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.     

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.     

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。     

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.     

A new approach for rapid detection of nearby thresholds in ecosystem time series

Massive changes to ecosystems sometimes cross thresholds from which recovery can be difficult, expensive and slow. These thresholds are usually discovered in post hoc analyses long after the event occurred. Anticipating these changes prior to their occurrence could give managers a chance to intervene. Here we present a novel approach for anticipating ecosystem thresholds that combines resilience indicators with Quickest detection of change points. Unlike existing methods, the Quickest detection method is updated every time a data point arrives, and minimizes the time to detect an approaching threshold given the users’ tolerance for false alarms. The procedure accurately detected an impending regime shift in an experimentally manipulated ecosystem. An ecosystem model was used to determine if the method can detect an approaching threshold soon enough to prevent a regime shift. When the monitored variable was directly involved in the interaction that caused the regime shift, detection was quick enough to avert collapse. When the monitored variable was only indirectly linked to the critical transition, detection came too late. The procedure is useful for assessing changes in resilience as ecosystems approach thresholds. However some thresholds cannot be detected in time to prevent regime shifts, and surprises will be inevitable in ecosystem management.     

Early detection of ecosystem regime shifts: a multiple method evaluation for management application

Critical transitions between alternative stable states have been shown to occur across an array of complex systems. While our ability to identify abrupt regime shifts in natural ecosystems has improved, detection of potential early-warning signals previous to such shifts is still very limited. Using real monitoring data of a key ecosystem component, we here apply multiple early-warning indicators in order to assess their ability to forewarn a major ecosystem regime shift in the Central Baltic Sea. We show that some indicators and methods can result in clear early-warning signals, while other methods may have limited utility in ecosystem-based management as they show no or weak potential for early-warning. We therefore propose a multiple method approach for early detection of ecosystem regime shifts in monitoring data that may be useful in informing timely management actions in the face of ecosystem change.     

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.     

Heteroskedasticity as a leading indicator of desertification in spatially explicit data

Regime shifts are abrupt transitions between alternate ecosystem states including desertification in arid regions due to drought or overgrazing. Regime shifts may be preceded by statistical anomalies such as increased autocorrelation, indicating declining resilience and warning of an impending shift. Tests for conditional heteroskedasticity, a type of clustered variance, have proven powerful leading indicators for regime shifts in time series data, but an analogous indicator for spatial data has not been evaluated. A spatial analog for conditional heteroskedasticity might be especially useful in arid environments where spatial interactions are critical in structuring ecosystem pattern and process. We tested the efficacy of a test for spatial heteroskedasticity as a leading indicator of regime shifts with simulated data from spatially extended vegetation models with regular and scale‐free patterning. These models simulate shifts from extensive vegetative cover to bare, desert‐like conditions. The magnitude of spatial heteroskedasticity increased consistently as the modeled systems approached a regime shift from vegetated to desert state. Relative spatial autocorrelation, spatial heteroskedasticity increased earlier and more consistently. We conclude that tests for spatial heteroskedasticity can contribute to the growing toolbox of early warning indicators for regime shifts analyzed with spatially explicit data.     

Regime Shifts in the Anthropocene: Drivers,Risks, and Resilience

Many ecosystems can experience regime shifts: surprising, large and persistent changes in the function and structure of ecosystems. Assessing whether continued global change will lead to further regime shifts, or has the potential to trigger cascading regime shifts has been a central question in global change policy. Addressing this issue has, however, been hampered by the focus of regime shift research on specific cases and types of regime shifts. To systematically assess the global risk of regime shifts we conducted a comparative analysis of 25 generic types of regime shifts across marine, terrestrial and polar systems; identifying their drivers, and impacts on ecosystem services. Our results show that the drivers of regime shifts are diverse and co-occur strongly, which suggests that continued global change can be expected to synchronously increase the risk of multiple regime shifts. Furthermore, many regime shift drivers are related to climate change and food production, whose links to the continued expansion of human activities makes them difficult to limit. Because many regime shifts can amplify the drivers of other regime shifts, continued global change can also be expected to increase the risk of cascading regime shifts. Nevertheless, the variety of scales at which regime shift drivers operate provides opportunities for reducing the risk of many types of regime shifts by addressing local or regional drivers, even in the absence of rapid reduction of global drivers.     

