Lakes are dynamic ecosystems that can transition among stable states. Since ecosystem-scale transitions can be detrimental and difficult to reverse, being able to predict impending critical transitions in state variables has become a major area of research. However, not all transitions are detrimental, and there is considerable interest in better evaluating the success of management interventions to support adaptive management strategies. Here, we retrospectively evaluated the agreement between time series statistics (i.e., standard deviation, autocorrelation, skewness, and kurtosis—also known as early warning indicators) and breakpoints in state variables in a lake (Lake Simcoe, Ontario, Canada) that has improved from a state of eutrophication. Long-term (1980 to 2019) monitoring data collected fortnightly throughout the ice-free season were used to evaluate historical changes in 15 state variables (e.g., dissolved organic carbon, phosphorus, chlorophyll a) and multivariate-derived time series at three monitoring stations (shallow, middepth, deep) in Lake Simcoe. Time series results from the two deep-water stations indicate that over this period Lake Simcoe transitioned from an algal-dominated state toward a state with increased water clarity (i.e., Secchi disk depth) and silica and lower nutrient and chlorophyll a concentrations, which coincided with both substantial management intervention and the establishment of invasive species (e.g., Dreissenid mussels). Consistent with improvement, Secchi depth at the deep-water stations demonstrated expected trends in statistical indicators prior to identified breakpoints, whereas total phosphorus and chlorophyll a revealed more nuanced patterns. Overall, state variables were largely found to yield inconsistent trends in statistical indicators, so many breakpoints were likely not reflective of traditional bifurcation critical transitions. Nevertheless, statistical indicators of state variable time series may be a valuable tool for the adaptive management and long-term monitoring of lake ecosystems, but we call for more research within the domain of early warning indicators to establish a better understanding of state variable behavior prior to lake changes.

中文翻译：

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应用预警指标来预测湖泊中发生多重变化的关键转变

湖泊是可以在稳定状态之间转换的动态生态系统。由于生态系统规模的转变可能是有害的并且难以逆转，因此能够预测状态变量即将发生的关键转变已成为研究的主要领域。然而，并不是所有的转变都是有害的，并且人们对更好地评估管理干预的成功以支持适应性管理战略有相当大的兴趣。在这里，我们回顾性地评估了时间序列统计（即标准差、自相关、偏度和峰度——也称为早期预警指标）与湖泊（加拿大安大略省锡姆科湖）中状态变量断点之间的一致性从富营养化状态。a ) 和多变量衍生的时间序列在锡姆科湖的三个监测站（浅、中、深）。两个深水站的时间序列结果表明，在此期间，西姆科湖从以​​藻类为主的状态转变为水透明度增加（即 Secchi 盘深度）和二氧化硅以及较低的养分和叶绿素a浓度的状态，这恰逢大量的管理干预和入侵物种的建立（例如，德莱森贻贝）。与改进一致的是，深水站的 Secchi 深度在确定断点之前显示出预期的统计指标趋势，而总磷和叶绿素a揭示了更细微的模式。总体而言，在很大程度上发现状态变量会在统计指标中产生不一致的趋势，因此许多断点可能无法反映传统的分叉关键转换。尽管如此，状态变量时间序列的统计指标可能是湖泊生态系统适应性管理和长期监测的宝贵工具，但我们呼吁在预警指标领域进行更多研究，以更好地了解状态变量行为的先验。到湖水变化。