Articles | Volume 22, issue 15
https://doi.org/10.5194/bg-22-3949-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-3949-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sensitivity of tropical woodland savannas to El Niño droughts
Simone Matias Reis
Laboratório de Ecologia de Ecossistemas Florestais e Savânicos, Centro de Ciências Biológicas e da Natureza, Universidade Federal do Acre, Rio Branco, Brazil
Laboratório de Ecologia Vegetal, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil
Yadvinder Malhi
School of Geography and the Environment, Environmental Change Institute, University of Oxford, Oxford, United Kingdom
Ben Hur Marimon Junior
Laboratório de Ecologia Vegetal, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil
Beatriz Schwantes Marimon
Laboratório de Ecologia Vegetal, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil
Huanyuan Zhang-Zheng
School of Geography and the Environment, Environmental Change Institute, University of Oxford, Oxford, United Kingdom
Igor Araújo
Laboratório de Ecologia Vegetal, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil
Renata Freitag
Laboratório de Ecologia Vegetal, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil
Edmar Almeida de Oliveira
Laboratório de Ecologia Vegetal, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil
Karine da Silva Peixoto
Laboratório de Ecologia Vegetal, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil
Luciana Januário de Souza
Laboratório de Ecologia Vegetal, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil
Ediméia Laura Souza da Silva
Laboratório de Ecologia Vegetal, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil
Eduarda Bernardes Santos
Laboratório de Ecologia Vegetal, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil
Kamila Parreira da Silva
Laboratório de Ecologia Vegetal, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil
Maélly Dállet Alves Gonçalves
Laboratório de Ecologia Vegetal, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil
Cécile Girardin
School of Geography and the Environment, Environmental Change Institute, University of Oxford, Oxford, United Kingdom
Cecilia Dahlsjö
School of Geography and the Environment, Environmental Change Institute, University of Oxford, Oxford, United Kingdom
Oliver L. Phillips
School of Geography, University of Leeds, Leeds, United Kingdom
Imma Oliveras Menor
CORRESPONDING AUTHOR
School of Geography and the Environment, Environmental Change Institute, University of Oxford, Oxford, United Kingdom
AMAP, Université de Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-226, https://doi.org/10.5194/essd-2026-226, 2026
Preprint under review for ESSD
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This study introduces the first global, spatially explicit dataset of tree diameter structure, capturing key aspects of forest organization, including average tree size, large-tree dominance, and within-stand variability. Built from over one million ground-based field plots combined with more than 50 satellite and environmental layers using machine learning, it provides a consistent representation of forest structure and supports ecosystem research, climate modeling, and forest management.
Cecilia Chavana-Bryant, Phil Wilkes, Wanxin Yang, Andrew Burt, Peter Vines, Amy C. Bennett, Georgia C. Pickavance, Declan L. M. Cooper, Simon L. Lewis, Oliver L. Phillips, Benjamin Brede, Alvaro Lau, Martin Herold, Iain M. McNicol, Edward T. A. Mitchard, David A. Coomes, Toby D. Jackson, Löic Makaga, Heddy O. Milamizokou Napo, Alfred Ngomanda, Stephan Ntie, Vincent Medjibe, Pacôme Dimbonda, Luna Soenens, Virginie Daelemans, Laetitia Proux, Reuben Nilus, Nicolas Labrière, Kathryn Jeffery, David F. R. P. Burslem, Dan Clewley, David Moffat, Lan Qie, Harm Bartholomeus, Gregoire Vincent, Nicolas Barbier, Geraldine Derroire, Katharine Abernethy, Klaus Scipal, and Mathias Disney
Earth Syst. Sci. Data, 18, 1243–1274, https://doi.org/10.5194/essd-18-1243-2026, https://doi.org/10.5194/essd-18-1243-2026, 2026
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The ForestScan project provides a comprehensive set of datasets of tropical forest 3D structural measurements using terrestrial, unpiloted aerial vehicle and aerial laser scanning, plus tree census data. Collected at three sites in French Guiana, Gabon, and Malaysia, these datasets are crucial for calibrating and validating earth observation-derived forest biomass estimates, therefore, expanding and enhancing their use, and aiding global conservation efforts.
Marina Corrêa Scalon, Imma Oliveras Menor, Renata Freitag, Karine S. Peixoto, Sami W. Rifai, Beatriz Schwantes Marimon, Ben Hur Marimon Junior, and Yadvinder Malhi
Biogeosciences, 19, 3649–3661, https://doi.org/10.5194/bg-19-3649-2022, https://doi.org/10.5194/bg-19-3649-2022, 2022
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We investigated dynamic nutrient flow and demand in a typical savanna and a transition forest to understand how similar soils and the same climate dominated by savanna vegetation can also support forest-like formations. Savanna relied on nutrient resorption from wood, and nutrient demand was equally partitioned between leaves, wood and fine roots. Transition forest relied on resorption from the canopy biomass and nutrient demand was predominantly driven by leaves.
Rahayu Adzhar, Douglas I. Kelley, Ning Dong, Charles George, Mireia Torello Raventos, Elmar Veenendaal, Ted R. Feldpausch, Oliver L. Phillips, Simon L. Lewis, Bonaventure Sonké, Herman Taedoumg, Beatriz Schwantes Marimon, Tomas Domingues, Luzmila Arroyo, Gloria Djagbletey, Gustavo Saiz, and France Gerard
Biogeosciences, 19, 1377–1394, https://doi.org/10.5194/bg-19-1377-2022, https://doi.org/10.5194/bg-19-1377-2022, 2022
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The MODIS Vegetation Continuous Fields (VCF) product underestimates tree cover compared to field data and could be underestimating tree cover significantly across the tropics. VCF is used to represent land cover or validate model performance in many land surface and global vegetation models and to train finer-scaled Earth observation products. Because underestimation in VCF may render it unsuitable for training data and bias model predictions, it should be calibrated before use in the tropics.
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Short summary
The 2015–2016 El Niño caused severe droughts in tropical forests, but its impact on the Cerrado, Brazil's largest savanna, was unclear. Our study tracked the productivity of two key Cerrado vegetation types over 5 years. Before the El Niño, productivity was higher in the transitional forest–savanna, but it dropped sharply during the event. Meanwhile, the savanna showed minor changes. These findings suggest that transitional ecosystems are particularly vulnerable to drought and climate change.
The 2015–2016 El Niño caused severe droughts in tropical forests, but its impact on the Cerrado,...
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