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:

Precipitation threshold-driven shifts in dominant controls of ecosystem nitrogen retention

Nature Geoscience (2026) Cite this article

Subjects

Abstract

Ecosystem nitrogen retention results from complex, long-term plant–soil–microbe interactions, yet integrating these processes across climatic gradient remains challenging. As a time-integrated tracer, the natural abundance of the stable nitrogen isotope (δ15N) in soil captures the cumulative balance of nitrogen inputs, transformations and losses, offering a robust proxy for ecosystem nitrogen retention. Although spatial patterns in δ15N have been widely documented, the drivers and their shifts across climatic thresholds remain unclear. Using data from 31 sites across the National Ecological Observatory Network in the United States, here we revealed that soil δ15N varies nonlinearly with mean annual precipitation, with a threshold (~700 mm) marking a shift in dominant controls. Below this threshold, soil δ15N decreased with precipitation and was shaped by plant community structure, microbial composition and soil nitrate concentration. Above the threshold, soil δ15N increased with precipitation, with soil physicochemical properties, particularly soil carbon/nitrogen ratio, nitrate concentration and clay content, exerting stronger influence. Precipitation thus regulates the ‘leakiness’ of the nitrogen cycle, shifting from rainfall-enhanced retention driven by plant–microbe competition in drier regions to rainfall-induced losses mediated by coupled hydrological and microbial transformations in wetter regions. These findings advance understanding of spatial variation in natural nitrogen cycling and provide a framework for predicting nitrogen dynamics under changing precipitation regimes.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: Soil δ15N serves as an integrative proxy of ecosystem N loss and retention.
The alternative text for this image may have been generated using AI.
Fig. 2: Spatial distribution of sampling sites and soil δ15N values in different ecosystem types.
The alternative text for this image may have been generated using AI.
Fig. 3: The relative influence of the predictor variables on soil δ15N across ecosystems.
The alternative text for this image may have been generated using AI.
Fig. 4: Precipitation-dependent patterns and environmental correlates of soil δ15N.
The alternative text for this image may have been generated using AI.
Fig. 5: Contrasting controls of soil δ15N across precipitation regimes.
The alternative text for this image may have been generated using AI.

Similar content being viewed by others

Data availability

Data supporting the findings of this study are available via Figshare at https://doi.org/10.6084/m9.figshare.29311124 (ref. 93). Source data are provided with this paper.

References

  1. Cleveland, C. C. et al. Patterns of new versus recycled primary production in the terrestrial biosphere. Proc. Natl Acad. Sci. USA 110, 12733–12737 (2013).

    Article  CAS  Google Scholar 

  2. Chapin, F. S., Matson, P. A. & Vitousek, P. M. Principles of Terrestrial Ecosystem Ecology (Springer, 2011).

  3. Firestone, M. K. & Davidson, E. A. in Exchange of Trace Gases Between Terrestrial Ecosystems and the Atmosphere (eds Andreae, M. O. & Schimel, D. S.) 7–21 (Wiley & Sons, 1989).

  4. Lu, C. et al. Century-long changes and drivers of soil nitrous oxide (N2O) emissions across the contiguous United States. Glob. Change Biol. 28, 2505–2524 (2022).

    Article  CAS  Google Scholar 

  5. Qiu, Y. et al. Intermediate soil acidification induces highest nitrous oxide emissions. Nat. Commun. 15, 2695 (2024).

    Article  CAS  Google Scholar 

  6. Craine, J. M. et al. Ecological interpretations of nitrogen isotope ratios of terrestrial plants and soils. Plant Soil 396, 1–26 (2015).

    Article  CAS  Google Scholar 

  7. Bai, E., Houlton, B. Z. & Wang, Y. P. Isotopic identification of nitrogen hotspots across natural terrestrial ecosystems. Biogeosciences 9, 3287–3304 (2012).

