Involving Stakeholders in Building Integrated Fisheries Models Using Bayesian Methods

• DOI: 10.1007/s00267-013-0041-9
• Publication Date: 2013-04-18T12:02:16Z
• Authors: Päivi Haapasaari; Samu Mäntyniemi; Sakari Kuikka
• Keywords: Problem framing; Bayesian model averaging; Fisheries; Fishes; Influence diagram; Bayes Theorem; Participatory modeling; Models, Theoretical; 01 natural sciences; Bayesian modeling; Stakeholders; Animals; Humans; Stock assessment; 14. Life underwater; Baltic herring; 0105 earth and related environmental sciences

A participatory Bayesian approach was used to investigate how the views of stakeholders could be utilized to develop models to help understand the Central Baltic herring fishery. In task one, we applied the Bayesian belief network methodology to elicit the causal assumptions of six stakeholders on factors that influence natural mortality, growth, and egg survival of the herring stock in probabilistic terms. We also integrated the expressed views into a meta-model using the Bayesian model averaging (BMA) method. In task two, we used influence diagrams to study qualitatively how the stakeholders frame the management problem of the herring fishery and elucidate what kind of causalities the different views involve. The paper combines these two tasks to assess the suitability of the methodological choices to participatory modeling in terms of both a modeling tool and participation mode. The paper also assesses the potential of the study to contribute to the development of participatory modeling practices. It is concluded that the subjective perspective to knowledge, that is fundamental in Bayesian theory, suits participatory modeling better than a positivist paradigm that seeks the objective truth. The methodology provides a flexible tool that can be adapted to different kinds of needs and challenges of participatory modeling. The ability of the approach to deal with small data sets makes it cost-effective in participatory contexts. However, the BMA methodology used in modeling the biological uncertainties is so complex that it needs further development before it can be introduced to wider use in participatory contexts.