Predicting coral mortality in South East Asia using open-source data

Coral reefs hold some of the most biodiverse and productive environments in the world while providing goods and services across a wide range of sectors. Unfortunately, due to climate change and other anthropogenic factors coral reefs are dying. However, majority of reef modeling literature looks at coral development rather than its mortality. In this study I modeled coral mortality using exclusively open source data and created a suitability score (concern index) for high concern corals that also considers the economic value of reefs. The goal was to help identify which community management units require attention from the marine conservation charity Blue Ventures. An inventory of global resolution datasets including environmental and management practices that may indicate coral stress were added to an empty linear model to predict mortality in South East Asia. The best model in predicting coral mortality utilized artisanal fishing data, the model had an R2 of 0.8673 and an accuracy of 48.51%. This model identified the locations of the highest mortality to be in Timor-Leste at Blue Ventures management unit sites TL1 and TL5, however adding economic value in the concern index then Papua New Guinea site PNG7 is more at risk. I hypothesize that a lack in variability and standardized approach in the coral mortality/bleaching explanatory data decreases the accuracy and real-world applicability of the model. The majority of explanatory variables were obtained from the online database Reef Base, some of the observations showed extreme outliers which were all indicated by citizen scientists. Going forward I would recommend that further education and standardization is included in observations to ensure the open source data has a higher accuracy to make better models.

珊瑚礁是全球生物多样性最丰富、生产力最高的生态系统之一，同时为诸多行业提供物资与服务。然而受气候变化与其他人为因素影响，全球珊瑚礁正持续退化消亡。但当前多数珊瑚礁建模相关研究多聚焦于珊瑚生长发育，而非其死亡过程。本研究仅采用开源数据构建珊瑚死亡率预测模型，并针对高风险珊瑚构建了兼顾珊瑚礁经济价值的适宜性评分（suitability score，关切指数）。本研究的目标是协助海洋保护慈善机构蓝色探险（Blue Ventures）确定需要优先开展保护工作的社区管理单元。本研究将涵盖可能表征珊瑚胁迫的环境与管理实践指标的全球分辨率数据集清单导入空白线性模型，以预测东南亚地区的珊瑚死亡率。在珊瑚死亡率预测任务中表现最优的模型纳入了手工渔业数据，该模型的决定系数（R²）为0.8673，预测准确率为48.51%。该模型识别出死亡率最高的区域位于东帝汶的蓝色探险（Blue Ventures）社区管理单元TL1与TL5站点；但在关切指数中纳入经济价值维度后，巴布亚新几内亚的PNG7站点则成为风险更高的区域。本研究推测，珊瑚死亡率/白化解释变量数据缺乏变异性与标准化处理流程，会降低模型的预测精度与实际应用价值。本研究使用的多数解释变量均来自在线数据库Reef Base，部分观测样本存在极端异常值，且均由公民科学家标注发现。后续研究建议在观测流程中纳入标准化培训与教育环节，以提升开源数据的质量与精度，从而构建性能更优的预测模型。