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 condition. To this end, we used data to evaluate the overlap between local stressors (overfishing and destructive fishing, 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). Each of the coral health indicators had Bayesian networks constructed globally and for Pacific, Atlantic, Australia, Middle East, Indian Ocean, and Southeast Asia coral reef locations. Sensitivity analysis helped evaluate the strength of the relationships between different stressors and reef condition indicators. The relationships between indicators and stressors were also evaluated with conditional analyses of linear and nonlinear interactions. In this process, a standardized direct effects analysis was emphasized with a target mean analysis 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 risks in the Middle East for watershed-based pollution with population bleaching and in Australia for overfishing and destructive fishing with living coral. For thermal stress, colony bleaching and recently killed coral in the Indian Ocean were found to have the strongest direct associations along with living coral in the Middle East. For acidification threat, Australia had a relatively strong association with colony bleaching, and the Middle East had the strongest overall association with recently killed coral, although extrapolated spatial data were used for the acidification estimates. The Bayesian network approach helped to explore the relationships among existing databases used for policy development in coral reef management by examining the sensitivity of multiple indicators of reef condition to spatially distributed stress. Integr Environ Assess Manag 2021;17:165-187. Published 2020. This article is a US Government work and is in the public domain in the USA.

Automated summary

This study utilized a Bayesian network approach to assess threats to coral reef condition from both local and global stressors, examining the overlap between stressors and indicators of reef health. The analysis found specific associations between different stressors and coral health indicators, providing valuable insights for coral reef management and policy development.