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Assessing Coral Reef Condition Indicators for Local and Global Stressors Using Bayesian Networks
Coral reefs are highly valued ecosystems currently threatened by both local and global stressors. Given the importance of coral reef ecosystems, a Bayesian network approach can benefit an evaluation of threats to reef conditions by revealing details about the relationships between variables. To this end, we used available data to evaluate the overlap between local stressors (overfishing, watershed-based pollution, marine-based pollution, and coastal development threats), global stressors (acidification and thermal stress) and management effectiveness with indicators of coral reef health (live coral index, live coral cover, population bleaching, colony bleaching and recently killed corals). We constructed Bayesian networks using available data for each coral health indicator both globally and for specified regions (Pacific, Atlantic, Australia, Middle East, Indian Ocean, and Southeast Asia). Sensitivity analysis helped evaluate the strength of the relationships between different stressors and reef condition indicators. Management effectiveness was also examined for directionality and strength of relationships. The relationships between indicators and stressors were evaluated with conditional analyses of linear and nonlinear interactions. This process used standardized direct effects and target mean analyses to predict changes in the mean value of the reef indicator from individual changes to the distribution of the predictor variables. The standardized direct effects analysis identified higher potential risks between coral reef indicators and stressors in and across regions when relationships approximated linearity. Additional measures, including the minimums and maximums of the target mean analysis, were used to support the relationship analysis. The Bayesian network approach helped characterize relationships among indicators used for coral reef management by examining the sensitivity of reef condition indicators to indicators of threats and management effectiveness.
EPA Disclaimer: The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
- John F. Carriger ([email protected]), Susan H. Yee, William S. Fisher
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Land Remediation and Technology Division, Environmental Decision Analytics Branch
John Carriger is a researcher with the U.S. Environmental Protection Agency’s Office of Research and Development. John received his Ph.D. in Marine Science from the College of William & Mary in 2009. His research interests are developing and applying causal modeling, decision analysis, and risk assessment tools to diverse environmental problems. John lives and works in Cincinnati, OH, USA.
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