Stochastic colonization and extinction of microbial species on marine aggregates

1. Stochastic colonization and extinction of microbial species on marine aggregates Collaborators: John Drake Maille Lyons Fred Dobbs Andrew Kramer Odum School of Ecology University of Georgia Photo by Maille Lyons

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

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. www-modeling.marsci.uga.edu 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

5. textbookofbacteriology.net 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)

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) Permanent attachment Colonization Detachment Birth Predation • Gillespie’s direct method: • Random time step • Single event occurs • Length of step and identity of event depend on probability of each event • Assumptions: • Well-mixed • No variation among species • No variation within species Pathogen Bacterial community Flagellate consumer Ciliate top predator (modified from Kiorboe 2003)

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

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. www.toptenz.net 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?

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)