Abstract
Urban greenspaces support multiple nature-based services, many of which depend on the amount of soil carbon (C). Yet, the environmental drivers of soil C and its sensitivity to warming are still poorly understood globally. Here we use soil samples from 56 paired urban greenspaces and natural ecosystems worldwide and combine soil C concentration and size fractionation measures with metagenomics and warming incubations. We show that surface soils in urban and natural ecosystems sustain similar C concentrations that follow comparable negative relationships with temperature. Plant productivity’s contribution to explaining soil C was higher in natural ecosystems, while in urban ecosystems, the soil microbial biomass had the greatest explanatory power. Moreover, the soil microbiome supported a faster C mineralization rate with experimental warming in urban greenspaces compared with natural ecosystems. Consequently, urban management strategies should consider the soil microbiome to maintain soil C and related ecosystem services.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
The raw data associated with this study are available in https://figshare.com/s/1eadef6619e74a8f2904 (https://doi.org/10.6084/m9.figshare.21025615).
References
Chien, S.-C. & Krumins, J. A. Natural versus urban global soil organic carbon stocks: a meta-analysis. Sci. Total Environ. 807, 150999 (2022).
Sun, Y., Xie, S. & Zhao, S. Valuing urban green spaces in mitigating climate change: a city‐wide estimate of aboveground carbon stored in urban green spaces of China’s Capital. Glob. Change Biol. 25, 1717–1732 (2019).
Bossio, D. et al. The role of soil carbon in natural climate solutions. Nat. Sustain. 3, 391–398 (2020).
Cambou, A. et al. Estimation of soil organic carbon stocks of two cities, New York City and Paris. Sci. Total Environ. 644, 452–464 (2018).
Epp Schmidt, D. J. et al. Urbanization erodes ectomycorrhizal fungal diversity and may cause microbial communities to converge. Nat. Ecol. Evol. 1, 0123 (2017).
Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).
García-Palacios, P. et al. Evidence for large microbial-mediated losses of soil carbon under anthropogenic warming. Nat. Rev. Earth Environ. 2, 507–517 (2021).
Pouyat, R., Groffman, P., Yesilonis, I. & Hernandez, L. Soil carbon pools and fluxes in urban ecosystems. Environ. Pollut. 116, S107–S118 (2002).
Edmondson, J. L. et al. Urban tree effects on soil organic carbon. PLoS ONE 9, e101872 (2014).
Weissert, L., Salmond, J. & Schwendenmann, L. Variability of soil organic carbon stocks and soil CO2 efflux across urban land use and soil cover types. Geoderma 271, 80–90 (2016).
Georgiou, K. et al. Global stocks and capacity of mineral-associated soil organic carbon. Nat. Commun. 13, 3797 (2022).
Cotrufo, M. F. & Lavallee, J. M. Soil organic matter formation, persistence, and functioning: a synthesis of current understanding to inform its conservation and regeneration. Adv. Agron. 172, 1–66 (2022).
Kleber, M. et al. Mineral–organic associations: formation, properties, and relevance in soil environments. Adv. Agron. 130, 1–140 (2015).
Cotrufo, M. F., Ranalli, M. G., Haddix, M. L., Six, J. & Lugato, E. Soil carbon storage informed by particulate and mineral-associated organic matter. Nat. Geosci. 12, 989–994 (2019).
Plaza, C. et al. Ecosystem productivity has a stronger influence than soil age on surface soil carbon storage across global biomes. Commun. Earth Environ. 3, 233 (2022).
Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).
IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).
Scharenbroch, B., Day, S., Trammell, T. & Pouyat, R. in Urban Soils (eds Lal, R. & Stewart, B. A.) Ch. 6 (CRC Press, 2017).
Crowther, T. W. et al. Sensitivity of global soil carbon stocks to combined nutrient enrichment. Ecol. Lett. 22, 936–945 (2019).
Delgado-Baquerizo, M. et al. The influence of soil age on ecosystem structure and function across biomes. Nat. Commun. 11, 4721 (2020).
