Atlantic Salmon Fishery in the Baltic Sea – A Case of Trivial Cooperation

This paper analyses the management of the Atlantic salmon stocks in the Baltic Sea through a coalition game in the partition function form. The signs of economic and biological over-exploitation of these salmon stocks over the last two decades indicate that cooperation among the harvesting countries, under the European Union's Common Fisheries Policy, has been superficial. Combining a two-stage game of four asymmetric players with a comprehensive bioeconomic model, we conclude that cooperation under the Relative Stability Principle is not a stable outcome. In contrast, the equilibrium of the game is non-cooperation. The paper also addresses the possibility of enhancing cooperation through more flexible fishing strategies. The results indicate that partial cooperation is stable under a specific sharing scheme. It is also shown that substantial economic benefits could have been realised by reallocating the fishing effort.Atlantic salmon, bioeconomic model, coalition formation, partition function, sharing rules, stability analysis, Research and Development/Tech Change/Emerging Technologies,

Integration of biological, economic and sociological knowledge by Bayesian belief networks: the interdisciplinary evaluation of potential Baltic salmon management plan

There is a growing need to evaluate fisheries management plans in a comprehensive interdisciplinary context involving stakeholders. In this paper we demonstrate a probabilistic management model to evaluate potential management plans for Baltic salmon fisheries. The analysis is based on several studies carried out by scientists from respective disciplines. The main part consisted of biological and ecological stock assessment with integrated economic analysis of the commercial fisheries. Recreational fisheries were evaluated separately. Finally, a sociological study was conducted aimed at understanding stakeholder perspectives and potential commitment to alternative management plans. In order to synthesize the findings from these disparate studies a Bayesian Belief Network (BBN) methodology is used. The ranking of management options can depend on the stakeholder perspective. The trade-offs can be analysed quantitatively with the BBN model by combining, according to the decision maker’s set of priorities, utility functions that represent stakeholders’ views. We show how BBN can be used to evaluate robustness of management decisions to different priorities and various sources of uncertainty. In particular, the importance of sociological studies in quantifying uncertainty about the commitment of fishermen to management plans is highlighted by modelling the link between commitment and implementation success.Baltic salmon, bio-economic modelling, Bayesian Belief Network, expert knowledge, fisheries management, commitment and implementation uncertainty, management plan, recreational fisheries, stakeholders., Resource /Energy Economics and Policy,

Identifying Nash equilibrium strategies for a multi-fleet multi-national fishery under uncertainty

Designing economic incentives to achieve a desired policy outcome from a fishery could be helped by exploring it within a game-theoretic context. We use Baltic salmon fishery to show how complex interactions between national fleets, offshore and coastal fishermen, and migratory stock dynamics can be analysed with a bio-economic model set up as a game of four asymmetric players. For Baltic salmon there is a stochastic and complex bio-economic model conditioned on a state-space Bayesian stock assessment which captures uncertainty in stock dynamics. We calculate Nash equilibrium under various cost and price scenarios for each combination of a salmon population model parameters, using an MCMC chain obtained from their joint posterior distribution so that the correlations between different life-history parameters are accounted for. We use results of many simulations to explore how natural variability and economic uncertainties affect Nash equilibrium strategies

The Potential for Insurance to Mediate Economic Risks in Marine Fisheries

In agriculture there has been a long history of using a levy or an insurance premium to create mutual funds to mediate economic risks to growers due to environmental variability and quarantine pests. In the United States the federal government, through the USDA, continues to underwrite funds (collected by private insurance agents) which are used to protect contributors from the effects of extreme weather and pest and disease losses. In Europe mutual funds such as the Kartoffelafgiftsfonden in Denmark and Potatopol in the Netherlands have been developed to mediate risks from some potato diseases in different ways. This paper uses established methods of economic risk management from agriculture and applies these to marine fisheries to demonstrate how financial risks could be mediated by the creation of insurance funds. Through the use of probabilistic estimates of future catches and prices, and the risk of depletion across various scenarios, we investigate how government and industry participation in creating and managing funds may encourage increased protection of fisheries, and compliance and enforcement for fishery regulations. The paper also explores how fund exposure may be reduced by the application of reinsurance from commercial insurers for the upper tail of high cost, low probability events, such as total fishery collapse

