Integrating multi-disciplinary data sources relating to inshore fisheries management via a Bayesian network. (1st July 2021)

• Record Type: Journal Article
• Authors: Parsons, Darren M.; Hartill, Bruce W.; Broekhuizen, Niall; McKenzie, Jeremy R.; Stephenson, Fabrice; Petersen, Grady L.; Lundquist, Carolyn J.
• Is Part Of: Ocean & coastal management. Volume 208(2021)
• Publication Date: 2021-07-01
• Subjects: Chrysophrys auratus -- Stakeholder engagement -- Ecosystem-based fishery management -- Ecosystem-based management -- BN; Bayesian network (BN) -- Ecosystem Based Management (EBM) -- Ecosystem based Fisheries management (EBFM) -- Quota Management Area (QMA) -- SNA 1 Strategy Group (S1SG) -- conditional probability table (CPT) -- Sea Surface Temperature (SST); Marine resources -- Management -- Periodicals; Coastal zone management -- Periodicals; Coastal ecology -- Periodicals; Ressources marines -- Périodiques; Littoral -- Aménagement -- Périodiques; Écologie littorale -- Périodiques; Coastal ecology; Coastal zone management; Marine resources -- Management; Periodicals; Electronic journals; 551.46
• DOI: 10.1016/j.ocecoaman.2021.105636
• Languages: English
• ISSNs: 0964-5691

Abstract

Abstract: The move towards broader management of marine ecosystems has received significant momentum in recent years. Fundamental to Ecosystem-Based Management (EBM) and Ecosystem-Based Fisheries management (EBFM) is the need to engage relevant stakeholder groups. Here we present a Bayesian network (BN) tool developed to describe the snapper population in the Hauraki Gulf, New Zealand, and investigate possible outcomes of alternative management scenarios on snapper abundance. The Hauraki Gulf is a location where the marine environment is both highly valued and in high demand for the goods and services it provides. Snapper are a key component of this social–ecological system. We developed a BN focussed on key snapper life stages, and the consequences of stressors, including fishing, uses of marine space, land use practises (which influence sediment and nutrient yield), biogenic and environmental habitat variables, and upper level drivers such as climate change, human population growth and the global economy. Parameters were populated through a range of information types, from empirical and model analysis to literature review and expert opinion. Parameters for the core of the BN model (i.e. the snapper life stages and fishing methods) were adjusted so that the predicted effects of fishing reflected those seen in model projections from an age structured population model that had recently been used to inform the management of the snapper population in the Hauraki Gulf. The two most influential factors within this BN were fishing and land–use practises. Although there was a strong impact on snapper populations from land–use practises and resulting sedimentation that detrimentally affects biogenic habitats that juvenile snapper depend on, the potential impact of fishery extraction was much stronger. While fishery extraction of snapper is generally well managed, under the existing management set up it is also the only major stressor that would likely be regulated in response to decreases in the snapper population (regardless of the source of any impact). Engaging stakeholders to address issues such as this remains the greatest challenge facing EBFM/EBM in the Hauraki Gulf, but may be possible with tools such as the BN presented here. Highlights: Bayesian network built describing Hauraki Gulf snapper and surrounding environment. Developed R code to populate multi-dimensional conditional probability tables. Fishing had by far the most influence on Hauraki Gulf snapper. Bayes net tools useful for engaging stakeholders within Ecosystem Based Management.