Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

When and where soil dryness matters to ecosystem photosynthesis

Nature Plants volume 11, pages 1390–1400 (2025)Cite this article

Subjects

Abstract

Projected increases in the intensity and frequency of droughts in the twenty-first century are expected to cause a substantial negative impact on terrestrial gross primary productivity (GPP). Yet, the relative role of soil water supply (indicated by soil moisture) and atmospheric water demand (indicated by vapour pressure deficit, VPD) on GPP remains debated, primarily due to their strong covariations, the presence of confounding factors and unresolved causal relationships among the interconnected hydrometeorological drivers of GPP. Here using a causality-guided explainable artificial intelligence framework, we show that soil moisture is the dominant regulator of water stress, surpassing the role of VPD, when and where soil water supply limits ecosystem functions. Temporally, we use in situ flux tower data to demonstrate that soil moisture dominates the GPP response during periods of insufficient soil water supply. Spatially, we assess the global spatial patterns of satellite sun-induced chlorophyll fluorescence (a proxy for GPP) in water-limited regions and demonstrate that they are mostly dominated by soil moisture. Conversely, VPD plays a greater role in controlling the temporal and spatial variations in GPP than soil moisture when and where soil water supply is not limited. The relative role of soil moisture and VPD is modulated by plant adaptation to long-term climatological aridity. Our findings advance the understanding of the impacts of soil and atmospheric dryness on ecosystem photosynthesis. They provide crucial insights into how terrestrial ecosystems respond to increasing aridity and more frequent droughts, particularly given the potential ecosystem shifts from energy to water limitation.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Conceptual diagram illustrating the impact of water availability on ecosystem photosynthesis and underlying SM distribution.
Fig. 2: Relative importance of hydrometeorological drivers in controlling temporal and spatial variations in ecosystem photosynthesis for water-limited and energy-limited conditions.
Fig. 3: Relative impacts of SM and VPD on global photosynthesis across space.
Fig. 4: Impacts of PFTs and aridity on the relative role of SM and VPD in controlling photosynthesis across space.

Similar content being viewed by others

Data availability

All data used in this study are openly available: FLUXNET2015 at https://fluxnet.org/data/fluxnet2015-dataset/, TROPOMI SIF at ftp://fluo.gps.caltech.edu/data/tropomi/, monthly ERA5-Land reanalysis data at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=form, the global land cover map of Moderate Resolution Imaging Spectroradiometer MCD12C1 product (version 6) for 2020 at https://lpdaac.usgs.gov/products/mcd12c1v006/, the TerraClimate wetness index at https://www.climatologylab.org/terraclimate.html, global apparent rooting depths at https://zenodo.org/records/5515246, Global Ψ50 at https://figshare.com/articles/dataset/Datasets_Global_ecosystem-scale_plant_hydraulic_traits_retrieved_using_model-data_fusion/13350713/4, global degrees of anisohydricity at https://github.com/agkonings/isohydricity and global canopy height for 2020 at https://www.research-collection.ethz.ch/handle/20.500.11850/609802.

Code availability

The corresponding R code scripts, trained XGBoost models, representative samples used for Shapley value calculation and original Shapley value results are available via Zenodo at https://doi.org/10.5281/zenodo.15314104 (ref. 84).

References

  1. Friedlingstein, P. et al. Global carbon budget 2022. Earth Syst. Sci. Data 14, 4811–4900 (2022).

    Article  Google Scholar 

  2. Fu, Z. et al. Uncovering the critical soil moisture thresholds of plant water stress for European ecosystems. Glob. Chang. Biol. 28, 2111–2123 (2022).

    Article  CAS  PubMed  Google Scholar 

  3. Fu, Z. et al. Global critical soil moisture thresholds of plant water stress. Nat. Commun. 15, 4826 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).

    Article  PubMed  Google Scholar 

  6. Ciais, P. H. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).

    Article  CAS  PubMed  Google Scholar 

  7. Humphrey, V. et al. Soil moisture–atmosphere feedback dominates land carbon uptake variability. Nature 592, 65–69 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Novick, K. A. et al. The impacts of rising vapour pressure deficit in natural and managed ecosystems. Plant Cell Environ. 47, 3561–3589 (2024).

