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Mapping the density of giant trees in the Amazon

  • Robson Borges de Lima*
  • , Diego Armando Silva da Silva
  • , Matheus Henrique Nunes
  • , Paulo R. de Lima Bittencourt
  • , Peter Groenendyk
  • , Cinthia Pereira de Oliveira
  • , Daniela Granato‐Souza
  • , Rinaldo L. Caraciolo Ferreira
  • , José A. Aleixo da Silva
  • , Jesús Aguirre‐Gutiérrez
  • , Toby Jackson
  • , João R. de Matos Filho
  • , Perseu da Silva Aparício
  • , Joselane P. Gomes da Silva
  • , José Julio de Toledo
  • , Marcelino Carneiro Guedes
  • , Danilo R. Alves de Almeida
  • , Niro Higuchi
  • , Fabien H. Wagner
  • , Jean Pierre Ometto
  • Eric Bastos Görgens
*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)

Abstract

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.
Original languageEnglish
Article number70634
Pages (from-to)152-168
Number of pages17
JournalNew Phytologist
Volume249
Issue number1
Early online date14 Oct 2025
DOIs
Publication statusE-pub ahead of print - 14 Oct 2025

Bibliographical note

Publisher Copyright:
© 2025 The Author(s). New Phytologist © 2025 New Phytologist Foundation.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • airborne LiDAR
  • spatial modeling
  • biogeographic provinces
  • tallest trees
  • environmental factors

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