浅水湖泊生态系统稳态转换的阈值判定方法

浅水湖泊生态系统对人类干扰的反应会随着干扰力度的改变或增强而出现突然的变化,即发生稳态转换;对其机理和驱动机制的揭示将有助于对湖泊富营养化的控制及恢复.基于“多稳态”理论的稳态转换研究已广泛开展,但对浅水湖泊生态系统稳态转换的驱动机制结论各异,采用的阈值判定方法相差很大,主要有实验观测、模型模拟和统计分析3种.实验观测多关注少数特定指标,指标筛选过程复杂且工作量大;模型模拟虽能从较为全面的尺度上理解生态系统稳态变化的特征和主要机理过程,但在模型误差和不确定性的处理等问题上尚存在不足;统计分析方法基于对长时间序列数据的统计变化规律分析,用以判断或者预警稳态转换现象的发生,是目前最为常用的方法.目前稳态转换领域的研究大都是对已发生的稳态转换进行机制分析或过程反演,对未来预测与预警的问题仍然亟需加强.     

干旱区生态系统稳态转换及其预警信号——基于景观格局特征的识别方法

在日益加剧的气候变化和土地开垦、放牧等人类活动干扰下,具有多稳态特征的干旱区生态系统可能会经历从相对健康状态到退化状态的稳态转换,导致生态系统的功能下降。早期预警信号的识别是生态系统稳态转换研究的热点,也是管理实践中防止生态系统退化的关键环节。以往预警信号研究聚焦于通用信号如自相关性、方差等统计学指标,然而这些指标对于具有特定机制的干旱区生态系统可能并不适用。基于干旱区景观格局特征所发展起来的空间指标为生态系统稳态转换提供了独特的空间视角,对于理解干旱区生态系统退化过程和机理具有科学意义和实践价值。介绍了干旱区生态系统稳态转换现象及其转换机制;聚焦景观生态学的指标和方法,从空间视角总结基于干旱区景观格局特征的关键预警指标(植被覆盖度、植被斑块形态、植被斑块大小频率分布和水文连通性等),重点剖析这些关键指标的概念、量化方法、识别特征及其实践应用;最后针对指标的优势和局限性对未来的研究方向进行展望,包括发掘潜在景观指标,加强干旱区生态系统变化的多种驱动要素的相互作用机制研究,开展多时空尺度的实证研究,构建生态系统稳态转换预警信号的整体分析框架,以及加强指标阈值的量化研究等方面。     

我国生态系统生态阈值研究基础

生态系统中广泛存在非线性变化,表现为系统状态随着压力的逐渐增加而发生骤然转变。为解释这种变化,国外生态学家提出了生态阈值和稳态转换的概念,不断完善理论和方法体系,开展机理和案例研究,深化对复杂系统演化机制的理解,并开始应用于环境管理。在我国,近几十年来在各类生态系统中开展了大量关于压力-响应的定量化研究,取得了丰富成果。这些研究在本质上与生态阈值和稳态转换理论范式紧密关联。本文以“中国生态阈值和稳态转换案例数据库”为基础,按照河流、湖泊、湿地、森林、草地、河口与海洋、农田、荒漠、城市、冻原10种生态系统类型,筛选归纳了相关生态阈值,并阐释了稳态转换机理。将研究案例与生态阈值、稳态转换理论范式进行衔接,目的是整合多领域研究成果,作为生态系统复杂性研究的基础,推动其在生态环境监测、生态安全预警以及生态标准创新发展领域中的应用。     

生态阈值:概念、方法与研究展望

生态阈值概念是20世纪70年代提出的,主要指生态系统的几个稳态之间突然改变的点或区域。在阐明生态系统结构与功能的关系、构建区域可持续发展范式以及服务于生态系统管理和生态红线的划定中,生态阈值的检测和量化有着重要的理论和实践意义。该文首先梳理了前人关于生态阈值的概念、类型的一些提法,从预警研究角度提出可以从两个层次理解生态阈值概念:生态阈值点是系统从量变到质变的转折点,类似于红色界限;而生态阈值带可以理解为量变过程中不同稳态之间的转换区域,类似于黄色与橙色预警边界带。黄色生态阈值表示生态系统可通过自身的调节能力重新达到稳定状态;橙色生态阈值表示需要排除干扰因子使得生态系统重新达到平衡;而红色生态阈值为关键阈值点,超过此阈值,生态系统将发生不可逆的退化甚至崩溃。该文还总结了目前确定生态阈值的主要方法,主要是基于野外观测数据的统计分析与模型模拟方法。最后,基于生态系统服务、生物多样性保护与生态系统管理等几个当今生态学热点研究领域,简单总结归纳了生态阈值的研究现状,并提出生态阈值未来的3个研究难点和方向:1)开展针对生态阈值检测和量化的研究;2)关注生态阈值的尺度效应并加强野外观测;3)发挥生态阈值的预警作用,指导"生态红线"的划定和生态系统管理。     

