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Research article - Peer-reviewed, 2023

The global biogeography of tree leaf form and habit

Ma, Haozhi; Crowther, Thomas W.; Mo, Lidong; Maynard, Daniel S.; Renner, Susanne S.; van den Hoogen, Johan; Zou, Yibiao; Liang, Jingjing; de-Miguel, Sergio; Nabuurs, Gert-Jan; Reich, Peter B.; Niinemets, Ulo; Abegg, Meinrad; Adou Yao, Yves C.; Alberti, Giorgio; Zambrano, Angelica M. Almeyda; Vilchez Alvarado, Braulio; Alvarez-Davila, Esteban; Alvarez-Loayza, Patricia; Alves, Luciana F.;
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Abstract

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4Gt), 54% (335.7Gt), 22% (136.2Gt) and 3% (18.7Gt), respectively. We further project that, depending on future emissions pathways, 17-34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling. Integrating inventory data with machine learning models reveals the global composition of tree types-needle-leaved evergreen individuals dominate, followed by broadleaved evergreen and deciduous trees-and climate change risks.

Published in

Nature Plants
2023, Volume: 9, number: 11, pages: 1795-1809
Publisher: NATURE PORTFOLIO