1. 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

2. Dynamics of small populations Extinction Invasion Outbreaks Important characteristics: - stochastic fluctuations - positive density dependence (Allee effects) biology.mcgill.ca Woodland caribou Gypsy moth caterpillar

3. 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

4. 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

5. 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

6. Pathogen presence and dynamics When will pathogenic bacteria be present? –Source of bacteria –Aggregate characteristics –Extinction? How many pathogenic bacteria? –Predation –Competition –Colonization/Detachment

7. 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

8. Higher density (1000/ml) Representative trajectories for 0.01 cm radius aggregate Extinctions Low density (10/ml)

9. 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

10. 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

11. Discussion

12. Knowledge gaps Pathogens are average? –Density –Colonization, extinction Does extinction occur? –Yes On what time scale? –Is it longer than aggregate persistence?

13. 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

14. 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?

15. 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)