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Competition among trees is an important driver of community structure and dynamics in tropical forests. Neighboring trees may impact an individual tree’s growth rate and probability of mortality, but large-scale geographic and environmental variation in these competitive effects has yet to be evaluated across the tropical forest biome. We quantified effects of competition on tree-level basal area growth and mortality for trees ≥10-cm diameter across 151 ~1-ha plots in mature tropical forests in Amazonia and tropical Africa by developing nonlinear models that accounted for wood density, tree size, and neighborhood crowding. Using these models, we assessed how water availability (i.e., climatic water deficit) and soil fertility influenced the predicted plot-level strength of competition (i.e., the extent to which growth is reduced, or mortality is increased, by competition across all individual trees). On both continents, tree basal area growth decreased with wood density and increased with tree size. Growth decreased with neighborhood crowding, which suggests that competition is important. Tree mortality decreased with wood density and generally increased with tree size, but was apparently unaffected by neighborhood crowding. Across plots, variation in the plot-level strength of competition was most strongly related to plot basal area (i.e., the sum of the basal area of all trees in a plot), with greater reductions in growth occurring in forests with high basal area, but in Amazonia, the strength of competition also varied with plot-level wood density. In Amazonia, the strength of competition increased with water availability because of the greater basal area of wetter forests, but was only weakly related to soil fertility. In Africa, competition was weakly related to soil fertility and invariant across the shorter water availability gradient. Overall, our results suggest that competition influences the structure and dynamics of tropical forests primarily through effects on individual tree growth rather than mortality and that the strength of competition largely depends on environment-mediated variation in basal area.

The Cerrado-Amazon Transition (CAT) in Brazil represents one of the most ecologically complex and dynamic tropical ecotones globally; however, it remains insufficiently characterized at high spatial resolution, primarily due to its intricate vegetation mosaics and the limited availability of reliable ground reference data. Accurate land cover maps are urgently needed to support conservation and sustainable land-use planning in this frontier region, especially for distinguishing critical vegetation types such as Amazon rainforest, Cerradão (dense woodland), and Savanna. In this study, we produce the first high-resolution land cover map of the CAT by integrating PlanetScope optical imagery, Sentinel-2 multispectral data, and Sentinel-1 SAR data within a U-net deep learning framework. This data fusion approach enables improved discrimination of ecologically similar vegetation types across heterogeneous landscapes. We systematically compare classification performance across single-sensor and fused datasets, demonstrating that multi-source fusion significantly outperforms single-source inputs. The highest overall accuracy was achieved using the fusion of PlanetScope, Sentinel-2, and Sentinel-1 (F1 = 0.85). Class-wise F1 scores for the best-performing model were 0.91 for Amazon Forest, 0.76 for Cerradão, and 0.76 for Savanna, indicating robust model performance in distinguishing ecologically important vegetation types. According to the best-performing model, 50.3% of the study area remains covered by natural vegetation. Cerradão, although ecologically important, covers only 8.4% of the landscape and appears highly fragmented, underscoring its vulnerability. These findings highlight the power of deep learning and multi-sensor integration for fine-scale land cover mapping in complex tropical ecotones and provide a critical spatial baseline for monitoring ecological changes in the CAT region.

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.

Climate, fire and soil nutrient limitation are important elements that affect vegetation dynamics in areas of the forest–savanna transition. In this paper, we use the dynamic vegetation model INLAND to evaluate the influence of interannual climate variability, fire and phosphorus (P) limitation on Amazon–Cerrado transitional vegetation structure and dynamics. We assess how each environmental factor affects net primary production, leaf area index and aboveground biomass (AGB), and compare the AGB simulations to an observed AGB map. We used two climate data sets (monthly average climate for 1961–1990 and interannual climate variability for 1948–2008), two data sets of total soil P content (one based on regional field measurements and one based on global data), and the INLAND fire module. Our results show that the inclusion of interannual climate variability, P limitation and fire occurrence each contribute to simulating vegetation types that more closely match observations. These effects are spatially heterogeneous and synergistic. In terms of magnitude, the effect of fire is strongest and is the main driver of vegetation changes along the transition. Phosphorus limitation, in turn, has a stronger effect on transitional ecosystem dynamics than interannual climate variability does. Overall, INLAND typically simulates more than 80 % of the AGB variability in the transition zone. However, the AGB in many places is clearly not well simulated, indicating that important soil and physiological factors in the Amazon–Cerrado border region, such as lithology, water table depth, carbon allocation strategies and mortality rates, still need to be included in the model.

