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

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)