Stochastic colonization and extinction of microbial species on marine aggregates Andrew Kramer Odum...
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Transcript of Stochastic colonization and extinction of microbial species on marine aggregates Andrew Kramer Odum...
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)