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

Towards a Data-Driven Estimation of Resilience in Networked Dynamical Systems: Designing a Versatile Testbed

Estimating resilience of adaptive, networked dynamical systems remains a challenge. Resilience refers to a system's capacity "to absorb exogenous and/or endogenous disturbances and to reorganize while undergoing change so as to still retain essentially the same functioning, structure, and feedbacks." The majority of approaches to estimate resilience requires exact knowledge of the underlying equations of motion; the few data-driven approaches so far either lack appropriate strategies to verify their suitability or remain subject of considerable debate. We develop a testbed that allows one to modify resilience of a multistable networked dynamical system in a controlled manner. The testbed also enables generation of multivariate time series of system observables to evaluate the suitability of data-driven estimators of resilience. We report first findings for such an estimator.

Visualizing the features of inflection point shown on a temporal bar graph using the data of COVID-19 pandemic

BACKGROUND

Exponential-like infection growth leading to peaks (denoted by inflection points [IP] or turning points) is usually the hallmark of infectious disease outbreaks, including coronaviruses. To determine the IPs of the novel coronavirus (COVID-19), we applied the item response theory model to detect phase transitions for each country/region and characterize the IP feature on the temporal bar graph (TBG).

METHODS

The IP (using the item difficulty parameter to locate) was verified by the differential equation in calculus and interpreted by the TBG with 2 virtual and real empirical data (i.e., from Collatz conjecture and COVID-19 pandemic in 2020). Comparisons of IPs, R2, and burst strength [BS = ln() denoted by the infection number at IP(Nip) and the item slope parameter(a) in item response theory were made for countries/regions and continents on the choropleth map and the forest plot.

RESULTS

We found that the evolution of COVID-19 on the TBG makes the data clear and easy to understand, the shorter IP (=53.9) was in China and the longest (=247.3) was in Europe, and the highest R2 (as the variance explained by the model) was in the US, with a mean R2 of 0.98. We successfully estimated the IPs for countries/regions on COVID-19 in 2020 and presented them on the TBG.

CONCLUSION

Temporal visualization is recommended for researchers in future relevant studies (e.g., the evolution of keywords in a specific discipline) and is not merely limited to the IP search in COVID-19 pandemics as we did in this study.

Paleoecological Investigation of Vegetation, Climate and Fire History in, and Adjacent to, Kootenay National Park, Southeastern British Columbia, Canada

Paleoecological investigation of two montane lakes in the Kootenay region of southeast British Columbia, Canada, reveal changes in vegetation in response to climate and fire throughout the Holocene. Pollen, charcoal, and lake sediment carbon accumulation rate analyses show seven distinct zones at Marion Lake, presently in the subalpine Engelmann Spruce-Subalpine Fir (ESSF) biogeoclimatic (BEC) zone of Kootenay Valley, British Columbia. Comparison of these records to nearby Dog Lake of Kootenay National Park of Canada in the Montane Spruce (MS) BEC zone of Kootenay Valley, British Columbia reveals unique responses of ecosystems in topographically complex regions. The two most dramatic shifts in vegetation at Marion Lake occur firstly in the early Holocene/late Pleistocene in ML Zone 3 (11,010–10,180 cal. yr. B.P.) possibly reflecting Younger Dryas Chronozone cooling followed by early Holocene xerothermic warming noted by the increased presence of the dry adapted conifer, Douglas-fir (Pseudotsuga menziesii) and increasing fire frequency. The second most prominent change occurred at the transition from ML Zone 5 through 6a (∼2,500 cal. yr. B.P.). This zone transitions from a warmer to a cooler/wetter climate as indicated by the increase in western hemlock (Tsuga heterophylla) and subsequent drop in fire frequency. The overall cooling trend and reduction in fire frequency appears to have occurred ∼700 years later than at Dog Lake (∼43 km to the south and 80 m lower in elevation), resulting in a closed montane spruce forest, whereas Marion Lake developed into a subalpine ecosystem. The temporal and ecological differences between the two study sites likely reflects the particular climate threshold needed to move these ecosystems from developed forests to subalpine conditions, as well as local site climate and fire conditions. These paleoecological records indicate future warming may result in the MS transitioning into an Interior Douglas Fir (IDF) dominated landscape, while the ESSF may become more forested, similar to the modern MS, or develop into a grassland-like landscape dependent on fire frequency. These results indicate that climate and disturbance over a regional area can dictate very different localized vegetative states. Local management implications of these dynamic landscapes will need to understand how ecosystems respond to climate and disturbance at the local or ecosystem/habitat scale.

Permutation Entropy as a Universal Disorder Criterion: How Disorders at Different Scale Levels Are Manifestations of the Same Underlying Principle

What do bacteria, cells, organs, people, and social communities have in common? At first sight, perhaps not much. They involve totally different agents and scale levels of observation. On second thought, however, perhaps they share everything. A growing body of literature suggests that living systems at different scale levels of observation follow the same architectural principles and process information in similar ways. Moreover, such systems appear to respond in similar ways to rising levels of stress, especially when stress levels approach near-lethal levels. To explain such communalities, we argue that all organisms (including humans) can be modeled as hierarchical Bayesian controls systems that are governed by the same biophysical principles. Such systems show generic changes when taxed beyond their ability to correct for environmental disturbances. Without exception, stressed organisms show rising levels of 'disorder' (randomness, unpredictability) in internal message passing and overt behavior. We argue that such changes can be explained by a collapse of allostatic (high-level integrative) control, which normally synchronizes activity of the various components of a living system to produce order. The selective overload and cascading failure of highly connected (hub) nodes flattens hierarchical control, producing maladaptive behavior. Thus, we present a theory according to which organic concepts such as stress, a loss of control, disorder, disease, and death can be operationalized in biophysical terms that apply to all scale levels of organization. Given the presumed universality of this mechanism, 'losing control' appears to involve the same process anywhere, whether involving bacteria succumbing to an antibiotic agent, people suffering from physical or mental disorders, or social systems slipping into warfare. On a practical note, measures of disorder may serve as early warning signs of system failure even when catastrophic failure is still some distance away.

Early warning signal reliability varies with COVID-19 waves

Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the emergence of disease outbreaks, offering hope that policymakers can make predictive rather than reactive management decisions. Here, using a novel, sequential analysis in combination with daily COVID-19 case data across 24 countries, we suggest that composite EWSs consisting of variance, autocorrelation and skewness can predict nonlinear case increases, but that the predictive ability of these tools varies between waves based upon the degree of critical slowing down present. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policymakers to improve the accuracy of urgent intervention decisions but best characterize hypothesized critical transitions.

