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

Tsun Fung Au, Justin Maxwell, Scott Robeson, Jinbao Li, Sacha Siani, Kimberly Novick, Matthew Dannenberg, Richard Phillips, Teng Li, Zhenju Chen, et al.

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## To cite this version:

Tsun Fung Au, Justin Maxwell, Scott Robeson, Jinbao Li, Sacha Siani, et al., Younger trees in the upper canopy are more sensitive but also more resilient to drought. Nature Climate Change, 2022, 12 (12), pp.1168-1174. 10.1038/s41558-022-01528-w. hal-03928624

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Submitted on 26 Oct 2023

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Younger trees in the upper canopy layer are more sensitive but also more

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resilient to drought: age matters

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Tsun Fung Au¹,²*, Justin T. Maxwell¹, Scott M. Robeson¹, Jinbao Li³, Sacha M. O. Siani¹, Kimberly

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Novick⁴, Matthew P. Dannenberg⁵, Richard Phillips⁶, Teng Li⁷, Zhenju Chen⁸, Jonathan Lenoir⁹

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¹ Department of Geography, Indiana University, Bloomington, IN, United States.

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² Institute for Global Change Biology, University of Michigan, MI, United States.

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³ Department of Geography, The University of Hong Kong, Hong Kong SAR, China.

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⁴ School of Public and Environmental Affairs, Indiana University, Bloomington, IN, United States.

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⁵ Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA, United States

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⁶ Department of Biology, Indiana University, Bloomington, IN, United States

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⁷ School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China

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⁸ College of Forestry, Shenyang Agricultural University, Shenyang 110866, China

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⁹ UMR CNRS 7058 ‘Ecologie et Dynamique des Systèmes Anthropisés’ (EDYSAN), Université de Picardie Jules Verne, Amiens, France

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*Corresponding author: tomau123@indiana.edu

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Abstract

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As forest demographics are altered by the global decline of old trees and subsequent reforestation, younger trees are expected to have an increasingly important influence on carbon sequestration and forest ecosystem functioning under more frequent drought conditions. 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, with angiosperms showing greater age differences than gymnosperms. The age-dependent sensitivity is more pronounced in humid climates compared to more arid regions.

However, younger canopy-dominant trees also recover more quickly from drought. The combination of increased drought events in the future together with an increased proportion of younger canopy-dominant trees suggests that droughts will have 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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Forests regulate global climate¹ and provide stable understory microclimates that promote biodiversity²,³. However, more frequent and intense drought can cause irreversible damage to plant hydraulics and induce forest dieback through concomitant abiotic and biotic stress⁴,⁵,⁶, which can reduce carbon sequestration and generate a positive feedback loop on the pace of climate change⁷,⁸,⁹. While previous work suggests that most species have similar vulnerability to drought¹⁰, substantial variability in drought responses have been shown for angiosperms and gymnosperms¹¹,¹². Drought can also alter the distribution of forest types, species composition, and ecosystem services⁴,¹³,¹⁴,¹⁵. For instance, widespread forest dieback can reduce the microclimatic buffering effect of canopy-dominant trees³, rapidly increasing subcanopy temperatures and their temporal rate of change with important consequences for forest biodiversity under climate change². While shifting forest species composition alters drought sensitivity of the forests¹⁶, warmer and drier conditions can hinder the ability of trees to return to pre-drought growth rates (i.e., drought resilience)¹⁷. Therefore, improved understanding of forest sensitivity and resilience to drought helps decrease the uncertainty in terrestrial carbon-cycle feedbacks.

