Salmon Action Plan impact assessment, 2007–2008
Integrated model, 2009–2010
|Funding||The Academy of Finland||European Commission||None|
|Aims||Increase knowledge of a complex fishery problem, and develop an interdisciplinary decision support tool.||Impact assessment of the Salmon Action Plan||Integrate biological, economic, and social knowledge in a decision support model|
How to deal with the high uncertainty in assessing the status of individual
salmon stocks and in setting management objectives?
How to justify the socioeconomic feasibility of a salmon stock restoration program to the local communities? How to help the local communities to cooperate for achieving the common goals?
|What are the biological, economic, and social impacts of the Salmon Action Plan and certain new management options and objectives for the future?||Which long-term management objective would lead to best implementation success of individual management measures in terms of fishers’ commitment, and further to best biological, commercial, recreational and/or social utility?|
Commitment to salmon: using Bayesian modeling to create a sustainable
fisheries management tool based on commitment of fishermen
(Haapasaari et al. 2005)A bioeconomic analysis of the Northern Baltic salmon
fishery: management of competing sequential fisheries (Kulmala et al. 2005)Interdisciplinary modeling through probabilistic networks: impact of fishermen’s commitment on the management of wild Baltic salmon stocks (Michielsens et al. 2005a) Interdisciplinary probabilistic network to examine the possibility to restore potential Baltic salmon rivers (Michielsens et al. 2005b) Reconciling economic and biological modelling of migratory fish stocks: optimal management of the northern Baltic salmon fishery (Kulmala et al. 2006) A Bayesian state-space mark-recapture model to estimate exploitation rates in mixed-stock fisheries (Michielsens et al. 2006b) Estimation of annual mortality rates caused by early mortality syndromes (EMS) and their impact on salmonid stock-recruit relationships (Michielsens et al. 2006a) Management measures and fishers’ commitment to sustainable exploitation: a case study of Atlantic salmon fisheries in the Baltic Sea (Haapasaari et al. 2007) Reconciling economic and biological modeling of migratory fish stocks: optimal management of the Atlantic salmon fishery in the Baltic Sea (Kulmala et al. 2008) Combining multiple Bayesian data analyses in a sequential Bayesian framework for quantitative fisheries stock assessment (Michielsens et al. 2008)
|The Report of the Data Analysis to Support the Development of a Baltic Sea Salmon Action Plan, S12.491891, FISH/2007/03—Lot 6 (Finnish Game and Fisheries Research Institute 2009) Formalizing expert knowledge to compare alternative management plans: sociological perspective to the future management of Baltic salmon stocks (Haapasaari and Karjalainen 2010)||Synthesizing biological, economic and sociological knowledge using Bayesian Belief Networks to support broadly based fisheries policy: the case of devising a new Baltic salmon management plan (Levontin et al. 2009) Integration of biological, economic and sociological knowledge by Bayesian belief networks: the interdisciplinary evaluation of potential Baltic salmon management plan (Levontin et al. 2011)|
|Forms of collaboration||Natural scientists and social scientists: composite multidisciplinarityNatural scientists and economists: methodological interdisciplinarityEconomists and social scientists: contextualizing multidisciplinarity||Natural scientists and social scientists: composite multidisciplinarity Natural scientists and economists: methodological interdisciplinarityEconomists and social scientists: contextualizing multidisciplinarity||All scientists: theoretical interdisciplinarity|
|Aim vs. output||Aim: theoretical interdisciplinarityOutput: contextualizing multidisciplinarity||Aim: composite multidisciplinarityOutput: composite multidisciplinarity||Aim: theoretical interdisciplinarityOutput: theoretical interdisciplinarity|