    Article  CAS  PubMed  Google Scholar 

  9. Wolf, S. et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl Acad. Sci. USA 113, 5880–5885 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Peng, C. et al. A drought-induced pervasive increase in tree mortality across Canada’s boreal forests. Nat. Clim. Chang. 1, 467–471 (2011).

    Article  Google Scholar 

  11. Lian, X. et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 2, 232–250 (2021).

    Article  Google Scholar 

  12. Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).

    Article  CAS  Google Scholar 

  13. Zhou, S. et al. Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl Acad. Sci. USA 116, 18848–18853 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhou, S., Zhang, Y., Park Williams, A. & Gentine, P. Projected increases in intensity, frequency, and terrestrial carbon costs of compound drought and aridity events. Sci. Adv. 5, eaau5740 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Denissen, J. M. C. et al. Widespread shift from ecosystem energy to water limitation with climate change. Nat. Clim. Chang. 12, 677–684 (2022).

    Article  Google Scholar 

  16. Green, J. K. The intricacies of vegetation responses to changing moisture conditions. New Phytol. 244, 2156–2162 (2024).

    Article  PubMed  Google Scholar 

  17. Li, W. et al. Widespread increasing vegetation sensitivity to soil moisture. Nat. Commun. 13, 3959 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Jiao, W. et al. Observed increasing water constraint on vegetation growth over the last three decades. Nat. Commun. 12, 3777 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhang, Y. et al. Enhanced dominance of soil moisture stress on vegetation growth in Eurasian drylands. Natl Sci. Rev. 10, nwad108 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Chang. 6, 1023–1027 (2016).

    Article  CAS  Google Scholar 

  21. Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Liu, L. et al. Soil moisture dominates dryness stress on ecosystem production globally. Nat. Commun. 11, 1–9 (2020).

    CAS  Google Scholar 

  23. Lu, H. et al. Large influence of atmospheric vapor pressure deficit on ecosystem production efficiency. Nat. Commun. 13, 10–13 (2022).

    Google Scholar 

  24. Xu, S. et al. Response of ecosystem productivity to high vapor pressure deficit and low soil moisture: lessons learned from the global eddy‐covariance observations. Earths Future 11, e2022EF003252 (2023).

    Article  Google Scholar 

  25. Zhao, D., Zhang, Z. & Zhang, Y. Soil moisture dominates the forest productivity decline during the 2022 China compound drought‐heatwave event. Geophys. Res. Lett. 50, e2023GL104539 (2023).

    Article  Google Scholar 

  26. Kannenberg, S. A., Anderegg, W. R. L., Barnes, M. L., Dannenberg, M. P. & Knapp, A. K. Dominant role of soil moisture in mediating carbon and water fluxes in dryland ecosystems. Nat. Geosci. 17, 38–43 (2024).

    Article  CAS  Google Scholar 

  27. Fu, Z. et al. Atmospheric dryness reduces photosynthesis along a large range of soil water deficits. Nat. Commun. 13, 1–10 (2022).

    Article  Google Scholar 

  28. Sulman, B. N. et al. High atmospheric demand for water can limit forest carbon uptake and transpiration as severely as dry soil. Geophys. Res. Lett. 43, 9686–9695 (2016).

    Article  CAS  Google Scholar 

  29. Gentine, P. et al. Coupling between the terrestrial carbon and water cycles—a review. Environ. Res. Lett. 14, 083003 (2019).

    Article  CAS  Google Scholar 

  30. Feldman, A. F., Short Gianotti, D. J., Trigo, I. F., Salvucci, G. D. & Entekhabi, D. Land-atmosphere drivers of landscape-scale plant water content loss. Geophys. Res. Lett. 47, e2020GL090331 (2020).

    Article  Google Scholar 

  31. Bernacchi, C. J., Singsaas, E. L., Pimentel, C., Portis, A. R. & Long, S. P. Improved temperature response functions for models of Rubisco-limited photosynthesis. Plant Cell Environ. 24, 253–259 (2001).

