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In this article, we claim that recent developments in climate science and renewable energy should prompt a reframing of debates surrounding climate change mitigation. Taken together, we argue that these developments suggest (1) global climate collapse in this century is a non-negligible risk, (2) mitigation offers substantial benefits to current generations, and (3) mitigation by some can generate social tipping dynamics that could ultimately make renewables cheaper than fossil fuels. We explain how these claims undermine familiar framings of climate change, wherein mitigation is understood as self-sacrifice that individuals and governments must be morally persuaded or incentivized to undertake.

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Complex spatio-temporal systems like lakes, forests and climate systems exhibit alternative stable states. In such systems, as the threshold value of the driver is crossed, the system may experience a sudden (discontinuous) transition or smooth (continuous) transition to an undesired steady state. Theories predict that changes in the structure of the underlying spatial patterns precede such transitions. While there has been a large body of research on identifying early warning signals of critical transitions, the problem of forecasting the type of transitions (sudden versus smooth) remains an open challenge. We address this gap by developing an advanced machine learning (ML) toolkit that serves as an early warning indicator of spatio-temporal critical transitions, Spatial Early Warning Signal Network (S-EWSNet). ML models typically resemble a black box and do not allow envisioning what the model learns in discerning the labels. Here, instead of naively relying upon the deep learning model, we let the deep neural network learn the latent features characteristic of transitions via an optimal sampling strategy (OSS) of spatial patterns. The S-EWSNet is trained on data from a stochastic cellular automata model deploying the OSS, providing an early warning indicator of transitions while detecting its type in simulated and empirical samples.

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The possibility to anticipate critical transitions through detecting loss of resilience has attracted attention in many fields. Resilience indicators rely on the mathematical concept of critical slowing down, which means that a system recovers more slowly from external perturbations when it gets closer to tipping point. This decrease in recovery rate can be reflected in rising autocorrelation and variance in data. To test whether resilience is changing, resilience indicators are often calculated using a moving window in long, continuous time series of the system. However, for some systems, it may be more feasible to collect several high-resolution time series in short periods of time, i.e., in bursts. Resilience indicators can then be calculated to detect a change of resilience between such bursts. Here, we compare the performance of both methods using simulated data and showcase the possible use of bursts in a case study using mood data to anticipate depression in a patient. With the same number of data points, the burst approach outperformed the moving window method, suggesting that it is possible to downsample the continuous time series and still signal an upcoming transition. We suggest guidelines to design an optimal sampling strategy. Our results imply that using bursts of data instead of continuous time series may improve the capacity to detect changes in resilience. This method is promising for a variety of fields, such as human health, epidemiology, or ecology, where continuous monitoring can be costly or unfeasible.

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Understanding the Amazon Rainforest's response to shifts in precipitation is paramount with regard to its sensitivity to climate change and deforestation. Studies using Dynamic Global Vegetation Models (DGVMs) typically only explore a range of socio-economically plausible pathways. In this study, we applied the state-of-the-art DGVM LPJmL to simulate the Amazon forest's response under idealized scenarios where precipitation is linearly decreased and subsequently increased between current levels and zero. Our results indicate a nonlinear but reversible relationship between vegetation Above Ground Biomass (AGB) and Mean Annual Precipitation (MAP), suggesting a threshold at a critical MAP value, below which vegetation biomass decline accelerates with decreasing MAP. We find that approaching this critical threshold is accompanied by critical slowing down, which can hence be expected to warn of accelerating biomass decline with decreasing rainfall. The critical precipitation threshold is lowest in the northwestern Amazon, whereas the eastern and southern regions may already be below their critical MAP thresholds. Overall, we identify the seasonality of precipitation and the potential evapotranspiration (PET) as the most important parameters determining the threshold value. While vegetation fires show little effect on the critical threshold and the biomass pattern in general, the ability of trees to adapt to water stress by investing in deep roots leads to increased biomass and a lower critical threshold in some areas in the eastern and southern Amazon where seasonality and PET are high. Our findings underscore the risk of Amazon forest degradation due to changes in the water cycle, and imply that regions that are currently characterized by higher water availability may exhibit heightened vulnerability to future drying.