    Article  CAS  Google Scholar 

  8. Robinson, D. δ15N as an integrator of the nitrogen cycle. Trends Ecol. Evol. 16, 153–162 (2001).

    Article  CAS  Google Scholar 

  9. Högberg, P. Tansley review no. 95: 15N natural abundance in soil–plant systems. New Phytol. 137, 179–203 (1997).

    Article  Google Scholar 

  10. Houlton, B. Z. & Bai, E. Imprint of denitrifying bacteria on the global terrestrial biosphere. Proc. Natl Acad. Sci. USA 106, 21713–21716 (2009).

    Article  CAS  Google Scholar 

  11. Choi, W.-J. et al. Patterns of δ15N in forest soils and tree foliage and rings between climate zones in relation to atmospheric nitrogen deposition: a review. Sci. Total Environ. 900, 165866 (2023).

    Article  CAS  Google Scholar 

  12. Wang, X. et al. Plant traits mediate foliar uptake of deposited nitrogen by mature woody plants. Plant Cell Environ. 47, 4870–4885 (2024).

    Article  CAS  Google Scholar 

  13. Houlton, B. Z., Sigman, D. M. & Hedin, L. O. Isotopic evidence for large gaseous nitrogen losses from tropical rainforests. Proc. Natl Acad. Sci. USA 103, 8745–8750 (2006).

    Article  CAS  Google Scholar 

  14. Austin, A. T. & Vitousek, P. M. Nutrient dynamics on a precipitation gradient in Hawai’i. Oecologia 113, 519–529 (1998).

    Article  Google Scholar 

  15. Handley, L. L. et al. The 15N natural abundance (δ15N) of ecosystem samples reflects measures of water availability. Aust. J. Plant Physiol. 26, 185–199 (1999).

    Article  Google Scholar 

  16. Amundson, R. et al. Global patterns of the isotopic composition of soil and plant nitrogen. Glob. Biogeochem. Cycles 17, 1031 (2003).

    Article  Google Scholar 

  17. Kleinebecker, T. et al. Evidence from the real world: 15N natural abundances reveal enhanced nitrogen use at high plant diversity in central European grasslands. J. Ecol. 102, 456–465 (2014).

    Article  CAS  Google Scholar 

  18. Hobbie, E. A. & Högberg, P. Nitrogen isotopes link mycorrhizal fungi and plants to nitrogen dynamics. New Phytol. 196, 367–382 (2012).

    Article  CAS  Google Scholar 

  19. Johnson, C., Schweinhart, S. & Buffam, I. Plant species richness enhances nitrogen retention in green roof plots. Ecol. Appl. 26, 2130–2144 (2016).

    Article  Google Scholar 

  20. 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 

  21. Högberg, M. N., Chen, Y. & Högberg, P. Gross nitrogen mineralisation and fungi-to-bacteria ratios are negatively correlated in boreal forests. Biol. Fertil. Soils 44, 363–366 (2007).

    Article  Google Scholar 

  22. Klemedtsson, L., Von Arnold, K., Weslien, P. & Gundersen, P. Soil CN ratio as a scalar parameter to predict nitrous oxide emissions. Glob. Change Biol. 11, 1142–1147 (2005).

    Article  Google Scholar 

  23. Lai, X., Zhu, Q., Castellano, M. J., Zan, Q. & Liao, K. Relationship between soil 15N natural abundance and soil water content at global scale: patterns and implications. Catena 222, 106879 (2023).

    Article  CAS  Google Scholar 

  24. Nielsen, U. N. & Ball, B. A. Impacts of altered precipitation regimes on soil communities and biogeochemistry in arid and semi-arid ecosystems. Glob. Change Biol. 21, 1407–1421 (2015).

    Article  Google Scholar 

  25. Wu, Q. et al. Contrasting effects of altered precipitation regimes on soil nitrogen cycling at the global scale. Glob. Change Biol. 28, 6679–6695 (2022).

    Article  CAS  Google Scholar 

  26. Santos, F. L. S. et al. Climatic control effect on the soil nitrogen isotopic composition in Alisols across the physiographic regions of Pernambuco State, Northeast Brazil. Geoderma Reg. 30, e00565 (2022).

    Article  Google Scholar 

  27. Zhang, Y. et al. Global evidence for joint effects of multiple natural and anthropogenic drivers on soil nitrogen cycling. Glob. Change Biol. 30, e17309 (2024).

