Critical transitions in the Amazon forest system.

The possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern1-3. For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system1. Long existing feedbacks between the forest and environmental conditions are being replaced by novel feedbacks that modify ecosystem resilience, increasing the risk of critical transition. Here we analyse existing evidence for five major drivers of water stress on Amazonian forests, as well as potential critical thresholds of those drivers that, if crossed, could trigger local, regional or even biome-wide forest collapse. By combining spatial information on various disturbances, we estimate that by 2050, 10% to 47% of Amazonian forests will be exposed to compounding disturbances that may trigger unexpected ecosystem transitions and potentially exacerbate regional climate change. Using examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories, involving different feedbacks and environmental conditions. We discuss how the inherent complexity of the Amazon adds uncertainty about future dynamics, but also reveals opportunities for action. Keeping the Amazon forest resilient in the Anthropocene will depend on a combination of local efforts to end deforestation and degradation and to expand restoration, with global efforts to stop greenhouse gas emissions.

Critical slowing down of the Amazon forest after increased drought occurrence.

Dynamic ecosystems, such as the Amazon forest, are expected to show critical slowing down behavior, or slower recovery from recurrent small perturbations, as they approach an ecological threshold to a different ecosystem state. Drought occurrences are becoming more prevalent across the Amazon, with known negative effects on forest health and functioning, but their actual role in the critical slowing down patterns still remains elusive. In this study, we evaluate the effect of trends in extreme drought occurrences on temporal autocorrelation (TAC) patterns of satellite-derived indices of vegetation activity, an indicator of slowing down, between 2001 and 2019. Differentiating between extreme drought frequency, intensity, and duration, we investigate their respective effects on the slowing down response. Our results indicate that the intensity of extreme droughts is a more important driver of slowing down than their duration, although their impacts vary across the different Amazon regions. In addition, areas with more variable precipitation are already less ecologically stable and need fewer droughts to induce slowing down. We present findings indicating that most of the Amazon region does not show an increasing trend in TAC. However, the predicted increase in extreme drought intensity and frequency could potentially transition significant portions of this ecosystem into a state with altered functionality.

Estimating the spatial amplification of damage caused by degradation in the Amazon.

The Amazon rainforests have been undergoing unprecedented levels of human-induced disturbances. In addition to local impacts, such changes are likely to cascade following the eastern-western atmospheric flow generated by trade winds. We propose a model of spatial and temporal interactions created by this flow to estimate the spread of effects from local disturbances to downwind locations along atmospheric trajectories. The spatial component captures cascading effects propagated by neighboring regions, while the temporal component captures the persistence of local disturbances. Importantly, all these network effects can be described by a single matrix, acting as a spatial multiplier that amplifies local forest disturbances. This matrix holds practical implications for policymakers as they can use it to easily map where the damage of an initial forest disturbance is amplified and propagated to. We identify regions that are likely to cause the largest impact throughout the basin and those that are the most vulnerable to shocks caused by remote deforestation. On average, the presence of cascading effects mediated by winds in the Amazon doubles the impact of an initial damage. However, there is heterogeneity in this impact. While damage in some regions does not propagate, in others, amplification can reach 250%. Since we only account for spillovers mediated by winds, our multiplier of 2 should be seen as a lower bound.

Double stress of waterlogging and drought drives forest-savanna coexistence.

Forest-savanna boundaries are ecotones that support complex ecosystem functions and are sensitive to biotic/abiotic perturbations. What drives their distribution today and how it may shift in the future are open questions. Feedbacks among climate, fire, herbivory, and land use are known drivers. Here, we show that alternating seasonal drought and waterlogging stress favors the dominance of savanna-like ecosystems over forests. We track the seasonal water-table depth as an indicator of water stress when too deep and oxygen stress when too shallow and map forest/savanna occurrence within this double-stress space in the neotropics. We find that under a given annual precipitation, savannas are favored in landscape positions experiencing double stress, which is more common as the dry season strengthens (climate driver) but only found in waterlogged lowlands (terrain driver). We further show that hydrological changes at the end of the century may expose some flooded forests to savanna expansion, affecting biodiversity and soil carbon storage. Our results highlight the importance of land hydrology in understanding/predicting forest-savanna transitions in a changing world.

Recurrent droughts increase risk of cascading tipping events by outpacing adaptive capacities in the Amazon rainforest.

