Presented by: Andrea Pearce, PhD. Rubenstein School of Environment and Natural Resources.
Abstract of her presentation:
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 were used to separate the dataset into no-bloom and bloom groups. Clustering was based on similarities in water and sediment chemistry, as well as non-cyanobacteria phytoplankton abundance. Our analysis focused on the contribution of individual parameters to no-bloom and bloom groupings. Application of the method to a second, more spatially diverse dataset, revealed similar no-bloom and bloom discrimination; yet a few samples possess all the physico-chemical characteristics of a bloom without the high cyanobacteria cell counts. This suggests that while specific environmental conditions can support a bloom, an environmental trigger may be required to initiate the bloom. The 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.