Evaluating the prevalence of early warning signals in palaeoecological time series of state change

评估状态变化的古生态时间序列中预警信号的普遍程度

• 批准号: RGPIN-2014-04032
• 负责人: Simpson, Gavin
• 金额: $ 2.11万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=610793
• 关键词: Evaluating; prevalence; early; warning; signals

Abrupt transition between states has emerged as a common phenomenon in a range of systems. Small changes may push a system past a tipping point resulting in a new mode of behaviour. The propensity to change state is related to resistance to change and resilience in the face of perturbation. A better understanding of the relationships between resistance, resilience and ecosystem change is particularly important considering projections for climate change. Critical transitions can affect the management of ecosystems; ecosystem function or services may be fundamentally altered or even lost following state change and reversal may be prohibitively costly. Early warning signals (EWSs) of impending transition have been derived through theoretical mathematical models, and include increased autocorrelation and variance of the state variable, increased asymmetry of fluctuations, or flickering. Identifying these system responses prior to threshold changes will allow us to better detect EWSs of future change. Our ability to confirm the ubiquity of EWSs in natural systems is hampered, however, by a general lack of long-term observational data prior to transition. Lake sediment cores provide a wealth of information on past ecosystem states yet a number of issues with the nature of the data need to be addressed before their potential is fully realized. The main focus of the proposed program is to develop an understanding of ecological state change through the use of lake sediments. In particular I will identify the statistical techniques needed to address difficulties in using sediment time series, which include irregular sampling intervals and compaction of sediments. Work will progress on three fronts through the use of ideal time series from well-studied lakes with annually-laminated fossil records (Baldeggersee, Switzerland; Kassjon, Sweden), new, high-resolution sequences from arctic lakes in Alaska, Greenland, and Russia, and new cores from prairie lakes of the northern Great Plains, Canada. Different lake types provide a novel apparatus with which to study ecosystem state change. Annually-laminated sequences are an ideal system to work on as they circumvent issues of irregular sampling and compaction. Prairie lakes are strongly impacted by climate variability having been shown to respond rapidly to changes in moisture availability. Vegetation in arctic lake catchments is thought to respond to climate warming with threshold-like behaviour yet how the biogeochemistry and ecology of arctic lakes react to this change is largely unknown. In each case I will examine the evidence for threshold responses and EWSs with analyses targeted at periods of rapid change using a range of biological and geochemical data. Previously, I developed additive models with residual time series processes for modelling palaeoecological data. These models assume that changes are smooth, which precludes examination of threshold behaviour. The use of adaptive splines offers one potential solution and I also envisage progress via the use of structural time series models. As most biological data are high-dimensional, methods suited to such data are also required. I will develop the combination of redundancy analysis with multi-scale measures of temporal variation to extract orthogonal patterns capable of detecting both rapid change and shifts in modes of variability. The proposed program will advance the quantitative modelling of long-term ecological and palaeolimnological data. I will apply these approaches to theories of CTs and EWSs. Subsequently, I expect to address the relative importance of stochasticity versus niche partitioning in structuring ecological communities in time and study macroecological patterns from a palaeoecological perspective.

国家之间的突然转换在一系列系统中已经成为一种普遍现象。微小的变化可能会将一个系统推过一个临界点，导致一种新的行为模式。改变状态的倾向与对改变的抵抗力和面对扰动时的韧性有关。考虑到对气候变化的预测，更好地理解阻力、复原力和生态系统变化之间的关系尤为重要。关键转变可能影响生态系统的管理；生态系统功能或服务可能在状态改变后发生根本改变，甚至丧失，逆转的代价可能高得令人望而却步。 过渡即将到来的预警信号(EWS)是通过理论数学模型得到的，包括状态变量的自相关性和方差增加，波动的不对称性增加，或闪烁。在阈值变化之前识别这些系统响应将使我们能够更好地检测未来变化的EWS。然而，由于在过渡之前普遍缺乏长期观测数据，我们确认EWS在自然系统中无处不在的能力受到了阻碍。 湖泊沉积物岩心提供了关于过去生态系统状态的丰富信息，但在充分实现其潜力之前，需要解决与数据性质有关的一些问题。拟议方案的主要重点是通过利用湖泊沉积物发展对生态状态变化的理解。特别是，我将确定解决使用沉积物时间序列的困难所需的统计技术，其中包括不规则的采样间隔和沉积物的压实。通过使用来自具有年度层状化石记录的经过充分研究的湖泊(瑞士波尔德盖尔；瑞典Kassjon)的理想时间序列，来自阿拉斯加、格陵兰和俄罗斯的北极湖泊的新的高分辨率序列，以及来自加拿大大平原北部草原湖泊的新岩心，工作将在三个方面取得进展。 不同的湖泊类型为研究生态系统状态变化提供了新的手段。每年分层的序列是一种理想的工作系统，因为它们避免了不规则采样和压实的问题。草原湖泊受到气候变化的强烈影响，已被证明对水分可利用性的变化做出快速反应。人们认为，北极湖流域的植被会对气候变暖做出类似临界点的反应，但北极湖的生物地球化学和生态对这种变化的反应在很大程度上是未知的。在每一种情况下，我都将使用一系列生物和地球化学数据，通过针对快速变化时期的分析，审查阈值反应和EWS的证据。 以前，我开发了带有剩余时间序列过程的加性模型来模拟古生态数据。这些模型假设变化是平稳的，这排除了对门槛行为的检查。自适应样条的使用提供了一种潜在的解决方案，我还设想通过使用结构化时间序列模型来取得进展。由于大多数生物数据都是高维的，因此也需要适合这些数据的方法。我将开发冗余分析与时间变化的多尺度测量相结合的方法，以提取能够检测变化模式的快速变化和转变的正交模式。 拟议的计划将推进长期生态和古潜水数据的定量模拟。我将把这些方法应用于CTS和EWSS理论。随后，我期望从古生态学的角度讨论随机性与生态位划分在构建生态群落中的相对重要性，并从古生态学的角度研究宏观生态格局。