Abrupt shifts in 21st-century plankton communities

Marine microbial communities sustain ocean food webs and mediate global elemental cycles. These communities will change with climate; these changes can be gradual or foreseeable but likely have much more substantial consequences when sudden and unpredictable. In a complex virtual marine microbial ecosystem, we find that climate change–driven shifts over the 21st century are often abrupt, large in amplitude and extent, and unpredictable using standard early warning signals. Phytoplankton with unique resource needs, especially fast-growing species such as diatoms, are more prone to abrupt shifts. Abrupt shifts in biomass, productivity, and community structure are concentrated in Atlantic and Pacific subtropics. Abrupt changes in environmental variables such as temperature and nutrients rarely precede these ecosystem shifts, indicating that rapid community restructuring can occur in response to gradual environmental changes, particularly in nutrient supply rate ratios.

Critical transitions and ecological resilience of large marine ecosystems in the Northwestern Pacific in response to global warming

Natural systems can undergo critical transitions, leading to substantial socioeconomic and ecological outcomes. "Ecological resilience" has been proposed to describe the capacity of natural systems to absorb external perturbation and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedbacks. However, the mere application of ecological resilience in theoretical research and the lack of quantitative approaches present considerable obstacles for predicting critical transitions and understanding their mechanisms. Large marine ecosystems (LMEs) in the Northwestern Pacific are characterized by great biodiversity and productivity, as well as remarkable warming in recent decades. However, no information is available on the critical transitions and ecological resilience of LMEs in response to warming. Therefore, we applied an integrated resilience assessment framework to fisheries catch data from seven LMEs covering a wide range of regions, from tropical to subarctic, in the Northwestern Pacific to identify critical transitions, assess ecological resilience, and reconstruct folded stability landscapes, with a specific focus on the effects of warming. The results provide evidence of the occurrence of critical transitions, with fold bifurcation and hysteresis in response to increasing sea surface temperatures (SSTs) in the seven LMEs. In addition, these LMEs show similarities and synchronies in structure variations and critical transitions forced by warming. Both dramatic increases in SST and small fluctuations at the corresponding thresholds may trigger critical transitions. Ecological resilience decreases when approaching the tipping points and is repainted as the LMEs shift to alternative stable states with different resilient dynamics. Folded stability landscapes indicate that the responses of LMEs to warming are discontinuous, which may be caused by the reorganization of LMEs as their sensitivity to warming changes. Our study clarifies the nonlinear responses of LMEs to anthropogenic warming and provides examples of quantifying ecological resilience in empirical systems at unprecedented spatial and temporal scales.

River connectivity and climate behind the long-term evolution of tropical American floodplain lakes

This study presents the long-term evolution of two floodplains lakes (San Juana and Barbacoas) of the Magdalena River in Colombia with varying degree of connectivity to the River and with different responses to climate events (i.e., extreme floods and droughts). Historical limnological changes were identified through a multiproxy-based reconstruction including diatoms, sedimentation, and sediment geochemistry, while historical climatic changes were derived from the application of the Standardised Precipitation-Evapotranspiration Index. The main gradients in climatic and limnological change were assessed via multivariate analysis and generalized additive models. The reconstruction of the more isolated San Juana Lake spanned the last c. 500 years. Between c. 1,620 and 1,750 CE, riverine-flooded conditions prevailed as indicated by high detrital input, reductive conditions, and dominance of planktonic diatoms. Since the early 1800s, the riverine meander became disconnected, conveying into a marsh-like environment rich in aerophil diatoms and organic matter. The current lake was then formed around the mid-1960s with a diverse lake diatom flora including benthic and planktonic diatoms, and more oxygenated waters under a gradual increase in sedimentation and nutrients. The reconstruction for Barbacoas Lake, a waterbody directly connected to the Magdalena River, spanned the last 60 years and showed alternating riverine-wetland-lake conditions in response to varying ENSO conditions. Wet periods were dominated by planktonic and benthic diatoms, while aerophil diatom species prevailed during dry periods; during the two intense ENSO periods of 1987 and 1992, the lake almost desiccated and sedimentation rates spiked. A gradual increase in sedimentation rates post-2000 suggests that other factors rather than climate are also influencing sediment deposition in the lake. We propose that hydrological connectivity to the Magdalena River is a main factor controlling lake long-term responses to human pressures, where highly connected lakes respond more acutely to ENSO events while isolated lakes are more sensitive to local land-use changes.

Distinctive patterns and signals at major environmental events and collapse zone boundaries

We studied the patterns of pre-collapse communities, the small-scale and the large-scale signals of collapses, and the environmental events before the collapses using four paleoecological and one modern data series. We applied and evaluated eight indicators in our analysis: the relative abundance of species, hierarchical cluster analysis, principal component analysis, total abundance, species richness, standard deviation (without a rolling window), first-order autoregression, and the relative abundance of the dominant species. We investigated the signals at the probable collapse triggering unusual environmental events and at the collapse zone boundaries, respectively. We also distinguished between pulse and step environmental events to see what signals the indicators give at these two different types of events. Our results show that first-order autoregression is not a good environmental event indicator, but it can forecast or indicate the collapse zones in climate change. The rest of the indicators are more sensitive to the pulse events than to the step events. Step events during climate change might have an essential role in initiating collapses. These events probably push the communities with low resilience beyond a critical threshold, so it is crucial to detect them. Before collapses, the total abundance and the species richness increase, the relative abundance of the species decreases. The hierarchical cluster analysis and the relative abundance of species together designate the collapse zone boundaries. We suggest that small-scale signals should be involved in analyses because they are often earlier than large-scale signals.

Deep learning for early warning signals of tipping points

Early warning signals (EWS) of tipping points are vital to anticipate system collapse or other sudden shifts. However, existing generic early warning indicators designed to work across all systems do not provide information on the state that lies beyond the tipping point. Our results show how deep learning algorithms (artificial intelligence) can provide EWS of tipping points in real-world systems. The algorithm predicts certain qualitative aspects of the new state, and is also more sensitive and generates fewer false positives than generic indicators. We use theory about system behavior near tipping points so that the algorithm does not require data from the study system but instead learns from a universe of possible models.

Many natural systems exhibit tipping points where slowly changing environmental conditions spark a sudden shift to a new and sometimes very different state. As the tipping point is approached, the dynamics of complex and varied systems simplify down to a limited number of possible “normal forms” that determine qualitative aspects of the new state that lies beyond the tipping point, such as whether it will oscillate or be stable. In several of those forms, indicators like increasing lag-1 autocorrelation and variance provide generic early warning signals (EWS) of the tipping point by detecting how dynamics slow down near the transition. But they do not predict the nature of the new state. Here we develop a deep learning algorithm that provides EWS in systems it was not explicitly trained on, by exploiting information about normal forms and scaling behavior of dynamics near tipping points that are common to many dynamical systems. The algorithm provides EWS in 268 empirical and model time series from ecology, thermoacoustics, climatology, and epidemiology with much greater sensitivity and specificity than generic EWS. It can also predict the normal form that characterizes the oncoming tipping point, thus providing qualitative information on certain aspects of the new state. Such approaches can help humans better prepare for, or avoid, undesirable state transitions. The algorithm also illustrates how a universe of possible models can be mined to recognize naturally occurring tipping points.

