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Tropical cyclones impact estuaries via a variety of mechanisms including storm surge, flooding from precipitation, high winds, and strong wave action. Prior studies have documented disturbances caused by tropical cyclones, including prolonged periods of depressed salinity from high freshwater discharge and increased or decreased dissolved oxygen concentrations from increased loading of organic matter and/or nutrients. However, most studies of disturbance and recovery in estuaries have been limited to one or a few locations or storm events, limiting generalizations about tropical cyclone impacts and characteristic patterns of ecosystem response and recovery. We analyzed responses to 59 tropical cyclones across 19 estuaries in the eastern United States by applying a new method for detecting disturbance and recovery to long-term and high-frequency measurements of salinity and dissolved oxygen from NOAA’s National Estuarine Research Reserve System. We quantified disturbance occurrence, timing, recovery time, and severity. Salinity disturbances generally started earlier and lasted longer than dissolved oxygen disturbances. Estuaries usually recovered within days, but some disturbances lasted weeks or months. Recovery time was positively correlated with disturbance severity for both variables. Tropical cyclone properties (especially precipitation) and location characteristics were both related to disturbance characteristics. Our findings demonstrate the power of high-frequency, long-term, and cross-system data, when combined with appropriate statistical methods, for analyzing hurricanes across many estuaries to quantify disturbances. Estuaries are resilient to hurricanes for the variables and time periods considered. However, persistent impacts can potentially damage resources provided by estuaries, eroding future resilience if hurricanes become more frequent and severe. 

Regime shifts have large consequences for ecosystems and the services they provide. However, understanding the potential for, causes of, proximity to, and thresholds for regime shifts in nearly all settings is difficult. Generic statistical indicators of resilience have been proposed and studied in a wide range of ecosystems as a method to detect when regime shifts are becoming more likely without direct knowledge of underlying system dynamics or thresholds. These early warning statistics (EWS) have been studied separately but there have been few examples that directly compare temporal and spatial EWS in ecosystem‐scale empirical data. To test these methods, we collected high‐frequency time series and high‐resolution spatial data during a whole‐lake fertilization experiment while also monitoring an adjacent reference lake. We calculated two common EWS, standard deviation and autocorrelation, in both time series and spatial data to evaluate their performance prior to the resulting algal bloom. We also applied the quickest detection method to generate binary alarms of resilience change from temporal EWS. One temporal EWS, rolling window standard deviation, provided advanced warning in most variables prior to the bloom, showing trends and between‐lake patterns consistent with theory. In contrast, temporal autocorrelation and both measures of spatial EWS (spatial SD, Moran's  I) provided little or no warning. By compiling time series data from this and past experiments with and without nutrient additions, we were able to evaluate temporal EWS performance for both constant and changing resilience conditions. True positive alarm rates were 2.5–8.3 times higher for rolling window standard deviation when a lake was being pushed towards a bloom than the rate of false positives when it was not. For rolling window autocorrelation, alarm rates were much lower and no variable had a higher true positive than false positive alarm rate. Our findings suggest temporal EWS provide advanced warning of algal blooms and that this approach could help managers prepare for and/or minimize negative bloom impacts.