title: A concept of Bayesian regulation in fisheries management.
abstract: Stochastic variability of biological processes and uncertainty of stock properties compel fisheries managers to look for tools to improve control over the stock. Inspired by animals exploiting hidden prey, we have taken a biomimetic approach combining catch and effort in a concept of Bayesian regulation (BR). The BR provides a real-time Bayesian stock estimate, and can operate without separate stock assessment. We compared the performance of BR with catch-only regulation (CR), alternatively operating with N-target (the stock size giving maximum sustainable yield, MSY) and F-target (the fishing mortality giving MSY) on a stock model of Baltic Sea herring. N-targeted BR gave 3% higher yields than F-targeted BR and CR, and 7% higher yields than N-targeted CR. The BRs reduced coefficient of variance (CV) in fishing mortality compared to CR by 99.6% (from 25.2 to 0.1) when operated with F-target, and by about 80% (from 158.4 to 68.4/70.1 depending on how the prior is set) in stock size when operated with N-target. Even though F-targeted fishery reduced CV in pre-harvest stock size by 19-22%, it increased the dominant period length of population fluctuations from 20 to 60-80 years. In contrast, N-targeted BR made the periodic variation more similar to white noise. We discuss the conditions when BRs can be suitable tools to achieve sustainable yields while minimizing undesirable fluctuations in stock size or fishing effort.
authors.affiliation: Systems Biology Research Centre, School of Bioscience, University of Skövde, Skövde, Sweden.
authors.forename: Noél Michael André
authors.initials: NM
authors.lastname: Holmgren
authors.affiliation: Systems Biology Research Centre, School of Bioscience, University of Skövde, Skövde, Sweden.
authors.forename: Niclas
authors.initials: N
authors.lastname: Norrström
authors.affiliation: University of Tartu, Estonian Marine Institute, Tallinn, Estonia.
authors.forename: Robert
authors.initials: R
authors.lastname: Aps
authors.affiliation: Fisheries and Environmental Management Group, Department of Environmental Sciences, University of Helsinki, Helsinki, Finland.
authors.forename: Sakari
authors.initials: S
authors.lastname: Kuikka
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D005398:Fisheries; D008962:Models, Theoretical