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Accurate phenology analyses require bud traits and energy budgets

Nature Plants volume 8, pages 915–922 (2022)Cite this article

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Abstract

Spring phenology is mainly driven by temperature in extratropical ecosystems. Recent evidence highlighted the key role of micrometeorology and bud temperature on delaying or advancing leaf unfolding. Yet, phenology studies, either using ground-based or remote sensing observations, always substitute plant tissue temperature by air temperature. In fact, temperatures differ substantially between plant tissues and the air because plants absorb and lose energy. Here, we build on recent observations and well-established energy balance theories to discuss how solar radiation, wind and bud traits might affect our interpretation of spring phenology sensitivity to warming. We show that air temperature might be an imprecise and biased predictor of bud temperature. Better characterizing the plants’ phenological response to warming will require new observations of bud traits and temperature for accurately quantifying their energy budget. As consistent micrometeorology datasets are still scarce, new approaches coupling energy budget modelling and plant traits could help to improve phenology analyses across scales.

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Fig. 1: Energy budget of buds and bud traits.
Fig. 2: Simulated differences in temperature between buds and the air (ΔT) from energy balance.
Fig. 3: Spatial variability in bud temperature.
Fig. 4: Potential changes in Tbud−Tair over Europe.
Fig. 5: Impact of ground albedo on simulated bud temperature.
Fig. 6: Impact of bud size on simulated bud temperature.

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Data availability

FLUXNET2015 data are available at https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/. CRU–JRA data are available at https://catalogue.ceda.ac.uk/uuid/13f3635174794bb98cf8ac4b0ee8f4ed. ERA5 soil temperature data are available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land. PEP725 phenology data are available at http://www.pep725.eu/.

Code availability

The R code of the model of energy budgets and datasets used to generate the figures and analysis of this manuscript are available from Github at https://github.com/mpeaucelle/Tbud. A version of the git repository is archived on Zenodo at https://zenodo.org/record/5897267 corresponding to tag v.2.0.

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Acknowledgements

We acknowledge C. Muir and R. Marchin Prokopavicius for their constructive feedback on our work. The project was funded by Fonds Wetenschappelijk Onderzoek (FWO; grant No. G018319N to M.P.), H2020 Marie Skłodowska-Curie Actions (LEAF-2-TBM, grant No. 891369 to M.P.), the European Research Council Synergy (grant No. ERC-SyG-2013-610028 IMBALANCE-P to J.P.), the Spanish Government (grant No. PID2019-110521GB-I00 to J.P.), the Fundación Ramon Areces (grant No. ELEMENTAL-CLIMATE to J.P.), the Catalan Government (grant No. SGR 2017-1005 to J.P.) and the European Research Council (grant No. 637643 TREECLIMBERS to H.V.).

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Authors and Affiliations

  1. INRAE, Université de Bordeaux, UMR 1391 ISPA, Villenave-d’Ornon, France

    Marc Peaucelle

  2. Computational and Applied Vegetation Ecology – CAVElab, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium

    Marc Peaucelle & Hans Verbeeck

  3. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Barcelona, Spain

    Josep Peñuelas

  4. CREAF, Cerdanyola del Vallès, Barcelona, Spain

    Josep Peñuelas

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  1. Marc Peaucelle
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Contributions

M.P. designed the study, performed the analysis and wrote the first version of the manuscript. J.P. and H.V. substantially contributed to the interpretation of the results and the revisions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Marc Peaucelle.

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Extended data

Extended Data Fig. 1 Distribution of sites with more than 20 years of leaf unfolding records over 1990-2015.

Sites including data for Common alder (Alnus glutinosa), horse chestnut (Aesculus hippocastanum), silver birch (Betula pendula), European beech (Fagus sylvatica), European ash (Fraxinus excelsior) and pedunculate oak (Quercus robur) from the PEP database.

Extended Data Fig. 2 Weight of each component of the bud energy budget in simulating bud temperature.

Response of the bud-air temperature difference (ΔT) is a complex nonlinear response to Rabs (red), H (orange) and LWbud (Blue). For more clarity, energy components were divided by 500, 100, 500 and 10 for Rabs, H, LWbud and E, respectively. Each point corresponds to the mean ΔT simulated for six species across Europe (n = 1059 sites) under idealized conditions using field observation of budburst and global meteorological data. All sites and species were pooled together. Solar absorptivity to shortwave radiation is set to 0.8. The error bars represent the spatial and species variability (±1SD around the mean, n = 5050). Panels b, c, d and e represent the weight of Rabs, LWbud, H and E in explaining ΔT variability, by showing the relationships of their respective partial residuals in the multiple linear regression ΔT=Rabs + H+LWbud+E (R² = 0.85) using global yearly averages. We observe that H, Rabs and LWbud account for most of the variability in ΔT. Red full lines represent the slope coefficient in the linear regression. Dashed lines represent the standard error associated to each coefficient.

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Supplementary Information

List of variables and parameters of the energy budget model.

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Peaucelle, M., Peñuelas, J. & Verbeeck, H. Accurate phenology analyses require bud traits and energy budgets. Nat. Plants 8, 915–922 (2022). https://doi.org/10.1038/s41477-022-01209-8

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