生态系统多稳态研究进展

在多稳态的生态系统中，外力可能导致生态系统状态突然之间发生不可逆转的转变，从而达到一个新的平衡状态。但目前对多稳态理论的系统研究很少，如何使用预警信号来预测生态系统的状态转变依旧是个难题。通过多稳态理论的梳理提出了一个更加综合的多稳态定义，并以放牧模型为例，系统总结了多稳态理论的相关概念，将多稳态理论应用在生态系统演替和扰沌理论的解释中；通过对生态系统稳态转换预警信号的原理、优缺点和应用条件的分析，对不同尺度下多稳态的研究方法进行了归纳；最后提出了目前多稳态领域的研究问题和未来的研究重点。结果表明：（1）将时间和空间预警信号结合在一起，并量化正确预警信号的概率，对错误预警信号的比例进行加权，可能会提供更准确的稳态转换的预报。（2）定量观测试验适用于小尺度的研究，而较大尺度的研究则采用简化的模型来模拟研究，选择正确的尺度极有可能改变预警信号的可靠性。（3）结合多稳态理论研究生态系统临界转换和反馈控制机制，并将基于性状的特征指标和进化动力学纳入其中，是生态系统修复实践的重要研究方向。（4）将多稳态相关理论和生态保护管理政策的实践相结合，是多稳态理论未来应用的前景。本研究为多稳态理论和实践的深入研究提供科学支撑。     

生态系统的多稳态与突变

许多生态系统可能在短时间内发生难以预料的状态突变, 其中一些生态系统突变的机理可以用多稳态理论进行解释。近年来生态系统的多稳态和突变现象及其机理吸引了研究者和管理者的广泛关注。本文重点对生态系统多稳态的理论基础、识别方法及稳态转换发生的早期预警信号进行综述, 并基于典型生态系统过程对现实世界中可能观测到的稳态转换进行实例分析, 最后对多稳态概念框架和理论应用中的潜在争议进行讨论, 以期为非线性生态系统动态的理论研究、管理实践和生物多样性保护等提供参考。     

草藻型稳态转换对湖泊微生物结构及其碳循环功能的影响

湖泊是地球表层系统中水、土、气等各个圈层相互作用的联结点,对区域物质如碳等元素循环具有重要影响.微生物是湖泊等水生态系统中的重要组成部分,是湖泊等生态系统中碳等元素物质循环的主要驱动者,是深入了解湖泊碳循环过程的关键.受人类活动等影响,湖泊生态系统,尤其是浅水湖泊生态系统往往表现出以高等水生植物(草型)为主要初级生产者的清水稳定态和以浮游藻类(藻型)为主要初级生产者的浊水稳定态,而随着湖泊营养负荷和湖泊环境条件的变化,这两个不同的稳定态之间可以发生转换或者剧变,这种剧变不仅影响湖泊生态系统中的微生物结构,而且对湖泊中有机碳的形成、循环过程及其微生物驱动机制产生重大影响.本文重点就湖泊生态系统中有机碳的转换与微生物关系以及草藻型稳定态的转换对微生物结构及其碳循环功能的影响等进行综述,进一步分析其中的关键科学问题,以期为深入了解湖泊生态系统中碳等元素循环的微生物驱动过程与机制提供帮助.     

生态系统重大突变检测研究进展北大核心CSCD

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。     

淡水湖泊生态系统退化驱动因子及修复技术研究进展

目前我国多数淡水湖泊污染、退化问题非常严重,诸多修复技术也已初见成效。影响淡水湖泊生态系统退化的驱动因子众多,既有生物因素也有非生物因素,它们之间相互联系,相互作用,且作用机理错综复杂。首先介绍了淡水湖泊生态系统退化的含义及形式;其次,分析、总结了淡水湖泊生态系统退化的驱动因子,从退化的生态学完整性意义和退化修复的技术手段上看,淡水湖泊生态系统主要受物理、化学和生物三大驱动因子影响,且基本遵循"环境变化-驱动力-压力(阈值)-状态-响应"原理;再次,在厘清湖泊生态系统退化驱动原理的基础上,从淡水湖泊生态系统功能模块和湖泊生态系统修复实践经验总结的角度出发,构建了淡水湖泊生态系统修复模块技术体系,并就湖泊富营养化和湖滨湿地生态系统退化修复的技术进行了讨论和对比;最后,对淡水湖泊生态系统修复的环境变化驱动因子的作用机制、作用途径和修复技术的长效机制等方面进行了展望。     