    Article  CAS  Google Scholar 

  28. Liao, K., Lai, X. & Zhu, Q. Soil δ15N is a better indicator of ecosystem nitrogen cycling than plant δ15N: a global meta-analysis. Soil 7, 733–742 (2021).

    Article  CAS  Google Scholar 

  29. Liao, J. D., Boutton, T. W. & Jastrow, J. D. Organic matter turnover in soil physical fractions following woody plant invasion of grassland: evidence from natural 13C and 15N. Soil Biol. Biochem. 38, 3197–3210 (2006).

    Article  CAS  Google Scholar 

  30. Craine, J. M. et al. Convergence of soil nitrogen isotopes across global climate gradients. Sci. Rep. 5, 8280 (2015).

    Article  CAS  Google Scholar 

  31. Jörgensen, K., Clemmensen, K. E., Wallander, H. & Lindahl, B. D. Do ectomycorrhizal exploration types reflect mycelial foraging strategies? New Phytol. 237, 576–584 (2023).

    Article  Google Scholar 

  32. Hobbie, E. A. & Agerer, R. Nitrogen isotopes in ectomycorrhizal sporocarps correspond to belowground exploration types. Plant Soil 327, 71–83 (2010).

    Article  CAS  Google Scholar 

  33. Seager, R. et al. Whither the 100th meridian? The once and future physical and human geography of America’s arid–humid divide. Part II: the meridian moves East. Earth Interact. 22, 1–24 (2018).

    Google Scholar 

  34. Cheng, W., Chen, Q., Xu, Y., Han, X. & Li, L. Climate and ecosystem 15N natural abundance along a transect of Inner Mongolian grasslands: contrasting regional patterns and global patterns. Glob. Biogeochem. Cycles 23, GB2005 (2009).

    Article  Google Scholar 

  35. Peri, P. L. et al. Carbon (δ13C) and nitrogen (δ15N) stable isotope composition in plant and soil in southern Patagonia’s native forests. Glob. Change Biol. 18, 311–321 (2012).

    Article  Google Scholar 

  36. Yang, Y. et al. Vegetation and soil 15N natural abundance in alpine grasslands on the Tibetan plateau: patterns and implications. Ecosystems 16, 1013–1024 (2013).

    Article  CAS  Google Scholar 

  37. Gubsch, M. et al. Foliar and soil δ15N values reveal increased nitrogen partitioning among species in diverse grassland communities. Plant Cell Environ. 34, 895–908 (2011).

    Article  CAS  Google Scholar 

  38. Kuzyakov, Y. & Xu, X. Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance. New Phytol. 198, 656–669 (2013).

    Article  CAS  Google Scholar 

  39. Strickland, M. S. & Rousk, J. Considering fungal:bacterial dominance in soils—methods, controls, and ecosystem implications. Soil Biol. Biochem. 42, 1385–1395 (2010).

    Article  CAS  Google Scholar 

  40. Wang, C. & Kuzyakov, Y. Mechanisms and implications of bacterial–fungal competition for soil resources. ISME J. 18, wrae073 (2024).

    Article  CAS  Google Scholar 

  41. Li, C., Aber, J., Stange, F., Butterbach-Bahl, K. & Papen, H. A process-oriented model of N2O and NO emissions from forest soils: 1. Model development. J. Geophys. Res. Atmos. 105, 4369–4384 (2000).

    Article  CAS  Google Scholar 

  42. Phillips, R. P., Brzostek, E. & Midgley, M. G. The mycorrhizal-associated nutrient economy: a new framework for predicting carbon–nutrient couplings in temperate forests. New Phytol. 199, 41–51 (2013).

    Article  CAS  Google Scholar 

  43. Deng, K., Yang, S. & Guo, Y. A global temperature control of silicate weathering intensity. Nat. Commun. 13, 1781 (2022).

    Article  CAS  Google Scholar 

  44. Elrys, A. S. et al. Patterns and drivers of global gross nitrogen mineralization in soils. Glob. Change Biol. 27, 5950–5962 (2021).

