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Biodiversity buffers the response of spring leaf unfolding to climate warming

Nature Climate Change (2024)Cite this article

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Abstract

Understanding the sensitivity of spring leaf-out dates to temperature (ST) is integral to predicting phenological responses to climate warming and the consequences for global biogeochemical cycles. While variation in ST has been shown to be influenced by local climate adaptations, the impact of biodiversity remains unknown. Here we combine 393,139 forest inventory plots with satellite-derived ST across the northern hemisphere during 2001–2022 to show that biodiversity greatly affects spatial variation in ST and even surpasses the importance of climate variables. High tree diversity significantly weakened ST, possibly driven by changes in root depth and soil processes. We show that current Earth system models fail to reproduce the observed negative correlation between ST and biodiversity, with important implications for phenological responses under future pathways. Our results highlight the need to incorporate the buffering effects of biodiversity to better understand the impact of climate warming on spring leaf unfolding and carbon uptake.

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Fig. 1: Negative correlations between biodiversity and ST.
Fig. 2: Mechanisms underlying the negative correlation between biodiversity and ST.
Fig. 3: Evaluation of model performances on ST with biodiversity.

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Data availability

All the data used in this study are available online via the following links: GFBI, https://www.gfbinitiative.org/data; ERA5, https://doi.org/10.24381/cds.e2161bac; Trendy, https://blogs.exeter.ac.uk/trendy; CMIP6, https://esgf-node.llnl.gov/projects/cmip6; elevation, https://doi.org/10.3133/ofr20111073; SoilGrids, https://doi.org/10.5194/soil-7-217-2021; evenness, https://doi.org/10.3929/ethz-b-000597256; forest age, https://doi.org/10.5194/essd-13-4881-2021; MCD12Q1v061, https://doi.org/10.5067/MODIS/MCD12Q1.061; MCD12Q2v061, https://doi.org/10.5067/MODIS/MCD12Q2.061; Ecoregions 2017, https://ecoregions.appspot.com; Köppen–Geiger maps, https://doi.org/10.1038/s41597-023-02549-6. Source data are provided with this paper.

Code availability

All the code used for data analysis and figure generation is available on GitHub at https://github.com/spjace/asc-for-bio-effect-on-lud (ref. 50). Furthermore, we packaged this code into the Python package phenology for phenological analysis and computing optimal pre-season length, released on the Python Package Index at https://pypi.org/project/phenology.

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Acknowledgements

This work was funded by the National Natural Science Foundation of China (grant nos 42125101 and 42271034). X.W. was funded by the Youth Innovation Promotion Association of the Chinese Academy of Sciences (grant no. 2022051). Y. Zhang was funded by the National Natural Science Foundation of China (grant no. 42125105). J.P. was funded by the TED2021-132627B-I00 grant funded by the Spanish MCIN, AEI/10.13039/501100011033, and by the European Union NextGenerationEU/PRTR, the Fundación Ramón Areces project no. CIVP20A6621 and the Catalan government grant no. SGR221-1333. C.M.Z. was funded by SNF Ambizione grant no. PZ00P3_193646. J.L. was supported by Science-i, of which the cyberinfrastructure was partially sponsored by the National Science Foundation of the United States (award no. 2311762).

Author information

Author notes

  1. These authors contributed equally: Pengju Shen, Xiaoyue Wang.

Authors and Affiliations

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

    Pengju Shen, Xiaoyue Wang & Chaoyang Wu

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

    Pengju Shen, Xiaoyue Wang & Chaoyang Wu

  3. Department of Environmental Systems Science, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland

    Constantin M. Zohner

  4. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Barcelona, Spain

    Josep Peñuelas

  5. CREAF, Barcelona, Spain

    Josep Peñuelas

  6. Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, USA

    Yuyu Zhou

  7. Institute of Ecology, College of Urban and Environmental Science and Key Laboratory for Earth Surface Processes of Ministry of Education, Peking University, Beijing, China

    Zhiyao Tang

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

    Jianyang Xia

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

    Hua Zheng

  10. College of Water Sciences, Beijing Normal University, Beijing, China

    Yongshuo Fu

  11. Forest Advanced Computing and Artificial Intelligence Laboratory, Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USA

    Jingjing Liang

  12. Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, China

    Weiwei Sun

  13. International Institute for Earth System Sciences, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing University, Nanjing, China

    Yongguang Zhang

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Contributions

C.W. designed the research. C.W. and P.S. wrote the first draft of the paper. P.S. and X.W. performed the data analysis. All authors assessed the research analyses and contributed to the writing of the paper.

Corresponding authors

Correspondence to Weiwei Sun, Yongguang Zhang or Chaoyang Wu.

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Nature Climate Change thanks Yanjun Du and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Spatially consistent evaluation of model performances on the sensitivity of spring leaf unfolding to warming (ST) with biodiversity.

a-d represent results for 15 Trendy models and 13 CMIP6 models under different shared socioeconomic pathways (SSP1-2.6, SSP2-4.5 and SSP5-8.5), respectively. + +, The model outcomes correspond harmoniously with the observed results, exhibiting a positive correlation; – –, both are negative.

Source data

Extended Data Fig. 2 Biodiversity impacts soil moisture and organic carbon (SOC) by influencing root depth, consequently shaping the sensitivity of spring leaf unfolding to warming (ST).

a-f, represent Partial correlation analysis results between biodiversity and root depth (a), biodiversity and spring soil moisture (b), biodiversity and SOC (c), root depth and soil organic carbon (d), root depth and spring soil moisture (e), spring soil moisture and ST (f), respectively. The significance was based on the t statistics using a two-tailed test and to control the false discovery rate, the Benjamini-Hochberg (BH) method was employed in a-f. *, P<0.05; **, P<0.01; NS, not significant; P, positive effect; and N, negative effect.

Source data

Extended Data Fig. 3 Enhancing soil fertility through the Influence of biodiversity on soil physicochemical properties.

a-f, represent the partial correlation analysis results between biodiversity and volumetric fraction of coarse fragments (VOCF) (a), VOCF and soil organic carbon (SOC) (b), VOCF and soil total nitrogen (N) (c), biodiversity and Soil pH (d), Soil pH and SOC (e), Soil pH and N (f), respectively. The significance was based on the t statistics using a two-tailed test and to control the false discovery rate, the Benjamini-Hochberg (BH) method was employed in a-f. *, P<0.05; **, P<0.01; NS, not significant; P, positive effect; and N, negative effect.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–11 and Tables 1–6.

Source data

Source Data Fig. 1

Source data for generating Fig. 1.

Source Data Fig. 2

Source data for generating Fig. 2.

Source Data Fig. 3

Source data for generating Fig. 3.

Source Data Extended Data Fig. 1

Source data for generating Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Source data for generating Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Source data for generating Extended Data Fig. 3.

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Shen, P., Wang, X., Zohner, C.M. et al. Biodiversity buffers the response of spring leaf unfolding to climate warming. Nat. Clim. Chang. (2024). https://doi.org/10.1038/s41558-024-02035-w

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