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Biodiversity buffers the response of spring leaf unfolding to climate warming


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,; ERA5,; Trendy,; CMIP6,; elevation,; SoilGrids,; evenness,; forest age,; MCD12Q1v061,; MCD12Q2v061,; Ecoregions 2017,; Köppen–Geiger maps, Source data are provided with this paper.

Code availability

All the code used for data analysis and figure generation is available on GitHub at (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


  1. Gu, H. et al. Warming-induced increase in carbon uptake is linked to earlier spring phenology in temperate and boreal forests. Nat. Commun. 13, 3698 (2022).

    Article  CAS  Google Scholar 

  2. Peñuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).

    Article  Google Scholar 

  3. Peñuelas, J. & Filella, I. Responses to a warming world. Science 294, 793–795 (2001).

    Article  Google Scholar 

  4. Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).

    Article  Google Scholar 

  5. Wang, T. et al. The influence of local spring temperature variance on temperature sensitivity of spring phenology. Glob. Change Biol. 20, 1473–1480 (2014).

    Article  Google Scholar 

  6. Bennie, J., Kubin, E., Wiltshire, A., Huntley, B. & Baxter, R. Predicting spatial and temporal patterns of bud-burst and spring frost risk in north-west Europe: the implications of local adaptation to climate. Glob. Change Biol. 16, 1503–1514 (2010).

    Article  Google Scholar 

  7. Gao, M. et al. Three-dimensional change in temperature sensitivity of northern vegetation phenology. Glob. Change Biol. 26, 5189–5201 (2020).

    Article  CAS  Google Scholar 

  8. Shen, M. et al. Earlier-season vegetation has greater temperature sensitivity of spring phenology in northern hemisphere. PLoS ONE 9, e88178 (2014).

    Article  Google Scholar 

  9. Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).

    Article  CAS  Google Scholar 

  10. Maina, F. Z., Kumar, S. V. & Gangodagamage, C. Irrigation and warming drive the decreases in surface albedo over High Mountain Asia. Sci. Rep. 12, 16163 (2022).

    Article  CAS  Google Scholar 

  11. Picard, G. et al. Bud-burst modelling in Siberia and its impact on quantifying the carbon budget. Glob. Change Biol. 11, 2164–2176 (2005).

    Article  Google Scholar 

  12. Furey, G. N. & Tilman, D. Plant biodiversity and the regeneration of soil fertility. Proc. Natl Acad. Sci. USA 118, e2111321118 (2021).

    Article  CAS  Google Scholar 

  13. Mori, A. S. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Change 11, 543–550 (2021).

    Article  Google Scholar 

  14. Rheault, G., Lévesque, E. & Proulx, R. Diversity of plant assemblages dampens the variability of the growing season phenology in wetland landscapes. BMC Ecol. Evol. 21, 91 (2021).

    Article  Google Scholar 

  15. Yin, R. et al. Experimental warming causes mismatches in alpine plant–microbe–fauna phenology. Nat. Commun. 14, 2159 (2023).

    Article  CAS  Google Scholar 

  16. Wolf, A. A., Zavaleta, E. S. & Selmants, P. C. Flowering phenology shifts in response to biodiversity loss. Proc. Natl Acad. Sci. USA 114, 3463–3468 (2017).

    Article  CAS  Google Scholar 

  17. Dronova, I., Taddeo, S. & Harris, K. Plant diversity reduces satellite-observed phenological variability in wetlands at a national scale. Sci. Adv. 8, eabl8214 (2022).

    Article  Google Scholar 

  18. Chen, X. et al. Tree diversity increases decadal forest soil carbon and nitrogen accrual. Nature (2023).

  19. Zhang, S., Dai, J. & Ge, Q. Responses of autumn phenology to climate change and the correlations of plant hormone regulation. Sci. Rep. 10, 9039 (2020).

    Article  CAS  Google Scholar 

  20. Liu, D., Wang, T., Peñuelas, J. & Piao, S. Drought resistance enhanced by tree species diversity in global forests. Nat. Geosci. 15, 800–804 (2022).

    Article  CAS  Google Scholar 

  21. Oliveira, B. F., Moore, F. C. & Dong, X. Biodiversity mediates ecosystem sensitivity to climate variability. Commun. Biol. 5, 628 (2022).

    Article  Google Scholar 

  22. García-Palacios, P., Gross, N., Gaitán, J. & Maestre, F. T. Climate mediates the biodiversity–ecosystem stability relationship globally. Proc. Natl Acad. Sci. USA 115, 8400–8405 (2018).

