Search Results (63)

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. Full article

Functional traits help to understand plant ecological strategies and play a determinant role in restoration. This study evaluated interspecific variability among 38 timber species of bioeconomic importance associated with natural forests and forest trials in the northern Colombian Amazon, identifying Plant Functional Types (PFTs) and their implications for productive restoration. Soft and hard traits were integrated, including tree morphological characteristics (diameter at breast height, total height, and crown cover) and wood functional traits (wood basic specific gravity, SG; maximum moisture content; fiber diameter and wall thickness; and vessel diameter and density). Correlations among these traits were also assessed. Five PFTs were identified. PFTs 1 and 2 grouped species with acquisitive strategies and high hydraulic efficiency, making them suitable for rapid vegetation cover recovery. In contrast, PFT 5 included conservative and hydraulically safe species, appropriate for enrichment processes once vegetation cover has been established. PFTs 3 and 4 represented intermediate strategies. Additionally, tree size was found to directly influence stem hydraulic architecture, and distinct anatomical configurations may occur within similar SG ranges, highlighting the need to integrate multi-trait approaches, as this trait alone does not fully capture the hydraulic and mechanical strategies of species. Full article

Cryphalus dilutus is an emerging invasive pest of tropical and subtropical regions, with Mangifera indica and Ficus carica being its primary host plants. Larval damage caused by this insect can lead to severe tree wilting, posing a direct threat to agricultural production and ecological security. Native to South Asia, C. dilutus has established introduced populations in the Near East, Mexico, and other areas. In recent years, it has invaded multiple regions, including southern China and southern Italy. Given the widespread global distribution of host plants and the intensification of climate change, their distribution ranges are expected to expand. However, research assessing the potential global geographical distribution of this pest under climate change is lacking. In this study, we used the Random Forest model to predict the potential distribution range of C. dilutus. Under historical climatic conditions between 1970 and 2000, suitable climatic regions for C. dilutus were primarily distributed across southern China, southeastern Brazil, southeastern Mexico, the Congo Basin periphery, and the Iberian Peninsula, with a total area of 12,192.42 × 10⁴ km². The Temperature Annual Range and Precipitation of Warmest Quarter were identified as key environmental determinants that shaped its distribution. Under the future RCP4.5 climate scenario projected for the 2050s, the total suitable area for C. dilutus is projected to contract. Specifically, high-, medium-, and low-suitability areas are projected to decline by 52.77%, 62.39%, and 24.02%, respectively. While the total area of the very low zones is expected to increase, the total area of the suitable region has been reduced to 11,891.17 ×10⁴ km². Future climate change is expected to drive the distribution northward to high-altitude areas and inland areas. Model projections indicate a poleward expansion of the fundamental climatic niche, with climatic suitability increasing in high-latitude and high-altitude regions, such as Northern Europe and western North America. Conversely, current core tropical habitats in the Indian subcontinent and the Amazon Basin are projected to face significant habitat degradation due to thermal stress. Agricultural regions previously considered relatively safe due to climatic constraints, such as northern China, the midwestern United States, and Eastern Europe, may face new challenges from pest infestation. These findings underscore the importance of proactive monitoring and implementation of preventive measures. This provides crucial decision support for countries and regions to formulate precise pest control strategies and offers a theoretical basis for early monitoring and prevention of cross-border invasions on a global scale. Full article

