Ocean warming endangers coastal ecosystems through increased risk of infectious disease, yet detection, surveillance, and forecasting of marine diseases remain limited. Eelgrass (Zostera marina) meadows provide essential coastal habitat and are vulnerable to a temperature-sensitive wasting disease caused by the protist Labyrinthula zosterae. We assessed wasting disease sensitivity to warming temperatures across a 3500 km study range by combining long-term satellite remote sensing of ocean temperature with field surveys from 32 meadows along the Pacific coast of North America in 2019. Between 11% and 99% of plants were infected in individual meadows, with up to 35% of plant tissue damaged. Disease prevalence was 3× higher in locations with warm temperature anomalies in summer, indicating that the risk of wasting disease will increase with climate warming throughout the geographic range for eelgrass. Large-scale surveys were made possible for the first time by the Eelgrass Lesion Image Segmentation Application, an artificial intelligence (AI) system that quantifies eelgrass wasting disease 5000× faster and with comparable accuracy to a human expert. This study highlights the value of AI in marine biological observing specifically for detecting widespread climate-driven disease outbreaks.

中文翻译：

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人工智能的疾病监测将鳗草消耗性疾病与跨纬度的海洋变暖联系起来

海洋变暖通过增加传染病的风险危及沿海生态系统，但对海洋疾病的检测、监测和预测仍然有限。Eelgrass ( Zostera marina ) 草地提供了重要的沿海栖息地，并且容易受到由原生动物 Labyrinthula zosterae引起的对温度敏感的消耗性疾病的影响. 我们通过将海洋温度的长期卫星遥感与 2019 年对北美太平洋沿岸 32 个草地的实地调查相结合，评估了在 3500 公里研究范围内浪费疾病对变暖温度的敏感性。11% 到 99% 的植物被在个别草地上感染，高达 35% 的植物组织受损。在夏季温度异常温暖的地区，疾病患病率高出 3 倍，表明随着整个地理范围内的气候变暖，病害的风险将增加。鳗草病变图像分割应用程序首次使大规模调查成为可能，这是一种人工智能 (AI) 系统，可将鳗草消耗性疾病的量化速度提高 5000 倍，准确度与人类专家相当。