Surface fires burn extensive areas of tropical forests each year, altering resource availability, biotic interactions, and, ultimately, plant diversity. In transitional forest between the Brazilian cerrado (savanna) and high stature Amazon forest, we took advantage of a long-term fire experiment to establish a factorial study of the interactions between fire, nutrient availability, and herbivory on early plant regeneration. Overall, five annual burns reduced the number and diversity of regenerating stems. Community composition changed substantially after repeated fires, and species common in the cerrado became more abundant. The number of recruits and their diversity were reduced in the burned area, but burned plots closed to herbivores with nitrogen additions had a 14 % increase in recruitment. Diversity of recruits also increased up to 50 % in burned plots when nitrogen was added. Phosphorus additions were related to an increase in species evenness in burned plots open to herbivores. Herbivory reduced seedling survival overall and increased diversity in burned plots when nutrients were added. This last result supports our hypothesis that positive relationships between herbivore presence and diversity would be strongest in treatments that favor herbivory—in this case herbivory was higher in burned plots which were initially lower in diversity. Regenerating seedlings in less diverse plots were likely more apparent to herbivores, enabling increased herbivory and a stronger signal of negative density dependence. In contrast, herbivores generally decreased diversity in more species rich unburned plots. Although this study documents complex interactions between repeated burns, nutrients, and herbivory, it is clear that fire initiates a shift in the factors that are most important in determining the diversity and number of recruits. This change may have long-lasting effects as the forest progresses through succession.

Understanding the nature and extent of ecosystem boundaries has important implications for the management and conservation of biodiversity. However, characterizing and establishing such boundary limits has been a persistent challenge worldwide. The Cerrado–Amazonia transition (CAT) in Brazil is the world’s largest savanna-forest transition. However, the CAT is represented in official maps used by Brazilian governmental agencies as a simple line separating the two biomes. Here, we demonstrate that the CAT is in fact broad, complex and interdigitating and that its traditional linear representation is not adequate for recognizing and conserving biodiversity in this region. Over the 30 years of our analysis, the CAT suffered more deforestation than the forests and savannas in each individual biomes (Amazonia and Cerrado). The complexity of tropical savanna-forest boundaries has been misunderstood and misrepresented by current maps, severely threatening the complex CAT biota. As a consequence, vegetation losses have reached levels close to collapse in areas of intense human activity.

Questions What are the trade‐offs and/or associated syndromes within and between fire‐associated traits? Does bud protection relate to bark properties and tree resprouting ability? Which traits will influence post‐fire tree survival (mortality rate and top‐kill) and tree recovery (canopy recovery and resprouting volume)? Do species with different leaf phenology have the same ecological strategies to survive and recover from fire? Location Tree community in a Neotropical savanna. Methods For each of the 24 most abundant species, we characterised the trade‐offs among bud protection, bark traits, mortality, canopy recovery and top‐kill, and resprouting strategies in both a burned and adjacent unburned area of Cerrado vegetation. Results Species with unprotected buds had a higher risk of dying, while high bud protection was associated to the ability to resprout from both the canopy and the base of the tree. We found three major trade‐offs defined by bark traits and plant properties. Cerrado woody species invest in either (a) high inner bark thickness and bark moisture, or (b) fast growth rate, height and bark density, or (c) thick outer bark and high wood density with high bud protection. Conclusions Cerrado species show different sets of fire‐related traits that seem to be important for both individual survival and community assembly. Here, we report these trade‐offs for Neotropical savannas, and our findings also shed light on how changes in fire regime may favour different groups of species, leading to changes in plant communities over time. In the Brazilian Cerrado, trees have developed distinct strategies to survive and recover after fire, by differently investing in bark structural traits and growth. Low bud protection is associated with a higher mortality risk, while species with highly protected buds promptly recover, resprouting both from the canopy and the base.

