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An earlier start of the thermal growing season enhances tree growth in cold humid areas but not in dry areas

Nature Ecology & Evolution volume 6, pages 397–404 (2022)Cite this article

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Abstract

Climatic warming alters the onset, duration and cessation of the vegetative season. While previous studies have shown a tight link between thermal conditions and leaf phenology, less is known about the impacts of phenological changes on tree growth. Here, we assessed the relationships between the start of the thermal growing season and tree growth across the extratropical Northern Hemisphere using 3,451 tree-ring chronologies and daily climatic data for 1948–2014. An earlier start of the thermal growing season promoted growth in regions with high ratios of precipitation to temperature but limited growth in cold–dry regions. Path analyses indicated that an earlier start of the thermal growing season enhanced growth primarily by alleviating thermal limitations on wood formation in boreal forests and by lengthening the period of growth in temperate and Mediterranean forests. Semi-arid and dry subalpine forests, however, did not benefit from an earlier onset of growth and a longer growing season, presumably due to associated water loss and/or more frequent early spring frosts. These emergent patterns of how climatic impacts on wood phenology affect tree growth at regional to hemispheric scales hint at how future phenological changes may affect the carbon sequestration capacity of extratropical forest ecosystems.

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Fig. 1: Responses of tree growth to changes in the onset of the TSOS across the extratropical Northern Hemisphere.
Fig. 2: Path diagrams and path effects for northern Asia, northern and central Europe, the Mediterranean region, the west and east coasts of the United States, the Colorado Plateau and the Tibetan Plateau.

Data availability

The reformatted data of the ITRDB were obtained from https://doi.org/10.5061/dryad.kh0qh06. Tree-ring width data from the ITPCAS tree-ring group are available from https://doi.org/10.11888/Terre.tpdc.271925. The Global Meteorological Forcing Dataset of the Terrestrial Hydrology Research Group at Princeton University was obtained from http://hydrology.princeton.edu/data.pgf.php. The NASA Global Land Data Assimilation System v.2 was obtained from https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary?keywords=GLDAS2.0. Source data are provided with this paper.

Code availability

Statistical analyses in this study were performed with publicly available packages in R (v.3.6.2, dplR and sem packages) and Python (v.3.8, scipy package) and the figures were produced using Python (matplotlib, cartopy and seaborn packages). The custom code for the analysis of the data are available from https://doi.org/10.11888/Terre.tpdc.271925.

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Acknowledgements

We acknowledge all contributors to the ITRDB for providing tree-ring data. This study was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0301), the National Natural Science Foundation of China (41907387, 42030508 and 41988101) and the China Postdoctoral Science Foundation (2019M660813). J.P. was funded by Spanish Government projects PID2019–110521GB-I00, Fundación Ramón Areces project ELEMENTAL-CLIMATE and Catalan government project SGR2017-1005.

Author information

Authors and Affiliations

  1. State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Shan Gao, Eryuan Liang, Ruishun Liu, Shilong Piao & Tao Wang

  2. School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA

    Flurin Babst

  3. Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA

    Flurin Babst

  4. Instituto Pirenaico de Ecología (IPE-CSIC), Zaragoza, Spain

    J. Julio Camarero

  5. College of Water Sciences, Beijing Normal University, Beijing, China

    Yongshuo H. Fu

  6. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Shilong Piao

  7. Département des Sciences Fondamentales, Université du Québec à Chicoutimi, Chicoutimi, Quebec, Canada

    Sergio Rossi

  8. Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China

    Sergio Rossi

  9. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Miaogen Shen

  10. CREAF, Cerdanyola del Valles, Barcelona, Spain

    Josep Peñuelas

  11. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Spain

    Josep Peñuelas

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  1. Shan Gao
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Contributions

S.G. and E.L. designed the research. S.G. and R.L. performed the analysis. S.G. drafted the manuscript. E.L., F.B., J.J.C., Y.H.F., S.P., S.R., M.S., T.W. and J.P. contributed ideas, interpreted the results and were involved in the editing and writing of the manuscript.

Corresponding author

Correspondence to Eryuan Liang.

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Nature Ecology & Evolution thanks Bao Yang, Yingying Xie 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 Responses of tree growth to changes in TSOS in the extratropical Northern Hemisphere.

Spatial distributions of simple correlation coefficients (A), partial correlation coefficients (B), significant (p < 0.1) simple correlation coefficients (C) and significant (p < 0.1) partial correlation coefficients (D) of TSOS and RWI. (E) Areas with significant (p < 0.1, dark blue) and nonsignificant (light blue) trends toward earlier TSOS between 1948 and 2016 in the extratropical Northern Hemisphere. (F) Areas with significant (p < 0.05, blue shaded area) trends toward earlier TSOS overlapping tree-ring chronologies with significant (p < 0.1) simple correlation coefficients of TSOS and RWI. The significance of the correlation analyses is estimated by two-tailed Student’s t-test. This figure was generated using the matplotlib and cartopy package in Python.

Source data

Extended Data Fig. 2 Scatter plots of TSOS–RWI relationships in different regions.

TSOS–RWI relationships of tree-ring chronologies with significant (p < 0.1) simple correlations for northern Asia (A), northern Europe (B), central Europe (C), the Mediterranean region (D), the west coast of the US (E), the east coast of the US (F), the Colorado Plateau (G) and the Tibetan Plateau (H). The predicted mean (solid lines) is bounded by the 95% confidence intervals (shaded areas). This figure was generated using the seaborn package, ‘lmplot’ function in Python.

Source data

Supplementary information

Supplementary Information

Supplementary text, Figs. 1–6, Tables 1–3 and references.

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Source data

Source Data Fig. 1

Statistical source data for individual data points.

Source Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data for individual data points.

Source Data Extended Data Fig. 2

Source data for individual data points.

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Gao, S., Liang, E., Liu, R. et al. An earlier start of the thermal growing season enhances tree growth in cold humid areas but not in dry areas. Nat Ecol Evol 6, 397–404 (2022). https://doi.org/10.1038/s41559-022-01668-4

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