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Stochastic colonization and extinction of microbial species on marine aggregates Andrew Kramer Odum School of Ecology University of Georgia Collaborators: John Drake Maille Lyons Fred Dobbs Photo by Maille Lyons
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Dynamics of small populations Extinction Invasion Outbreaks Important characteristics: - stochastic fluctuations - positive density dependence (Allee effects) biology.mcgill.ca Woodland caribou Gypsy moth caterpillar
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Tools Experiments: zooplankton, bacteria (planned) Computer models –Stochasticity crucial –Simulation approaches Programmed in R and Matlab Parallelization to speed computation time –Computing time remains substantial No experience with individual-based approaches –Want to relax assumptions, such as no inter-individual variation
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Bacteria on marine aggregates Lifespan: days to weeks (Alldredge and Silver 1988, Kiorboe 2001) –Carry material out of water column Variable size, shape, porosity Microbial community on aggregate: –bacteria –phytoplankton –flagellates –ciliates www-modeling.marsci.uga.edu
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Aggregates and disease Enriched in bacteria –Active colonization –Higher replication (e.g. 6x higher (Grossart et al. 2003) ) Favorable microhabitat for waterborne, human pathogens –Vibrio sp., E. Coli, Enterococcus, Shigella, and others (Lyons et al 2007) textbookofbacteriology.net
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Pathogen presence and dynamics When will pathogenic bacteria be present? –Source of bacteria –Aggregate characteristics –Extinction? How many pathogenic bacteria? –Predation –Competition –Colonization/Detachment
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Pathogen dynamics model (Non-linear stochastic birth-death process) (modified from Kiorboe 2003) Gillespie’s direct method: 1.Random time step 2.Single event occurs 3.Length of step and identity of event depend on probability of each event Assumptions: 1.Well-mixed 2.No variation among species 3.No variation within species Ciliate top predator Flagellate consumer Bacterial community Colonization Birth Detachment Predation Permanent attachment Pathogen
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Higher density (1000/ml) Representative trajectories for 0.01 cm radius aggregate Extinctions Low density (10/ml)
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Motivations and challenges Increased understanding of importance of individual variation in bacteria Computational techniques –Scaling up –Model validation, model-data comparison Unpracticed with individual-based and spatially explicit modeling techniques
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Possible further application: Aggregate as mechanical vector –Extend pathogen lifespan –Transport –Facilitate accumulation in shellfish (Kach and Ward 2008) Shellfish uptake, agent-based model –What scale? Shellfish bed or individual animal? www.toptenz.net
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Discussion
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Knowledge gaps Pathogens are average? –Density –Colonization, extinction Does extinction occur? –Yes On what time scale? –Is it longer than aggregate persistence?
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Testing the models Experimental tests –Isolate mechanisms –Measure parameters for prediction Use new techniques to parameterize stochastic models with data –Particle filtering method to estimate maximum likelihood
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Hypotheses Are species-specific traits important? –Detachment Are aggregates a source of new pathogen? –Mortality –Competition (Grossart et al 2004a,b) –Predation Do pathogens interact with aggregates in distinct ways?
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Implications Identify new environmental correlates for human risk Quantification of human exposure and infection risk Surveillance techniques for current and emerging waterborne pathogens Improved control: – hydrological connections between pollution source and shellfish beds –Aggregate formation and lifespan (e.g. mixing)
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