Thermal stress and differential recovery of coral reefs

珊瑚礁是世界上最多样化的海洋生态系统，为全球数百万人提供了无价的商品和服务。然而，全世界的珊瑚礁都在经历热应力事件，珊瑚礁的群落也在发生变化。虽然粗粒度的气候模型预测，在未来一个世纪，几乎没有珊瑚礁能在3摄氏度的海面温度上升中幸存下来，但实地研究表明，即使在高温条件下，珊瑚的存活和恢复也是局部的。量化从热应力事件中的恢复对于准确预测珊瑚礁在不久的将来的轨迹是至关重要的。这项研究考察了全球在热应激事件后珊瑚恢复的不同速度，并确定了区域和当地条件对恢复的影响程度。这项研究利用了时空分析的最新进展。这项工作最具变革性的方面之一是确定珊瑚回收率与预期不同的地方，以及这些差异与区域和当地条件的关系。这项研究与所有在珊瑚礁附近生活和工作的人有关。珊瑚礁发生的事情会对其他与珊瑚礁相关的生物产生连锁影响，并影响珊瑚礁能否跟上海平面的上升。该项目正在通过培训一名博士后学者和一名博士生进行大数据分析并使这些分析技术广泛可用来提高科学能力。高质量和免费的在线教程支持R高中数学、科学和计算机教师的标准驱动教学，R是一种用于统计计算和图形的编程语言和软件环境。该项目正在产生大规模的数据和计算资源，使学生、科学家、资源管理人员和更广泛的公众等不同用户受益。目前快速的气候变化威胁着珊瑚礁。量化从热应力事件中恢复的情况是准确预测珊瑚礁在不久的将来的轨迹的关键。不同地理区域和不同当地条件下的珊瑚种群对热应激的耐受能力或恢复能力各不相同。然而，珊瑚的反应如何以及为什么不同仍然知之甚少。显然需要准确预测热应力事件后的珊瑚轨迹，并确定哪些相互作用因素对珊瑚恢复影响最大。这项研究描述了珊瑚恢复率、热应激事件的频率和强度、地理位置、栖息地和减缓或促进珊瑚恢复的当地条件之间的关系。目前正在使用四种方法来分析珊瑚恢复：(1)二元方法，(2)荟萃分析方法，(3)反问题方法，和(4)状态空间方法。正在利用贝叶斯框架内时空分析的最新发展，量化珊瑚恢复率的空间和时间差异。正在将珊瑚恢复的观察结果与预测结果进行比较，以确定恢复程度高于或低于预期的地区，并评估珊瑚恢复情况与当地和区域条件的相关性。这项研究最具变革性的方面是确定恢复率高于预期的地区，这可以指导未来的保护决策，使管理者能够通过将具有特定特征的珊瑚礁指定为潜在的避难所来保护它们免受人类干扰，因为海洋继续变暖。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Coral reefs are the world’s most diverse marine ecosystem that provide invaluable goods and services for millions of people worldwide. Yet, coral reefs are experiencing thermal-stress events worldwide and their communities are changing. While coarse-grained climate models predict that few coral reefs will survive the 3ºC sea-surface temperature rise in the coming century, field studies show localized pockets of coral survival and recovery, even under high temperature conditions. Quantifying recovery from thermal-stress events is central to making accurate predictions of coral reef trajectories into the near future. This study examines the differential rates of coral recovery following thermal-stress events, globally, and determines the extent to which regional and local conditions influence recovery. This research is taking advantage of the recent progress in spatio-temporal analyses. One of the most transformative aspects of this work is determining where coral recovery rates differ from expectations, and how those differences relate to regional and local conditions. The research is of relevance to all persons that live and work near coral reefs. What happens to reef corals has cascading consequences on other reef-associated organisms and influences whether coral reefs can keep up with sea-level rise. This project is increasing scientific capacity by training a post-doctoral scholar and a PhD student in big-data analysis and making these analysis techniques broadly available. High quality and free online tutorials are supporting standards-driven instruction for high school math, science, and computer teachers in R, a programming language and software environment used for statistical computing and graphics. The project is producing large-scale data and computational resources, which are benefitting diverse users such as students, scientists, resource managers and the broader public. The current rapid rate of climate change threatens coral reefs. Quantifying recovery from thermal-stress events is central to making accurate predictions of coral-reef trajectories into the near future. Coral populations in different geographic regions and under different local conditions vary in their capacity to tolerate or recover from thermal stress. However, how and why coral responses differ remains poorly understood. There is a clear need for accurate predictions of coral trajectories following thermal-stress events and for determining which interacting factors most influence coral recovery. This study is characterizing the relationships between the rates of coral recovery, frequency and intensity of thermal-stress events, geographic location, habitat, and local conditions that slow or enhance coral recovery. Four approaches are being used to analyze coral recovery: (i) a binary approach, (ii) a meta-analysis approach, (iii) an inverse-problem approach, and (iv) a state-space approach. Spatial and temporal differences in rates of coral recovery are being quantified by capitalizing on the latest developments in spatio-temporal analyses within a Bayesian framework. Observed outcomes of coral recovery are being compared with predicted outcomes to identify areas where recoveries are either higher or lower than expected, and to assess context-dependencies of coral recovery in relation to local and regional conditions. The most transformative aspect of the study is the identification of localities with greater than expected recovery rates, which could guide future conservation decisions by enabling managers to target coral reefs with specific characteristics for protection from human disturbances by designating them as potential refuges as the oceans continue to warm.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.