Tipping elements are nonlinear subsystems of the Earth system that have the potential to abruptly shift to another state if environmental change occurs close to a critical threshold with large consequences for human societies and ecosystems. Among these tipping elements may be the Amazon rainforest, which has been undergoing intensive anthropogenic activities and increasingly frequent droughts. Here, we assess how extreme deviations from climatological rainfall regimes may cause local forest collapse that cascades through the coupled forest-climate system. We develop a conceptual dynamic network model to isolate and uncover the role of atmospheric moisture recycling in such tipping cascades. We account for heterogeneity in critical thresholds of the forest caused by adaptation to local climatic conditions. Our results reveal that, despite this adaptation, a future climate characterized by permanent drought conditions could trigger a transition to an open canopy state particularly in the southern Amazon. The loss of atmospheric moisture recycling contributes to one-third of the tipping events. Thus, by exceeding local thresholds in forest adaptive capacity, local climate change impacts may propagate to other regions of the Amazon basin, causing a risk of forest shifts even in regions where critical thresholds have not been crossed locally.

Linking plant hydraulics and the fast-slow continuum to understand resilience to drought in tropical ecosystems.

Tropical ecosystems have the highest levels of biodiversity, cycle more water and absorb more carbon than any other terrestrial ecosystem on Earth. Consequently, these ecosystems are extremely important components of Earth's climatic system and biogeochemical cycles. Plant hydraulics is an essential discipline to understand and predict the dynamics of tropical vegetation in scenarios of changing water availability. Using published plant hydraulic data we show that the trade-off between drought avoidance (expressed as deep-rooting, deciduousness and capacitance) and hydraulic safety (P50 - the water potential when plants lose 50% of their maximum hydraulic conductivity) is a major axis of physiological variation across tropical ecosystems. We also propose a novel and independent axis of hydraulic trait variation linking vulnerability to hydraulic failure (expressed as the hydraulic safety margin (HSM)) and growth, where inherent fast-growing plants have lower HSM compared to slow-growing plants. We surmise that soil nutrients are fundamental drivers of tropical community assembly determining the distribution and abundance of the slow-safe/fast-risky strategies. We conclude showing that including either the growth-HSM or the resistance-avoidance trade-off in models can make simulated tropical rainforest communities substantially more vulnerable to drought than similar communities without the trade-off. These results suggest that vegetation models need to represent hydraulic trade-off axes to accurately project the functioning and distribution of tropical ecosystems.

Disturbance maintains alternative biome states.

Understanding the mechanisms controlling the distribution of biomes remains a challenge. Although tropical biome distribution has traditionally been explained by climate and soil, contrasting vegetation types often occur as mosaics with sharp boundaries under very similar environmental conditions. While evidence suggests that these biomes are alternative states, empirical broad-scale support to this hypothesis is still lacking. Using community-level field data and a novel resource-niche overlap approach, we show that, for a wide range of environmental conditions, fire feedbacks maintain savannas and forests as alternative biome states in both the Neotropics and the Afrotropics. In addition, wooded grasslands and savannas occurred as alternative grassy states in the Afrotropics, depending on the relative importance of fire and herbivory feedbacks. These results are consistent with landscape scale evidence and suggest that disturbance is a general factor driving and maintaining alternative biome states and vegetation mosaics in the tropics.

Thresholds for boreal biome transitions.

Although the boreal region is warming twice as fast as the global average, the way in which the vast boreal forests and tundras may respond is poorly understood. Using satellite data, we reveal marked alternative modes in the frequency distributions of boreal tree cover. At the northern end and at the dry continental southern extremes, treeless tundra and steppe, respectively, are the only possible states. However, over a broad intermediate temperature range, these treeless states coexist with boreal forest (∼75% tree cover) and with two more open woodland states (∼20% and ∼45% tree cover). Intermediate tree covers (e.g., ∼10%, ∼30%, and ∼60% tree cover) between these distinct states are relatively rare, suggesting that they may represent unstable states where the system dwells only transiently. Mechanisms for such instabilities remain to be unraveled, but our results have important implications for the anticipated response of these ecosystems to climatic change. The data reveal that boreal forest shows no gradual decline in tree cover toward its limits. Instead, our analysis suggests that it becomes less resilient in the sense that it may more easily shift into a sparse woodland or treeless state. Similarly, the relative scarcity of the intermediate ∼10% tree cover suggests that tundra may shift relatively abruptly to a more abundant tree cover. If our inferences are correct, climate change may invoke massive nonlinear shifts in boreal biomes.

Tipping points in tropical tree cover: linking theory to data.

