Abstract: Environmental managers are increasingly required to forecast long-term effects and/or the resilience and vulnerability of biophysical systems to human-generated stresses. We research and develop a classification tool to be used by decision-makers to identify how channel, floodplain and watershed scale stressors affect hydrological processes and in doing so, alter the physical structure and habitat values of streams. In the development of this work, we are using the rapid geomorphic assessment protocols (RGA), as well as, the rapid habitat assessment protocols (RHA) from over 800 Vermont stream reaches assessed by the Vermont Agency of Natural Resources (VTANR). We extend previous work focused on RGA to include RHA because natural communities are directly and/or indirectly affected by land use history, stream geomorphology and disturbance regime history. Our approach integrates spatial statistics with artificial neural networks (ANNs) visualized in GIS to examine the effect of land use and geomorphology on biodiversity. A specific data-driven ANN, known as a counterpropagation neural network originally developed by Hecht-Nielsen  will be used to: (1) provide a standardized, expert-trained approach for classifying the sensitivity of river networks in various contexts (erosion hazard mitigation, habitat restoration and conservation) and (2) document the weights experts place on various parameters when classifying stream geomorphic condition, inherent vulnerability, and overall sensitivity at the reach-scale. The procedure is data-driven, and therefore does not require the development of site-specific, process-based stream models, or sets of if-then-else rules associated with expert systems. The ANN architecture is sufficiently flexible to allow for its continual update and refinement in light of new and expanded understandings of fluvial geomorphology. This has potential to save time and resources, while enabling a truly adaptive management approach using expert opinion. The RGA and RHA provide multiple data types from the stream cross-section, reach and watershed scale and corresponding estimates of stream habitat biodiversity (based on macro-invertebrate and fish abundance), thus providing a training set that enables the ANN to accurately classify habitat integrity at the reach scale to: (1) more fully utilize the geomorphic and habitat data collected by the VTANR, and (2) capture the analytical process used by the experts when experts are not available. The ability to characterize streams with high environmental risk is essential for a proactive adaptive watershed management approach and, in addition, allows managers to classify river network sensitivity in various contexts and on different spatial scales.
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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).