Cited by 607Ecosystem dynamics and resiliencePlant Water Relations and Carbon DynamicsConservation, Biodiversity, and Resource Management

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Critical transitions in the Amazon forest system

Open access

Abstract

The possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern 1–3 . 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 system 1 . 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.

Overview

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TL;DR

This synthesis assesses evidence that the Amazon forest system is approaching critical transitions under compounding water-stress drivers, estimating that by 2050 10–47% of Amazonian forests could be exposed to compounding disturbances. It identifies critical thresholds and safe boundaries for five drivers and three plausible post-disturbance ecosystem trajectories, concluding resilience depends on ending deforestation and degradation, expanding restoration, and stopping greenhouse gas emissions.

Key findings

  • By 2050, 10% of the Amazon forest biome has relatively high transition potential (more than 2 disturbance types), 47% has moderate transition potential (more than 1 disturbance type), and 53% has low transition potential; if broader CMIP6 bistability projections are considered, the proportion that could transition into low tree cover would be much larger.
  • Critical thresholds and safe boundaries include: annual rainfall threshold at 1,000 mm (bistability 1,000–1,800 mm; safe boundary 1,800 mm); maximum cumulative water deficit threshold at approximately −450 mm (bistability −350 to −450 mm; safe boundary −350 mm); dry season length threshold at 8 months (bistability 5–8 months; safe boundary 5 months); accumulated deforestation threshold at 20% (safe boundary 10%); and global warming threshold range of 2–6 °C (suggested safe boundary 1.5 °C).
  • Currently, 86% of the Amazon biome may be in a stable forest state, but 17% of remaining Amazon forest has been degraded by human disturbances, rising to 38% when repeated extreme droughts are included. Under rainfall trends, 6% of the biome may change from stable forest to bistable, 3% may pass into stable savanna, and 8% of bistable areas may turn into stable forest by 2050.
  • Three plausible ecosystem trajectories are identified: degraded forest (for example, Vismia, liana, and Guadua bamboo forests), white-sand savanna (confirmed in the Negro river basin after repeated fires), and degraded open-canopy ecosystem (with 5–6% of the southern Amazon landscape already shifted due to deforestation and fires).

Methods

  • The paper synthesizes paleorecords, observational data, and modelling studies, using spatial datasets including MODIS Vegetation Continuous Fields for 2001 at 250-m resolution, CHIRPS rainfall 1981–2020, CRU temperature, MapBiomas land cover, MODIS fire data, and RAISG road and protected-area layers.
  • Potential analysis was used to construct empirical stability landscapes of tree cover against mean annual precipitation, maximum cumulative water deficit, and dry season length, with sensitivity tests of kernel smoothing and thresholds.
  • A relative transition-potential index was calculated at 5-km resolution by additively combining dry-season temperature slopes, 2050 ecosystem stability classes, repeated extreme drought events, road proximity, and protected areas/Indigenous territories; atmospheric moisture flows between Amazonian countries were tracked with the UTrack Lagrangian model using ERA5 data for 2008–2017.

Limitations

  • The paper states that the simple additive transition-potential approach does not consider synergies between compounding disturbances that could trigger unexpected ecosystem transitions, and that the inherent complexity of the Amazon adds uncertainty about future dynamics.
  • The paper states that many questions remain about environmental heterogeneity and whether it can mitigate a systemic transition, that CO2 fertilization responses are still poorly understood, and that most Earth system models do not simulate dynamic vegetation cover or key disturbances.
  • Evident from the text: the transition-potential map is a relative spatial measure for governance, not a predictive model; it excludes accumulated deforestation and savannas, uses road proximity as a proxy for degrading activities, and relies on MODIS VCF data that can contain errors at lower tree cover ranges.