Frostegård, Å., Bååth, E. & Tunlio, A. Shifts in the structure of soil microbial communities in limed forests as revealed by phospholipid fatty acid analysis. Soil Biol. Biochem. 25, 723–730 (1993).
Qin, S. et al. Temperature sensitivity of SOM decomposition governed by aggregate protection and microbial communities. Sci. Adv. 5, eaau1218 (2019).
Delgado-Baquerizo, M. et al. Global homogenization of the structure and function in the soil microbiome of urban greenspaces. Sci. Adv. 7, eabg5809 (2021).
Mundim, K. C., Baraldi, S., Machado, H. G. & Vieira, F. M. Temperature coefficient (Q10) and its applications in biological systems: beyond the Arrhenius theory. Ecol. Model. 431, 109127 (2020).
Wang, C. et al. The temperature sensitivity of soil: microbial biodiversity, growth, and carbon mineralization. ISME J. 15, 2738–2747 (2021).
Harris, D., Horwáth, W. R. & van Kessel, C. Acid fumigation of soils to remove carbonates prior to total organic carbon or carbon‐13 isotopic analysis. Soil Sci. Soc. Am. J. 65, 1853–1856 (2001).
Sokol, N. W. & Bradford, M. A. Microbial formation of stable soil carbon is more efficient from belowground than aboveground input. Nat. Geosci. 12, 46–53 (2019).
Fick, S. & Hijmans, R. WorldClim 2: nouvelles surfaces climatiques de résolution spatiale de 1 km pour les zones terrestres mondiales. Int. J. Climatol. 37, 4302–4315 (2017).
Lembrechts, J. J. et al. Global maps of soil temperature. Glob. Change Biol. 28, 3110–3144 (2021).
Vermote, E., Justice, C., Claverie, M. & Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens. Environ. 185, 46–56 (2016).
Zhang, L. et al. Direct and indirect impacts of urbanization on vegetation growth across the world’s cities. Sci. Adv. 8, eabo0095 (2022).
Richards, D. R. & Belcher, R. N. Global changes in urban vegetation cover. Remote Sens. 12, 23 (2019).
Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).
Frostegård, Å., Tunlid, A. & Bååth, E. Use and misuse of PLFA measurements in soils. Soil Biol. Biochem. 43, 1621–1625 (2011).
Shi, B. et al. Temporal changes in the spatial variability of soil respiration in a meadow steppe: the role of abiotic and biotic factors. Agric. Meteorol. 287, 107958 (2020).
Dacal, M., Bradford, M. A., Plaza, C., Maestre, F. T. & García-Palacios, P. Soil microbial respiration adapts to ambient temperature in global drylands. Nat. Ecol. Evol. 3, 232–238 (2019).
Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Natl Acad. Sci. USA 109, 21390–21395 (2012).
Fierer, N. et al. Reconstructing the microbial diversity and function of pre-agricultural tallgrass prairie soils in the United States. Science 342, 621–624 (2013).
Meyer, F. et al. The metagenomics RAST server–a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9, 386 (2008).
Oksanen, J. et al. Package ‘vegan’: community ecology. R package version 2.2-0 (2014); http://CRAN.Rproject.org/package=vegan
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013); http://www.R-project.org/
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Kunzetsova, A., Brockhoff, P. & Christensen, R. lmerTest package: tests in linear mixed effect models. J. Stat. Softw. 82, 1–26 (2017).
Menard, S. Applied Logistic Regression Analysis 2nd edn (SAGE Publications, 2001).
Schermelleh-Engel, K., Moosbrugger, H. & Müller, H. Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit measures. Methods Psychol. Res. 8, 23–74 (2003).
Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Contr. 19, 716–723 (1974).
Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).
Feng, Y. et al. Temperature thresholds drive the global distribution of soil fungal decomposers. Glob. Change Biol. 28, 2779–2789 (2022).
Fong, Y., Huang, Y., Gilbert, P. B. & Permar, S. R. chngpt: threshold regression model estimation and inference. BMC Bioinformatics 18, 454 (2017).
