Wilkinson Grace M, Walter Jonathan A, Albright Ellen A, King Rachel F, Moody Eric K, Ortiz David A
Center for Limnology, University of Wisconsin - Madison, 680N Park Street, Madison, WI 53706, USA.
Center for Watershed Sciences, University of California - Davis, One Shields Ave., Davis, CA 95616, USA.
Harmful Algae. 2024 Aug;137:102679. doi: 10.1016/j.hal.2024.102679. Epub 2024 Jun 17.
Algal blooms can threaten human health if cyanotoxins such as microcystin are produced by cyanobacteria. Regularly monitoring microcystin concentrations in recreational waters to inform management action is a tool for protecting public health; however, monitoring cyanotoxins is resource- and time-intensive. Statistical models that identify waterbodies likely to produce microcystin can help guide monitoring efforts, but variability in bloom severity and cyanotoxin production among lakes and years makes prediction challenging. We evaluated the skill of a statistical classification model developed from water quality surveys in one season with low temporal replication but broad spatial coverage to predict if microcystin is likely to be detected in a lake in subsequent years. We used summertime monitoring data from 128 lakes in Iowa (USA) sampled between 2017 and 2021 to build and evaluate a predictive model of microcystin detection as a function of lake physical and chemical attributes, watershed characteristics, zooplankton abundance, and weather. The model built from 2017 data identified pH, total nutrient concentrations, and ecogeographic variables as the best predictors of microcystin detection in this population of lakes. We then applied the 2017 classification model to data collected in subsequent years and found that model skill declined but remained effective at predicting microcystin detection (area under the curve, AUC ≥ 0.7). We assessed if classification skill could be improved by assimilating the previous years' monitoring data into the model, but model skill was only minimally enhanced. Overall, the classification model remained reliable under varying climatic conditions. Finally, we tested if early season observations could be combined with a trained model to provide early warning for late summer microcystin detection, but model skill was low in all years and below the AUC threshold for two years. The results of these modeling exercises support the application of correlative analyses built on single-season sampling data to monitoring decision-making, but similar investigations are needed in other regions to build further evidence for this approach in management application.
如果蓝藻产生微囊藻毒素等蓝藻毒素，藻华就会威胁人类健康。定期监测娱乐用水中的微囊藻毒素浓度以指导管理行动是保护公众健康的一种手段；然而，监测蓝藻毒素需要耗费资源和时间。识别可能产生微囊藻毒素的水体的统计模型有助于指导监测工作，但湖泊之间以及年份之间藻华严重程度和蓝藻毒素产生的变异性使得预测具有挑战性。我们评估了一个基于一个季节水质调查开发的统计分类模型的技能，该调查时间重复率低但空间覆盖范围广，以预测随后几年湖泊中是否可能检测到微囊藻毒素。我们使用了2017年至2021年期间在美国爱荷华州128个湖泊的夏季监测数据，构建并评估了一个微囊藻毒素检测的预测模型，该模型是湖泊物理和化学属性、流域特征、浮游动物丰度和天气的函数。基于2017年数据构建的模型确定pH值、总养分浓度和生态地理变量是该湖泊群体中微囊藻毒素检测的最佳预测因子。然后，我们将2017年的分类模型应用于随后几年收集的数据，发现模型技能有所下降，但在预测微囊藻毒素检测方面仍然有效（曲线下面积，AUC≥0.7）。我们评估了将前几年的监测数据纳入模型是否可以提高分类技能，但模型技能仅略有提高。总体而言，分类模型在不同气候条件下仍然可靠。最后，我们测试了是否可以将季节早期观测结果与经过训练的模型相结合，以提供夏末微囊藻毒素检测的早期预警，但所有年份的模型技能都很低，有两年低于AUC阈值。这些建模练习的结果支持将基于单季采样数据的相关分析应用于监测决策，但其他地区需要进行类似的调查，以进一步证明这种方法在管理应用中的有效性。
