Human and ecological health have been threatened by the increase of cyanobacteria harmful algal blooms (cyanoHABs) in freshwater systems. Successful mitigation of this risk requires understanding the factors driving cyanoHABs at a broad scale. To inform management priorities and decisions, we employed random forest modeling to identify major cyanoHAB drivers in 369 freshwater lakes distributed across 15 upper Midwest states during the 2011 bloom season (July–October). We used Cyanobacteria Index (CI_cyano)—A remotely sensed product derived from the MEdium Resolution Imaging Spectrometer (MERIS) aboard the European Space Agency's Envisat satellite—as the response variable to obtain variable importance metrics for 75 landscape and lake physiographic predictor variables. Lakes were stratified into high and low elevation categories to further focus CI_cyano variable importance identification by anthropogenic and natural influences. “High elevation” watershed land cover (LC) was primarily forest or natural vegetation, compared with “low elevation” watersheds LC dominated by anthropogenic landscapes (e.g., agriculture and municipalities). We used the top ranked 25 Random Forest variables to create a classification and regression tree (CART) for both low and high elevation lake designations to identify variable thresholds for possible management mitigation. Mean CI_cyano was 3 times larger for “low elevation” lakes than for “high elevation” lakes, with both mean values exceeding the “High” World Health Organization recreational guidance/action level threshold for cyanobacteria (100,000 cells/mL). Agrarian-related variables were prominent across all 369 lakes and low elevation lakes. High elevation lakes showed more influence of lakeside LC than for the low elevation lakes.

中文翻译：

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模拟人为和环境对 MERIS 在美国中部检测到的淡水有害藻华发展的影响

淡水系统中蓝藻有害藻华 (cyanoHAB) 的增加已经威胁到人类和生态健康。成功缓解这种风险需要广泛了解推动氰基HABs的因素。为了告知管理优先事项和决策，我们采用随机森林模型来确定 2011 年开花季节（7 月至 10 月）分布在 15 个中西部上游州的 369 个淡水湖中的主要 cyanoHAB 驱动因素。我们使用蓝藻指数 (CI_cyano) - 一种源自欧洲航天局 Envisat 卫星上的中分辨率成像光谱仪 (MERIS) 的遥感产品 - 作为响应变量，以获得 75 个景观和湖泊地貌预测变量的变量重要性指标。湖泊被分为高海拔和低海拔类别，以进一步关注人为和自然影响对 CI_cyano 变量重要性的识别。“高海拔”流域土地覆盖（LC）主要是森林或自然植被，而“低海拔”流域土地覆盖主要是人为景观（例如农业和城市）。我们使用排名最高的 25 个随机森林变量为低海拔和高海拔湖泊指定创建分类和回归树 (CART)，以确定可能的管理缓解的变量阈值。“低海拔”湖泊的平均 CI_cyano 是“高海拔”湖泊的 3 倍，两个平均值都超过了“高”世界卫生组织蓝藻娱乐指导/行动水平阈值（100,000 个细胞/mL）。农业相关变量在所有 369 个湖泊和低海拔湖泊中都很突出。与低海拔湖泊相比，高海拔湖泊受湖滨LC的影响更大。