1. Marilyn Brandt, Tyler B. Smith, Akima George and Robert Stolz Investigating the role of coral disease in a potential reef refuge

2. 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

4. Smith et al. (2010) Coral Reefs

7. Response of Virgin Islands reefs to 2005 bleaching event The 2005 mass bleaching event was caused by unusually warm temperatures that persisted into the winter October 5, 2010, Site: Savannah, ~10 m Photo by T. Smith

8. October 6, 2010, Site: South Fish, ~30 m Photo by T. Smith Mesophotic reefs also exhibited bleaching but at significantly less intensity than in shallow habitats

9. ShallowMesophotic Mesophotic reefs showed significantly less bleaching than shallow reef habitats Data from the Virgin Islands Territorial Coral Reef Monitoring Program (VI-TCRMP)

10. ShallowMesophotic Outbreaks of disease that followed bleaching were as high on mesophotic reefs as in shallow habitats

11. 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 ShallowMesophotic

12. ShallowMesophotic 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%)

13. Do the different properties of these reefs make them more or less susceptible to disease compared with shallow reefs? Mesophotic reefs are affected by diseases

14. Mesophotic reefs have higher coral cover and higher densities of the primary species affected by white disease (Montastraea annularis species complex) Potential for greater transmissibility of disease MACX Density (#/10 m)

15. 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, 40.3m6 May 2011, Site: Grammanik, 40.3m *t test: t = 10.68, df = 30, p < 0.01

16. 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

17. Disease in SICO is determined by several disease parameters including: Brandt and McManus (2009) Dis. Aquatic Org. v. 87 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

18. Disease in SICO is determined by several disease parameters including: Brandt and McManus (2009) Dis. Aquatic Org. v. 87 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

19. Disease in SICO is determined by several disease parameters including: Brandt and McManus (2009) Dis. Aquatic Org. v. 87 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

20. 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

21. 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

22. 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

23. 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) Sensitivity analysis

24. 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

25. 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

26. 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.