Abstract：
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.
摘要：
如果蓝细菌产生诸如微囊藻毒素之类的蓝藻毒素，则藻类水华可能威胁人类健康。定期监测娱乐水域的微囊藻毒素浓度以告知管理行动是保护公众健康的工具；然而，监测氰基毒素是资源和时间密集的。识别可能产生微囊藻毒素的水体的统计模型可以帮助指导监测工作，但是湖泊和年份之间水华严重程度和蓝藻毒素产生的差异使得预测具有挑战性。我们评估了从一个季节的水质调查中开发的统计分类模型的技能，该模型具有较低的时间复制性，但具有广泛的空间覆盖范围，以预测随后几年是否可能在湖泊中检测到微囊藻毒素。我们使用了2017年至2021年之间来自爱荷华州（美国）128个湖泊的夏季监测数据，以建立和评估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的函数。流域特征，浮游动物丰富，和天气。根据2017年的数据建立的模型确定了pH值，总营养素浓度，和生态地理变量是该湖泊种群中微囊藻毒素检测的最佳预测因子。然后，我们将2017年分类模型应用于随后几年收集的数据，发现模型技能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)。我们评估了分类技能是否可以通过将前几年的监测数据吸收到模型中来提高，但是模型技能只有最低限度的增强。总的来说,分类模型在不同的气候条件下仍然可靠。最后,我们测试了早期的季节观测是否可以与训练过的模型相结合，为夏末的微囊藻毒素检测提供预警，但是模型技能在所有年份都很低，并且在两年内低于AUC阈值。这些建模练习的结果支持将建立在单季节采样数据上的相关分析应用于监测决策，但是在其他地区需要进行类似的调查，以便为这种方法在管理应用中的进一步证据。
