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Funding: Data collection was largely funded by the UK Natural Environment Research Council (NERC) project TREMOR (NE/N004655/1) to D.G., E.G. and O.P., with further funds from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES, finance code 001) to J.V.T. and a University of Leeds Climate Research Bursary Fund to J.V.T. D.G., E.G. and O.P. acknowledge further support from a NERC-funded consortium award (ARBOLES, NE/S011811/1). This paper is an outcome of J.V.T.’s doctoral thesis, which was sponsored by CAPES (GDE 99999.001293/2015-00). J.V.T. was previously supported by the NERC-funded ARBOLES project (NE/S011811/1) and is supported at present by the Swedish Research Council Vetenskapsrådet (grant no. 2019-03758 to R.M.). E.G., O.P. and D.G. acknowledge support from NERC-funded BIORED grant (NE/N012542/1). O.P. acknowledges support from an ERC Advanced Grant and a Royal Society Wolfson Research Merit Award. R.S.O. was supported by a CNPq productivity scholarship, the São Paulo Research Foundation (FAPESP-Microsoft 11/52072-0) and the US Department of Energy, project GoAmazon (FAPESP 2013/50531-2). M.M. acknowledges support from MINECO FUN2FUN (CGL2013-46808-R) and DRESS (CGL2017-89149-C2-1-R). C.S.-M., F.B.V. and P.R.L.B. were financed by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES, finance code 001). C.S.-M. received a scholarship from the Brazilian National Council for Scientific and Technological Development (CNPq 140353/2017-8) and CAPES (science without borders 88881.135316/2016-01). Y.M. acknowledges the Gordon and Betty Moore Foundation and ERC Advanced Investigator Grant (GEM-TRAITS, 321131) for supporting the Global Ecosystems Monitoring (GEM) network (gem.tropicalforests.ox.ac.uk), within which some of the field sites (KEN, TAM and ALP) are nested. The authors thank Brazil–USA Collaborative Research GoAmazon DOE-FAPESP-FAPEAM (FAPESP 2013/50533-5 to L.A.) and National Science Foundation (award DEB-1753973 to L. Alves). They thank Serrapilheira Serra-1709-18983 (to M.H.) and CNPq-PELD/POPA-441443/2016-8 (to L.G.) (P.I. Albertina Lima). They thank all the colleagues and grants mentioned elsewhere [8,36] that established, identified and measured the Amazon forest plots in the RAINFOR network analysed here. The authors particularly thank J. Lyod, S. Almeida, F. Brown, B. Vicenti, N. Silva and L. Alves. This work is an outcome approved Research Project no. 19 from ForestPlots.net, a collaborative initiative developed at the University of Leeds that unites researchers and the monitoring of their permanent plots from the world’s tropical forests [61]. The authros thank A. Levesley, K. Melgaço Ladvocat and G. Pickavance for ForestPlots.net management. They thank Y. Wang and J. Baker, respectively, for their help with the map and with the climatic data. The authors acknowledge the invaluable help of M. Brum for kindly providing the comparison of vulnerability curves based on PAD and on PLC shown in this manuscript. They thank J. Martinez-Vilalta for his comments on an early version of this manuscript. The authors also thank V. Hilares and the Asociación para la Investigación y Desarrollo Integral (AIDER, Puerto Maldonado, Peru); V. Saldaña and Instituto de Investigaciones de la Amazonía Peruana (IIAP) for local field campaign support in Peru; E. Chavez and Noel Kempff Natural History Museum for local field campaign support in Bolivia; ICMBio, INPA/NAPPA/LBA COOMFLONA (Cooperativa mista da Flona Tapajós) and T. I. Bragança-Marituba for the research support. Tropical forests face increasing climate risk1,2, yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, Ψ50) and hydraulic safety margins (for example, HSM50) are important predictors of drought-induced mortality risk3-5, little is known about how these vary across Earth's largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters Ψ50 and HSM50 vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both Ψ50 and HSM50 influence the biogeographical distribution of Amazon tree species. However, HSM50 was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM50 are gaining more biomass than are low HSM50 forests. We propose that this may be associated with a growth-mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM50 in the Amazon6,7, with strong implications for the Amazon carbon sink. Publisher PDF