Land-use conversion from forest-to-pasture in the Amazon can affect soil physical quality and hydraulic functioning. The study evaluates the effects of land use (forest and pasture) and soil texture (fine and coarse) on soil structure and hydraulic properties, using the soil water retention curve as an integrative indicator. The study was conducted with soil samples from the Tapajós National Forest region, Pará State, Brazil, with eight sites (four forest and four pasture), balanced by texture. Undisturbed samples were collected from five profile layers (0–10, 10–20, 20–30, and 30–40 cm) for each site, totaling 160 samples. Samples were saturated and measured at soil water matric potentials from −0.1 to −15,000 hPa to obtain the soil water retention curve, which was fitted using the van Genuchten–Mualem model. Pore size distribution was derived from the relationship between soil water matric potential and equivalent pore diameter. Results are reported for the 0–40 cm soil profile (integrating the four sampled layers). Forest-to-pasture conversion altered soil pore structure and water retention in a texture-dependent manner. For fine-textured soils, bulk density increased from 1.03 to 1.31 Mg m⁻³ (+27%) from forest to pasture. In coarse-textured soils, the drainable pore volume up to −15,000 hPa, equivalent diameter > 0.2 µm) decreased from 0.296 to 0.147 m³ m⁻³ (−50%) from forest to pasture. Plant-available water across the 0–40 cm profile ranged from 0.107 m³ m⁻³ (pasture, fine-textured) to 0.137 m³ m⁻³ (forest, coarse-textured). Coarse-textured soils showed a marked reduction in macroporosity, water retention, and plant-available water, whereas fine-texture soils showed smaller changes in water availability but reduced aeration associated with macropore reduction. These results indicate higher physical quality vulnerability of coarse-textured soils following forest-to-pasture conversion. Full article

Tropical forests are the most plant-diverse ecosystems on Earth, characterized by extremely high species richness and playing essential roles in ecosystem stability, carbon sequestration, and hydrological regulation. Although remote sensing has been widely applied to monitoring tropical forest plant diversity in recent decades, a systematic understanding of its actual monitoring capacity remains limited. Based on a bibliometric analysis of 15,878 publications from 1960 to 2025, this study draws several key conclusions: (1) Global research is highly unevenly distributed, with most studies concentrated in China’s tropical monsoon forests, Brazil’s Amazon rainforest, Costa Rica’s tropical rainforests, and Mexico’s tropical dry forests, while many other regions remain understudied; (2) The Sentinel-2 and Landsat series are the most widely used satellite sensors, and indirect indicators are applied more frequently than direct spectral metrics in monitoring models. Hyperspectral data, Light Detection and Ranging (LiDAR), and nonlinear models generally achieve higher accuracy than multispectral data, Synthetic Aperture Radar (SAR), and linear models; (3) Sampling scales range from 64 m² to 1600 ha, with the highest accuracy achieved when plot size is within 400 m² < Area ≤ 2500 m², and spatial resolutions below 10 m perform best. Based on these findings, we propose four priority directions for future research: (1) Quantifying spectral indicators and models; (2) Assessing the influence of canopy structure on biodiversity remote sensing accuracy; (3) Strengthening the application of high-resolution data and reducing intraspecific spectral variability; and (4) Enhancing functional diversity monitoring and advancing research on the relationship between biodiversity and ecosystem functioning. Full article

Understanding how vegetation structure influences invertebrate diversity is critical for tropical forest conservation because invertebrates play key roles in ecosystem functioning. This study investigates the role of vegetation and selected microhabitats in shaping harvestmen assemblages across primary and planted forests in the Amazon rainforest. Our findings challenge the traditional view that vegetation quantity alone drives invertebrate distribution, revealing that specific plant species play a key role in shaping harvestmen assemblages. Notably, Geaya sp. (Sclerosomatidae) was strongly associated with specific arboreal species, especially Tetragastris altissima and Attalea maripa, and was identified as a bioindicator of trees. Tree diversity provides critical habitats in primary forests, illustrating how changes in tree composition can disproportionately impact specialist species. Two species of harvestmen were also identified as bioindicators of forest quality. For instance, Geaya sp. was exclusively linked to primary forests, while the cosmetid Gryne sp. emerged as moderately associated with this type of forest with high structural complexity. By identifying the specific relationships between harvestmen and vegetation, this study demonstrates their potential for monitoring ecosystem health and emphasizes the importance of preserving keystone plant species to maintain ecological integrity in tropical forests. Full article

