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This is the full dataset that is needed to create a worldmap, which is shown as a figure in the following publication: van de Vijsel, R.C., Scheffer, M. & Hoitink, A.J.F. (2024). Tipping points in river deltas. Nature Reviews Earth & Environment. This worldmap shows how three main drivers of change are projected to change in the near future, for 47 of the world's major river deltas. These drivers are: i) changes in compound flood risk due to combined storm surge and river flood, ii) changes in sediment budget, and iii) changes in relative sea level. The current dataset contains: a) instructions on how to download three existing (published) datasets that are used to make the worldmap; b) the computer scripts (Matlab and Jupyter Notebook) that are needed to analyze these existing datasets and to produce the worldmap; c) output from these computer scripts (figures and calculation results).

What propelled the human ‘revolutions' that started the Anthropocene? and what could speed humanity out of trouble? Here, we focus on the role of reinforcing feedback cycles, often comprised of diverse, unrelated elements (e.g. fire, grass, humans), in propelling abrupt and/or irreversible, revolutionary changes. We suggest that differential ‘spread of the cycles' has been critical to the past human revolutions of fire use, agriculture, rise of complex states and industrialization. For each revolution, we review and map out proposed reinforcing feedback cycles, and describe how new systems built on previous ones, propelling us into the Anthropocene. We argue that to escape a bleak Anthropocene will require abruptly shifting from existing unsustainable ‘vicious cycles’, to alternative sustainable ‘virtuous cycles' that can outspread and outpersist them. This will need to be complemented by a revolutionary cultural shift from maximizing growth to maximizing persistence (sustainability). To achieve that we suggest that non-human elements need to be brought back into the feedback cycles underlying human cultures and associated measures of progress.

Using data from a wide range of natural communities including the human microbiome, plants, fish, mushrooms, rodents, beetles, and trees, we show that universally just a few percent of the species account for most of the biomass. This is in line with the classical observation that the vast bulk of biodiversity is very rare. Attempts to find traits allowing the tiny fraction of abundant species to escape rarity have remained unsuccessful. Here, we argue that this might be explained by the fact that hyper-dominance can emerge through stochastic processes. We demonstrate that in neutrally competing groups of species, rarity tends to become a trap if environmental fluctuations result in gains and losses proportional to abundances. This counter-intuitive phenomenon arises because absolute change tends to zero for very small abundances, causing rarity to become a "sticky state", a pseudoattractor that can be revealed numerically in classical ball-in-cup landscapes. As a result, the vast majority of species spend most of their time in rarity leaving space for just a few others to dominate the neutral community. However, fates remain stochastic. Provided that there is some response diversity, roles occasionally shift as stochastic events or natural enemies bring an abundant species down allowing a rare species to rise to dominance. Microbial time series spanning thousands of generations support this prediction. Our results suggest that near-neutrality within niches may allow numerous rare species to persist in the wings of the dominant ones. Stand-ins may serve as insurance when former key species collapse.

Superimposed on long-term late Paleocene–early Eocene warming (~59 to 52 million years ago), Earth’s climate experienced a series of abrupt perturbations, characterized by massive carbon input into the ocean-atmosphere system and global warming. Here, we examine the three most punctuated events of this period, the Paleocene-Eocene Thermal Maximum and Eocene Thermal Maximum 2 and 3, to probe whether they were initiated by climate-driven carbon cycle tipping points. Specifically, we analyze the dynamics of climate and carbon cycle indicators acquired from marine sediments to detect changes in Earth system resilience and to identify positive feedbacks. Our analyses suggest a loss of Earth system resilience toward all three events. Moreover, dynamic convergent cross mapping reveals intensifying coupling between the carbon cycle and climate during the long-term warming trend, supporting increasingly dominant climate forcing of carbon cycle dynamics during the Early Eocene Climatic Optimum when these recurrent global warming events became more frequent.

To reduce the consequences of infectious disease outbreaks, the timely implementation of public health measures is crucial. Currently used early-warning systems are highly context-dependent and require a long phase of model building. A proposed solution to anticipate the onset or termination of an outbreak is the use of so-called resilience indicators. These indicators are based on the generic theory of critical slowing down and require only incidence time series. Here we assess the potential for this approach to contribute to outbreak anticipation. We systematically reviewed studies that used resilience indicators to predict outbreaks or terminations of epidemics. We identified 37 studies meeting the inclusion criteria: 21 using simulated data and 16 real-world data. 36 out of 37 studies detected significant signs of critical slowing down before a critical transition (i.e., the onset or end of an outbreak), with a highly variable sensitivity (i.e., the proportion of true positive outbreak warnings) ranging from 0.03 to 1 and a lead time ranging from 10 days to 68 months. Challenges include low resolution and limited length of time series, a too rapid increase in cases, and strong seasonal patterns which may hamper the sensitivity of resilience indicators. Alternative types of data, such as Google searches or social media data, have the potential to improve predictions in some cases. Resilience indicators may be useful when the risk of disease outbreaks is changing gradually. This may happen, for instance, when pathogens become increasingly adapted to an environment or evolve gradually to escape immunity. High-resolution monitoring is needed to reach sufficient sensitivity. If those conditions are met, resilience indicators could help improve the current practice of prediction, facilitating timely outbreak response. We provide a step-by-step guide on the use of resilience indicators in infectious disease epidemiology, and guidance on the relevant situations to use t

