Toward Integrative Management Advice of Water Quality, Oil Spills, and Fishery in the Gulf of Finland: A Bayesian Approach

Abstract

Understanding and managing ecosystems affected by several anthropogenic stressors require methods that enable analyzing the joint effects of different factors in one framework. Further, as scientific knowledge about natural systems is loaded with uncertainty, it is essential that analyses are based on a probabilistic approach. We describe in this article about building a Bayesian decision model, which includes three stressors present in the Gulf of Finland. The outcome of the integrative model is a set of probability distributions for future nutrient concentrations, herring stock biomass, and achieving the water quality targets set by HELCOM Baltic Sea Action Plan. These distributions can then be used to derive the probability of reaching the management targets for each alternative combination of management actions.

How to value biodiversity in environmental management?

“…Decision analysis can help to structure the problem, to integrate knowledge and any prevailing uncertainty, and to visualise the results (Cooper, 2012;Lehikoinen et al, 2014;Rahikainen et al, 2014). The ultimate goal of decision analysis is to successfully select the management alternative that minimises risks and costs while maximising benefits and public acceptance (Keeney, 1982;Burgman, 2005;Kiker et al, 2005).…”

Fisheries Volume 43 Number 3 March 2018

“…Bayesian inference has been used to fi nd lost ships, crack the "unbreakable" Enigma code of World War II, predict the outcomes of elections, forecast nuclear meltdowns, and predict Major League Baseball player performance (McGrayne 2012 ); it has also likely been used at some point in your own life to fi nd a new fi shing spot. Within our own fi eld and more recently, Bayesian inference has been used in a variety of analyses, including generalized linear models, species distribution modeling, incorporating phylogeny into standard models describing trends in abundance, and stock assessments (Punt and Hilborn 1997 ;Jacquemin and Doll 2014 ;Rahikainen et al 2014 ). Bayesian inference is all around us and is commonly used in fi sheries science, yet many may not be familiar enough with it to appreciate (1) its fl exibility for addressing both simple and complex problems and (2) how it can take advantage of all available information to help produce clear and direct inferences.…”

Options for integrating ecological, economic, and social objectives in evaluation and management of fisheries

“…The utility of BBNs as a tool for facilitation of stakeholder participation and integration of diverse knowledge is emphasized in the literature (Table , Figure ; Cain, Batchelor, & Waughray, ; Duespohl et al., ). In addition to their graphical construction, which clearly shows the assumed relationships among system variables, their use of Bayesian statistics means that BBNs can synthesize both empirical data (including missing observations) and expert knowledge in representations of modeled system and alternative beliefs about how impacts and interventions propagate through that system (Kuikka et al., ; Rahikainen et al., ). They are additionally highly interactive and can be developed quickly using a number of computationally efficient commercial modeling software packages (e.g., WinBUGS; Lunn, Thomas, Best, & Spiegelhalter, ).…”