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.     

Contrasting energy pathways at the community level as a consequence of regime shifts

Ecological regime shifts typically result in abrupt changes in ecosystem structure through several trophic levels, which leads to rapid ecosystem reconfiguration between regimes. An interesting aspect of the impact of regime shift is that alternative regimes may induce distinct shifts in energy pathways; these have been less tested than structural changes. This paper addresses this by using stable isotopes to establish the energy pathways in fish communities. We specifically focus on the impact of regime shift on changes of the energy pathways, and how the magnitude and direction of these changes affect the local community. We found that energy pathways significantly varied among the planktivorous, benthivorous, and piscivorous trophic guilds as a result of the alternative regimes. The regime shift from a clear to a turbid state altered the food web towards planktonic energy pathways and truncated food chain length, which is indicative of less ecological efficiency. This was confirmed by the adaptive foraging strategies of prevalent omnivores in the current communities. These structural and functional characteristics of trophic interactions might not facilitate classic trophic cascading effects in such a turbid regime and suppress the system’s response to environmental changes, e.g., nutrient loading, and restoration efforts in turbid to clear water regime shifts.     

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.     

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.     

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.     

Shifts in the dynamics of productivity signal ecosystem state transitions at the biome‐scale

Understanding ecosystem dynamics and predicting directional changes in ecosystem in response to global changes are ongoing challenges in ecology. Here we present a framework that links productivity dynamics and ecosystem state transitions based on a spatially continuous dataset of aboveground net primary productivity (ANPP) from the temperate grassland of China. Across a regional precipitation gradient, we quantified spatial patterns in ANPP dynamics (variability, asymmetry and sensitivity to rainfall) and related these to transitions from desert to semi‐arid to mesic steppe. We show that these three indices of ANPP dynamics displayed distinct spatial patterns, with peaks signalling transitions between grassland types. Thus, monitoring shifts in ANPP dynamics has the potential for predicting ecosystem state transitions in the future. Current ecosystem models fail to capture these dynamics, highlighting the need to incorporate more nuanced ecological controls of productivity in models to forecast future ecosystem shifts.     

Theoretical basis for predicting climate-induced abrupt shifts in the oceans

Among the responses of marine species and their ecosystems to climate change, abrupt community shifts (ACSs), also called regime shifts, have often been observed. However, despite their effects for ecosystem functioning and both provisioning and regulating services, our understanding of the underlying mechanisms involved remains elusive. This paper proposes a theory showing that some ACSs originate from the interaction between climate-induced environmental changes and the species ecological niche. The theory predicts that a substantial stepwise shift in the thermal regime of a marine ecosystem leads indubitably to an ACS and explains why some species do not change during the phenomenon. It also explicates why the timing of ACSs may differ or why some studies may detect or not detect a shift in the same ecosystem, independently of the statistical method of detection and simply because they focus on different species or taxonomic groups. The present theory offers a way to predict future climate-induced community shifts and their potential associated trophic cascades and amplifications.     

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.     

Precursors of state transitions in stochastic systems with delay

Ecosystem dynamics may exhibit alternative stable states induced by positive feedbacks between the state of the system and environmental drivers. Bistable systems are prone to abrupt shifts from one state to another in response to even small and gradual changes in external drivers. These transitions are often catastrophic and difficult to predict by analyzing the mean state of the system. Indicators of the imminent occurrence of phase transitions can serve as important tools to warn ecosystem managers about an imminent transition before the bifurcation point is actually reached. Thus, leading indicators of phase transitions can be used either to prepare for or to prevent the occurrence of a shift to the other state. In recent years, theories of leading indicators of ecosystem shift have been developed and applied to a variety of ecological models and geophysical time series. It is unclear, however, how some of these indicators would perform in the case of systems with a delay. Here, we develop a theoretical framework for the investigation of precursors of state shift in the presence of drivers acting with a delay. We discuss how the effectiveness of leading indicators of state shift based on rising variance may be affected by the presence of delays. We apply this framework to an ecological model of desertification in arid grasslands.     

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.     

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.     

Plant Invasions or Fire Policy: Which Has Altered Fire Behavior More in Tallgrass Prairie?

Human behavior has rapidly evolved from fire-promoting to aggressively attempting to minimize its magnitude and variability. This global shift in human behavior has contributed to the adoption of strict policies that govern the purposeful and planned use of fire in ecosystem science and management. However, it remains unclear the extent to which modern-day prescribed fire policies are altering the potential magnitude and variation of fire behavior in scientific investigations and ecosystem management. Here, we modeled the theoretical historical range of variability (ROV) in fire behavior for the tallgrass prairie ecosystem of North America. We then compared sensitivities in the magnitude and variation in the historical ROV in fire behavior as a result of (1) policies governing prescribed fire and (2) woody and herbaceous plant invasions. Although considerably more attention has focused on changes in fire behavior as a result of biological invasions, our model demonstrates that contemporary fire management policies can meet or surpass these effects. Policies governing prescribed fire management in tallgrass prairie reduced the magnitude and variability of surface fire behavior more than tall fescue invasion and rivaled reductions in fire behavior from decades of Juniperus encroachment. Consequently, fire and its potential as a driver of ecosystem dynamics has been simplified in the study and management of this system, which may be contributing to misleading conclusions on the potential responses of many highly researched environmental priorities. We emphasize the need to study changes in fire dynamics as a function of both social and ecological drivers, in an effort to advance our basic understanding of the role of fire in nature and its potential usefulness in ecosystem management.     