The physiology of all living organisms is striving for maintenance of a homeostatic balance. Both diseases and external stressors disturb this balance and may cause the organism to pass a in principle reversible tipping point towards a disease state. However, tipping points also occur in other complex systems, as we have seen for banks in the recent economic crises, and for health care institutions in the covid-19 pandemic. Many studies have been carried out to elucidate the behavior of such systems in the proximity of tipping points. The common principle among all complex adaptive systems turned out to be the critical slowing of the system close to it's tipping point. Additionally, the concepts of frailty and resilience have been developed to respectively characterize the risk for passing tipping points and the chances to return to a healthy balance. Both can be quantified and have shown prognostic value for the dynamics of complex (living) systems around their tipping points., De menselijke fysiologie streeft net als die van andere levende organismen naar een homeostatisch evenwicht. Zowel ziekten als externe stressoren kunnen dit evenwicht verstoren en ervoor zorgen dat het systeem een kantelpunt passeert en (tijdelijk) in een nieuw, minder gunstig evenwicht raakt. Iemand is dan ziek. Kantelpunten komen echter ook veelvuldig voor bij complexe sociale systemen, zoals we hebben gezien bij banken en gezondheidszorginstellingen in financiële crises en tijdens de covid-19-pandemie. Wetenschappelijk bezien blijken gelijke principes te gelden, ongeacht de aard van het kantelpunt. Centraal hierbij staat de tragere reactie op verstoringen van het evenwicht als een organisme dicht bij een kantelpunt is.We spreken verder van kwetsbaarheid of ‘frailty’ als een persoon gevoelig is voor kanteling van de gezondheid door lichte stressoren en van veerkracht als het vermogen tot herstel na het passeren van een kantelpunt. Zowel kwetsbaarheid als veerkracht kunnen in toenemende mate worden gekwantificeerd en hebben voorspellende waarde voor de dynamiek rond kantelpunten.

Theory suggests that increasingly long, negative feedback loops of many interacting species may destabilize food webs as complexity increases. Less attention has, however, been paid to the specific ways in which these delayed negative feedbacks’ may affect the response of complex ecosystems to global environmental change. Here, we describe five fundamental ways in which these feedbacks might pave the way for abrupt, large-scale transitions and species losses. By combining topological and bioenergetic models, we then proceed by showing that the likelihood of such transitions increases with the number of interacting species and/or when the combined effects of stabilizing network patterns approach the minimum required for stable coexistence. Our findings thus shift the question from the classical question of what makes complex, unaltered ecosystems stable to whether the effects of, known and unknown, stabilizing food-web patterns are sufficient to prevent abrupt, large-scale transitions under global environmental change.

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.

The costs of climate change are often estimated in monetary terms but this raises ethical issues. Here we express them in terms of numbers of people left outside the ‘human climate niche’ – defined as the historically highly-conserved distribution of relative human population density with respect to mean annual temperature (MAT). We show that climate change has already put ~9% of people (>600 million) outside this niche. By end-of-century (2080-2100), current policies leading to around 2.7 °C global warming could leave one 22 third (22-39%) of people outside the niche. Reducing global warming from 2.7 to 1.5 °C results in a ~5-fold decrease in the population exposed to unprecedented heat (MAT ≥29 °C). The lifetime emissions of ~3.5 global average citizens today (or ~1.2 average US citizens) expose 1 future person to unprecedented heat by end-of-century. That person comes from a place where emissions today are around half of the global average. These results highlight the need for more decisive policy action to limit the human costs and inequities of climate change.

The published files are two maps of North American boreal forest tree cover trends between 2000 and 2019. Pixel values are annual trends in tree cover expressed as % change per year. The trends are based on annual tree cover estimates from the MODIS Vegetation Continuous Field version 6 product. We quantified tree cover trends per pixel through Theil-Sen's slope estimation using the 'zyp' package in R. We followed the Yue-Pilon pre-whitening method to account for temporal autocorrelation. We created a trend map for all data points within the boreal biome boundary following Gauthier et al. 2015, Science (DOI: 10.1126/science.aaa9092) and added a 120km buffer around it (tcchange_all_points_clipped.tif). We also produced a map where we masked out non-significant trends based on a Mann-Kendall-test (tcchange_significant_points.tif). Both maps have a spatial resolution of around 1,000m. The map forms the key results in our manuscript: 'Tree cover changes reveal contraction of North American boreal forests', Rotbarth et al. (In Review)

