Examination of the relationship between host worm community structure on transmission of the parasite, Myxobolus cerebralis by developing taxon-specific probes for multiplex qPCR to identify worm taxa in stream communities
Abstract: Fish diseases are often caused by waterborne parasites, making them ideal systems for modeling the non-linear relationships between disease dynamics, stream dwelling oligochaete communities and geochemical features. Myxobolus cerebralis, the causative agent of whirling disease in salmonid fishes, has been a major contributor to the loss of wild rainbow trout populations in numerous streams within the Intermountain West. The parasite alternates between an invertebrate and vertebrate host, being transmitted between the sediment feeding worm Tubifex tubifex (T.tubifex) and salmonid fishes. Worm community biodiversity and abundance are influenced by biogeochemical features and have been linked to disease severity in fish. The worm (T.tubifex) lives in communities with 3-4 other types of worms in stream sediments. Unfortunately, taxonomic identification of oligochaetes is largely dependent on morphological characteristics of sexually mature adults. We have collected and identified ~700 worms from eight sites using molecular genetic probes and a taxonomic key. Additionally, ~1700 worms were identified using only molecular genetic probes. To facilitate distinguishing among tubificids, we developed two multiplex molecular genetic probe-based quantitative polymerase reaction (qPCR) assays to assess tubificid communities in the study area. Similar qPCR techniques specific for M.cerebralis used to determine if individual worms were infected with the parasite. We show how simple Bayesian analysis of the qPCR data can predict the worm community structure and reveal relationships between biodiversity of host communities and host-parasite dynamics. To our knowledge, this is the first study that combines molecular data of both the host and the parasite to examine the effects of host community structure on the transmission of a parasite. Our work can be extended to examine the links between worm community structure and biogeochemical features using molecular genetics and Bayesian statistics to assist in identifying new nonlinear relationships and suggest new subsets of input parameters. Future work includes the development of a new complex systems tool capable of assimilating biological DNA sequence data and biogeochemical features using artificial neural networks and Bayesian analysis. The methodologies developed here helped mine the relationships between biodiversity of host communities and host-parasite dynamics. The results from our study will be useful to managers and researchers for assessing the risk of whirling disease in drainages where tubificid community composition data are needed. This collaboration between modelers, field ecologists and geneticists will prove useful in modeling efforts and will enable more effective, high-volume hypothesis generation. The ability to characterize areas of high whirling disease risk is essential for improving our understanding of the dynamics of M.cerebralis such that appropriate management strategies can be implemented.
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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).