    Article  Google Scholar 

  23. Gould, I. J., Quinton, J. N., Weigelt, A., De Deyn, G. B. & Bardgett, R. D. Plant diversity and root traits benefit physical properties key to soil function in grasslands. Ecol. Lett. 19, 1140–1149 (2016).

    Article  Google Scholar 

  24. Ding, J. et al. Decadal soil carbon accumulation across Tibetan permafrost regions. Nat. Geosci. 10, 420–424 (2017).

    Article  CAS  Google Scholar 

  25. Chen, S. et al. Plant diversity enhances productivity and soil carbon storage. Proc. Natl Acad. Sci. USA 115, 4027–4032 (2018).

    Article  CAS  Google Scholar 

  26. Beugnon, R. et al. Tree diversity and soil chemical properties drive the linkages between soil microbial community and ecosystem functioning. ISME Commun. 1, 41 (2021).

    Article  Google Scholar 

  27. Zhang, J. et al. Variation and evolution of C:N ratio among different organs enable plants to adapt to N-limited environments. Glob. Change Biol. 26, 2534–2543 (2020).

    Article  Google Scholar 

  28. Wang, C., Cao, R., Chen, J., Rao, Y. & Tang, Y. Temperature sensitivity of spring vegetation phenology correlates to within-spring warming speed over the Northern Hemisphere. Ecol. Indic. 50, 62–68 (2015).

    Article  Google Scholar 

  29. Xin, Q. et al. A semiprognostic phenology model for simulating multidecadal dynamics of global vegetation leaf area index. J. Adv. Model. Earth Syst. 12, e2019MS001935 (2020).

    Article  Google Scholar 

  30. Shen, M. et al. Plant phenology changes and drivers on the Qinghai–Tibetan Plateau. Nat. Rev. Earth Environ. 3, 633–651 (2022).

    Article  Google Scholar 

  31. Liang, J. et al. Positive biodiversity–productivity relationship predominant in global forests. Science 354, aaf8957 (2016).

    Article  Google Scholar 

  32. Friedl, M. A., Gray, J. & Sulla-Menashe, D. MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V061 [MCD12Q2] (NASA EOSDIS Land Processes Distributed Active Archive Center, 2022).

  33. Muñoz-Sabater, J. ERA5-Land Monthly Averaged Data from 1950 to Present (Copernicus Climate Change Service Climate Data Store, 2019).

  34. Poggio, L. et al. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. SOIL 7, 217–240 (2021).

    Article  CAS  Google Scholar 

  35. Yu, Z. et al. Forest expansion dominates China’s land carbon sink since 1980. Nat. Commun. 13, 5374 (2022).

    Article  CAS  Google Scholar 

  36. Zhu, B. et al. Constrained tropical land temperature–precipitation sensitivity reveals decreasing evapotranspiration and faster vegetation greening in CMIP6 projections. NPJ Clim. Atmos. Sci. 6, 91 (2023).

    Article  Google Scholar 

  37. Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).

    Article  Google Scholar 

  38. Friedl, M. A. & Sulla-Menashe, D. MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061 [MCD12Q1] (NASA EOSDIS Land Processes Distributed Active Archive Center, 2022).

  39. Beck, H. E. et al. Present and future Köppen–Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).

    Article  Google Scholar 

  40. Hordijk, I. et al. Evenness mediates the global relationship between forest productivity and richness. J. Ecol. 111, 1308–1326 (2023).

    Article  Google Scholar 

  41. Gonsamo, A., Chen, J. M. & D’Odorico, P. Deriving land surface phenology indicators from CO2 eddy covariance measurements. Ecol. Indic. 29, 203–207 (2013).

    Article  CAS  Google Scholar 

  42. Kong, D. et al. phenofit: an R package for extracting vegetation phenology from time series remote sensing. Methods Ecol. Evol. 13, 1508–1527 (2022).

    Article  Google Scholar 

  43. Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. in Proc. 9th Python in Science Conference (eds van der Walt, S. & Millman, J.) 92–96 (2010).

  44. Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).

    Article  CAS  Google Scholar 

  45. Vallat, R. Pingouin: statistics in Python. J. Open Source Softw. 3, 1026 (2018).

    Article  Google Scholar 

  46. Rey, S. J. & Anselin, L. PySAL: A Python Library of Spatial Analytical Methods. in Handbook of Applied Spatial Analysis (eds Fischer, M. & Getis, A.) (Springer, 2010).

  47. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  48. Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. in Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016).

  49. Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).

    Article  Google Scholar 

  50. Shen, P. Python code for ‘Biodiversity buffers the response of spring leaf unfolding to climate warming’. GitHub (2024).

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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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Authors and Affiliations



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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The authors declare no competing interests.

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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.

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Source Data Fig. 1

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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).

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