Ocean acidification and fisheries - a Bayesian network approach to assessing a wicked problem

• Main Authors: Richards, Russell, Meynecke, Jan-Olaf, Sahin, Oz, Tiller, Rachel, Liu, Yaije
• Format: Text
• Language: unknown
• Published: BYU ScholarsArchive 2014
• Subjects: ocean acidification; fisheries management; climate change; Bayesian network model; Civil Engineering; Data Storage Systems; Environmental Engineering; Hydraulic Engineering; Other Civil and Environmental Engineering; Queensland; Ocean acidification
• Online Access: https://scholarsarchive.byu.edu/iemssconference/2014/Stream-H/67; https://scholarsarchive.byu.edu/context/iemssconference/article/1170/viewcontent/4_Ocean_acidification_and_fisheries_a_Bayesian_network_approach_to_assessing_a_wicked_problem.pdf

Increased carbon dioxide emissions are driving changes in the chemistry of seawater in a process termed 'ocean acidification' (OA). Globally, this is predicted to impact on coastal fisheries, especially those consisting of calcifying organisms (e.g. mollusks and crustaceans). The impact might also depend on synergistic co-stressors such as a concomitant increase in seawater temperature and the resilience of other biota. However, the framing of OA as a future problem coupled with the complexity of coastal systems means that assessing fisheries vulnerability to OA is characterised by strong variability and uncertainty. It is further characterised by strong socio-economic dimensions given the increasing demand on seafood as a protein source. We have examined the vulnerabilities of, and potential management interventions for, mollusk and crustacean fisheries in Queensland (Australia) to OA using a Bayesian network (BN) modelling framework. An advantage of this approach is that it provides a probabilistic framework for assessing causality between drivers, impacts and responses while conditional probabilities allow for straightforward integration of environmental, social and economic data. It also enables models to be developed, even when data is scarce or uncertain. We have drawn upon "expert opinion· in developing this model, including construction of the causal network and the underlying probabilities. The resulting BN indicates that the crustacean fishery (represented in our model by two prawn species) is more resilient to increasing OA conditions than the mollusk fishery (represented by one scallop species).