Review on detection of critical transition in ecosystems

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。     

Resilience indicators: prospects and limitations for early warnings of regime shifts

In the vicinity of tipping points—or more precisely bifurcation points—ecosystems recover slowly from small perturbations. Such slowness may be interpreted as a sign of low resilience in the sense that the ecosystem could easily be tipped through a critical transition into a contrasting state. Indicators of this phenomenon of ‘critical slowing down (CSD)’ include a rise in temporal correlation and variance. Such indicators of CSD can provide an early warning signal of a nearby tipping point. Or, they may offer a possibility to rank reefs, lakes or other ecosystems according to their resilience. The fact that CSD may happen across a wide range of complex ecosystems close to tipping points implies a powerful generality. However, indicators of CSD are not manifested in all cases where regime shifts occur. This is because not all regime shifts are associated with tipping points. Here, we review the exploding literature about this issue to provide guidance on what to expect and what not to expect when it comes to the CSD-based early warning signals for critical transitions.     

Early warning signals of demographic regime shifts in invading populations

The Allee effect can cause alternative stable states in population abundance of invasive species. Sudden eruption of invading populations from low to high abundance may be viewed as a regime shift from one alternative state to another. Previous research proposed several types of early warning signals to predict regime shifts in ecological systems such as polluted lakes and semiarid grasslands. This paper explores theoretically the potential of such indicators in predicting demographic regime shifts of invading populations. I analyzed a stochastic differential equation model for the population dynamics of an invasive species subject to Allee effects and propagule pressure. Diffusion approximation to the stochastic model suggests that persistent propagule pressure makes demographic regime shifts inevitable, but Allee effects can lengthen the mean time until regime shifts virtually indefinitely. To compare the potential of indicators, I examined standard deviation, skewness, and estimated return rates of longitudinal population abundance. I found that standard deviation showed a distinct increase as regime shifts became more likely, but skewness and return rates showed no clear trends. This result suggests that standard deviation might be a useful warning signal for forecasting an impending demographic regime shift of invading populations during the period when their abundance is still low.     

Spatial early warning signals for impending regime shifts: A practical framework for application in real‐world landscapes

Prediction of ecosystem response to global environmental change is a pressing scientific challenge of major societal relevance. Many ecosystems display nonlinear responses to environmental change, and may even undergo practically irreversible ‘regime shifts’ that initiate ecosystem collapse. Recently, early warning signals based on spatiotemporal metrics have been proposed for the identification of impending regime shifts. The rapidly increasing availability of remotely sensed data provides excellent opportunities to apply such model‐based spatial early warning signals in the real world, to assess ecosystem resilience and identify impending regime shifts induced by global change. Such information would allow land‐managers and policy makers to interfere and avoid catastrophic shifts, but also to induce regime shifts that move ecosystems to a desired state. Here, we show that the application of spatial early warning signals in real‐world landscapes presents unique and unexpected challenges, and may result in misleading conclusions when employed without careful consideration of the spatial data and processes at hand. We identify key practical and theoretical issues and provide guidelines for applying spatial early warning signals in heterogeneous, real‐world landscapes based on literature review and examples from real‐world data. Major identified issues include (1) spatial heterogeneity in real‐world landscapes may enhance reversibility of regime shifts and boost landscape‐level resilience to environmental change (2) ecosystem states are often difficult to define, while these definitions have great impact on spatial early warning signals and (3) spatial environmental variability and socio‐economic factors may affect spatial patterns, spatial early warning signals and associated regime shift predictions. We propose a novel framework, shifting from an ecosystem perspective towards a landscape approach. The framework can be used to identify conditions under which resilience assessment with spatial remotely sensed data may be successful, to support well‐informed application of spatial early warning signals, and to improve predictions of ecosystem responses to global environmental change.     