Anthropogenic disturbances such as deforestation and selective logging, combined with climate-induced threats, have caused the decline of old canopy-dominant trees¹⁸,¹⁹ that sequester large amounts of carbon, while simultaneously buffering and stabilizing microclimates in the understory²,³,²⁰,²¹. Globally, subsequent reforestation, whether through natural succession or tree-planting²², has led and will further lead to forests that are dominated by a younger age structure¹⁹. The area covered by younger trees in the temperate biome (4 million km², < 140 years old) already far exceeds the area covered by older trees (2.2 million km²)¹⁹. Therefore, younger trees reaching the upper canopy layer, hereby referred as younger canopy-dominant trees, are an increasingly important component of forest ecosystems and functions, and it is critically important to understand the extent to which these less mature trees share the same resistance to climate extremes than older trees within the upper canopy layer. In response to drought, larger trees generally have greater decreases in growth than smaller trees²³. However, while taller or bigger trees are assumed to be older trees, tree size, either measured by height or diameter at breast height, does not necessarily indicate and reflect tree age, especially in harsh environments (e.g., cold and dry climates) or for populations located close to the species' range limits where individual trees of similar size from the same species could differ by several centuries in age²⁴. If there are clear links (i.e., allometric equations) to
relate tree height and tree diameter at breast height²⁵, the link between tree size (e.g., tree height) and tree age is much less evident, especially so when comparing trees from the upper canopy layer. Besides, tree age is an important variable, and probably more relevant than tree height, for understanding and addressing global change challenges due to the ability of older trees to have longer carbon residence times and more carbon storage, in the end, than fast-growing younger trees from the upper canopy layer²⁴,²⁶. The demographic shift towards a predominance of younger canopy-dominant trees and the ecological importance of older trees, necessitate a better understanding of age-dependent responses to drought stress within the upper canopy layer that provides a myriad microclimate in the understory. At the stand level, the relationship between stand age and drought responses has been examined for selected species and regions²⁷,²⁸, but mean stand age across several vegetation layers may dilute potential age impacts, making it impossible to separate within-stand variations across multiple species and higher taxa. Further, how the age of canopy-dominant trees impacts drought resilience is largely unknown, but critical to resolve for a holistic perspective on how shifting age distributions will affect forest growth and survival under climate change.

Here, we investigate drought responses across tree-age cohorts of canopy-dominant angiosperm and gymnosperm species by assessing: (1) drought sensitivity (i.e., the percentage of growth reduction between drought and non-drought conditions); (2) drought resistance (i.e., growth rate during drought compared to pre-drought levels); and (3) drought resilience (i.e., the capacity to resume growth to pre-drought levels) (Methods). Drought conditions were defined by the 3-month averaged standardized precipitation evapotranspiration index (SPEI), which accounts for site variations in both moisture supply and demand. We gathered tree-ring width data from 119 drought-sensitive species (38 angiosperm species and 81 gymnosperm species) across North and South America, Eurasia, Africa, and Oceania. We then classified 21,964 canopy-dominant individual trees into 3 age cohorts based on species-specific age distributions. The youngest and the oldest 25% of a given species' population were attributed to young and old canopy-dominant cohorts, respectively, while the remaining 50% (i.e., the interquartile) were attributed to the intermediate canopy-dominant cohort (Extended Data Fig. 1), allowing us to examine how tree age influences the drought responses and how that influence varies across tree taxa and biomes. We also ran supplementary analyses (Methods) using tree age as a continuous variable to examine drought-driven growth reduction instead of using a factor variable with three levels (young, intermediate, and old canopy-dominant cohorts).
103 **Results**

104 *Growth reduction in response to drought*

105 The younger cohort of canopy-dominant individuals for both angiosperms and gymnosperms
106 experienced significantly greater growth reductions in response to drought than the respective
107 older canopy-dominant cohort of trees. Under drought conditions, the youngest canopy-
108 dominant cohort of angiosperms showed a 28% reduction in growth, on average, whereas tree
109 growth in the intermediate and older canopy-dominant cohorts was only reduced by 25% and
110 20%, respectively (Fig. 1a, *P*<sub>young-inter</sub>, *P*<sub>young-old</sub> < 0.001). Drought also reduced the growth of
111 the youngest canopy-dominant cohort of gymnosperms by 27%, on average, and the response
112 is nearly identical to that of the intermediate cohort (0.6% greater; Fig. 1a, *P*<sub>young-inter</sub>= 0.227)
113 but 2.4% greater than that of the oldest cohort (Fig. 1a, *P*<sub>young-old</sub> < 0.001) of canopy-dominant
114 trees.

