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Evidence for widespread thermal optimality of ecosystem respiration

Nature Ecology & Evolution volume 7, pages 1379–1387 (2023)Cite this article

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Abstract

Ecosystem respiration (ER) is among the largest carbon fluxes between the biosphere and the atmosphere. Understanding the temperature response of ER is crucial for predicting the climate change–carbon cycle feedback. However, whether there is an apparent optimum temperature of ER (\({{T}}_{{\rm{opt}}}^{\,{\rm{ER}}}\)) and how it changes with temperature remain poorly understood. Here we analyse the temperature response curves of ER at 212 sites from global FLUXNET. We find that ER at 183 sites shows parabolic temperature response curves and \({{T}}_{{\rm{opt}}}^{\,{\rm{ER}}}\) at which ER reaches the maximum exists widely across biomes around the globe. Among the 15 biotic and abiotic variables examined, \({{T}}_{{\rm{opt}}}^{\,{\rm{ER}}}\) is mostly related to the optimum temperature of gross primary production (GPP, \({{T}}_{{\rm{opt}}}^{\,{\rm{GPP}}}\)) and annual maximum daily temperature (Tmax). In addition, \({{T}}_{{\rm{opt}}}^{\,{\rm{ER}}}\) linearly increases with Tmax across sites and over vegetation types, suggesting its thermal adaptation. The adaptation magnitude of \({{T}}_{{\rm{opt}}}^{\,{\rm{ER}}}\), which is measured by the change in \({{T}}_{{\rm{opt}}}^{\,{\rm{ER}}}\) per unit change in Tmax, is positively correlated with the adaptation magnitude of \({{T}}_{{\rm{opt}}}^{\,{\rm{GPP}}}\). This study provides evidence of the widespread existence of \({{T}}_{{\rm{opt}}}^{\,{\rm{ER}}}\) and its thermal adaptation with Tmax across different biomes around the globe. Our findings suggest that carbon cycle models that consider the existence of \({{T}}_{{\rm{opt}}}^{\,{\rm{ER}}}\) and its adaptation have the potential to more realistically predict terrestrial carbon sequestration in a world with changing climate.

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Fig. 1: Distribution of \({\boldsymbol{T}}_{\mathbf{opt}}^{\,{\mathbf{ER}}}\) derived from flux-tower sites.
Fig. 2: The relationship of \({\boldsymbol{T}}_{{\mathbf{opt}}}^{\,{\mathbf{ER}}}\) to Tmax.
Fig. 3: The relationship between \({{\boldsymbol{T}}}_{{\mathbf{opt}}}^{\,{\mathbf{ER}}}\) and influencing factors.
Fig. 4: Relationship between the thermal adaptation magnitudes of \({{\boldsymbol{T}}}_{{\mathbf{opt}}}^{\,{\mathbf{GPP}}}\) and \({{\boldsymbol{T}}}_{{\mathbf{opt}}}^{\,{\mathbf{ER}}}\) across different vegetation types.
Fig. 5: The relationship between annual ER and \({{\boldsymbol{T}}}_{{\mathbf{opt}}}^{\,{\mathbf{ER}}}\).
Fig. 6: Projections of \({\boldsymbol{T}}_{{\mathbf{opt}}}^{\,{\mathbf{ER}}}\).

Data availability

All FLUXNET data can be downloaded at https://fluxnet.fluxdata.org. Soil properties were retrieved from the Regridded Harmonized World Soil Database v.1.2 in the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (https://daac.ornl.gov/SOILS/guides/HWSD.html). Biomass data were obtained at http://wald.anu.edu.au/data_services/data/global-above-ground-biomass-carbon-v1-0/. Soil moisture used to extract data for sites without providing soil water conditions was obtained from the European Space Agency’s (ESA) Soil Moisture Climate Change Initiative (CCI) project (https://www.esa-soilmoisture-cci.org/). Leaf area index data were obtained from https://www.ncei.noaa.gov/products/climate-data-records/leaf-area-index-and-fapar. Current air temperature and relative humidity data were obtained from the Climatic Research Unit/National Centers for Environmental Protection (CRU/NCEP) 6-hourly dataset (https://data.ucar.edu/en/dataset/cruncep-version-7-atmospheric-forcing-data-for-the-community-land-model). Future daily air temperature and relative humidity data were obtained from the Lawrence Livermore National Library (https://esgf-node.llnl.gov/projects/esgf-llnl/).

Code availability

Code used for data analysis in this study is available at https://figshare.com/articles/online_resource/code_docx/23514492.

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Acknowledgements

This study was financially supported by the National Natural Science Foundation of China (31988102), the National Key Technology R & D Program of China (2022YFF0802102) and the International Partnership Program of the Chinese Academy of Science (177GJHZ2022020BS). We used the eddy-covariance data of the FLUXNET community by the following networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, Swiss FluxNet and USCCC. The ERA-Interim reanalysis data were provided by ECMWF and processed by Laboratoire des sciences du climat et de l’environnement (LSCE). The FLUXNET eddy-covariance data processing and harmonization was carried out by the European Fluxes Database Cluster and the AmeriFlux Management Project (with support by European Union H2020 projects and the US Department of Energy Office of Science, respectively), with contributions from the Carbon Dioxide Information Analysis Center, ICOS Ecosystem Thematic Centre, and OzFlux, ChinaFlux and AsiaFlux offices.

