Abstract: Myxobolus cerebralis, the parasite that causes whirling disease, has been a major contributor to the reduction in trout populations in the Intermountain West, U.S.A. Myxobolus cerebralis has a obligate, two host life cycle: trout produce spores that are infective to the tubificid worm host, Tubifex tubifex, and the worm produces spores that are infective to trout. Evidence collected from both field surveys in watersheds in Montana as well as laboratory experiments suggest that the structure of the worm community plays an important role in the prevalence and severity of disease in fish. Tubifex tubifex is comprised of several genetic lineages that often coexist in stream communities. Our field work has shown lineage I and III (TI and TIII) coexist in Montana streams. Several studies have shown that TIII is a highly susceptible host, more likely to be infected than TI and sometimes producing ten times the number of spores that infect fish than TI. Our field studies using caged trout fry have also shown infection of young fish is directly linked to the density of infected T. tubifex and the most susceptible worm, TIII, dominated the communities in areas of highest whirling disease risk at both within and among watershed spatial scales. Laboratory experiments manipulating communities including the abundances of high, low and unsusceptible hosts have revealed complex interactions within the worm community that influence the production of number of spores that are infective to fish. For example, the presence of a worm taxon that cannot transmit the parasite did not decrease the prevalence of infection in highly susceptible TIII; however, infection prevalence in TIII was low when it coexisted with a low susceptibility lineage, but the result was not statistically significant. Thus, the complex ecological interactions within worm communities that determine parasite success are the focus of our current research.
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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).