The Amazon rainforest is threatened by both anthropogenic climate and land use change. Observations and some modelling results suggest that the Amazon rainforest might abruptly transition to a savanna-like state and that this transition would be practically irreversible. Here, we analyze observations of precipitation, temperature and tree cover together with high-resolution simulations of several comprehensive climate models to investigate the combined effect global warming and a potential collapse of the Atlantic Meridional Overturning Circulation (AMOC) will have on the Amazon. Our results show that, while high levels of warming lead to forest dieback, an AMOC collapse would tend to stabilize the forest by increasing rainfall and decreasing temperature in most of the Amazon basin. Although an AMOC collapse would have devastating impacts globally, our results hence suggest that it may delay or even prevent parts of the Amazon rainforest from transitioning to a savanna-like state.

Since the foundational paper by Lenton et al. (2008, PNAS), tipping elements in the climate system have attracted great attention within the scientific community and beyond. One of the most important tipping elements is the Amazon rainforest. Under ongoing global warming, it is suspected that extreme droughts such as those in 2005 and 2010 occur significantly more often, up to nine out of ten years from the mid to late 21st century onwards (e.g. Cox et al., 2008, Nature; Cook et al., 2020, Earth’s Future).In this work, we quantify how climates ranging from normal rainfall conditions to extreme droughts may generate cascading tipping events through the coupled forest-climate system. For that purpose, we make use of methods from nonlinear dynamical systems theory and complex networks to create a conceptual model of the Amazon rainforest, which is dependent on itself through atmospheric moisture recycling.We reveal that, even when the rainforest is adapted to past local conditions of rainfall and evaporation, parts of the rainforest may still tip when droughts intensify. We uncover that forest-induced moisture recycling exacerbates tipping events by causing tipping cascades that make up to one-third (mean+-s.d. = 35.9+-4.9%) of all tipping events. Our results imply that if the speed of climate change might exceed the adaptation capacity of the forest, knock-on effects through moisture recycling impede further adaptation to climate change.Further, we use a network analysis method to compare the four main terrestrial moisture recycling hubs: the Amazon Basin, the Congo Rainforest, South Asia and the Indonesian Archipelago. By evaluating so-called network motifs, i.e. local-scale network structures, we quantify the fundamentally different functioning of these regions. Our results indicate that the moisture recycling streams in the Amazon Basin are more vulnerable to disturbances than in the three other main moisture recycling hubs.

The Amazon forest is at risk of dieback due to climate change, in particular decreasing mean annual precipitation (MAP) and increasing mean annual temperature (MAT). This study assesses the influence on South American vegetation under two possible future climate change scenarios: global warming, and global warming combined with an AMOC collapse. We consider MAT and MAP as control parameters and use their projected changes from climate model simulations with the Earth System Model HadGEM3. We then estimate the most probable states of vegetation based on empirical relationships between these parameters and tree cover. Our results suggest that an AMOC collapse would not contribute to further rainforest dieback over most of the Amazon basin. Instead, in parts of tropical South America, MAP increases and MAT decreases after AMOC collapse, which tends to stabilize the Amazon forest and hence delay the Amazon dieback compared to the default global warming scenario.

Territorios indígenas (ITs, en sus siglas en inglés) en la Amazonia protegen aproximadamente 24,GtC sobre el suelo, funcionan como barreras significativas contra la deforestación y la degradación del bosque, y sirven como una defensa importante contra el cambio climático. Los ITs demarcados tienen significativamente menos deforestación en comparación con tierras no reconocidas, demostrando la importancia de demarcar ITs para proteger los sustentos y la cultura de los indígenas Amazónicos y para conservar sus bosques y ríos.

Global climate change, the conversion of large areas of tropical forest to agriculture and rangelands, and forest degradation driven by wildfires are pushing the Amazon towards irreversible thresholds, often called tipping points. We need an immediate deforestation moratorium in areas more likely to cross a tipping point (e.g., Southern Amazon), and in protected areas and Indigenous territories; so that there is zero deforestation in the Amazon by 2030.

