de Matos Filho, J. R. | Search bioRxiv

bioRxiv2025

Mapping giant-tree density in the Amazon

Tall trees are pivotal in maintaining tropical forests biodiversity and regulating biogeochemical cycles, mainly through their influence on carbon storage, nutrient cycling, and habitat structuring. Despite their ecological importance, the drivers behind their spatial distribution and the environmental factors driving the density of these emergent trees remain unexplained. Here we analyzed tall-tree density data derived from airborne LiDAR across 900 randomly distributed transects throughout the Brazilian Amazon. We paired each transect location with climatic, topographic, atmospheric, and soil metrics to investigate regional-level environmental drivers of large tree density using spatial modeling. Water availability and stable climatic conditions (low incidence of storms and of lightning) were the main drivers of tall-tree density at the regional level. The occurrence of tall trees is aggregated along the Amazon, with 14% occurring within 1.3% of the Amazon and 50% concentrated in 11.2% of the area. These patterns suggest that tall trees dominate localized regions of the biome and that high densities of tall trees are primarily dependent on mechanical rather than physiological drivers. Understanding the dynamics of 55.5 million emergent trees is imperative for advancing research in biogeochemistry, ecology, and climate resilience. Our results are especially relevant in the context of conserving nature and carbon stocks under increasing anthropogenic and environmental pressures.

ecology↗

bioRxiv2025

Warmer climate threatens the occurrence of giant trees in the Amazon basin

Giant trees in the Amazon play crucial ecological roles by acting as substantial carbon sinks and supporting diverse forest ecosystems. However, these emergent trees are increasingly vulnerable to climatic changes, and recent research suggests that the distribution and niche of many emergent species are severely threatened. This study employs ecological niche modeling using data LiDAR and forest inventory, global repository data, and bioclimatic variables to project the potential larger tree distributions under past, current, and future climate scenarios (SSP1-2.6 and SSP5-8.5) in different biogeographic provinces in the Amazon basin. We used algorithms MARS, RandomForest, MaxEnt, and GAM to assess the impact of critical climatic factors, including isothermality, maximum temperatures, and precipitation patterns, on the habitat suitability of these larger trees and species Dinizia excelsa and Goupia glabra. Our results show that the tall trees dataset, Dinizia excelsa and Goupia glabra exhibit congruent distinct responses to climate variables, with Dinizia excelsa being more sensitive to increased temperature extremes, particularly in the Guiana Shield and Roraima Provinces. In contrast, Goupia glabra displays a broader tolerance to precipitation variability. Under the high-emission scenario (SSP5-8.5), both species are projected to lose up to 45% of their suitable habitats by 2080, primarily in southern Amazon provinces like Xingu-Tapajos, where increased drought frequency and temperature extremes are expected. Conversely, the low-emission scenario (SSP1-2.6) suggests potential habitat stability or even slight expansions in the northern Amazon regions due to moderate temperature and rainfall conditions. These findings highlight the urgent need for conservation strategies to protect critical refugia and enhance ecosystem resilience. By integrating niche modeling with climate projections, our study provides vital insights into managing Amazonian giant trees, emphasizing their potential role in mitigating global climate change impacts.

ecology↗