    Article  CAS  Google Scholar 

  45. Butterbach-Bahl, K., Baggs, E. M., Dannenmann, M., Kiese, R. & Zechmeister-Boltenstern, S. Nitrous oxide emissions from soils: how well do we understand the processes and their controls? Phil. Trans. R. Soc. B 368, 20130122 (2013).

    Article  Google Scholar 

  46. Millard, P. & Grelet, G. A. Nitrogen storage and remobilization by trees: ecophysiological relevance in a changing world. Tree Physiol. 30, 1083–1095 (2010).

    Article  CAS  Google Scholar 

  47. Homyak, P. M. et al. Aridity and plant uptake interact to make dryland soils hotspots for nitric oxide (NO) emissions. Proc. Natl Acad. Sci. USA 113, E2608–E2616 (2016).

    Article  CAS  Google Scholar 

  48. Homyak, P. M., Allison, S. D., Huxman, T. E., Goulden, M. L. & Treseder, K. K. Effects of drought manipulation on soil nitrogen cycling: a meta-analysis. J. Geophys. Res. Biogeosci. 122, 3260–3272 (2017).

    Article  CAS  Google Scholar 

  49. Groffman, P. M. et al. Long-term integrated studies show complex and surprising effects of climate change in the northern hardwood forest. BioScience 62, 1056–1066 (2012).

    Article  Google Scholar 

  50. Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).

    Article  Google Scholar 

  51. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Article  Google Scholar 

  52. Soil Physical and Chemical Properties, Periodic (DP1.10086.001), RELEASE-2023 (NEON, accessed 17 May 2023); https://doi.org/10.48443/0phb-j505

  53. Soil Physical and Chemical Properties, Megapit (DP1.00096.001), RELEASE-2023 (NEON, accessed 25 May 2023); https://doi.org/10.48443/t70z-np08

  54. Buyer, J. S. & Sasser, M. High throughput phospholipid fatty acid analysis of soils. Appl. Soil Ecol. 61, 127–130 (2012).

    Article  Google Scholar 

  55. Soil Microbe Biomass (DP1.10104.001), RELEASE-2023 (NEON, accessed 28 March 2023); https://doi.org/10.48443/rwbj-ry66

  56. Gorka, S. et al. Beyond PLFA: concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities. Soil Biol. Biochem. 187, 109205 (2023).

    Article  CAS  Google Scholar 

  57. Veum, K. S., Lorenz, T. & Kremer, R. J. Phospholipid fatty acid profiles of soils under variable handling and storage conditions. Agron. J. 111, 1090–1096 (2019).

    Article  CAS  Google Scholar 

  58. Soil Microbe Marker Gene Sequences (DP1.10108.001), RELEASE-2023 (NEON, accessed 17 March 2023); https://doi.org/10.48443/y0ry-xp72

  59. NEON (National Ecological Observatory Network). Soil Microbe Community Composition (DP1.10081.001), RELEASE-2023 (NEON, accessed 17 March 2023); https://doi.org/10.48443/p0ge-z118

  60. Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277 (2016).

    Article  CAS  Google Scholar 

  61. Põlme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2020).

    Article  Google Scholar 

  62. Unestam, T. & Sun, Y. P. Extramatrical structures of hydrophobic and hydrophilic ectomycorrhizal fungi. Mycorrhiza 5, 301–311 (1995).

    Article  Google Scholar 

  63. Agerer, R. Exploration types of ectomycorrhizae. Mycorrhiza 11, 107–114 (2001).

    Article  Google Scholar 

  64. Lilleskov, E. A., Hobbie, E. A. & Horton, T. R. Conservation of ectomycorrhizal fungi: exploring the linkages between functional and taxonomic responses to anthropogenic N deposition. Fungal Ecol. 4, 174–183 (2011).

