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An algorithm for detecting and quantifying disturbance and recovery in high‐frequency time series

Informally Refereed
Authors: Jonathan A. Walter, Cal D. Buelo, Alice F. Besterman, Spencer J. Tassone, Jeff W. Atkins, Michael L. Pace
Year: 2022
Type: Scientific Journal
Station: Southern Research Station
Source: Limnology and Oceanography: Methods


Determining when a disturbance has occurred, its severity, and when the system recovered, is important tonumerous questions in the aquatic sciences. This problem can be conceptualized as the timing and degree ofperturbation from a typical state, and when the system returns to that typical state. We present an algorithm fordetecting disturbance and recovery designed for high-frequency time series, e.g., data produced by automated sampling devices in instrumented buoys and flux towers. The algorithm quantifies differences in the empiricalcumulative distribution functions of moving windows over reference and evaluation periods, and is sensitive tochanges in the mean, variance, and higher statistical moments. Tests on simulated data show it accurately identifiesdisturbance and recovery. Three case studies illustrate the application of our algorithm in different empiricalsettings. A case study on dissolved oxygen in a Florida, USA estuary following a hurricane identified thedisturbance and recovery 73 d later. A case study on air temperature and net ecosystem exchange in the Floridaeverglades identified cold snaps coinciding with periods of reduced carbon uptake. A case study on rotifer abundancefollowing zebra mussel invasion in the Hudson River, NY showed rotifer collapse following invasion andrecovery over a decade later. Methods such as ours can improve understanding response to disturbance and facilitate comparative and synthetic study of disturbance impacts across ecosystems.


Walter, Jonathan A.; Buelo, Cal D.; Besterman, Alice F.; Tassone, Spencer J.; Atkins, Jeff W.; Pace, Michael L. 2022. An algorithm for detecting and quantifying disturbance and recovery in high frequency time series. Limnology and Oceanography: Methods. 4: 83-.