A stochastic viability approach to ecosystem-based fisheries management

Doyen, Luc; Thébaud, Olivier; Béné, Christophe; Martinet, Vincent; Gourguet, Sophie; Bertignac, Michel; Fifas, Spyros; Blanchard, Gérard

doi:10.1016/j.ecolecon.2012.01.005

Cited by 55 publications

(33 citation statements)

References 27 publications

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“…The models we used represented the best understanding of the population dynamics, and associated uncertainty in predicting the future population states. Such models are well established for exploring population viability and the implications of management (Morris & Doak 2002;Bunnefeld et al 2011;Doyen et al 2012).…”

Section: Discussionmentioning

confidence: 99%

Environmental Derivatives, Risk Analysis, and Conservation Management

Little

Parslow

Fay

et al. 2013

Conservation Letters

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Two key challenges in conservation management are: (1) how to quantify and manage the risk that natural populations will fall below critical thresholds and (2) how to fund recovery plans should a population do so. Statistically estimated, process-based simulation models of two distinct fish populations are used to forecast the species population levels, and capture the risk of crossing a management defined trigger point. We show how to calculate the environmental derivative price, which is the amount a risk-neutral investor would require for promising a pay-out should the species abundance fall below the trigger level. The approach provides the potential for environmental derivatives to support species recovery, and a method for measuring the underlying "health" of a managed population and calculating risk-cost tradeoffs among alternative management strategies.

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Section: Discussionmentioning

confidence: 99%

Environmental Derivatives, Risk Analysis, and Conservation Management

Little

Parslow

Fay

et al. 2013

Conservation Letters

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“…Last but not least, dynamic programming algorithms work in theory for any system described by equation (14), which would make this method very general. Its area of application, however, is limited in practice by the curse of dimensionality, which causes both the required computational time and memory to increase exponentially with the dimension of the state space.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, minimizing the cost on the trajectory may sometimes con ict with maximizing the probability to reach the viability kernel, and one should de ne a tradeo between these objectives. Viability theory allows for the exploration of trade-o s in the latter case under the so-called framework of co-viability [14], but without addressing the issue of resilience.…”

Section: Discussionmentioning

confidence: 99%

“…From a technical point of view, the present work uses the stochastic extensions to viability theory in the discrete-time case [8,13] and builds on the robust and stochastic viability frameworks, both based on dynamic programming, and which have been successfully applied to sheries management [12,9,14,27]. One can expect three main di erences between the deterministic and stochastic viability frameworks:…”

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confidence: 99%

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Extending the viability theory framework of resilience to uncertain dynamics, and application to lake eutrophication

Rougé¹,

Mathias²,

Deffuant³

2013

Ecological Indicators

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)>IJH=?J Resilience, the capacity for a system to recover from a perturbation so as to keep its properties and functions, is of growing concern to a wide range of environmental systems. The challenge is often to render this concept operational without betraying it, nor diluting its content. The focus here is on building on the viability theory framework of resilience to extend it to discrete-time stochastic dynamical systems. The viability framework describes properties of the system as a subset of its state space. This property is resilient to a perturbation if it can be recovered and kept by the system after a perturbation: its trajectory can come back and stay in the subset. This is shown to re ect a general de nition of resilience. With stochastic dynamics, the stochastic viability kernel describes the robust states, in which the system has a high probability of staying in the subset for a long time. Then, probability of resilience is de ned as the maximal probability that the system reaches a robust state within a time horizon. Management strategies that maximize the probability of resilience can be found through dynamic programming. It is then possible to compute a range of statistics on the time for restoring the property. The approach is illustrated on the example of lake eutrophication and shown to foster the use of di erent indicators that are adapted to distinct situations. Its relevance for the management of ecological systems is also discussed.

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“…In recent decades, traditional fishery management, focused on single species or stocks and resources, has gradually evolved towards ecosystem-based fisheries management (EBFM). However, the way to operationalize EBFM remains unclear and challenging, and fishery managers are still seeking guidance on what measures or combination thereof would best meet the multiple and often conflicting objectives of EBFM [1][2][3][4]. Such an approach requires additional tools and indicators that are effectively managed at a finer scale [5].…”

Section: Decision Support Systemsmentioning

confidence: 99%

Decision Support to Sustainable Management of Bottom Trawl Fleet

2016

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Abstract:A decision support concept (DSC) for sustainable management of the bottom trawl fleet was created in line with ecosystem-based management. It is based on principles that integrate ecological, social and techno-economic aspects of trawl fisheries in a multicriteria analysis approach. For the sake of greater transparency and improved stakeholder participation, elements of the proposed multicriteria models were discussed, generated and evaluated in collaboration with designated experts from four stakeholder groups: fishers, environmentally focused non-governmental organizations, fisheries scientists and government representatives. The proposed DSC management could facilitate management and assist decision makers in adequately using data and scientific advice to shape management strategies and related policies for the bottom trawl fleet. It may also assist in finding compromise solutions based on deliverables from the multicriteria analysis, while taking stakeholder requirements into account by using the multicriteria Analytic Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) methods. The final decision is then based on a vast amount of knowledge and relevant information collected from different sources. The proposed DSC represents a novel approach to fishery fleet management and assists in systematizing management processes and instruments to make it operational at the strategic level. The method was applied to the Adriatic bottom trawl fishery, and the obtained results confirmed its managerial potential in the strategic decision-making process, aimed at improving conventional management, while considering the specific requirements of an ecosystem-based approach and ensuring stakeholder participation.Keywords: sustainable fisheries management; decision support concept; ecosystem-based approach; bottom trawl fleet; strategy planning tool for the Adriatic fishing fleet

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A stochastic viability approach to ecosystem-based fisheries management

Cited by 55 publications

References 27 publications

Environmental Derivatives, Risk Analysis, and Conservation Management

Environmental Derivatives, Risk Analysis, and Conservation Management

Extending the viability theory framework of resilience to uncertain dynamics, and application to lake eutrophication

Decision Support to Sustainable Management of Bottom Trawl Fleet

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