Department of Oceanography, University of Hawai'i at Mānoa, Honolulu, HI, United States; Daniel K. Inouye Center for Microbial Oceanography: Research and Education (C-MORE), Honolulu, HI, United States; Sea Grant College Program, University of Hawai'i at Mānoa, Honolulu, HI, United States.
Daniel K. Inouye Center for Microbial Oceanography: Research and Education (C-MORE), Honolulu, HI, United States; Department of Biological Sciences, Virginia Institute of Marine Science, Gloucester Point, VA, United States.
Sci Total Environ. 2022 Jul 10;829:154075. doi: 10.1016/j.scitotenv.2022.154075. Epub 2022 Feb 24.
The south shore of O'ahu, Hawai'i is one of the most visited coastal tourism areas in the United States with some of the highest instances of recreational waterborne disease. A population of the pathogenic bacterium Vibrio vulnificus lives in the estuarine Ala Wai Canal in Honolulu which surrounds the heavily populated tourism center of Waikīkī. We developed a statistical model to predict V. vulnificus dynamics in this system using environmental measurements from moored oceanographic and atmospheric sensors in real time. During a year-long investigation, we analyzed water from 9 sampling events at 3 depths and 8 sites along the canal (n = 213) for 36 biogeochemical variables and V. vulnificus concentration using quantitative polymerase chain reaction (qPCR) of the hemolysin A gene (vvhA). The best multiple linear regression model of V. vulnificus concentration, explaining 80% of variation, included only six predictors: 5-day average rainfall preceding water sampling, daily maximum air temperature, water temperature, nitrate plus nitrite, and two metrics of humic dissolved organic matter (DOM). We show how real-time predictions of V. vulnificus concentration can be made using these models applied to the time series of water quality measurements from the Pacific Islands Ocean Observing System (PacIOOS) as well as the PacIOOS plume model based on the Waikīkī Regional Ocean Modeling System (ROMS) products. These applications highlight the importance of including DOM variables in predictive modeling of V. vulnificus and the influence of rain events in elevating nearshore concentrations of V. vulnificus. Long-term climate model projections of locally downscaled monthly rainfall and air temperature were used to predict an overall increase in V. vulnificus concentration of approximately 2- to 3-fold by 2100. Improving these predictive models of microbial populations is critical for management of waterborne pathogen risk exposure, particularly in the wake of a changing global climate.
瓦胡岛南岸是美国最受欢迎的沿海旅游区之一,也是休闲性水上疾病发病率最高的地区之一。一种致病性弧菌——创伤弧菌存在于火奴鲁鲁的阿拉瓦伊运河中,该运河环绕着人口稠密的威基基旅游中心。我们开发了一个统计模型,使用实时系泊海洋和大气传感器的环境测量数据来预测该系统中的创伤弧菌动态。在为期一年的调查中,我们分析了运河 8 个地点 3 个深度共 9 次采样的水,使用定量聚合酶链反应(qPCR)检测血弧素 A 基因(vvhA)来检测 36 种生物地球化学变量和创伤弧菌浓度(n = 213)。创伤弧菌浓度的最佳多元线性回归模型仅包含 6 个预测因子,可解释 80%的变异,包括采样前 5 天的平均降雨量、日最高空气温度、水温、硝酸盐加亚硝酸盐以及两种腐殖质溶解有机物质(DOM)指标。我们展示了如何使用这些模型对来自太平洋岛屿海洋观测系统(PacIOOS)的水质测量时间序列以及基于威基基区域海洋建模系统(ROMS)产品的 PacIOOS 羽流模型进行实时预测,以及如何对创伤弧菌浓度进行实时预测。这些应用强调了在预测创伤弧菌时纳入 DOM 变量的重要性,以及降雨事件对近岸创伤弧菌浓度升高的影响。使用本地降尺度的月降雨和空气温度的长期气候模型预测表明,到 2100 年,创伤弧菌浓度将总体增加约 2-3 倍。改善这些微生物种群的预测模型对于管理水源性病原体风险暴露至关重要,尤其是在全球气候变化的背景下。