Sustainable management of the Amazon rainforest is fundamental for supporting life on earth because of its crucial role in sequestering carbon. One of the species grown in the forest is açaí (Euterpe oleracea), which is an important food and income source for its inhabitant. The acai seed, resulting from the processing of the fruit, is a solid organic residue, which has been an agent of undesirable environmental impacts such as natural landscape modifications, clogging sewers and water courses, eutrophication of surface waters. In this research, we evaluated the use of wood chips as a source of energy in a rustic oven to produce acai biochar so that family farmers carry out sustainable management of the residue and use biochar to improve soil quality and produce seedlings of native plants to regenerate degraded forests. The experiment was conducted in Pará, Brazil, Amazon region, using a randomized complete block design. A factorial treatment structure was implemented consisting of four biochar particle sizes (3, 5, 7, and 12 mm), 4 application rates (4, 8, 16, and 32 t ha-1), and a biochar-free control, with 5 replications. The results showed that the methodology for biochar production was easy to apply and low cost, allowing its use by family farmers. The combination of biochar rate and particle size affected soil properties and the development of black pepper seedlings in different ways. The soil properties affected were water retention capacity, moisture, fluorescein diacetate hydrolysis and arylsulphatase activity. The growth parameters of the affected black pepper seedlings were height and root system development.

Seasonal floodplains in the Amazon basin are important sources of methane (CH4), while upland forests are known for their sink capacity. Climate change effects, including shifts in rainfall patterns and rising temperatures, may alter the functionality of soil microbial communities, leading to uncertain changes in CH4 cycling dynamics. To investigate the microbial feedback under climate change scenarios, we performed a microcosm experiment using soils from two floodplains (i.e., Amazonas and Tapajós rivers) and one upland forest. We employed a two-factorial experimental design comprising flooding (with non-flooded control) and temperature (at 27 °C and 30 °C, representing a 3 °C increase) as variables. We assessed prokaryotic community dynamics over 30 days using 16S rRNA gene sequencing and qPCR. These data were integrated with chemical properties, CH4 fluxes, and isotopic values and signatures. In the floodplains, temperature changes did not significantly affect the overall microbial composition and CH4 fluxes. CH4 emissions and uptake in response to flooding and non-flooding conditions, respectively, were observed in the floodplain soils. By contrast, in the upland forest, the higher temperature caused a sink-to-source shift under flooding conditions and reduced CH4 sink capability under dry conditions. The upland soil microbial communities also changed in response to increased temperature, with a higher percentage of specialist microbes observed. Floodplains showed higher total and relative abundances of methanogenic and methanotrophic microbes compared to forest soils. Isotopic data from some flooded samples from the Amazonas river floodplain indicated CH4 oxidation metabolism. This floodplain also showed a high relative abundance of aerobic and anaerobic CH4 oxidizing Bacteria and Archaea. Taken together, our data indicate that CH4 cycle dynamics and microbial communities in Amazonian floodplain and upland forest soils may respond differently to climate change effects. We also highlight the potential role of CH4 oxidation pathways in mitigating CH4 emissions in Amazonian floodplains.

When looking for new ingredients to process red meat, poultry, and fish products, it is essential to consider using vegetable resources that can replace traditional ingredients such as animal fat and synthetic antioxidants that may harm health. The Amazon, home to hundreds of edible fruit species, can be a viable alternative for new ingredients in processing muscle food products. These fruits have gained interest for their use as natural antioxidants, fat replacers, colorants, and extenders. Some of the fruits that have been tested include açai, guarana, annatto, cocoa bean shell, sacha inchi oil, and peach palm. Studies have shown that these fruits can be used as dehydrated products or as liquid or powder extracts in doses between 250 and 500 mg/kg as antioxidants. Fat replacers can be added directly as flour or used to prepare emulsion gels, reducing up to 50% of animal fat without any detrimental effects. However, oxidation problems of the gels suggest that further investigation is needed by incorporating adequate antioxidant levels. In low doses, Amazon fruit byproducts such as colorants and extenders have been shown to have positive technological and sensory effects on muscle food products. While evidence suggests that these fruits have beneficial health effects, their in vitro and in vivo nutritional effects should be evaluated in muscle food products containing these fruits. This evaluation needs to be intended to identify safe doses, delay the formation of key oxidation compounds that directly affect health, and investigate other factors related to health.

This study aims to analyze deforestation in the Brazilian Amazon from 1999 to 2020 using machine learning techniques to assess 16 critical factors. Our approach leverages the capabilities of machine learning, particularly Random Forest, which proved to be the most accurate model in terms of determination coefficient, mean squared error, and mean absolute error. The analysis revealed that the harvested area of permanent crops is the most influential variable in predicting deforestation, followed by the area of temporary crops. Furthermore, our findings indicate a significant inverse relationship between public spending and deforestation rates. These results contribute to understanding deforestation dynamics and offer potential strategies for improving conservation efforts.

Here, we report 27 metagenome-assembled bacterial genomes (MAGs) from litter samples of a secondary forest located in Brazil over an Amazonian Dark Earth pool. The data set includes members from the phyla Pseudomonadata (14 MAGs), Actinomycetota (7 MAGs), Bacteroidota (4 MAGs), Bacillota (1 MAG), and Bdellovibrionota (1 MAG).