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116 To ensure that these results are robust regardless of age groupings, we used a linear mixed-
117 effects model based on continuous variation of age (i.e., using age as a continuous fixed effect:
118 see Methods) and found that younger canopy-dominant trees had a larger growth reduction to
119 drought (Extended Data Table 1, *P* < 0.001). We also found that tree age had an interactive
120 effect with tree taxa such that the growth was reduced by 3.6% for angiosperms but only by
121 0.2% for gymnosperms every 100 years (Extended Data Table 1, *P* < 0.001). Noteworthy, tree
122 height and tree age are poorly correlated within the subset of individual trees for which we had
123 data on both age and height (r = 0.21, n = 540). Thus, examining the effect of tree height alone
124 on drought-driven growth reduction may tell a totally different story than the effect of tree age,
125 especially so for canopy-dominant trees. We then employed linear mixed-effects models to
126 control for the potential confounding effects from tree height. Tree age was still the most
127 prominent variable to explain drought-driven growth reduction within the canopy-dominant
128 individual trees with both age and height data (Tables S1, S2 and S3). The growth was reduced
129 by 4.3% per one unit of standardized age while the growth reduction was increased by 0.7%
130 per one unit of standardized height (Table S3, *P*<sub>age</sub> < 0.01, *P*<sub>height</sub> = 0.72).

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132 For angiosperms, the difference in growth among canopy-dominant cohorts was the greatest
133 when moisture availability was the most limiting. Specifically, the youngest cohort of canopy-
134 dominant trees for angiosperms experienced 17% more growth reduction than the oldest
135 canopy-dominant cohort during extreme drought (SPEI < -3) (Fig. 1b, P < 0.001). Under
136 extremely wet conditions (SPEI > 3), the youngest cohort of canopy-dominant trees for
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angiosperms had only 1.9% higher radial growth than the oldest canopy-dominant cohort.
Gymnosperms experienced a wider range of SPEI (likely because they tend to inhabit more
arid environments), but the differences in radial growth among age cohorts of canopy-dominant
trees were minimal, even during extreme droughts. The youngest canopy-dominant cohort for
gymnosperms only experienced 2% and 3% greater growth reduction than the oldest canopy-
dominant cohort when SPEI went below -1.5 and -3, respectively (Fig. 1b, *P* < 0.1). The
nonlinear relationship between moisture availability and radial growth, particularly for young
canopy-dominant angiosperms, suggests that decreases in growth during increasingly extreme
dry years may not be sufficiently offset by increases in growth during increasingly extreme wet
years²⁹.

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Younger canopy-dominant trees were especially more vulnerable to drought-driven growth
reductions in Mediterranean, temperate, and alpine/boreal regions (*P* < 0.05), but not in deserts
and tropical regions (Fig. 2a). The average growth reduction of the youngest canopy-dominant
cohort for angiosperms in Mediterranean (38%), temperate (26%), and alpine/boreal (20%)
regions is 4%, 8%, and 12% higher than that of the oldest canopy-dominant cohort during
drought (Fig. 2b, *P* < 0.05). The youngest canopy-dominant cohort for gymnosperm showed
22%, 28%, and 25% growth reductions in those same regions, which is 3-4% higher than
growth reductions of the oldest canopy-dominant cohort (Fig. 2b, *P* < 0.05). In deserts, the
differences in drought response between the youngest and oldest canopy-dominant cohorts for
gymnosperms narrowed, with the youngest canopy-dominant cohort experiencing 32% growth
reduction, on average, compared to 29% growth reduction for the oldest canopy-dominant
cohort (Fig. 2b, *P* < 0.1). By contrast, in tropical regions, drought sensitivity was greater in the
oldest canopy-dominant cohort for both angiosperms and gymnosperms, being on average
5.7% and 4.4% more sensitive to drought than the youngest canopy-dominant cohort,
respectively (Fig. 2b, *P* < 0.05).
<figure><img src="image_3.png" /><figcaption>Fig. 1: Drought-induced growth reduction of younger canopy-dominant trees is more pronounced than for older canopy-dominant trees. a, Boxplots of drought-induced tree growth reduction as a percentage of difference between mean standardized growth during drought (standardized precipitation evapotranspiration index: SPEI ≤ -1.5) and mean growth during non-drought condition (SPEI > -1.5) for young (orange), intermediate (green), and old (blue) cohorts of canopy-dominant trees for both angiosperms (left panel) and gymnosperms (right panel). The numbers at the top of panels represent the *p*-values of pairwise differences in percentage of growth reduction between age cohorts that were identified by Tukey honest</figcaption></figure>
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significant differences. The numbers at the bottom of panels represent number of tree
individuals for the youngest, intermediate and oldest age cohort of canopy-dominant trees,
respectively. **b**, Standardized growth of angiosperms and gymnosperms with species-specific
age cohorts across moisture variability from dry (left) to wet (right) in terms of the SPEI from
generalized additive mixed-effects models. Each curve represents the mean response of each
age cohort with corresponding 95% confidence interval. The horizontal grey line (standardized
growth = 1) indicates mean standardized growth for individual trees, where standardized
growth above 1 indicates enhanced growth and below 1 indicates reduced growth. Numbers in
the lower right of panels indicate *R*² and *p*-values for both models. 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. Age cohorts were assigned
using species-specific age cutoffs (Extended Data Fig. 1 & Extended Data Table 2).

