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Global evidence for the acclimation of ecosystem photosynthesis to light

Abstract

Photosynthesis responds quickly to changes in light, increasing with incoming photosynthetic photon flux density (PPFD) until the leaves become light saturated. This instantaneous response to PPFD, which is widely studied and incorporated into models of photosynthesis, is overlaid on non-instantaneous photosynthetic changes resulting from the acclimation of plants to average PPFD over intermediate timescales of a week to months \(\left( {\overline {{\mathrm{PPFD}}} } \right)\). Such photosynthetic light acclimation is not typically incorporated into models, due to the lack of observational constraints. Here, we use eddy covariance observations from globally distributed and automated sensor networks, along with photosynthesis estimates from nine terrestrial biosphere models (TBMs), to quantify and assess photosynthetic acclimation to light in natural environments. We also use recent theoretical developments to incorporate light acclimation in a TBM. Our results show widespread light acclimation of ecosystem photosynthesis. On average, a 1 μmol m−2 s−1 increase in \(\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}\) (ten-day average PPFD) leads to a 0.031 ± 0.013 μmol C m−2 s−1 increase in the maximum photosynthetic assimilation rate (Amax), with croplands having stronger acclimation rates than grasslands and forests. Our analysis shows that the TBMs examined either neglect or substantially underestimate light acclimation. By updating a TBM to include photosynthetic acclimation, successfully reproducing the \(\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}\)Amax relationship, we provide a robust method for the incorporation of photosynthetic light acclimation in future models.

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Fig. 1: The relationships between the Amax of ecosystems and \({\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}}\).
Fig. 2: Ecosystem γA changes with fAPAR, Tair, PFTs and VPD.
Fig. 3: Incorporating photosynthetic light acclimation into TBMs.

Data availability

This study used openly available eddy covariance measurements provided by the FLUXNET2015 Tier 1 dataset (https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/) and the NACP site-level interim synthesis data downloaded from https://daac.ornl.gov/NACP/. The MODIS fAPAR time series (MOD15A2H) for eddy covariance sites were acquired from https://lpdaac.usgs.gov/tools/appeears.

Code availability

The code to derive the maximum ecosystem photosynthetic rates from eddy covariance measurements is available at https://github.com/bgctw/REddyProc. The code for the optimality model for Vcmax is available at https://github.com/SmithEcophysLab/optimal_vcmax_R. The code for BEPS is available at https://github.com/JChen-UToronto/BEPS_hourly_site_4.02.

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Acknowledgements

X.L. and T.F.K. were supported by the NASA Terrestrial Ecology Program IDS Award NNH17AE86I. T.F.K. also acknowledges support by the Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy under Contract DE-AC02-05CH11231 as part of the RUBISCO SFA. This work used 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. The eddy covariance site used is listed in Supplementary Table 2. We thank NACP for making their site-level synthesis and modelling data publicly available. The TBMs and the eddy covariance sites participating in NACP are acknowledged in Supplementary Tables 3 and 4.

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X.L. and T.F.K. designed the study. X.L. performed the analysis and led the writing. T.F.K. contributed to the writing.

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Correspondence to Xiangzhong Luo or Trevor F. Keenan.

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Extended data

Extended Data Fig. 1 Photosynthetic light acclimation rate (γA) derived from eddy covariance observations using different time windows (that is 7, 10, 15 and 30 days).

a, the distribution of γA; b, the number of valid bins in which we have at least 20 pairs of Amax and \(\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}\) used to derive γA; c, γA changes with fAPAR; d, the detectability of photosynthetic light acclimation changes with fAPAR; (e) γA changes with Tair; f, the detectability of photosynthetic light acclimation changes with Tair. Note that there are too few samples to plot the changes in γA derived from 30-day window to fAPAR and Tair.

Extended Data Fig. 2 Using ecosystem photosynthesis standardized to PPFD of 2000 µmol m−2 s−1 to study light acclimation.

a, Comparison between Amax and A2000 (that is ecosystem photosynthesis under PPFD of 2000 µmol m−2 s−1) ; b, γA calculated based on the regression of A2000 to \(\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}\); impacts of c, fAPAR and d, daytime air temperature (Tair) on γA calculated based on the regression of A2000 to \(\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}\).

Extended Data Fig. 3 Ecosystem photosynthetic light acclimation explained by light metrics other than light intensity.

The relationships between the maximum photosynthetic rate (Amax) of ecosystems and a, photoperiod (number of daytime hours per day) and b, total amount of photons (mol m−2 day−1) received by vegetation over the same 10-day windows used for \(\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}\).

Extended Data Fig. 4 The fraction of sunlit and shaded leaves under different fAPAR and leaf area index (LAI).

According to Beer’s law, \({\mathrm{fAPAR}} = 1 - {\mathrm{e}}^{ - 0.5{\mathrm{LAI}}}\). The separation of sunlit and shaded leaves is based on a theoretical algorithm23, assuming clumping index is 1 and solar zenith angle is 45°.

Extended Data Fig. 5 The number of pairs of Amax and \(\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}\) for each plant functional type (PFT) to get γA.

The acronyms of PFTs stand for croplands (CRO), deciduous broadleaf forests (DBF), evergreen broadleaf forests (EBF), evergreen needleleaf forests (ENF), mixed forests (MF) and grasslands (GRA).

Extended Data Fig. 6 The rate of photosynthetic light acclimation (γA) for each plant functional type (PFT).

Red indicates the existence of light acclimation and blue means no light acclimation within the bins defined by similar fAPAR and Tair. The black dots indicate where the linear correlation between \(\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}\) and Amax relationship is significant (p < 0.1). The acronyms of PFTs stand for croplands (CRO), deciduous broadleaf forests (DBF), evergreen broadleaf forests (EBF), evergreen needleleaf forests (ENF), mixed forests (MF) and grasslands (GRA).

Extended Data Fig. 7 The number of pairs of maximum photosynthetic rate (Amax) of ecosystems and 10-day average PPFD \(\left( {\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}} \right)\) within each bin.

We only calculated the photosynthetic light acclimation rate (γA) for bins that have more than 20 pairs of Amax and \(\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}\). Each bin is confined by similar fAPAR and Tair, where fAPAR is the fraction of absorbed PPFD, indicating vegetation foliage density and Tair is daytime air temperature.

Extended Data Fig. 8 An example light response curve fitted to eddy covariance measurements.

The y axis is the negative half-hourly net ecosystem exchange (NEE) and the x axis is the half-hourly PPFD. The daytime flux observations in a dynamic window of 2–14 days are used to constrain a light response curve to derive Amax, and every day in the dynamic window is assumed to have the same Amax.

Extended Data Fig. 9 Percentage of the different qualities of the fitted light response curve.

High quality means all parameters in the fitted curve are within a reasonable range, moderate quality means the light response curve is fitted but some of the parameters are out of the reasonable range, and low quality means the light response curve cannot be well constrained using available data in two weeks.

Extended Data Fig. 10 The rate of light acclimation (γA) estimated by 9 terrestrial biosphere models.

Red indicates the existence of light acclimation and blue means no light acclimation within the bins defined by similar fAPAR and Tair. The black dots indicate where the linear correlation between \(\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}\) and Amax relationship is significant (p < 0.1). The summary of all models is in Supplementary Table 3.

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Luo, X., Keenan, T.F. Global evidence for the acclimation of ecosystem photosynthesis to light. Nat Ecol Evol 4, 1351–1357 (2020). https://doi.org/10.1038/s41559-020-1258-7

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