Päivi Elisabet Haapasaari

Growing into interdisciplinarity: how to converge biology, economics and social science in fisheries research?

It has been acknowledged that natural sciences alone cannot provide an adequate basis for the management of complex environmental problems. The scientific knowledge base has to be expanded in a more holistic direction by incorporating social and economic issues. As well, the multifaceted knowledge has to be summarized in a form that can support science-based decision making. This is, however, difficult. Interdisciplinary skills, practices, and methodologies are needed that enable the integration of knowledge from conceptually different disciplines. Through a focus on our research process, we analyzed how and what kind of interdisciplinarity between natural scientists, environmental economists, and social scientists grew from the need to better understand the complexity and uncertainty inherent to the Baltic salmon fisheries, and how divergent knowledge was integrated in a form that can support science-based decision making. The empirical findings suggest that interdisciplinarity is an extensive learning process that takes place on three levels: between individuals, between disciplines, and between types of knowledge. Such a learning process is facilitated by agreeing to a methodological epoch and by formulating a global question at the outset of a process.

Baltic Herring Fisheries Management: Stakeholder Views to Frame the Problem

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.

Toward integrative management advice of water quality, oil spills, and fishery in the Gulf of Finland: a Bayesian approach.

Understanding and managing ecosystems affected by several anthropogenic stressors require methods that enable analyzing the joint effects of different factors in one framework. Further, as scientific knowledge about natural systems is loaded with uncertainty, it is essential that analyses are based on a probabilistic approach. We describe in this article about building a Bayesian decision model, which includes three stressors present in the Gulf of Finland. The outcome of the integrative model is a set of probability distributions for future nutrient concentrations, herring stock biomass, and achieving the water quality targets set by HELCOM Baltic Sea Action Plan. These distributions can then be used to derive the probability of reaching the management targets for each alternative combination of management actions.

Involving stakeholders in building integrated fisheries models using Bayesian methods

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.