Territorios indígenas (ITs, en sus siglas en inglés) en la Amazonia protegen aproximadamente 24,GtC sobre el suelo, funcionan como barreras significativas contra la deforestación y la degradación del bosque, y sirven como una defensa importante contra el cambio climático. Los ITs demarcados tienen significativamente menos deforestación en comparación con tierras no reconocidas, demostrando la importancia de demarcar ITs para proteger los sustentos y la cultura de los indígenas Amazónicos y para conservar sus bosques y ríos.

Global climate change, the conversion of large areas of tropical forest to agriculture and rangelands, and forest degradation driven by wildfires are pushing the Amazon towards irreversible thresholds, often called tipping points. We need an immediate deforestation moratorium in areas more likely to cross a tipping point (e.g., Southern Amazon), and in protected areas and Indigenous territories; so that there is zero deforestation in the Amazon by 2030.

Territórios Indígenas (TIs) na Amazônia protegem aproximadamente 24.5 gigatoneladas de carbono (GtC) acima do solo, atuam como barreiras significativas contra o desmatamento e a degradação florestal, e funcionam como importantes amortecedores contra as mudanças climáticas. TIs demarcadas apresentam desmatamento significativamente menor do que terras não reconhecidas oficialmente, demonstrando a importância de se demarcar TIs tanto para proteger os meios de subsistência e as culturas dos povos nativos da Amazônia, quanto para conservar suas florestas e rios.

Aquí revisamos y discutimos la evidencia existente de los cambios en curso en el sistema forestal amazónico que pueden conducir a la pérdida de resiliencia y al cruce de puntos de inflexión más allá de los cuales el ecosistema puede cambiar persistentemente a un estado alternativo.

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., We suggest two different approaches of how rainfall manipulation experiments in grasslands can be carried out to investigate critical transitions of productivity. We aim to trigger a perspective shift and guide researchers along the trajectory to 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.

Tropical forests and savannas can co-occur in a range of macro-environmental conditions. In these conditions, disturbances and resource availability are thought to control savanna and forest transitions, although the mechanisms involved are disputed. We hypothesized that, in Neotropical regions where fire activity is high, fire is the main factor controlling functional differences between savanna and forest, as well as their relative resistance to biome shifts. We sampled plant functional traits and soil and determined fire history, for 198 plots distributed across three landscapes with distinct fire frequencies (high, mid, and low). In each landscape, plots covered a woody cover gradient (from wooded grasslands to forests). We tested whether the sharpness and the magnitude of the functional distinction between savanna and forest were affected by fire. We also computed the environmental hyperspace (niche space) to evaluate how biome relative stability changed in relation to fire. Functional thresholds were detected only in the high and mid landscapes, where savanna and forest plots formed a multidimensional bimodal distribution in functional trait space. The stability of savannas in relation to forest increased abruptly with fire, whereas functional differences between forest and savanna increased gradually. Our results suggest that savanna can occur as an alternative vegetation state to forest where a fire burns every 18 years (on average), but higher frequencies are required for savannas to occupy large unique portions of the environmental niche space., The authors are grateful to the São Paulo Research Foundation (FAPESP; processes 2013/50169-1, 2014/06100-0, and 2014/06453-0) for the financial support and scholarships granted to the authors. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. M.H. is currently supported by a grant from Instituto Serrapilheira/Serra-1709-18983, R.O. by a Grant from FAPESP 19/07773-1, and J.P. by a Grant from the Spanish government (PGC2018-096569-B-I00).

Despite its great ecological importance, the main factors governing tree cover in tropical savannas as well as savanna-forest boundaries are still largely unknown. Here we address this issue by investigating marine sediment records of long-chain n-alkane stable carbon (δ13Cwax) and hydrogen (δDwax) isotopes from a core collected off eastern tropical South America spanning the last ca. 45 thousand years. While δ13Cwax is a proxy for the main photosynthetic pathway of terrestrial vegetation, tracking the relative proportion of C3 (mainly trees) versus C4 (mainly grasses) plants, δDwax is a proxy for continental precipitation, tracking the intensity of rainfall. The investigated core was collected off the mouth of the São Francisco River drainage basin, a tropical savanna-dominated region with dry austral autumn, winter and spring. On top of millennial-scale changes, driven by anomalies in the amount of precipitation associated with Heinrich Stadials, we identify a marked obliquity control over the expansion and contraction of tree and grass cover. During periods of maximum (minimum) obliquity, trees (grasses) reached maximum coverage. We suggest that maximum (minimum) obliquity decreased (increased) the length of the dry season allowing (hampering) the expansion of tree-dominated vegetation. Periods of maximum obliquity induced an anomalous heating (cooling) of the summer (winter) hemisphere that in combination with a delayed response of the climate system slightly increased autumn precipitation over the São Francisco River drainage basin, through a shift of the Intertropical Convergence Zone towards or further into the anomalously heated hemisphere. We found that atmospheric CO2 concentration has only a secondary effect on tree cover. Our results underline the importance of the dry season length as a governing factor in the long-term control of tree cover in tropical landscapes. ISSN:0277-3791

