There is growing interest in understanding resilience of ecosystems because of the potential of abrupt and possibly irreversible shifts between alternative ecosystem states. Tipping points are observed in systems with strong positive feedback, providing early warning signals of potential instability. These points can be detected through metrics like critical slowing down (CSD), such as increased recovery time, variance, and autocorrelation. These indicators have been tested in laboratory experiments and field settings, ignoring trait changes. Here we present a long-term temporal analysis of several large, intensely monitored constructed wetlands, the Everglades Stormwater Treatment Areas (STAs), in which sudden changes in plant community composition have been observed. Using wavelet analysis, significant increases and decreases of variance properties (long-term flow data, water quality and nutrient TP loads) across these systems can indicate when and which STAs are less resilient to perturbations. In this study, continuous wavelet transform (CWT) was used to determine the periodicity of any cyclical activity in the data and to determine changes in autocorrelation and variance as measures of CSD. The change detection methods were used to find significant changes in variations and correlations across the time series. By employing these techniques, we were able to spot substantial shifts in model-observed wavelet correlation and model residual wavelet variance and thereby identify where these systems exhibit CSD. Although our analysis is limited to historical data, the proposed approach has practical value in that it identifies STAs that may be vulnerable to perturbation. The study also presents one of the few studies in which CSD is observed in practice rather than modelled in theory.

由于替代生态系统状态之间可能发生突然且可能不可逆转的转变，人们对了解生态系统的恢复力越来越感兴趣。在具有强烈正反馈的系统中观察到临界点，提供潜在不稳定的早期预警信号。这些点可以通过关键减速 (CSD) 等指标来检测，例如恢复时间增加、方差和自相关。这些指标已经在实验室实验和现场设置中进行了测试，忽略了性状的变化。在这里，我们对几个大型的、受到严格监测的人工湿地、大沼泽地雨水处理区（STA）进行了长期时间分析，在这些湿地中观察到了植物群落组成的突然变化。使用小波分析，这些系统中方差特性（长期流量数据、水质和营养物 TP 负荷）的显着增加和减少可以表明何时以及哪些 STA 对扰动的弹性较差。在本研究中，连续小波变换 (CWT) 用于确定数据中任何循环活动的周期性，并确定自相关和方差的变化作为 CSD 的度量。变化检测方法用于发现时间序列中变化和相关性的显着变化。通过采用这些技术，我们能够发现模型观察到的小波相关性和模型残余小波方差的显着变化，从而确定这些系统在何处表现出 CSD。尽管我们的分析仅限于历史数据，但所提出的方法具有实用价值，因为它识别出可能容易受到扰动的 STA。该研究还提出了少数几项在实践中观察 CSD 而不是在理论上建模的研究之一。