Climate change is expected to shift the boreal biome northward through expansion at the northern and contraction at the southern boundary respectively. However, biome-scale evidence of such a shift is rare. Here, we used remotely-sensed tree cover data to quantify temporal changes across the North American boreal biome from 2000 to 2019. We reveal a strong north-south asymmetry in tree cover change, coupled with a range shrinkage of tree cover distributions. We found no evidence for tree cover expansion in the northern biome, while tree cover increased markedly in the core of the biome range. By contrast, tree cover declined along the southern biome boundary, where losses were related largely to wildfires and timber logging. We show that these contrasting trends are structural indicators for a possible onset of a biome contraction which may lead to long-term carbon declines.

The costs of climate change are often estimated in monetary terms but this raises ethical issues. Here we express them in terms of numbers of people left outside the ‘human climate niche’ – defined as the historically highly-conserved distribution of relative human population density with respect to mean annual temperature (MAT). We show that climate change has already put ~9% of people (>600 million) outside this niche. By end-of-century (2080-2100), current policies leading to around 2.7 °C global warming could leave one third (22-39%) of people outside the niche. Reducing global warming from 2.7 to 1.5 °C results in a ~5-fold decrease in the population exposed to unprecedented heat (MAT ≥29 °C). The lifetime emissions of ~3.5 global average citizens today (or ~1.2 average US citizens) expose 1 future person to unprecedented heat by end-of-century. That person comes from a place where emissions today are around half of the global average. These results highlight the need for more decisive policy action to limit the human costs and inequities of climate change.

The stability and resilience of the Earth system and human well-being are inseparably linked1,2,3, yet their interdependencies are generally under-recognized; consequently, they are often treated independently4,5. Here, we use modelling and literature assessment to quantify safe and just Earth system boundaries (ESBs) for climate, the biosphere, water and nutrient cycles, and aerosols at global and subglobal scales. We propose ESBs for maintaining the resilience and stability of the Earth system (safe ESBs) and minimizing exposure to significant harm to humans from Earth system change (a necessary but not sufficient condition for justice)4. The stricter of the safe or just boundaries sets the integrated safe and just ESB. Our findings show that justice considerations constrain the integrated ESBs more than safety considerations for climate and atmospheric aerosol loading. Seven of eight globally quantified safe and just ESBs and at least two regional safe and just ESBs in over half of global land area are already exceeded. We propose that our assessment provides a quantitative foundation for safeguarding the global commons for all people now and into the future.

How states and great powers rise and fall is an intriguing enigma of human history. Are there any patterns? Do polities become more vulnerable over time as they age? We analyze longevity in hundreds of premodern states using survival analysis to help provide initial insights into these questions. This approach is commonly used to study the risk of death in biological organisms or failure in mechanical systems. The results reveal that the risk of state termination increased steeply over approximately the first two centuries after formation and stabilized thereafter. This provides the first quantitative support for the hypothesis that the resilience of political states decreases over time. Potential mechanisms that could drive such declining resilience include environmental degradation, increasing complexity, growing inequality, and extractive institutions. While the cases are from premodern times, such dynamics and drivers of vulnerability may remain relevant today.

Theory suggests that increasingly long, negative feedback loops of many interacting species may destabilize food webs as complexity increases. Less attention has, however, been paid to the specific ways in which these ‘delayed negative feedbacks’ may affect the response of complex ecosystems to global environmental change. Here, we describe five fundamental ways in which these feedbacks might pave the way for abrupt, large‐scale transitions and species losses. By combining topological and bioenergetic models, we then proceed by showing that the likelihood of such transitions increases with the number of interacting species and/or when the combined effects of stabilizing network patterns approach the minimum required for stable coexistence. Our findings thus shift the question from the classical question of what makes complex, unaltered ecosystems stable to whether the effects of, known and unknown, stabilizing food‐web patterns are sufficient to prevent abrupt, large‐scale transitions under global environmental change.

There is growing awareness of pollinator declines worldwide. Conservation efforts have mainly focused on finding the direct causes, while paying less attention to building a systemic understanding of the fragility of these communities of pollinators. To fill this gap, we need operational measures of network resilience that integrate two different approaches in theoretical ecology. First, we should consider the range of conditions compatible with the stable coexistence of all of the species in a community. Second, we should address the rate and shape of network collapse once this safe operational space is exited. In this review, we describe this integrative approach and consider several mechanisms that may enhance the resilience of pollinator communities, chiefly rewiring the network of interactions, increasing heterogeneity, allowing variance, and enhancing coevolution. The most pressing need is to develop ways to reduce the gap between these theoretical recommendations and practical applications. This perspective shifts the emphasis from traditional approaches focusing on the equilibrium states to strategies that allow pollination networks to cope with global environmental change.