Understanding how main plant functional strategies scale from species to communities is critical for guiding restoration in tropical disturbed areas by unsustainable livestock grazing; yet, the patterns and drivers of functional trait space along successional trajectories remain poorly understood. Here, we investigated functional trait space using principal component analyses (PCAs) based on eight traits related to leaf, stem, and seed morphology across 226 tree species and 33 forest communities along a chronosequence of natural regeneration following cattle ranching abandonment in deforested landscapes of the Colombian Amazon. We identified three species-level functional axes—namely, the ‘Structural–Reproductive Allocation Axis’, the ‘Mechanical Support and Tissue Investment Axis’, and the ‘Leaf Economics Axis’—and two community-level axes: the ‘Colonization–Longevity Axis’ and the ‘Persistence–Acquisition Axis’. These axes aligned with the life-history strategies of short-lived pioneers, long-lived pioneers, and old-growth species, and reflected their relationships with key environmental drivers. Community-level functional composition reflected species-level patterns, but was also shaped by soil properties, microclimate, and tree species richness. Forest age and precipitation promoted conservative strategies, while declining soil fertility suggested a decoupling between above- and belowground recovery. Functional richness and divergence were highest in mid-successional forests dominated by long-lived pioneers. Our findings highlight the role of environmental and successional filters in shaping functional trait space and emphasize the value of functionally diverse communities. Particularly, our results indicate that long-lived pioneers (LLP) such as Astrocaryum chambira Burret and Pouteria campanulata Baehni, with traits like large height, intermediate wood density, and larger seed size, represent ideal candidates for early enrichment strategies due to their facilitation roles in succession supporting restoration efforts in regenerating Amazonian forests. Full article

Monitoring the height of secondary forest regrowth is essential for assessing ecosystem recovery, but current methods rely on field surveys, airborne or UAV LiDAR, and 3D reconstruction from high-resolution UAV imagery, which are often costly or limited by logistical constraints. Here, we address the challenge of scaling up canopy height monitoring by evaluating a recent deep learning model, trained on data from the Amazon and Atlantic Forests, developed to extract canopy height from RGB-NIR Planet NICFI imagery. The research questions are as follows: (i) How are canopy height estimates from the model affected by slope and orientation in natural forests, based on a large and well-balanced experimental design? (ii) How effectively does the model capture the growth trajectories of Pinus and Eucalyptus plantations over an eight-year period following planting? We find that the model closely tracks Pinus growth at the parcel scale, with predictions generally within one standard deviation of UAV-derived heights. For Eucalyptus, while growth is detected, the model consistently underestimates height, by more than 10 m in some cases, until late in the cycle when the canopy becomes less dense. In stable natural forests, the model reveals seasonal artifacts driven by topographic variables (slope × aspect × day of year), for which we propose strategies to reduce their influence. These results highlight the model’s potential as a cost-effective and scalable alternative to field-based and LiDAR methods, enabling broad-scale monitoring of forest regrowth and contributing to innovation in remote sensing for forest dynamics assessment. Full article

What is the potential impact on carbon storage of the native and introduced tree species commonly associated with coffee in the central Peruvian Amazon? Coffee is a pivotal crop within the Peruvian economy. Nevertheless, the establishment of new plantations—driven by the subsistence needs of smallholder farmers—has led to expansion into forested areas. Given the significance of this crop and the demonstrated ecosystem benefits of agroforestry systems (AFSs), the aim of this study was to evaluate the influence of native and introduced shade tree species on carbon storage in coffee plantations. This study was observational and exhibited characteristics of an unbalanced incomplete block design. Agroforestry systems (AFSs) with shade tree species such as Inga, Retrophyllum rospigliosii, Eucalyptus and Pinus, and three unshaded coffee plantations, were included in this study. The total carbon stored in each AFS was higher than in unshaded coffee plantations. Soil contributed between 47% and 91% to total carbon storage, shade trees (24–46%), coffee (2–7%), leaf litter (0.6–1.9%) and shrubs and herbaceous plants (0.02–0.3%). The AFS with R. rospigliosii achieved the highest carbon storage with 190.38 Mg ha⁻¹, highlighting the compatibility of this species with coffee plantations, as well as its positive effect on climate change mitigation in deforested areas. Full article

