A concept of Bayesian regulation in fisheries management

Noél Michael André Holmgren; Niclas Norrström; Robert Aps; Sakari Kuikka

doi:10.1371/journal.pone.0111614

A concept of Bayesian regulation in fisheries management

PLoS One. 2014 Nov 3;9(11):e111614. doi: 10.1371/journal.pone.0111614. eCollection 2014.

Authors

Noél Michael André Holmgren¹, Niclas Norrström¹, Robert Aps², Sakari Kuikka³

Affiliations

¹ Systems Biology Research Centre, School of Bioscience, University of Skövde, Skövde, Sweden.
² University of Tartu, Estonian Marine Institute, Tallinn, Estonia.
³ Fisheries and Environmental Management Group, Department of Environmental Sciences, University of Helsinki, Helsinki, Finland.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Bayes Theorem*
Fisheries*
Models, Theoretical*

Grants and funding

The study is part of the IBAM project “Integrated Bayesian risk analysis of ecosystem management – Gulf of Finland as a case study”, which received funding from the European Community’s Seventh Framework Programme under grant agreement 217246 made with the joint Baltic Sea research and development programme BONUS (http://www.bonusportal.org/), from FORMAS, Sweden, the Academy of Finland, and an Estonian Science Foundation grant 7609, Estonian target financed theme SF0180104s08. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.