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The Amazon Forest, the largest contiguous tropical forest in the world, stores a significant fraction of the carbon on land. Changes in climate and land use affect total carbon stocks, making it critical to continuously update and revise the best estimates for the region, particularly considering changes in forest dynamics. Forest inventory data cover only a tiny fraction of the Amazon region, and the coverage is not sufficient to ensure reliable data interpolation and validation. This paper presents a new forest above-ground biomass map for the Brazilian Amazon and the associated uncertainty both with a resolution of 250 meters and baseline for the satellite dataset the year of 2016 (i.e., the year of the satellite observation). A significant increase in data availability from forest inventories and remote sensing has enabled progress towards high-resolution biomass estimates. This work uses the largest airborne LiDAR database ever collected in the Amazon, mapping 360,000 km2 through transects distributed in all vegetation categories in the region. The map uses airborne laser scanning (ALS) data calibrated by field forest inventories that are extrapolated to the region using a machine learning approach with inputs from Synthetic Aperture Radar (PALSAR), vegetation indices obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite, and precipitation information from the Tropical Rainfall Measuring Mission (TRMM). A total of 174 field inventories geolocated using a Differential Global Positioning System (DGPS) were used to validate the biomass estimations. The experimental design allowed for a comprehensive representation of several vegetation types, producing an above-ground biomass map varying from a maximum value of 518 Mg ha−1, a mean of 174 Mg ha−1, and a standard deviation of 102 Mg ha−1. This unique dataset enabled a better representation of the regional distribution of the forest biomass and structure, providing further studies and critical information for decision-making concerning forest conservation, planning, carbon emissions estimate, and mechanisms for supporting carbon emissions reductions. [ABSTRACT FROM AUTHOR]

The Amazon Forest, the largest contiguous tropical forest in the world, stores a significant fraction of the carbon on land. Changes in climate and land use affect total carbon stocks, making it critical to continuously update and revise the best estimates for the region, particularly considering changes in forest dynamics. Forest inventory data cover only a tiny fraction of the Amazon region, and the coverage is not sufficient to ensure reliable data interpolation and validation. This paper presents a new forest above-ground biomass map for the Brazilian Amazon and the associated uncertainty both with a resolution of 250 meters and baseline for the satellite dataset the year of 2016 (i.e., the year of the satellite observation). A significant increase in data availability from forest inventories and remote sensing has enabled progress towards high-resolution biomass estimates. This work uses the largest airborne LiDAR database ever collected in the Amazon, mapping 360,000 km2 through transects distributed in all vegetation categories in the region. The map uses airborne laser scanning (ALS) data calibrated by field forest inventories that are extrapolated to the region using a machine learning approach with inputs from Synthetic Aperture Radar (PALSAR), vegetation indices obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite, and precipitation information from the Tropical Rainfall Measuring Mission (TRMM). A total of 174 field inventories geolocated using a Differential Global Positioning System (DGPS) were used to validate the biomass estimations. The experimental design allowed for a comprehensive representation of several vegetation types, producing an above-ground biomass map varying from a maximum value of 518 Mg ha−1, a mean of 174 Mg ha−1, and a standard deviation of 102 Mg ha−1. This unique dataset enabled a better representation of the regional distribution of the forest biomass and structure, providing further studies and critical information for decision-making concerning forest conservation, planning, carbon emissions estimate, and mechanisms for supporting carbon emissions reductions. [ABSTRACT FROM AUTHOR]

We present a thorough analysis of the scientific production in climate change and adaptation in Brazil. We conducted a bibliometric and bibliographic review to reveal and discuss how climate change research has been carried out. We compared a broader climate change research dataset with a more specific climate adaptation research dataset, to understand the main differences and convergence points, and how science conducted from a mitigation and adaptation perspective demonstrates potential to confront the climate change challenges and drivers in Brazil. Four main clusters of investigation within climate change were detected: (1) impacts on forest and plant development, (2) land use and ecology, (3) adaptation/governance, and (4) climate/atmospheric studies. Only about 5% of the total studies on climate change address adaptation, for which three main clusters of research were identified: (1) adaptation actions and policies; (2) urban environment, vulnerability, and health and (3) food and coastal impacts. Although there are strong research trends in climate change for the Amazon Forest, there was less evidence of studies concerning climate adaptation for this and other Brazilian biomes, smaller cities, rural and traditional communities, and poorer regions. Our results shed a light on the more commonly chosen research topics, their strongest points and potential gaps and trends. This can contribute to the scientific communication and implementation of climate actions in Brazil, and a better understanding of the climate science knowledge from the perspective of a middle-income country. [ABSTRACT FROM AUTHOR]