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Crossref · 10.1038/s41586-023-06970-0

Cites

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Figures

Fig. 1 Exploring ecosystem transition potential across the Amazon forest biome as a result of compounding disturbances. a, Changes in the dry season (July–October) mean temperature reveal widespread warming, estimated using simple regressions between time and temperature observed between 1981 and 2020 (with P < 0.1). b, Potential ecosystem stability classes estimated for year 2050, adapted from current stability classes (Extended Data Fig. 1b) by considering only areas with significant regression slopes between time and annual rainfall observed from 1981 through 2020 (with P < 0.1) (see Extended Data Fig. 3 for areas with significant changes). c, Repeated extreme drought events between 2001–2018 (adapted from ref. 39). d, Road network from where illegal deforestation and degradation may spread. e, Protected areas and Indigenous territories reduce deforestation and fire disturbances. f, Ecosystem transition potential (the possibility of forest shifting into an alternative structural or compositional state) across the Amazon biome by year 2050 inferred from compounding disturbances (a–d) and high-governance areas (e). We excluded accumulated deforestation until 2020 and savannas. Transition potential rises with compounding disturbances and varies as follows: less than 0 (in blue) as low; between 1 and 2 as moderate (in yellow); more than 2 as high (orange–red). Transition potential represents the sum of: (1) slopes of dry season mean temperature (as in a, multiplied by 10); (2) ecosystem stability classes estimated for year 2050 (as in b), with 0 for stable forest, 1 for bistable and 2 for stable savanna; (3) accumulated impacts from extreme drought events, with 0.2 for each event; (4) road proximity as proxy for degrading activities, with 1 for pixels within 10 km from a road; (5) areas with higher governance within protected areas and Indigenous territories, with −1 for pixels inside these areas. For more details, see Methods.
Fig. 1 Exploring ecosystem transition potential across the Amazon forest biome as a result of compounding disturbances. a, Changes in the dry season (July–October) mean temperature reveal widespread warming, estimated using simple regressions between time and temperature observed between 1981 and 2020 (with P < 0.1). b, Potential ecosystem stability classes estimated for year 2050, adapted from current stability classes (Extended Data Fig. 1b) by considering only areas with significant regression slopes between time and annual rainfall observed from 1981 through 2020 (with P < 0.1) (see Extended Data Fig. 3 for areas with significant changes). c, Repeated extreme drought events between 2001–2018 (adapted from ref. 39). d, Road network from where illegal deforestation and degradation may spread. e, Protected areas and Indigenous territories reduce deforestation and fire disturbances. f, Ecosystem transition potential (the possibility of forest shifting into an alternative structural or compositional state) across the Amazon biome by year 2050 inferred from compounding disturbances (a–d) and high-governance areas (e). We excluded accumulated deforestation until 2020 and savannas. Transition potential rises with compounding disturbances and varies as follows: less than 0 (in blue) as low; between 1 and 2 as moderate (in yellow); more than 2 as high (orange–red). Transition potential represents the sum of: (1) slopes of dry season mean temperature (as in a, multiplied by 10); (2) ecosystem stability classes estimated for year 2050 (as in b), with 0 for stable forest, 1 for bistable and 2 for stable savanna; (3) accumulated impacts from extreme drought events, with 0.2 for each event; (4) road proximity as proxy for degrading activities, with 1 for pixels within 10 km from a road; (5) areas with higher governance within protected areas and Indigenous territories, with −1 for pixels inside these areas. For more details, see Methods.CC BY · PubMed Central
Fig. 2 Connectivity between Amazonian countries involving atmospheric moisture flow. Brazil holds 60% of the Amazon forest biome and has a major responsibility towards its neighbouring countries in the west. Brazil is the largest supplier of rainfall to western Amazonian countries. Up to one-third of the total annual rainfall in Amazonian territories of Bolivia, Peru, Colombia and Ecuador depends on water originating from Brazil’s portion of the Amazon forest. This international connectivity illustrates how policies related to deforestation, especially in the Brazilian Amazon, will affect the climate in other countries. Arrow widths are proportional to the percentage of the annual rainfall received by each country within their Amazonian areas. We only show flows with percentages higher than 10% (see Methods for details).
