生态系统重大突变检测研究进展

doi:10.3724/SP.J.1258.2013.00109

摘要/Abstract

摘要：

当一个存在多稳态的生态系统临近突变阈值点时, 外界条件即使发生一个微小变化, 也会引发生态系统的剧烈响应, 使之进入结构和功能截然不同的另一稳定状态, 这种现象称为重大突变(critical transition)。重大突变所导致的稳态转换总是伴随着生态系统服务的急剧变化, 可能对人类可持续发展产生重大影响。预测生态系统突变的发生非常困难, 但科学家在此领域的大量研究结果表明, 通过监测一些通用指标可以判断生态系统是否不断临近重大突变阈值点, 进而可以进行生态系统重大突变预警。该文对近年来生态系统重大突变检测领域所取得的成果进行总结与归纳, 论述了生态系统重大突变的产生机制及其后果, 介绍了生态系统突变预警信号提取的理论基础, 从时间和空间两个维度总结了近年来生态系统重大突变预警信号的提取方法, 概述了当前研究面临的挑战, 指出生态系统突变预警信号的检测应充分利用时空动态数据, 并且联合多个指标, 从多个角度进行综合预警, 此外, 还应重视生态系统结构与重大突变之间的关系, 增强生态系统突变预警能力。

关键词: 临界放缓, 重大突变, 早期预警信号, 生态系统弹性, 稳态

Abstract:

Ecosystem with alternative stable states would respond abruptly to minor changes in the external conditions and switch into an alternative stable state with different ecosystem structures and functions when the system approaches the transition threshold. This phenomenon is called critical transition. It is often the case that such transition can result in marked changes in ecosystem services, which are much likely to impact the sustainable development of human being. It is difficult to predict the critical transitions in ecosystems, but the large amount of research in this field show that by monitoring some generic properties (i.e. early-warning signals) relating to ecosystem status, we are able to discern if the system approaches the transition threshold; this can be used to predict the critical transitions in ecosystems. This paper summarizes the major findings and achievements in the field of detecting critical transitions in ecosystems. It first discusses the mechanism and consequence of critical transitions, and then introduces the basic theory behind the early-warning signals. We sum up the methods used to extract early-warning signals both from temporal and spatial dimensions. Finally, challenges confronting the contemporary research are summarized. In future, the application of early-warning signals should make full use of both temporal and spatial data and combine different indicators to improve our ability to forecast unfavorable environmental events. Also, special attention needs to be paid to the relationship between critical transitions and ecosystem structures so that we can strengthen the ability to predict critical transitions in ecosystems.

Key words: critical slowing down, critical transition, early-warning signal, ecological resilience, stable state

孙云,于德永,刘宇鹏,郝蕊芳. 生态系统重大突变检测研究进展. 植物生态学报, 2013, 37(11): 1059-1070. DOI: 10.3724/SP.J.1258.2013.00109

SUN Yun,YU De-Yong,LIU Yu-Peng,HAO Rui-Fang. Review on detection of critical transition in ecosystems. Chinese Journal of Plant Ecology, 2013, 37(11): 1059-1070. DOI: 10.3724/SP.J.1258.2013.00109

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链接本文: https://www.plant-ecology.com/CN/10.3724/SP.J.1258.2013.00109

https://www.plant-ecology.com/CN/Y2013/V37/I11/1059

图/表 7

参考文献 59

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主要突变检测方法 Key methods of detecting critical transition	主要内容 Main contents	计算公式 Formula	局限性 Limitation
测量恢复时间 Measuring the recovery time	直接测量系统遭受微小事件扰动时恢复到最初平衡态的时间 Directly measuring the recovery time to equilibrium after a small perturbation		适用于模拟试验, 对现实生态系统可操作性差 Suitable to simulation experiments, but not feasible in reality
计算自相关系数、方差, 功率谱分析 Computing autocorrelation and variance, and performing power spectrum analysis	计算指示系统状态的变量的自相关系数、方差, 对时间序列进行功率谱分析 Computing autocorrelation and variance of the variables that indicate the status of the ecosystem, and analyzing the power spectrum of the time series	$\rho =\frac{E[(\mathop{z}_{t}-\mu )(\mathop{z}_{t+1}-\mu )]}{{}^{\mathop{\mathop{s}_{z}}^{2}}}$	数据获取困难和数据处理过程带入的误差和主观性 Difficult to acquire data; error and subjectivity are easily brought to the processing
		$\mathop{s}^{2}=\frac{1}{n}\sum\limits_{t=1}^{n}{\mathop{(\mathop{z}_{t}-\mu )}^{2}}$
		μ和s_z²分别表示状态变量z_t的平均值和方差 μ and s_z² represent the average and variance of z_t_,respectively
计算偏度 Computing the skewness	由一个描述系统状态变量的时间序列数据计算出偏度绝对值 Computing the absolute value of skewness from the time series of variables that indicate ecosystem state	$\mathop{s}_{k}=\frac{\frac{1}{n}\sum\limits_{t=1}^{n}{\mathop{(\mathop{z}_{t}-\mu )}^{3}}}{\sqrt{\frac{1}{n}\sum\limits_{t=1}^{n}{\mathop{(\mathop{z}_{t}-\mu )}^{2}}}}$	偏度计算结果有时候仅反映出外部噪声变化并非生态系统本身状态 Skewness sometimes reflects external noise rather than the ecosystem itself
观察频繁波动 Observing flickering	计算生态系统状态变量的方差、频率分布的偏度或观察是否出现双峰性 Computing the variance, skewness of frequency distribution of the variables that indicates ecosystem state or observing the bimodality		适用于出现频繁波动现象的生态系统 Suitable to the ecosystems that flick