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Ecological resilience is the capability of an ecosystem to maintain the same structure and function and avoid crossing catastrophic tipping points (i.e. undergoing irreversible regime shifts). While fundamental for management, concrete ways to estimate and interpret resilience in real ecosystems are still lacking. Here, we develop an empirical approach to estimate resilience based on the stochastic cusp model derived from catastrophe theory. The cusp model models tipping points derived from a cusp bifurcation. We extend cusp in order to identify the presence of stable and unstable states in complex natural systems. Our Cusp Resilience Assessment (CUSPRA) has three characteristics: (i) it provides estimates on how likely a system is to cross a tipping point (in the form of a cusp bifurcation) characterized by hysteresis, (ii) it assesses resilience in relation to multiple external drivers and (iii) it produces straightforward results for ecosystem-based management. We validate our approach using simulated data and demonstrate its application using empirical time series of an Atlantic cod population and marine ecosystems in the North Sea and the Mediterranean Sea. We show that Cusp Resilience Assessment is a powerful method to empirically estimate resilience in support of a sustainable management of our constantly adapting ecosystems under global climate change.

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The sustainability of social-ecological systems within river deltas globally is in question as rapid development and environmental change trigger "negative" or "positive" tipping points depending on actors' perspectives, e.g. regime shift from abundant sediment deposition to sediment shortage, agricultural sustainability to agricultural collapse or shift from rural to urban land use. Using a systematic review of the literature, we show how cascading effects across anthropogenic, ecological, and geophysical processes have triggered numerous tipping points in the governance, hydrological, and land-use management of the world's river deltas. Crossing tipping points had both positive and negative effects that generally enhanced economic development to the detriment of the environment. Assessment of deltas that featured prominently in the review revealed how outcomes of tipping points can inform the long-term trajectory of deltas towards sustainability or collapse. Management of key drivers at the delta scale can trigger positive tipping points to place social-ecological systems on a pathway towards sustainable development.

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Scholars of disaster politics debate how far natural hazards cause or catalyse political change. This paper builds on recent scholarship on tipping points and social contracts to argue that two case studies of historical earthquakes in 1930s British-colonised India invite a focus on the dynamics of cooperation and conflict between state and non-state actors. Officials of the colonial state and its nationalist rivals cooperated after one earthquake even though they otherwise bitterly opposed each other. Cooperation broke down after the second event, just one year later. Yet, in both cases, officials and nationalist leaders shared a broad vision for Indian society, which pushed both sides actively to seek to recover the social and economic status quo ante, preventing potential tipping points from crystallising. These case studies reveal how and why highly fraught social contracts can survive major disasters. The colonial state's transient and reactive approach to disaster governance continued to impact on post-independence India.

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Several studies explored the links between perfectionism and sport performance. However, only a few studies examined this relationship in the context of real sport competition and with a focus on the possible interactive effects of the dimensions of perfectionism. The present study aimed to investigate whether the two higher-order dimensions of perfectionism - perfectionistic strivings and perfectionistic concerns - interact in predicting performance in mountain trail running competitions. 167 athletes (54 females, 113 males) aged 19 to 65 (M = 39.32, SD = 9.35) completed measures of perfectionism in the week prior to the competition. Regression analyses revealed that perfectionistic strivings were a positive predictor of the runners' performance while perfectionistic concerns showed no significant associations with performance. However, a more in-depth exploration within the framework of the 2 × 2 model of perfectionism showed that the beneficial effects of high perfectionistic strivings are no longer significant when accompanied by high levels of perfectionistic concerns. These results support the notion that perfectionistic concerns may be detrimental to sport performance even if their overall, direct effects are not significant, adding first evidence of the existence of standard but also flipped perfectionistic tipping points in the context of sport performance.

