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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566251
• 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.

国家之间的突然转变已成为一系列制度中的普遍现象。微小的变化可能会推动系统突破临界点，从而产生新的行为模式。改变状态的倾向与对改变的抵抗力和面对扰动的弹性有关。考虑到气候变化的预测，更好地理解阻力、恢复力和生态系统变化之间的关系尤为重要。关键转变可能会影响生态系统的管理；生态系统的功能或服务可能会在状态变化后发生根本性的改变，甚至丧失，而逆转的代价可能会非常高昂。即将发生转变的预警信号 (EWS) 是通过理论数学模型得出的，包括状态变量的自相关和方差增加、波动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，由于过渡前普遍缺乏长期观测数据，我们确认自然系统中普遍存在的 EWS 的能力受到了阻碍。湖泊沉积物岩心提供了有关过去生态系统状态的丰富信息，但在充分发挥其潜力之前，需要解决与数据性质有关的许多问题。该计划的主要重点是通过利用湖泊沉积物来了解生态状态的变化。我将特别确定解决使用沉积物时间序列时遇到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。工作将在三个方面取得进展，包括使用来自经过充分研究的湖泊的理想时间序列，以及每年层压的化石记录（瑞士巴尔德格湖；瑞典卡斯琼），来自阿拉斯加、格陵兰岛和俄罗斯北极湖泊的新高分辨率序列，以及来自加拿大北部大平原草原湖泊的新核心。不同的湖泊类型为研究生态系统状态变化提供了一种新颖的仪器。每年层压的序列是一个理想的系统，因为它们规避了不规则采样和压缩的问题。草原湖泊受到气候变化的强烈影响，事实证明，它们对水分供应的变化反应迅速。人们认为北极湖泊流域的植被会以类似阈值的行为对气候变暖做出反应，但北极湖泊的生物地球化学和生态如何对这种变化做出反应尚不清楚。在每种情况下，我都会使用一系列生物和地球化学数据，针对快速变化时期进行分析，检查阈值响应和 EWS 的证据。此前，我开发了具有残差时间序列过程的加性模型，用于对古生态数据进行建模。这些模型假设变化是平滑的，这排除了对阈值行为的检查。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时间序列模型取得进展。由于大多数生物数据都是高维的，因此也需要适合此类数据的方法。我将开发冗余分析与时间变化的多尺度测量的组合，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湖泊学数据的定量建模。我将把这些方法应用到 CT 和 EWS 理论中。随后，我希望及时解决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模式。