Experiences in Bayesian Inference in Baltic Salmon Management

TL;DR

This paper discusses the successful application of Bayesian inference in Baltic Sea salmon management, highlighting its role in complex population modeling, decision-making, and future research directions in fisheries science.

Contribution

It presents a comprehensive review of Bayesian methods in fisheries management, emphasizing their practical use and potential for hierarchical modeling of under-studied species.

Findings

Bayesian models are effectively used for salmon population management.

Hierarchical Bayesian models help estimate parameters for data-scarce species.

Bayesian inference enhances decision-making in fisheries management.

Abstract

We review a success story regarding Bayesian inference in fisheries management in the Baltic Sea. The management of salmon fisheries is currently based on the results of a complex Bayesian population dynamic model, and managers and stakeholders use the probabilities in their discussions. We also discuss the technical and human challenges in using Bayesian modeling to give practical advice to the public and to government officials and suggest future areas in which it can be applied. In particular, large databases in fisheries science offer flexible ways to use hierarchical models to learn the population dynamics parameters for those by-catch species that do not have similar large stock-specific data sets like those that exist for many target species. This information is required if we are to understand the future ecosystem risks of fisheries.