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**Fig. 2: Drought-induced growth reduction varies across biomes.** a, Geographical coverage and biomes of the tree-ring chronologies. Circle and square represent angiosperm (n = 502) and gymnosperm (n = 928) clusters, respectively. b, Percentage of growth reduction during drought events across age cohorts of canopy-dominant trees (Y: young, I: intermediate, O: old) in five different biomes: alpine/boreal forests; deserts; Mediterranean regions; temperate regions; and tropical regions. Biomes and tree taxa (angiosperms vs. gymnosperms) are arranged based on the growth reduction percentage of the youngest cohort where the youngest cohort in the left panel has the highest growth reduction. The numbers at the top of panel in 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 in b represent number of tree individuals for the youngest, intermediate, and oldest canopy-dominant cohorts, respectively. Age cohorts were assigned using species-specific age
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cutoffs. Note that sample size for angiosperm in the Alpine/Boreal is low and sampling in the tropics is under-represented, which may limit inference in these regions.

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### *Drought resistance and resilience*

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The youngest cohort of canopy-dominant trees for both angiosperms and gymnosperms was less drought-resistant than the respective oldest cohort of canopy-dominant trees, albeit the oldest canopy-dominant cohort of angiosperms was more drought-resistant than that of gymnosperms (Fig. 3a, 3b, *P* < 0.001). The growth of the youngest cohort of canopy-dominant trees was also more resilient in subsequent years after accounting for the growth reduction during drought (i.e., relative resilience). Relative to pre-drought growth rate, the youngest cohort of canopy-dominant angiosperms recovered by 29%, on average, whereas the oldest cohort only recovered 19% of their pre-drought growth rate, meaning that younger canopy-dominant trees of angiosperms are more resilient to drought (Fig. 3c, *P* < 0.001). The youngest cohort of canopy-dominant gymnosperms also had a higher mean relative resilience (27%) than that of the oldest cohort (24%) (Fig. 3d, *P* < 0.001).

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Although age differences in resistance and mean relative resilience were found in angiosperms, none of the angiosperms' cohorts of canopy-dominant trees returned to pre-drought growth levels after the first year (i.e., all had year-1 resilience index below 100%). The growth of angiosperms exceeded pre-drought levels (i.e., 100%) in the second year after drought, with the youngest cohort of canopy-dominant angiosperms generally having better growth-restoring capacity than the other two older cohorts (Fig. 3a). For gymnosperms, different age cohorts of canopy-dominant trees showed very similar resilience patterns after a drought occurred (Fig. 3b). The growth of all age cohorts during the first-year post-drought was nearly the same (on average) as the pre-drought growth, with the resilience index ranging from 99.5% to 100%. In years 2-4 after drought, all age cohorts for both angiosperms and gymnosperms had resilience indices greater than 100% (indicating growth above pre-drought levels). The youngest cohort of canopy-dominant angiosperms tended to have a higher resilience than older cohorts in years 2 and 4 (Fig. 3a) while there were no significant differences among the gymnosperm cohorts of canopy-dominant trees (Fig. 3b).

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In contrast, previous research on “drought legacies” (i.e., extended post-drought growth reductions and prolonged recoveries) showed that gymnosperms had more prevalent legacy effects³⁰. While both angiosperms and gymnosperms had a resilience index above 100% for

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several years after drought (Fig. 3a, 3b), age-dependent resiliency varied in some genera for both angiosperms and gymnosperms (Fig. 4). For gymnosperms, *Larix*, *Picea*, and *Tsuga* showed reduced resilience for all age cohorts in the first year after drought, and the growth was restored to pre-drought levels by the second year (Fig. 4a). Older trees of *Larix* and *Tsuga* showed even more growth reduction one year after a drought event than during the drought event (Fig. 4a). Nevertheless, the growth of *Juniperus* and *Taxodium* was completely restored for all age cohorts of canopy-dominant trees in the first year after drought (Fig. 4a). For angiosperms, reduced resilience in the first year after drought was shown for all canopy-dominant age cohorts of *Fagus*, *Liriodendron*, and *Acer* (Fig. 4b), suggesting that longer-term legacy effects may be present in some angiosperm genera¹⁶.

