Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8845268 | Ecological Indicators | 2018 | 10 Pages |
Abstract
There have been significant regime shifts in the ecosystem structure and function in a large number of lakes worldwide due to the increasing human disturbance and climate change in recent decades. It has become a critical issue in lake conservation and management to identify the characteristics of regime shifts in lakes and explore potential early-warning signals prior to regime shifts. However, research on identifying and predicting regime shifts in lakes is still a difficult task since recent modelling approaches cannot fully grasp the non-linear processes among multiple ecosystem components and the ecological time series data are too scarce to support the detection in most lakes. In this study, multi-proxy paleolimnological records were used to obtain long time-series ecological data and determine the inflection points of regime shifts in the Baiyangdian Lake, northern China. First, the sediment chronology was established, and macrophyte pollen as well as nutrient conditions in each dated sediment layer were identified. Then the heuristic segmentation algorithm and Pettitt test were used to determine the most significant inflection points of regime shifts. Additionally, multiple early-warning indicators including variance, autocorrelation and skewness were used to test their ability to forecast the major ecosystem regime shift. Results show that the most important abrupt change in the Baiyangdian Lake occurred in the early 1960s. The increasing variance coupled with decreasing autocorrelation and skewness started in 1-16 years before this regime shift, which is consistent with a flickering phenomenon rather than critical slowing down. The detection results of regime shifts and early-warning signals can provide valuable reference information for the lake management and aquatic ecosystem conservation.
Keywords
Related Topics
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Agricultural and Biological Sciences
Ecology, Evolution, Behavior and Systematics
Authors
Ying Yang, Xinan Yin, Zhifeng Yang, Tao Sun, Cui Xu,