    Article  Google Scholar 

  65. Plant Presence and Percent Cover (DP1.10058.001), RELEASE-2023 (NEON, accessed 17 May 2023); https://doi.org/10.48443/9579-a253

  66. Vegetation Structure (DP1.10098.001), RELEASE-2023 (NEON, accessed 17 May 2023); https://doi.org/10.48443/73zn-k414

  67. Gonzalez-Akre, E. et al. allodb: an R package for biomass estimation at globally distributed extratropical forest plots. Methods Ecol. Evol. 13, 330–338 (2022).

    Article  Google Scholar 

  68. Conti, G. et al. Developing allometric models to predict the individual aboveground biomass of shrubs worldwide. Global Ecol. Biogeogr. 28, 961–975 (2019).

    Article  Google Scholar 

  69. Herbaceous Clip Harvest (DP1.10023.001), RELEASE-2023 (NEON, accessed 20 February 2024); https://doi.org/10.48443/kbnv-bz87

  70. Soudzilovskaia, N. A. et al. FungalRoot: global online database of plant mycorrhizal associations. New Phytol. 227, 955–966 (2020).

    Article  Google Scholar 

  71. Lang, A. K., Pett-Ridge, J., McFarlane, K. J. & Phillips, R. P. Climate, soil mineralogy and mycorrhizal fungi influence soil organic matter fractions in eastern US temperate forests. J. Ecol. 111, 1254–1269 (2023).

    Article  CAS  Google Scholar 

  72. Ónodi, G. et al. Estimating aboveground herbaceous plant biomass via proxies: the confounding effects of sampling year and precipitation. Ecol. Indic. 79, 355–360 (2017).

    Article  Google Scholar 

  73. Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).

    Article  Google Scholar 

  74. Root Biomass and Chemistry, Periodic (DP1.10067.001), RELEASE-2023 (NEON, accessed 26 April 2023); https://doi.org/10.48443/ecg2-af83

  75. Koteen, L. E. & Baldocchi, D. D. A randomization method for efficiently and accurately processing fine roots, and separating them from debris, in the laboratory. Plant Soil 363, 383–398 (2013).

    Article  CAS  Google Scholar 

  76. Wet Deposition Chemical Analysis (DP1.00013.001), RELEASE-2023 (NEON, accessed 23 March 2025); https://doi.org/10.48443/naax-q953

  77. Ackerman, D., Millet, D. B. & Chen, X. Global estimates of inorganic nitrogen deposition across four decades. Glob. Biogeochem. Cycles 33, 100–107 (2019).

    Article  CAS  Google Scholar 

  78. Homer, C. G., Fry, J. A. & Barnes, C. A. The National Land Cover Database https://pubs.usgs.gov/publication/fs20123020 (2012).

  79. Cavanaugh, J. E. & Neath, A. A. The Akaike information criterion: background, derivation, properties, application, interpretation, and refinements. WIiley Interdiscip. Rev. Comput. Stat. 11, e1460 (2019).

    Article  Google Scholar 

  80. Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).

    Article  Google Scholar 

  81. Muggeo, V. M. R. segmented: an R package to fit regression models with broken-line relationships. R News 8, 20–25 (2008).

    Google Scholar 

  82. Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, 2012).

  83. Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).

    Article  CAS  Google Scholar 

  84. Pebesma, E. J. Multivariable geostatistics in S: the gstat package. Comput. Geosci. 30, 683–691 (2004).

    Article  Google Scholar 

  85. Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).

    Article  Google Scholar 

  86. Shams, M. Y. et al. in The Power of Data: Driving Climate Change with Data Science and Artificial Intelligence Innovations (eds Hassanien, A. E. & Darwish, A.) 61–81 (Springer, 2023).

  87. Verhaeghe, J., Van Der Donckt, J., Ongenae, F. & Van Hoecke, S. in Machine Learning and Knowledge Discovery in Databases (eds Amini, M.-R. et al.) 71–87 (Springer, 2023).

  88. Maynard, D. S. et al. Global relationships in tree functional traits. Nat. Commun. 13, 3185 (2022).

    Article  CAS  Google Scholar 

  89. Yan, Y. et al. Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening. Nat. Ecol. Evol. 8, 912–923 (2024).

    Article  Google Scholar 

  90. 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 

  91. Cotrufo, M. F. et al. In-N-Out: a hierarchical framework to understand and predict soil carbon storage and nitrogen recycling. Glob. Change Biol. 27, 4465–4468 (2021).

