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。     

宏观生态系统科学整合研究的多学科知识融合及其技术途径

综合认识大尺度的宏观生态系统结构功能、空间变异和动态演变的过程机理和模式机制,实现对生态系统变化及其对人类福祉影响的定量模拟、科学评估和预测预警,服务生态系统的利用保护及调控管理,是当代宏观生态系统科学的重要发展方向,正在孕育并形成大尺度的宏观生态系统科学整合生态学(IEMES)研究新领域。本研究通过对宏观生态系统科学整合生态学研究的基础理论、多学科知识融合途径及其关键技术问题的系统分析,形成以下几个基本认识: 1)宏观生态系统科学整合生态学研究是以区域、大陆和全球尺度的宏观生态系统为研究对象,采用多学科知识融合方法和技术,致力于解决人类社会发展的食物安全、资源安全、生态安全、环境安全等重大资源环境问题。2)宏观生态系统科学整合生态学研究的基本科技任务是: 理解宏观生态系统的结构功能基本属性,监测生态系统状态变化,解释生态系统时空演变规律,认知生态系统运维过程机理,定量评估生态系统功能状态及服务能力,预测生态系统动态演变及地理格局,预警生态系统变化及生态环境灾害。3)宏观生态系统科学整合生态学研究需要重新构造“多源数据分析-多模型模拟-多学科知识融合”的理论和方法学体系,发展“多尺度观测、多方法印证、多过程融合、跨尺度模拟”的多学科知识融合关键技术。4)大陆尺度的地基-空基-天基多时空尺度生态系统观测试验网络是承载多学科知识深度融合研究的基础科技设施,需要围绕区域、大陆和全球尺度的宏观生态系统科学问题,发展多要素-多过程-多界面-多介质-多尺度-多方法的多学科维度生态学知识融合关键技术。     

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。     

生态系统稳态转换检测研究进展

在气候变化、人类活动等影响下,生态系统结构和功能可能发生大规模的突变,导致生态系统从一个相对稳定的状态进入另一个稳定状态,这种现象称为稳态转换。由于生态系统的复杂性,准确刻画生态系统多稳态并界定其临界点尚存在挑战,提升对生态系统稳态转换的检测和预测能力依旧是生态学领域研究的热点和难题。基于多稳态理论和稳态转换经典概念框架,阐释了稳态转换检测的理论基础;归纳总结出四种稳态转换检测方法的原理和优劣势;鉴于稳态转换的尺度依赖性,梳理了单一生态系统、区域综合生态系统和全球生态系统不同尺度下的稳态转换检测方法、研究思路和应用案例。基于研究进展和问题现状,提出在未来研究中,亟待发展适应复杂系统的综合检测方法;创新稳态转换多尺度分析的技术方法体系;深化生态系统稳态转换驱动机制研究,构建多元耦合机理模型;进而深化稳态转换检测结果链接生态系统管理的实践研究;解析生态系统服务和可持续发展机制。     

大尺度陆地生态系统动态变化与空间变异的过程模型及模拟系统

当代生态系统科学研究更加关注区域生态环境及生态系统状态变化的监测、评估、预测、预警及生态环境可持续管理。在深入理解陆地生态系统的要素、过程、功能、格局及其相互作用机理基础上,发展生态系统定量化描述方法和数值模拟技术,集成构建大陆尺度的“多过程耦合-多技术集成-多目标应用”的陆地生态系统数值模拟器已成为生态系统与全球变化及其资源、环境和灾害效应科学研究的重要科技任务。本研究围绕宏观生态系统模拟分析方法问题,在回顾陆地生态系统模型研究现状和发展趋势的基础上,深入讨论开发大尺度陆地生态系统动态变化和空间变异及其资源环境效应模拟系统的理念,以及模拟系统的功能定位、结构设计等基本问题,为构造中国陆地生态系统数值模拟器提供参考。     

