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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 Dynamics of small populations • Extinction • Invasion • Outbreaks Woodland caribou Gypsy moth caterpillar biology.mcgill.ca Important characteristics: - stochastic fluctuations - positive density dependence (Allee effects) 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 Bacteria on marine aggregates • Lifespan: days to weeks (Alldredge and Silver 1988, Kiorboe 2001) – Carry material out of water column www-modeling.marsci.uga.edu • Variable size, shape, porosity • Microbial community on aggregate: – – – – bacteria phytoplankton flagellates ciliates 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,textbookofbacteriology.net Shigella, and others (Lyons et al 2007) ) Pathogen presence and dynamics • When will pathogenic bacteria be present? – Source of bacteria – Aggregate characteristics – Extinction? • How many pathogenic bacteria? – Predation – Competition – Colonization/Detachment Pathogen dynamics model (Non-linear stochastic birth-death process) Permanent Detachment Birth Predation attachment dPU F B PD PU B PU FPU PU dt 1 F F BT Pathogen dPA F PU PA FPA dt 1 F F BT dBU F B BD BU B BU FBU BU dt 1 F F BT Bacterial community dBA F BU BA FBA dt 1 F F BT C F dF Flagellate F FD YF FBT F F CF dt 1 B 1 F consumer F F T C C C dC Ciliate C CD YC FC C C top predator dt 1 C C F Colonization • 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 (modified from Kiorboe 2003) Representative trajectories for 0.01 cm radius aggregate Higher density (1000/ml) Extinctions Low density (10/ml) 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 Possible further application: • Aggregate as mechanical vector – Extend pathogen lifespan – Transport – Facilitate accumulation in shellfish (Kach and Ward 2008) www.toptenz.net • Shellfish uptake, agent-based model – What scale? Shellfish bed or individual animal? Discussion Knowledge gaps • Pathogens are average? – Density – Colonization, extinction • Does extinction occur? – Yes • On what time scale? – Is it longer than aggregate persistence? 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 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? 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)