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Acquisitive plants exhibit stronger phenological shifts in response to warming: insights from meta-analysis and long-term monitoring

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Abstract

As climate warming accelerates, shifts in plant phenology are reshaping the functioning and stability of terrestrial ecosystems. While the roles of climatic drivers in shaping phenological responses to warming are well established, the influence of intrinsic plant functional traits remains poorly understood. Here, we combine two complementary approaches through a meta-analysis of 124 field warming experiments and an analysis of long-term phenological monitoring networks (CPON and USA‑NPN) to evaluate phenological responses to warming across a spectrum of resource-use strategies in seasonally cold biomes. Our meta-analysis demonstrates that resource-acquisitive plants, characterized by higher nutrient concentrations and thinner leaves, show significantly stronger phenological responses to experimental warming. This pattern is observed consistently across both leaf-out in spring and senescence in autumn. These results from meta-analysis are further supported by two long-term observational datasets, which also show more pronounced phenological shifts in acquisitive species under long-term warming. Our findings present a trait-climate integration framework that extends beyond conventional environmental drivers, providing a mechanistic foundation to enhance the accuracy of forecasts for plant responses to climate change.

Data availability

The data generated in this study have been deposited in Figshare at https://doi.org/10.6084/m9.figshare.29917052. Plant traits data were obtained from TRY Plant Trait Database (https://www.try-db.org/), climate data from WorldClim (https://www.worldclim.org/) and Climate Research Unit (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.08/). The long-term ground phenological data of USA National Phenology Network (USA-NPN) are available from the website: https://www.usanpn.org/ results/data. The phenological data from China Phenological Observation Network (CPON) were provided by the Meteorological Information Center of the China Meteorological Administration. Source data are provided with this paper.

Code availability

Codes for analysis are available in the Figshare repository (https://doi.org/10.6084/m9.figshare.29917052).

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 32422055 (H.L.), 32130065 (H.L.) and 42125101 (C.W.)) and the National R&D Program of China (Grant No. 2023YFF0806800 (H.L.)). H.L. also acknowledges support from the Shanghai Rising-Star Program (Grant No. 23QA1402900) and Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (JYB2025XDXM904). We would like to express our gratitude to all the authors of the published papers included in our meta-analysis, as well as to the contributors of the two long-term phenological datasets. We also thank the contributors of the TRY database, as well as the authors who provided the spatial distribution data of the traits used in Fig. 4.

Author information

Authors and Affiliations

  1. Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, Institute of Eco-Chongming, Zhejiang Zhoushan Island Ecosystem Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China

    Kexin Xiong, Chunyan Lu, Huimin Zhou, Xinxin Wang & Huiying Liu

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

    Peter B. Reich

  3. Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia

    Peter B. Reich

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

    Peter B. Reich

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

    Philippe Ciais

  6. State Key Laboratory of Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China

    Huimin Zhou

  7. CREAF, Cerdanyola del Vallès, Barcelona, Catalonia, Spain

    Josep Peñuelas

  8. CSIC, Global Ecology Unit, CREAF-CSIC-UAB, Cerdanyola del Vallès, Barcelona, Catalonia, Spain

    Josep Peñuelas

  9. The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Chaoyang Wu

  10. University of the Chinese Academy of Sciences, Beijing, China

    Chaoyang Wu

Authors

  1. Kexin Xiong
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  2. Peter B. Reich
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  3. Philippe Ciais
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  4. Chunyan Lu
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  5. Huimin Zhou
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  6. Xinxin Wang
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  7. Josep Peñuelas
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  8. Chaoyang Wu
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  9. Huiying Liu
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Contributions

H.L. and C.W. conceived the study. K.X., H.Z., and H.L. conducted the analysis with inputs from C.L. and X.W. P.R., P.C., J.P., and C.W. provided significant revisions. K.X., C.W., and H.L. wrote the manuscript, with contributions from all co-authors.

Corresponding authors

Correspondence to
Chaoyang Wu or Huiying Liu.

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Xiong, K., Reich, P.B., Ciais, P. et al. Acquisitive plants exhibit stronger phenological shifts in response to warming: insights from meta-analysis and long-term monitoring.
Nat Commun (2026). https://doi.org/10.1038/s41467-026-70474-4

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