تجزیه و تحلیل زیستی و اقتصادی از قوانین کنترل برداشت در شیلات COD شمال شرقی قطب شمال
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|29058||2013||10 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Marine Policy, Volume 39, May 2013, Pages 172–181
Harvest control rules (HCRs) have been implemented for many fisheries worldwide. However, in most instances, those HCRs are not based on the explicit feedbacks between stock properties and economic considerations. This paper develops a bio-economic model that evaluates the HCR adopted in 2004 by the Joint Norwegian–Russian Fishery Commission to manage the world's largest cod stock, Northeast Arctic cod (NEA). The model considered here is biologically and economically detailed, and is the first to compare the performance of the stock's current HCR with that of alternative HCRs derived with optimality criteria. In particular, HCRs are optimized for economic objectives including fleet profits, economic welfare, and total yield and the emerging properties are analyzed. The performance of these optimal HCRs was compared with the currently used HCR. This paper show that the current HCR does in fact comes very close to maximizing profits. Furthermore, the results reveal that the HCR that maximizes profits is the most precautionary one among the considered HCRs. Finally, the HCR that maximizes yield leads to un-precautionary low levels of biomass. In these ways, the implementation of the HCR for NEA cod can be viewed as a success story that may provide valuable lessons for other fisheries.
1.1. Northeast Arctic cod and its current management plan Northeast Arctic (NEA) cod (Gadus morhua) is currently the world's largest cod stock, distributed from its feeding grounds in the Barents Sea to its spawning grounds off the Lofoten islands in the Norwegian Sea . The fishery consists of two parts that are geographically separate: the feeding-ground fishery in the north and the spawning-ground fishery further south ( Fig. 1). Humans have been fishing on the spawning grounds for more than a thousand years, beginning with the export of cod during the Viking Age . Until the 1930s, the spawning-ground fishery dominated catches, due to its proximity to coastal villages and ports. However, during the 1930s the advent of industrial fishing technology facilitated the expansion of the NEA cod fishery into the Barents Sea. This expansion led to a shift of catches toward the stock's feeding grounds, as well as to an increase in the total fishing mortality ( Fig. 2a). In 2010, ICES (the International Council for Exploration of the Sea) estimated the spawning-stock biomass (SSB) of NEA cod to reach 1,145,000 t, the highest amount that has been observed since 1947 . The stock's total biomass has also increased, even though not concomitantly with the SSB ( Fig. 2b). In addition to possible climate effects, this recent increase in SSB could have at least two explanations: First, illegal fishing has been reduced from the maximum of 166,000 t in 2005 to approximately zero in 2009 . This decline is most likely due to the introduction of port control in 2007, requiring all vessels to document that their landings are legally caught. Second, a joint Norwegian–Russian harvest control rule (HCR) that determines the total allowable catch (TAC) has been implemented since 2004, to ensure that the stock is not at “risk of being harvested unsustainably” or “suffering reduced reproductive capacity”  and .NEA cod is an economically very important fish resource  and  mostly situated in the exclusive economic zones of Norway and Russia (Fig. 1). For years, NEA cod has been managed jointly by those two countries, though not without scientific and political disagreements . To enable more farsighted management and to simplify the annual negotiations on harvest levels, an HCR was agreed upon by the two countries in 2004 (Fig. 2c). In general, an HCR is an algorithm and a tactical management tool that translates biological information, such as a stock's current SSB, into management information such as a TAC for that stock during the next fishing season. An HCR is often designed with the help of reference points for target biomass and fishing mortality. In particular, the precautionary reference points for biomass and fishing mortality, Bpa and Fpa, respectively, act as buffers to account for natural variability and uncertainty in the stock assessment: Bpa implements a “safety margin” to reduce the risk that the true SSB falls below a limit reference point Blim below which the stock is expected to suffer from reduced reproductive capacity. Likewise, Fpa is meant to avoid a true fishing mortality that exceeds the limit reference point Flim above which SSB is expected to drop below Blim . The range of these buffers depends on the level of uncertainty and on the level of risk fisheries managers are willing to accept on behalf of society. In autumn 2004, the 33rd session of the Joint Norwegian–Russian Fishery Commission adopted a HCR stipulating that the fishing mortality is allowed to be at Fpa as long as SSB exceeds Bpa, but is required linearly to decrease from Fpa to 0 as SSB decreases from Bpa to 0 ( Fig. 2c). Therefore, fishing can take place at all SSB levels . The HCR contains additional elements that aim to restrict how much the TAC can change from one year to the next. However, the TAC advised by the adopted HCR is not always followed. For example in 2009, due to the high SSB, the TAC was decided by the Joint Norwegian–Russian Fishery Commission to be 525,000 tonnes, while the adopted HCR advised 473,000 tonnes . Today, the NEA stock is classified as having “full reproductive capacity” and being “harvested sustainably”  and . 