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Universal temperature sensitivity of denitrification nitrogen losses in forest soils

Nature Climate Change volume 13, pages 726–734 (2023)Cite this article

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Abstract

Soil nitrous oxide (N2O) and dinitrogen (N2) emissions from denitrification are crucial to the nitrogen (N) cycle. However, the temperature sensitivities (Q10) of gaseous N losses in forest soils are poorly understood, with implications for prediction of N cycle responses to warming. Here, we quantify temperature sensitivities of denitrification-derived potential N2O and N2 production. Using soils from 18 forest sites in China along a 4,000 km north–south transect we find that N2O and N2 production rates increased with temperature, with large variations across soils. In contrast, the Q10 values for N2O (2.1 ± 0.5) and N2 (2.6 ± 0.6) were similar across soils. N2 was more sensitive to temperature than N2O, suggesting that warming could promote complete denitrification. Moreover, the Q10 for denitrification (2.3 ± 0.5) was comparable to Q10 for aquatic sediments. This finding of universal temperature sensitivity of gaseous N losses from denitrification will facilitate modelling N losses in response to warming globally.

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Fig. 1: Temperature responses of potential N2O, N2 and N2O + N2 production and N2O/(N2O + N2) ratio for different Chinese forest soils.
Fig. 2: Temperature sensitivity of microbial NO3− consumption, N2O, N2 and N2O + N2 production rates.
Fig. 3: Pearson correlation analysis between climate factors, soil properties, functional gene abundances, N gas production rates, microbial NO3− consumption rates and their Q10 values across all forest sites.
Fig. 4: Experimental sites and temperature sensitivities for denitrification.
Fig. 5: Temperature dependences of N losses, microbial NO3− consumption, and CH4 and CO2 production.
Fig. 6: N2O and N2 production from global forest ecosystems in response to warming.

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Data availability

The data relating to the current study are available in the Dryad Digital Repository (https://doi.org/10.5061/dryad.hqbzkh1jg)74. Monthly mean values of temperature, cloud cover and monthly precipitation totals for the historical simulation (1950–2014) are from the Climatic Research Unit TS v.4.05 dataset70. The climate datasets for the future projections (2015–2100) are provided by the Earth System Federation (https://esgf-node.llnl.gov/search/cmip6/). Global atmospheric N deposition values for 1860, 1993 and 2050 are obtained from ref. 71. Atmospheric CO2 concentrations for the historical simulation (1850–2013) and future projections (2015–2100) are obtained from ref. 72. The shapefile of the world continents is publicly obtained from http://www.soest.hawaii.edu/pwessel/gshhg/. The global forest cover map is derived from the University of Maryland global land cover data (https://doi.org/10.3334/ORNLDAAC/969)73. The shapefile of China is publicly obtained from https://www.csgpc.org/list/254.html (drawing approval no. GS(2020)4619). The forest cover map of China is provided by National Cryosphere Desert Data Center (https://cstr.cn/11738.11.ncdc.Westdc.2020.632)75.

Code availability

The codes used in this work are available in the Dryad Digital Repository (https://doi.org/10.5061/dryad.hqbzkh1jg)74.

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Acknowledgements

This research was financially supported by the National Key Research and Development Program of China (2016YFA0600802), K.C. Wong Education Foundation (GJTD-2018-07), Research and Development Project of Scientific Instruments and Equipment of Chinese Academy of Sciences (YJKYYQ20190054), Liaoning Vitalization Talents Program (XLYC1902016), National Natural Science Foundation of China (41773094) to Y.F., National Natural Science Foundation of China (31600358 to D.L.) and the Youth Innovation Promotion Association CAS (2021195 to Z.Q.). H.Y. was supported by the Chinese Scholarship Council Fellowship to study at the Norwegian University of Life Sciences. We wish to thank X. Fang, W. Zhou, A. Wang, Y. Tu, G. Zhu, W. Zhang, M. Gao, T. Sun, T. Yan and L. Yin for their assistance in collecting soils. Special thanks to Y. Liu for his help in maps. We are grateful to the following field stations for providing the experimental sites and relevant support: National Research Station of Jianfengling National Key Field Station, Xishuangbanna Station for Tropical Rain Forest Ecosystems, Dinghushan Forest Ecosystem Research Station, Huitong Experimental Station of Forest Ecology, Henan Dabieshan National Field Observation & Research Station of Forest Ecosystem, Beijing Forest Ecosystem Research Station, Saihanba Ecological Station of Peking University, Qingyuan Forest CERN, Changbai Mountain Forest Ecosystems and Laoshan Forest Research Station.