International Fisheries Management and Recreational Benefits: The Case of Baltic Salmon

This article studies how accounting for the benefits of recreational fisheries affects the formation and stability of an international fisheries agreement (IFA) on the management of Baltic salmon stocks. The interaction between four countries is modelled through a partition function game, under two scenarios. In the first scenario, countries take their participation decision for the IFA based only on the net present value of profits from commercial fisheries. In the second scenario, the net present value of the recreational benefits from angling is also considered. The results show that accounting for recreational benefits leads to the formation of the grand coalition, whereas only partial cooperation occurs when payoffs are confined to profits from commercial fisheries

On the role of visualisation in fisheries management

Environmental change has focused the attention of scientists, policy makers and the wider public on the uncertainty inherent in interactions between people and the environment. Governance in fisheries is required to involve stakeholder participation and to be more inclusive in its remit, which is no longer limited to ensuring a maximum sustainable yield from a single stock but considers species and habitat interactions, as well as social and economic issues. The increase in scope, complexity and awareness of uncertainty in fisheries management has brought methodological and institutional changes throughout the world. Progress towards comprehensive, explicit and participatory risk management in fisheries depends on effective communication. Graphic design and data visualisation have been underused in fisheries for communicating science to a wider range of stakeholders. In this paper, some of the general aspects of designing visualisations of modelling results are discussed and illustrated with examples from the EU funded MYFISH project. These infographics were tested in stakeholder workshops, and improved through feedback from that process. It is desirable to convey not just modelling results but a sense of how reliable various models are. A survey was developed to judge reliability of different components of fisheries modelling: the quality of data, the quality of knowledge, model validation efforts, and robustness to key uncertainties. The results of these surveys were visualized for ten different models, and presented alongside the main case study.Versión del editor1,86

Stability of International Fisheries Agreements: Implications of Non-Market Benefits and the Country of Origin

Salmon is an anadromous species that during its feeding and spawning migrations is sought after catch for commercial and recreational fisheries. The management of salmon fisheries is complicated by the combination of mixed and single stock fisheries. Thus, the country of origin has sovereign control over harvest of a salmon stock only at the last steps of the gauntlet. This paper addresses the stability of an international fisheries agreement on the Baltic salmon. This setting is modeled through a coalition game in the partition function form with four asymmetric players. Countries payoffs depend both on commercial fishery’s profits and net benefits from recreational harvest. Moreover, the country of origin must ensure that each of the salmon stocks achieve or maintain a sustainable size. The economic sub-model is calibrated using commercial fisheries statistics and existing non-market valuation studies. The underlying population dynamics model accounts for 15 salmon stocks and it is used in the actual stock assessment. The results indicate that by considering economic aspects of recreational fisheries it is possible to stabilize the grand coalition. However, the cooperative strategies of the grand coalition do not ensure biologically sound harvesting of all salmon stocks

Best practices for the provision of prior information for Bayesian stock assessment

