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Päivi Haapasaari1, Samu Mäntyniemi, Sakari Kuikka

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This study shows that Bayesian methods effectively integrate stakeholder knowledge for fisheries models, particularly for the Central Baltic herring. This participatory approach suits complex ecological systems better than traditional objective methods.

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Area of Science:

  • Fisheries Science
  • Ecological Modeling
  • Bayesian Statistics

Background:

  • Stakeholder involvement is crucial for effective fisheries management.
  • Traditional modeling approaches often struggle to incorporate diverse stakeholder perspectives.
  • Understanding factors influencing herring stock dynamics requires integrating various knowledge sources.

Purpose of the Study:

  • To investigate the utility of a participatory Bayesian approach for modeling the Central Baltic herring fishery.
  • To elicit and integrate stakeholder causal assumptions into a meta-model.
  • To assess the suitability of Bayesian methods for participatory modeling practices.

Main Methods:

  • Bayesian belief networks were used to elicit stakeholder causal assumptions on herring mortality, growth, and egg survival.
  • Bayesian model averaging (BMA) integrated stakeholder views into a meta-model.
  • Influence diagrams qualitatively explored stakeholder framing of the herring fishery management problem.

Main Results:

  • The Bayesian approach successfully integrated stakeholder knowledge on herring population dynamics.
  • Stakeholder perspectives were qualitatively mapped using influence diagrams.
  • Bayesian theory's subjective knowledge perspective aligns well with participatory modeling needs.

Conclusions:

  • Participatory Bayesian modeling offers a flexible and cost-effective tool for fisheries management, especially with limited data.
  • The methodology is adaptable to various participatory modeling challenges.
  • Further development of complex methods like BMA is needed for broader application in participatory contexts.