School of Engineering, ‡Rubenstein School of Environment and Natural Resources, and §Department of Geology, University of Vermont , Burlington, Vermont 05405.
Environ Sci Technol. 2013 Dec 17;47(24):14267-74. doi: 10.1021/es403490g. Epub 2013 Nov 26.
Exploratory data analysis on physical, chemical, and biological data from sediments and water in Lake Champlain reveals a strong relationship between cyanobacteria, sediment anoxia, and the ratio of dissolved nitrogen to soluble reactive phosphorus. Physical, chemical, and biological parameters of lake sediment and water were measured between 2007 and 2009. Cluster analysis using a self-organizing artificial neural network, expert opinion, and discriminant analysis separated the data set into no-bloom and bloom groups. Clustering was based on similarities in water and sediment chemistry and non-cyanobacteria phytoplankton abundance. Our analysis focused on the contribution of individual parameters to discriminate between no-bloom and bloom groupings. Application to a second, more spatially diverse data set, revealed similar no-bloom and bloom discrimination, yet a few samples possess all the physicochemical characteristics of a bloom without the high cyanobacteria cell counts, suggesting that while specific environmental conditions can support a bloom, another environmental trigger may be required to initiate the bloom. Results highlight the conditions coincident with cyanobacteria blooms in Missisquoi Bay of Lake Champlain and indicate additional data are needed to identify possible ecological contributors to bloom initiation.
对尚普兰湖沉积物和水中的物理、化学和生物数据进行探索性数据分析，揭示了蓝藻、沉积物缺氧和溶解氮与可溶反应性磷的比例之间的强相关性。在 2007 年至 2009 年期间测量了湖底沉积物和水的物理、化学和生物参数。使用自组织人工神经网络、专家意见和判别分析对数据进行聚类，将数据集分为无藻华和藻华组。聚类是基于水和沉积物化学以及非蓝藻浮游植物丰度的相似性。我们的分析集中在各个参数对区分无藻华和藻华分组的贡献上。应用于第二个空间更具多样性的数据集，揭示了类似的无藻华和藻华区分，但有几个样本具有藻华的所有理化特征，而蓝藻细胞计数却不高，这表明虽然特定的环境条件可以支持藻华，但可能需要另一个环境触发因素来引发藻华。结果突出了尚普兰湖米西索基湾蓝藻藻华的条件，并表明需要更多的数据来确定藻华开始的可能生态贡献者。