Climate change threatens many native species from the Amazon Forest. Among the endangered species is the Açaí (Euterpe oleracea), which is a species with great national and international interest, due to the nutritional benefits and medicinal properties of its fruits. However, there is still no information on the ecophysiological responses of Açaí to climate change. Thus, the objective of this work was to evaluate the effect of increased temperature and CO2 concentration change on the ecophysiology of Açaí seedlings. To do so, Açaí seedlings were subjected, for 90 days, to three different climatic scenarios: current Amazon; RCP4.5 (current average temperature in the Amazon + 2.5 °C and 538 ppm of carbon dioxide concentration i.e. CO2); and RCP8.5 (+ 4.5 °C and 936 ppm of CO2 concentration). In addition, two irrigation levels were applied within each climatic scenario: seedlings maintained at 90% (not stressed) and 40% (stressed) of the water holding capacity of the substrate. Gas exchange, water status, fluorescence parameters, enzymatic antioxidants activity and dry matter production were evaluated. High CO2 concentration enhanced Açaí gas exchange (increasing CO2 assimilation), regardless of substrate water availability and temperature. However, high temperature and high vapor-pressure deficit reduced quantum yield and increased the minimum fluorescence and enzymatic antioxidants activity. With that, Açaí seedlings did not convert the additional assimilated carbon (due to higher CO2 concentration) into biomass, showing decreased total dry mass accumulation for RCP4.5 and RCP8.5 climate scenarios. Our results indicated that the positive impacts of increased CO2 concentration to gas exchange may not offset the negative impacts of increased air temperature and VPD to Açaí growth. [ABSTRACT FROM AUTHOR]

The climate change endangers many species of the Amazon Forest. An example, in the endangered medicinal species Pilocarpus microphyllus (popularly known as jaborandi). The jaborandi is a natural source of alkaloids used for serious diseases, essential for medical use. However, the potential impacts of climate change on this species are still unknown. Thus, the objective was to evaluate the effect of high temperature and high atmospheric concentrations of CO2, projected to occur with climate change, combined with water stress, on the ecophysiology of jaborandi seedlings. For this, seedlings were exposed to three different climatic scenarios in a controlled environment, characterized as current Amazon: RCP4.5 (current Amazon average temperature + 2.5 °C and 538 ppm of [CO2]) and RCP 8.5 (+ 4.5 °C and 936 ppm of [CO2]). Within each climatic scenario, two levels of irrigation were applied: 90% (well-watered) and 40% (water-stressed) of the substrate water holding capacity. Growth variables, plant water status, fluorescence parameters, and enzymatic antioxidants activity were evaluated. The results showed that the jaborandi seedlings showed greater total dry mass production when grown in the current Amazon with no water stress. The seedlings' growth was negatively affected by the scenarios enriched with CO2, especially RCP 8.5. Climate change scenarios had the greatest negative impact when the seedlings were subject to water stress. Under these scenarios of climate change and water stress, the seedlings allocated more carbohydrates to the root system. The negative effect of water stress on jaborandi physiology and growth was attenuated by the RCP 8.5 scenario. Our results indicated that the predicted climate changes negatively impact ecophysiology jaborandi, being a threat to the extinction of this species. [ABSTRACT FROM AUTHOR]

The Amazon Rainforest is facing high rates of deforestation, leaving anthropogenically dominated landscapes across the Amazon. Understanding the biodiversity response to the effects of rainfall seasonality can provide important examples of how species may respond to drastic climate conditions in anthropogenic landscapes. Here, we sampled dung beetles across the same Amazon forest sites during the rainy and dry seasons to evaluate the effect of seasonality on biodiversity patterns. We found a significant space–time interaction for the whole set of forest sites indicating that the spatial structure of the species abundance data has changed significantly between seasons. Dung beetle abundance and species richness was higher in the rainy season when evaluating all species and separately for nesting behavior group. For presence-absence and abundance data of the whole assemblage, the mean differences between losses and gains over all forest sites was negative, indicating dominance of species and individuals-per-species losses across all forest sites. Eleven species (5 tunnellers, 4 rollers, and 2 dwellers) had significant decreases in abundances in the forest sites from the rainy to the dry season, whereas one roller species increased in abundance from the rainy to the dry season. Rainfall seasonality drives the temporal patterns of dung beetle diversity in the Amazonian forests in the study region in the arc of deforestation. Implications for insect conservation: Understanding species vulnerability to changes in climatic conditions could help to predict species distributions in an increased scenario of higher temperatures and longer drought periods during dry seasons in the near future. [ABSTRACT FROM AUTHOR]

The Andean-Amazon foothills region, shaped by Andean moist forests and Amazon forests in southwestern Colombia, Napo province in Ecuador, and Ucayali Province and Napo Basin in Peru, provides local and global ecosystem services as food, water, world climate regulation, water purification, and carbon absorption. However, it faces major problems of land-use change that are exacerbated by climate change that affects these ecosystem services. For instance, conventional agriculture contribute to deforestation, soil degradation, and biodiversity loss, which might be further aggravated by climate change–induced droughts, thus reducing staple crop production and, consequently, food security. Cassava (Manihot esculenta Crantz), maize (Zea mays L.), and plantain (Musa paradisiaca L.) are major staple crops in the region. They play a key role for food security and local farmers' income but are highly exposed to climate risks. This article aims to quantify the level of exposure to climate change (measured as climatic suitability) of these crops in the Andean-Amazon foothills by using the EcoCrop model by the 2030s, 2050s, and 2080s under Representative Concentration Pathway 2.6, 4.5, and 8.5 scenarios. EcoCrop results showed that, whereas cassava will not lose climatic suitability, maize will lose more than half of its current suitable area, and plantain will gain and lose area, which would affect local food security. Globally, these results are important in highlighting adaptive and cost-effective strategies in agriculture and suggest that agricultural crop diversification may improve resilience by promoting the use of local crops varieties. [ABSTRACT FROM AUTHOR]

