Modeling environmentally driven uncertainties in Baltic cod (Gadus morhua) management by Bayesian influence diagrams

Abstract

The large variability in Baltic cod (Gadus morhua) recruitment has been attributed both to environmental factors dependent on the inflow of saline water (oxygen and salinity in spawning deeps) and to the size of the spawning stock. Due to the complex interactions between hydrographic and biological processes, future recruitment levels of cod will remain highly uncertain and increase uncertainties in stock predictions and management advice. We assessed the effects of the exploitation level and mesh size used by a trawl fishery on some variables of management interest under different environmental conditions. The modeling consisted of three separate steps: (i) modeling of selectivity, (ii) estimation of uncertainties by Monte Carlo simulations, and (iii) decision analysis by Bayesian influence diagrams, focusing on the structural uncertainties and model selection. Realistic assumptions of environmental conditions and present fishing mortality rates suggest that the current Baltic cod fishery is unsustainable. We use our approach to identify robust management measures that reduce the risk of overfishing and the sensitivity to management information. The value of information analysis demonstrates the advantages of a larger mesh size as a management measure.Résumé : La grande variabilité dans le recrutement de la morue franche (Gadus morhua) de la Baltique a été attribuée à des facteurs environnementaux qui dépendent de l'apport d'eau salée (oxygène et salinité dans les profondeurs où s'effectue la fraye) et à la taille du stock de géniteurs. En raison des interactions complexes entre les processus hydrographiques et biologiques, les niveaux futurs de recrutement chez la morue resteront très incertains, ce qui accroîtra les incertitudes dans les prévisions des stocks et les recommandations en matière de gestion. Nous avons évalué les effets du niveau d'exploitation et du maillage utilisé par les chalutiers sur certaines variables prises en compte dans la gestion des stocks dans différentes conditions environnementales. La modélisation a été effectuée en trois étapes distinctes : (i) modélisation de la sélectivité, (ii) estimation des incertitudes par des simulations de Monte Carlo et (iii) analyse de décision au moyen de diagrammes d'influence Bayésiens, qui se concentrait sur les incertitudes structurales et le choix du modèle. Selon des hypothèses réalistes sur les conditions environnementales et les taux présents de mortalité par pêche, la pêche actuelle de la morue de la Baltique n'est pas viable. Nous utilisons notre approche pour concevoir des mesures de gestion énergiques qui réduisent le risque de surpêche et la sensibilité à l'information de gestion. L'analyse fondée sur la valeur de l'information montre que l'agrandissement du maillage est une mesure de gestion bénéfique.[Traduit par la Rédaction] Kuikka et al. 641

Bayesian Networks for Modeling Dredging Decisions

“…Predictions were used to guide future management and research efforts and improve understanding of the causal factors leading to the species decline at different sites within the reserve. In another example, Kuikka et al (1999) developed an influence diagram to consider whether or not a change in mesh size would benefit a Baltic cod fishery. The authors assess how environmentally driven uncertainties in recruitment and growth, including alternative models for recruitment, might affect cod management.…”

“…Model simulation and data from elicitation - Kuikka et al 1999 Determine the best mesh size for use in the Baltic cod (Gadus morhua) fishery and how uncertainties in recruitment and growth might affect cod management decisions.…”

A Bayesian belief network model for community-based coastal resource management in the Kei Islands, Indonesia

“…Such features make them well suited for small-scale fisheries where availability of quantitative data is often limited. A number of BBNs have been developed to aid natural resource management decisions, including fisheries (Varis and Kuikka 1997, Kuikka et al 1999, Little et al 2004, Haapasaari and Karjalainen 2010, Levontin et al 2011, van Putten et al 2013. The importance of integrating biological, economic, and sociological information into fisheries management plans was illustrated by Levontin et al (2011), who highlighted the link between commitment and management success.…”

Risk of collapse in the eastern Baltic cod fishery

“…From the mid 1980s, the eastern stock (ICES subdivisions 25 to 32) has declined dramatically and recruitment has been at low levels since 1989 (ICES 1999, Kuikka et al 1999. Cod is the dominant piscivore in the Baltic Sea and one of its the most valuable commercial species (Bagge et al 1994).…”

“…However, the Baltic cod fishery is experiencing a major crisis (Cardinale & Arrhenius 2000, Hjerne & Hansson 2001. The combination of current environmental conditions, fishing mortality rates and stock size suggests that the Baltic cod fishery is not sustainable (Kuikka et al 1999).…”