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。     

沈阳应用生态所的研究方向任务简介

1987年10月27日经中国科学院批准,林业土壤研究所改名为沈阳应用生态研究所,其方向任务为: 1.研究方向:以现代生态学为主攻学科,以陆地生态系统为主要研究对象,重点发展生态系统生态学,重视和发展景观生态学、系统生态学、城市生态学,加速发展服务于资源开发和生态环境建设的生态规划、生态工程等应用技术以及分析测试技术的研究所。     

生态系统状态跃变的早期预警信号及检测

生态系统在气候变化和土地利用及人类活动等的影响下其状态会由某一稳态转变到另一稳态。由于环境压力的复杂性、非线性、随机性等特征，往往导致状态转变表现为非线性、突变、跃变等特点。准确界定系统状态跃变的拐点或阈值点存在很大的挑战，而捕捉接近临界拐点前的生态系统结构和属性上的变化特征作为早期预警信号是切实可行的。早期预警信号理论经历理论框架构建、方法确立、机理认知等近半个多世纪的探索，已经由最初的通过仅依赖检测临界点恢复力的速率减慢、方差增加、系统自相关增强等统计学信号过度到更加多样化的检测方法，如检测系统组分属性的变化特征，诊断系统组分各属性之间的关系变化，系统组分的性状变化、系统组分网络结构变化等等，并且试图整合多信号提高预警的精确性。利用来自自然生态系统的长时间高密度数据集和空间代替时间的数据集，基于多度及性状信号的早期预警，结合稳定性、临界恢复力的减速、以及统计参数的指示作用对系统跃变进行早期诊断和预警是预测生态学的主旨。早期预警信号的深入研究不仅能够完善已有理论的不足，同时还能够为生态系统的保护和管理提供切实有效的理论指导。     

浅水湖泊稳态转换预警识别方法局限与展望

浅水湖泊水体底泥交换强烈,极易受人类活动干扰,超过一定阈值即可能发生灾难性的稳态转换,对其有效识别有助于湖泊富营养化的及时防控与修复。浅水湖泊稳态转换可通过系统关键变量(叶绿素、溶解氧、浮游动物、鱼类等)的时间序列(判别不同稳态)、预警信号及阈值等进行识别,其中预警识别可为湖泊生态系统稳态转换提供预判信息,有利于早预警早行动。目前,浅水湖泊稳态转换预警识别因子(方差及自相关性等)主要用于"临界慢化"现象,但在强大外力作用、强烈随机扰动及极端事件下,这些"临界慢化"因子则可能出现误用或错用。基于浅水湖泊基本特征,针对稳态转换的不同驱动机制,探讨"临界慢化"因子的适用性与局限性,并展望其未来发展方向,旨在为湖泊生态系统稳态转换预警识别提供科学参考。     

土壤生态系统微生物多样性-稳定性关系的思考

自20世纪50年代以来,生物多样性与生态系统稳定性的关系一直是生态学中重点讨论的理论问题之一.在当今人类活动对自然生态系统产生重大影响的情况下,全面理解生态系统多样性与稳定性的关系,有助于我们更好地应对环境变化和生物多样性丧失等生态问题.在陆地生态系统中,关注重点多集中在地上植物生态系统;而对地下生态系统,尤其是对微生物多样性与系统稳定性关系的研究尚重视不够.事实上,土壤微生物作为生命元素循环的驱动者,主导和参与地下生态系统中一系列重要生态过程,对土壤能否正常有序地执行各项生态功能至关重要.对土壤微生物多样性的研究,能使我们明确土壤中微生物对各种环境条件(包括自然和人为因素)变化的响应机制,更好地维持土壤生态系统的稳定性及其生态服务功能.本文在介绍土壤微生物多样性概念、研究方法、地下生态系统稳定性的基础上,重点讨论了土壤微生物多样性对土壤生态系统稳定性的影响,对多样性-稳定性关系在土壤微生物生态学中的应用进行了较为深入和全面的思考.作者提出,土壤微生物系统是一个动态变化的自组织系统,通过遗传来维持其组成和结构的相对稳定性,通过变异而适应外界干扰,共同构成土壤微生物系统的抵抗力(resistance)和恢复力(resilience),维护土壤生态系统的稳定性.今后土壤微生物多样性-稳定性关系的研究,需要注重地上与地下生态系统的结合与统一,借鉴宏观生态学理论来构建微生物生态学的理论框架,建立微生物多样性-稳定性关系的机理模型,从定性描述向定量表征方向发展.     

