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Linking spatially distributed biogeochemical data with a two-host life-cycle pathogen:A model of whirling disease dynamics in salmonid fishes in the Intermountain West

Preprint, 2010

Status: Published


Cite: [bibtex]

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Abstract: Fish diseases are often caused by waterborne parasites, making them ideal systems for modeling the non-linear relationships between biogeochemical features and disease dynamics. Myxobolus cerebralis, the causative agent of whirling disease, has been a major contributor to the loss of wild rainbow trout populations in numerous streams within the Intermountain West (Colorado, Idaho, Montana, Utah, Wyoming). The parasite alternates between an invertebrate and vertebrate host, being transmitted between the sediment feeding worm T.Tubifex and salmonid fishes. A greater understanding of the linkage between biological stream integrity, geomorphic features, water quality parameters and whirling disease risk is needed to improve current management techniques. Biodiversity and abundance of the worm communities are influenced by biogeochemical features and linked to disease severity in fish. We collected and identified ~700 worms from eight sites using molecular genetic probes and a taxonomic key. Additionally, ~1700 worms were identified using only a taxonomic key. Our work examines the links between worm community structure and biogeochemical features. We use a modified Self-Organizing-Map (SOM), which is a non-parametric clustering method based on an artificial neural network (ANN). Clustering methods are particularly attractive for exploratory data analyses because they do not require either the target number of groupings or the data structure be specified at the outset. ANN clustering methods have been shown to be more robust and to account for more data variability than traditional methods when applied to clustering geo-hydrochemical and microbiological datasets. The SOM highlights spatial variation of worm community structure between sites; and is used in tandem with expert knowledge (Lamb and Kerans) of local worm communities and a Madison River, MT physiochemical dataset (GIS-derived layers, water quality parameters). We iteratively clustered the physiochemical data and then compared the resulting groups to site-specific worm community structures. The SOM mined patterns from this highly dimensional data and produced 2-D visualizations of the data clusters. This process, in concert with iterative feedback with stream ecologists, led to the adaptation of new nonlinear relations and suggests new subsets of input parameters that guide the next round of SOM simulations, expand the pool of concepts, hone existing hypotheses, generate new hypotheses, and so on. The methodologies developed here helped mine the relationship between dominant biogeochemical features and the distribution of an alternative host of a vertebrate disease. This collaboration between modelers, field ecologists and geneticists will prove useful in guiding future data gathering and modeling efforts. (i.e., identifying missing data gaps and sampling frequency), and will enable more effective, high-volume hypothesis generation that, in turn, will better guide complex experimental designs providing integrated understanding of disease dynamics.

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Joshua Bongard - Department of Computer Science, Associate Professor

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.

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Chris Danforth -Department of Mathematics and Statistics, Flint Professor of Mathematical, Natural, and Technical Sciences

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.

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Laurent Hébert-Dufresne - Assistant Professor, Computer Science

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.

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Paul Hines - School of Engineering, Associate Professor

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.

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James Bagrow - Assistant Professor, Department of Mathematics and Statistics

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).

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