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Research has recently begun to examine the relationship between multidimensional perfectionism and athletes' post-competition mood. However, to date, there have been few attempts to examine the interaction between dimensions of perfectionism or model possible explanatory processes. To address these limitations, in the current study we tested a novel conditional process model whereby the relationship between perfectionistic strivings and post-competition affect was mediated by the degree to which goals were considered to have been met (goal-realization) and that this indirect effect was, in turn, moderated by levels of perfectionistic concerns. We tested this model in a sample of 251 athletes who took part in a "Runmageddon" event - a cross-country obstacle race. Athletes completed measures of perfectionism (perfectionistic strivings and perfectionistic concerns) before the race and measures of goal-realization and mood (tense arousal, energetic arousal, and hedonic tone) between 24 and 48 h after the race. Analyses revealed that perfectionistic strivings were indirectly linked to a more unpleasant post-competition mood (higher tense arousal and lower hedonic tone) via perceptions of lower goal-realization. In addition, these two indirect effects were statistically significant only when perfectionistic concerns were medium and high. The results support the proposed conditional model and suggest the interplay between dimensions of perfectionism is important for athletes' post-competition mood, and the level of perfectionistic concerns, especially.

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Environmental management in coastal ecosystems has been challenged by the complex cumulative effects that occur when many small issues result in large ecological shifts. Current environmental management of these spaces focuses on identifying and limiting problematic stressors via a series of assessment techniques. Whilst there is a strong desire among managers to consider complexity in ecological responses to cumulative effects, current approaches for assessing risk focus on breaking down the issues into multiple cause and effect relationships. However, uncertainty arises when data and information for a place are limited, as is commonly the case, and this creates decision paralysis while more information is generated. Here, we discuss how ecological understanding of network interactions in coastal marine ecosystems can be used as a lens to bring together multiple lines of evidence and create actions. We list and describe four characteristics of marine ecosystem interaction networks including the possibility for; 1) indirect effects, 2) effects that emerge as stressor magnitude increases the number of network components implicated, 3) network interactions that amplify these indirect effects, and 4) feedbacks that reinforce or stabilise against indirect effects. We then link these four characteristics to three case studies of common coastal environmental issues to demonstrate how a general understanding of ecological interaction networks can enhance priorities for stressor management that can be applied even when specific data is limited.

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More frequent and severe droughts are driving increased forest mortality around the globe. We urgently need to describe and predict how drought affects forest carbon cycling and identify thresholds of environmental stress that trigger ecosystem collapse. Quantifying the effects of drought at an ecosystem level is complex because dynamic climate-plant relationships can cause rapid and/or prolonged shifts in carbon balance. We employ the CARbon DAta MOdel fraMework (CARDAMOM) to investigate legacy effects of drought on forest carbon pools and fluxes. Our Bayesian model-data fusion approach uses tower observed meteorological forcing and carbon fluxes to determine the response and sensitivity of aboveground and belowground ecological processes associated with the 2012-2015 California drought. Our study area is a mid-montane mixed conifer forest in the Southern Sierras. CARDAMOM constrained with gross primary productivity (GPP) estimates covering 2011-2017 show a ~75% reduction in GPP, compared to negligible GPP change when constrained with 2011 only. Precipitation across 2012-2015 was 45% (474 mm) lower than the historical average and drove a cascading depletion in soil moisture and carbon pools (foliar, labile, roots, and litter). Adding 157 mm during an especially stressful year (2014, annual rainfall = 293 mm) led to a smaller depletion of water and carbon pools, steering the ecosystem away from a state of GPP tipping-point collapse to recovery. We present novel process-driven insights that demonstrate the sensitivity of GPP collapse to ecosystem foliar carbon and soil moisture states-showing that the full extent of GPP response takes several years to arise. Thus, long-term changes in soil moisture and carbon pools can provide a mechanistic link between drought and forest mortality. Our study provides an example for how key precipitation threshold ranges can influence forest productivity, making them useful for monitoring and predicting forest mortality events.

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Coverage of climate tipping points has rapidly increased over the past 20 years. Despite this upsurge, there has been precious little research into how the public perceives these abrupt and/or irreversible large-scale risks. This article provides a nationally representative view on public perceptions of climate tipping points and possible societal responses to them (n = 1773). Developing a mixed-methods survey with cultural cognition theory, it shows that awareness among the British public is low. The public is doubtful about the future effectiveness of humanity's response to climate change in general, and significantly more doubtful about its response to tipping points specifically. Significantly more people with an egalitarian worldview judge tipping points likely to be crossed and to be a significant threat to humanity. All possible societal responses received strong support. The article ends by considering the prospects for 'cultural tipping elements' to tip support for climate policies across divergent cultural worldviews.