Understanding the critical rate of environmental change for ecosystems, cyanobacteria as an example

Recently it has been show that in some ecosystems fast rates of change of environmental drivers may trigger a critical transition, whereas change of the same magnitude but at slower rates would not. So far, few studies describe this phenomenon of rate-induced tipping, while it is important to understand this phenomenon in the light of the ongoing rapid environmental change. Here, we demonstrate rate-induced tipping in a simple model of cyanobacteria with realistic parameter settings. We explain graphically that there is a range of initial conditions at which a gradual increase in environmental conditions can cause a collapse of the population, but only if the change is fast enough. In addition, we show that a pulse in the environmental conditions can cause a temporary collapse, but that is dependent on both the rate and the duration of the pulse. Furthermore, we study whether the autocorrelation of stochastic environmental conditions can influence the probability of inducing rate-tipping. As both the rate of environmental change, and autocorrelation of the environmental variability are increasing in parts of the climate, the probability for rate-induced tipping to occur is likely to increase. Our results imply that, even though the identification of rate sensitive ecosystems in the real world will be challenging, we should incorporate critical rates of change in our ecosystem assessments and management.

Local climate and biodiversity affect the stability of China's grasslands in response to drought

The stability of ecosystems is of great significance to the supply of ecosystem services and human well-being. Frequently occurring drought events seriously threaten the stability of terrestrial ecosystems. In particular, in grasslands with low rainfall, ecosystems are more vulnerable to drought. To date, most studies have focused on forest ecosystems, while the difference in the stability of various types of grassland ecosystems under drought is less studied. Here, we selected China's grasslands as the study system and used the standardized precipitation evapotranspiration index (SPEI) to identify drought years and drought events (2001-2015) that occurred in China. Subsequently, we used the satellite-based enhanced vegetation index (EVI) to calculate the resistance (the ability to maintain the original EVI level in a drought year), resilience (the capacity of ecosystem functioning to recover to its normal state after a drought year), and recovery time (how long an ecosystem requires to recover to its predrought EVI) of different grassland types in China from 2001 to 2015. Finally, random forest analysis was used to identify the factors affecting the spatial patterns of the three indicators of stability. The results showed that the grassland ecosystem vulnerability to drought was significantly different among grassland types. The alpine steppe and alpine meadow ecosystems located on the Qinghai-Tibet Plateau have the strongest resistance, the weakest resilience, and the longest recovery time. The meadow steppe and typical steppe ecosystems located in Inner Mongolia have the weakest resistance, the strongest resilience, and the shortest recovery time. The stability of grassland ecosystems is mainly affected by the characteristics of drought events (drought severity and duration), local climate factors (precipitation and temperature), and biodiversity. These results provide a scientific basis for taking appropriate management measures to address the impacts of future drought events on various types of grassland ecosystems.

A resilience concept based on system functioning: A dynamical systems perspective

We introduce a new framework for resilience, which is traditionally understood as the ability of a system to absorb disturbances and maintain its state, by proposing a shift from a state-based to a system functioning-based approach to resilience, which takes into account that several different coexisting stable states could fulfill the same functioning. As a consequence, not every regime shift, i.e., transition from one stable state to another, is associated with a lack or loss of resilience. We emphasize the importance of flexibility-the ability of a system to shift between different stable states while still maintaining system functioning. Furthermore, we provide a classification of system responses based on the phenomenological properties of possible disturbances, including the role of their timescales. Therefore, we discern fluctuations, shocks, press disturbances, and trends as possible disturbances. We distinguish between two types of mechanisms of resilience: (i) tolerance and flexibility, which are properties of the system, and (ii) adaptation and transformation, which are processes that alter the system's tolerance and flexibility. Furthermore, we discuss quantitative methods to investigate resilience in model systems based on approaches developed in dynamical systems theory.

Evaluating the performance of multivariate indicators of resilience loss

Various complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These 'tipping points' are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.

Loss of resilience preceded transformations of pre-Hispanic Pueblo societies

Collapse of civilizations remains one of the most enigmatic phenomena in human history. In this paper we provide quantitative evidence that loss of resilience systematically preceded collapses. We take advantage of unique time series documenting both construction activity and climate conditions for pre-Columbian societies of the southwestern United States on an annual basis over a period of eight centuries. These data cover five transformations encompassing shifts to novel constellations of beliefs, social practices, and styles of art and architecture. The remarkable high-resolution time series allowed us to quantify the dynamics of social fragility using numerical techniques for probing resilience. Our results demonstrate that all but one of these transformations happened after decades of rising social instability.

Climate extremes are thought to have triggered large-scale transformations of various ancient societies, but they rarely seem to be the sole cause. It has been hypothesized that slow internal developments often made societies less resilient over time, setting them up for collapse. Here, we provide quantitative evidence for this idea. We use annual-resolution time series of building activity to demonstrate that repeated dramatic transformations of Pueblo cultures in the pre-Hispanic US Southwest were preceded by signals of critical slowing down, a dynamic hallmark of fragility. Declining stability of the status quo is consistent with archaeological evidence for increasing violence and in some cases, increasing wealth inequality toward the end of these periods. Our work thus supports the view that the cumulative impact of gradual processes may make societies more vulnerable through time, elevating the likelihood that a perturbation will trigger a large-scale transformation that includes radically rejecting the status quo and seeking alternative pathways.

No warning for slow transitions

A rise in fragility as a system approaches a tipping point may be sometimes estimated using dynamical indicators of resilience (DIORs) that measure the characteristic slowing down of recovery rates before a tipping point. A change in DIORs could be interpreted as an early warning signal for an upcoming critical transition. However, in order to be able to estimate the DIORs, observational records need to be long enough to capture the response rate of the system. As we show here, the required length of the time series depends on the response rates of the system. For instance, the current rate of anthropogenic climate forcing is fast relative to the response rate of some parts of the climate system. Therefore, we may expect difficulties estimating the resilience from modern time series. So far, there have been no systematic studies of the effects of the response rates of the dynamical systems and the rates of forcing on the detectability trends in the DIORs prior to critical transitions. Here, we quantify the performance of the resilience indicators variance and temporal autocorrelation, in systems with different response rates and for different rates of forcing. Our results show that the rapid rise of anthropogenic forcing to the Earth may make it difficult to detect changes in the resilience of ecosystems and climate elements from time series. These findings suggest that in order to determine with models whether the use of the DIORs is appropriate, we need to use realistic models that incorporate the key processes with the appropriate time constants.