Fig. 2 Connectivity between Amazonian countries involving atmospheric moisture flow. Brazil holds 60% of the Amazon forest biome and has a major responsibility towards its neighbouring countries in the west. Brazil is the largest supplier of rainfall to western Amazonian countries. Up to one-third of the total annual rainfall in Amazonian territories of Bolivia, Peru, Colombia and Ecuador depends on water originating from Brazil’s portion of the Amazon forest. This international connectivity illustrates how policies related to deforestation, especially in the Brazilian Amazon, will affect the climate in other countries. Arrow widths are proportional to the percentage of the annual rainfall received by each country within their Amazonian areas. We only show flows with percentages higher than 10% (see Methods for details).CC BY · PubMed Central
Fig. 3 Drivers of water stress on the Amazon forest, their critical thresholds, safe boundaries and interactions. a, Five critical drivers of water stress on Amazonian forests affect (directly or indirectly) the underlying tipping point of the system. For each driver, we indicate potential critical thresholds and safe boundaries that define a safe operating space for keeping the Amazon forest resilient11,12. We followed the precautionary principle and considered the most conservative thresholds within the ranges, when confidence was low. b, Conceptual model showing how the five drivers may interact (arrows indicate positive effects) and how these interactions may strengthen a positive feedback between water stress and forest loss. These emerging positive feedback loops could accelerate a systemic transition of the Amazon forest15. At global scales, driver 1 (global warming) intensifies with greenhouse gas emissions, including emissions from deforestation. At local scales, driver 5 (accumulated deforestation) intensifies with land use changes. Drivers 2 to 4 (regional rainfall conditions) intensify in response to drivers 1 and 5. The intensification of these drivers may cause widespread tree mortality for instance because of extreme droughts and fires76. Water stress affects vegetation resilience globally79,104, but other stressors, such as heat stress34,36, may also have a role. In the coming decades, these five drivers could change at different rates, with some approaching a critical threshold faster than others. Therefore, monitoring them separately can provide vital information to guide mitigation and adaptation strategies.
Fig. 3 Drivers of water stress on the Amazon forest, their critical thresholds, safe boundaries and interactions. a, Five critical drivers of water stress on Amazonian forests affect (directly or indirectly) the underlying tipping point of the system. For each driver, we indicate potential critical thresholds and safe boundaries that define a safe operating space for keeping the Amazon forest resilient11,12. We followed the precautionary principle and considered the most conservative thresholds within the ranges, when confidence was low. b, Conceptual model showing how the five drivers may interact (arrows indicate positive effects) and how these interactions may strengthen a positive feedback between water stress and forest loss. These emerging positive feedback loops could accelerate a systemic transition of the Amazon forest15. At global scales, driver 1 (global warming) intensifies with greenhouse gas emissions, including emissions from deforestation. At local scales, driver 5 (accumulated deforestation) intensifies with land use changes. Drivers 2 to 4 (regional rainfall conditions) intensify in response to drivers 1 and 5. The intensification of these drivers may cause widespread tree mortality for instance because of extreme droughts and fires76. Water stress affects vegetation resilience globally79,104, but other stressors, such as heat stress34,36, may also have a role. In the coming decades, these five drivers could change at different rates, with some approaching a critical threshold faster than others. Therefore, monitoring them separately can provide vital information to guide mitigation and adaptation strategies.CC BY · PubMed Central
Fig. 4 Alternative ecosystem trajectories for Amazonian forests that transition due to compounding disturbances. From examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories related to the types of disturbances, feedbacks and local environmental conditions. These alternative trajectories may be irreversible or transient depending on the strength of the novel interactions15. Particular combinations of interactions (arrows show positive effects described in the literature) may form feedback loops15 that propel the ecosystem through these trajectories. In the ‘degraded forest’ trajectory, feedbacks often involve competition between trees and other opportunistic plants85,90,92, as well as interactions between deforestation, fire and seed limitation84,87,105. At the landscape scale, secondary forests are more likely to be cleared than mature forests, thus keeping forests persistently young and landscapes fragmented83. In the ‘degraded open-canopy ecosystem’ trajectory, feedbacks involve interactions among low tree cover and fire97, soil erosion60, seed limitation105, invasive grasses and opportunistic plants96. At the regional scale, a self-reinforcing feedback between forest loss and reduced atmospheric moisture flow may increase the resilience of these open-canopy degraded ecosystems42. In the ‘white-sand savanna’ trajectory, the main feedbacks result from interactions among low tree cover and fire, soil erosion, and seed limitation106. Bottom left, floodplain forest transition to white-sand savanna after repeated fires (photo credit: Bernardo Flores); bottom centre, forest transition to degraded open-canopy ecosystem after repeated fires (photo credit: Paulo Brando); bottom right, forest transition to Vismia degraded forest after slash-and-burn agriculture (photo credit: Catarina Jakovac).