This chapter reviews and discusses existing evidence of ongoing changes in the Amazon forest system that may lead to resilience loss and the potential to cross tipping points in which the ecosystem may shift either gradually or abruptly to a persistent, environmentally degraded configuration.

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.

Reported changes in precipitation may impact processes linked to growth and survival, with potential consequences to carbon balance in the tropics. Furthermore, precipitation regimes partially modulate the occurrence of forests and savannas in these regions. However, the local mechanisms responsible for the resilience of those ecosystems remain uncertain. We aim at understanding how forests and savannas under the same precipitation regime respond in terms of GPP to seasonal rainfall variability. We hypothesize that savannas respond faster to precipitation by changing their greenness (EVI2) because savanna trees and grasses usually occur in areas far away from the water table, while riparian forests occur in valleys with shallower water tables. We sampled a total of 20 (200m2) field plots, with 10 in cerrado stricto sensu (savanna) and 10 in riparian evergreen forests, at the Chapada dos Veadeiros National Park (PNCV), located within the Cerrado biome, in Brazil. We measured tree cover using a concave densiometer, and calculated the coupling, i.e. Kendall correlation, between EVI2 from Landsat8 and the monthly precipitation calculated from CHIRPS dataset, using time-series from 2013 through 2018. In savannas where trees and grasses co-exist, we standardized coupling values by tree cover to account only for the effect savanna trees have on the EVI2. Forests and savannas respond differently to precipitation. Maximum coupling in savannas is greater than in forests but when considering only trees in both ecosystems, we found similar responses. Furthermore, savannas respond faster than forests. Our results indicate that the GPP may depend on the proximity of the water table. However, further studies are necessary to understand which mechanisms are driving the patterns found here. Therefore, forests and savannas at the PNCV are functionally distinct, in agreement with structural differences they present, particularly taking into account the differences in the response time of forest and savanna communities.

Tipping elements are nonlinear subsystems of the Earth system that can potentially abruptly and irreversibly shift if environmental change occurs. Among these tipping elements is the Amazon rainforest, which is threatened by anthropogenic activities and increasingly frequent droughts. Here, we assess how extreme deviations from climatological rainfall regimes may cause local forest-savanna transitions that cascade through the coupled forest-climate system. We develop a dynamical network model to uncover the role of atmospheric moisture recycling in such tipping cascades. We account for the heterogeneity in critical thresholds of the forest caused by adaptation to local climatic conditions. Our results reveal that, despite this adaptation, increased dry-season intensity may trigger tipping events particularly in the southeastern Amazon. Moisture recycling is responsible for one-fourth of the tipping events. If the rate of climate change exceeds the adaptive capacity of some parts of the forest, secondary effects through moisture recycling may exceed this capacity in other regions, increasing the overall risk of tipping across the Amazon rainforest.

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.

Tree water access via roots is crucial for forest functioning and therefore forests have developed a vast variety of rooting strategies across the globe. However, Dynamic Global Vegetation Models (DGVMs), which are increasingly used to simulate forest functioning, often condense this variety of tree rooting strategies into biome-scale averages, potentially under- or overestimating forest response to intra- and inter-annual variability in precipitation. Here we present a new approach of implementing variable rooting strategies and dynamic root growth into the LPJmL4.0 DGVM and apply it to tropical and sub-tropical South-America under contemporary climate conditions. We show how competing rooting strategies which underlie the trade-off between above- and below-ground carbon investment lead to more realistic simulated intra-annual productivity and evapotranspiration, and consequently forest cover and spatial biomass distribution. We find that climate and soil depth determine a spatially heterogeneous pattern of mean rooting depth and belowground biomass across the study region.