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Authors and Affiliations

  1. CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China

    Haoming Yu, Yihang Duan, Kai Huang, Ronghua Kang, Chao Wang, Zhi Quan, Feifei Zhu, Dongwei Liu & Yunting Fang

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

    Haoming Yu

  3. Key Laboratory of Stable Isotope Techniques and Applications, Shenyang, China

    Yihang Duan, Kai Huang, Ronghua Kang, Chao Wang, Zhi Quan, Feifei Zhu, Dongwei Liu & Yunting Fang

  4. Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway

    Jan Mulder & Peter Dörsch

  5. Department of Biological Sciences, Binghamton University, State University of New York, Binghamton, NY, USA

    Weixing Zhu

  6. State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Xu-Ri

  7. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Zhoutao Zheng & Yangjian Zhang

  8. Qingyuan Forest CERN, National Observation and Research Station, Shenyang, China

    Feifei Zhu & Dongwei Liu

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

    Shushi Peng

  10. School of Life Sciences, Qufu Normal University, Qufu, China

    Shijie Han

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Contributions

Y.F. and H.Y. designed the study. H.Y. and Y.D. performed the experiment. Data analysis was conducted by H.Y., K.H., Z.Z., X.-R., Y.Z. and Y.F. The paper was written by H.Y., Y.D. and Y.F. and all other co-authors contributed to improve it.

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Correspondence to Yangjian Zhang or Yunting Fang.

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Nature Climate Change thanks Sam Bonnett, Michael Dannenmann and Shuh-Ji Kao for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Locations of the studied forest sites along a latitudinal gradient in China.

Colour shading of dots reflects the mean annual air temperature (MAT) at the forest sites. DXAL, Daxinganling; MES, Maoershan; CBS, Changbaishan; QY, Qingyuan; SHB, Saihaiba; DLS, Donglingshan; JGS, Jigongshan; HT, Huitong; DHS, Dinghuishan; XSBN, Xishuangbanna; JFL, Jianfengling.

Extended Data Fig. 2 Potential production rates of N2O, N2 and N2O + N2 of each forest soil.

a–g, Temperature responses of N gases production for soils collected from tropical forests. h–r, temperature responses of N gases production for soils collected from temperate forests. Data are presented as mean ± s.d.; (n = 5). The average temperature of the growing season (May-September for temperate forest and April-October for tropical forest) of each site is shown at black triangle on the X axis as the in-situ temperature. See Extended Data Fig. 1 and Supplementary Table 1 for forest site locations and abbreviations.

Extended Data Fig. 3 Potential production and consumption rates at 20 °C by site and biome.

(a) soil microbial consumption, (c) N2O production, (e) N2 production, (g) N2O + N2 production, which data are presented as mean ± s.d.; (n = 5). Panel (b, d, f, h) differences between tropical (n = 1 in panel b, n = 7 in panel d, f, h) and temperate (n = 8 in panel b, n = 11 in panel d, f, h) forest soils. NO3− consumption rates were measured for only 9 of the 18 studied forests. Statistical significance is determined by a two-sided Wilcoxon test (b,f) or t-test (d,h) depending on whether the data conformed to normal distribution. *, **, *** indicate significant differences between the two biomes at P values of <0.05, <0.01 and <0.001, respectively, and NS indicates no significant difference. Box plots are standard Tukey plots, where the centre line represents the median, the lower and upper hinges represent the first and third quartiles, and whiskers represent +1.5× the interquartile range. See Extended Data Fig. 1 and Supplementary Table 1 for forest site locations and abbreviations.

Extended Data Fig. 4 Mean temperature response of the N2O/(N2O + N2) ratio of denitrification.

Error bars denote the standard deviation of 18 sample sites.

Extended Data Fig. 5 Temperature responses of microbial NO3− consumption rates.

Potential microbial NO3− consumption rates were measured for 9 of the 18 studied soils. Data are presented as mean ± s.d.; (n = 5). See Extended Data Fig. 1 and Supplementary Table 1 for forest site locations and abbreviations.

Extended Data Fig. 6 Effect of temperature on gaseous N recovered as denitrification-derived N2O and N2 relative to microbial NO3− consumed.

a, d, N2O production/microbial NO3− consumption ratio. b, e, N2 production/microbial NO3− consumption ratio. c, f, (N2O + N2) production/microbial NO3− consumption ratio. a–c, Soils collected from tropical forests. d–f, Soils collected from temperate forests. Data are presented as mean ± s.d.; (n = 5). See Extended Data Fig. 1 and Supplementary Table 1 for forest site locations and abbreviations.

Extended Data Fig. 7 Global pattern of N2O, N2 and N flux for 1991–2000.

The simulated N2O (a), N2 (b) and the respective latitudinal averages of N gas flux (c) by denitrification from global forest ecosystems.

Supplementary information

Supplementary Information

Supplementary Results, Model description, Discussion, Figs. 1–3 and Tables 1–6.

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Yu, H., Duan, Y., Mulder, J. et al. Universal temperature sensitivity of denitrification nitrogen losses in forest soils. Nat. Clim. Chang. 13, 726–734 (2023). https://doi.org/10.1038/s41558-023-01708-2

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