Vibrio Vignette: Part II

Welcome back! This week, we continue our Vibrio vignette initiated last week!

Top Solid line: Annual integral of probability of Vibrio cholarae occurrence at the CBNERRs Jug Bay site over the period from 2004 to 2014. Dashed Line: Modelled annual probability based on the winter extreme climate index, TXn. Bottom Solid Line: Annual integral of probability of Vibrio vulnificus occurrence at the CBNERRs Taskinas Creek site. Dashed Line: Modelled annual probability based on the spring extreme climate index, TX90p.

Top Solid line: Annual integral of probability of Vibrio cholarae occurrence at the CBNERRs Jug Bay site over the period from 2004 to 2014. Dashed Line: Modeled annual probability based on the winter extreme climate index, TXn. Bottom Solid Line: Annual integral of probability of Vibrio vulnificus occurrence at the CBNERRs Taskinas Creek site. Dashed Line: Modeled annual probability based on the spring extreme climate index, TX90p.

To understand how the intensity of the Vibrio probability combines with how long Vibrio persists, we summed up the probabilities over a year from 2004 to 2014.  There is substantial interannual variability in the integrated probabilities for both V. cholarae and V. vulnificus.

Since much of the interannual change for both species is associated with temperature, we analyze our thermal extreme climate indices to determine whether they might predict Vibrio.  A number of indices have modest predictive power. But, the best model for V. cholerae occurs with the winter season (December, January, February) integrated TXn index, which measures the coldest maximum daily temperature in a month. High values are associated with a lack of cold spells. This index is positively correlated with the winter North Atlantic Oscillation index, and also with growing season length and the Warm Spell Duration Index.

For the V. vulnificus, the best fit model is based on the spring (March, April, May) integrated TX90p index. This index is a measure of the number of days in a month when the maximum daily temperature exceeds the 90th percentile. Thus, warm spring temperatures are correlated with higher annual V. vulnificus. This index is also negatively correlated with TX10p and TN10p, measures of very cold events and slightly positively correlated with the Atlantic Multidecadal Oscillation.

Top Squares: Annual integral of probability of Vibrio cholarae occurrence at the CBNERRs Jug Bay site over the period from 1900 to 2100 based on observed TXn. Dots: Past and future annual integral of probability based on the CMIP 5 model ensemble of winter extreme climate index, TXn. Bottom Squares: Annual integral of probability of Vibrio cholarae occurrence at the CBNERRs Taskinas Creek site over the period from 1900 to 2100 based on observed TX90p. Dots: Past and future annual integral of probability based on the CMIP 5 model ensemble of winter extreme climate index, TX90p.

Top Squares: Annual integral of probability of Vibrio cholarae occurrence at the CBNERRs Jug Bay site over the period from 1900 to 2100 based on observed TXn. Dots: Past and future annual integral of probability based on the CMIP 5 model ensemble of winter extreme climate index, TXn. Bottom Squares: Annual integral of probability of Vibrio cholarae occurrence at the CBNERRs Taskinas Creek site over the period from 1900 to 2100 based on observed TX90p. Dots: Past and future annual integral of probability based on the CMIP 5 model ensemble of winter extreme climate index, TX90p.

Because we have only eleven data points for IAP calculated from the SWMP dataset, we do not have sufficient information to begin to validate the model linking extreme event indices to the Vibrio presence model. This would require sufficient data to separate training data from the data used in development of the model.  However, under the assumption that the model has some skill, linking the extreme event indices to the Vibrio presence models allows us to make both backwads predictions and forecasts of Vibrio presence. Back predictions for V. cholarae and V. vulnificus use the observed extreme event indices with North and South Chesapeake regions aggregated together. Back predictions and forecast predictions of the climate model ensemble are also shown (see right). The model range and variability are consistent with the observed estimates for annual integrals of the probability of occurrence. More interannual variability is observed in both the data based model and the numerical model based V. cholera estimates than for the V. vulnificus estimates.

The water quality sonde and weather station at Jug Bay.

The water quality sonde and weather station at Jug Bay. Credit

Both time series exhibit an increasing trend as is expected based on the linear trend analysis for both the TXn and TX90p indices. There is evidence for increasing numbers of human health impacts from both Vibrio species (Newton et al. 2012), however the reporting standards have changed over time, and the links between Vibrio in the water, and infection from shellfish or direct contact with a wound are complex to unravel. Thus, these models should not be interpreted as a justification for the increased cases of Vibrio infection in humans in recent years. These models do suggest that V. cholarae and V. vulnificus will continue to show increased probability of occurrence in Chesapeake Bay due to climate changed induced increases in temperature.

Works Cited

Brown, C.W., R.R. Hood, W. Long, J. Jacobs, D.L. Ramers, C. Wazniak, J.D. Wiggert, R. Wood, and J. Xu. 2013. Ecological forecasting in Chesapeake Bay: Using a mechanistic–empirical modeling approach. Journal of Marine Systems 125: 113–125. doi:10.1016/j.jmarsys.2012.12.007.

Jacobs, John M, Matt Rhodes, Christopher W Brown, Raleigh R Hood, Andrew Leight, Wen Long, and Robert Wood. 2014. Modeling and Forecasting the Distribution of Vibrio vulnificus in Chesapeake Bay. Journal of Applied Microbiology. doi:10.1111/jam.12624.

Louis, V.R., Estelle Russek-Cohen, Nipa Choopun, Irma N G Rivera, Brian Gangle, Sunny C Jiang, Andrea Rubin, Jonathan a Patz, Anwar Huq, and Rita R Colwell. 2003. Predictability of Vibrio cholerae in Chesapeake Bay. Applied and environmental microbiology 69: 2773–2785. doi:10.1128/AEM.69.5.2773.

De Magny, Guillaume Constantin, Wen Long, Christopher W. Brown, Raleigh R. Hood, Anwar Huq, Raghu Murtugudde, and Rita R. Colwell. 2009. Predicting the distribution of Vibrio spp. in the Chesapeake Bay: A vibrio cholerae case study. EcoHealth 6: 378–389. doi:10.1007/s10393-009-0273-6.

Newton, Anna, Magdalena Kendall, Duc J. Vugia, Olga L. Henao, and Barbara E. Mahon. 2012. Increasing Rates of Vibriosis in the United States, 1996–2010: Review of Surveillance Data From 2 Systems. Clin Infect Dis. 54: S391–S395. doi:10.1093/cid/cis243.Increasing.

Scallan, Elaine, Robert M. Hoekstra, Frederick J. Angulo, Robert V. Tauxe, Marc Alain Widdowson, Sharon L. Roy, Jeffery L. Jones, and Patricia M. Griffin. 2011. Foodborne illness acquired in the United States-Major pathogens. Emerging Infectious Diseases 17: 7–15. doi:10.3201/eid1701.P11101.

Victoria Coles

About Victoria Coles

I am a physical oceanographer, someone who studies ocean currents, who is also interested in ecology and how it’s shaped by the distribution of elements in the ocean and how in turn ocean ecology influences global climate. Some of my research questions can only be addressed at the scale of entire oceans, others, such as this project ask how our local Chesapeake Bay environment is influenced by climate variability and change.
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