    Article  CAS  Google Scholar 

  32. Baldocchi, D. How eddy covariance flux measurements have contributed to our understanding of Global Change Biology. Glob. Chang. Biol. 26, 242–260 (2020).

    Article  PubMed  Google Scholar 

  33. Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Frankenberg, C. et al. New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 38, L17706 (2011).

    Article  Google Scholar 

  35. Li, X. & Xiao, J. TROPOMI observations allow for robust exploration of the relationship between solar-induced chlorophyll fluorescence and terrestrial gross primary production. Remote Sens. Environ. 268, 112748 (2022).

    Article  Google Scholar 

  36. Köhler, P. et al. Global retrievals of solar‐induced chlorophyll fluorescence with TROPOMI: first results and intersensor comparison to OCO‐2. Geophys. Res. Lett. 45, 10456–10463 (2018).

    Article  Google Scholar 

  37. Miller, D. L. et al. Increased photosynthesis during spring drought in energy-limited ecosystems. Nat. Commun. 14, 7828 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794. https://doi.org/10.1145/2939672.2939785 (2016).

  39. Shwartz-Ziv, R. & Armon, A. Tabular data: deep learning is not all you need. Inf. Fusion 81, 84–90 (2022).

    Article  Google Scholar 

  40. Heskes, T., Sijben, E., Bucur, I. G. & Claassen, T. Causal Shapley values: exploiting causal knowledge to explain individual predictions of complex models. Adv. Neural Inf. Process. Syst. 33, 4778–4789 (2020).

    Google Scholar 

  41. Pearl, J. The do-calculus revisited. Preprint at https://arxiv.org/abs/1210.4852 (2012).

  42. Shapley, L. S. A value for n-person games. in Contributions to the Theory of Games II (eds Kuhn, H. and Tucker, A.) 307–317 (Princeton Univ. Press, 1953).

  43. Rozemberczki, B., et al. The Shapley value in machine learning. In Proc. 31st Int. Jt Conf. Artificial Intelligence (ed. De Raedt, L.) 5572–5579 (IJCAI, 2022).

  44. Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4768–4777 (2017).

    Google Scholar 

  45. Pearl, J. Causal diagrams for empirical research. Biometrika 82, 669–688 (1995).

    Article  Google Scholar 

  46. Stocker, B. D. et al. Global patterns of water storage in the rooting zones of vegetation. Nat. Geosci. 16, 250–256 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Liu, Y., Holtzman, N. M. & Konings, A. G. Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion. Hydrol. Earth Syst. Sci. 25, 2399–2417 (2021).

    Article  CAS  Google Scholar 

  48. Konings, A. G. & Gentine, P. Global variations in ecosystem‐scale isohydricity. Glob. Chang. Biol. 23, 891–905 (2017).

    Article  PubMed  Google Scholar 

  49. Lang, N., Jetz, W., Schindler, K. & Wegner, J. D. A high-resolution canopy height model of the Earth. Nat. Ecol. Evol. 7, 1778–1789 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Yu, T. et al. Disentangling the relative effects of soil moisture and vapor pressure deficit on photosynthesis in dryland Central Asia. Ecol. Indic. 137, 108698 (2022).

    Article  CAS  Google Scholar 

  52. Qiu, R. et al. Soil moisture dominates the variation of gross primary productivity during hot drought in drylands. Sci. Total Environ. 899, 165686 (2023).

    Article  CAS  PubMed  Google Scholar 

  53. Wang, H. et al. Exploring complex water stress–gross primary production relationships: impact of climatic drivers, main effects, and interactive effects. Glob. Chang. Biol. 28, 4110–4123 (2022).

    Article  CAS  PubMed  Google Scholar 

  54. He, B. et al. Worldwide impacts of atmospheric vapor pressure deficit on the interannual variability of terrestrial carbon sinks. Natl Sci. Rev. 9, nwab150 (2022).