It has recently been found that the frequency distribution of remotely sensed tree cover in the tropics has three distinct modes, which seem to correspond to forest, savanna, and treeless states. This pattern has been suggested to imply that these states represent alternative attractors, and that the response of these systems to climate change would be characterized by critical transitions and hysteresis. Here, we show how this inference is contingent upon mechanisms at play. We present a simple dynamical model that can generate three alternative tree cover states (forest, savanna, and a treeless state), based on known mechanisms, and use this model to simulate patterns of tree cover under different scenarios. We use these synthetic data to show that the hysteresis inferred from remotely sensed tree cover patterns will be inflated by spatial heterogeneity of environmental conditions. On the other hand, we show that the hysteresis inferred from satellite data may actually underestimate real hysteresis in response to climate change if there exists a positive feedback between regional tree cover and precipitation. Our results also indicate that such positive feedback between vegetation and climate should cause direct shifts between forest and a treeless state (rather than through an intermediate savanna state) to become more likely. Finally, we show how directionality of historical change in conditions may bias the observed relationship between tree cover and environmental conditions.

Socioeconomic development role in hospitalization related to air pollution and meteorology: A study case in southern Brazil.

Air pollution is one of the foremost environmental threats to human health. However, the meteorological and social factors that lead to respiratory and cardiovascular diseases have not been fully elucidated. In this study, we use Principal Component Analysis and Generalized Linear Model (PCA-GLM) to investigate the combined effect of socioeconomic development and air pollution on cardiorespiratory hospitalization in southern Brazil. This region has the highest rates of hospitalization by cardiorespiratory diseases in the country. We analyze three main sources of data: (i) air pollutants density from TROPOMI/Sentinel-5p satellite; (ii) temperature, humidity, and planetary boundary layer height (PBLH) modeled with the Weather Research Forecast model; and (iii) hospitalization by cardiorespiratory diseases obtained from the Brazilian National Health System. We estimate the Relative Risk (RR) using the PCA-GLM coefficients and interquartile variations of air pollutants density and meteorological parameters. Our results show that the population living in colder and drier municipalities is more prone to cardiorespiratory hospitalization. Regarding respiratory hospitalization, municipalities with lower socioeconomic development are more sensitive to meteorology and pollution variability than highly developed ones. In less developed municipalities, we observe the highest rates of cardiorespiratory hospitalization even if air pollution is low, which we interpret in terms of higher vulnerability. The RR analysis suggests that air pollution is an important environmental risk to cardiovascular diseases and respiratory diseases is more sensitive to air pollution and meteorology than cardiovascular ones. Our findings corroborate the mounting evidence that social vulnerability is a significant factor affecting the increase of cardiorespiratory hospitalization in the world.

Mapping native and non-native vegetation in the Brazilian Cerrado using freely available satellite products.

Native vegetation across the Brazilian Cerrado is highly heterogeneous and biodiverse and provides important ecosystem services, including carbon and water balance regulation, however, land-use changes have been extensive. Conservation and restoration of native vegetation is essential and could be facilitated by detailed landcover maps. Here, across a large case study region in Goiás State, Brazil (1.1 Mha), we produced physiognomy level maps of native vegetation (n = 8) and other landcover types (n = 5). Seven different classification schemes using different combinations of input satellite imagery were used, with a Random Forest classifier and 2-stage approach implemented within Google Earth Engine. Overall classification accuracies ranged from 88.6-92.6% for native and non-native vegetation at the formation level (stage-1), and 70.7-77.9% for native vegetation at the physiognomy level (stage-2), across the seven different classifications schemes. The differences in classification accuracy resulting from varying the input imagery combination and quality control procedures used were small. However, a combination of seasonal Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (surface reflectance) imagery resulted in the most accurate classification at a spatial resolution of 20 m. Classification accuracies when using Landsat-8 imagery were marginally lower, but still reasonable. Quality control procedures that account for vegetation burning when selecting vegetation reference data may also improve classification accuracy for some native vegetation types. Detailed landcover maps, produced using freely available satellite imagery and upscalable techniques, will be important tools for understanding vegetation functioning at the landscape scale and for implementing restoration projects.

The climatic sensitivity of the forest, savanna and forest-savanna transition in tropical South America.

*We used a climate-vegetation-natural fire (CVNF) conceptual model to evaluate the sensitivity and vulnerability of forest, savanna, and the forest-savanna transition to environmental changes in tropical South America. *Initially, under current environmental conditions, CVNF model results suggested that, in the absence of fires, tropical forests would extend c. 200 km into the presently observed savanna domain. *Environmental changes were then imposed upon the model in temperature, precipitation and lightning strikes. These changes ranged from 2 to 6 degrees C warming, +10 to -20% precipitation change and 0 to 15% increase in lightning frequency, which, in aggregate form, represent expected future climatic changes in response to global warming and deforestation. *The most critical vegetation changes are projected to take place over the easternmost portions of the basin, with a widening of the forest-savanna transition. The transition width would increase from 150 to c. 300 km, with tree cover losses ranging from 20 to 85%. This means that c. 6% of the areas currently covered by forests could potentially turn into grass-dominated savanna landscapes. The mechanism driving tree cover reduction consists of the combination of less favorable climate conditions for trees and more fire activity. In addition, this sensitivity analysis predicts that the current dry shrubland vegetation of northeast Brazil could potentially turn into a bare soil landscape.

