Ecological forecasts are becoming increasingly valuable tools for conservation and management. However, there are few examples of near‐real‐time forecasting systems that account for the wide range of ecological complexities. We developed a new coral disease ecological forecasting system that explores a suite of ecological relationships and their uncertainty and investigates how forecast skill changes with shorter lead times. The Multi‐Factor Coral Disease Risk product introduced here uses a combination of ecological and marine environmental conditions to predict the risk of white syndromes and growth anomalies across reefs in the central and western Pacific and along the east coast of Australia and is available through the US National Oceanic and Atmospheric Administration Coral Reef Watch program. This product produces weekly forecasts for a moving window of 6 months at a resolution of ~5 km based on quantile regression forests. The forecasts show superior skill at predicting disease risk on withheld survey data from 2012 to 2020 compared with predecessor forecast systems, with the biggest improvements shown for predicting disease risk at mid‐ to high‐disease levels. Most of the prediction uncertainty arises from model uncertainty, so prediction accuracy and precision do not improve substantially with shorter lead times. This result arises because many predictor variables cannot be accurately forecasted, which is a common challenge across ecosystems. Weekly forecasts and scenarios can be explored through an online decision support tool and data explorer, co‐developed with end‐user groups to improve use and understanding of ecological forecasts. The models provide near‐real‐time disease risk assessments and allow users to refine predictions and assess intervention scenarios. This work advances the field of ecological forecasting with real‐world complexities and, in doing so, better supports near‐term decision making for coral reef ecosystem managers and stakeholders. Secondarily, we identify clear needs and provide recommendations to further enhance our ability to forecast coral disease risk.

中文翻译：

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多因素珊瑚病风险：早期预警和管理的新产品


生态预测正在成为越来越有价值的保护和管理工具。然而，能够解释广泛的生态复杂性的近实时预测系统的例子很少。我们开发了一种新的珊瑚病生态预测系统，该系统探索一系列生态关系及其不确定性，并研究预测技能如何在较短的交付时间内发生变化。这里介绍的多因素珊瑚病风险产品结合了生态和海洋环境条件来预测中太平洋和西太平洋以及澳大利亚东海岸珊瑚礁出现白色综合症和生长异常的风险，可通过美国购买国家海洋和大气管理局珊瑚礁观察计划。该产品基于分位数回归森林，以约 5 公里的分辨率生成 6 个月移动窗口的每周预测。与之前的预测系统相比，这些预测在根据 2012 年至 2020 年保留的调查数据预测疾病风险方面表现出卓越的能力，其中在预测中高疾病水平的疾病风险方面取得了最大的进步。大多数预测不确定性源于模型不确定性，因此预测准确性和精度不会随着交付时间的缩短而大幅提高。出现这一结果是因为许多预测变量无法准确预测，这是整个生态系统面临的共同挑战。可以通过与最终用户团体共同开发的在线决策支持工具和数据浏览器来探索每周预测和情景，以提高对生态预测的使用和理解。 这些模型提供近乎实时的疾病风险评估，并允许用户完善预测和评估干预场景。这项工作推进了具有现实世界复杂性的生态预测领域，并在此过程中更好地支持珊瑚礁生态系统管理者和利益相关者的近期决策。其次，我们确定明确的需求并提供建议，以进一步提高我们预测珊瑚疾病风险的能力。