Global distribution and changes of leaf-level intrinsic water use efficiency and their responses to water stress

Intrinsic water use efficiency (iWUE) at the leaf level measures water expenditures by terestrial plants during photosynthesis, yet its global spatiotemporal dynamics and responses to water stress remain poorly understood. Using machine-learning models and carbon isotope observations in C<sub>3</sub> foliage, here we elucidate global patterns, trends, and water-stress responses of leaf iWUE. We find high iWUE in cold, arid regions and lower values in warm, humid areas. From 2001 to 2020, global iWUE increases at 0.2 ± 0.02 μmol mol<sup>-1</sup> year<sup>-1</sup>, with strong biome specific differences. Grasslands exhibit the highest mean iWUE but the slowest increase, whereas evergreen broadleaf forests show the lowest iWUE yet the fastest increase. iWUE rises with increasing water stress, but the rate of growth diminishes as water stress intensifies. Vapor pressure deficit influence iWUE more broadly than soil moisture. The ecological optimality model reproduces the spatial patterns of leaf iWUE and identifies vapor pressure deficit as the dominant driver, but overestimates mean iWUE and its trend. Our findings suggest that increasing water stress may slow the rate of global iWUE increase as the climate continues to warm.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2026-01-07
DOI
https://doi.org/10.1038/s41467-025-68252-9
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Global distribution and changes of leaf-level intrinsic water use efficiency and their responses to water stress

Paul C. Stoy, David Makowski, N. Buchmann, Trevor F. Keenan et al.
Nature Communications
Plant Water Relations and Carbon Dynamics
article

Global distribution and changes of leaf-level intrinsic water use efficiency and their responses to water stress

Paul C. Stoy, David Makowski, N. Buchmann, Trevor F. Keenan, Martin G. De Kauwe, G Z Chen, Songhan Wang, Jingfeng Xiao, Fangyue Zhang, Philippe Ciais, William K. Smith, Lixin Wang, Xing Li, Qiaoyun Xie, Josep Penuelas, Han Wang, Shuli Niu, Zheng Fu, Xiaoying Gong, Xiang Wang
article en

Abstract

Intrinsic water use efficiency (iWUE) at the leaf level measures water expenditures by terestrial plants during photosynthesis, yet its global spatiotemporal dynamics and responses to water stress remain poorly understood. Using machine-learning models and carbon isotope observations in C<sub>3</sub> foliage, here we elucidate global patterns, trends, and water-stress responses of leaf iWUE. We find high iWUE in cold, arid regions and lower values in warm, humid areas. From 2001 to 2020, global iWUE increases at 0.2 ± 0.02 μmol mol<sup>-1</sup> year<sup>-1</sup>, with strong biome specific differences. Grasslands exhibit the highest mean iWUE but the slowest increase, whereas evergreen broadleaf forests show the lowest iWUE yet the fastest increase. iWUE rises with increasing water stress, but the rate of growth diminishes as water stress intensifies. Vapor pressure deficit influence iWUE more broadly than soil moisture. The ecological optimality model reproduces the spatial patterns of leaf iWUE and identifies vapor pressure deficit as the dominant driver, but overestimates mean iWUE and its trend. Our findings suggest that increasing water stress may slow the rate of global iWUE increase as the climate continues to warm.

Nature CommunicationsVol. 17(1)
Fujian Normal University (CN), Nanjing Agricultural University (CN), Centre National de la Recherche Scientifique (FR), University of Wisconsin–Madison (US), University of Arizona (US), Lawrence Berkeley National Laboratory (US), Sun Yat-sen University (CN), University of New Hampshire (US), The University of Western Australia (AU), Université de Versailles Saint-Quentin-en-Yvelines (FR), Chinese Academy of Sciences (CN), AgroParisTech (FR), Beijing Normal University (CN), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Chengdu University of Technology (CN), ETH Zurich (CH), University of Bristol (GB), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Laboratoire des Sciences du Climat et de l'Environnement (FR), CEA Paris-Saclay (FR), Centre for Research on Ecology and Forestry Applications (ES), Institute of Geographic Sciences and Natural Resources Research (CN), Indiana University Indianapolis (US), Global Ecology Unit CREAF-CSIC-UAB, Indiana University – Purdue University Indianapolis (US), University of California, Berkeley (US), Tsinghua University (CN)
Clean water and sanitation
Openalex Percentile: Top 96%
Plant Water Relations and Carbon Dynamics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.