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.

中文翻译：

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使用贝叶斯网络评估当地和全球压力源的珊瑚礁状况指标

珊瑚礁是目前受到当地和全球压力源威胁的高度重视的生态系统。鉴于珊瑚礁生态系统的重要性，贝叶斯网络方法有助于评估对珊瑚礁状况的威胁。为此，我们使用数据来评估当地压力源（过度捕捞和破坏性捕捞、流域污染、海洋污染和沿海发展威胁）、全球压力源（酸化和热应力）之间的重叠，以及管理有效性指标珊瑚礁健康（活珊瑚指数、活珊瑚覆盖、种群白化、群落白化和最近杀死的珊瑚）。每个珊瑚健康指标都在全球以及太平洋、大西洋、澳大利亚、中东、印度洋和东南亚珊瑚礁位置构建了贝叶斯网络。敏感性分析有助于评估不同压力源和珊瑚礁状况指标之间关系的强度。指标和压力源之间的关系也通过线性和非线性相互作用的条件分析进行了评估。在这个过程中，强调了标准化的直接影响分析和目标均值分析，以预测珊瑚礁指标的平均值从个体变化到预测变量分布的变化。标准化的直接影响分析确定了中东因人口白化而导致流域污染的风险较高，而澳大利亚因活珊瑚过度捕捞和破坏性捕捞而存在较高风险。对于热应力，发现印度洋的群落漂白和最近被杀死的珊瑚与中东的活珊瑚有着最强烈的直接关联。对于酸化威胁，澳大利亚与殖民地漂白的关联性相对较强，而中东与最近杀死的珊瑚的总体关联最强，尽管外推空间数据用于酸化估计。贝叶斯网络方法通过检查珊瑚礁条件的多个指标对空间分布压力的敏感性，帮助探索用于珊瑚礁管理政策制定的现有数据库之间的关系。和中东与最近杀死的珊瑚的总体关联最强，尽管外推的空间数据被用于酸化估计。贝叶斯网络方法通过检查珊瑚礁条件的多个指标对空间分布压力的敏感性，帮助探索用于珊瑚礁管理政策制定的现有数据库之间的关系。和中东与最近杀死的珊瑚的总体关联最强，尽管外推的空间数据被用于酸化估计。贝叶斯网络方法通过检查珊瑚礁条件的多个指标对空间分布压力的敏感性，帮助探索用于珊瑚礁管理政策制定的现有数据库之间的关系。2021 年整合环境评估管理；17：165–187。2020 年出版。本文是美国政府的作品，在美国属于公共领域。