    Article  Google Scholar 

  92. Rosseel, Y. lavaan: an R package for structural equation modeling. J. Stat. Softw. 48, 1–36 (2012).

    Article  Google Scholar 

  93. Peng, Y. et al. Data for ‘Precipitation threshold-driven shifts in dominant controls of ecosystem nitrogen retention’. Figshare https://doi.org/10.6084/m9.figshare.29311124 (2026).

Download references

Acknowledgements

This work was funded by the National Natural Science Foundation of China (32125025 to L.L., 32330066 to L.L. and 32301359 to Y.P.). We acknowledge the National Ecological Observatory Network (NEON) for providing data used in this study. NEON is a programme sponsored by the National Science Foundation and operated under a cooperative agreement by Battelle Memorial Institute.

Author information

Authors and Affiliations

  1. State Key Laboratory of Forage Breeding-by-Design and Utilization, Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China

    Yong Peng, Jie Luo, Lulu Guo, Hongyi Chen, Yuxuan Gao, Ziyang Peng & Lingli Liu

  2. China National Botanical Garden, Beijing, China

    Yong Peng, Jie Luo, Lulu Guo, Hongyi Chen, Yuxuan Gao, Ziyang Peng & Lingli Liu

  3. University of Chinese Academy of Sciences, Beijing, China

    Hongyi Chen, Yuxuan Gao & Lingli Liu

  4. School of Life Sciences, Institute of Life Science and Green Development, Hebei University, Baoding, China

    Ziyang Peng

Authors
  1. Yong Peng
    View author publications

    Search author on:PubMed Google Scholar

  2. Jie Luo
    View author publications

    Search author on:PubMed Google Scholar

  3. Lulu Guo
    View author publications

    Search author on:PubMed Google Scholar

  4. Hongyi Chen
    View author publications

    Search author on:PubMed Google Scholar

  5. Yuxuan Gao
    View author publications

    Search author on:PubMed Google Scholar

  6. Ziyang Peng
    View author publications

    Search author on:PubMed Google Scholar

  7. Lingli Liu
    View author publications

    Search author on:PubMed Google Scholar

Contributions

This study was conceived and designed by L.L. and Y.P. Data collection was performed by Y.P. Statistical analyses were conducted by Y.P., with contributions from J.L., L.G., H.C., Y.G. and Z.P. All authors contributed to data interpretation. Y.P. drafted the initial version of the paper, with L.L. providing primary revisions. All co-authors contributed to the subsequent revisions and approved the final version.

Corresponding author

Correspondence to Lingli Liu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks Eric Davidson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang and Carolina Ortiz Guerrero, in collaboration with the Nature Geoscience team.

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 Correlations between soil δ15N and geography, climate, and N deposition.

(a) latitude, (b) mean annual temperature (MAT, °C), (c) mean annual precipitation (MAP, mm), (d) wet N deposition (kg N ha−1 yr−1), (e) dry N deposition (kg N ha−1 yr−1), and (f) total N deposition (kg N ha−1 yr−1). Lines represent fitted regression relationships from either linear or quadratic models selected based on AIC. Solid and dashed lines indicate significant (p < 0.05) and non-significant (p > 0.05) relationships, respectively. All tests were two-sided, and shaded areas denote 95% confidence intervals around the fitted values (n = 225).

Source Data

Extended Data Fig. 2 Correlations between soil δ15N and vegetation characteristics.

(a) plant species richness (Pdiv), (b) plant aboveground biomass (AGB, kg m−2, ln-transformed), (c) root biomass (RB, kg m−2, ln-transformed), (d) proportion of arbuscular mycorrhizal (AM)-associated plants, (e) proportion of ectomycorrhizal (EcM)-associated plants, and (f) proportion of legumes (ln-transformed). Lines represent fitted regression relationships from either linear or quadratic models selected based on AIC. Solid lines indicate significant relationships (p < 0.05), and shaded areas denote 95% confidence intervals around the fitted values (n = 225). All tests were two-sided.

