results provide the first fishery-independent estimate of the scale of shark catches worldwide and indicate that shark biomass in the fin trade is three to four times higher than shark catch figures reported in the only global data base. Comparison of our estimates to approximated stock assessment reference points for one of the most commonly traded species, blue shark, suggests that current trade volumes in numbers of sharks are close to or possibly exceeding the maximum sustainable yield levels.
The burgeoning and largely unregulated trade in shark fins represents one of the most serious threats to shark populations worldwide. In Hong Kong, the world's largest shark fin market, fins are classified by traders into Chinese-name categories on the basis of market value, but the relationship between market category 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 numbers of fins from traders and categories under conditions of limited market access because of heightened trader sensitivities. Based on information from traders and morphological inspection, we hypothesized matches between market names and shark taxa for fins within 11 common trade categories. These hypotheses were tested using DNA-based species identification techniques to determine the concordance between market category and species. Only 14 species made up approximately 40% of the auctioned fin weight. The proportion of samples confirming the hypothesized match, or concordance, varied from 0.64 to 1 across the market categories. We incorporated the concordance information and available market auction records for these categories into stochastic models to estimate the contribution of each taxon by weight to the fin trade. Auctioned fin weight was dominated by the blue shark (Prionace glauca), which was 17% of the overall market. Other taxa, including the 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 to marketplace monitoring of wildlife products isparticularly applicable to situations in which quantitative data at the source of resource extraction are sparse and large-scale genetic testing is limited by budgetary or other market access constraints.
We present a model of the effects of a marine reserve on spawning stock biomass (SSB) and short-and long-term yield for a size-structured species that exhibits seasonal movements. The model considers the effects of protecting nursery and (or) spawning grounds under a range of fishing mortalities and fish mobility rates. We consider two extremes of effort redistribution following reserve establishment and analyze the effects of a reserve when the fishery targets either mature or immature fish. We apply the model to the Mediterranean hake (Merluccius merluccius) and show that a marine reserve could be highly beneficial for this species. We demonstrate benefits from reserves not just for overexploited stocks of low-mobility species, but also (to a lesser extent) for underexploited stocks and highmobility species. Greatly increased resilience to overfishing is also found in the majority of cases. We show that a reserve provides benefits additional to those obtained from simple effort control. Benefits from reserves depend to a major extent on the amount of effort redistribution following reserve establishment and on fishing selectivity; hence, these factors should be key components of any evaluation of reserve effectiveness.Résumé : On trouvera ici un modèle qui décrit les effets d'une réserve marine sur la biomasse du stock des reproducteurs et sur les rendements à court et à long termes chez une espèce à structure de taille définie qui fait des déplacements saisonniers. Le modèle examine les effets de la protection des zones d'alevinage et (ou) de fraye sur une gamme de mortalités dues à la pêche et de taux de mobilité des poissons. Nous considérons deux extrêmes dans la redistribution de l'effort de pêche à la suite de l'établissement d'une réserve et nous analysons les effets de la réserve quand la pêche cible les poissons immatures ou alors les poissons adultes. Nous appliquons le modèle au merlu commun (Merluccius merluccius) et démontrons qu'une réserve marine lui serait très bénéfique. Les bénéfices d'une réserve s'appliquent non seulement aux stocks surexploités d'espèces à mobilité réduite, mais aussi, bien que dans une plus faible mesure, aux stocks sous-exploités et aux espèces très mobiles. Dans la majorité des cas, il existe aussi une résilience fortement accrue à la surpêche. La réserve apporte des bénéfices additionnels à ceux que génère le simple contrôle de l'effort de pêche. Ces bénéfices dépendent en grande partie de l'importance de la redistribution de l'effort de pêche après l'établissement de la réserve et de la sélectivité de la pêche; ces facteurs devraient donc être les éléments essentiels de toute évaluation de l'efficacité d'une réserve.[Traduit par la Rédaction] Apostolaki et al. 415
Scientific advice to fishery managers needs to be expressed in probabilistic terms to convey uncertainty about the consequences of alternative harvesting policies (policy performance indices). In most Bayesian approaches to such advice, relatively few of the model parameters used can be treated as uncertain, and deterministic assumptions about population dynamics are required; this can bias the degree of certainty and estimates of policy performance indices. We reformulate a Bayesian approach that uses the sampling/importance resampling algorithm to improve estimates of policy performance indices; it extends the number of parameters that can be treated as uncertain, does not require deterministic assumptions about population dynamics, and can use any of the types of fishery assessment models and data. Application of the approach to New Zealand's western stock of hoki (Macruronus novaezelandiae) shows that the use of Bayesian prior information for parameters such as the constant of proportionality for acoustic survey abundance indices can enhance management advice by reducing uncertainty in current stock size estimates; it also suggests that assuming historic recruitment is deterministic can create large negative biases (e.g., 26%) in estimates of biological and economic risks of alternative harvesting policies and that a stochastic recruitment assumption can be more appropriate.
Fisheries scientists habitually consider uncertainty in parameter values, but often neglect uncertainty about model structure. The importance of this latter source of uncertainty is likely to increase with the greater emphasis on ecosystem models in the move to an ecosystem approach to fisheries (EAF). It is therefore necessary to increase awareness about pragmatic approaches with which fisheries modellers and managers can account for model uncertainty and so we review current ways of dealing with model uncertainty in fisheries and other disciplines. These all involve considering a set of alternative models representing different structural assumptions, but differ in how those models are used. The models can be used to identify bounds on possible outcomes, find management actions that will perform adequately irrespective of the true model, find management actions that best achieve one or more objectives given weights assigned to each model, or formalise hypotheses for evaluation through experimentation. Data availability is likely to limit the use of approaches that involve weighting alternative models in an ecosystem setting, and the cost of experimentation is likely to limit its use. Practical implementation of the EAF should therefore be based on management approaches that acknowledge the uncertainty inherent in model predictions and are robust to it. Model results must be presented in a way that represents the risks and trade-offs associated with alternative actions and the degree of uncertainty in predictions. This presentation should not disguise the fact that, in many cases, estimates of model uncertainty may be based on subjective criteria. The problem of model uncertainty is far from unique to fisheries, 3 and coordination among fisheries modellers and modellers from other communities will therefore be useful.Keywords Bayesian methods, Ecosystem Approach to Fisheries, ecosystem models, fisheries management, model uncertainty, operational management procedures. Abstract
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