1. Student: Paul Welle Collaborators: Ines Azevedo Mitchell Small Sarah Cooley Scott Doney THE IMPACT OF CLIMATE STRESSORS ON CORAL BLEACHING AND MORTALITY : A CASE STUDY OF THE 2005 CARIBBEAN SUMMER 1

2.  Eakin et al. (2010)  Caribbean summer 2005  Bleaching, Mortality (dependent variables)  Temperature (independent variable) 2 BACKGROUND Reproduced from Eakin (2010)

3. 3 TEMPERATURES 2005 Retrieved from http://coralreefwatch.noaa.gov/satellite/dhw.php

4. 4 THE DATA n=2945

5. (1) Limited by the functional form of OLS We expand to a non-linear model. (2) Uncontrolled spatial correlation We add in fixed effects. (3) Limited number of explanatory variables We extend the dataset to include photosynthetically active radiation (PAR) and pH. We also recalculate DHW. 5 ANALYSIS CAN BE IMPROVED

6.  DHW correlates well with bleaching and mortality, although there are indications that the 12-week interval should be lengthened.  PAR Anomaly correlates well with bleaching and mortality (PAR does not), but seems to be of less importance than DHW.  In predicting mortality, it is best to use the maximum value of a stressor, while predicting bleaching the recent (observed) temperatures are more important.  Depth is very protective against PAR. For deep corals (13.5m, or 80 th percentile), PAR plays almost no part in predicting bleaching. For shallow corals (5m, or 20 th percentile), PAR is roughly as important a stressor as DHW. 6 WHAT WE LEARNED

7. 7 METHOD (OLS VS FRACTIONAL LOGIT) 100

8. 8 METHOD (MANIPULATION OF CONTINUOUS DATA) PAR, DHW, pH… time observed maximu m

9. 9 METHOD (VARIABLES)  Four stressor formulations  Temperature - Degree Heating Weeks (DHW) – 12 week  Photosynthetically Active Radiation – PAR 12 week average  Photosynthetically Active Radiation – PAR Anomaly  Simulated pH – Monthly average  Each formulation has 2 forms  “Maximum” – Hypothesized to be important for mortality  “Observed” – Hypothesized to be important for bleaching Bleaching -and- Mortality = MaxDHW, ObsDHW, MaxPAR, MaxPAR Anomaly, ObsPAR, ObsPAR Anomaly, MaxPH, ObsPH f()

10. 10 RESULTS

11. 11 RESULTS - MORTALITY Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

12. 12 RESULTS - MORTALITY

13. 13 RESULTS - BLEACHING Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

14. 14 RESULTS - BLEACHING

15. 15 RESULTS BLEACHING Depth = 5 m Depth = 13.5 m

16.  DHW correlates well with bleaching and mortality, although there are indications that the 12-week interval should be lengthened.  PAR Anomaly correlates well with bleaching and mortality (PAR does not), but seems to be of less importance than DHW.  In predicting mortality, it is best to use the maximum value of a stressor, while predicting bleaching the recent (observed) temperatures are more important.  Depth is very protective against PAR. For deep corals (13.5m, or 80 th percentile), PAR plays almost no part in predicting bleaching. For shallow corals (5m, or 20 th percentile), PAR is roughly as important a stressor as DHW. 16 WHAT WE LEARNED

17.  Eakin, C. M., Morgan, J. a, Heron, S. F., Smith, T. B., Liu, G., Alvarez-Filip, L., … Bouchon, C. (2010). Caribbean corals in crisis: record thermal stress, bleaching, and mortality in 2005. PloS one, 5(11), e13969.  Hoegh-Guldberg, O., Mumby, P. J., Hooten, a J., Steneck, R. S., Greenfield, P., Gomez, E., … Hatziolos, M. E. (2007). Coral reefs under rapid climate change and ocean acidification. Science (New York, N.Y.), 318(5857), 1737–42.  McWilliams, J., Côté, I., & Gill, J. (2005). Accelerating impacts of temperature- induced coral bleaching in the Caribbean. Ecology, 86(8), 2055–2060.  Wilkinson, C. "Coral bleaching and mortality–The 1998 event 4 years later and bleaching to 2002." Status of coral reefs of the world (2002): 33-44.  Wilkinson, Clive R., and David Souter, eds. Status of Caribbean coral reefs after bleaching and hurricanes in 2005. Global Coral Reef Monitoring Network, 2008.  Yee, S. H., Santavy, D. L., & Barron, M. G. (2008). Comparing environmental influences on coral bleaching across and within species using clustered binomial regression. Ecological Modelling, 218(1-2), 162–174.  Yee, S. H., & Barron, M. G. (2010). Predicting coral bleaching in response to environmental stressors using 8 years of global-scale data. Environmental monitoring and assessment, 161(1-4), 423–38. 17 REFERENCES

18. This work would not be possible without support by 18 SUPPORT

19. 19 DATA >30% <30% & >0% 0%

20. 20 BLEACHING

21. 21 MORTALITY

22. 22 DESCRIPTIVE STATISTICS

23. 23 DEGREE HEATING WEEKS Typical Hottest Month

24. 24 DATA

25. 25 DATA >30% <30% & >0% 0%

26. 26 CORRELATIONS

27.  Questions: Which stressor form fits best- maximum, observed, or weighted average? Bleaching – Weighted Average Mortality – Maximum Does PAR or PAR anomaly fit the data better? Bleaching – PAR Anomaly Mortality – PAR Anomaly Does measuring independent maximums of temperature and radiation suffice, or must one account for simultaneously high peaks? Bleaching – Independent Mortality – Independent Is there evidence for a depth-stressor interaction? Bleaching – Yes Mortality - No 27 SUMMARY

28. 28 MORTALITY MODELS

29. 29 MORTALITY MODELS

30. 30 BLEACHING MODELS

31. 31 BLEACHING MODELS

32.  Fixed Effects Fractional Logit Model  Logit – Used for binary dependent variables  Fractional Logit – Repurposed for bounded dependent variable  Fixed Effects – Used to control for homogeneity within groups  Maximize quasi-likelihood function:  Returns sigmoid in range (0,1) 32 MODEL

33. 33 VARIABLES Maximum DHW Maximum PAR Anomaly Depth Constant Log-Likelihood AIC Observations Coefficients 0.188*** (0.0286) 0.0283*** (0.0085) -0.0479*** (0.0118) -5.50*** (0.430) -62.07 0.1628 1,045 Marginal Effects (at means) 0.00152*** (0.000197) 0.000229*** (0.0000587) -0.000139*** (0.0000342)