Estimating Ecological Responses to Climatic Variability on Reclaimed and Unmined Lands Using Enhanced Vegetation Index

Climatic impact on re-established ecosystems at reclaimed mined lands may have changed. However, little knowledge is available about the difference in vegetation–climate relationships between reclaimed and unmined lands. In this study, ecological responses to climatic variability on reclaimed and neighbouring unmined lands were estimated using remote-sensing data at the Pingshuo Mega coal mine, one of the largest coal mines with long-term reclamation history in China. Time-series MODIS enhanced vegetation index (EVI) data and meteorological data from 1997 to 2017 were collected. Results show significantly different vegetation–climate relationships between reclaimed and unmined lands. First, the accumulation periods of all climatic variables were much longer on reclaimed mining lands. Second, vegetation on reclaimed lands responded to variabilities in temperature, rainfall, air humidity, and wind speed, while undisturbed vegetation only responded to variabilities of temperature and air humidity. Third, climatic variability made a much higher contribution to EVI variation on reclaimed land (20.0–46.5%) than on unmined land (0.7–1.7%). These differences were primarily caused by limited ecosystem resilience, and changed site hydrology and microclimate on reclaimed land. Thus, this study demonstrates that the legacy effects of surface mining can critically change on-site vegetation–climate relationships, which impacts the structure, functions, and stability of reclaimed ecosystems. Vegetation–climate relationships of reclaimed ecosystems deserve further research, and remote-sensing vegetation data are an effective source for relevant studies.

Variations in stability revealed by temporal asymmetries in contraction of phase space flow

Empirical diagnosis of stability has received considerable attention, often focused on variance metrics for early warning signals of abrupt system change or delicate techniques measuring Lyapunov spectra. The theoretical foundation for the popular early warning signal approach has been limited to relatively simple system changes such as bifurcating fixed points where variability is extrinsic to the steady state. We offer a novel measurement of stability that applies in wide ranging systems that contain variability in both internal steady state dynamics and in response to external perturbations. Utilizing connections between stability, dissipation, and phase space flow, we show that stability correlates with temporal asymmetry in a measure of phase space flow contraction. Our method is general as it reveals stability variation independent of assumptions about the nature of system variability or attractor shape. After showing efficacy in a variety of model systems, we apply our technique for measuring stability to monthly returns of the S&P 500 index in the time periods surrounding the global stock market crash of October 1987. Market stability is shown to be higher in the several years preceding and subsequent to the 1987 market crash. We anticipate our technique will have wide applicability in climate, ecological, financial, and social systems where stability is a pressing concern.

Sports Injury Forecasting and Complexity: A Synergetic Approach

The understanding that sports injury is the result of the interaction among many factors and that specific profiles could increase the risk of the occurrence of a given injury was a significant step in establishing programs for injury prevention. However, injury forecasting is far from being attained. To be able to estimate future states of a complex system (forecasting), it is necessary to understand its nature and comply with the methods usually used to analyze such a system. In this sense, sports injury forecasting must implement the concepts and tools used to study the behavior of self-organizing systems, since it is by self-organizing that systems (i.e., athletes) evolve and adapt (or not) to a constantly changing environment. Instead of concentrating on the identification of factors related to the injury occurrence (i.e., risk factors), a complex systems approach looks for the high-order variables (order parameters) that describe the macroscopic dynamic behavior of the athlete. The time evolution of this order parameter informs on the state of the athlete and may warn about upcoming events, such as injury. In this article, we describe the fundamental concepts related to complexity based on physical principles of self-organization and the consequence of accepting sports injury as a complex phenomenon. In the end, we will present the four steps necessary to formulate a synergetics approach based on self-organization and phase transition to sports injuries. Future studies based on this experimental paradigm may help sports professionals to forecast sports injuries occurrence.

Sediment availability provokes a shift from Brownian to Lévy-like clonal expansion in a dune building grass

In biogeomorphic landscapes, plant traits can steer landscape development through plant-mediated feedback interactions. Interspecific differences in clonal expansion strategy can therefore lead to the emergence of different landscape organisations. Yet, whether landscape-forming plants adopt different clonal expansion strategies depending on their physical environment remains to be tested. Here, we use a field survey and a complementary mesocosm approach to investigate whether sediment deposition affects the clonal expansion strategy employed by dune-building marram grass individuals. Our results reveal a consistent shift in expansion pattern from more clumped, Brownian-like, movement in sediment-poor conditions, to patchier, Lévy-like, movement under high sediment supply rates. Additional model simulations illustrate that the sediment-dependent shift in movement strategies induces a shift in optimisation of the cost-benefit relation between landscape engineering (i.e. dune formation) and expansion. Plasticity in expansion strategy may therefore allow landscape-forming plants to optimise their engineering ability depending on their physical landscape.

Merging dynamical and structural indicators to measure resilience in multispecies systems

Resilience is broadly understood as the ability of an ecological system to resist and recover from perturbations acting on species abundances and on the system's structure. However, one of the main problems in assessing resilience is to understand the extent to which measures of recovery and resistance provide complementary information about a system. While recovery from abundance perturbations has a strong tradition under the analysis of dynamical stability, it is unclear whether this same formalism can be used to measure resistance to structural perturbations (e.g. perturbations to model parameters). Here, we provide a framework grounded on dynamical and structural stability in Lotka-Volterra systems to link recovery from small perturbations on species abundances (i.e. dynamical indicators) with resistance to parameter perturbations of any magnitude (i.e. structural indicators). We use theoretical and experimental multispecies systems to show that the faster the recovery from abundance perturbations, the higher the resistance to parameter perturbations. We first use theoretical systems to show that the return rate along the slowest direction after a small random abundance perturbation (what we call full recovery) is negatively correlated with the largest random parameter perturbation that a system can withstand before losing any species (what we call full resistance). We also show that the return rate along the second fastest direction after a small random abundance perturbation (what we call partial recovery) is negatively correlated with the largest random parameter perturbation that a system can withstand before at most one species survives (what we call partial resistance). Then, we use a dataset of experimental microbial systems to confirm our theoretical expectations and to demonstrate that full and partial components of resilience are complementary. Our findings reveal that we can obtain the same level of information about resilience by measuring either a dynamical (i.e. recovery) or a structural (i.e. resistance) indicator. Irrespective of the chosen indicator (dynamical or structural), our results show that we can obtain additional information by separating the indicator into its full and partial components. We believe these results can motivate new theoretical approaches and empirical analyses to increase our understanding about risk in ecological systems.

Reflections on psychological resilience: a comparison of three conceptually different operationalizations in predicting mental health

BACKGROUND

Psychological resilience refers to the ability to maintain mental health or recover quickly after stress. Despite the popularity of resilience research, there is no consensus understanding or operationalization of resilience.

OBJECTIVE

We plan to compare three indicators of resilience that each involve a different operationalization of the construct: a) General resilience or one's self-reported general ability to overcome adversities; b) Daily resilience as momentarily experienced ability to overcome adversities; and c) Recovery speed evident in the pattern of negative affect recovery after small adversities in daily life. These three indicators are constructed per person to investigate their cross-sectional associations, stability over time, and predictive validity regarding mental health.

