Deoclecio Jardim Amorim

Silicon (Si) modulates plant responses to abiotic stress, but its role in legumes remains unclear. We investigated the effects of Si supply on drought responses in common bean genotypes contrasting in drought tolerance (BAT 477, tolerant; IAC Carioca 80 SH, susceptible), focusing on leaf gas exchange, carbon isotope discrimination (δ ¹³ C), leaf nitrogen (N), and C/N. Plants were grown under greenhouse upon different water regimes (80% and 40% of soil water holding capacity) and Si supply (0 and 200 mg dm ⁻³ ). Silicon application increased intercellular CO ₂ concentration (Ci) in both genotypes, regardless of water regime, suggesting changes in the balance between stomatal and non‐stomatal limitations to photosynthesis. Effects on water‐use efficiency (WUE) were genotype and stage‐dependent, with increases observed only in the susceptible genotype under well‐watered conditions. Leaf N increased with Si supply, particularly under drought in the tolerant genotype, while changes in the C/N reflected shifts in carbon‐nitrogen balance associated with improved physiological performance. Despite higher Si accumulation in IAC Carioca 80 SH, this genotype did not exhibit superior drought tolerance, highlighting that Si accumulation per se does not determine physiological responsiveness. Multivariate analysis revealed coordinated associations among Si, WUE, δ ¹³ C, and C/N under drought, suggesting that Si modulates the balance between carbon assimilation, nutrient status, and water use. Our findings demonstrate that Si regulates integrated physiological responses rather than directly enhancing individual traits, with effects dependent on genotype and environment. These results highlight the potential of Si as a strategy to improve the physiological performance of legumes under drought.

Stable oxygen isotopes (δ¹⁸O) in wood provide integrative records of plant water use and regional hydroclimatic processes, offering a powerful framework for spatial ecological analysis in tropical forests. Here, we present the first regional-scale δ¹⁸O isoscapes for Amazonian wood based on 387 trees sampled across 25 sites. After α-cellulose extraction, δ¹⁸O values were modeled using multiple linear regression (MLR) and Random Forest (RF) approaches. A Moran’s I test revealed no significant spatial autocorrelation (p = 0.73), indicating that geostatistical interpolation methods such as kriging were not appropriate for this dataset. The MLR model based on site-average data achieved an R² of 0.70, with a mean absolute error (MAE) of 0.56‰ and root mean square error (RMSE) of 0.68‰. The RF model showed comparable performance (R² = 0.67; MAE = 0.64‰; RMSE = 0.77‰). Both approaches reproduced a coherent southeast-to-northwest gradient, with lower δ¹⁸O values in the western Amazon and higher values in the east, consistent with regional patterns in precipitation isotopic composition and evapotranspiration. These findings demonstrate that climate-driven statistical modeling effectively captures large-scale isotopic structure across the Amazon basin, providing a robust spatial representation of δ¹⁸O variability in tropical forest wood.

Stable oxygen isotopes (δ¹⁸O) in wood provide integrative records of plant water use and regional hydroclimatic processes, offering a powerful framework for spatial ecological analysis in tropical forests. Here, we present the first regional-scale δ¹⁸O isoscapes for Amazonian wood based on 387 trees sampled across 25 sites. After α-cellulose extraction, δ¹⁸O values were modeled using multiple linear regression (MLR) and Random Forest (RF) approaches. A Moran’s I test revealed no significant spatial autocorrelation (p = 0.73), indicating that geostatistical interpolation methods such as kriging were not appropriate for this dataset. The MLR model based on site-average data achieved an R² of 0.70, with a mean absolute error (MAE) of 0.56‰ and root mean square error (RMSE) of 0.68‰. The RF model showed comparable performance (R²= 0.67; MAE = 0.64‰; RMSE = 0.77‰). Both approaches reproduced a coherent southeast-to-northwest gradient, with lower δ¹⁸O values in the western Amazon and higher values in the east, consistent with regional patterns in precipitation isotopic composition and evapotranspiration. These findings demonstrate that climate-driven statistical modeling effectively captures large-scale isotopic structure across the Amazon basin, providing a robust spatial representation of δ¹⁸O variability in tropical forest wood.

Illegal logging is one of the most prevalent environmental infractions in the Amazon, led by organized networks that cause substantial ecological and economic impacts. Official control mechanisms, such as Brazil’s Forest Origin Document (DOF), remain vulnerable to the fraudulent manipulation of virtual timber credits and inconsistencies in digital traceability. These deficiencies highlight the need for independent, scientifically based methodologies for timber traceability that can support law enforcement and ensure reliable provenance verification. Here, we tested whether the isotopic composition of carbon (δ13C) and nitrogen (δ15N) in wood can trace Amazonian timber origin. We developed basin-wide δ13C and δ15N isoscapes using machine-learning models to predict spatial variability. A total of 571 trees from 47 sites were analyzed for both isotopes. Tree disks or wedges were sampled from the basal trunk, sectioned transversely, and sub-sampled from heartwood to near the sapwood boundary to obtain whole-tree isotopic composition. The δ13C and, more strongly, the δ15N values exhibited substantial within-site heterogeneity, indicating individual-level physiological controls, interspecific differences, and/or small-scale environmental variation influencing isotope fractionation. Despite these sources of noise, isotopic values showed independent and predictable spatial patterns across the basin (R2=0.67 for δ15N and R2=0.60 for δ13C). Nitrogen isotopes were primarily controlled by edaphic factors, while carbon isotopes revealed a broad longitudinal gradient linked to climate. Together, these isotopic markers provide complementary information for basin-scale timber provenancing and form a robust, high-resolution framework for Amazon-wide traceability.

