Bayesian adaptive design: Improving the effectiveness of monitoring of the Great Barrier Reef

Kang, Su Yun, McGree, James M., Drovandi, Christopher C., Caley, M. Julian, & Mengersen, Kerrie L. (2016) Bayesian adaptive design: Improving the effectiveness of monitoring of the Great Barrier Reef. Ecological Applications, 26(8), pp. 2637-2648.

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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 21 cost-effective than an existing (non-adaptive) 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.

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ID Code: 98492
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: adaptive design, Bayesian inference, coral reef ecosystems, reef monitoring, utility functions
DOI: 10.1002/eap.1409
ISSN: 1939-5582
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Statistical Theory (010405)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: Copyright 2016 Ecological Society of America
Deposited On: 29 Aug 2016 22:54
Last Modified: 15 Apr 2017 07:31

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