Fig. 4 Alternative ecosystem trajectories for Amazonian forests that transition due to compounding disturbances. From examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories related to the types of disturbances, feedbacks and local environmental conditions. These alternative trajectories may be irreversible or transient depending on the strength of the novel interactions15. Particular combinations of interactions (arrows show positive effects described in the literature) may form feedback loops15 that propel the ecosystem through these trajectories. In the ‘degraded forest’ trajectory, feedbacks often involve competition between trees and other opportunistic plants85,90,92, as well as interactions between deforestation, fire and seed limitation84,87,105. At the landscape scale, secondary forests are more likely to be cleared than mature forests, thus keeping forests persistently young and landscapes fragmented83. In the ‘degraded open-canopy ecosystem’ trajectory, feedbacks involve interactions among low tree cover and fire97, soil erosion60, seed limitation105, invasive grasses and opportunistic plants96. At the regional scale, a self-reinforcing feedback between forest loss and reduced atmospheric moisture flow may increase the resilience of these open-canopy degraded ecosystems42. In the ‘white-sand savanna’ trajectory, the main feedbacks result from interactions among low tree cover and fire, soil erosion, and seed limitation106. Bottom left, floodplain forest transition to white-sand savanna after repeated fires (photo credit: Bernardo Flores); bottom centre, forest transition to degraded open-canopy ecosystem after repeated fires (photo credit: Paulo Brando); bottom right, forest transition to Vismia degraded forest after slash-and-burn agriculture (photo credit: Catarina Jakovac).CC BY · PubMed Central
Extended Data Fig. 1 Alternative stable states in Amazonian tree cover relative to rainfall conditions. Potential analysis of tree cover distributions across rainfall gradients in the Amazon basin suggest the existence of critical thresholds and alternative stable states in the system. For this, we excluded accumulated deforestation until 2020 and included large areas of tropical savanna biome in the periphery of the Amazon basin (see Methods). Solid black lines indicate two stable equilibria. Small grey arrows indicate the direction towards equilibrium. (a) The overlap between ~ 1,000 and 1,800 mm of annual rainfall suggests that two alternative stable states may exist (bistability): a high tree cover state ~ 80 % (forests), and a low tree cover state ~ 20% (savannas). Tree cover around 50 % is rare, indicating an unstable state. Below 1,000 mm of annual rainfall, forests are rare, indicating a potential critical threshold for abrupt forest transition into a low tree cover state79,104 (arrow 1). Between 1,000 and 1,800 mm of annual rainfall, the existence of alternative stable states implies that forests can shift to a low tree cover stable state in response to disturbances (arrow 2). Above 1,800 mm of annual rainfall, low tree cover becomes rare, indicating a potential critical threshold for an abrupt transition into a high tree cover state. In this stable forest state, forests are expected to always recover after disturbances (arrow 3), although composition may change47,85. (b) Currently, the stable savanna state covers 1 % of the Amazon forest biome, bistable areas cover 13 % of the biome (less than previous analysis using broader geographical ranges78) and the stable forest state covers 86 % of the biome. Similar analyses using the maximum cumulative water deficit (c) and the dry season length (d) also suggest the existence of critical thresholds and alternative stable states. When combined, these critical thresholds in rainfall conditions could result in a tipping point of the Amazon forest in terms of water stress, but other factors may play a role, such as groundwater availability64. MODIS VCF may contain some level of uncertainty for low tree cover values, as shown by the standard deviation of tree cover estimates across the Amazon (Extended Data Fig. 8). However, the dataset is relatively robust for assessing bistability within the tree cover range between forest and savanna126.