Understanding variation in key functional traits across gradients in high diversity systems and the ecology of community changes along gradients in these systems is crucial in light of conservation and climate change. We examined inter‐ and intraspecific variation in leaf mass per area (LMA) of sun and shade leaves along a 3330‐m elevation gradient in Peru, and in sun leaves across a forest–savanna vegetation gradient in Brazil. We also compared LMA variance ratios (T‐statistics metrics) to null models to explore internal (i.e., abiotic) and environmental filtering on community structure along the gradients. Community‐weighted LMA increased with decreasing forest cover in Brazil, likely due to increased light availability and water stress, and increased with elevation in Peru, consistent with the leaf economic spectrum strategy expected in colder, less productive environments. A very high species turnover was observed along both environmental gradients, and consequently, the first source of variation in LMA was species turnover. Variation in LMA at the genus or family levels was greater in Peru than in Brazil. Using dominant trees to examine possible filters on community assembly, we found that in Brazil, internal filtering was strongest in the forest, while environmental filtering was observed in the dry savanna. In Peru, internal filtering was observed along 80% of the gradient, perhaps due to variation in taxa or interspecific competition. Environmental filtering was observed at cloud zone edges and in lowlands, possibly due to water and nutrient availability, respectively. These results related to variation in LMA indicate that biodiversity in species rich tropical assemblages may be structured by differential niche‐based processes. In the future, specific mechanisms generating these patterns of variation in leaf functional traits across tropical environmental gradients should be explored. We examined inter‐ and intraspecific variation in leaf mass per area (LMA) of sun and shade leaves along a 3330‐m elevation gradient in Peru, and in sun leaves across a forest–savanna vegetation gradient in Brazil. We also compared LMA variance ratios (T‐statistics metrics) to null models to explore filtering on community structure along the gradients. Patterns in LMA across both gradients were consistent with the leaf economic spectrum and perhaps due to internal filtering in montane forests, and environmental filtering in forests in lowlands and cloud zone edges and in dry savannas.

Land regions are warming rapidly. While in a warming world at extra-tropical latitudes vegetation adapted to higher temperatures may move in from lower latitudes this is not possible in the tropics. Thus, the limits of plant functioning will determine the nature and composition of future vegetation. The most temperature sensitive component of photosynthesis is photosystem II. Here we report the thermal safety margin (difference between photosystem II thermotolerance (T 50 ) and maximum leaf temperature) during the beginning of the dry season for four tree species co-occurring across the forest-savanna transition zone in Brazil, a region which has warmed particularly rapidly over the recent decades. The species selected are evergreen in forests but deciduous in savannas. We find that thermotolerance declines with growth temperature >40 °C for individuals in the savannas. Current maximum leaf temperatures exceed T 50 in some species and will exceed T 50 in a 2.5 °C warmer world in most species evaluated. Despite plasticity in leaf thermal traits to increase leaf cooling in hotter environments, the results show this is not sufficient to maintain a safe thermal safety margin in hotter savannas. Overall, the results suggest that tropical forests may become increasingly deciduous and savanna-like in the future.

Drought and fire reduce productivity and increase tree mortality in tropical forests. Fires also produce pyrogenic carbon (PyC), which persists in situ for centuries to millennia, and represents a legacy of past fires, potentially improving soil fertility and water holding capacity and selecting for the survival and recruitment of certain tree life-history (or successional) strategies. We investigated whether PyC is correlated with physicochemical soil properties, wood density, aboveground carbon (AGC) dynamics and forest resistance to severe drought. To achieve our aim, we used an Amazon-wide, long-term plot network, in forests without known recent fires, integrating site-specific measures of forest dynamics, soil properties and a unique soil PyC concentration database. We found that forests with higher concentrations of soil PyC had both higher soil fertility and lower wood density. Soil PyC was not associated with AGC dynamics in non-drought years. However, during extreme drought events (10% driest years), forests with higher concentrations of soil PyC experienced lower reductions in AGC gains (woody growth and recruitment), with this drought-immunizing effect increasing with drought severity. Forests with a legacy of ancient fires are therefore more likely to continue to grow and recruit under increased drought severity. Forests with high soil PyC concentrations (third quartile) had 3.8% greater AGC gains under mean drought, but 33.7% greater under the most extreme drought than forests with low soil PyC concentrations (first quartile), offsetting losses of up to 0.68 Mg C ha –1 yr –1 of AGC under extreme drought events. This suggests that ancient fires have legacy effects on current forest dynamics, by altering soil fertility and favoring tree species capable of continued growth and recruitment during droughts. Therefore, mature forest that experienced fires centuries or millennia ago may have greater resistance to current short-term droughts.