1.2. Need for adaptive management and clear objectives Despite considerable attention to the management of marine ecosystems, most fisheries have yet to be optimized to reach management goals , ,  and . Political obstacles and roadblocks play an important role in failures of fisheries management . Also, some scientific models for optimal management are not easily applicable to real-world situations, and may be based on hidden and/or overly simple assumptions . Another obstacle for successful fisheries management is the fact that it is often not explicit, or evident a priori, which particular objectives should be pursued ,  and . At a very basic level, a specific fish stock can provide income to society, but also serves as an important food source. Therefore, one may favour a harvesting rate that provides the highest perpetual yield, known as the maximum sustainable yield (MSY), and this objective has been endorsed in various international agreements . Economic science has added an important refinement to the purely biological consideration of MSY by accounting for the costs and benefits associated with resource extraction  and . This allows deriving an exploitation path that maximizes profits from harvesting, but is based on the simplifying assumption that the government, at least theoretically, is the “sole owner” of the resource. The contrast between these two basic approaches already shows that a crucial prerequisite for achieving optimal exploitation is the clear specification of management goals. A policy-maker may, for instance, take into account that parts of society are concerned about nature conservation in general, or about a specific species or ecosystem in particular. Accordingly, the policy-maker may decide to harvest less than what would be optimal if only yields or profits were to be maximized. The opposite can be true when fishing is considered part of a region's cultural heritage, which society finds worth preserving even at the expense of reducing profits through subsidies or over-fishing. It is important to acknowledge that fisheries management—just as every other part of public policy—is, inherently political. Nevertheless, objectives that politicians consider important should be clearly defined; otherwise, hidden objectives can sneak in through the backdoor and take precedence . It is therefore desirable to devise models that are flexible enough to evaluate realistic political options and transparent enough to communicate their consequences effectively to all stakeholders. Technically, an HCR is a feedback control that links one or more control variables (e.g., catch) to one or more state variables (e.g., SSB) of the stock. If an operational biological model is in place and is sufficiently simple, such a feedback control can be derived analytically , ,  and . There is, however, a critical trade-off between analytic tractability and realistic complexity, implying that sufficiently detailed biological models will often be too complicated for deriving an optimal HCR analytically. In such cases, it is necessary to sacrifice analytical rigor for biological realism and use numerical analyses instead. When setting up an HCR, policy-makers can express their resource-management objectives by emphasizing quantitative goals, which different scientific disciplines can then jointly help to assess. HCRs are readily based on such an approach, and accordingly offer various advantages for modern fisheries management, including (i) a reduced need for annual negotiations on how to set harvest quotas, (ii) the integration of interdisciplinary research into policy-making, and (iii) the strengthening of a constructive dialogue between policy-makers, stakeholders, and the scientific community. Harvest policies formulated through HCRs therefore represent an ideal platform for policy makers and scientists on which to interact. Positive practical experiences with the HCR framework have been highlighted in recent reviews ,  and . 1.3. Aims of this study The approach here is to use a detailed bio-economic model for the NEA cod fishery to evaluate the current HCR and to inform policy-makers about how this HCR performs compared to alternative HCRs that are optimized for different objectives. The purpose of this study is to provide an overview of the strengths and weaknesses associated with HCRs devised to meet the different objectives. In doing so, this study aims to examine how these alternate HCRs for the management of NEA cod perform in comparison with the currently implemented HCR. Kovalev and Bogstad in 2005  addressed the performance of the current HCR, however, their model is purely biological and thus does not include economic objectives. While their biological model operates at the population level, ours is individual-based. This allows us to incorporate more biological detail and realistic complexity than other biological models used in previous bio-economic studies. This level of realism is needed: to evaluate the merits of any HCR, the used biological model must match the observational data it represents sufficiently well, if inferences for future fishing pressures are to be trusted. Analogous considerations apply to the used economic model. The bio-economic model presented below is the most detailed such model developed for NEA cod, and the first applied to evaluating HCRs.
نتیجه گیری انگلیسی
This bio-economic model predicts that the current HCR rule is practically identical with the economically optimal one, suggesting that economic and biological sustainability can go hand in hand. A relatively low fishing mortality is a major factor in achieving both. Also, yield maximization alone has been demonstrated to potentially result in a lack of precaution. The design of HCRs provides a platform for promoting and structuring the dialogue between policy-makers, managers, scientists, and stakeholders. With this in mind, HCRs can be tailored according to a variety of management objectives. The benefits of translating a harvest policy into an HCR are epitomized by the phrase “quantification leads to clarification” : unclear objectives and “gut-feeling” policies do not lend themselves to being quantified as part of harvest-strategy evaluation. Nonetheless, it is important to realize that quantification alone might increase the precision, but not necessarily the accuracy, of results. Therefore, intensive and open dialogue between managers, who set operational fisheries objectives, and scientists, who aid in designing and testing management strategies, will remain essential in pursuing the sustainable management of aquatic resources.