METHODS

Data will be derived from the prospective MIRORR study that comprises 96 individuals at different levels of psychosis risk and contains both single-time assessed questionnaires and 90-days intensive longitudinal data collection at baseline (T0) and three yearly follow-up waves (T1-T3). General resilience is assessed using the Brief Resilience Scale (BRS) at baseline. Daily resilience is measured by averaging daily resilience scores across 90 days. For recovery speed, vector-autoregressive models with consecutive impulse response simulations will be applied to diary data on negative affect and daily stressors to calculate pattern of affect recovery. These indicators will be correlated concurrently (at T0) to assess their overlap and prospectively (between T0 and T1) to estimate their stability. Their predictive potential will be assessed by regression analysis with mental health (SCL-90) as an outcome, resilience indicators as predictors, and stressful life events as a moderator.

CONCLUSION

The comparison of different conceptualizations of psychological resilience can increase our understanding of its multifaceted nature and, in future, help improve diagnostic, prevention and intervention strategies aimed at increasing psychological resilience.

Facultative mutualisms: A double-edged sword for foundation species in the face of anthropogenic global change

Ecosystems worldwide depend on habitat-forming foundation species that often facilitate themselves with increasing density and patch size, while also engaging in facultative mutualisms. Anthropogenic global change (e.g., climate change, eutrophication, overharvest, land-use change), however, is causing rapid declines of foundation species-structured ecosystems, often typified by sudden collapse. Although disruption of obligate mutualisms involving foundation species is known to precipitate collapse (e.g., coral bleaching), how facultative mutualisms (i.e., context-dependent, nonbinding reciprocal interactions) affect ecosystem resilience is uncertain. Here, we synthesize recent advancements and combine these with model analyses supported by real-world examples, to propose that facultative mutualisms may pose a double-edged sword for foundation species. We suggest that by amplifying self-facilitative feedbacks by foundation species, facultative mutualisms can increase foundation species' resistance to stress from anthropogenic impact. Simultaneously, however, mutualism dependency can generate or exacerbate bistability, implying a potential for sudden collapse when the mutualism's buffering capacity is exceeded, while recovery requires conditions to improve beyond the initial collapse point (hysteresis). Thus, our work emphasizes the importance of acknowledging facultative mutualisms for conservation and restoration of foundation species-structured ecosystems, but highlights the potential risk of relying on mutualisms in the face of global change. We argue that significant caveats remain regarding the determination of these feedbacks, and suggest empirical manipulation across stress gradients as a way forward to identify related nonlinear responses.

A unifying framework for studying and managing climate-driven rates of ecological change

During the Anthropocene and other eras of rapidly changing climates, rates of change of ecological systems can be described as fast, slow or abrupt. Fast ecological responses closely track climate change, slow responses substantively lag climate forcing, causing disequilibria and reduced fitness, and abrupt responses are characterized by nonlinear, threshold-type responses at rates that are large relative to background variability and forcing. All three kinds of climate-driven ecological dynamics are well documented in contemporary studies, palaeoecology and invasion biology. This fast-slow-abrupt conceptual framework helps unify a bifurcated climate-change literature, which tends to separately consider the ecological risks posed by slow or abrupt ecological dynamics. Given the prospect of ongoing climate change for the next several decades to centuries of the Anthropocene and wide variations in ecological rates of change, the theory and practice of managing ecological systems should shift attention from target states to target rates. A rates-focused framework broadens the strategic menu for managers to include options to both slow and accelerate ecological rates of change, seeks to reduce mismatch among climate and ecological rates of change, and provides a unified conceptual framework for tackling the distinct risks associated with fast, slow and abrupt ecological rates of change.

Resilience of Spanish forests to recent droughts and climate change

A widespread increase in forest cover is underway in northern Mediterranean forests because of land abandonment and decreased wood demand, but the resilience of these successional forests to climate change remains unresolved. Here we use 18-year time series of canopy greenness derived from satellite imagery (NDVI) to evaluate the impacts of climate change on Spain's forests. Specifically, we analyzed how NDVI was influenced by the climatic water balance (i.e. Standardized Precipitation-Evapotranspiration Index, SPEI), using monthly time-series extracted from 3,100 pixels of forest, categorized into ten forest types. The forests increased in leaf area index by 0.01 per year on average (from 1.7 in 2000 to 1.9 in 2017) but there was enormous variation among years related to climatic water balance. Forest types varied in response to drought events: those dominated by drought-avoiding species showed strong covariance between greenness and SPEI, while those dominated by drought-tolerant species showed weak covariance. Native forests usually recovered more than 80% of greenness within the 18 months and the remainder within 5 years, but plantations of Eucalyptus were less resilient. Management to increase the resilience of forests-a key goal of forestry in the Mediterranean region-appears to have had a positive effect: canopy greenness within protected forests was more resilient to drought than within non-protected forests. In conclusion, many of Spain's successional forests have been resilient to drought over the past 18 years, from the perspective of space. Future studies will need to combine remote sensing with field-based analyses of physiological tolerances and mortality processes to understand how Mediterranean forests will respond to the rapid climate change predicted for this region in the coming decades.

Personality, Resilience, and Psychopathology: A Model for the Interaction between Slow and Fast Network Processes in the Context of Mental Health

Network theories have been put forward for psychopathology (in which mental disorders originate from causal relations between symptoms) and for personality (in which personality factors originate from coupled equilibria of cognitions, affect states, behaviours, and environments). Here, we connect these theoretical strands in an overarching personality–resilience–psychopathology model. In this model, factors in personality networks control the shape of the dynamical landscape in which symptom networks evolve; for example, the neuroticism item ‘I often feel blue’ measures a general tendency to experience negative affect, which is hypothesized to influence the threshold parameter of the symptom ‘depressed mood’ in the psychopathology network. Conversely, events at the level of the fast–evolving psychopathology network (e.g. a depressive episode) can influence the slow–evolving personality variables (e.g. by increasing feelings of worthlessness). We apply the theory to neuroticism and major depressive disorder. Through simulations, we show that the model can accommodate important phenomena, such as the strong relation between neuroticism and depression and individual differences in the change of neuroticism levels and development of depression over time. The results of the simulation are implemented in an online, interactive simulation tool. Implications for research into the relationship between personality and psychopathology are discussed. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology

Predicting the Outbreak Risks and Inflection Points of COVID-19 Pandemic with Classic Ecological Theories

Predicting the outbreak risks and/or the inflection (turning or tipping) points of COVID-19 can be rather challenging. Here, it is addressed by modeling and simulation approaches guided by classic ecological theories and by treating the COVID-19 pandemic as a metapopulation dynamics problem. Three classic ecological theories are harnessed, including TPL (Taylor's power-law) and Ma's population aggregation critical density (PACD) for spatiotemporal aggregation/stability scaling, approximating virus metapopulation dynamics with Hubbell's neutral theory, and Ma's diversity-time relationship adapted for the infection-time relationship. Fisher-Information for detecting critical transitions and tipping points are also attempted. It is discovered that: (i) TPL aggregation/stability scaling parameter (b > 2), being significantly higher than the b-values of most macrobial and microbial species including SARS, may interpret the chaotic pandemic of COVID-19. (ii) The infection aggregation critical threshold (M 0) adapted from PACD varies with time (outbreak-stage), space (region) and public-health interventions. Exceeding M 0, local contagions may become aggregated and connected regionally, leading to epidemic/pandemic. (iii) The ratio of fundamental dispersal to contagion numbers can gauge the relative importance between local contagions vs. regional migrations in spreading infections. (iv) The inflection (turning) points, pair of maximal infection number and corresponding time, are successfully predicted in more than 80% of Chinese provinces and 68 countries worldwide, with a precision >80% generally.