Temporal trends and variability in a high-arctic ecosystem in Greenland: multidimensional analyses of limnic and terrestrial ecosystems

The high arctic is undergoing a faster change in climate than most other regions of the planet, with already observed ecological consequences. Combined with the characteristics of high-arctic ecosystems, such as low species redundancy, high seasonality and weather extremes, shifts in individual species performance and phenology may lead to altered interaction dynamics through trophic mismatch and cascades. An ecosystem approach is therefore desirable in the attempt to understand the multidimensional impacts of climate. Here, we present ecosystem-wide trend analyses of a long-term dataset on terrestrial and limnic biota with focus on the distribution of observed trends and associated variation across the ecosystem. We used 114 time series drawn from 11 abiotic variables, 19 terrestrial and 7 limnic biotic species/taxa and compared temporal trends, changes and abrupt shifts in the variation within and across the two biota. A total of 36 % of the time series analysed showed a significant trend during the study period with a higher frequency of trends occurring within performance variables. Overall, the changes tended to be negative, indicating advances in phenology but reduced species performance. General system variance was also higher in the limnic biota than in the terrestrial biota, both exhibiting increasing variance up through the trophic system. Overall, our results suggest that multiple biotic responses to the climatic changes in this high-arctic ecosystem are not synchronised across trophic levels and may differ qualitatively and quantitatively between terrestrial and limnic biota.     

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。     

Ecosystem feedbacks constrain the effect of day-to-day weather variability on land–atmosphere carbon exchange

Whole-ecosystem interactions and feedbacks constrain ecosystem responses to environmental change. The effects of these constraints on responses to climate trends and extreme weather events have been well studied. Here we examine how these constraints respond to changes in day-to-day weather variability without changing the long-term mean weather. Although environmental variability is recognized as a critical factor affecting ecological function, the effects of climate change on day-to-day weather variability and the resultant impacts on ecosystem function are still poorly understood. Changes in weather variability can alter the mean rates of individual ecological processes because many processes respond non-linearly to environmental drivers. We assessed how these individual-process responses to changes in day-to-day weather variability interact with one another at an ecosystem level. We examine responses of arctic tundra to changes in weather variability using stochastic simulations of daily temperature, precipitation, and light to drive a biogeochemical model. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates in our model. However, responses of some processes (e.g., respiration) were inconsistent with expectations because ecosystem feedbacks can moderate, or even reverse, direct process responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere; less variability led to higher carbon sequestration on land. The magnitude of modeled ecosystem response to weather variability was comparable to that predicted for the effects of climate mean trends by the end of the century.     

Knowing when (not) to attempt ecological restoration

Here we argue that there are two important steps in the decision process to restore ecological system that are often ignored. First, consideration of restoration is in response to observed change in a system, but ecological systems can fluctuate widely in their normal dynamic. Thus, there is an imperative to interpret ecological change; shifts in community structure that represent “typical” fluctuations in a properly functioning ecosystem do not warrant restoration, while change associated with phase shift in the system may well demand restoration action. Second, where restoration effort is warranted, it needs to be determined whether management responses are likely to be successful within resource constraints. Where ecological change involves pronounced hysteresis, even massive effort may have little chance in effecting recovery to a preferred ecosystem state. Theory and models indicate that consideration of the characteristic length scales (CLSs) of ecological systems provides an unambiguous interpretation of ecological change, enabling differentiation of “typical” fluctuations from phase shift, and here we show that CLSs can be calculated for real communities from their species' dynamics, and that their behavior is as predicted from theory. We also show that for ecological systems where local interactions and forcings are well understood, validated simulation models can provide a ready means to identify hysteresis and estimate its magnitude. We conclude that there are useful tools available for ecologists to address the key questions of (1) whether restoration attempts are warranted in the first place and, if they are, (2) whether it is practical to pursue them.     

Shifts in fisheries management: adapting to regime shifts

For many years, fisheries management was based on optimizing yield and maintaining a target biomass, with little regard given to low-frequency environmental forcing. However, this policy was often unsuccessful. In the last two to three decades, fisheries science and management have undergone a shift towards balancing sustainable yield with conservation, with the goal of including ecosystem considerations in decision-making frameworks. Scientific understanding of low-frequency climate–ocean variability, which is manifested as ecosystem regime shifts and states, has led to attempts to incorporate these shifts and states into fisheries assessment and management. To date, operationalizing these attempts to provide tactical advice has met with limited success. We review efforts to incorporate regime shifts and states into the assessment and management of fisheries resources, propose directions for future investigation and outline a potential framework to include regime shifts and changes in ecosystem states into fisheries management.     

Hydrologic variability in dryland regions: impacts on ecosystem dynamics and food security

Research on ecosystem and societal response to global environmental change typically considers the effects of shifts in mean climate conditions. There is, however, some evidence of ongoing changes also in the variance of hydrologic and climate fluctuations. A relatively high interannual variability is a distinctive feature of the hydrologic regime of dryland regions, particularly at the desert margins. Hydrologic variability has an important impact on ecosystem dynamics, food security and societal reliance on ecosystem services in water-limited environments. Here, we investigate some of the current patterns of hydrologic variability in drylands around the world and review the major effects of hydrologic fluctuations on ecosystem resilience, maintenance of biodiversity and food security. We show that random hydrologic fluctuations may enhance the resilience of dryland ecosystems by obliterating bistable deterministic behaviours and threshold-like responses to external drivers. Moreover, by increasing biodiversity and the associated ecosystem redundancy, hydrologic variability can indirectly enhance post-disturbance recovery, i.e. ecosystem resilience.