Extended Data Fig. 1 Alternative stable states in Amazonian tree cover relative to rainfall conditions. Potential analysis of tree cover distributions across rainfall gradients in the Amazon basin suggest the existence of critical thresholds and alternative stable states in the system. For this, we excluded accumulated deforestation until 2020 and included large areas of tropical savanna biome in the periphery of the Amazon basin (see Methods). Solid black lines indicate two stable equilibria. Small grey arrows indicate the direction towards equilibrium. (a) The overlap between ~ 1,000 and 1,800 mm of annual rainfall suggests that two alternative stable states may exist (bistability): a high tree cover state ~ 80 % (forests), and a low tree cover state ~ 20% (savannas). Tree cover around 50 % is rare, indicating an unstable state. Below 1,000 mm of annual rainfall, forests are rare, indicating a potential critical threshold for abrupt forest transition into a low tree cover state79,104 (arrow 1). Between 1,000 and 1,800 mm of annual rainfall, the existence of alternative stable states implies that forests can shift to a low tree cover stable state in response to disturbances (arrow 2). Above 1,800 mm of annual rainfall, low tree cover becomes rare, indicating a potential critical threshold for an abrupt transition into a high tree cover state. In this stable forest state, forests are expected to always recover after disturbances (arrow 3), although composition may change47,85. (b) Currently, the stable savanna state covers 1 % of the Amazon forest biome, bistable areas cover 13 % of the biome (less than previous analysis using broader geographical ranges78) and the stable forest state covers 86 % of the biome. Similar analyses using the maximum cumulative water deficit (c) and the dry season length (d) also suggest the existence of critical thresholds and alternative stable states. When combined, these critical thresholds in rainfall conditions could result in a tipping point of the Amazon forest in terms of water stress, but other factors may play a role, such as groundwater availability64. MODIS VCF may contain some level of uncertainty for low tree cover values, as shown by the standard deviation of tree cover estimates across the Amazon (Extended Data Fig. 8). However, the dataset is relatively robust for assessing bistability within the tree cover range between forest and savanna126.CC BY · PubMed Central
Extended Data Fig. 2 Changes in dry-season temperatures across the Amazon basin. (a) Dry season temperature averaged from mean annual data observed between 1981 and 2010. (b) Changes in dry season mean temperature based on the difference between the projected future (2021−2050) and observed historical (1981−2010) climatologies. Future climatology was obtained from the estimated slopes using historical CRU data128 (shown in Fig. 1a). (c, d) Changes in the distributions of dry season mean and maximum temperatures for the Amazon basin. (e) Correlation between dry-season mean and maximum temperatures observed (1981–2010) across the Amazon basin (r = 0.95).
Extended Data Fig. 2 Changes in dry-season temperatures across the Amazon basin. (a) Dry season temperature averaged from mean annual data observed between 1981 and 2010. (b) Changes in dry season mean temperature based on the difference between the projected future (2021−2050) and observed historical (1981−2010) climatologies. Future climatology was obtained from the estimated slopes using historical CRU data128 (shown in Fig. 1a). (c, d) Changes in the distributions of dry season mean and maximum temperatures for the Amazon basin. (e) Correlation between dry-season mean and maximum temperatures observed (1981–2010) across the Amazon basin (r = 0.95).CC BY · PubMed Central
Extended Data Fig. 3 Changes in annual precipitation and ecosystem stability across the Amazon forest biome. (a) Slopes of annual rainfall change between 1981 and 2020 estimated using simple regressions (only areas with significant slopes, p < 0.1). (b) Changes in ecosystem stability classes projected for year 2050, based on significant slopes in (a) and critical thresholds in annual rainfall conditions estimated in Extended Data Fig. 1. Data obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), at 0.05° spatial resolution127.
Extended Data Fig. 3 Changes in annual precipitation and ecosystem stability across the Amazon forest biome. (a) Slopes of annual rainfall change between 1981 and 2020 estimated using simple regressions (only areas with significant slopes, p < 0.1). (b) Changes in ecosystem stability classes projected for year 2050, based on significant slopes in (a) and critical thresholds in annual rainfall conditions estimated in Extended Data Fig. 1. Data obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), at 0.05° spatial resolution127.CC BY · PubMed Central
Extended Data Fig. 4 Changes in ecosystem stability by 2050 across the Amazon based on annual rainfall projected by CMIP6 models. (a) Changes in stability classes estimated using an ensemble with the five CMIP6 models that include vegetation modules (coupled for climate-vegetation feedbacks) for two emission scenarios (Shared Socio-economic Pathways - SSPs). (b) Changes in stability classes estimated using an ensemble with all 33 CMIP6 models for the same emission scenarios. Stability changes may occur between stable forest (F), stable savanna (S) and bistable (B) classes, based on the bistability range of 1,000 – 1,800 mm in annual rainfall, estimated from current rainfall conditions (see Extended Data Fig. 1). Projections are based on climate models from the 6th Phase of the Coupled Model Intercomparison Project (CMIP6). SSP2-4.5 is a low-emission scenario of future global warming and SSP5-8.5 is a high-emission scenario. The five coupled models analysed separately in (a) were: EC-Earth3-Veg, GFDL-ESM4, MPI-ESM1-2-LR, TaiESM1 and UKESM1-0-LL (Supplementary Information Table 1).