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As we cross the 2030 deadline to achieve the Sustainable Development Goals (SDGs), there is a growing sense of urgency around the need to accelerate the necessary transformations. These encompass a broad range of systems and require fundamental changes in system goals and design. In this paper, we undertake a narrative review of the literature relating to the acceleration of transformations and offer a framework for unlocking and accelerating transformations to the SDGs. While there is no blueprint for acceleration, there is an expanding knowledge base on important dynamics, impediments and enabling conditions across diverse literatures which can help to inform strategic interventions by actors. The emerging literature on positive tipping points and deep leverage points identifies opportunities to rewire systems design so that important system feedbacks create the conditions for acceleration. Transformation takes time and actors will need to build momentum to reorient systems around new goals, informed by knowledge of common policy, technology and behavioural feedbacks that govern system dynamics. Where resistance is strong, actors can seek to augment system design in ways that weaken balancing feedbacks that stabilise existing system configurations and strengthen reinforcing feedbacks that promote emerging system configurations oriented towards the SDGs. Well-designed and sequenced interventions can promote innovation and behaviour change and build and maintain political support. This can build critical enabling conditions and push systems towards large-scale tipping points, paving the way for decisive policy action that is crucial for triggering acceleration. We conclude by highlighting gaps and priorities for further research.

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The Late Ordovician mass extinction event is the oldest of the five great extinction events in the fossil record. It has long been regarded as an outlier among mass extinctions, primarily due to its association with a cooling climate. However, recent temporally better resolved fossil biodiversity estimates complicate this view, providing growing evidence for a prolonged but punctuated biodiversity decline modulated by changes in atmospheric composition, ocean chemistry, and viable habitat area. This evolving view invokes extinction drivers similar to those that occurred during other major extinctions; some are even factors in the current human-induced biodiversity crisis. Even this very ancient and, at first glance, exceptional event conveys important lessons about the intensifying 'sixth mass extinction'.

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Although the trait concept is increasingly used in research, quantitative relations that can support in determining ecological tipping points and serve as a basis for environmental standards are lacking. This study determines changes in trait abundance along a gradient of flow velocity, turbidity and elevation, and develops trait-response curves, which facilitate the identification of ecological tipping points. Aquatic macroinvertebrates and abiotic conditions were determined at 88 different locations in the streams of the Guayas basin. After trait information collection, a set of trait diversity metrics were calculated. Negative binomial regression and linear regression were applied to relate the abundance of each trait and trait diversity metrics, respectively, to flow velocity, turbidity and elevation. Tipping points for each environmental variable in relation to traits were identified using the segmented regression method. The abundance of most traits increased with increasing velocity, while they decreased with increasing turbidity. The negative binomial regression models revealed that from a flow velocity higher than 0.5 m/s, a substantial increase in abundance occurs for several traits, and this is even more substantially noticed at values higher than 1 m/s. Furthermore, significant tipping points were also identified for elevation, wherein an abrupt decline in trait richness was observed below 22 m a.s.l., implying the need to focus water management in these altitudinal regions. Turbidity is potentially caused by erosion; thus, measures that can reduce or limit erosion within the basin should be implemented. Our findings suggest that measures mitigating the issues related to turbidity and flow velocity may lead to better aquatic ecosystem functioning. This quantitative information related to flow velocity might serve as a good basis to determine ecological flow requirements and illustrates the major impacts that hydropower dams can have in fast-running river systems. These quantitative relations between invertebrate traits and environmental conditions, as well as related tipping points, provide a basis to determine critical targets for aquatic ecosystem management, achieve improved ecosystem functioning and warrant trait diversity.

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Human activity is leading to changes in the mean and variability of climatic parameters in most locations around the world. The changing mean has received considerable attention from scientists and climate policy makers. However, recent work indicates that the changing variability, that is, the amplitude and the temporal autocorrelation of deviations from the mean, may have greater and more imminent impact on ecosystems. In this paper, we demonstrate that changes in climate variability alone could drive cyclic predator-prey ecosystems to extinction via so-called phase-tipping (P-tipping), a new type of instability that occurs only from certain phases of the predator-prey cycle. We construct a mathematical model of a variable climate and couple it to two self-oscillating paradigmatic predator-prey models. Most importantly, we combine realistic parameter values for the Canada lynx and snowshoe hare with actual climate data from the boreal forest. In this way, we demonstrate that critically important species in the boreal forest have increased likelihood of P-tipping to extinction under predicted changes in climate variability, and are most vulnerable during stages of the cycle when the predator population is near its maximum. Furthermore, our analysis reveals that stochastic resonance is the underlying mechanism for the increased likelihood of P-tipping to extinction.

