DeCarlo Thomas M
Hawaii Pacific University, Honolulu, HI, United States of America.
Red Sea Research Center, Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
PeerJ. 2020 Jul 3;8:e9449. doi: 10.7717/peerj.9449. eCollection 2020.
Few coral reefs remain unscathed by mass bleaching over the past several decades, and much of the coral reef science conducted today relates in some way to the causes, consequences, or recovery pathways of bleaching events. Most studies portray a simple cause and effect relationship between anomalously high summer temperatures and bleaching, which is understandable given that bleaching rarely occurs outside these unusually warm times. However, the statistical skill with which temperature captures bleaching is hampered by many "false alarms", times when temperatures reached nominal bleaching levels, but bleaching did not occur. While these false alarms are often not included in global bleaching assessments, they offer valuable opportunities to improve predictive skill, and therefore understanding, of coral bleaching events. Here, I show how a statistical framework adopted from weather forecasting can optimize bleaching predictions and validate which environmental factors play a role in bleaching susceptibility. Removing the 1 °C above the maximum monthly mean cutoff in the typical degree heating weeks (DHW) definition, adjusting the DHW window from 12 to 9 weeks, using regional-specific DHW thresholds, and including an El Niño threshold already improves the model skill by 45%. Most importantly, this framework enables hypothesis testing of other factors or metrics that may improve our ability to forecast coral bleaching events.
在过去几十年里，很少有珊瑚礁未受到大规模白化的影响，如今开展的许多珊瑚礁科学研究都在某种程度上与白化事件的成因、后果或恢复途径有关。大多数研究描绘了夏季异常高温与白化之间简单的因果关系，鉴于白化很少在这些异常温暖的时期之外发生，这是可以理解的。然而，温度捕捉白化现象的统计能力受到许多“误报”的阻碍，即温度达到名义上的白化水平但白化并未发生的时期。虽然这些误报通常不包括在全球白化评估中，但它们为提高对珊瑚白化事件的预测能力（进而增进理解）提供了宝贵机会。在此，我展示了一种从天气预报中采用的统计框架如何优化白化预测，并验证哪些环境因素在白化易感性中起作用。在典型的热应力周（DHW）定义中，去除高于月平均最高值1摄氏度的部分，将DHW窗口从12周调整为9周，使用特定区域的DHW阈值，并纳入厄尔尼诺阈值，已经将模型技能提高了45%。最重要的是，这个框架能够对其他可能提高我们预测珊瑚白化事件能力的因素或指标进行假设检验。
