Unraveling Associations between Cyanobacteria Blooms and In-Lake Environmental Conditions in Missisquoi Bay, Lake Champlain, USA, Using a Modified Self-Organizing Map
Environmental Science & Technology, 47, , 2013
Abstract: 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 dataset 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 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 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.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).