<figure><img src="image_6.png" /><figcaption>**Fig. 3:** Age-dependent drought resistance and relative resilience converge between tree taxonomic groups. **a**, **b**, Temporal trends in drought resistance (Year 0) and resilience (Year 1–4) for young (orange), intermediate (green), and old (blue) cohorts of canopy-dominant trees for both angiosperms (**a**) and gymnosperms (**b**) under non-drought conditions (SPEI > -1.5) with shaded 95% confidence interval. Year 0 indicates averaged resistance coefficients during</figcaption></figure>
drought and years 1, 2, 3, and 4 indicate the resilience coefficients after 1, 2, 3, and 4 years following the drought events, relative to the pre-drought growth rate. The horizontal line at 100% indicates the mean pre-drought growth level. The coefficients that are below the line indicate growth reduction while the coefficients that are above the line indicate growth enhancement compared to pre-drought conditions. c, d, Mean drought relative resilience for young (orange), intermediate (green), and old (blue) cohorts of angiosperms (c) and gymnosperms (d) with 95% confidence interval. The relative resilience is averaged from year 1 to 4 after drought and accounted for the weighting of drought impacts on growth. A higher mean relative resilience value indicates post-drought growth enhancement and recovery from drought-induced reduction relative to the pre-drought growth rate. Age cohorts are assigned based on species-specific age distribution (Extended Data Fig. 1, Extended Data Table 2).

<figure><img src="image_7.png" /><figcaption>**Fig. 4: Variations of drought resistance and resilience in major tree genera.** a, Temporal trends in drought resistance (Year 0) and resilience (Year 1–4) relative to non-drought conditions, with shaded 95% confidence interval, for young (orange), intermediate (green), and old (blue) cohorts of canopy-dominant trees for 8 different genera (*Juniperus*, *Taxodium*, *Abies*, *Larix*, *Picea*, *Pinus*, *Pseudotsuga*, *Tsuga*) belonging to 91% of gymnosperm samples and b, 8 different genera (*Betula*, *Fagus*, *Quercus*, *Carya*, *Juglans*, *Liriodendron*, *Nothofagus*, *Acer*) belonging to 97% of angiosperm samples. Year 0 indicates averaged resistance coefficient during the drought year while year 1, 2, 3, and 4 indicate the resilience coefficient 1, 2, 3, and 4 years respectively after drought happens. The horizontal line at 100% indicates growth</figcaption></figure>
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condition relative to the pre-drought level. The coefficients that are below the line indicate

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growth reduction while the coefficients that are above the line indicate growth enhancement

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compared to pre-drought condition. Age cohorts are assigned based on species-specific age

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distribution.

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Discussion

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Overall, younger canopy-dominant angiosperms have greater growth reduction during drought but recover faster than older ones. Gymnosperms have a less prominent age-dependency in drought sensitivity, but growth recovery is also faster for younger canopy-dominant gymnosperms after drought. While our results appear to contradict some former studies²³, a unique feature of this analysis is its focus on canopy-dominant trees, which reduces the confounding influence of effects from tree height and size in our analysis. Several causal mechanisms likely underlie the age and taxonomic differences in drought resistance and resilience. The differences in drought sensitivity across age cohorts of canopy-dominant trees could reflect the development of more extensive root systems as trees grow older, allowing better access to subsurface water³¹,³² and thus buffering the immediate impacts of most droughts³³. Younger canopy-dominant trees have shallower and less dense root systems that limit water uptake to sustain tree growth, making them more susceptible to the immediate impacts of droughts, even the less perceptible ones³⁴. The mean rooting depth for *Quercus* (5.23m) is deeper than *Pinus* (2.45m)³⁵ and the differences in rooting depth could lead to higher overall drought resistance for oaks (Fig. 4). Mature, older canopy-dominant trees with more extensive canopy cover and greater transpiration efficiency, through access to water in the deep soil layers, can also better regulate and stabilize the understory microclimate²,³,³², potentially reducing the drought severity and buffering the water demand of the subcanopy trees and herbs.

