Survey on Driving Mechanism and Threshold Detection Methods of Regime Shift in Shallow Lakes
LI Yuzhao 1，LIU Yong 1*，ZHAO Lei 2，ZOU Rui 3，WANG Cuiyu1，GUO Huaicheng
（1. College of Environmental Science and Engineering, Peking University, The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Beijing 100871, China；2. Yunnan Institute for Environmental Sciences, Kunming 650034；3. Tetra Tech, Inc. 10306 Eaton Place, Ste 340, Fairfax, VA 22030, USA）
Abstract: Ecosystems can undergo regime shifts when there is an abrupt change from one state to another. It is essential for ecosystem management and decision making to verify, forecast and hindcast the state changes. Regime shift has been observed worldwide for the lake ecosystem. Thus, the studies on driving mechanism of regime shift in lake ecosystems can help decision makers and public to distinguish if a lake has or will change to an undesirable state from human perspective. More importantly, it will be greatly necessary for the decision makers to determine the lake restoration efforts if a lake is under regime shift. There are several methods used to detect and identify threshold of regime shifts. Empirical evidence for large-scale abrupt changes in ecosystems such as lakes and vegetation of semi-arid regions is growing. Despite the fact that experimental observation pays more attention to a few specific targets, the indicators selection process is complex. Regime shifts detection or foreshadow using experimental observation is limited. We analyze the simple ecological models that show a catastrophic transition as a control parameter is varied and propose a novel early warning signal that exploits two ubiquitous features of ecological systems, i.e. nonlinearity and large external fluctuations. However, the remaining model deviation, as well as uncertainty, made it not a good reference to detect threshold of regime shifts. The current studies revealed that either reduced resilience or increased external fluctuations can tip ecosystems to an alternative stable state. Thus the changes in asymmetry in the distribution of time series data can be a model-independent and reliable early warning signal for both routes to regime shifts, which can be quantified by rising variance, changing skewness recovery rate, conditional heteroscedasticity and auto-correlation. The studies proved that statistic analysis of long time series data will be the useful and common method of regime shift detection, due to the advantage that statistical analysis method is independent of complex mechanism of lake ecosystems as well as the fact that it is relatively easy and reliable to analyze a long time series data. Some potential research focus was proposed based on the intensive literature review, including (a) mechanism analysis of shallow lake ecosystem, (b) uncertainty analysis in model simulations, and (c) detection methods for regime shifts in shallow lake ecosystem.
Keywords: Shallow Lake, Regime Shift, Driving Mechanism, Threshold Determination, Statistic Analysis