Extended Data Fig. 4 Changes in ecosystem stability by 2050 across the Amazon based on annual rainfall projected by CMIP6 models. (a) Changes in stability classes estimated using an ensemble with the five CMIP6 models that include vegetation modules (coupled for climate-vegetation feedbacks) for two emission scenarios (Shared Socio-economic Pathways - SSPs). (b) Changes in stability classes estimated using an ensemble with all 33 CMIP6 models for the same emission scenarios. Stability changes may occur between stable forest (F), stable savanna (S) and bistable (B) classes, based on the bistability range of 1,000 – 1,800 mm in annual rainfall, estimated from current rainfall conditions (see Extended Data Fig. 1). Projections are based on climate models from the 6th Phase of the Coupled Model Intercomparison Project (CMIP6). SSP2-4.5 is a low-emission scenario of future global warming and SSP5-8.5 is a high-emission scenario. The five coupled models analysed separately in (a) were: EC-Earth3-Veg, GFDL-ESM4, MPI-ESM1-2-LR, TaiESM1 and UKESM1-0-LL (Supplementary Information Table 1).CC BY · PubMed Central
Extended Data Fig. 5 Deforestation continues to expand within the Amazon forest system. (a) Map highlighting deforestation and fire activity between 2012 and 2021, a period when environmental governance began to weaken again, as indicated by increasing rates of annual deforestation in (b). In (b), annual deforestation rates for the entire Amazon biome were adapted with permission from Smith et al.83.
Extended Data Fig. 5 Deforestation continues to expand within the Amazon forest system. (a) Map highlighting deforestation and fire activity between 2012 and 2021, a period when environmental governance began to weaken again, as indicated by increasing rates of annual deforestation in (b). In (b), annual deforestation rates for the entire Amazon biome were adapted with permission from Smith et al.83.CC BY · PubMed Central
Extended Data Fig. 6 Environmental heterogeneity in the Amazon forest system. Heterogeneity involves myriad factors, but two in particular, related to water availability, were shown to contribute to landscape-scale heterogeneity in forest resilience; topography shapes fine-scale variations of forest drought-tolerance135,136, and floodplains may reduce forest resilience by increasing vulnerability to wildfires65. Datasets: topography is shown by the Shuttle Radar Topography Mission (SRTM; https://earthexplorer.usgs.gov/)137 at 90 m resolution; floodplains and uplands are separated with the Amazon wetlands mask138 at 90 m resolution.
Extended Data Fig. 6 Environmental heterogeneity in the Amazon forest system. Heterogeneity involves myriad factors, but two in particular, related to water availability, were shown to contribute to landscape-scale heterogeneity in forest resilience; topography shapes fine-scale variations of forest drought-tolerance135,136, and floodplains may reduce forest resilience by increasing vulnerability to wildfires65. Datasets: topography is shown by the Shuttle Radar Topography Mission (SRTM; https://earthexplorer.usgs.gov/)137 at 90 m resolution; floodplains and uplands are separated with the Amazon wetlands mask138 at 90 m resolution.CC BY · PubMed Central
Extended Data Fig. 7 The Amazon is biologically and culturally diverse. (a) Tree species richness and (b) language richness illustrate how biological and cultural diversity varies across the Amazon. Diverse tree communities and human cultures contribute to increasing forest resilience in various ways that are being undermined by land-use and climatic changes. Datasets: (a) Amazon Tree Diversity Network (ATDN, https://atdn.myspecies.info). (b) World Language Mapping System (WLMS) obtained under license from Ethnologue139.
Extended Data Fig. 7 The Amazon is biologically and culturally diverse. (a) Tree species richness and (b) language richness illustrate how biological and cultural diversity varies across the Amazon. Diverse tree communities and human cultures contribute to increasing forest resilience in various ways that are being undermined by land-use and climatic changes. Datasets: (a) Amazon Tree Diversity Network (ATDN, https://atdn.myspecies.info). (b) World Language Mapping System (WLMS) obtained under license from Ethnologue139.CC BY · PubMed Central
Extended Data Fig. 8 Uncertainty of the MODIS VCF dataset across the Amazon basin. Map shows standard deviation (SD) of tree cover estimates from MODIS VCF124. We masked deforested areas until 2020 using the MapBiomas Amazonia Project (2022; https://amazonia.mapbiomas.org).
Extended Data Fig. 8 Uncertainty of the MODIS VCF dataset across the Amazon basin. Map shows standard deviation (SD) of tree cover estimates from MODIS VCF124. We masked deforested areas until 2020 using the MapBiomas Amazonia Project (2022; https://amazonia.mapbiomas.org).CC BY · PubMed Central