Network based early warning indicators of vegetation changes in a land–atmosphere model

Numerous model studies demonstrate that ecosystems might not shift smoothly with a gradual change in resource concentration. At specific points, vegetation can suddenly shift from one stable state to another. To predict such undesirable shifts, statistical indicators are proposed for early warning prediction. These so-called classical indicators can address whether vegetation state is moving towards the tipping point of an abrupt transition, however when the transition will occur is hard to predict. Recent studies suggest that complex network based indicators can improve early warning signals of abrupt transitions in complex dynamic systems. In this study, both classical and network based indicators are tested in a coupled land–atmosphere ecological model in which a scale-dependent hydrology-infiltration feedback and a large scale vegetation–precipitation feedback are represented. Multiple biomass equilibria are found in the model and abrupt transitions can occur when rainfall efficiency is decreased. Interaction network based indicators of these transitions are compared with classical indicators, such as the lag-1 autocorrelation and Moran's coefficient, with particular focus on the transition associated with desertification. Two criteria are used to evaluate the quality of these early warning indicators and several high quality network based indicators are identified.     

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.     

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。     

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.     

Rate of forcing and the forecastability of critical transitions

Critical transitions are qualitative changes of state that occur when a stochastic dynamical system is forced through a critical point. Many critical transitions are preceded by characteristic fluctuations that may serve as model‐independent early warning signals, implying that these events may be predictable in applications ranging from physics to biology. In nonbiological systems, the strength of such early warning signals has been shown partly to be determined by the speed at which the transition occurs. It is currently unknown whether biological systems, which are inherently high dimensional and typically display low signal‐to‐noise ratios, also exhibit this property, which would have important implications for how ecosystems are managed, particularly where the forces exerted on a system are anthropogenic. We examine whether the rate of forcing can alter the strength of early warning signals in (1) a model exhibiting a fold bifurcation where a state shift is driven by the harvesting of individuals, and (2) a model exhibiting a transcritical bifurcation where a state shift is driven by increased grazing pressure. These models predict that the rate of forcing can alter the detectability of early warning signals regardless of the underlying bifurcation the system exhibits, but that this result may be more pronounced in fold bifurcations. These findings have important implications for the management of biological populations, particularly harvested systems such as fisheries, and suggest that knowing the class of bifurcations a system will manifest may help discriminate between true‐positive and false‐positive signals.     

Biodiversity collapse and early warning indicators in a spatial phase transition between neutral and niche communities

The dynamics of ecological communities have been described by neutral and niche theories that are now increasingly integrated into unified models. It is known that a critical transition exists between these two states, but the spatial aspect of this transition has not been studied. Our aim is to study the spatial aspect of the transition and propose early warning signals to detect it. We used a stochastic, spatially explicit model that spans a continuum from neutral to niche communities, and is driven by the intensity of hierarchical competition. The transition is indicated by the emergence of a large patch formed by one species that connects the whole area. The properties of this patch can be used as early warning indicators of a critical transition. If competition intensity increases beyond the critical point, our model shows a sudden decrease of the Shannon diversity index and a gentle decline in species richness. The critical point occurs at a very low value of competitive intensity, with the rate of migration from the metacommunity greatly influencing the position of this critical point. As an example, we apply our new method of early warning indicators to the Barro Colorado Tropical forest, which, as expected, appears to be far from a critical transition. Low values of competitive intensity were also reported by previous studies for different high‐diversity real communities, suggesting that these communities are located before the critical point. A small increase of competitive interactions could push them across the transition, however, to a state in which diversity is much lower. Thus this new early warnings indicator could be used to monitor high diversity ecosystems that are still undisturbed.     

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.