Critical transitions in rainfall manipulation experiments on grasslands.

As a result of climate and land-use changes, grasslands have been subjected to intensifying drought regimes. Extreme droughts could interfere in the positive feedbacks between grasses and soil water content, pushing grasslands across critical thresholds of productivity and leading them to collapse. If this happens, systems may show hysteresis and costly management interventions might be necessary to restore predrought productivity. Thus, neglecting critical transitions may lead to mismanagement of grasslands and to irreversible loss of ecosystem services. Rainfall manipulation experiments constitute a powerful approach to investigate the risk of such critical transitions. However, experiments performed to date have rarely applied extreme droughts and have used resilience indices that disregard the existence of hysteresis. Here, we suggest how to incorporate critical transitions when designing rainfall manipulation experiments on grasslands and when measuring their resilience to drought. The ideas presented here have the potential to trigger a perspective shift among experimental researchers, into a new state where the existence of critical transitions will be discussed, experimentally tested, and largely considered when assessing and managing vegetation resilience to global changes.

Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks.

Reduced rainfall increases the risk of forest dieback, while in return forest loss might intensify regional droughts. The consequences of this vegetation-atmosphere feedback for the stability of the Amazon forest are still unclear. Here we show that the risk of self-amplified Amazon forest loss increases nonlinearly with dry-season intensification. We apply a novel complex-network approach, in which Amazon forest patches are linked by observation-based atmospheric water fluxes. Our results suggest that the risk of self-amplified forest loss is reduced with increasing heterogeneity in the response of forest patches to reduced rainfall. Under dry-season Amazonian rainfall reductions, comparable to Last Glacial Maximum conditions, additional forest loss due to self-amplified effects occurs in 10-13% of the Amazon basin. Although our findings do not indicate that the projected rainfall changes for the end of the twenty-first century will lead to complete Amazon dieback, they suggest that frequent extreme drought events have the potential to destabilize large parts of the Amazon forest.

A Changing Number of Alternative States in the Boreal Biome: Reproducibility Risks of Replacing Remote Sensing Products.

Publicly available remote sensing products have boosted science in many ways. The openness of these data sources suggests high reproducibility. However, as we show here, results may be specific to versions of the data products that can become unavailable as new versions are posted. We focus on remotely-sensed tree cover. Recent studies have used this public resource to detect multi-modality in tree cover in the tropical and boreal biomes. Such patterns suggest alternative stable states separated by critical tipping points. This has important implications for the potential response of these ecosystems to global climate change. For the boreal region, four distinct ecosystem states (i.e., treeless, sparse and dense woodland, and boreal forest) were previously identified by using the Collection 3 data of MODIS Vegetation Continuous Fields (VCF). Since then, the MODIS VCF product has been updated to Collection 5; and a Landsat VCF product of global tree cover at a fine spatial resolution of 30 meters has been developed. Here we compare these different remote-sensing products of tree cover to show that identification of alternative stable states in the boreal biome partly depends on the data source used. The updated MODIS data and the newer Landsat data consistently demonstrate three distinct modes around similar tree-cover values. Our analysis suggests that the boreal region has three modes: one sparsely vegetated state (treeless), one distinct 'savanna-like' state and one forest state, which could be alternative stable states. Our analysis illustrates that qualitative outcomes of studies may change fundamentally as new versions of remote sensing products are used. Scientific reproducibility thus requires that old versions remain publicly available.

Global resilience of tropical forest and savanna to critical transitions.

It has been suggested that tropical forest and savanna could represent alternative stable states, implying critical transitions at tipping points in response to altered climate or other drivers. So far, evidence for this idea has remained elusive, and integrated climate models assume smooth vegetation responses. We analyzed data on the distribution of tree cover in Africa, Australia, and South America to reveal strong evidence for the existence of three distinct attractors: forest, savanna, and a treeless state. Empirical reconstruction of the basins of attraction indicates that the resilience of the states varies in a universal way with precipitation. These results allow the identification of regions where forest or savanna may most easily tip into an alternative state, and they pave the way to a new generation of coupled climate models.