Abstract
Extreme weather events can temporarily alter the structure of coastal systems and generate floodwaters that are contaminated with fecal indicator bacteria (FIB); however, every coastal system is unique, so identification of trends and commonalities in these episodic events is challenging. To improve our understanding of the resilience of coastal systems to the disturbance of extreme weather events, we monitored water quality, FIB at three stations within Clear Lake, an estuary between Houston and Galveston, and three stations in bayous that feed into the estuary. Water samples were collected immediately before and after Hurricane Harvey (HH) and then throughout the fall of 2017. FIB levels were monitored by culturing E. coli and Enterococci. Microbial community structure was profiled by high throughput sequencing of PCR-amplified 16S rRNA gene fragments. Water quality and FIB data were also compared to historical data for these water body segments. Before HH, salinity within Clear Lake ranged from 9 to 11 practical salinity units (PSU). Immediately after the storm, salinity dropped to < 1 PSU and then gradually increased to historical levels over 2 months. Dissolved inorganic nutrient levels were also relatively low immediately after HH and returned, within a couple of months, to historical levels. FIB levels were elevated immediately after the storm; however, after 1 week, E. coli levels had decreased to what would be acceptable levels for freshwater. Enterococci levels collected several weeks after the storm were within the range of historical levels. Microbial community structure shifted from a system dominated by Cyanobacteria sp. before HH to a system dominated by Proteobacteria and Bacteroidetes immediately after. Several sequences observed only in floodwater showed similarity to sequences previously reported for samples collected following Hurricane Irene. These changes in beta diversity corresponded to salinity and nitrate/nitrite concentrations. Differential abundance analysis of metabolic pathways, predicted from 16S sequences, suggested that pathways associated with virulence and antibiotic resistance were elevated in floodwater. Overall, these results suggest that floodwater generated from these extreme events may have high levels of fecal contamination, antibiotic resistant bacteria and bacteria rarely observed in other systems.