title: Bayesian adaptive design: improving the effectiveness of monitoring of the Great Barrier Reef.
abstract: Monitoring programs are essential for understanding patterns, trends, and threats in ecological and environmental systems. However, such programs are costly in terms of dollars, human resources, and technology, and complex in terms of balancing short- and long-term requirements. In this work, We develop new statistical methods for implementing cost-effective adaptive sampling and monitoring schemes for coral reef that can better utilize existing information and resources, and which can incorporate available prior information. Our research was motivated by developing efficient monitoring practices for Australia's Great Barrier Reef. We develop and implement two types of adaptive sampling schemes, static and sequential, and show that they can be more informative and cost-effective than an existing (nonadaptive) monitoring program. Our methods are developed in a Bayesian framework with a range of utility functions relevant to environmental monitoring. Our results demonstrate the considerable potential for adaptive design to support improved management outcomes in comparison to set-and-forget styles of surveillance monitoring.
authors.affiliation: Mathematical Sciences School and Institute for Future Environments, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia.
authors.forename: Su Yun
authors.initials: SY
authors.lastname: Kang
authors.affiliation: Mathematical Sciences School and Institute for Future Environments, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia.|ARC Centre of Excellence for Mathematical & Statistical Frontiers, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia.
authors.forename: James M
authors.initials: JM
authors.lastname: McGree
authors.affiliation: Mathematical Sciences School and Institute for Future Environments, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia.|ARC Centre of Excellence for Mathematical & Statistical Frontiers, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia.
authors.forename: Christopher C
authors.initials: CC
authors.lastname: Drovandi
authors.affiliation: ARC Centre of Excellence for Mathematical & Statistical Frontiers, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia.|Australian Institute of Marine Science, PMB No.3, Townsville MC, Townsville, Queensland, 4810, Australia.
authors.forename: M Julian
authors.initials: MJ
authors.lastname: Caley
authors.affiliation: Mathematical Sciences School and Institute for Future Environments, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia.|ARC Centre of Excellence for Mathematical & Statistical Frontiers, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia.
authors.forename: Kerrie L
authors.initials: KL
authors.lastname: Mengersen
mesh_terms: D000818:Animals; D037421:Anthozoa; D001315:Australia; D001499:Bayes Theorem; D057894:Coral Reefs; D004784:Environmental Monitoring; D006801:Humans
keywords: Australia; Bayesian inference; Great Barrier Reef; adaptive design; coral reef ecosystems; reef monitoring; utility functions