    Article  PubMed  Google Scholar 

  55. Farrior, C. E., Bohlman, S. A., Hubbell, S. & Pacala, S. W. Dominance of the suppressed: power-law size structure in tropical forests. Science 351, 155–157 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Liu, Y., Konings, A. G., Kennedy, D. & Gentine, P. Global coordination in plant physiological and rooting strategies in response to water stress. Glob. Biogeochem. Cycles 35, e2020GB006758 (2021).

    Article  CAS  Google Scholar 

  57. Volaire, F. A unified framework of plant adaptive strategies to drought: crossing scales and disciplines. Glob. Chang. Biol. 24, 2929–2938 (2018).

    Article  PubMed  Google Scholar 

  58. Konings, A. G., Williams, A. P. & Gentine, P. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nat. Geosci. 10, 284–288 (2017).

    Article  CAS  Google Scholar 

  59. Anderegg, W. R. L. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).

    Article  CAS  PubMed  Google Scholar 

  60. Zhou, L. et al. Widespread decline of Congo rainforest greenness in the past decade. Nature 509, 86–90 (2014).

    Article  CAS  PubMed  Google Scholar 

  61. Flores, B. M. et al. Critical transitions in the Amazon forest system. Nature 626, 555–564 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).

    Article  CAS  PubMed  Google Scholar 

  63. Fernández‐de‐Uña, L., Martínez‐Vilalta, J., Poyatos, R., Mencuccini, M. & McDowell, N. G. The role of height‐driven constraints and compensations on tree vulnerability to drought. New Phytol. 239, 2083–2098 (2023).

    Article  PubMed  Google Scholar 

  64. Khanal, P. et al. Relevance of near-surface soil moisture vs. terrestrial water storage for global vegetation functioning. Biogeosciences 21, 1533–1547 (2024).

    Article  Google Scholar 

  65. Fang, L. et al. Neglecting acclimation of photosynthesis under drought can cause significant errors in predicting leaf photosynthesis in wheat. Glob. Chang. Biol. 29, 505–521 (2023).

    Article  CAS  PubMed  Google Scholar 

  66. Reichstein, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Chang. Biol. 11, 1424–1439 (2005).

    Article  Google Scholar 

  67. Lasslop, G. et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Glob. Chang. Biol. 16, 187–208 (2010).

    Article  Google Scholar 

  68. Zhou, H. et al. Distinguishing the main climatic drivers to the variability of gross primary productivity at global FLUXNET sites. Environ. Res. Lett. 18, 124007 (2023).

    Article  Google Scholar 

  69. Li, X. et al. Solar‐induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: first global analysis based on OCO‐2 and flux tower observations. Glob. Chang. Biol. 24, 3990–4008 (2018).

    Article  PubMed  Google Scholar 

  70. Marrs, J. K. et al. Solar‐induced fluorescence does not track photosynthetic carbon assimilation following induced stomatal closure. Geophys. Res. Lett. 47, e2020GL087956 (2020).

    Article  CAS  Google Scholar 

  71. Zhan, W., Lian, X., Liu, J. & Gentine, P. Inappropriateness of space-for-time and variability-for-time approaches to infer future dryland productivity changes. Front. Environ. Sci. 10, 1010269 (2022).

    Article  Google Scholar 

  72. Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).

    Article  Google Scholar 

  73. Li, W. et al. Revisiting global vegetation controls using multi‐layer soil moisture. Geophys. Res. Lett. 48, e2021GL092856 (2021).

    Article  Google Scholar 

  74. Friedl, M. & Sulla-Menashe, D. MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006. NASA EOSDIS Land Processes DAAC (NASA, 2015).

  75. Huettner, F. & Sunder, M. Axiomatic arguments for decomposing goodness of fit according to Shapley and Owen values. Electron. J. Statist. 6, 1239–1250 (2012).

    Article  Google Scholar 

  76. Jiang, S. et al. How interpretable machine learning can benefit process understanding in the geosciences. Earth’s Future 12, e2024EF004540 (2024).

    Article  Google Scholar 

  77. Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).

