Time scale of resilience loss: Implications for managing critical transitions in water quality


Autoři: Ryan D. Batt aff001;  Tarsha Eason aff004;  Ahjond Garmestani aff005
Působiště autorů: National Research Council, United States Environmental Protection Agency, Cincinnati, Ohio, United States of America aff001;  Rensselaer Polytechnic Institute, Department of Biological Sciences, Troy, New York, United States of America aff002;  Rutgers University, Department of Ecology, Evolution, and Natural Resources, New Brunswick, New Jersey, United States of America aff003;  United States Environmental Protection Agency, Office of Research and Development, Research Triangle Park, North Carolina, United States of America aff004;  United States Environmental Protection Agency, Office of Research and Development, Cincinnati, Ohio, United States of America aff005;  Utrecht Centre for Water, Oceans and Sustainability Law, Utrecht University School of Law, Utrecht, Netherlands aff006
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223366

Souhrn

Regime shifts involving critical transitions are a type of rapid ecological change that are difficult to predict, but may be preceded by decreases in resilience. Time series statistics like lag-1 autocorrelation may be useful for anticipating resilience declines; however, more study is needed to determine whether the dynamics of autocorrelation depend on the resolution of the time series being analyzed, i.e., whether they are time-scale dependent. Here, we examined timeseries simulated from a lake eutrophication model and gathered from field measurements. The field study involved collecting high frequency chlorophyll fluorescence data from an unmanipulated reference lake and a second lake undergoing experimental fertilization to induce a critical transition in the form of an algal bloom. As part of the experiment, the fertilization was halted in response to detected early warnings of the algal bloom identified by increased autocorrelation. We tested these datasets for time-scale dependence in the dynamics of lag-1 autocorrelation and found that in both the simulation and field experiment, the dynamics of autocorrelation were similar across time scales. In the simulated time series, autocorrelation increased exponentially approaching algal bloom development, and in the field experiment, the difference in autocorrelation between the manipulated and reference lakes increased sharply. These results suggest that, as an early warning indicator, autocorrelation may be robust to the time scale of the analysis. Given that a time scale can be shortened by increasing sampling frequency, or lengthened by aggregating data during analysis, these results have important implications for management as they demonstrate the potential for detecting early warning signals over a wide range of monitoring frequencies and without requiring analysts to make situation-specific decisions regarding aggregation. Such an outcome provides promise that data collection procedures, especially by automated sensors, may be used to monitor and manage ecosystem resilience without the need for strict attention to time scale.

Klíčová slova:

Algae – Ecosystems – Chlorophyll – Lakes – Surface water – System stability – Water quality – Fertilization


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2019 Číslo 10

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