The value of information in fisheries management: North Sea herring as an example

Mäntyniemi, Samu; Kuikka, Sakari; Rahikainen, Mika; Kell, Laurence T.; Kaitala, Veijo

doi:10.1093/icesjms/fsp206

Cited by 52 publications

(45 citation statements)

References 20 publications

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“…As an alternative, meta-modeling approaches, capable of estimating error propagation from data to model uncertainty with reduced computational cost exist (Scavia et al 1981a, b;Ratto et al 2007). More recently, also Bayesian uncertainty analyses have been applied to ecosystem models (Mäntyniemi et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Uncertainties in a Baltic Sea Food-Web Model Reveal Challenges for Future Projections

Niiranen

Blenckner

Hjerne

et al. 2012

AMBIO

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Models that can project ecosystem dynamics under changing environmental conditions are in high demand. The application of such models, however, requires model validation together with analyses of model uncertainties, which are both often overlooked. We carried out a simplified model uncertainty and sensitivity analysis on an Ecopath with Ecosim food-web model of the Baltic Proper (BaltProWeb) and found the model sensitive to both variations in the input data of pre-identified key groups and environmental forcing. Model uncertainties grew particularly high in future climate change scenarios. For example, cod fishery recommendations that resulted in viable stocks in the original model failed after data uncertainties were introduced. In addition, addressing the trophic control dynamics produced by the food-web model proved as a useful tool for both model validation, and for studying the food-web function. These results indicate that presenting model uncertainties is necessary to alleviate ecological surprises in marine ecosystem management.

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Section: Introductionmentioning

confidence: 99%

Uncertainties in a Baltic Sea Food-Web Model Reveal Challenges for Future Projections

Niiranen

Blenckner

Hjerne

et al. 2012

AMBIO

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“…A higher error cost due to the choice of x can lead to the choice of x , except if the prior probability of being in the state of nature s is itself greater. We already know that this maximal a priori uncertainty context provides a higher value of information [14]. Some authors have attempted to estimate the prior probabilities in a subjective way [18] by questioning the decision makers [16] [17] or by using Monte Carlo simulations [19].…”

Section: Decision Rule Without Additional Informationmentioning

confidence: 99%

An Analysis of the Value of Additional Information Provided by Water Quality Measurement Network

Destandau¹,

Diop²

2016

JWARP

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European Community policy concerning water is placing increasing demands on the acquisition of information about the quality of aquatic environments. The cost of this information has led to a reflection on the rationalization of monitoring networks and, therefore, on the economic value of information produced by these networks. The aim of this article is to contribute to this reflection. To do so, we used the Bayesian framework to define the value of additional information in relation to the following three parameters: initial assumptions (prior probabilities) on the states of nature, costs linked to a poor decision (error costs) and accuracy of additional information. We then analyzed the impact of these parameters on this value, particularly the combined role of prior probabilities and error costs that increased or decreased the value of information depending on the initial uncertainty level. We then illustrated the results using a case study of a stream in the Bas-Rhin department in France.

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“…Collecting and analyzing social and economic knowledge would further enable the evaluation of the causal assumptions of the stakeholders and potentially lead to concentrating on those models that show a capability for predicting responses of the whole fishery to changes in individual factors. Decision analysis using stakeholder models could reveal the sensitivity of the scientific management advice to differences in both knowledge and objectives (Mäntyniemi et al 2009b). This could lead to focusing research on the issues most important for successful fishery management.…”

Section: Usability Of the Bbn Approach For Fisheries Managementmentioning

confidence: 99%

“…Our aim is threefold: (1) to examine both the potential and the challenges of BBNs for participatory problem framing, (2) to assess the implications of such problem framing for management, and (3) to form a basis for a follow-up study that proceeds from qualitative problem framing to quantitative model building, and to evaluate the value of different types of information needed for a holistic model (Mäntyniemi et al 2009b).…”

Section: Introductionmentioning

confidence: 99%

Baltic Herring Fisheries Management: Stakeholder Views to Frame the Problem

Haapasaari¹,

Mäntyniemi²,

Kuikka³

2012

E&S

Self Cite

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ABSTRACT. Comprehensive problem framing that includes different perspectives is essential for holistic understanding of complex problems and as the first step in building models. We involved five stakeholders to frame the management problem of the Central Baltic herring fishery. By using the Bayesian belief networks (BBNs) approach, the views of the stakeholders were built into graphical influence diagrams representing variables and their dependencies. The views of the scientists involved concentrated on biological concerns, whereas the fisher, the manager, and the representative of an environmental nongovernmental organization included markets and fishing industry influences. Management measures were considered to have a relatively small impact on the development of the herring stock; their impact on socioeconomic objectives was greater. Overall, the framings by these stakeholders propose a focus on socioeconomic issues in research and management and explicitly define management objectives, not only in biological but also in social and economic terms. We find the approach an illustrative tool to structure complex issues systematically. Such a tool can be used as a forum for discussion and for decision support that explicitly includes the views of different stakeholder groups. It enables the examination of social and biological factors in one framework and facilitates bridging the gap between social and natural sciences. A benefit of the BBN approach is that the graphical model structures can be transformed into a quantitative form by inserting probabilistic information.

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The value of information in fisheries management: North Sea herring as an example

Cited by 52 publications

References 20 publications

Uncertainties in a Baltic Sea Food-Web Model Reveal Challenges for Future Projections

Uncertainties in a Baltic Sea Food-Web Model Reveal Challenges for Future Projections

An Analysis of the Value of Additional Information Provided by Water Quality Measurement Network

Baltic Herring Fisheries Management: Stakeholder Views to Frame the Problem

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