Transpiration of the younger canopy-dominant trees could be less efficient at maintaining a sufficient buffering capacity from drought²,³, resulting in an increase of the drought severity experienced and higher water demand for the subcanopy. Gymnosperms tend to be more isohydric, closing their stomates more quickly during drought to prevent dehydration and hydraulic damage. In contrast, angiosperms are more anisohydric, keeping stomates open for longer periods during droughts and allowing more stable gas exchange, transpiration, and photosynthesis¹⁰,³⁶. Among the angiosperms, the more isohydric genera (e.g., *Liriodendron* and *Acer*) were less resistant to drought than the more anisohydric genus *Quercus*¹⁶ (Fig. 4b). Altogether, mature angiosperms could access deeper water reserves, have a better buffering capacity to maintain stable microclimate for a longer time period, and have a higher carbon assimilation during drought³⁷, allowing them to have less growth reduction and, thereby, be more drought tolerant.

The youngest canopy-dominant angiosperms showed greater capacity to restore growth once favorable water status was returned even though angiosperms, using a more anisohydric
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strategy, tend to be more susceptible to xylem embolism¹⁰. Such plastic responses may be due
to higher availability of parenchyma to allocate nonstructural carbohydrates for repairing
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xanthophylls, α-tocopherol, and ascorbate were reported in young individuals of an oak species
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The age-dependent drought sensitivity is common and widespread across biomes and tree taxa, potentially with large implications for the global carbon cycle. For example, the infamous European drought event in 2003 reduced gross primary productivity by 30% (similar to the mean growth reduction of the youngest canopy-dominant cohort in temperate forests, 28%, Fig. 2b) and temporarily converted the ecosystem into a net carbon source by releasing 0.5 Peta-gram carbon yr⁻¹ into the atmosphere, which is equivalent to four years of net carbon storage in European ecosystems⁷. Thus, the substantially lower growth reduction of older canopy-dominant trees during drought, even on average of only 7-8% less compared to younger canopy-dominant trees, when taken at the *global* scale, could have huge impacts to the regional carbon storage and the global carbon budget, particularly in temperate forests that currently are among the largest carbon sinks worldwide³⁹. During extreme drought, such impacts of age-dependent sensitivity on carbon cycle are magnified with older angiosperms having 17% less growth reduction (Fig. 1b). In alpine/boreal regions experiencing the greatest magnitude of climate warming, drought could also cause more pronounced impacts on younger canopy-dominant angiosperms than older ones, with important consequences for carbon sequestration and climate feedback loops in these ecosystems²⁸.

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Drought sensitivity also varies across biomes, with growth in humid biomes being less sensitive than in arid regions but with more age-dependent differences. Drought conditions defined by low SPEI values indicate the water balance of a given site is lower than usual, but the water balance may still be favorable for tree growth in humid regions, even when SPEI values are low⁴⁰. Trees growing in arid regions such as deserts could have been evolutionarily adapted to the xeric environments by having more rapid responses. For example, increased vapor pressure deficit in xeric environments like deserts may cause increased stomatal sensitivity of older canopy-dominant gymnosperms and limit evapotranspiration with less local regulation of microclimate³⁴,³⁶,⁴⁰, leading to similar growth reduction of all age cohorts among canopy-dominant trees. Taller and older trees in the tropics were also less sensitive to precipitation

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variability³², but radial growth data from tropical regions are currently under-represented due
to the long-held perception that indistinct seasonality prohibits tree-ring formation in tropical
regions⁴¹, which inhibits robust interpretation of age-dependent drought sensitivity here.