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Authors and Affiliations

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

    Weinan Chen, Song Wang, Jinsong Wang, Guirui Yu & Shuli Niu

  2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, P. R. China

    Weinan Chen, Song Wang, Guirui Yu & Shuli Niu

  3. Research Center for Global Change and Complex Ecosystems, East China Normal University, Shanghai, China

    Jianyang Xia

  4. School of Integrative Plant Science, Cornell University, Ithaca, NY, USA

    Yiqi Luo

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  1. Weinan Chen
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Contributions

W.C. and S.N. conceived the ideas and designed the study. W.C. and S.W. collected and analysed the data. W.C. and S.N. wrote the first draft of the manuscript. J.W., J.X., Y.L. and G.Y. offered thoughts on the analysis and contributed critically to the writing through multiple rounds of revisions.

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Correspondence to Shuli Niu.

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Nature Ecology & Evolution thanks Kristine Crous and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Response of ecosystem respiration (ER) to daily temperature at the 11 sites from 11 different vegetation types.

Points and Error bars indicate mean ± s.d. for each temperature bin. The vegetation types are as follows: (a) croplands (CRO), (b) deciduous broadleaf forests (DBF), (c) deciduous needle-leaf forest (DNF), (d) evergreen needle leaf forests (ENF), (e) evergreen broadleaf forests (EBF), (f) grasslands (GRA), (g) mixed forests (MF), (h) opened shrublands (OSH), (i) savanna (SAV), (j) wetlands (WET), (k) woody savanna (WSA).

Extended Data Fig. 2 Goodness-of-fit of the fitted quadratic function at \({{\boldsymbol{T}}}_{{\mathbf{opt}}}^{\,{\mathbf{ER}}}\) for the 183 sites with temperature optima.

(a) Distribution of adjusted R square of the fitted quadratic function. (b) Distribution of P value of the fitted quadratic function at the 0.05 level.

Extended Data Fig. 3 Bivariate plots between \({{\boldsymbol{T}}}_{{\mathbf{opt}}}^{\,{\mathbf{ER}}}\) and influencing variables. Symbol point size is proportional to numbers of site-years in each site.

The influencing variables are as follows: (a) annual minimum daily temperature (Tmin, °C); (b) growing season temperature (GST, °C); (c) vapor pressure deficit (VPD, kPa); (d) mean annual temperature (MAT, °C); (e) mean annual precipitation (MAP, mm yr−1); (f) global solar radiation (GSR, W/m2); -(g) soil moisture (SM); (h) aridity index (AI); (i) aboveground biomass (Biomass, Mg ha-1), soil growing season temperature (GST, °C); vapor pressure deficit (VPD, kPa); soil moisture (SM); aridity index (AI); aboveground biomass (Biomass, Mg ha-1), (j) soil organic carbon (SOC, %), (k) soil pH (pH), (l) clay fraction (Clay, %) (m) soil bulk density (BD, kg dm−3).

Extended Data Fig. 4 The relationship of \({{\boldsymbol{T}}}_{{\mathbf{opt}}}^{\,{\mathbf{ER}}}\) with maximum temperature (Tmax) (a) and \({{\boldsymbol{T}}}_{{\mathbf{opt}}}^{\,{\mathbf{GPP}}}\) (b) across years at the 15 sites with > 10 years of data.

Each colored line indicates one site. The red dotted line was the fixed-effect linear regression slope between sites considering site-level random effects estimated from the linear-mixed model.

Extended Data Fig. 5 Adaptation magnitude of GPP and ER among different vegetation types.

The adaptation magnitude of \({{T}}_{{\rm{opt}}}^{\,{\rm{ER}}}\) or \({{T}}_{{\rm{opt}}}^{\,{\rm{GPP}}}\) was calculated as the slope between \({{T}}_{{\rm{opt}}}^{\,{\rm{ER}}}\) or \({{T}}_{{\rm{opt}}}^{\,{\rm{GPP}}}\) and Tmax. The vegetation types are as follows: croplands (CRO, n = 17), deciduous broadleaf forests (DBF, n = 22), evergreen needle leaf forests (ENF, n = 45), evergreen broadleaf forests (EBF, n = 10), grasslands (GRA, n = 35), mixed forests (MF, n = 8), opened shrublands (OSH, n = 14), wetlands (WET, n = 17) and savanna (SAV, n = 13). Points and error bars indicate mean ± 95% confidence intervals.

Extended Data Fig. 6

Comparison of modeled \({{T}}_{{\rm{opt}}}^{\,{\rm{ER}}}\) with the compiled \({{T}}_{{\rm{opt}}}^{\,{\rm{ER}}}\) (R2 = 0.70; P < 0.001).

Extended Data Fig. 7

Uncertainty of global estimation of \({{\boldsymbol{T}}}_{{\mathbf{opt}}}^{\,{\mathbf{ER}}}\) with an empirical model. (a) Current uncertainty. (b) Future uncertainty under SSP2-4.5 scenario.

Supplementary information

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Supplementary Figs. 1–5, Discussion and Tables 1–5.

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Chen, W., Wang, S., Wang, J. et al. Evidence for widespread thermal optimality of ecosystem respiration. Nat Ecol Evol 7, 1379–1387 (2023). https://doi.org/10.1038/s41559-023-02121-w

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