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.     

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.     

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。     

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。     

湖北省若干浅水湖泊沉积物有机质与富营养化的关系

分析了湖北省月湖、龙阳湖、后官湖、梁子湖、牛山湖、保安湖、鲁湖等7个湖泊表层沉积物有机质、总氮与总磷的含量,并考察了其在月湖、龙阳湖沉积物中的垂直分布及月湖、龙阳湖与保安湖表层沉积物的颗粒组成,结果表明：位于城市与城郊的月湖与龙阳湖表层沉积物有机质含量较高,且表现出沿岸带较丰富的空间分布特征。在垂直及水平方向上,有机质与总氮和总磷含量均显著相关,颗粒组成与有机质和总氮含量亦显著相关。有机质富集应是促进城市湖泊富营养化的重要因素。     

城市小型浅水人工湖泊浮游藻类与水质特征研究

研究了广州城区某富营养化小型浅水人工湖泊浮游藻类和水质特征及其变化规律。根据镜检共鉴定出7门54属浮游藻类,以绿藻门、蓝藻门、硅藻门和裸藻门为主,其中绿藻门的绿球藻目占绝对优势,达26属62种,占总属数的45.6%;在整个观测期内,浮游藻类密度均较高,05年2月藻类密度高达10.8×106cell·mL-1;根据Kolkwitz划分的水域类型,该湖泊中浮游藻类属于α、β-中污带指示种类的最多。由以修正的卡森(Carlson)指数为基础的综合营养型评价方法和Margalef生物多样性指数判断,该湖泊水体属重富营养化水平,受到重污染,且污染水平季节变化明显:冬季>秋季>夏季。     

气候变暖与富营养化交互作用对浅水湖泊水-气界面N2O通量的影响

研究通过构建中尺度控温围隔模拟系统,模拟21世纪末气候变化与富营养化趋势,探讨未来气候变暖与富营养化趋势下浅水湖泊水-气界面N₂O交换过程的响应特征及机制。结果表明:(1)恒定与波动升温引起的代谢过程及生物间相互作用的改变显著促进了水-气界面间N₂O的排放及年累积释放量,而磷的添加可能因为影响了水体中反硝化代谢的效率而削弱了水-气界面N₂O排放及年累积释放量;(2)实验期间随季节转换,控制系统内优势的初级生产者由水生植物转变为浮游植物,水体中有机质含量亦不断积累,研究结果表明季节变化及初级生产者转换均对水-气界面N₂O排放量的增加起到了显著促进作用。在气候变化与富营养化趋势下浅水湖泊水-气界面的N₂O交换过程主要受到水体中氮磷含量及其比例的变化、水生植物与浮游植物的转换及有机质的积累过程的影响。因此,气候变暖(恒定和波动升温)能够促进湖泊N₂O排放量的上升,而变暖和营养盐的交互作用会使水-气界面N₂O交换更加复杂。     

稳态转换视角下生态系统服务变化过程与作用机制

优化生态系统服务供给是实现人类社会与自然生态系统和谐发展的必然途径。生态系统在平衡与非平衡之间复杂的转化模式使生态系统服务研究备受阻力,如何科学地解析生态系统服务内在调控机制是实现从自然资源利用到生态系统功能优化的关键。从论述生态系统稳态转换驱动机制入手,阐明扰动发生后稳态转换的路径、生态系统功能对扰动的响应模式;基于稳态转换视角深入诠释生态系统服务内涵及变化过程,以"结构-过程-功能-服务-人类福祉-可持续性"为核心架构来发展生态系统服务理论框架,并从生态系统敏感性和恢复力等内在属性探讨生态系统服务对结构和功能变化的响应情况;解析当土地利用变化超过生态系统阈值时,各项生态系统服务间的互馈作用。基于稳态转换视角评述生态系统服务变化过程与作用机制,以期为生态系统服务研究及生态系统管理提供新视角。     

长江中下游浅水湖泊沉积物多酚氧化酶与过氧化物酶活性分布

采样分析了长江中下游浅水湖泊(鲁湖、梁子湖、后官湖、牛山湖、三角湖、龙阳湖、墨水湖、月湖以及太湖)沉积物多酚氧化酶与过氧化物酶活性的分布及其与微生物的关系.结果表明,在水平方向上,沉积物有机质含量较高的湖泊酶的活性明显较高,湖内酶的活性亦有明显的异质性,排污口、水生植物凋落区以及未疏浚点的活性明显较高.在垂直方向上,有机质含量较高的表层显示较高的多酚氧化酶活性.因此,不同来源的有机质均能诱导酶的产生;过氧化物酶活性随深度变化的趋势不明显,且在疏浚与未疏浚点显示相近水平,这种现象可能源于酶与腐殖质的结合;多酚氧化酶与过氧化物酶活性显著正相关,初步揭示了二者在有机质降解与腐质化过程中的偶联;细菌和放线菌(而非真菌)似为酶的主要生产者.并讨论了氧化还原酶在湖泊富营养化过程中的作用.     

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.     

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.     

Changing skewness: an early warning signal of regime shifts in ecosystems

Empirical evidence for large-scale abrupt changes in ecosystems such as lakes and vegetation of semi-arid regions is growing. Such changes, called regime shifts, can lead to degradation of ecological services. We study simple ecological models that show a catastrophic transition as a control parameter is varied and propose a novel early warning signal that exploits two ubiquitous features of ecological systems: nonlinearity and large external fluctuations. Either reduced resilience or increased external fluctuations can tip ecosystems to an alternative stable state. It is shown that changes in asymmetry in the distribution of time series data, quantified by changing skewness, is a model-independent and reliable early warning signal for both routes to regime shifts. Furthermore, using model simulations that mimic field measurements and a simple analysis of real data from abrupt climate change in the Sahara, we study the feasibility of skewness calculations using data available from routine monitoring.     

Total phosphorus thresholds for regime shifts are nearly equal in subtropical and temperate shallow lakes with moderate depths and areas

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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.     

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.     

Robustness of early warning signals for catastrophic and non‐catastrophic transitions

Early warning signals (EWS) are statistical indicators that a rapid regime shift may be forthcoming. Their development has given ecologists hope of predicting rapid regime shifts before they occur. Accurate predictions, however, rely on the signals being appropriate to the system in question. Most of the EWS commonly applied in ecology have been studied in the context of one specific type of regime shift (the type brought on by a saddle‐node bifurcation, at which one stable equilibrium point collides with an unstable equilibrium and disappears) under one particular perturbation scheme (temporally uncorrelated noise that perturbs the net population growth rate in a density independent way). Whether and when these EWS can be applied to other ecological situations remains relatively unknown, and certainly underappreciated. We study a range of models with different types of dynamical transitions (including rapid regime shifts) and several perturbation schemes (density‐dependent uncorrelated or temporally‐correlated noise) and test the ability of EWS to warn of an approaching transition. We also test the sensitivity of our results to the amount of available pre‐transition data and various decisions that must be made in the analysis (i.e. the rolling window size and smoothing bandwidth used to compute the EWS). We find that EWS generally work well to signal an impending saddle‐node bifurcation, regardless of the autocorrelation or intensity of the noise. However, EWS do not reliably appear as expected for other types of transition. EWS were often very sensitive to the length of the pre‐transition time series analyzed, and usually less sensitive to other decisions. We conclude that the EWS perform well for saddle‐node bifurcation in a range of noise environments, but different methods should be used to predict other types of regime shifts. As a consequence, knowledge of the mechanism behind a possible regime shift is needed before EWS can be used to predict it.