Decrease in social cohesion in a colonial seabird under a perturbation regime

Social interactions, through influence on behavioural processes, can play an important role in populations’ resilience (i.e. ability to cope with perturbations). However little is known about the effects of perturbations on the strength of social cohesion in wild populations. Long-term associations between individuals may reflect the existence of social cohesion for seizing the evolutionary advantages of social living. We explore the existence of social cohesion and its dynamics under perturbations by analysing long-term social associations, in a colonial seabird, the Audouin’s gull Larus audouinii, living in a site experiencing a shift to a perturbed regime. Our goals were namely (1) to uncover the occurrence of long-term social ties (i.e. associations) between individuals and (2) to examine whether the perturbation regime affected this form of social cohesion. We analysed a dataset of more than 3500 individuals from 25 years of monitoring by means of contingency tables and within the Social Network Analysis framework. We showed that associations between individuals are not only due to philopatry or random gregariousness but that there are social ties between individuals over the years. Furthermore, social cohesion decreased under the perturbation regime. We sustain that perturbations may lead not only to changes in individuals’ behaviour and fitness but also to a change in populations’ social cohesion. The consequences of decreasing social cohesion are still not well understood, but they can be critical for the population dynamics of social species.

Forecasting resilience profiles of the run-up to regime shifts in nearly-one-dimensional systems

The forecasting of sudden, irreversible shifts in natural systems is a challenge of great importance, whose realization could allow pre-emptive action to be taken to avoid or mitigate catastrophic transitions, or to help systems adapt to them. In recent years, there have been many advances in the development of such early warning signals. However, much of the current toolbox is based around the tracking of statistical trends and therefore does not aim to estimate the future time scale of transitions or resilience loss. Metric-based indicators are also difficult to implement when systems have inherent oscillations which can dominate the indicator statistics. To resolve these gaps in the toolbox, we use additional system properties to fit parsimonious models to dynamics in order to predict transitions. Here, we consider nearly-one-dimensional systems-higher dimensional systems whose dynamics can be accurately captured by one-dimensional discrete time maps. We show how the nearly one-dimensional dynamics can be used to produce model-based indicators for critical transitions which produce forecasts of the resilience and the time of transitions in the system. A particularly promising feature of this approach is that it allows us to construct early warning signals even for critical transitions of chaotic systems. We demonstrate this approach on two model systems: of phosphorous recycling in a shallow lake, and of an overcompensatory fish population.

Toward General Principles for Resilience Engineering

Maintaining the performance of infrastructure-dependent systems in the face of surprises and unknowable risks is a grand challenge. Addressing this issue requires a better understanding of enabling conditions or principles that promote system resilience in a universal way. In this study, a set of such principles is interpreted as a group of interrelated conditions or organizational qualities that, taken together, engender system resilience. The field of resilience engineering identifies basic system or organizational qualities (e.g., abilities for learning) that are associated with enhanced general resilience and has packaged them into a set of principles that should be fostered. However, supporting conditions that give rise to such first-order system qualities remain elusive in the field. An integrative understanding of how such conditions co-occur and fit together to bring about resilience, therefore, has been less clear. This article contributes to addressing this gap by identifying a potentially more comprehensive set of principles for building general resilience in infrastructure-dependent systems. In approaching this aim, we organize scattered notions from across the literature. To reflect the partly self-organizing nature of infrastructure-dependent systems, we compare and synthesize two lines of research on resilience: resilience engineering and social-ecological system resilience. Although some of the principles discussed within the two fields overlap, there are some nuanced differences. By comparing and synthesizing the knowledge developed in them, we recommend an updated set of resilience-enhancing principles for infrastructure-dependent systems. In addition to proposing an expanded list of principles, we illustrate how these principles can co-occur and their interdependencies.

The Ecosystem Resilience Concept Applied to Hydrogeological Systems: A General Approach

We have witnessed the great changes that hydrogeological systems are facing in the last decades: rivers that have dried up; wetlands that have disappeared, leaving their buckets converted into farmland; and aquifers that have been intensively exploited for years, among others. Humans have caused the most part of these results that can be worsened by climate change, with delayed effects on groundwater quantity and quality. The consequences are negatively impacting ecosystems and dependent societies. The concept of resilience has not been extensively used in the hydrogeological research, and it can be a very useful concept that can improve the understanding and management of these systems. The aim of this work is to briefly discuss the role of resilience in the context of freshwater systems affected by either climate or anthropic actions as a way to increase our understanding of how anticipating negative changes (transitions) may contribute to improving the management of the system and preserving the services that it provides. First, the article presents the basic concepts applied to hydrogeological systems from the ecosystem’s resilience approach. Second, the factors controlling for hydrogeological systems’ responses to different impacts are commented upon. Third, a case study is analyzed and discussed. Finally, the useful implications of the concept are discussed.

Conceptualizing alternate regimes in a large floodplain-river ecosystem: Water clarity, invasive fish, and floodplain vegetation

Regime shifts - persistent changes in the structure and function of an ecosystem - are well-documented for some ecosystems and have informed research and management of these ecosystems. In floodplain-river ecosystems, there is growing interest from restoration practitioners in ecological resilience, yet regime shifts remain poorly understood in these ecosystems. To understand how regime shifts may apply to floodplain-river ecosystems, we synthesize our understanding of ecosystem dynamics using an alternate regimes conceptual framework. We present three plausible sets of alternate regimes relevant to natural resource management interests within the Upper Mississippi River and Illinois River. These alternate regimes include: 1) a clear water and abundant vegetation regime vs. a turbid water and sparse vegetation regime in lentic, off-channel areas, 2) a diverse native fish community regime vs. an invasive-dominated fish community regime, and 3) a regime characterized by a diverse and dynamic mosaic of floodplain vegetation types vs. one characterized as a persistent invasive wet meadow monoculture. For each set of potential alternate regimes, we review available literature to synthesize known or hypothesized feedback mechanisms that reinforce regimes, controlling variables that drive regime transitions, and current restoration pathways. Our conceptual models provide preliminary support for the existence of alternate regimes in floodplain-river ecosystems. Quantitatively testing hypotheses contained within the conceptual model are important next steps in evaluating the model. Ultimately, the synthesis and evaluation of alternate regimes can inform the utility of resilience concepts in restoration and management on the Upper Mississippi River and Illinois River and improve our understanding of ecosystem dynamics in other large, heavily managed floodplain-river ecosystems.

