Abstract
Extreme weather events are occurring more frequently, and research has shown that plant diversity can help mitigate the impacts of climate change by increasing plant productivity and ecosystem stability. Although soil temperature and its stability are key determinants of essential ecosystem processes, no study has yet investigated whether plant diversity buffers soil temperature fluctuations over long-term community development. Here we have conducted a comprehensive analysis of a continuous 18-year dataset from a grassland biodiversity experiment with high spatial and temporal resolutions. Our findings reveal that plant diversity acts as a natural buffer, preventing soil heating in hot weather and cooling in cold weather. This diversity effect persists year-round, intensifying with the aging of experimental communities and being even stronger under extreme climate conditions, such as hot days or dry years. Using structural equation modelling, we found that plant diversity stabilizes soil temperature by increasing soil organic carbon concentrations and, to a lesser extent, plant leaf area index. Our results suggest that, in lowland grasslands, the diversity-induced stabilization of soil temperature may help to mitigate the negative effects of extreme climatic events such as soil carbon decomposition, thus slowing global warming.
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Data availability
The data supporting the results of this study are publicly available in the Jena Experiment Information System (https://doi.org/10.25829/F1PZ-Q045)55.
Code availability
The code supporting the results of this study is deposited in the Jena Experiment Information System (https://doi.org/10.25829/F1PZ-Q045).
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Acknowledgements
We express our gratitude to the fieldworkers of the Jena Experiment. We thank the technical staff S. Eismann, G. Kratzsch, U. Köber, K. Kunze and H. Scheffler for their work in maintaining the field site and the many student helpers for weeding the experimental plots and assisting with the measurements. We also thank J. Hines for providing valuable data-analysis suggestions. Special thanks go to G. Rada (iDiv) for her exceptional artistic contribution to the illustration of Fig. 4. We would also like to thank the source of the image ‘Soil with roots’ from V. Romanov/stock.adobe.com, which was skilfully incorporated into the illustration. The Jena Experiment is funded by the German Research Foundation (DFG, FOR 5000). We gratefully acknowledge the support of iDiv, which is funded by the German Research Foundation (DFG – FZT 118, 202548816). M.S. is supported by the German Research Foundation (DFG, SH 1682/1-1). N.E. is also funded by the German Research Foundation (DFG, EI 862/29-1).
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E.-D.S., O.K. and K.K. installed and maintained the soil temperature measurement system. N.E. sourced the funding. Y.H. conceived the project. Y.H., G.S. and H.D. cleaned and analysed the data. D.E. made substantial contributions to time-series analysis using a Bayesian approach, while N.E., B.S., H.S., A.E., J.D. and M.S. provided valuable insights and ideas for methodology development and data analysis. Y.H. and G.S. wrote the first draft of the paper. A.E. is the scientific coordinator of the Jena Experiment. G.G., A.H., M.L., C.R., B.S., A.W. and W.W.W. originally created the dataset of the covariate variables. All authors contributed to the development of the ideas, discussed the analysis and results, and edited the paper text.
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Nature Geoscience thanks Pieter De Frenne and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.
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Extended data
Extended Data Fig. 1 Temperature time series at different heights and soil depths.
Data are from plots in block 2.
Extended Data Fig. 2 Air temperature at 2 m (a), precipitation (b), soil temperature at 8 cm (c), and drought index (SPEI) (d) change with time at the field site of the Jena Experiment.
In panels a and c, the lines represent predictions derived from simple linear regression models, with the shaded bands denoting the corresponding 95% confidence intervals (n = 18).
Extended Data Fig. 3 Experiment design (a) and photos of cable boxes and the temperature sensor in the plot (b, c).
a, Plots are arranged in four blocks following the randomized block design. Each plot has an area of 20 × 20 m. Different colours represent different levels of the sown plant diversity. Notably, two monoculture plots marked with a cross in panel a were excluded from our study in 2009 due to insufficient coverage of the target species. b, A photo showing the cable box in each plot for the soil temperature sensors. c, Soil temperature sensors at three different layers (5, 15, 60 cm) in block 2. In other three blocks, only the first two layers (5 and 15 cm) were measured. Photo credit: Karl Kübler.
Extended Data Fig. 4 Extreme climate days in plant communities with different plant diversity levels in each year.
Colour gradient represents the sown plant species richness from 0 to 60. The circles represent the corresponding data points. The mean number of heat days (maximum soil temperature at 5 cm is higher than 25 °C) and standard error (n = 14, 16 or 4) are shown in panel a. The mean number of freezing days (minimum soil temperature at 5 cm is lower than 0 °C) and standard error (n = 14, 16, or 4) are shown in panel b. Note that in 2013, summer data (June, July and August) is missing due to the flood.
Extended Data Fig. 5 Linear relationship between air temperature and the effects of plant diversity on daily soil temperature offset at 5 cm during days without snow cover (a) and the effects of snow cover depth on the plant diversity effects (b).
Here, plant diversity is depicted by sown plant species richness. a, Different colours represent different seasons. The lines were predicted from linear models (n = 6095). b, The black linear line and grey band representing the 95% confidence intervals are predicted from a linear mixed-effects model with the year, the months within year, and the autocorrelation of the plant diversity effects between days within year as random terms (n = 190). The symbols F and P in panel b represent the F-ratios and the corresponding P value, respectively, derived from the ANOVA table of linear mixed-effects models. The degrees of freedom for both the numerator and denominator are given in the subscript of the F value.
Extended Data Fig. 6 Nonlinear evolution of plant diversity effects over time.
Multiple nonlinear models were examined, with the logarithmic curve demonstrating the best fit. The linear year was logarithmically transformed, followed by a straightforward linear regression analysis to assess the influence of the transformed year on the effects of plant diversity on soil temperature annual stability. The depicted nonlinear curve and 95% confidence intervals band represents the model’s prediction after backtransformation of the independent variable year (n = 18).
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Supplementary Appendices 1 and 2, Figs. 1–13 and Tables 1–3.
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Huang, Y., Stein, G., Kolle, O. et al. Enhanced stability of grassland soil temperature by plant diversity. Nat. Geosci. 17, 44–50 (2024). https://doi.org/10.1038/s41561-023-01338-5
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DOI: https://doi.org/10.1038/s41561-023-01338-5
