RAPID: Measuring the response of stream communities to Hurricane Harvey across a semi-arid to sub-humid gradient

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Understanding how natural systems change and recover from a catastrophic disturbance, such as a major hurricane, is important to help society respond effectively to these events. Natural systems, like streams, can provide a buffer to lessen storm impacts and quicken recovery of natural processes that benefit humans. This research aims to understand how streams along the Gulf Coast of Texas responded to flooding and strong winds after Hurricane Harvey made landfall in August 2017. The enormous amount of rainfall and associated storm surges are likely to change the number and kinds of organisms that live in nearby streams. Although the 'community' of microbes, plants and animals in streams is expected to change, it is also expected to recover over time to pre-storm conditions. The time to recovery is thought to differ in predictable ways between streams and between kinds of organisms. To test this idea, it is necessary to have data on stream flows, physical stream characteristics, and community composition before and after the storm. Researchers supported by this award developed and implemented a stream monitoring program prior to Harvey's landfall in south Texas. This award will allow the researchers to collect data immediately after the storm and over the expected recovery period. They will explore if the recovery period depends on whether a stream occurs in a typically wet or typically dry area. Results of this project will help urban planners and natural resource managers understand responses of streams to hurricanes. In addition, the research will directly support faculty to train a diverse group of local scientists and managers who can help their communities respond effectively to future hurricanes. This project addresses how climate regime mediates community response to disturbance. Specifically, it tests predictions about which biotic communities and associated ecosystem processes are most or least resilient to climate-related disturbances. These predictions provide an important test of disturbance theory and are of great relevance to forecasting the ecological impacts of climate variation. A major disturbance event, Hurricane Harvey, affected a region with a sharp climate gradient from semi-arid to sub-humid. The research team, including faculty and students at a Hispanic-serving institution, will use a series of stream ecosystems along the climate gradient, with pre-storm community and ecosystem data, as a model system. In each stream, the research team will conduct high frequency repeated surveys of communities (e.g., microbial, invertebrates), ecosystem components (nutrients, benthic algae, organic matter, habitat), and collect continuous high-frequency data using automated loggers (DO, light, temperature, discharge, depth). These data will track temporal changes in community composition, basal resources, and ecosystem processes that collectively provide measurement of the immediate response and longer-term impacts of the disturbance. It is expected that community resistance to the disturbance will be higher in drier systems with greater stochasticity in abiotic conditions. Likewise, it is expected that communities with higher annual precipitation and more riparian vegetation will experience a positive response driven by organic matter inputs and increased light and primary production. These general functional responses will vary in rate among trophic groups as a function of generation time.