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.     

Synchronous marine pelagic regime shifts in the Northern Hemisphere

Regime shifts are characterized by sudden, substantial and temporally persistent changes in the state of an ecosystem. They involve major biological modifications and often have important implications for exploited living resources. In this study, we examine whether regime shifts observed in 11 marine systems from two oceans and three regional seas in the Northern Hemisphere (NH) are synchronous, applying the same methodology to all. We primarily infer marine pelagic regime shifts from abrupt shifts in zooplankton assemblages, with the exception of the East Pacific where ecosystem changes are inferred from fish. Our analyses provide evidence for quasi-synchronicity of marine pelagic regime shifts both within and between ocean basins, although these shifts lie embedded within considerable regional variability at both year-to-year and lower-frequency time scales. In particular, a regime shift was detected in the late 1980s in many studied marine regions, although the exact year of the observed shift varied somewhat from one basin to another. Another regime shift was also identified in the mid- to late 1970s but concerned less marine regions. We subsequently analyse the main biological signals in relation to changes in NH temperature and pressure anomalies. The results suggest that the main factor synchronizing regime shifts on large scales is NH temperature; however, changes in atmospheric circulation also appear important. We propose that this quasi-synchronous shift could represent the variably lagged biological response in each ecosystem to a large-scale, NH change of the climatic system, involving both an increase in NH temperature and a strongly positive phase of the Arctic Oscillation. Further investigation is needed to determine the relative roles of changes in temperature and atmospheric pressure patterns and their resultant teleconnections in synchronizing regime shifts at large scales.     

Prolonged Instability Prior to a Regime Shift

Regime shifts are generally defined as the point of ‘abrupt’ change in the state of a system. However, a seemingly abrupt transition can be the product of a system reorganization that has been ongoing much longer than is evident in statistical analysis of a single component of the system. Using both univariate and multivariate statistical methods, we tested a long-term high-resolution paleoecological dataset with a known change in species assemblage for a regime shift. Analysis of this dataset with Fisher Information and multivariate time series modeling showed that there was a∼2000 year period of instability prior to the regime shift. This period of instability and the subsequent regime shift coincide with regional climate change, indicating that the system is undergoing extrinsic forcing. Paleoecological records offer a unique opportunity to test tools for the detection of thresholds and stable-states, and thus to examine the long-term stability of ecosystems over periods of multiple millennia.     

Rising variance: a leading indicator of ecological transition

Regime shifts are substantial, long-lasting reorganizations of complex systems, such as ecosystems. Large ecosystem changes such as eutrophication, shifts among vegetation types, degradation of coral reefs and regional climate change often come as surprises because we lack leading indicators for regime shifts. Increases in variability of ecosystems have been suggested to foreshadow ecological regime shifts. However, it may be difficult to discern variability due to impending regime shift from that of exogenous drivers that affect the ecosystem. We addressed this problem using a model of lake eutrophication. Lakes are subject to fluctuations in recycling associated with regime shifts, as well as fluctuating nutrient inputs. Despite the complications of noisy inputs, increasing variability of lake-water phosphorus was discernible prior to the shift to eutrophic conditions. Simulations show that rising standard deviation (SD) could signal impending shifts about a decade in advance. The rising SD was detected by studying variability around predictions of a simple time-series model, and did not depend on detailed knowledge of the actual ecosystem dynamics.     