This manual represents a review of the potential sources and methods to be applied when providing prior information to Bayesian stock assessments and marine risk analysis. The manual is compiled as a product of the EC Framework 7 ECOKNOWS project (www.ecoknows.eu). The manual begins by introducing the basic concepts of Bayesian inference and the role of prior information in the inference. Bayesian analysis is a mathematical formalization of a sequential learning process in a probabilistic rationale. Prior information (also called ”prior knowledge”, ”prior belief”, or simply a ”prior”) refers to any existing relevant knowledge available before the analysis of the newest observations (data) and the information included in them. Prior information is input to a Bayesian statistical analysis in the form of a probability distribution (a prior distribution) that summarizes beliefs about the parameter concerned in terms of relative support for different values. Apart from specifying probable parameter values, prior information also defines how the data are related to the phenomenon being studied, i.e. the model structure. Prior information should reflect the different degrees of knowledge about different parameters and the interrelationships among them. Different sources of prior information are described as well as the particularities important for their successful utilization. The sources of prior information are classified into four main categories: (i) primary data, (ii) literature, (iii) online databases, and (iv) experts. This categorization is somewhat synthetic, but is useful for structuring the process of deriving a prior and for acknowledging different aspects of it. A hierarchy is proposed in which sources of prior information are ranked according to their proximity to the primary observations, so that use of raw data is preferred where possible. This hierarchy is reflected in the types of methods that might be suitable – for example, hierarchical analysis and meta-analysis approaches are powerful, but typically require larger numbers of observations than other methods. In establishing an informative prior distribution for a variable or parameter from ancillary raw data, several steps should be followed. These include the choice of the frequency distribution of observations which also determines the shape of prior distribution, the choice of the way in which a dataset is used to construct a prior, and the consideration related to whether one or several datasets are used. Explicitly modelling correlations between parameters in a hierarchical model can allow more effective use of the available information or more knowledge with the same data. Checking the literature is advised as the next approach. Stock assessment would gain much from the inclusion of prior information derived from the literature and from literature compilers such as FishBase (www.fishbase.org), especially in data-limited situations. The reader is guided through the process of obtaining priors for length–weight, growth, and mortality parameters from FishBase. Expert opinion lends itself to data-limited situations and can be used even in cases where observations are not available. Several expert elicitation tools are introduced for guiding experts through the process of expressing their beliefs and for extracting numerical priors about variables of interest, such as stock–recruitment dynamics, natural mortality, maturation, and the selectivity of fishing gears. Elicitation of parameter values is not the only task where experts play an important role; they also can describe the process to be modelled as a whole. Information sources and methods are not mutually exclusive, so some combination may be used in deriving a prior distribution. Whichever source(s) and method(s) are chosen, it is important to remember that the same data should not be used twice. If the 2 | ICES Cooperative Research Report No. 328 plan is to use the data in the analysis for which the prior distribution is needed, then the same data cannot be used in formulating the prior. The techniques studied and proposed in this manual can be further elaborated and fine-tuned. New developments in technology can potentially be explored to find novel ways of forming prior distributions from different sources of information. Future research efforts should also be targeted at the philosophy and practices of model building based on existing prior information. Stock assessments that explicitly account for model uncertainty are still rare, and improving the methodology in this direction is an important avenue for future research. More research is also needed to make Bayesian analysis of non-parametric models more accessible in practice. Since Bayesian stock assessment models (like all other assessment models) are made from existing knowledge held by human beings, prior distributions for parameters and model structures may play a key role in the processes of collectively building and reviewing those models with stakeholders. Research on the theory and practice of these processes will be needed in the future

WORKSHOP ON GUIDELINES FOR MANAGEMENT STRATEGY EVALUATIONS (WKGMSE2)

The purpose of the meeting was to bring up to date the methodologies and technical specifications that should be incorporated in Management Strategy Evaluation (MSE) work in ICES. The workshop was tasked with reviewing recent methodological and practical MSE work conducted in ICES and around the world, as well as the guidelines provided by the 2013 ICES Workshop on Guidelines for Management Strategy Evaluations (WKGMSE). The Terms of Reference indicated that the revision should include all aspects involved in MSE, while paying specific attention to several issues that had been identified through ICES practice. The Terms of Reference also requested WKGMSE 2 to consider how best to disseminate the guidelines to experts within the ICES community and the need for training courses. The workshop addressed all its Terms of Reference. The main results of the workshop are the revised MSE guidelines, as well as recommendations in relation to the ICES criterion for defining a management strategy as precautionary and in relation to the evaluation and advice on rebuilding strategies.publishedVersio