Abstract
Plants assimilate carbon through photosynthesis (gross primary productivity, GPP) while losing water via transpiration (Trans), with both processes responding nonlinearly to temperature. Although the air temperature optimum of GPP (({T}_{mathrm{opt}}^{mathrm{GPP}})) is well studied, the thermal response of Trans (({T}_{mathrm{opt}}^{mathrm{Trans}})) remains unknown. Here, using global eddy covariance observations and sap flow measurements along with simulations from an Earth system model, we find that ({T}_{mathrm{opt}}^{mathrm{Trans}}) is consistently higher than ({T}_{mathrm{opt}}^{mathrm{GPP}}) across biomes and climates, indicating greater heat tolerance in Trans. Despite a strong correlation, their divergence suggests carbon uptake is more vulnerable to warming than water loss. Machine learning identifies maximum air temperature as the key driver of both optima, while their difference (({Delta T}_{mathrm{opt}})) is associated with vegetation water content. The Earth system model predicts spatial patterns of ({T}_{mathrm{opt}}^{mathrm{Trans}}) and ({T}_{mathrm{opt}}^{mathrm{GPP}}) that align with observations, but the model significantly underestimates the magnitudes of ({T}_{mathrm{opt}}^{,mathrm{Trans}}), ({T}_{mathrm{opt}}^{mathrm{GPP}}) and ({Delta T}_{mathrm{opt}}). These results reveal a critical decoupling of carbon–water coordination under heat stress, with ecosystems sustaining Trans beyond ({T}_{mathrm{opt}}^{mathrm{GPP}}) to cool leaves, but ultimately reducing Trans to conserve water.
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Evidence for widespread thermal optimality of ecosystem respiration
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Dryland dominance in the slowdown of global vegetation carbon uptake
Data availability
The eddy covariance measurements are downloaded via ICOS at https://data.icos-cp.eu/portal, via AmeriFlux at https://ameriflux.lbl.gov/ and via FLUXNET2015 datasets at https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/. Sap flow data are obtained via the SAPFLUXNET datasets at https://sapfluxnet.creaf.cat/. The independent Trans dataset is downloaded from the NEON datasets using five different flux partitioning methods via Zenodo at https://doi.org/10.5281/zenodo.12191876 (ref. 31). The second-generation Köppen–Geiger climate classification dataset (1-km resolution) is available at https://www.gloh2o.org/koppen/. The third-generation global Aridity Index and potential ET dataset are accessible via figshare at https://doi.org/10.6084/m9.figshare.7504448.v5 (ref. 84). Vegetation optical depth data are available at https://doi.org/10.48436/t74ty-tcx62. LAI data are obtained via the MOD15A2H product at https://ladsweb.modaps.eosdis.nasa.gov/. LST data were obtained via the MOD11A1 Collection 6.1 daily product at https://ladsweb.modaps.eosdis.nasa.gov/. CH data are obtained from the global integrated CH map at https://doi.org/10.3929/ethz-b-000609802. Root mass fraction data are derived from the global root mass fraction map (https://doi.org/10.1038/s41559-021-01485-1). Aboveground and belowground biomass data are obtained from the global 1-km root biomass dataset via figshare at https://doi.org/10.6084/m9.figshare.12199637.v1 (ref. 89). Root-zone water storage data are sourced from the global map of root-zone water storage capacity via Zenodo at https://doi.org/10.5281/zenodo.5515246 (ref. 90). ESM outputs are accessed via CMIP6 archive at https://esgf-data.dkrz.de/search/cmip6-dkrz/. Source data are provided with this paper.
Code availability
The code used for this study is available via Zenodo at https://doi.org/10.5281/zenodo.18604934 (ref. 91).
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Acknowledgements
This work was supported by the National Key R&D Program of China (grant no. 2024YFF1309000 to Z.F.), the National Natural Science Foundation of China (grant nos. 42471122 and 32588202 to Z.F.) and the NSFC Excellent Young Scientists Fund (Overseas). J.P. was funded by the European Union grant CONCERTO (grant no. HORIZON-CL5-2024-D1-01). This work used global eddy covariance data acquired and shared by the FLUXNET 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 ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization were carried out by the European Fluxes Database Cluster, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center and the OzFlux, ChinaFlux and AsiaFlux offices.
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Z.F. designed the study. H.X. and F.Z. performed the data analysis. Z.F. and H.X. wrote the initial manuscript. F.Z., P.C., P.C.S., J.P., X.L., Y.-P.W., D.M., Y.L., S.N., G.Y., J.H., X.W. and E.Z. provided methodological suggestions and contributed to the interpretation of the results. All co-authors reviewed the paper and provided feedback.
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Xia, H., Zhang, F., Ciais, P. et al. Higher optimal temperature for vegetation transpiration than for photosynthesis.
Nat. Plants (2026). https://doi.org/10.1038/s41477-026-02263-2
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DOI: https://doi.org/10.1038/s41477-026-02263-2
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