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Acclimation of phenology relieves leaf longevity constraints in deciduous forests

Nature Ecology & Evolution volume 7, pages 198–204 (2023)Cite this article

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Abstract

Leaf phenology is key for regulating total growing-season mass and energy fluxes. Long-term temporal trends towards earlier leaf unfolding are observed across Northern Hemisphere forests. Phenological dates also vary between years, whereby end-of-season (EOS) dates correlate positively with start-of-season (SOS) dates and negatively with growing-season total net CO2 assimilation (Anet). These associations have been interpreted as the effect of a constrained leaf longevity or of premature carbon (C) sink saturation—with far-reaching consequences for long-term phenology projections under climate change and rising CO2. Here, we use multidecadal ground and remote-sensing observations to show that the relationships between Anet and EOS are opposite at the interannual and the decadal time scales. A decadal trend towards later EOS persists in parallel with a trend towards increasing Anet—in spite of the negative Anet–EOS relationship at the interannual scale. This finding is robust against the use of diverse observations and models. Results indicate that acclimation of phenology has enabled plants to transcend a constrained leaf longevity or premature C sink saturation over the course of several decades, leading to a more effective use of available light and a sustained extension of the vegetation CO2 uptake season over time.

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Fig. 1: Relationship of CO2 assimilation and autumn phenology from ground observations.
Fig. 2: Relationships of CO2 assimilation and autumn phenology from remote-sensing observations.

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Data availability

Ground phenology data provided by the members of the PEP725 project are freely available at http://www.pep725.eu. Remote-sensing phenology data from the MODIS C6 MCD12Q2 land surface dynamics product are freely accessible at https://lpdaac.usgs.gov/products/mcd12q2v006/. Eddy covariance data are freely available by the FLUXNET community at https://fluxnet.org.

Code availability

Code for the data analysis of this study is available at the Github repository https://zenodo.org/record/7245870.

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Acknowledgements

We thank T. Keenan, J. Madrigal-González, H. Bugmann, M. Meier and Y. Vitasse for their valuable feedback on the study. L.M. and B.D.S. were funded by the Swiss National Science Foundation grant no. PCEFP2_181115. K.H. was supported by the generosity of E. and W. Schmidt by recommendation of the Schmidt Futures programme. C.M.Z. was funded by the Ambizione grant PZ00P3_193646. Ground phenology data were provided by the members of the PEP725 project. 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 FLUXNET eddy covariance data processing and harmonization was 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.

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Authors and Affiliations

  1. Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland

    Laura Marqués, Koen Hufkens & Benjamin D. Stocker

  2. Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland

    Laura Marqués, Koen Hufkens & Benjamin D. Stocker

  3. Institute of Geography, University of Bern, Bern, Switzerland

    Laura Marqués, Koen Hufkens & Benjamin D. Stocker

  4. Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

    Laura Marqués, Koen Hufkens & Benjamin D. Stocker

  5. Department of Environmental Systems Science, Institute of Terrestrial Ecosystems, ETH Zurich, Zurich, Switzerland

    Christof Bigler

  6. Department of Environmental Systems Science, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland

    Thomas W. Crowther & Constantin M. Zohner

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  1. Laura Marqués
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Contributions

B.D.S. and L.M. conceived and developed the study. B.D.S., K.H. and L.M. gathered the MODIS data, ran the P-model simulations and conducted the statistical analyses. L.M. and B.D.S. led the writing of the manuscript. C.B. contributed critically to the analyses and the writing. C.M.Z. and T.W.C. gave substantial inputs to the manuscript. All authors gave final approval for publication.

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Correspondence to Laura Marqués.

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Extended data

Extended Data Fig. 1 Temporal trends of CO2 assimilation and phenological dates from ground observations.

(A) Trend towards delayed EOS (expressed as day-of-year, DOY), (B) increased Anet from P-model and (C) from LPJ model simulations, and (D) advanced SOS (DOY), based on linear mixed-effect models (LMMs) with year as a single fixed effect and site and species as grouping variables of the random intercepts. Black lines represent the expected mean values from LMMs and grey ranges their 95% confidence intervals. Colour hexagonal heatmap represents the observed data adjusted for the effects of the covariates.

Extended Data Fig. 2 Relationship of CO2 assimilation and autumn phenology from ground observations.