主要突变检测方法 Key methods of detecting critical transition	主要内容 Main contents	计算公式 Formula	局限性 Limitation
测量恢复时间 Measuring the recovery time	直接测量系统遭受微小事件扰动时恢复到最初平衡态的时间 Directly measuring the recovery time to equilibrium after a small perturbation		适用于模拟试验, 对现实生态系统可操作性差 Suitable to simulation experiments, but not feasible in reality
计算自相关系数、方差, 功率谱分析 Computing autocorrelation and variance, and performing power spectrum analysis	计算指示系统状态的变量的自相关系数、方差, 对时间序列进行功率谱分析 Computing autocorrelation and variance of the variables that indicate the status of the ecosystem, and analyzing the power spectrum of the time series	$\rho =\frac{E[(\mathop{z}_{t}-\mu )(\mathop{z}_{t+1}-\mu )]}{{}^{\mathop{\mathop{s}_{z}}^{2}}}$	数据获取困难和数据处理过程带入的误差和主观性 Difficult to acquire data; error and subjectivity are easily brought to the processing
		$\mathop{s}^{2}=\frac{1}{n}\sum\limits_{t=1}^{n}{\mathop{(\mathop{z}_{t}-\mu )}^{2}}$
		μ和s_z²分别表示状态变量z_t的平均值和方差 μ and s_z² represent the average and variance of z_t_,respectively
计算偏度 Computing the skewness	由一个描述系统状态变量的时间序列数据计算出偏度绝对值 Computing the absolute value of skewness from the time series of variables that indicate ecosystem state	$\mathop{s}_{k}=\frac{\frac{1}{n}\sum\limits_{t=1}^{n}{\mathop{(\mathop{z}_{t}-\mu )}^{3}}}{\sqrt{\frac{1}{n}\sum\limits_{t=1}^{n}{\mathop{(\mathop{z}_{t}-\mu )}^{2}}}}$	偏度计算结果有时候仅反映出外部噪声变化并非生态系统本身状态 Skewness sometimes reflects external noise rather than the ecosystem itself
观察频繁波动 Observing flickering	计算生态系统状态变量的方差、频率分布的偏度或观察是否出现双峰性 Computing the variance, skewness of frequency distribution of the variables that indicates ecosystem state or observing the bimodality		适用于出现频繁波动现象的生态系统 Suitable to the ecosystems that flick

主要突变检测方法 Key methods of detecting critical transition	主要内容 Main contents	计算公式 Formula	局限性 Limitation
计算空间自相关性和方差 Computing spatial autocorrelation and variance	计算指示系统状态的变量的空间自相关系数和方差 Computing spatial autocorrelation and variance of the variables that indicate the ecosystem state		数据获取困难和数据处理过程带入的误差和主观性 Difficult to acquire data; error and subjectivity are easily brought to the processing
观察power law现象 Observing power law	分析样地斑块大小S与斑块数目N(S), 观察它们的对数是否满足特定的方程 Analyzing the patch size S and number of patches N(S), and see if their logarithm can meet specific equation	N(S)=CS^-γ	适用地区受限, 计算结果受数据计算方式影响大 Restricted to certain areas and the result is greatly influenced by the computation method
观察规则斑块 Observing regular patches	观察植被斑块是否呈现规则形状 Observing if the vegetation presents regular patches		难以对斑块进行量化与判断 Difficult to quantify and judge the patches

主要突变检测方法 Key methods of detecting critical transition	主要内容 Main contents	计算公式 Formula	局限性 Limitation
计算空间自相关性和方差 Computing spatial autocorrelation and variance	计算指示系统状态的变量的空间自相关系数和方差 Computing spatial autocorrelation and variance of the variables that indicate the ecosystem state		数据获取困难和数据处理过程带入的误差和主观性 Difficult to acquire data; error and subjectivity are easily brought to the processing
观察power law现象 Observing power law	分析样地斑块大小S与斑块数目N(S), 观察它们的对数是否满足特定的方程 Analyzing the patch size S and number of patches N(S), and see if their logarithm can meet specific equation	N(S)=CS^-γ	适用地区受限, 计算结果受数据计算方式影响大 Restricted to certain areas and the result is greatly influenced by the computation method
观察规则斑块 Observing regular patches	观察植被斑块是否呈现规则形状 Observing if the vegetation presents regular patches		难以对斑块进行量化与判断 Difficult to quantify and judge the patches