Identifying the true drivers of abrupt changes in ecosystem state with a focus on time lags: Extreme precipitation can determine water quality in shallow lakes

Abstract

A better understanding of abrupt ecosystem changes is needed to improve prediction of future ecosystem states under climate change. Chronological analysis based on long-term monitoring data is an effective way to estimate the frequency and magnitude of abrupt ecosystem changes. In this study, we used abrupt-change detection to differentiate changes of algal community composition in two Japanese lakes and to identify the causes of long-term ecological transitions. Additionally, we focused on finding statistically significant relationships between abrupt changes to aid with factor analysis. To estimate the strengths of driver-response relationships underlying abrupt algal transitions, the timing of the algal transitions was compared to that of abrupt changes in climate and basin characteristics to identify any synchronicities between them. The timing of abrupt algal changes in the two study lakes corresponded most closely to that of heavy runoff events during the past 30-40 years. This strongly suggests that changes in the frequency of extreme events (e.g., heavy rain, prolonged drought) have a greater effect on lake chemistry and community composition than do shifts in the means of climate and basin factors. Our analysis of synchronicity (with a focus on time lags) could provide an easy method to identify better adaptative strategies for future climate change.

Keywords: Abrupt-change detection; Chronological clustering analysis; Phytoplankton community; Runoff event; Synchronicity analysis; Time lag.