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Thermal adaptation of respiration in terrestrial ecosystems alleviates carbon loss

Nature Climate Change volume 15, pages 873–879 (2025)Cite this article

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Abstract

Ecosystem respiration (ER) is the largest contributor to terrestrial carbon loss. ER responds positively to increasing temperature, so a warming world is hypothesized to lead to additional CO2 release, potentially further exacerbating climate warming. The long-term influence of thermal changes on this carbon–climate feedback, however, remains unresolved. Here, by compiling data from 221 eddy covariance sites worldwide, we observe decreases in the temperature sensitivity and reference respiration rates of ER with increasing mean annual temperature, suggesting that ER adapts to temperature changes. Our results further reveal that thermal adaptation would eliminate 17.91–31.41% of the anticipated increase in the respiration of unadapted ecosystems under future warming scenarios, equivalent to a net carbon loss of 0.85–11.83 Pg C per year. The increase in respiration rates of terrestrial ecosystems in response to climate warming may thus be lower than predicted, with important consequences for modulating future terrestrial carbon–climate feedback.

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Fig. 1: Changes in ecosystem-level temperature responses with the MAT gradient across 221 eddy covariance sites.
Fig. 2: Downregulation of the respiratory thermal responses for site-years with increasing annual temperature across different carbon availability levels (n = 1,358).
Fig. 3: Correlations between the respiratory thermal responses and annual temperature by site-year under various aridity levels (n = 1,358).
Fig. 4: Effects of thermal adaptation on ER.

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Data availability

The data supporting the main findings of this study are presented in the paper and/or the Supplementary Information. The eddy covariance measurements of the carbon fluxes used in this study are available from the FLUXNET2015 dataset (https://fluxnet.org/) and AmeriFlux (https://ameriflux.lbl.gov/). ERA5-Land data are available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview. Model simulations of CMIP6 are available at https://pcmdi.llnl.gov/CMIP6/. The soil physicochemical attributes of the SoilGrids datasets can be obtained from https://soilgrids.org/.

Code availability

The code used in this study is available via figshare at https://doi.org/10.6084/m9.figshare.28246940 (ref. 84).

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Acknowledgements

We greatly appreciate the FLUXNET and AmeriFlux community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia and USCCC. The FLUXNET eddy covariance data processing and harmonization were carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. This work was supported by the National Natural Science Foundation of China (grant nos 32430065, 92251305, 32301402 and 32471831), the Shanghai Pilot Program for Basic Research—Fudan University 21TQ1400100 (grant no. 21TQ004) and the Science and Technology Plan Project of Shanghai (grant no. 23DZ1202700).

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  1. State Key Laboratory of Wetland Conservation and Restoration, National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, and Institute of Eco-Chongming, School of Life Sciences, Fudan University, Shanghai, China

    Xiaoni Xu, Jinquan Li, Xiangyi Li, Changming Fang, Bo Li & Ming Nie

  2. State Key Laboratory of Vegetation Structure, Functions and Construction (VegLab), Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China, Institute of Biodiversity, School of Ecology and Environmental Science, and Southwest United Graduate School, Yunnan University, Kunming, China

    Bo Li

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Contributions

M.N. developed the idea. X.X. performed the analysis with assistance from M.N. and X.L. C.F., J.L. and B.L. provided critical suggestions on the results. X.X. wrote the first draft, and M.N., X.L. and J.L. revised the paper.

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Correspondence to Ming Nie.

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Extended data

Extended Data Fig. 1 Global site distribution and climate information.

a Geographic locations of the 221 sites included in our analysis. b Two-dimensional climate space represented by the mean annual temperature and mean annual precipitation superimposed on Whittaker’s biomes. Panel a is created with ggplot2 package and panel b is created with plotbiomes and ggplot2 packages in R software. plotbiomes, © 2017, Valentin Stefan. ggplot2, 2024, ggplot2 core developer team.

Extended Data Fig. 2 Theoretical diagrams describing the thermal adaptation of ecosystems in terms of their respiration rates.

a Relationship of temperature sensitivity (Q10; shape of the temperature‒respiration curve) with the temperature of the source environment. b Changes in the basal respiration rate at a reference temperature (Rref; intercept of the temperature‒respiration curve) with the temperature of the source environment. Thermal adaptation is evidenced by systematically higher Q10 or Rref values in cold-adapted versus warm-adapted ecosystems. The lines in panel b correspond to different reference temperatures, with T1 < T2 < T3. Since the respiration rate exponentially increases with increasing temperature, the respiration rate is weak at T1, intermediate at T2 and high at T3. Moreover, the differences in respiration across reference temperatures should be greater in colder environments than in warmer environments because of the greater effects of thermal adaptation in warmer conditions.