Long-term digital device-enabled monitoring of functional status: Implications for management of persons with Alzheimer's disease

INTRODUCTION

Informal caregiving is an essential element of health-care delivery. Little data describes how caregivers structure care recipients' lives and impact their functional status.

METHODS

We performed observational studies of community dwelling persons with dementia (PWD) to measure functional status by simultaneous assessment of physical activity (PA) and lifespace (LS). We present data from two caregiver/care-recipient dyads representing higher and average degrees of caregiver involvement.

RESULTS

We acquired >42,800 (subject 1); >41,300 (subject 2) PA data points and >154,500 (subject 1); >119,700 (subject 2) LS data points over 15 months of near continuous observation. PA and LS patterns provided insights into the caregiver's role in structuring the PWD's day-to-day function and change in function over time.

DISCUSSION

We show that device-enabled functional monitoring (FM) can successfully gather and display data at resolutions required for dementia care studies. Objective quantification of individual caregiver/care-recipient dyads provides opportunities to implement patient-centered care.

Towards a Comparative Framework of Demographic Resilience

In the current global biodiversity crisis, the development of tools to define, quantify, compare, and predict resilience is essential for understanding the responses of species to global change. However, disparate interpretations of resilience have hampered the development of a common currency to quantify and compare resilience across natural systems. Most resilience frameworks focus on upper levels of biological organization, especially ecosystems or communities, which complicates measurements of resilience using empirical data. Surprisingly, there is no quantifiable definition of resilience at the demographic level. We introduce a framework of demographic resilience that draws on existing concepts from community and population ecology, as well as an accompanying set of metrics that are comparable across species.

The role of multiple global change factors in driving soil functions and microbial biodiversity

Soils underpin terrestrial ecosystem functions, but they face numerous anthropogenic pressures. Despite their crucial ecological role, we know little about how soils react to more than two environmental factors at a time. Here, we show experimentally that increasing the number of simultaneous global change factors (up to 10) caused increasing directional changes in soil properties, soil processes, and microbial communities, though there was greater uncertainty in predicting the magnitude of change. Our study provides a blueprint for addressing multifactor change with an efficient, broadly applicable experimental design for studying the impacts of global environmental change.

Forecasting semi-arid biome shifts in the Anthropocene

Shrub encroachment, forest decline and wildfires have caused large-scale changes in semi-arid vegetation over the past 50 years. Climate is a primary determinant of plant growth in semi-arid ecosystems, yet it remains difficult to forecast large-scale vegetation shifts (i.e. biome shifts) in response to climate change. We highlight recent advances from four conceptual perspectives that are improving forecasts of semi-arid biome shifts. Moving from small to large scales, first, tree-level models that simulate the carbon costs of drought-induced plant hydraulic failure are improving predictions of delayed-mortality responses to drought. Second, tracer-informed water flow models are improving predictions of species coexistence as a function of climate. Third, new applications of ecohydrological models are beginning to simulate small-scale water movement processes at large scales. Fourth, remotely-sensed measurements of plant traits such as relative canopy moisture are providing early-warning signals that predict forest mortality more than a year in advance. We suggest that a community of researchers using modeling approaches (e.g. machine learning) that can integrate these perspectives will rapidly improve forecasts of semi-arid biome shifts. Better forecasts can be expected to help prevent catastrophic changes in vegetation states by identifying improved monitoring approaches and by prioritizing high-risk areas for management.

Resilience of aquatic systems: Review and management implications

Our understanding of how ecosystems function has changed from an equilibria-based view to one that recognizes the dynamic, fluctuating, nonlinear nature of aquatic systems. This current understanding requires that we manage systems for resilience. In this review, we examine how resilience has been defined, measured and applied in aquatic systems, and more broadly, in the socioecological systems in which they are embedded. Our review reveals the importance of managing stressors adversely impacting aquatic system resilience, as well as understanding the environmental and climatic cycles and changes impacting aquatic resources. Aquatic resilience may be enhanced by maintaining and enhancing habitat connectivity as well as functional redundancy and physical and biological diversity. Resilience in aquatic socioecological system may be enhanced by understanding and fostering linkages between the social and ecological subsystems, promoting equity among stakeholders, and understanding how the system is impacted by factors within and outside the area of immediate interest. Management for resilience requires implementation of adaptive and preferably collaborative management. Implementation of adaptive management for resilience will require an effective monitoring framework to detect key changes in the coupled socioecological system. Research is needed to (1) develop sensitive indicators and monitoring designs, (2) disentangle complex multi-scalar interactions and feedbacks, and (3) generalize lessons learned across aquatic ecosystems and apply them in new contexts.

Catastrophic dynamics limit Atlantic cod recovery

Collapses and regime changes are pervasive in complex systems (such as marine ecosystems) governed by multiple stressors. The demise of Atlantic cod (Gadus morhua) stocks constitutes a text book example of the consequences of overexploiting marine living resources, yet the drivers of these nearly synchronous collapses are still debated. Moreover, it is still unclear why rebuilding of collapsed fish stocks such as cod is often slow or absent. Here, we apply the stochastic cusp model, based on catastrophe theory, and show that collapse and recovery of cod stocks are potentially driven by the specific interaction between exploitation pressure and environmental drivers. Our statistical modelling study demonstrates that for most of the cod stocks, ocean warming could induce a nonlinear discontinuous relationship between fishing pressure and stock size, which would explain hysteresis in their response to reduced exploitation pressure. Our study suggests further that a continuing increase in ocean temperatures will probably limit productivity and hence future fishing opportunities for most cod stocks of the Atlantic Ocean. Moreover, our study contributes to the ongoing discussion on the importance of climate and fishing effects on commercially exploited fish stocks, highlighting the importance of considering discontinuous dynamics in holistic ecosystem-based management approaches, particularly under climate change.

Measuring resilience prospectively as the speed of affect recovery in daily life: a complex systems perspective on mental health

INTRODUCTION

There is growing evidence that mental disorders behave like complex dynamic systems. Complex dynamic systems theory states that a slower recovery from small perturbations indicates a loss of resilience of a system. This study is the first to test whether the speed of recovery of affect states from small daily life perturbations predicts changes in psychopathological symptoms over 1 year in a group of adolescents at increased risk for mental disorders.

METHODS

We used data from 157 adolescents from the TWINSSCAN study. Course of psychopathology was operationalized as the 1-year change in the Symptom Checklist-90 sum score. Two groups were defined: one with stable and one with increasing symptom levels. Time-series data on momentary daily affect and daily unpleasant events were collected 10 times a day for 6 days at baseline. We modeled the time-lagged effect of daily unpleasant events on negative and positive affect after each unpleasant event experienced, to examine at which time point the impact of the events is no longer detectable.

RESULTS

There was a significant difference between groups in the effect of unpleasant events on negative affect 90 min after the events were reported. Stratified by group, in the Increase group, the effect of unpleasant events on both negative (B = 0.05, p < 0.01) and positive affect (B = - 0. 08, p < 0.01) was still detectable 90 min after the events, whereas in the Stable group this was not the case.