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The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and often irreversible shift is well established, but prediction of these events using standard forecast modelling techniques is quite difficult. This has led to the development of an alternative suite of methods that seek to identify signatures of critical phenomena in data, which are expected to occur in advance of many classes of dynamical bifurcation. Crucially, the manifestations of these critical phenomena are generic across a variety of systems, meaning that data-intensive deep learning methods can be trained on (abundant) synthetic data and plausibly prove effective when transferred to (more limited) empirical datasets. This paper provides a proof of concept for this approach as applied to lattice phase transitions: a deep neural network trained exclusively on two-dimensional Ising model phase transitions is tested on a number of real and simulated climate systems with considerable success. Its accuracy frequently surpasses that of conventional statistical indicators, with performance shown to be consistently improved by the inclusion of spatial indicators. Tools such as this may offer valuable insight into climate tipping events, as remote sensing measurements provide increasingly abundant data on complex geospatially resolved Earth systems.

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Ecosystems under stress may respond abruptly and irreversibly through tipping points. Although mechanisms leading to alternative stable states are much studied, little is known about how such ecosystems could have emerged in the first place. We investigate whether evolution by natural selection along resource gradients leads to bistability, using shallow lakes as an example. There, tipping points occur between two alternative states dominated by either submersed or floating macrophytes depending on nutrient loading. We model the evolution of macrophyte depth in the lake, identify the conditions under which the ancestor population diversifies and investigate whether alternative stable states dominated by different macrophyte phenotypes occur. We find that eco-evolutionary dynamics may lead to alternative stable states, but under restrictive conditions. Such dynamics require sufficient asymmetries in the acquisition of both light and nutrient. Our analysis suggests that competitive asymmetries along opposing resource gradients may allow bistability to emerge by natural selection.

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Sudden changes in populations are ubiquitous in ecological systems, especially under perturbations. The agents of global change may increase the frequency and severity of anthropogenic perturbations, but complex populations' responses hamper our understanding of their dynamics and resilience. Furthermore, the long-term environmental and demographic data required to study those sudden changes are rare. Fitting dynamical models with an artificial intelligence algorithm to population fluctuations over 40 y in a social bird reveals that feedback in dispersal after a cumulative perturbation drives a population collapse. The collapse is well described by a nonlinear function mimicking social copying, whereby dispersal made by a few individuals induces others to leave the patch in a behavioral cascade for decision-making to disperse. Once a threshold for deterioration of the quality of the patch is crossed, there is a tipping point for a social response of runaway dispersal corresponding to social copying feedback. Finally, dispersal decreases at low population densities, which is likely due to the unwillingness of the more philopatric individuals to disperse. In providing the evidence of copying for the emergence of feedback in dispersal in a social organism, our results suggest a broader impact of self-organized collective dispersal in complex population dynamics. This has implications for the theoretical study of population and metapopulation nonlinear dynamics, including population extinction, and managing of endangered and harvested populations of social animals subjected to behavioral feedback loops.

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Successfully anticipating sudden major changes in complex systems is a practical concern. Such complex systems often form a heterogeneous network, which may show multi-stage transitions in which some nodes experience a regime shift earlier than others as an environment gradually changes. Here we investigate early warning signals for networked systems undergoing a multi-stage transition. We found that knowledge of both the ongoing multi-stage transition and network structure enables us to calculate effective early warning signals for multi-stage transitions. Furthermore, we found that small subsets of nodes could anticipate transitions as well as or even better than using all the nodes. Even if we fix the network and dynamical system, no single best subset of nodes provides good early warning signals, and a good choice of sentinel nodes depends on the tipping direction and the current stage of the dynamics within a multi-stage transition, which we systematically characterize.