Temperature patterns and mechanisms influencing coral bleaching during the 2016 El Nino
Temperature patterns and mechanisms influencing coral bleaching during the 2016 El Nino
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DOI：
10.1038/s41558-019-0576-8
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发表时间：
2019-11-01
影响因子：
30.7
通讯作者：
Leblond, Julien
中科院分区：
文献类型：
--
作者：
McClanahan, Tim R.;Darling, Emily S.;Leblond, Julien
在极端的热压力下，珊瑚会排出共生的藻类和颜色(即“漂白”)，这往往会导致广泛的死亡。对加强或缓解珊瑚漂白的大规模环境条件的预测仍然没有解决，并限制了战略性保护行动(1，2)。在这里，我们通过协调努力评估珊瑚对2014-2016年厄尔尼诺/南方涛动热异常的响应，评估了226个地点和26个环境变量的珊瑚白化情况，这些变量代表了从东非到斐济的不同应激反应机制。我们应用常见的时间序列方法研究了急性热应激的时间模式，并评估了传统和新的海洋表面温度指标和机制在预测漂白严重程度方面的有效性。最好的模型表明了最高最热温度、最低温度持续时间和温度双峰的重要性，其解释的方差接近50%，而普通的采暖周温度指数仅解释9%。我们的发现表明，阈值概念作为一种单独解释漂白的机制，不如压力的多维交互作用那么强大，压力的多维交互作用包括相对于平均局部条件的热和冷极端温度的持续时间和时间模式。
Under extreme heat stress, corals expel their symbiotic algae and colour (that is, 'bleaching'), which often leads to widespread mortality. Predicting the large-scale environmental conditions that reinforce or mitigate coral bleaching remains unresolved and limits strategic conservation actions(1,2). Here we assessed coral bleaching at 226 sites and 26 environmental variables that represent different mechanisms of stress responses from East Africa to Fiji through a coordinated effort to evaluate the coral response to the 2014-2016 El Nino/Southern Oscillation thermal anomaly. We applied common time-series methods to study the temporal patterning of acute thermal stress and evaluated the effectiveness of conventional and new sea surface temperature metrics and mechanisms in predicting bleaching severity. The best models indicated the importance of peak hot temperatures, the duration of cool temperatures and temperature bimodality, which explained similar to 50% of the variance, compared to the common degree-heating week temperature index that explained only 9%. Our findings suggest that the threshold concept as a mechanism to explain bleaching alone was not as powerful as the multidimensional interactions of stresses, which include the duration and temporal patterning of hot and cold temperature extremes relative to average local conditions.