Regime shifts: catastrophic responses of ecosystems to human impacts

Evidence of abrupt changes in ecosystem states, such as sudden eutrophication in lakes, has been increasingly reported in a variety of aquatic and terrestrial systems. Ecosystems may have more than one state with a self-stabilizing mechanism, so that a shift between states does not occur frequently and is not readily reversible. These big changes are termed regime shifts where often one state is preferred over another. Thus, regime shifts are problematic for ecosystem managers, and the need exists for studies that lead to the identification of thresholds of key variables that trigger regime shifts. Regime shifts are currently difficult to predict and in many cases may be caused by the human pursuit of efficiency in land and water productivity in the last few decades. Here I briefly introduce a theoretical approach to predict the shift between a clear-water state and a turbid state in lakes, the best-studied example of regime shifts. This paper also discusses alternative states in other natural systems besides ecosystems to draw more attention to the research currently being performed on regime shifts. Motomi Genkai-Kato is the recipient of the 10th Denzaburo Miyadi Award.     

How Regime Shifts in Connected Aquatic Ecosystems Are Affected by the Typical Downstream Increase of Water Flow

All over the world freshwater ecosystems like ponds, ditches and lakes suffer from nutrient-driven regime shifts from submerged plants to dominance by algae or free-floating plants. Although freshwaters are often connected and part of a network, most of our current knowledge on regime shifts comes from studies of isolated ecosystems. The few studies that have assessed the spatial manifestation of regime shifts overlooked the hydrological fact that the water flow through connected waters typically increases in the downstream direction. Here, we use a complex ecosystem model to show that this increase in flow does not lead to spatial differences in ecosystem state. We support these findings with a simple, analytically tractable, nutrient retention model on connected waterbodies. The model shows that all bodies have the same nutrient concentration despite spatial gradients in the flow of water as well as nutrients carried by the water. As a consequence, each connected waterbody is equally vulnerable to a regime shift, implying a regime shift to be system-wide. Furthermore, it appeared that each connected waterbody behaves the same as an isolated waterbody, implying that the vast body of theory on isolated systems, like alternative stable states theory, can still be useful for connected systems. Although these findings are violated when there is heterogeneity in lateral runoff or waterbody characteristics—leading to spatial differences in ecosystem state and therefore to differences in the vulnerability to a regime shift—they show that the typical downstream build-up of water flow does not necessarily lead to differences in ecological state, and thereby provide a basic concept to better understand the ecology of connected freshwaters.     

Architecture of collapse: regime shift and recovery in an hierarchically structured marine ecosystem

By the late 20th century, a series of events or ‘natural experiments’, for example the depletion of apex predators, extreme eutrophication and blooms of invasive species, had suggested that the Black Sea could be considered as a large ecosystem ‘laboratory’. The events resulted in regime shifts cascading through all trophic levels, disturbing ecosystem functioning and damaging the water environment. Causal pathways by which the external (hydroclimate, overfishing) and internal (food web interactions) drivers provoke regime shifts are investigated. Statistical data analyses supported by an interpretative framework based on hierarchical ecosystem theory revealed mechanisms of hierarchical incorporation of environmental factors into the ecosystem. Evidence links Atlantic teleconnections to Black Sea hydroclimate, which together with fishing shapes variability in fish stocks. The hydroclimatic signal is conveyed through the food web via changes in productivity at all levels, to planktivorous fish. Fluctuating fish abundance is believed to induce a lagged change in competitor jelly plankton that cascades down to phytoplankton and influences water quality. Deprived of the stabilising role of apex predators, the Black Sea's hierarchical ecosystem organisation is susceptible to both environmental and anthropogenic stresses, and increased fishing makes fish stock collapses highly probable. When declining stocks are confronted with burgeoning fishing effort associated with the inability of fishery managers and decision‐makers to adapt rapidly to changes in fish abundance, there is overfishing and stock collapse. Management procedures are ineffective at handling complex phenomena such as ecosystem regime shifts because of the shortage of suitable explanatory models. The proposed concepts and models reported here relate the hydroclimate, overfishing and invasive species to shifts in ecosystem functioning and water quality, unravelling issues such as the causality of ecosystem interactions and mechanisms and offering potential for finding ways to reverse regime shifts. We advocate a management approach aiming at restoring ecosystem hierarchy that might mitigate the costly consequences of regime shifts.     

