Moving Beyond Temperature Metrics in Coral Bleaching Prediction Using Interpretable Machine Learning

IF 6 1区 环境科学与生态学 Q1 ECOLOGY
Mandy W. M. Cheung, Milani Chaloupka, Karlo Hock, Peter J. Mumby
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引用次数: 0

Abstract

Aim

Marine heatwaves are the greatest threat to coral reefs, but the interplay between other physical environmental factors often influences the thermal sensitivity of corals. While existing coral bleaching algorithms largely depend on temperature-related metrics, such relationships may not hold under climate change when corals experience thermal and environmental variability that may shape bleaching susceptibility. Our aim is to use an interpretable machine learning-based approach to explore the effects and critical thresholds of thermal history and environmental drivers on bleaching outcomes.

Time Period

2016–2020.

Location

Great Barrier Reef (GBR), Australia.

Major Taxa Studied

Scleractinia corals.

Methods

A spatially cross-validated ordinal random forest model was applied to predict 2643 observed coral bleaching outcomes of three levels using 19 potentially informative environmental parameters (i.e., predictors) across three bleaching events on the GBR. We estimated the importance and marginal effects of each predictor using the SHapley Additive exPlanations method. Using the 10 most important predictors, we then fitted and applied a model to predict bleaching on unsurveyed reefs with predictor properties that the model had high confidence in.

Results

Our model predicted bleaching intensities with 80% accuracy. While accumulated heat stress was the strongest predictor, non-linear interactions between drivers resolved observed bleaching outcomes and showed that heat stress alone could not always predict bleaching responses. Reefs with weak currents or high water clarity showed higher bleaching risk even with moderate heat stress. Severely heated reefs with high cloud cover or recent exposure to higher thermal stress exhibited lower bleaching risk.

Main Conclusions

We show that corals respond to acute heat stress differently depending on thermal history, water flow and light availability. Integrating environmental heterogeneity into coral bleaching algorithms, reef vulnerability assessment and spatial conservation planning will be critical for identifying bleaching refugia, facilitating coral adaptation and supporting reef persistence under climate change.

Abstract Image

使用可解释的机器学习在珊瑚白化预测中超越温度指标
目的海洋热浪是对珊瑚礁的最大威胁,但其他物理环境因素之间的相互作用往往影响珊瑚的热敏性。虽然现有的珊瑚白化算法在很大程度上依赖于与温度相关的指标,但当珊瑚经历可能影响白化敏感性的温度和环境变化时,这种关系可能在气候变化下不成立。我们的目标是使用一种可解释的基于机器学习的方法来探索热历史和环境驱动因素对漂白结果的影响和临界阈值。2016-2020年。位置:澳大利亚大堡礁(GBR)。主要分类群研究了核珊瑚。方法采用空间交叉验证的有序随机森林模型,利用19个潜在信息环境参数(即预测因子)预测GBR上三个漂白事件中2643个观察到的三个级别的珊瑚漂白结果。我们使用SHapley加性解释方法估计了每个预测因子的重要性和边际效应。使用10个最重要的预测因子,我们然后拟合并应用一个模型来预测未调查珊瑚礁的白化,该模型具有很高的置信度。结果该模型预测漂白强度的准确率为80%。虽然累积的热应激是最强的预测因子,但驱动因素之间的非线性相互作用解决了观察到的漂白结果,并表明单独的热应激并不总是能预测漂白反应。即使在适度的热压力下,水流弱或水清澈度高的珊瑚礁也显示出更高的漂白风险。高度云层覆盖或最近暴露于较高热应力的严重加热珊瑚礁显示出较低的漂白风险。研究表明,珊瑚对急性热应激的反应不同,这取决于热历史、水流和光的可用性。将环境异质性纳入珊瑚白化算法、珊瑚礁脆弱性评估和空间保护规划,对于识别白化避难所、促进珊瑚适应和支持气候变化下的珊瑚礁持久性至关重要。
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来源期刊
Global Ecology and Biogeography
Global Ecology and Biogeography 环境科学-生态学
CiteScore
12.10
自引率
3.10%
发文量
170
审稿时长
3 months
期刊介绍: Global Ecology and Biogeography (GEB) welcomes papers that investigate broad-scale (in space, time and/or taxonomy), general patterns in the organization of ecological systems and assemblages, and the processes that underlie them. In particular, GEB welcomes studies that use macroecological methods, comparative analyses, meta-analyses, reviews, spatial analyses and modelling to arrive at general, conceptual conclusions. Studies in GEB need not be global in spatial extent, but the conclusions and implications of the study must be relevant to ecologists and biogeographers globally, rather than being limited to local areas, or specific taxa. Similarly, GEB is not limited to spatial studies; we are equally interested in the general patterns of nature through time, among taxa (e.g., body sizes, dispersal abilities), through the course of evolution, etc. Further, GEB welcomes papers that investigate general impacts of human activities on ecological systems in accordance with the above criteria.
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