Verification

Claims the paper makes

69 Partly supported · 23 Own interpretation · 20 No full text of the sources · 19 Supported · 15 Own result · 12 Not checked yet · 8 No basis given · 6 Supported so far · 4 Not supported · 3 Couldn’t confirm · 3 Sources not found · 3 Own method · 1 Mixed support · accuracy not yet measured

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  • It may state two separate claims as one. 85
  • Probably not a substantial claim. 18
97 of 97
  • The paper relies on this without support.

    The additive sum of warming/drying slopes, ecosystem stability classes, drought impacts, road proximity, and governance can serve as a proxy for ecosystem transition potential.

    “Transition potential represents the sum of: (1) slopes of dry season mean temperature (as in a, multiplied by 10); (2) ecosystem stability classes estimated for year 2050 (as in b), with 0 for stable forest, 1 for bistable and 2 for stable savanna; (3) accumulated impacts from extreme drought events, with 0.2 for each event; (4) road proximity as proxy for degrading activities, with 1 for pixels”
  • The paper relies on this without support.

    Northwestern Amazon forests rarely experience water stress.

    “Northwestern forests rarely experience water stress, which makes them relatively more resilient than southeastern forests that may experience water stress in the dry season, and therefore are more likely to shift into a low tree cover state.”
  • The paper relies on this without support.