Meshing evidence from multiple datasets unveils Earth's mechanisms for adapting to environmental changes

We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a technique successfully used in a wide variety of optimization problems. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in pixelwise classification tasks. We used the GP-based solution to evaluate complex classification problems, such as those that are related to the discrimination of vegetation types within and between tropical biomes. Using time series defined in terms of the learned spectral indices, we show that the GP framework leads to superior results than other indices that are used to discriminate and classify tropical biomes., Comment: MDPI Remote Sensing - Special Issue "Current Limits and New Challenges and Opportunities in Soft Computing, Machine Learning and Computational Intelligence for Remote Sensing"

Background: Tropical forests are threatened by intensifying natural and anthropogenic disturbance regimes. Disturbances reduce tree cover and leave the organic topsoil vulnerable to erosion processes, but when resources are still abundant forests usually recover. Scope: Across the tropics, variation in rainfall erosivity – a measure of potential soil exposure to water erosion – indicates that soils in the wetter regions would experience high erosion rates if they were not protected by tree cover. However, twenty-first-century global land cover data reveal that in wet South America tropical tree cover is decreasing and bare soil area is increasing. Here we address the role of soil erosion in a positive feedback mechanism that may persistently alter the functioning of disturbed tropical forests. Conclusions: Based on an extensive literature review, we propose a conceptual model in which soil erosion reinforces disturbance effects on tropical forests, reducing their resilience with time and increasing their likelihood of being trapped in an alternative vegetation state that is persistently vulnerable to erosion. We present supporting field evidence from two distinct forests in central Amazonia that have been repeatedly disturbed. Overall, the strength of the erosion feedback depends on disturbance types and regimes, as well as on local environmental conditions, such as topography, flooding, and soil fertility. As disturbances intensify in tropical landscapes, we argue that the erosion feedback may help to explain why certain forests persist in a degraded state and often undergo critical functional shifts.

ropical dry forests and savannas harbour unique biodiversity and provide critical ES, yet they are under severe pressure globally. We need to improve our understanding of how and when this pressure provokes tipping points in biodiversity and the associated social-ecological systems. We propose an approach to investigate how drivers leading to natural vegetation decline trigger biodiversity tipping and illustrate it using the example of the Dry Diagonal in South America, an understudied deforestation frontier. The Dry Diagonal represents the largest continuous area of dry forests and savannas in South America, extending over three million km² across Argentina, Bolivia, Brazil, and Paraguay. Natural vegetation in the Dry Diagonal has been undergoing large-scale transformations for the past 30 years due to massive agricultural expansion and intensification. Many signs indicate that natural vegetation decline has reached critical levels. Major research gaps prevail, however, in our understanding of how these transformations affect the unique and rich biodiversity of the Dry Diagonal, and how this affects the ecological integrity and the provisioning of ES that are critical both for local livelihoods and commercial agriculture. Fil: Thonicke, Kirsten. Institute for Climate Impact Research ; Alemania Fil: Langerwisch, Fanny. Institute for Climate Impact Research ; Alemania. Czech University of Life Sciences Prague; República Checa Fil: Baumann, Matthias. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; Alemania Fil: Leitão, Pedro J.. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; Alemania Fil: Václavík, Tomáš. Helmholtz Centre for Environmental Research; Alemania. Palacký University Olomouc; República Checa Fil: Alencar, Anne. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; Brasil Fil: Simões, Margareth. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); Brasil Fil: Scheiter, Simon. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); Brasil. Universidade Federal do Rio de Janeiro; Brasil Fil: Langan, Liam. Senckenberg Biodiversity and Climate Research Centre; Alemania Fil: Bustamante, Mercedes. Universidade do Brasília; Brasil Fil: Gasparri, Nestor Ignacio. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina Fil: Hirota, Marina. Universidade Federal de Santa Catarina; Brasil. Universidade Estadual de Campinas; Brasil Fil: Börner, Jan. Universitat Bonn; Alemania Fil: Rajao, Raoni. Universidade Federal de Minas Gerais; Brasil Fil: Soares Filho, Britaldo. Universidade Federal de Minas Gerais; Brasil Fil: Yanosky, Alberto. Consejo Nacional de Ciencia y Tecnología; Paraguay Fil: Ochoa Quinteiro, José Manuel. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt; Colombia Fil: Seghezzo, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; Argentina Fil: Conti, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina Fil: de la Vega Leiner, Anne Cristina. Universität Greifswald; Alemania

