用卫星数据预测葡萄牙沿岸有害藻华，准确率达77%。
Predicting Pseudo-nitzschia harmful algal blooms along the Portuguese Coast using satellite-derived predictors
- 基于十年卫星数据构建时空机器学习模型，仅用遥感变量预测藻华。
- 加入生物指标后模型准确率提升至0.77，优于传统方法。
- 适合海洋生态预警、渔业管理和气候适应研究者参考。
Pseudo-nitzschia硅藻对葡萄牙大西洋沿岸的海岸生态系统和贝类捕捞构成持续威胁。本文开发并评估了一种时空机器学习框架，仅使用卫星遥感变量，在真实预报条件下预测有害藻华（HAB）发生。利用5,882个观测数据刻画了贝类生产区（L1-L9）的环境与生物变异，为系统提供全局背景。针对藻华热点区域L1-L2，基于2013-2023年共1,440个观测数据及超过1,000个卫星衍生预测因子（包括海表温度、上升流指数、叶绿素a和浮游生物功能类型），构建预测模型。通过考虑河流影响的空间聚类方法划分采样区域，并采用严格时空交叉验证策略，同时剔除整年和空间簇，避免信息泄露，贴近实际预报场景。结果显示，不同模型类别和特征配置下，藻华具有中等可预测性。集成树模型表现最佳：仅使用环境变量时，随机森林达到0.74±0.05；加入生物变量后，极端随机树达0.77±0.06。特征重要性分析表明，季节结构、空间背景和滞后环境条件主导模型决策，而生物指标则在物理适宜期细化藻华概率。该框架在东部边界上升流海岸具备实际应用价值，支持卫星驱动的藻华早期预警系统。
原文摘要 · Abstract (English)
Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and shellfish harvesting along the Portuguese Atlantic coast. Here we develop and evaluate a spatio-temporal machine-learning framework to predict harmful algal bloom (HAB) occurrence using exclusively satellite-derived predictors under realistic forecasting constraints. We characterised environmental and biological variability across shellfish production zones (L1-L9) using 5,882 observations, providing system-wide context. Predictive models were developed for zones L1-L2, a hotspot for Pseudo-nitzschia and domoic acid events, using a decade-long dataset (2013-2023; 1,440 observations; more than 1,000 satellite-based predictors including sea surface temperature, an upwelling index, chlorophyll-a, and plankton functional types). Sampling locations were partitioned into ecologically meaningful sub-regions using a river-aware spatial clustering scheme. A stringent spatio-temporal cross-validation strategy that simultaneously withholds entire years and spatial clusters prevents leakage and closely mimics real-world forecasting conditions. HAB occurrence proved moderately predictable across model classes and feature configurations. Ensemble tree-based methods achieved the strongest discrimination: Random Forest reached 0.74 +/- 0.05 with environmental predictors; Extra Trees reached 0.77 +/- 0.06 with biological variables added. Feature-importance analyses revealed that seasonal structure, spatial context, and lagged environmental conditions dominate model decisions, while biological indicators refine bloom likelihood within physically favourable periods. The framework demonstrates operationally relevant skill for satellite-supported HAB early-warning systems along eastern boundary upwelling coasts.
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