Identification of regime shifts in time series using neighborhood statistics

Sudden and significant changes in biotic and abiotic variables have been observed across a variety of systems. The identification of such regime shifts in time series includes both model-fitting and statistical approaches. We introduce two methods that use state- or measurement-space neighborhood statistics to pick out regime shifts. Analysis of simulated and real data sets shows that these methods can be an effective means of identifying regime shifts for single variable as well as multivariable time series. In addition, these methods can be used on systems with non-equilibrium steady states. However, care must be taken in interpreting results as these methods do respond to changes in time series that are not consistent with the regime shift concept.     

Conditional heteroscedasticity as a leading indicator of ecological regime shifts

Regime shifts are massive, often irreversible, rearrangements of nonlinear ecological processes that occur when systems pass critical transition points. Ecological regime shifts sometimes have severe consequences for human well-being, including eutrophication in lakes, desertification, and species extinctions. Theoretical and laboratory evidence suggests that statistical anomalies may be detectable leading indicators of regime shifts in ecological time series, making it possible to foresee and potentially avert incipient regime shifts. Conditional heteroscedasticity is persistent variance characteristic of time series with clustered volatility. Here, we analyze conditional heteroscedasticity as a potential leading indicator of regime shifts in ecological time series. We evaluate conditional heteroscedasticity by using ecological models with and without four types of critical transition. On approaching transition points, all time series contain significant conditional heteroscedasticity. This signal is detected hundreds of time steps in advance of the regime shift. Time series without regime shifts do not have significant conditional heteroscedasticity. Because probability values are easily associated with tests for conditional heteroscedasticity, detection of false positives in time series without regime shifts is minimized. This property reduces the need for a reference system to compare with the perturbed system.     

Marine ecosystem regime shifts: challenges and opportunities for ecosystem-based management

Regime shifts have been observed in marine ecosystems around the globe. These phenomena can result in dramatic changes in the provision of ecosystem services to coastal communities. Accounting for regime shifts in management clearly requires integrative, ecosystem-based management (EBM) approaches. EBM has emerged as an accepted paradigm for ocean management worldwide, yet, despite the rapid and intense development of EBM theory, implementation has languished, and many implemented or proposed EBM schemes largely ignore the special characteristics of regime shifts. Here, we first explore key aspects of regime shifts that are of critical importance to EBM, and then suggest how regime shifts can be better incorporated into EBM using the concept of integrated ecosystem assessment (IEA). An IEA uses approaches that determine the likelihood that ecological or socio-economic properties of systems will move beyond or return to acceptable bounds as defined by resource managers and policy makers. We suggest an approach for implementing IEAs for cases of regime shifts where the objectives are either avoiding an undesired state or returning to a desired condition. We discuss the suitability and short-comings of methods summarizing the status of ecosystem components, screening and prioritizing potential risks, and evaluating alternative management strategies. IEAs are evolving as an EBM approach that can address regime shifts; however, advances in statistical, analytical and simulation modelling are needed before IEAs can robustly inform tactical management in systems characterized by regime shifts.     

A hypothesis-testing framework for studies investigating ontogenetic niche shifts using stable isotope ratios

Ontogenetic niche shifts occur across diverse taxonomic groups, and can have critical implications for population dynamics, community structure, and ecosystem function. In this study, we provide a hypothesis-testing framework combining univariate and multivariate analyses to examine ontogenetic niche shifts using stable isotope ratios. This framework is based on three distinct ontogenetic niche shift scenarios, i.e., (1) no niche shift, (2) niche expansion/reduction, and (3) discrete niche shift between size classes. We developed criteria for identifying each scenario, as based on three important resource use characteristics, i.e., niche width, niche position, and niche overlap. We provide an empirical example for each ontogenetic niche shift scenario, illustrating differences in resource use characteristics among different organisms. The present framework provides a foundation for future studies on ontogenetic niche shifts, and also can be applied to examine resource variability among other population sub-groupings (e.g., by sex or phenotype).