Illegal logging is one of the most prevalent environmental infractions in the Amazon, led by organized networks that cause substantial ecological and economic impacts. Official control mechanisms, such as Brazil’s Forest Origin Document (DOF), remain vulnerable to the fraudulent manipulation of virtual timber credits and inconsistencies in digital traceability. These deficiencies highlight the need for independent, scientifically based methodologies for timber traceability that can support law enforcement and ensure reliable provenance verification. Here, we tested whether the isotopic composition of carbon (δ¹³C) and nitrogen (δ¹⁵N) in wood can trace Amazonian timber origin. We developed basin-wide δ¹³C and δ¹⁵N isoscapes using machine-learning models to predict spatial variability. A total of 571 trees from 47 sites were analyzed for both isotopes. Tree disks or wedges were sampled from the basal trunk, sectioned transversely, and sub-sampled from heartwood to near the sapwood boundary to obtain whole-tree isotopic composition. The δ¹³C and, more strongly, the δ¹⁵N values exhibited substantial within-site heterogeneity, indicating individual-level physiological controls, interspecific differences, and/or small-scale environmental variation influencing isotope fractionation. Despite these sources of noise, isotopic values showed independent and predictable spatial patterns across the basin (R² = 0.67 for δ¹⁵N and R² = 0.60 for δ¹³C). Nitrogen isotopes were primarily controlled by edaphic factors, while carbon isotopes revealed a broad longitudinal gradient linked to climate. Together, these isotopic markers provide complementary information for basin-scale timber provenancing and form a robust, high-resolution framework for Amazon-wide traceability.

Illegal logging is one of the most prevalent environmental infractions in the Amazon, led by organized networks that cause substantial ecological and economic impacts. Official control mechanisms, such as Brazil’s Forest Origin Document (DOF), remain vulnerable to the fraudulent manipulation of virtual timber credits and inconsistencies in digital traceability. These deficiencies highlight the need for independent, scientifically based methodologies for timber traceability that can support law enforcement and ensure reliable provenance verification. Here, we tested whether the isotopic composition of carbon (δ¹³C) and nitrogen (δ¹⁵N) in wood can trace Amazonian timber origin. We developed basin-wide δ¹³C and δ¹⁵N isoscapes using machine-learning models to predict spatial variability. A total of 571 trees from 47 sites were analyzed for both isotopes. Tree disks or wedges were sampled from the basal trunk, sectioned transversely, and sub-sampled from heartwood to near the sapwood boundary to obtain whole-tree isotopic composition. The δ¹³C and, more strongly, the δ¹⁵N values exhibited substantial within-site heterogeneity, indicating individual-level physiological controls, interspecific differences, and/or small-scale environmental variation influencing isotope fractionation. Despite these sources of noise, isotopic values showed independent and predictable spatial patterns across the basin (R² = 0.67 for δ¹⁵N and R² = 0.60 for δ¹³C). Nitrogen isotopes were primarily controlled by edaphic factors, while carbon isotopes revealed a broad longitudinal gradient linked to climate. Together, these isotopic markers provide complementary information for basin-scale timber provenancing and form a robust, high-resolution framework for Amazon-wide traceability.

Spent yeast represents a promising opportunity for value-added applications. This study proposes its biofortification as a source of organic selenium (Se)-enriched supplements. Se is an essential component of the glutathione (GSH) system, playing a critical role in decomposing lipid peroxidation products and protecting cellular membranes. We evaluated the effects of sodium selenite (Na₂SeO₃) supplementation on enzymatic activity, oxidative stress markers, and biomass production of Saccharomyces cerevisiae Thermosacc ^® , cultivated in corn hydrolysate—a non-synthetic medium that provides a more realistic representation of industrial environments—under aerobic and anaerobic conditions. Antioxidant responses were assessed via glutathione peroxidase (GPx), glutathione reductase (GR), and glutathione S-transferase (GST) activities, while oxidative stress was measured through hydrogen peroxide (H₂O₂) and malondialdehyde (MDA) levels. Yeasts were grown with 0, 200, and 400 mg L ⁻¹ Na₂SeO₃. The highest enzymatic activities were observed in AE400 (GPx: 5.35 μmol mg ⁻¹ , GR: 3.39 μmol mg ⁻¹ , GST: 0.035 μmol mg ⁻¹ ), indicating enhanced antioxidant defenses under aerobic Se supplementation. However, increased Se concentrations also elevated H₂O₂ and MDA levels—especially in aerobic conditions—likely due to intensified ROS generation. Consequently, biomass production and growth parameters declined, suggesting an energy trade-off in which antioxidant defense is prioritized over cell proliferation. These findings highlight Se’s dual role as both an antioxidant and a pro-oxidant at elevated concentrations. This study advances understanding of yeast redox biology and supports the integration of Se-enriched yeast production into industrial fermentation as a sustainable strategy for generating high-value functional ingredients for food and feed applications.