Extended Data Fig. 3 Effects of soil, climate and vegetation factors on the temperature sensitivity (Q10) and basal respiration rate (Rref) of ER (n = 221).

a Q10. b R10. c R20. d R30. The soil factors include pH, soil organic carbon (SOC), bulk density (BD) and organic carbon stock (OCS). The climate variables include the mean annual temperature (MAT), shortwave radiation (SW), aridity index (AI) and vapor pressure deficit (VPD). The vegetation factor is gross primary production (GPP). We performed multiple linear regression to relate Q10 and Rref to these factors. Each predictor variable was standardized by subtracting the mean value and dividing the results by the standard deviation to eliminate disturbances from scale, quantity and other attributes. The black and gray circles indicate significant (P < 0.05) and nonsignificant effects (P > 0.05) of a variable, respectively. The error bars indicate the standard errors, centered on the mean.

Extended Data Fig. 4 The sensitivity of Q10 and Rref to changes in MAT for each ecosystem type.

a Q10. b R10. c R20. d R30. Statistical analysis revealed that there was no significant difference in the sensitivity of Rref among biomes. The error bars indicate standard errors, centered on the mean, and the values indicate the number of sites belonging to the ecosystems. SHR, shrublands (n = 22). GRA, grasslands (n = 4). CRO, croplands (n = 28). FOR, forests (n = 102). WET, wetlands (n = 9). SAV, savanna (n = 15).

Extended Data Fig. 5 Temperature responses of ecosystem respiration in the control and warming groups.

a Q10. b Rref. W, warming. Eco, ecosystem type. For an adequate assessment of Rref, we selected 3 reference temperatures (10, 20 and 30 °C) within the favorable range for biotic activity. Ecosystems were classified into six groups: savanna (SAV), shrublands (SHR), grasslands (GRA), croplands (CRO), forests (FOR) and wetlands (WET). Based on the data from 221 sites, paired t tests were conducted to explore the effects of warming on Q10 and Rref, and two-way analysis of variance (ANOVA) was employed to statistically assess the interaction effects of warming and ecosystem type on Q10 and Rref. P values are shown in the figures. The results revealed that warming significantly decreased Q10, R20 and R30. However, ecosystem type had no influence on the warming effect, as the interaction effect between ecosystems and warming was nonsignificant. Boxplots show 25th-75th percentiles (boxes) and 10th-90th percentiles (whiskers).

Extended Data Fig. 6 Changes in the temperature responses of ER with the mean annual temperature gradient according to linear mixed-effects models across 221 sites.

a Relationship between Q10 and mean annual temperature (MAT). b Effects of MAT on the basal respiration rates at 10, 20 and 30 °C (R10, R20, and R30). Ecosystem type and climate zone were included as random factors. Linear mixed-effects models revealed that Q10 and Rref remained negatively correlated with MAT. R2c, conditional R2. R2m, marginal R2. Lines represent the linear relationship between thermal responses and annual temperature, with P values less than 0.05. The shaded areas represent 95% confidence intervals. Detailed descriptions are shown in Fig. 1 in the main text.

Extended Data Fig. 7 Temporal relationship between the temperature response of ER and the annual temperature.

a Q10. b R10. c R20. d R30. A total of 92 sites with at least 6 years of data were collected, and the temporal relationships were analyzed at each site. The line type shows the significance of the fit, with solid and dashed lines representing P < 0.05 and P > 0.05, respectively. Only a few sites present a reduction in Q10 and Rref in response to increasing temperature. Each point represents one site-year of data.

Extended Data Fig. 8 Results of the van’t Hoff model and macromolecular rate theory (MMRT) in fitting temperature‒respiration relationships across 1362 site-years.

a Frequency distribution of ΔC‡P from the MMRT model, with a positive ΔC‡P at 493 site-years. b The optimum temperature (Topt) of MMRT for 582 site-years is higher than the recorded maximum temperature (Tmax). c A total of 276 site-years are better fitted by the MMRT model than by the van’t Hoff model according to a comparison of their Akaike information criterion (AIC) values (ΔAIC > 2).

Extended Data Fig. 9 Decreases in the temperature responses of ER with increasing annual temperature over time at 9 sites.

a Q10. b Rref. Linear correlation analysis was used to test the significance of correlations, with solid and no lines indicating P < 0.05 and P > 0.05.

Extended Data Fig. 10 Increases in the strength of thermal adaptation with increasing temperature through both time and space.

a Temporal relationship. b Spatial relationship (n = 9). On the basis of the interannual temperatures at each of the nine eddy covariance sites, we paired temperature‒ER curves from the control (the lowest annual temperature) and warming (a particular annual temperature) years and found that the adaptation strength increased with temperature differences (black). In addition, the increasing trend remained unchanged regardless of whether the data with temperature changes over 10 °C were excluded (gray). The temporal temperature difference is the difference in annual temperature between the control and warming years. The strength of thermal adaptation was then averaged over multiple pairs at each site, and the resulting averages and standard errors are represented as points and error bars in the figures, respectively. A linear mixed-effects model is used to test the correlations that are represented by lines. All correlations are significant with P < 0.05.

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Xu, X., Li, J., Li, X. et al. Thermal adaptation of respiration in terrestrial ecosystems alleviates carbon loss. Nat. Clim. Chang. 15, 873–879 (2025). https://doi.org/10.1038/s41558-025-02377-z

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  • DOI: https://doi.org/10.1038/s41558-025-02377-z

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