Acknowledgements
We thank the researchers involved in the MUSGONET project for collection of field data. This study was supported by a 2019 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation (URBANFUN), and by BES Grant Agreement No. LRB17\1019 (MUSGONET). M.D-B., P.G-P., J.D. and A.R. acknowledge support from TED2021-130908B-C41/AEI/10.13039/501100011033/Unión Europea NextGenerationEU/PRTR. M.D.-B. also acknowledges support from the Spanish Ministry of Science and Innovation for the I + D + i project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033. M.D.-B. was also supported by a project of the Fondo Europeo de Desarrollo Regional (FEDER) and the Consejería de Transformación Económica, Industria, Conocimiento y Universidades of the Junta de Andalucía (FEDER Andalucía 2014-2020 Objetivo temático ‘01 - Refuerzo de la investigación, el desarrollo tecnológico y la innovación’) associated with the research project P20_00879 (ANDABIOMA). D.J.E. was supported by the Hermon Slade Foundation. J.P.V. thanks the Science and Engineering Research Board (SERB) (EEQ/2021/001083, SIR/2022/000626) and the Department of Science and Technology (DST), India (DST/INT/SL/P-31/2021) and Banaras Hindu Univeristy-IoE (6031)-incentive grant for financial assistance for research in plant-microbe interaction and soil microbiome. J.D. and A. Rodríguez acknowledge support from the FCT (2020.03670.CEECIND and SFRH/BDP/108913/2015, respectively), as well as from the MCTES, FSE, UE and the CFE (UIDB/04004/2021) research unit financed by FCT/MCTES through national funds (PIDDAC).
Author information
Authors and Affiliations
Contributions
M.D.-B., P.G.-P., M.A.B. and C.P. conceptualized the project. M.D.-B., D.J.E., M.B., T.S.-S., Y.-R.L., F.A., S.A., A.R.B., F.B., J.L.B.-P., J.D., J.J.G., J.G.I., T.G., T.P.M., D.K.J., T.U.N., G.F.P.-B., A. Rey, A. Rodriguez, C.S., A.L.T., W.S., P.T., J.P.V., L.W., J.W., T.Y., E.Z., X.Z., X.-Q.Z. and C.P. developed the methodology. M.D.-B., P.G.-P., M.A.B., D.J.E., M.B., T.S.-S., Y.-R.L., F.A., S.A., A.R.B., F.B., J.L.B.-P., J.D., J.J.G., J.G.I., T.G., T.P.M., D.K.J., T.U.N., G.F.P.-B., A. Rey, A. Rodriguez, C.S., A.L.T., W.S., P.T., J.P.V., L.W., J.W., T.Y., E.Z., X.Z., X.-Q.Z. and C.P. conducted the investigation. M.D.-B., M.B. and C.P. performed visualization. M.D.-B., D.J.E., C.P. and J.P.V. acquired funding. M.D.-B. administered and supervised the project. M.D.-B., P.G.-P., M.A.B., C.P. and D.J.E. wrote the original draft. M.D.-B., P.G.-P., M.A.B., D.J.E., M.B., T.S.-S., Y.-R.L., F.A., S.A., A.R.B., F.B., J.L.B.-P., J.D., J.J.G., J.G.I., T.G., T.P.M., D.K.J., T.U.N., G.F.P.-B., A. Rey, A. Rodriguez, C.S., A.L.T., W.S., P.T., J.P.V., L.W., J.W., T.Y., E.Z., X.Z., X.-Q.Z. and C.P. reviewed and edited the paper.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Climate Change thanks Jennifer Adams Krumins, Victor Allory, Claudia Canedoli and Tae Kyung Yoon 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.
Supplementary information
Supplementary Information
Supplementary Figs. 1–13 and Tables 1–7.
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.
About this article
Cite this article
Delgado-Baquerizo, M., García-Palacios, P., Bradford, M.A. et al. Biogenic factors explain soil carbon in paired urban and natural ecosystems worldwide. Nat. Clim. Chang. 13, 450–455 (2023). https://doi.org/10.1038/s41558-023-01646-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41558-023-01646-z
This article is cited by
-
Black carbon in urban soils: land use and climate drive variation at the surface
Carbon Balance and Management (2024)
-
Global distribution of surface soil organic carbon in urban greenspaces
Nature Communications (2024)