Source Data

Extended Data Fig. 3 Correlations between soil δ15N and soil physicochemical properties.

(a) soil organic carbon concentration (SOC, %, ln-transformed), (b) total nitrogen concentration (STN, %, ln-transformed), (c) soil C:N ratio, (d) soil pH, (e) soil ammonium concentration (NH4+, mg kg−1, ln-transformed as ln(NH4+ + 1)), (f) soil nitrate concentration (NO3−, mg kg−1, ln-transformed as ln(NO3− + 1)), (g) soil water content (SWC, %), and (h) clay content (Clay, %). Lines represent fitted regression relationships from either linear or quadratic models selected based on AIC. Solid and dashed lines indicate significant (p < 0.05) and non-significant (p > 0.05) relationships, respectively. All tests were two-sided, and shaded areas denote 95% confidence intervals around the fitted values (n = 225).

Source Data

Extended Data Fig. 4 Correlations between soil δ15N and microbial attributes.

(a) fungal-to-bacterial (F:B) ratio, the relative abundances of (b) N2-fixing bacteria (NFB, %), (c) nitrate-reducing bacteria (NRB, %, ln-transformed), (d) saprotrophic fungi (SapF, %), (e) hydrophilic ectomycorrhizal fungi (EcMhi, %, ln-transformed as ln(EcMhi + 1)), and (f) hydrophobic ectomycorrhizal fungi (EcMho, %, ln-transformed as ln(EcMho + 1)). Lines represent fitted regression relationships from either linear or quadratic models selected based on AIC. Solid lines indicate significant relationships (p < 0.05), and shaded areas denote 95% confidence intervals around the fitted values (n = 225). All tests were two-sided.

Source Data

Extended Data Fig. 5 Robustness of MAP threshold and regression slopes.

Comparison of MAP thresholds (a) and regression slopes (b) estimated from 200 bootstrap resamples (orange) and 200 spatially thinned subsamples (blue). Dashed lines in panel (a) denote mean threshold estimates, with shaded bands showing the 95% confidence intervals. Left and right violin plots in (b) illustrate regression slopes below and above threshold, respectively.

Source Data

Extended Data Fig. 6 Precipitation threshold of soil δ15N in forest ecosystems.

(a) Distribution of MAP thresholds estimated from 200 bootstrap resampling using segmented regression. The vertical dashed line indicates the mean threshold (~ 550 mm), and the shaded band represents the 95% confidence interval (546–556 mm). (b) Violin plots showing the distribution of regression slopes below (orange) and above (blue) the threshold; the difference between slopes was significant (two-sided t-test, p < 0.001). (c) Relationship between soil δ15N and log-transformed MAP in forest ecosystems; red and blue lines denote linear regressions fitted below and above the threshold, respectively, with significance assessed using two-sided tests.

Source Data

Extended Data Fig. 7 Relative influence of the predictor variables on soil δ15N across precipitation regimes.

(a, j) Shapley values quantify the importance of each predictor, ranked by their significance and relative importance within the model. Variables in bold with an asterisk indicate a statistically significant (p < 0.05) contribution. Yellow points represent observations with higher values for a given predictor, while blue points denote the lowest values. (b-i) Partial dependence plots of Shapley values for the eight significant variables in low precipitation regions. (k-r) Partial dependence plots of Shapley values for the eight significant variables in high precipitation regions. Positive values for a variable suggest it increases soil δ15N, while negative values indicate a decreasing effect. The solid lines represent loess fits, with the shaded areas indicating the two-sided 95% confidence intervals.

Source Data

Supplementary information

Supplementary Information (download PDF )

Supplementary Notes 1 and 2, Figs. 1–6, Table 1, and References.

Source data

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

Peng, Y., Luo, J., Guo, L. et al. Precipitation threshold-driven shifts in dominant controls of ecosystem nitrogen retention. Nat. Geosci. (2026). https://doi.org/10.1038/s41561-026-01992-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41561-026-01992-5

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

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