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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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).
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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.
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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.
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.
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.
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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. 14, 863–868 (2024). https://doi.org/10.1038/s41558-024-02035-w
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DOI: https://doi.org/10.1038/s41558-024-02035-w
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