Can we detect ecosystem critical transitions and signals of changing resilience from paleo-ecological records?
Can we detect ecosystem critical transitions and signals of changing resilience from paleo-ecological records?
复制标题
我们能否从古生态记录中检测到生态系统的关键转变和恢复能力变化的信号？
DOI：
10.1002/ecs2.2438
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发表时间：
2018
期刊：
影响因子：
2.7
通讯作者：
Perga, Marie-Elodie
中科院分区：
文献类型：
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作者：
Taranu, Zofia E.;Carpenter, Stephen R.;Frossard, Victor;Jenny, Jean-Philippe;Thomas, Zoë;Vermaire, Jesse C.;Perga, Marie-Elodie
在真实的世界生态系统中，对外界压力变化的非线性响应研究越来越多。然而，由于生态学家观察到的许多变化超出了监测记录，系统从一个平衡状态被推到另一个平衡状态的关键转变的发生仍然难以检测。因此，古生态记录代表了一个独特的机会，可以扩展我们的时间视角，考虑政权转移和关键过渡，以及这些事件是否是例外而不是规则。然而，沉积物岩心记录可能会受到其自身偏差的影响，例如沉积物混合或压缩，对通常用于评估制度转变、复原力或关键转变的统计数据产生未知后果。为了解决这一缺点，我们开发了一个协议，模拟古湖泊记录进行政权转移或临界过渡到交替状态和测试，使用模拟和真实的核心记录，如何混合和压缩影响我们的能力，以检测过去的突变。建立在古湖沼学数据集的平滑显然干扰了滚动窗口指标的信号，特别是自相关。因此，我们转向时变自回归（在线动态线性模型，DLMs;和时变自回归状态空间模型，TVARSS），以评估检测模拟和真实的岩心记录中的状态转移和临界转变的可能性。对于真实的岩心，我们检查了纹孔（每年层压沉积物）和非纹孔岩心，因为前者的混合问题有限。我们的研究结果表明，状态空间模型可以用来检测一些古湖沼数据中的状态转移和临界转换，特别是当信噪比很强时。然而，如果记录是嘈杂的，在线DLM和TVARSS检测沉积物记录中的关键转变有局限性。
Nonlinear responses to changing external pressures are increasingly studied in real‐world ecosystems. However, as many of the changes observed by ecologists extend beyond the monitoring record, the occurrence of critical transitions, where the system is pushed from one equilibrium state to another, remains difficult to detect. Paleo‐ecological records thus represent a unique opportunity to expand our temporal perspective to consider regime shifts and critical transitions, and whether such events are the exception rather than the rule. Yet, sediment core records can be affected by their own biases, such as sediment mixing or compression, with unknown consequences for the statistics commonly used to assess regime shifts, resilience, or critical transitions. To address this shortcoming, we developed a protocol to simulate paleolimnological records undergoing regime shifts or critical transitions to alternate states and tested, using both simulated and real core records, how mixing and compression affected our ability to detect past abrupt shifts. The smoothing that is built into paleolimnological data sets apparently interfered with the signal of rolling window indicators, especially autocorrelation. We thus turned to time‐varying autoregressions (online dynamic linear models, DLMs; and time‐varying autoregressive state‐space models, TVARSS) to evaluate the possibility of detecting regime shifts and critical transitions in simulated and real core records. For the real cores, we examined both varved (annually laminated sediments) and non‐varved cores, as the former have limited mixing issues. Our results show that state‐space models can be used to detect regime shifts and critical transitions in some paleolimnological data, especially when the signal‐to‐noise ratio is strong. However, if the records are noisy, the online DLM and TVARSS have limitations for detecting critical transitions in sediment records.