    Article  PubMed  Google Scholar 

  78. Baldocchi, D. `Breathing' of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 56, 1–26 (2008).

    Article  CAS  Google Scholar 

  79. Jung, M. et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature 541, 516–520 (2017).

    Article  CAS  PubMed  Google Scholar 

  80. Chen, J. L., Reynolds, J. F., Harley, P. C. & Tenhunen, J. D. Coordination theory of leaf nitrogen distribution in a canopy. Oecologia 93, 63–69 (1993).

    Article  PubMed  Google Scholar 

  81. Liu, J. et al. Evidence for widespread thermal acclimation of canopy photosynthesis. Nat. Plants 10, 1919–1927 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Wang, J. & Wen, X. Excess radiation exacerbates drought stress impacts on canopy conductance along aridity gradients. Biogeosciences 19, 4197–4208 (2022).

    Article  Google Scholar 

  83. Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).

    Article  CAS  PubMed  Google Scholar 

  84. Liu, J. When and where soil dryness matters to ecosystem photosynthesis. Zenodo https://doi.org/10.5281/zenodo.15314104 (2025).

Download references

Acknowledgements

This research is a contribution to Understanding and Modelling the Earth System with Machine Learning European Research Council grant (ERC CU18-3746). X.L. was supported by the Land Ecosystem Models Based on New Theory, Observations and Experiments project, funded through the generosity of E. and W. Schmidt by recommendation of the Schmidt Futures programme. W.Z. acknowledges the funding from Max Planck-Caltech-Carnegie-Columbia MC3 4 Earth Center. P.G. acknowledges the funding from the US National Science Foundation Learning the Earth with Artificial Intelligence and Physics science and technology center (AGS-2019625). We thank L. Zhang for her assistance in creating the conceptual figure.

Author information

Authors and Affiliations

  1. Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA

    Jiangong Liu, Weiwei Zhan, Xu Lian & Pierre Gentine

  2. Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA

    Qiren Wang

  3. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Xu Lian

  4. LEAP Science and Technology Center, School of Engineering and Applied Sciences, Climate School, Columbia University, New York, NY, USA

    Pierre Gentine

Authors
  1. Jiangong Liu
    View author publications

    Search author on:PubMed Google Scholar

  2. Qiren Wang
    View author publications

    Search author on:PubMed Google Scholar

  3. Weiwei Zhan
    View author publications

    Search author on:PubMed Google Scholar

  4. Xu Lian
    View author publications

    Search author on:PubMed Google Scholar

  5. Pierre Gentine
    View author publications

    Search author on:PubMed Google Scholar

Contributions

J.L. and P.G. conceptualized the study. J.L. designed the research. J.L., Q.W., W.Z. and X.L. compiled the data. J.L. performed the analysis and drafted the initial paper. All co-authors commented on the results and contributed to the writing of the paper.

Corresponding author

Correspondence to Jiangong Liu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Plants thanks Rene Orth and Jean-Pierre Wigneron for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Schematic of the local explanation of GPP prediction using the Shapley value.

The Shapley value can provide local explanations for predictions made by a ‘black-box’ machine learning model, that is the contributions of individual predictors to the difference between a specific model prediction and a baseline value. SW: incoming shortwave radiation; TA: air temperature; VPD: vapour pressure deficit; SM: soil moisture.

Extended Data Fig. 2 Comparison of long-term climatological aridity and ecosystem-scale traits between SM- and VPD-dominated pixels across water-limited regions.

Comparison of wetness index (a), apparent rooting depth (Zr, b), xylem water potential measured at 50% xylem conductivity loss (Ψ50, c), degrees of anisohydricity (d), and canopy height (e) between SM- and VPD-dominated pixels across water-limited regions. Only the PFTs with a sample size greater than 50 for both SM- and VPD-dominated pixels are shown. The central lines of box plots indicate the median values, the upper and lower box limits represent the 75% and 25% percentiles, and the upper and lower whiskers extend to 1.5 times the interquartile range, respectively. The comparison is done by Welch’s two-tailed t-tests, with adjusted p-values (Bonferroni correction) indicating different significant levels: P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), and P < 0.0001 (****). The numbers in parentheses (e) represent the sample size for each PFT in a-e.