With ongoing global warming, the amount and frequency of precipitation in the tropics is projected to change substantially. While it has been shown that tropical forests and savannahs are sustained within the same intermediate mean annual precipitation range, the mechanisms that lead to the resilience of these ecosystems are still not fully understood. In particular, the long-term impact of rainfall variability on resilience is as yet unclear. Here we present observational evidence that both tropical forest and savannah exposed to a higher rainfall variability—in particular on interannual scales—during their long-term past are overall more resilient against climatic disturbances. Based on precipitation and tree cover data in the Brazilian Amazon basin, we constructed potential landscapes that enable us to systematically measure the resilience of the different ecosystems. Additionally, we infer that shifts from forest to savannah due to decreasing precipitation in the future are more likely to occur in regions with a precursory lower rainfall variability. Long-term rainfall variability thus needs to be taken into account in resilience analyses and projections of vegetation response to climate change. Tropical forests and savannah are more resilient to climate disturbances when they have been exposed to higher rainfall variability in the long-term past, finds an analysis of Brazilian rainfall and tree-cover observations.

International audience; Suitable time series representations play an important role in classification tasks. In this letter, we investigate the use of recurrence-plot-(RP)-based representations in the classification of eucalyptus regions in remote sensing images. The proposed framework is composed of three steps. First, time series associated with image pixels are represented by RP images; next, RP images are characterized by means of visual description approaches; finally, we use a soft computing framework based on genetic programing to discover an effective combination of time series dissimilarity functions to combine extracted features. Performed experiments in a eucalyptus classification problem demonstrated that the proposed framework is effective when compared to approaches based on the use of time series itself.

Remote sensing images have been broadly employed over the past decades to detect and investigate temporal changes on Earth's surface. Appropriate tools are needed to support complex analyses of temporal data in various domains, ranging from purely scientific to educational. There are many existing tools, but it is often necessary to switch among them several times. Typical inter operability issues include system mismatch and the variety of user profiles and data types.

During the last decades, several climate-modelling studies have forecasted a decrease in precipitation in Southern Amazonia, projecting scenarios of a drier Amazon for the future in relation with deforestation (or forest cover). In this area, only a limited number of analyses using forest cover data and rainfall time series have considered the transitional zones between the Amazon Forest and the Cerrado biome. In this work, we evaluated whether forest cover and rainfall patterns are correlated. The analysis encompassed rainfall times series from 207 rain gauges during the 1971–2010 period, and forest cover data acquired from LANDSAT5 satellite images. The results indicate that, at local level (1–15 km), both seasonal and annual precipitation values are not correlated to forest cover, whereas at the regional scale (30–50 km) contrasting to annual values, significant correlation occurs between forest cover and seasonal precipitation. Additionally, the study suggests that the larger the forest areas, the greater the probabilities of those influencing precipitation at regional scale, in opposite direction to the observed local level effects.

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., Relatively little is understood about seasonal effect of climate change on the Amazon rainforest. Here, the authors show that Amazon forest loss in response to dry-season intensification during the last glacial period was likely self-amplified by regional vegetation-rainfall feedbacks.

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.

Recent work suggests that episodes of drought and heat canbring forests across climate zones to a threshold for massivetree mortality. As complex systems approach a threshold forcollapse they tend to exhibit a loss of resilience, as reflected indeclining recovery rates from perturbations. Trees may be noexception, as at the verge of drought-induced death, trees arefound to be weakened in multiple ways, aecting their abilityto recover from stress. Here we use worldwide time seriesof satellite images to show that temporal autocorrelation, anindicator of slow recovery rates, rises steeply as mean annualprecipitation declines to levels known to be critical for tropicalforests. This implies independent support for the idea thatsuch forests may have a tipping point for collapse at dryingconditions. Moreover, the demonstration that reduced rates ofrecovery (slowing down) may be detected from satellite datasuggests a novel way to monitor resilience of tropical forests,as well as other ecosystems known to be vulnerable to collapse

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.