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Mapping the density of giant trees in the Amazon

Abstract:
Summary: Tall trees (height ≥ 60 m) are keystone elements of tropical forests, strongly influencing biodiversity, carbon storage, and ecosystem resilience. Yet, their density and spatial distribution remain poorly quantified, especially in remote Amazonian regions, limiting our understanding of their ecological roles and contribution to forest–climate interactions. We combined airborne LiDAR data from 900 transects across the Brazilian Amazon with environmental predictors to model tall‐tree density. Spatial extrapolations allowed us to generate regional distribution estimates and assess associations with climate, topography, and disturbance regimes. Our model predicts that tall trees are unevenly distributed, with c. 14% of the estimated density concentrated in c. 1% of the Amazon and c. 50% within c. 11%. The highest densities occur in Roraima and the Guiana Shield provinces, where water availability is high and lightning or storm incidence is low. Modeled density strongly correlates with aboveground biomass, highlighting the disproportionate contribution of tall trees to carbon stocks. We estimate c. 55.5 million tall trees across the Brazilian Amazon. These findings demonstrate that tall‐tree distribution is a crucial but underused predictor for biomass models. Understanding their ecological and spatial dynamics is vital for forest conservation and climate‐resilience strategies under increasing anthropogenic pressures.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1111/nph.70634

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Author
ORCID:
0000-0001-5915-4045
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Role:
Author
ORCID:
0000-0003-1018-3640
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Author
ORCID:
0000-0001-9979-6456
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Author
ORCID:
0000-0002-1618-9077
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Role:
Author
ORCID:
0000-0003-2752-6195



Publisher:
Wiley
Journal:
New Phytologist More from this journal
Publication date:
2025-10-14
Acceptance date:
2025-09-22
DOI:
EISSN:
1469-8137
ISSN:
0028646X, 0028-646X


Language:
English
Keywords:
Pubs id:
2308865
Local pid:
pubs:2308865
Source identifiers:
3369549
Deposit date:
2025-10-14
ARK identifier:
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