The humid forests of Amazonia are experiencing longer and more intense dry seasons, which are predicted to intensify by the end of the 21st century. Although tree species often have long generation times, they may still have the capacity to rapidly respond to changing climatic conditions through adaptive phenotypic plasticity. We, therefore, predicted that Amazonian trees have shifted their leaf morphology in response to the recent drier climate. We tested this prediction by analysing historical herbarium specimens of six Amazonian tree species collected over a 60-year period and comparing changes in leaf morphology with historical precipitation data. Moreover, we explored spatial and temporal biases in herbarium specimens and accounted for their potentially confounding effect in our analysis. We found pronounced biases in herbarium specimens, with nearly 20% of specimens collected in close geographic proximity and around the 1975s. When accounting for such biases, our results indicate a trend of decreasing leaf size after the 1970s, which may have been spurred by an observed reduction in rainfall. Our findings support the hypothesis that (some) Amazonian trees have the capacity to adaptively change their leaf phenotypes in response to the recent drier climate. Nevertheless, the unavoidable spatial and temporal biases in herbarium specimens call for caution when generalizing our findings to all Amazonian trees. [ABSTRACT FROM AUTHOR]

The Amazon rainforest is disproportionately important for global carbon storage and biodiversity. The system couples the atmosphere and land, with moist forest that depends on convection to sustain gross primary productivity and growth. Earth system models that estimate future climate and vegetation show little agreement in Amazon simulations. Here we show that biases in internally generated climate, primarily precipitation, explain most of the uncertainty in Earth system model results; models, empirical data and theory converge when precipitation biases are accounted for. Gross primary productivity, above-ground biomass and tree cover align on a hydrological relationship with a breakpoint at ~2000mm annual precipitation, where the system transitions between water and radiation limitation of evapotranspiration. The breakpoint appears to be fairly stable in the future, suggesting resilience of the Amazon to climate change. Changes in precipitation and land use are therefore more likely to govern biomass and vegetation structure in Amazonia. [ABSTRACT FROM AUTHOR]

Reconstruction of Highway BR-319 (Manaus-Porto Velho) would allow for access from the 'arc of deforestation' in the southern part of Brazil's Amazon region to vast blocks of forests in central and northern Amazonia. Building roads is known to be a major driver of deforestation, allowing entry of squatters, and other actors. Rather than deforestation along the highway route, here we consider the road's potential for stimulating deforestation in a separate location, approximately 550 km north of BR-319's endpoint in Manaus. Reconstructing BR-319 has great potential impact to start a new wave of migration to this remote region. The southern portion of the state of Roraima, the focus of our study, is already connected to Manaus by Highway BR-174. We modeled deforestation in southern Roraima and simulated carbon emissions between 2007 and 2030 under four scenarios. Simulations used the AGROECO model in DINAMICA-EGO © software. Two scenarios were considered with reconstruction of BR-319 and two without this road connection. For each of the two possibilities regarding BR-319, simulations were developed for (1) a 'conservation' (CONSERV) scenario that assumes the creation of a series of protected areas, and (2) a 'business-as-usual' (BAU) scenario that assumes no additional protected areas. Results show that by 2030, with BR-319 rebuilt, deforestation carbon emissions would increase between 19 % (CONSERV) and 42 % (BAU) over and above those corresponding to no-road scenarios. [ABSTRACT FROM AUTHOR]

Tropical forests modify the conditions they depend on through feedbacks at different spatial scales. These feedbacks shape the hysteresis (history-dependence) of tropical forests, thus controlling their resilience to deforestation and response to climate change. Here, we determine the emergent hysteresis from local-scale tipping points and regional-scale forest-rainfall feedbacks across the tropics under the recent climate and a severe climate-change scenario. By integrating remote sensing, a global hydrological model, and detailed atmospheric moisture tracking simulations, we find that forest-rainfall feedback expands the geographic range of possible forest distributions, especially in the Amazon. The Amazon forest could partially recover from complete deforestation, but may lose that resilience later this century. The Congo forest currently lacks resilience, but is predicted to gain it under climate change, whereas forests in Australasia are resilient under both current and future climates. Our results show how tropical forests shape their own distributions and create the climatic conditions that enable them. Tropical rainforests partly create their own climatic conditions by promoting precipitation, therefore rainforest losses may trigger dramatic shifts. Here the authors combine remote sensing, hydrological modelling, and atmospheric moisture tracking simulations to assess forest-rainfall feedbacks in three major tropical rainforest regions on Earth and simulate potential changes under a severe climate change scenario. [ABSTRACT FROM AUTHOR]