    Protected areas and Indigenous territories have lower exposure to human land-use disturbances such as deforestation and forest fires.

    “protected areas and Indigenous territories as areas with lower exposure to human (land use) disturbances, such as deforestation and forest fires, with −1 for pixels inside these areas.”
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Citations

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Research works printed but not linked: 3

  1. IPCC. Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) https://www.ipcc.ch/report/ar6/wg1/#FullReport (Cambridge Univ. Press, 2021).
  2. Holling, C. S. Engineering Resilience versus Ecological Resilience (National Academy Press, 1996).
  3. Silverman, B. W. Density Estimation for Statistics and Data Analysis (Chapman & Hall/CRC Taylor & Francis Group, 1998).

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Metadata

Authors
Flores, B. M., Montoya, E., Sakschewski, B., Nascimento, N., Staal, A., Betts, R. A., Levis, C., Lapola, D. M., Esquível-Muelbert, A., Jakovac, C., Nobre, C. A., Oliveira, R. S., Borma, L. S., Nian, D., Boers, N., Hecht, S. B., ter Steege, H., Arieira, J., Lucas, I. L., Berenguer, E., Marengo, J. A., Gatti, L. V., Mattos, C. R. C., & Hirota, M.
Affiliations
  • Universidade Federal de Santa Catarina (BR)
  • Leibniz Association (DE)
  • Universidade de São Paulo (BR)
  • Utrecht University (NL)
  • Met Office (GB)
  • Universidade Estadual de Campinas (UNICAMP) (BR)
  • Instituto Nacional de Pesquisas Espaciais (BR)
  • University of California, Los Angeles (US)
  • Korea University (KR)
  • Princeton University (US)
Corresponding author
  • Flores, Bernardo M.
  • Hirota, Marina
Venue
Nature
Published
2024-02-14
Volume
626
Issue
7999
Pages
555-564
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
OpenAlex
W4391809192
Cited by
607
References
136
Citations by year
2026: 252 · 2025: 254 · 2024: 101
FWCI
106.58
Type
journal article
Language
en
Open access
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  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, cc-by)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
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  • Graduate Institute Geneva Institutional Repository (Graduate Institute of International and Development Studies) (submittedVersion)
  • The Digital Academic Repository of Naturalis Biodiversity Center (Naturalis Biodiversity Center) (submittedVersion, cc-by)
  • Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research) (submittedVersion)
Funders
  • Princeton University
  • UK Research and Innovation (NE/V021346/1, 2018-1)
  • European Commission (956170, HORIZON2020, 758873, 820970)
  • Met Office
  • Volkswagen Foundation (956170)
  • Fundação de Amparo à Pesquisa do Estado de São Paulo (2014/50848-, 2019/, 2014/, 20/08940-6, 2019/07773‐ 1, 88887, 14/50848-9)
  • Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (88887)
  • Bundesministerium für Bildung und Forschung (01LS2001A)
  • Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VI.Veni.202.170)
  • Ministério da Ciência, Tecnologia e Inovação
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (465501/2014-, #465501/2014‐1, 2014-1, 309074/2021‐5, 159440/2018-1)
  • Instituto Serrapilheira (Serra‐1709–18983, 1709-18983)
  • Foreign, Commonwealth and Development Office
  • Natural Environment Research Council (NE/Y003942/1, NE/V021346/1)
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Ecosystem dynamics and resilience
  • Physical Sciences › Environmental Science › Global and Planetary Change › Plant Water Relations and Carbon Dynamics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Conservation, Biodiversity, and Resource Management
Keywords
Amazon rainforest, tipping points, ecosystem resilience, water stress, drought, deforestation, forest fires, climate change, feedback, restoration, greenhouse gas emissions, Anthropocene
MeSH terms
Wildfires, Feedback, Trees, Uncertainty, Environmental Restoration and Remediation, Droughts, Global Warming, Forests

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-11-03, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-11-03 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Cited sources are checked with a language model’s help (gpt-6-sol).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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