(A, B) Partial relationships of a multiple LMM, where EOS is the response variable and (A) the long-term trend (year) and (B) Anet are treated as fixed effects. (C) EOS versus Anet based on an LMM with Anet as a single fixed effect. Anet estimates are simulated by the LPJ model. In both bivariate and univariate models, site and species are treated as grouping variables of random intercepts. Black lines represent the expected mean values from LMMs and grey ranges their 95% confidence intervals. Colour hexagonal heatmap represents the observed data adjusted for the effects of the covariates.

Extended Data Fig. 3 Sensitivity analysis of the relationship of CO2 assimilation and autumn phenology from ground observations.

Partial relationships of multiple LMMs, where EOS is the response variable and (A, D, G) the long-term trend (year) and (B, E, H) Anet are treated as fixed effects. Anet estimates are simulated by the P-model and considering (B) a daylength threshold of 10.0 hours, (E) a fixed DOY cut-off on the 23rd of September, and (H) a fixed DOY cut-off on the 21st of June. (C, F, I) EOS versus Anet based on an LMM with Anet as a single fixed effect. In all bivariate and univariate models, site and species are treated as grouping variables of random intercepts. Black lines represent the expected mean values from LMMs and grey ranges their 95% confidence intervals. Colour hexagonal heatmap represents the observed data adjusted for the effects of the covariates.

Extended Data Fig. 4 Sensitivity analysis of the relationship of CO2 assimilation and autumn phenology from remote-sensing observations.

Partial relationships of multiple LMMs, where EOS is the response variable and (A, C, E) mean Anet and (B, D, F) anomalies Anet relative to the mean value are treated as fixed effects, while site and year are treated as grouping variables of random intercepts. Anet estimates are simulated by the P-model and considering (A, B,) a daylength threshold of 10.0 hours, (C, D) a fixed DOY cut-off on the 23rd of September, and (E, F) a fixed DOY cut-off on the 21st of June. Black lines represent the expected mean values from LMMs and grey ranges their 95% confidence intervals. Colour hexagonal heatmap represents the observed data adjusted for the effects of the covariates.

Extended Data Fig. 5 Comparative relationships of autumn phenology and total gross ecosystem-level CO2 assimilation from both observations and simulations for the selected FLUXNET sites.

Partial relationships of a multiple LMM, with GPP (A, B) estimated from FLUXNET 2015 observations or (C, D) simulated using the P-model, and where both (A, C) mean GPP and (B, D) anomalies GPP relative to the mean value are treated as fixed effects, and site and year are treated as grouping variables of random intercepts. Black lines represent the expected mean values from LMMs and grey ranges their 95% confidence intervals.

Extended Data Fig. 6 Relationships of autumn phenology and simulated total net ecosystem-level CO2 assimilation for the selected FLUXNET sites.

Partial relationships of a multiple LMM, with GPP minus ecosystem-level dark respiration (GPPnet) simulated using the P-model, and where (A) mean GPPnet and (B) anomalies GPPnet relative to the mean value are treated as fixed effects, and site and year are treated as grouping variables of random intercepts. Black lines represent the expected mean values from LMMs and grey ranges their 95% confidence intervals.

Extended Data Fig. 7 Relationship of spring and autumn phenological dates from ground observations.

(A, B) Partial relationships of a multiple LMM, where EOS is the response variable and (A) the long-term trend (year) and (B) SOS are treated as fixed effects. (C) EOS versus SOS based on an LMM with SOS as a single fixed effect. In both bivariate and univariate models, site and species are treated as grouping variables of random intercepts. Black lines represent the expected mean values from LMMs and grey ranges their 95% confidence intervals. Colour hexagonal heatmap represents the observed data adjusted for the effects of the covariates.

Extended Data Fig. 8 Relationships of spring and autumn phenological dates from remote-sensing observations.

(A, B) Partial relationships of a multiple LMM where EOS is the response variable and (A) mean SOS and (B) anomalies SOS relative to the mean value are treated as fixed effects, and site and year are treated as grouping variables of random intercepts. Black lines represent the expected mean values from LMMs and grey ranges their 95% confidence intervals. Colour hexagonal heatmap represents the observed data adjusted for the effects of the covariates.

Extended Data Fig. 9 Site locations of the phenological observations selected from (A) ground and (B) remote-sensing observations.

Colour transparency indicates the density of the data points.

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Marqués, L., Hufkens, K., Bigler, C. et al. Acclimation of phenology relieves leaf longevity constraints in deciduous forests. Nat Ecol Evol 7, 198–204 (2023). https://doi.org/10.1038/s41559-022-01946-1

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