cangas are iron-rich outcrops where rupestrian fields develop in the Carajás Mountain Range (CMR). canga formations are ancient ecosystems characterized by high levels of endemic and threatened plant species that thrive on iron-rich substrates in the southeastern Amazon uplands. The recent taxonomic validation of these species enables more accurate distribution modeling across past, present, and future time scales. This work presents a comprehensive palynological database for the Amazon canga vegetation, resulting from extensive field and herbarium surveys, as well as the compilation and taxonomic validation of species in the Carajás Mountain Range (CMR). This atlas includes 204 plant species: 10 ferns and lycophytes, 62 monocots, and 132 eudicots and magnoliids (mainly herbs, lianas, and trees). Most flowering plants are pollinated by bees, with secondary pollination by other insects and wind. The taxa co-occur in two geoenvironments: (1) forested slopes and caves over plinthosols and ferralsols and (2) slopes with canga vegetation over plinthosols. Seventeen species are potential domesticates used by Indigenous peoples. This highlights canga vegetation as a unique and diverse ecosystem with various survival strategies, emphasizing the need for precise habitat definitions in paleoenvironmental and paleoclimate reconstructions. This atlas provides a valuable reference for palynological studies, enhancing the vegetation reconstruction, climate history analysis, pre-Columbian influences on vegetation patterns, and ecological monitoring. Full article

The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse (GHG) emissions over the study region during the last two decades of the 21st century by integrating calibrated and validated satellite-derived products of estimations of burned biomass area, land cover, vegetation greenness, rainfall, land surface temperature (LST), carbon monoxide (CO), and nitrogen dioxide (NO₂) through geospatial techniques. The results revealed a strong impact of fire activity on GHG emissions, with abrupt changes in CO and NO₂ emission factors between early and middle dry season fires (July–September). Among these seven variables analyzed, we found a positive relationship between the total biomass burned area and fire-derived GHG emission factors (r² = 0.30) due to the complex dynamics of plant moisture and associated CO and NO₂ emissions generated by fire. Nevertheless, other land surface drivers showed the weakest relationships (r²~0.1) with fire-derived GHG emissions due to other factors that drive their regional distribution. Our analysis suggests the importance of continued research on the response of fire season to other land surface characteristics that represent the processes driving fire over the study region such as fuel load, composition, and structure, as well as prevailing weather conditions. These determinants drive fire-related GHG emissions and fire-related carbon cycling relationships and can, therefore, appropriately inform policy fire-abatement guidelines. Full article

Understanding genetic diversity is crucial for plant adaptation in a changing world. The neutral genetic variation (NGD) is correlated to adaptation capacity, which is crucial for long-term conservation of threatened species. Brazil, a megadiverse nation with habitats encompassing a great variety of ecosystems, harbors a wealth of plant biodiversity, yet studies on NGD remain scarce. This work analyzed published data on NGD in native Brazilian plant populations, identifying 731 papers through a systematic search on the Scopus database. Results indicated microsatellite markers as the most used for population studies, followed by ISSR. The SNP marker is still underutilized, possibly due to its higher costs and labor-intensiveness. Fabaceae, Bromeliaceae, and Arecaceae were the most studied families. Moreover, the two most studied species were Euterpe edulis and Hancornia speciosa, both economically important species. Notably, trees and herbs dominated the studies with a focus on the Atlantic Forest biome. However, Cerrado and Amazon biomes were also well represented, underscoring the importance of broader investigation across all Brazilian ecosystems. These findings reveal a critical gap in knowledge, where traditional molecular markers are most used and few economically important species are intensively studied. The number of threatened species studied is negligible, and most are not endemic. With looming climate and landscape changes, more comprehensive studies of NGD of threatened flora in Brazil are vital. The lack of genetic diversity information of native species may threaten any conservation efforts in the long term. Full article

