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Contrasting age-dependent leaf acclimation strategies drive vegetation greening across deciduous broadleaf forests in mid- to high latitudes

Nature Plants volume 11, pages 1748–1758 (2025)Cite this article

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Abstract

Increasing leaf area and extending vegetation growing seasons are two primary drivers of global greening, which has emerged as one of the most significant responses to climate change. However, it remains unclear how these two leaf acclimation strategies would vary across forests at a large spatial scale. Here, using multiple satellite-based datasets and field measurements, we analysed the temporal changes (Δ) in maximal leaf area index (LAImax) and length of the growing season (LOS) from 2002 to 2021 across deciduous broadleaf forests (DBFs) in the middle to high latitudes. Contrary to the widely held assumption of coordination, our results revealed a negative correlation between ΔLAImax and ΔLOS. Notably, the trade-offs between ΔLAImax and ΔLOS were strongly explained by stand age. Younger DBFs, with lower baseline LAImax, predominantly located in eastern Asia, displayed an increase in LAImax with small changes in LOS. This acquisitive strategy facilitated younger DBFs to grow more photosynthetically efficient leaves with low leaf mass per area, enhancing their light use efficiency. Conversely, older DBFs with a higher baseline LAImax, primarily located in North America and Europe, extended their LOS by increasing leaf mass per area. This conservative strategy facilitated older DBFs to produce thicker, but less photosynthetically efficient leaves, resulting in decreased light use efficiency. Our findings offer new insights into the contrasting changes in leaf area and growing season length and highlight their divergent impacts on ecosystem functioning.

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Fig. 1: Contrasting changes in maximal leaf area index (∆LAImax) and length of growing season (∆LOS) from 2002 to 2021 across DBFs in the middle to high latitudes.
Fig. 2: Temporal changes in leaf mass per area (∆LMA) and leaf moisture content (∆LMC) from 2002 to 2016 across DBFs in the middle to high latitudes.
Fig. 3: Cascading influences on changes in ecosystem light use efficiency (∆LUE) from 2002 to 2016 across DBFs in the middle to high latitudes.
Fig. 4: Schematic representation of age-dependent leaf acclimation strategies driving vegetation greening across DBFs in the middle to high latitudes.

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Data availability

All the relevant data come from publicly available sources. The GLASS LAI (01B01.V60) product is available at http://www.glass.umd.edu/LAI/MODIS/0.05D/; the satellite leaf unfolding and dormancy dates product is available at https://lpdaac.usgs.gov/products/mcd12q2v061/; the MPI-BGC stand age data are available at https://doi.org/10.17871/ForestAgeBGI.2021; the MODIS Nadir BRDF-Adjusted Reflectance (NBAR) products (MCD43A4) are available via Google Earth Engine at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A4; the LMC data are available via Zenodo at https://doi.org/10.5281/zenodo.6545571; the BESS v2.0 GPP data are available at https://www.environment.snu.ac.kr/bessv2; the GLASS PAR (04B01.V60) product is available at http://www.glass.umd.edu/PAR/; the GLASS fAPAR (09B01.V60) product is available at http://www.glass.umd.edu/FAPAR/MODIS/0.05D/; the Global Land cover data are available at https://lpdaac.usgs.gov/products/mcd12c1v061/; the MOD13Q1 V061 EVI data are available at https://lpdaac.usgs.gov/products/mod13q1v061/; the in situ leaf unfolding date products of Europe, Russia and the USA, respectively, are available at http://www.pep725.eu, https://doi.org/10.1038/s41597-020-0376-z and https://www.usanpn.org/data/observational; the TRY database is available at https://www.try-db.org/; the MODIS FireCCILT11 (version 1.1) data are available at https://catalogue.ceda.ac.uk/uuid/b1bd715112ca43ab948226d11d72b85e/; the GFW Global Forest Change v1.9 data are available at https://glad.earthengine.app/view/global-forest-change; the Palmer Drought Severity Index data are available at https://climate.northwestknowledge.net/TERRACLIMATE.

Code availability

The code used for this study is available via Zenodo at https://doi.org/10.5281/zenodo.15765680 (ref. 87).

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (grant nos. 42471326, 31971458 and U21A6001), the National Key R&D Program of China (grant no. 2024YFF1306600) and the Science and Technology Program of Guangdong (grant no. 2024B1212070012).

Author information

Author notes

  1. These authors contributed equally: Fangyi Wang, Meimei Xue.

Authors and Affiliations

  1. Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, School of Ecology, Sun Yat-sen University, Zhuhai, China

    Fangyi Wang, Meimei Xue, Xing Li, Yixuan Pan, Xueqin Yang & Xiuzhi Chen

  2. Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY, USA

    Liming Zhou

  3. School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA

    Christopher E. Doughty & Julia K. Green

  4. Laboratoire des Sciences du Climat et de l’Environnement, IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif sur Yvette, France

    Philippe Ciais & Liyang Liu

  5. Institute for Global Change Biology and School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA

    Peter B. Reich

  6. Department of Forest Resources, University of Minnesota, St. Paul, MN, USA

    Peter B. Reich

  7. Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada

    Jiali Shang

  8. Department of Geography and Planning, University of Toronto, Toronto, Ontario, Canada

    Jing Ming Chen & Jane Liu

  9. Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA

    Dalei Hao

  10. Institute of Ecology, Peking University, Beijing, China

    Shengli Tao

  11. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China

    Yanjun Su & Lingli Liu

  12. School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China

    Jianyang Xia

  13. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

    Han Wang

  14. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Kailiang Yu

  15. School of Urban Planning and Design, Peking University, Beijing, China

    Zaichun Zhu

  16. School of Biological Sciences, The University of Hong Kong, Hong Kong, China

    Peng Zhu

  17. South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China

    Hui Liu

  18. College of Land Science and Technology, China Agricultural University, Beijing, China

    Yelu Zeng

  19. Center for GeoData and Analysis, State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Kai Yan & Yongshuo H. Fu

  20. Department of Agricultural and Environmental Sciences, University of Bari ‘A. Moro’, Bari, Italy

    Raffaele Lafortezza

  21. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Yongxian Su

  22. School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, China

    Yanqiong Meng

  23. School of Forestry, Northeast Forestry University, Harbin, China

    Nianpeng He

  24. College of Urban and Environmental Sciences, School of Urban Planning and Design, Peking University, Beijing, China

    Wenping Yuan

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  33. Xiuzhi Chen
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X.C. designed the study, wrote the initial paper and revised the paper. F.W. and M.X. collected the data, performed the analysis, drew the figures and wrote the Methods section. L.Z., C.E.D., P.C., P.B.R., J.S., J.M.C., J.L., J.K.G., D.H., S.T., Y.J.S., Lingli Liu, J.X., H.W., K. Yu, Z.Z., P.Z., X.L., H.L., Y.Z., K. Yan, Liyang Liu, R.L., Y.S., Y.M., Y.P., X.Y., Y.H.F., N.H. and W.Y. contributed to discussing the scientific question, as well as writing and revising the paper.

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Correspondence to Xiuzhi Chen.

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Wang, F., Xue, M., Zhou, L. et al. Contrasting age-dependent leaf acclimation strategies drive vegetation greening across deciduous broadleaf forests in mid- to high latitudes. Nat. Plants 11, 1748–1758 (2025). https://doi.org/10.1038/s41477-025-02096-5

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