Regime shifts in lake ecosystems: Testing theory with long-term observational data and large scale experiments (LakeShift)

长期生态研究已成为科学奋进的基石，以更好地了解生态系统动态和对环境变化（如全球变暖）的突然反应（政权转移）。理论预测，政权转移可以归因于随机事件，如极端事件或物种入侵，超过临界阈值或纯粹由内部产生的波动。然而，在关于食物网相互作用和制度转变的驱动力的理论框架与它们对自然界中实际观察到的情景的适用性之间存在明显的差距;即用观测数据测试生态理论。过去缺乏用观测数据来检验生态理论，部分原因是缺乏关于时间序列长度及其时间分辨率的足够数据;鉴于目前的状况转变理论需要大量数据。我们将把湖泊生态系统的长期观测数据、物理建模方法和大规模实验装置中人工诱导的制度转变联系起来，以解决环境波动的频率和幅度如何影响整个生态系统或单个非生物和生物生态系统水平从一种状态转变为另一种状态的概率的问题。此外，我们打算通过生态时间序列中的统计异常来确定这种突然变化之前的预警信号。这些研究的价值是多方面的。它将导致更好地理解驱动观察到的政权转移的基本力量的性质，即驱动变量和响应变量之间的关系是渐进的还是阈值驱动的性质。它将促进假说的发展，以确定驱动观察到的政权转变的重要机制。此外，它将提高我们预测在未来气候变化情景下观察到的热状况可能发生的变化和随后的生物群变化的能力。我们最感兴趣的是在测试是否观察到的和实验启动的政权转移的各种性质，在不同的时间尺度（几周到几十年），遵循一般的固有模式，制定政权转移理论，以及它们是否可以预见的预警信号。

Long-term ecological research has become a cornerstone of the scientific endeavor to better understand ecosystem dynamics and abrupt responses (regime shifts) to variability in environmental changes such as global warming. Theory predicts that regime shifts can be attributed to stochastic events such as extreme events or species invasion, surpassing of critical thresholds or purely by internally generated fluctuations. There is an apparent gap, however, between theoretical frameworks concerning driving forces of food web interactions and regime shifts and their applicability to scenarios actually observed in nature; i.e. testing ecological theory with observational data. The lack of testing ecological theory with observational data in the past has been partly due to the lack of adequate data with respect to the length of the time series and their temporal resolution; given that current scenarios of regime shift theory demand large amounts of data. We will link long-term observational data of lake ecosystems, physical modeling approaches and artificially induced regime shifts in large scale experimental set-ups to tackle the question as to how the frequency and magnitude of environmental fluctuations affect the probability that entire ecosystems or single abiotic and biotic ecosystem levels will shift from one state into another. Moreover, we intend to pinpoint early warning signals via statistical anomalies in ecological time series which precede such abrupt shifts. The value of such studies is manifold. It will lead to a better understanding of the nature of the underlying forces driving observed regime shifts, i.e. whether the relationship between driver and response variables is of a gradual or of a threshold driven nature. It will foster the development of hypothesis to identify important mechanisms driving observed regime shifts. Moreover, it will enhance our capability to predict likely shifts in thermal regime and subsequent changes in the biota observed under future climate change scenarios. Our foremost interest lies in testing whether observed and experimentally initiated regime shifts of various natures, which operate on various time scales (weeks to decades), follow general inherent pattern as formulated by regime shift theory and whether they can be foreseen by early warning signals.