Extended Data Fig. 3 Comparison of long-term climatological aridity and ecosystem-scale traits between SM- and VPD-dominated pixels across energy-limited regions.

Comparison of wetness index (a), apparent rooting depth (Zr, b), xylem water potential measured at 50% xylem conductivity loss (Ψ50, c), degrees of anisohydricity (d), and canopy height (e) between SM- and VPD-dominated pixels across energy-limited regions. Only the PFTs with a sample size greater than 50 for both SM- and VPD-dominated pixels are shown. The central lines of box plots indicate the median values, the upper and lower box limits represent the 75% and 25% percentiles, and the upper and lower whiskers extend to 1.5 times the interquartile range, respectively. The comparison is done by Welch’s two-tailed t-tests, with adjusted p-values (Bonferroni correction) indicating different significant levels: P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), and P < 0.0001 (****). The numbers in parentheses (e) represent the sample size for each PFT in a-e.

Extended Data Fig. 4 Dependencies of Causal Shapley values under water-limited conditions.

a-p, Relationships between Causal Shapley values and SW, TA, VPD, and SM for eddy covariance measurements across biomes of forests (a-d), savannahs and shrublands (e-h), and grasslands (i-l), as well as TROPOMI SIF observations over global arid and semi-arid regions (m-p). Data from two sandy forests are indicated by a dashed box in (d). Causal Shapley values are presented in consistent units with the eddy covariance half-hourly GPP data (µmol m−2 s−1, a-l) and TROPOMI SIF (mW m−2 sr−1 nm−1, m-p). SW: incoming shortwave radiation; TA: air temperature; VPD: vapour pressure deficit; VWC: volumetric water content.

Extended Data Fig. 5 Dependencies of Causal Shapley values under energy-limited conditions.

a-p, Relationships between Causal Shapley values and SW, TA, VPD, and SM for eddy covariance measurements across biomes of forests (a–d), drylands (e–h), and grasslands (i–l), as well as TROPOMI SIF observations over global humid regions (m–p). Causal Shapley values are presented in consistent units with the eddy covariance half-hourly GPP data (µmol m−2 s−1, a–l) and TROPOMI SIF (mWm−2 sr−1 nm−1, m–p). SW: incoming shortwave radiation; TA: air temperature; VPD: vapour pressure deficit; VWC: volumetric water content.

Extended Data Fig. 6 Relative contributions of SM and VPD to GPP along the gradient of SM under water-limited conditions.

Bars represent the ratios of the average absolute causal Shapely values for SM to those for VPD when SM is filtered based on its percentiles. Asterisks indicate a statistically significant difference between the absolute causal Shapely values for SM and VPD for different biomes (Welch’s two-tailed t-tests, P < 0.05).

Extended Data Fig. 7 Relationships between evaporative fraction (EF) and soil volumetric water content (VWC) across eddy covariance sites.

Critical soil moisture threshold are identified for forests (a), savannahs-shrublands (b), and grasslands (c) using linear-plateau models (see Methods). n indicates the number of sites involved in the analysis. The fitted parameters are statistically significant (two-sided, P < 0.001).

Extended Data Fig. 8 Relative contributions of SM and VPD to TROPOMI SIF in regions where they are deemed comparably equivalent.

The global pattern of the ratio between absolute causal Shapley values for SM and VPD across water-limited and energy-limited regions where the contributions of SM and VPD are deemed comparatively equivalent. These regions are defined as areas where the absolute causal Shapley value for SM (VPD) exceeds that for VPD (SM) by no more than 20%. Locations and categories of the FLUXNET2015 sites used in this study are indicated in the figure. Latitude and longitude are in degrees.

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2 and Figs. 1–9.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Wang, Q., Zhan, W. et al. When and where soil dryness matters to ecosystem photosynthesis. Nat. Plants 11, 1390–1400 (2025). https://doi.org/10.1038/s41477-025-02024-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41477-025-02024-7

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing