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Younger trees in the upper canopy are more sensitive but also more resilient to drought

Nature Climate Change volume 12, pages 1168–1174 (2022)Cite this article

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Abstract

As forest demographics are altered by the global decline of old trees and reforestation efforts, younger trees are expected to have an increasingly important influence on carbon sequestration and forest ecosystem functioning. However, the relative resilience of these younger trees to climate change stressors is poorly understood. Here we examine age-dependent drought sensitivity of over 20,000 individual trees across five continents and show that younger trees in the upper canopy layer have larger growth reductions during drought. Angiosperms show greater age differences than gymnosperms, and age-dependent sensitivity is more pronounced in humid climates compared with more arid regions. However, younger canopy-dominant trees also recover more quickly from drought. The future combination of increased drought events and an increased proportion of younger canopy-dominant trees suggests a larger adverse impact on carbon stocks in the short term, while the higher resilience of younger canopy-dominant trees could positively affect carbon stocks over time.

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Fig. 1: Drought-induced growth reduction of younger canopy-dominant trees is more pronounced than that for older canopy-dominant trees.
Fig. 2: Drought-induced growth reduction varies across biomes.
Fig. 3: Age-dependent drought resistance and relative resilience converge between tree taxonomic groups.
Fig. 4: Variations of drought resistance and resilience in major tree genera.

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Data availability

The data are accessible on the International Tree‐Ring Data Bank (https://www.ncei.noaa.gov/products/paleoclimatology/tree-ring) and the DendroEcological Network (https://www.uvm.edu/femc/dendro#data)43.

Code availability

The codes used to calculate the results reported in this study have been deposited on Figshare77: https://figshare.com/projects/Younger_trees_in_the_upper_canopy_are_more_sensitive_but_also_more_resilient_to_drought/150312

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Acknowledgements

We thank N. Pederson, Y. Zhao, Y. Jin, W. S. Ma and S. L. Kong for providing feedback and data. We thank all contributors to the ITRDB and DEN to make this analysis feasible. T.F.A. received support from Indiana University College of Arts and Sciences Dissertation Research Fellowship. J.Li received support from Hong Kong Research Grants Council (no. 17303017). Z.C. received support from the National Natural Science Foundation of China (no. 41888101, 41871027, 41601045, 41571094, 31570632). T.L. received support from the National Natural Science Foundation of China (no. 42105155). This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute.

Author information

Authors and Affiliations

  1. Department of Geography, Indiana University, Bloomington, IN, USA

    Tsun Fung Au, Justin T. Maxwell, Scott M. Robeson & Sacha M. O. Siani

  2. Institute for Global Change Biology, School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA

    Tsun Fung Au

  3. Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA

    Tsun Fung Au

  4. Center for the Analysis of Social-Ecological Landscapes (CASEL), Indiana University, Bloomington, IN, USA

    Tsun Fung Au & Sacha M. O. Siani

  5. Department of Geography, The University of Hong Kong, Hong Kong, China

    Jinbao Li

  6. O’Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN, USA

    Kimberly A. Novick

  7. Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA, USA

    Matthew P. Dannenberg

  8. Department of Biology, Indiana University, Bloomington, IN, USA

    Richard P. Phillips

  9. School of Geography and Remote Sensing, Guangzhou University, Guangzhou, China

    Teng Li

  10. Tree-Ring Laboratory/Research Station of Liaohe-River Plain Forest Ecosystem CFERN, College of Forestry, Shenyang Agricultural University, Shenyang, China

    Zhenju Chen

  11. Qingyuan Forest CERN, Chinese Academy of Sciences, Shenyang, China

    Zhenju Chen

  12. Key Laboratory of Desert and Desertification, Chinese Academy of Sciences, Lanzhou, China

    Zhenju Chen

  13. National Research Station of Changbai Forest Ecosystem, Er’daobaihe, China

    Zhenju Chen

  14. UMR CNRS 7058 ‘Ecologie et Dynamique des Systèmes Anthropisés’ (EDYSAN), Université de Picardie Jules Verne, Amiens, France

    Jonathan Lenoir

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Contributions

T.F.A., J. Li and J. Lenoir conceived the research, and T.F.A., J.T.M., S.M.R., J. Li, S.M.O.S., K.A.N., M.P.D., R.P.P., T.L., Z.C. and J. Lenoir designed the study. J.T.M., J.Li, M.P.D., T.L. and Z.C. contributed data. T.F.A. performed analyses with contribution from J.T.M., S.M.R., J. Li, S.M.O.S., K.A.N., M.P.D., R.P.P., T.L., Z.C. and J. Lenoir. All authors discussed, interpreted results, drew conclusions and participated in writing the paper.

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Correspondence to Tsun Fung Au.

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Extended data

Extended Data Fig. 1 The relationship between tree age and tree size of individual canopy-dominant trees.

The relationship between individual tree age and diameter at breast height (DBH) for 68 canopy-dominant trees Liriodendron tulipifera, representing ~20% of total L. tulipifera samples. The dashed red lines indicate the first quartile for the cutoff age of young and intermediate cohort, and the dashed blue lines indicate the third quartile for the cutoff age of intermediate and old cohort for L. tulipifera. Shaded ribbon indicates the 95% confidence interval for prediction from a linear model. The exact cutoff ages for L. tulipifera are listed in Supplementary Table 12.

Extended Data Fig. 2 Individual tree age is a better metric than mean stand age for examining age-dependent drought responses.

Comparison of Quercus alba drought responses at 18 sites between composite chronology with mean stand age (a) and individual series with individual tree age approach (b) using the same dataset as in Au et al.16. Hence, the same data can lead to very different results due to diluting effect of aggregating data at the coarser stand level. Here, we advocate for analysing the raw data on individual tree-ring time series rather than analysing the aggregated the information at the stand level for age-dependent drought responses. Shaded ribbon in panel a indicates the 95% confidence interval for prediction from a linear model. The age cohort classification in panel b follows the cutoff age for Quercus alba listed in the Supplementary Table 12. The numbers at the top of panel b represent the p-values of pairwise differences in percentage of growth reduction between age cohorts that were identified by Tukey honest significant differences. The numbers at the bottom of panel b represent number of tree individuals for the youngest, intermediate and oldest age cohort of Quercus alba, respectively. Boxes show the interquartile range (IQR) while upper and lower whiskers are defined as the third quartile (Q3) plus 1.5×IQR and the first quartile (Q1) minus 1.5×IQR, respectively. Values that are less than Q1–1.5×IQR or greater than Q3+1.5×IQR are plotted as closed circles. The bold lines and open squares in the boxplot represent the median and the mean values, respectively.

Extended Data Fig. 3 Distribution of correlation coefficients between site-optimized 3-month SPEI and site chronologies.

Numbers in the upper right and the parentheses indicate mean correlation and total number of sites, respectively for angiosperms (a) and gymnosperms (b).

Extended Data Fig. 4 Empirical probability densities of the Standardized Precipitation-Evapotranspiration Index across age cohorts.

Numbers in the panels indicate standard deviations for the young (Y, orange), intermediate (I, green), and old (O, blue) age cohort, separately for angiosperms (a) and gymnosperms (b), indicating that each age cohort experienced similar interannual moisture variability across different locations.

Extended Data Fig. 5 Empirical probability densities of standardized ring width across age cohorts.

Numbers in the panels indicate standard deviations of the young (Y, orange), intermediate (I, green), and old (O, blue) age cohort, separately for angiosperms (a) and gymnosperms (b), indicating that the standardization did not lead to variability-induced sensitivity differences between age cohorts.

Extended Data Fig. 6 Age grouping based on species-specific age distribution and longevity.

Examples of age grouping into young (Y), intermediate (I), and old (O) tree cohorts based on species-specific distribution for an angiosperm species (Quercus macrocarpa) (a) and a gymnosperm species (Pinus jeffreyi) (b) in North America. The dashed red lines indicate the first quartile for the cutoff age of young and intermediate cohort, and the dashed blue lines indicate the third quartile for the cutoff age of intermediate and old cohort. The exact cutoff ages are listed in Supplementary Table 12 and the maximum, mean, median, and minimum ages of each age cohort of angiosperm and gymnosperm are listed in Supplementary Table 13.

Extended Data Fig. 7 Time span of all individual tree series of young, intermediate, and old age cohorts after age grouping from species-specific age distribution.

The period between the two vertical dashed lines of each panel indicates the available period of global SPEI dataset (1901–2015) for drought responses analyses. The maximum, mean, median, and minimum ages of each age cohort of angiosperm and gymnosperm are listed in Supplementary Table 13. Note the x-axis scales are different in each panel.

Supplementary information

Supplementary Information

Supplementary Figs. 1–9, Tables 1–15 and Sensitivity Analysis.

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Au, T.F., Maxwell, J.T., Robeson, S.M. et al. Younger trees in the upper canopy are more sensitive but also more resilient to drought. Nat. Clim. Chang. 12, 1168–1174 (2022). https://doi.org/10.1038/s41558-022-01528-w

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