Sustainable biomasses are vital to ensure preservation of the Amazon biome within the Mato Grosso State whilst enabling energy generation for the region and its population. Here, the potential of the elephant grass cultivar BRS Capiaçu as an alternative to replace native forest wood as biomass for energy generation is investigated, considering the whole process from plant cultivation to biomass characterisation in terms of productivity of green and dry mass per hectare; density, moisture, ash, volatile and fixed carbon content, as well as higher heating value (HHV). MANOVA indicates that the effects of plant parts and age on density and proximate analysis parameters are influenced by the plant parts and age interaction, whereas HHV can be considered similar between them. The cultivar BRS Capiaçu showed suitable energetic values (17,922 < HHV < 18,918 kJ.kg⁻¹) compared to that of native Amazon wood. Energetic results combined with cultivation outputs of high productivity (dry mass production of 44.1 tonnes.ha⁻¹ at 180 days) with a short cutting interval (3 months), adaptation to the region’s climate and soil, and the possibility of cultivation in areas currently consolidated for agriculture demonstrate the potential of BRS Capiaçu as biomass to reduce native wood usage and deforestation rates. Full article

The rapid urbanization process in recent decades has altered the carbon cycle and exacerbated the impact of climate change, prompting many cities to develop tree planting and green area preservation as mitigation and adaptation measures. While numerous studies have estimated the carbon stocks of urban trees in temperate and subtropical cities, data from tropical regions, including tropical botanic gardens, are scarce. This study aimed to quantify the aboveground biomass and carbon (AGB and AGC, respectively) stocks in trees at the Rio de Janeiro Botanical Garden arboretum, Rio de Janeiro, Brazil. Our survey included 6793 stems with a diameter at breast height (DBH) ≥ 10 cm. The total AGB was 8047 ± 402 Mg, representing 4024 ± 201 Mg of AGC. The AGB density was 207 ± 10 Mg·ha⁻¹ (AGC = 104 ± 5 Mg·ha⁻¹), which is slightly lower than the density stored in Brazil’s main forest complexes, the Atlantic and Amazon forests, but much higher than in many cities worldwide. Our results suggest that, in addition to their global importance for plant conservation, tropical botanic gardens could function as significant carbon sinks within the urban matrix. Full article

The ecotone zone, located between the Cerrado and Amazon biomes, has been under intensive anthropogenic pressures due to the expansion of commodity agriculture and extensive cattle ranching. This has led to habitat loss, reducing biodiversity, depleting biomass, and increasing CO₂ emissions. In this study, we employed an artificial neural network, field data, and remote sensing techniques to develop a model for estimating biomass in the remaining native vegetation within an 18,864 km² ecotone region between the Amazon and Cerrado biomes in the state of Mato Grosso, Brazil. We utilized field data from a plant ecology laboratory and vegetation indices from Sentinel-2 satellite imagery and trained artificial neural networks to estimate aboveground biomass (AGB) in the study area. The optimal network was chosen based on graphical analysis, mean estimation errors, and correlation coefficients. We validated our chosen network using both a Student’s t-test and the aggregated difference. Our results using an artificial neural network, in combination with vegetation indices such as AFRI (Aerosol Free Vegetation Index), EVI (Enhanced Vegetation Index), and GNDVI (Green Normalized Difference Vegetation Index), which show an accurate estimation of aboveground forest biomass (Root Mean Square Error (RMSE) of 15.92%), can bolster efforts to assess biomass and carbon stocks. Our study results can support the definition of environmental conservation priorities and help set parameters for payment for ecosystem services in environmentally sensitive tropical regions. Full article