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首页> 外文期刊>Environmental Science & Technology >Revealing Biotic and Abiotic Controls of Harmful Algal Blooms in a Shallow Subtropical Lake through Statistical Machine Learning
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Revealing Biotic and Abiotic Controls of Harmful Algal Blooms in a Shallow Subtropical Lake through Statistical Machine Learning

机译：通过统计机器学习揭示浅亚热带湖泊有害藻华的生物和非生物控制

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摘要

Harmful algal blooms are a growing human and environmental health hazard globally. Eco-physiological diversity of the cyanobacteria genera that make up these blooms creates challenges for water managers tasked with controlling the intensity and frequency of blooms, particularly of harmful taxa (e.g., toxin producers, N_(2) fixers). Compounding these challenges is the ongoing debate over the efficacy of nutrient management strategies (phosphorus-only versus nitrogen and phosphorus), which increases decision-making uncertainty. To improve our understanding of how different cyanobacteria respond to nutrient levels and other biophysical factors, we analyzed a unique 17 year data set comprising monthly observations of cyanobacteria genera and zooplankton abundances, water quality, and flow in a bloom-impacted, subtropical, flow-through lake in Florida (United States). Using the Random Forests machine learning algorithm, an ensemble modeling approach, we characterized and quantified relationships among environmental conditions and five dominant cyanobacteria genera. Results highlighted nonlinear relationships and critical thresholds between cyanobacteria genera and environmental covariates, the potential for hydrology and temperature to limit the efficacy of cyanobacteria bloom management actions, and the importance of a dual nutrient management strategy for reducing bloom risk in the long term.
机译：有害的藻华在全球范围内对人类和环境健康的危害日益严重。组成这些开花的蓝细菌属的生态生理多样性给水管理人员带来了挑战，他们需要控制开花的强度和频率，特别是有害类群（例如，毒素生产者，N_（2）固色剂）。营养管理策略的有效性（仅磷与氮和磷）增加了决策的不确定性，这些争论不断加剧。为了增进我们对不同蓝藻对营养水平和其他生物物理因素的反应的了解，我们分析了一个独特的17年数据集，其中包括对蓝藻属和浮游动物的丰度，水质和水华影响下的，亚热带水流的流量的每月观测。穿过佛罗里达州（美国）的湖泊。使用随机森林机器学习算法（一种集成建模方法），我们表征并量化了环境条件与五个优势蓝细菌属之间的关系。结果强调了蓝藻属与环境协变量之间的非线性关系和临界阈值，水文和温度可能会限制蓝藻开花管理措施的效力以及长期采用双重养分管理策略降低开花风险的重要性。

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  • 来源
    《Environmental Science & Technology》 |2018年第6期|3527-3535|共9页
  • 作者

    NatalieG. Nelson; Rafael Muñoz-Carpena; Edward J. Phlips; David Kaplan; Peter Sucsy; John Hendrickson;

  • 作者单位

    Hydrology & Water Quality, Agricultural & Biological Engineering, University of Florida, Gainesville, Florida, United States,Biological & Agricultural Engineering, North Carolina State University, Raleigh, North Carolina, United States;

    Hydrology & Water Quality, Agricultural & Biological Engineering, University of Florida, Gainesville, Florida, United States;

    Fisheries & Aquatic Sciences, School of Forest Resources & Conservation, University of Florida, Gainesville, Florida, United States;

    Engineering School of Sustainable Infrastructure and Environment, Environmental Engineering Sciences Department, University of Florida, Gainesville, Florida, United States;

    St. Johns River Water Management District, Palatka, Florida, United States;

    St. Johns River Water Management District, Palatka, Florida, United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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