Artisanal and small-scale gold mining (ASGM) is the primary global source of anthropogenic mercury (Hg) emissions. It has impacted the Amazon rainforest in the Peruvian region of Madre de Dios. However, few studies have investigated Hg\'s distribution in terrestrial ecosystems in this region. We studied Hg\'s distribution and its predictors in soil and native plant species from artisanal mining sites. Total Hg concentrations were determined in soil samples collected at different depths (0-5 cm and 5-30 cm) and plant samples (roots, shoots, leaves) from 19 native plant species collected in different land cover categories: naked soil (L1), gravel piles (L2), natural regeneration (L3), reforestation (L4), and primary forest (L5) in the mining sites. Hg levels in air were also studied using passive air samplers. The highest Hg concentrations in soil (average 0.276 and 0.210 mg kg-1 dw.) were found in the intact primary forest (L5) at 0-5 cm depth and in the plant rooting zones at 5-30 cm depth, respectively. Moreover, the highest Hg levels in plants (average 0.64 mg kg-1 dw) were found in foliage of intact primary forest (L5). The results suggest that the forest in these sites receives Hg from the atmosphere through leaf deposition and that Hg accumulates in the soil surrounding the roots. The Hg levels found in the plant leaves of the primary forest are the highest ever recorded in this region, exceeding values found in forests impacted by Hg pollution worldwide and raising concerns about the extent of the ASGM impact in this ecosystem. Correlations between Hg concentrations in soil, bioaccumulation in plant roots, and soil physical-chemical characteristics were determined. Linear regression models showed that the soil organic matter content (SOM), pH, and electrical conductivity (EC) predict the Hg distribution and accumulation in soil and bioaccumulation in root plants.

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Solitary bees and wasps that nest in cavities in tree trunks are important components of terrestrial ecosystems, providing pollination services, and in the case of wasps, the regulation of their prey populations. However, little is known about the vertical strata where bees and wasps build their nests. This is especially the case of urban forest remnants in the Amazon, which is relevant in the context of the global crisis in insect losses. We investigated the existence of vertical stratification in the nesting of solitary bees and wasps in an urban forest in Rio Branco, state of Acre, in the western Brazilian Amazon. We focused on whether wood temperature, ants, and termites are predictors of bee and wasp nesting. We sampled bee and wasp nests in the forest using trap-nests made with wooden blocks containing cavities with three different diameters for twelve months. Trap-nests were installed randomly at three heights in the forest. We collected 145 nests of 25 species, belonging to 11 genera and 6 families. A higher number of nests and species were collected in the upper stratum of the forest, strengthening the hypothesis that there is vertical stratification in the assemblage of solitary bees and wasps. Wood surface temperature and termite attacks on trap-nests were significantly different between strata, which may explain the vertical stratification of bee and wasp assemblages. Considering the importance of these insects for tropical forest ecosystems, the conservation of structurally complex and stratified forests is of paramount importance to maintain the diversity of this insect group.

Deforestation reduces the capacity of the terrestrial biosphere to take up toxic pollutant mercury (Hg) and enhances the release of secondary Hg from soils. The consequences of deforestation for Hg cycling are not currently considered by anthropogenic emission inventories or specifically addressed under the global Minamata Convention on Mercury. Using global Hg modeling constrained by field observations, we estimate that net Hg fluxes to the atmosphere due to deforestation are 217 Mg year-1 (95% confidence interval (CI): 134-1650 Mg year-1) for 2015, approximately 10% of global primary anthropogenic emissions. If deforestation of the Amazon rainforest continues at business-as-usual rates, net Hg emissions from the region will increase by 153 Mg year-1 by 2050 (CI: 97-418 Mg year-1), enhancing the transport and subsequent deposition of Hg to aquatic ecosystems. Substantial Hg emissions reductions are found for two potential cases of land use policies: conservation of the Amazon rainforest (92 Mg year-1, 95% CI: 59-234 Mg year-1) and global reforestation (98 Mg year-1, 95% CI: 64-449 Mg year-1). We conclude that deforestation-related emissions should be incorporated as an anthropogenic source in Hg inventories and that land use policy could be leveraged to address global Hg pollution.

Social media has become a one-stop shop for consuming news and expressing political views. Politics has become increasingly emotional, and the ensuing polarization has created echo chambers that favor narratives and stories that repeat only one point of view. In this article, we investigated the role of political activity through Twitter (now \'X\') engagement as a predictor of destructive fires and deforestation in the Brazilian Legal Amazon (BLA). We used a machine learning approach based on sentiment analysis and satellite data. To test the consistency of the sentiment analysis, we compared the timing of messages related to fire and deforestation events with daily fire data from satellites. When comparing positive and negative comments about fires in the BLA, the results showed that the best model for predicting fire outbreaks is the decision tree regressor. We found evidence that positive comments about agriculture, industry, and the Amazon rainforest in response to speeches and statements by high-ranking Brazilian politicians tend to induce positive comments about fire outbreaks and deforestation. These comments then become good predictors of fire outbreaks with a 6-day lag. These results support the view that high-ranking politicians have enormous power to influence damaging events that can have severe impacts on communities, the environment, and the economy. Brazil has seen an unprecedented increase in deforestation and fires in the Amazon rainforest in recent years. Our findings contribute to the growing literature on the role of social media in real-world events and how machine learning approaches can be used to address this class of problems.