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Stochastic colonization and extinction of microbial species on
marine aggregates
Andrew KramerOdum School of
EcologyUniversity of Georgia
Collaborators:John Drake
Maille LyonsFred Dobbs
Photo by Maille Lyons
Dynamics of small populations
• Extinction
• Invasion
• Outbreaks
Important characteristics:- stochastic fluctuations
- positive density dependence
(Allee effects)
biology.mcgill.ca
Woodland caribou
Gypsy moth caterpillar
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
• Variable size, shape, porosity
• Microbial community on aggregate:– bacteria– phytoplankton– flagellates– ciliates
www-modeling.marsci.uga.edu
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
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)
1
1
1
1
1 1
1
U FB D U B U U U
F F T
A FU A A
F F T
U FB D U B U U U
F F T
A FU A A
F F T
CFF D F T F
F F T C C
CC D C C
C C
dPP P P FP P
dt B
dPP P FP
dt B
dBB B B FB B
dt B
dBB B FB
dt B
dFF Y FB F CF
dt B F
dCC Y FC C
dt F
(modified from Kiorboe 2003)
• Gillespie’s direct method:1. Random time step2. Single event
occurs3. Length of step and
identity of event depend on probability of each event
• Assumptions:1. Well-mixed2. No variation
among species3. No variation within
species
Ciliatetop predator
Flagellateconsumer
Bacterialcommunity
ColonizationBirthDetachment
PredationPermanentattachment
Pathogen
Higher density (1000/ml)
Representative trajectories for 0.01 cm radius aggregate
ExtinctionsExtinctionsLow 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)
• Shellfish uptake, agent-based model– What scale? Shellfish bed or individual
animal?
www.toptenz.net
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)