Conditional Heteroskedasticity Forecasts Regime Shift in a Whole-Ecosystem Experiment

Regime shifts in stochastic ecosystem models are often preceded by early warning signals such as increased variance and increased autocorrelation in time series. There is considerable theoretical support for early warning signals, but there is a critical lack of field observations to test the efficacy of early warning signals at spatial and temporal scales relevant for ecosystem management. Conditional heteroskedasticity is persistent periods of high and low variance that may be a powerful leading indicator of regime shift. We evaluated conditional heteroskedasticity as an early warning indicator by applying moving window conditional heteroskedasticity tests to time series of chlorophyll-a and fish catches derived from a whole-lake experiment designed to create a regime shift. There was significant conditional heteroskedasticity at least a year prior to the regime shift in the manipulated lake but there was no significant conditional heteroskedasticity in an adjacent reference lake. Conditional heteroskedasticity was an effective leading indicator of regime shift for the ecosystem manipulation.     

陆地生态系统临界转换理论及其生态学机制研究进展

随着气候变化和人类活动对陆地生态系统双重扰动的不断加剧,越来越多的研究已经意识到生态系统结构和功能会发生难以预知的突变,并且恢复起来需要很长时间.开发判别典型生态系统临界转换的早期预警模型及理解其生态学机制成为生态学研究的热点.目前,基于跨越多个时空尺度的理论和实验研究,提出了多种预警陆地生态系统临界转换的理论框架和指...     

Robustness of early warning signals of regime shifts in time-delayed ecological models

Various ecological and other complex dynamical systems may exhibit abrupt regime shifts or critical transitions, wherein they reorganize from one stable state to another over relatively short time scales. Because of potential losses to ecosystem services, forecasting such unexpected shifts would be valuable. Using mathematical models of regime shifts, ecologists have proposed various early warning signals of imminent shifts. However, their generality and applicability to real ecosystems remain unclear because these mathematical models are considered too simplistic. Here, we investigate the robustness of recently proposed early warning signals of regime shifts in two well-studied ecological models, but with the inclusion of time-delayed processes. We find that the average variance may either increase or decrease prior to a regime shift and, thus, may not be a robust leading indicator in time-delayed ecological systems. In contrast, changing average skewness, increasing autocorrelation at short time lags, and reddening power spectra of time series of the ecological state variable all show trends consistent with those of models with no time delays. Our results provide insights into the robustness of early warning signals of regime shifts in a broader class of ecological systems.     

Leading indicators of trophic cascades

Regime shifts are large, long-lasting changes in ecosystems. They are often hard to predict but may have leading indicators which are detectable in advance. Potential leading indicators include wider swings in dynamics of key ecosystem variables, slower return rates after perturbation and shift of variance towards lower frequencies. We evaluated these indicators using a food web model calibrated to long-term whole-lake experiments. We investigated whether impending regime shifts driven by gradual increase in exploitation of the top predator can create signals that cascade through food webs and be discerned in phytoplankton. Substantial changes in standard deviations, return rates and spectra occurred near the switch point, even two trophic levels removed from the regime shift in fishes. Signals of regime shift can be detected well in advance, if the driver of the regime shift changes much more slowly than the dynamics of key ecosystem variables which can be sampled frequently enough to measure the indicators. However, the regime shift may occur long after the driver has passed the critical point, because of very slow transient dynamics near the critical point. Thus, the ecosystem can be poised for regime shift by the time the signal is discernible. Field tests are needed to evaluate these indicators.     

Early warning signals also precede non‐catastrophic transitions

Synthesis The quickly expanding literature on early warning signals for critical transitions in ecosystems suggests that critical slowing down is a key phenomenon to measure the distance to a tipping point in ecosystems. Such work is broadly misinterpreted as showing that slowing down is specific to tipping points. In this contribution, we show why this is not the case. Early warning signals based on critical slowing down indicate a broader class of situations where a system becomes increasingly sensitive to perturbations. Ecosystem responses to external changes can surprise us by their abruptness and irreversibility. Models have helped identifying indicators of impending catastrophic shifts, referred to as ‘generic early warning signals’. These indicators are linked to a phenomenon known as ‘critical slowing down’ which describes the fact that the recovery rate of a system after a perturbation decreases when the system approaches a bifurcation – such as the classical fold bifurcation associated to catastrophic shifts. However, contrary to what has sometimes been suggested in the literature, a decrease in recovery rate cannot be considered as specific to approaching catastrophic shifts. Here, we analyze the behavior of early warning signals based on critical slowing down in systems approaching a range of catastrophic and non‐catastrophic situations. Our results show that slowing down generally happens in situations where a system is becoming increasingly sensitive to external perturbations, independently of whether the impeding change is catastrophic or not. These results highlight that indicators specific to catastrophic shifts are still lacking. More importantly, they also imply that in systems where we have no reason to expect catastrophic transitions, slowing down may still be used in a more general sense as a warning signal for a potential decrease in stability.