CONCLUSION

Findings cautiously suggest that adolescents who develop more symptoms in the following year may display a slower affect recovery from daily perturbations at baseline. This supports the notion that mental health may behave according to the laws of a complex dynamic system. Future research needs to examine whether these dynamic indicators of system resilience may prove valuable for personalized risk assessment in this field.

Soil microbial community responses to climate extremes: resistance, resilience and transitions to alternative states

A major challenge for advancing our understanding of the functional role of soil microbial communities is to link changes in their structure and function under climate change. To address this challenge requires new understanding of the mechanisms that underlie the capacity of soil microbial communities to resist and recover from climate extremes. Here, we synthesize emerging understanding of the intrinsic and extrinsic factors that influence the resistance and resilience of soil microbial communities to climate extremes, with a focus on drought, and identify drivers that might trigger abrupt changes to alternative states. We highlight research challenges and propose a path for advancing our understanding of the resistance and resilience of soil microbial communities to climate extremes, and of their vulnerability to transitions to alternative states, including the use of trait-based approaches. We identify a need for new approaches to quantify resistance and resilience of soil microbial communities, and to identify thresholds for transitions to alternative states. We show how high-resolution time series coupled with gradient designs will enable detecting response patterns to interacting drivers. Finally, to account for extrinsic factors, we suggest that future studies should use environmental gradients to track soil microbial community responses to climate extremes in space and time. This article is part of the theme issue 'Climate change and ecosystems: threats, opportunities and solutions'.

Social tipping dynamics for stabilizing Earth's climate by 2050

Achieving a rapid global decarbonization to stabilize the climate critically depends on activating contagious and fast-spreading processes of social and technological change within the next few years. Drawing on expert elicitation, an expert workshop, and a review of literature, which provides a comprehensive analysis on this topic, we propose concrete interventions to induce positive social tipping dynamics and a rapid global transformation to carbon-neutral societies. These social tipping interventions comprise removing fossil-fuel subsidies and incentivizing decentralized energy generation, building carbon-neutral cities, divesting from assets linked to fossil fuels, revealing the moral implications of fossil fuels, strengthening climate education and engagement, and disclosing greenhouse gas emissions information.

Safely achieving the goals of the Paris Climate Agreement requires a worldwide transformation to carbon-neutral societies within the next 30 y. Accelerated technological progress and policy implementations are required to deliver emissions reductions at rates sufficiently fast to avoid crossing dangerous tipping points in the Earth’s climate system. Here, we discuss and evaluate the potential of social tipping interventions (STIs) that can activate contagious processes of rapidly spreading technologies, behaviors, social norms, and structural reorganization within their functional domains that we refer to as social tipping elements (STEs). STEs are subdomains of the planetary socioeconomic system where the required disruptive change may take place and lead to a sufficiently fast reduction in anthropogenic greenhouse gas emissions. The results are based on online expert elicitation, a subsequent expert workshop, and a literature review. The STIs that could trigger the tipping of STE subsystems include 1) removing fossil-fuel subsidies and incentivizing decentralized energy generation (STE1, energy production and storage systems), 2) building carbon-neutral cities (STE2, human settlements), 3) divesting from assets linked to fossil fuels (STE3, financial markets), 4) revealing the moral implications of fossil fuels (STE4, norms and value systems), 5) strengthening climate education and engagement (STE5, education system), and 6) disclosing information on greenhouse gas emissions (STE6, information feedbacks). Our research reveals important areas of focus for larger-scale empirical and modeling efforts to better understand the potentials of harnessing social tipping dynamics for climate change mitigation.

Early-warning signals using dynamical network markers selected by covariance

It is an important issue, particularly in the context of sustainable society, to predict critical transitions across which a system state abruptly shifts toward a contrasting state. In this study, we propose an indicator of critical transitions in multivariate dynamical systems, based on the concept of the dynamical network marker (DNM). The DNM is originally defined based on the eigendecomposition of the Jacobian matrix of a nonlinear system and corresponds to large-magnitude components of the dominant eigenvector, which contributes primarily to transitions. Our DNM-based indicator is derived from the sample covariance matrix of state variables in a target system. Simulation results to predict transitions in complex network systems consisting of a harvesting model consistently show the superiority of our indicator as a precursor of transitions regardless of network structure characteristics, as compared to a conventional indicator.

Period doubling as an indicator for ecosystem sensitivity to climate extremes

The predictions for a warmer and drier climate and for increased likelihood of climate extremes raise high concerns about the possible collapse of dryland ecosystems, and about the formation of new drylands where native species are less tolerant to water stress. Using a dryland-vegetation model for plant species that display different tradeoffs between fast growth and tolerance to droughts, we find that ecosystems subjected to strong seasonal variability, typical for drylands, exhibit a temporal period-doubling route to chaos that results in early collapse to bare soil. We further find that fast-growing plants go through period doubling sooner and span wider chaotic ranges than stress-tolerant plants. We propose the detection of period-doubling signatures in power spectra as early indicators of ecosystem collapse that outperform existing indicators in their ability to warn against climate extremes and capture the heightened vulnerability of newly-formed drylands. The proposed indicator is expected to apply to other types of ecosystems, such as consumer–resource and predator–prey systems. We conclude by delineating the conditions ecosystems should meet in order for the proposed indicator to apply.

Anticipating critical transitions in epithelial-hybrid-mesenchymal cell-fate determination

In the vicinity of a tipping point, critical transitions occur when small changes in an input condition cause sudden, large, and often irreversible changes in the state of a system. Many natural systems ranging from ecosystems to molecular biosystems are known to exhibit critical transitions in their response to stochastic perturbations. In diseases, an early prediction of upcoming critical transitions from a healthy to a disease state by using early-warning signals is of prime interest due to potential application in forecasting disease onset. Here, we analyze cell-fate transitions between different phenotypes (epithelial, hybrid-epithelial/mesenchymal [E/M], and mesenchymal states) that are implicated in cancer metastasis and chemoresistance. These transitions are mediated by a mutually inhibitory feedback loop-microRNA-200/ZEB-driven by the levels of transcription factor SNAIL. We find that the proximity to tipping points enabling these transitions among different phenotypes can be captured by critical slowing down-based early-warning signals, calculated from the trajectory of ZEB messenger RNA level. Further, the basin stability analysis reveals the unexpectedly large basin of attraction for a hybrid-E/M phenotype. Finally, we identified mechanisms that can potentially elude the transition to a hybrid-E/M phenotype. Overall, our results unravel the early-warning signals that can be used to anticipate upcoming epithelial-hybrid-mesenchymal transitions. With the emerging evidence about the hybrid-E/M phenotype being a key driver of metastasis, drug resistance, and tumor relapse, our results suggest ways to potentially evade these transitions, reducing the fitness of cancer cells and restricting tumor aggressiveness.