Investigating the
role of coral disease
in a potential reef
refuge
Marilyn Brandt, Tyler B. Smith,
Akima George and
Robert Stolz
Session 9D: Refuges for corals in time and space
Long version of the session title:
Refuges for corals and coral reefs in changing
environments: progress and priorities
Co-occurring threats to corals and coral reefs:
• Climate change and its associated impacts: bleaching,
acidification
• Land based sources of pollution
• Overexploitation
Focus of this mini-symposium:
To examine what ecological, physical, and geological
evidence has been identified for coral and coral reef refuges
Smith et al. (2010) Coral Reefs
Smith et al. (2010) Coral Reefs
Response of Virgin Islands reefs
to 2005 bleaching event
October 5, 2010, Site: Savannah, ~10 m
Photo by T. Smith
The 2005 mass bleaching event was caused by unusually
warm temperatures that persisted into the winter
Mesophotic reefs also exhibited bleaching but at
significantly less intensity than in shallow habitats
October 6, 2010, Site: South Fish, ~30 m
Photo by T. Smith
Mesophotic reefs showed significantly less bleaching than
shallow reef habitats
Shallow
Mesophotic
Data from the Virgin Islands Territorial Coral Reef Monitoring Program (VI-TCRMP)
Outbreaks of disease that followed bleaching were as high on
mesophotic reefs as in shallow habitats
Shallow
Mesophotic
Disease was the primary cause of coral tissue loss during the
bleaching/disease event and coral cover declined significantly
in both shallow and mesophotic reefs
Shallow
Mesophotic
Absolute change in coral cover was similar between the two
habitats (~8%) but change relative to coral cover before the
event was much higher on shallow reefs (~50%) mesophotic
reefs (~22%)
Shallow
Mesophotic
Mesophotic reefs are affected by diseases
Do the different
properties of these
reefs make them more
or less susceptible to
disease compared with
shallow reefs?
Mesophotic reefs have higher coral cover and higher densities of the
primary species affected by white disease (Montastraea annularis
species complex)
MACX Density (#/10 m)
Potential for greater transmissibility of disease
Disease lesion expansion rates significantly* slower at
mesophotic depths
Shallow: 0.23 cm/day ± 0.03 (SE)
Deep: 0.0051 cm/day ± 0.002 (SE)
25 April 2011, Site: Grammanik, 40.3m
6 May 2011, Site: Grammanik, 40.3m
*t test: t = 10.68, df = 30, p < 0.01
Spatially explicit individual-based model of white disease
Simulation of Infected Corals (SICO)
Implemented in Java using Recursive
Porous Agent Simulation Toolkit
(RePast) class libraries (U. Chicago)
Spatially-explicit
Individual-based: colonies the
fundamental unit
Ability to simulate variability of
populations
–
–
–
–
Density/Cover
Species composition
Size distributions
Aggregation
Details of SICO parameterization and initial testing in Brandt and McManus
(2009) Dis. Aquatic Org. v. 87
Disease in SICO is determined by several disease
parameters including:
The mortality rate of the disease
The susceptibility of a colony
The transmission ability of the disease (ρ)
Frequency of introductions of disease into the system
Intensity of the disease introduction
Brandt and McManus (2009) Dis. Aquatic Org. v. 87
Disease in SICO is determined by several disease
parameters including:
The mortality rate of the disease
The susceptibility of a colony
The transmission ability of the disease (ρ)
Frequency of introductions of disease into the system
Intensity of the disease introduction
Brandt and McManus (2009) Dis. Aquatic Org. v. 87
Disease in SICO is determined by several disease
parameters including:
The mortality rate of the disease
The susceptibility of a colony
The transmission ability of the disease (ρ)
Frequency of introductions of disease into the system
Intensity of the disease introduction
Brandt and McManus (2009) Dis. Aquatic Org. v. 87
Best fitting disease parameters were where:
Disease was transmissible to a close area (distance
had a large impact on disease spread)
Susceptibility was low
Disease was periodically introduced into the system
Simulations were initialized with USVI data from 2005 from
one shallow and one deep site and were run for a time period
equivalent to the monitoring period in the field (2005-2010)
Simulations with the best fitting parameters produced accurate
patterns of coral cover change, change in size of average
colonies, and spatial distributions of disease.
Field vs contagious scenario: χ = 7.04, p > 0.05
Field vs. non-contagious scenario: χ = 62.17, p<0.001
A sensitivity analysis of disease parameters was performed to
compare how the mesophotic system would respond in
comparison to the shallow reef system in the face of changes
in disease parameters including:
• Mortality rate
• Susceptibility of the host
• Transmissibility
• Disease introduction intensity
• Disease introduction frequency
Sensitivity analysis
For both shallow and mesophotic coral population settings
disease parameters were varied 50% above and below their
values and then 10 replicate simulations were run
Average change in coral cover was used as a measure of
sensitivity
Multiple linear regression was used to determine relationships
between parameter values and change in coral cover (CC).
A sensitivity index was then calculated by multiplying the
regression coefficient for each parameter by the default
parameter value and dividing by the change in CC at the default
value (Law 2007)
The sensitivity index provides a relative measure of the
influence of that parameter on model outcomes (CC change)
Disease parameter sensitivity analysis
In both habitats, the model outcomes (CC change) were most
sensitive to changes in the transmission range parameter, ρ
The mesophotic habitat was much less sensitive to changes in
disease parameters, including to the frequency and intensity of
disease events
Sensitivity analysis of coral population characteristics
Mesophotic habitats were somewhat less sensitive than
shallow habitats to changes in population structure
Changes in the size distribution had the greatest effect on
model outcomes
Acknowledgements
CMES
This study was supported by VI-EPSCoR under National Science Foundation Grant #0814417. Any
opinions, findings, conclusions, or recommendations expressed in the material are those of the
authors and do not necessarily reflect the views of NSF.