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Many other organisms exhibit age-dependent responses to external stress, and age-dependent drought responses, after accounting for height-dependent responses, may be much more widespread among angiosperms than previously known. Given that angiosperms are more abundant and diverse than gymnosperms in most biomes and forest stands are increasingly dominated by younger trees in the upper canopy layer, the short-term impacts of drought on the terrestrial carbon sink and the buffering capacity of the upper canopy may be more pronounced. Over the long term, younger canopy-dominant angiosperms are more resilient to droughts. In a warmer and dryer future with higher risks of prolonged droughts, angiosperms might be better prepared for drought¹² and therefore predominate the forests. As a result, plant traits, plant functional types, and ecosystem functioning of the forests could shift in the future, which would influence the predictions from dynamic global vegetation models. Recent advances in carbon cycle simulations allow inclusion of tree age but do not necessarily represent age-dependent sensitivity to climate extremes⁴². Considering tree age along with species composition and tree height could help improve the simulation of carbon cycle feedbacks. While reforestation with native tree species is beneficial, it takes considerable time for young trees to attain maturity, reach the upper canopy layer and gain the associated resistance to drought stress reported here. Thus, from a climate mitigation perspective, conservation priorities should still focus on preserving existing older canopy-dominant trees for their exceptional carbon residence and storage capacity²⁶,³⁹ so that the forests could have a diverse structure and composition to withstand and mitigate future emerging climate extremes¹¹.

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## Methods

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### *Tree growth data*

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We accessed individual tree-ring width measurements of canopy-dominant trees from the International Tree-Ring Data Bank (ITRDB), the DendroEcological Network (DEN)⁴³, and previous publications¹⁶,⁴⁴,⁴⁵. The ITRDB consists predominantly of canopy-dominant trees for climate reconstruction⁴¹ such that the size and height of trees sampled at a given site are usually similar. Yet, substantial variations in individual tree ages are still observed for the canopy-dominant trees from the ITRDB (Extended Data Fig. 1, Extended Data Fig. 2, Extended Data Fig. 3, Extended Data Table 2, Extended Data Table 3). We added the DEN database to our

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analyses in order to have, for the same individual canopy-dominant tree, information on both tree age and tree diameter to test and control for the potential confounding effects of tree height, which was obtained by feeding allometric equations with tree diameter data (see below), on drought responses. We analyzed the impacts of tree age on drought-induced growth reduction, resistance, and resilience. Both site chronologies and tree ring time series that ended before the 1940s (i.e., the outermost ring) were not considered to ensure sufficient overlap with the observational records of the standardized precipitation evapotranspiration index (SPEI, i.e., from 1901 to 2015 see below) and instances of drought and wet conditions (Extended Data Fig. 2).

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

To allow meaningful age-dependent drought sensitivity, resistance, and resilience analysis, we first standardized individual tree-ring time series with a two-thirds smoothing spline and then computed site chronologies using the Tukey's biweight in the *dplR* package in R<sup>46</sup>. Further, this standardization procedure removes any potential confounding effects with tree aging. Removing these low frequency signals associated with long-term biological growth trends and forest dynamics allows fair comparisons of drought sensitivity and responses between tree individuals with different ages (e.g., the known decay of ring width when trees are aging as well as the suppression and release for shade-tolerant species)<sup>16,46,47,48,49</sup>.

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

Given that temperature is typically the most limiting and sometimes the only factor affecting tree radial growth at high latitudes<sup>40,49,50</sup>, most of our study sites are situated between 60°N and 60°S where tree growth typically is more sensitive to soil moisture rather than to temperature. We then selected 1430 sites from both the ITRDB and DEN database that had a positive relationship (Extended Data Fig. 4) between standardized growth of site chronology and hydroclimate variability (SPEI03, see below). From those 1430 sites, we then only considered individual tree-ring time series with positive relationships (Pearson's correlation coefficient *r* > 0.1) to hydroclimate variability to avoid potential sampling error. Ultimately, we standardized the raw ring-width measurements of 21,964 individual trees with the same method as stated above (i.e., two-thirds smoothing spline). The tree-growth data consisted of 81 gymnosperm species and 38 angiosperm species (119 species in total) from 32 genera that inhabit diverse biomes from tropical to boreal forests.

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We used the Olson et al.⁵¹ biome classification, which classified the land surface into 14 distinct biomes: (1) tropical moist broadleaf forests; (2) tropical dry broadleaf forests; (3) tropical coniferous forests; (4) temperate broadleaf & mixed forests; (5) temperate conifer forests; (6) boreal forests/taiga; (7) tropical grasslands, savannas & shrublands; (8) temperate grasslands, savannas & shrublands; (9) flooded grasslands & savannas; (10) montane grasslands & shrublands; (11) tundra; (12) Mediterranean forests, woodlands and scrubs; (13) deserts & xeric shrublands; and (14) rock & ice.

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Using geographic coordinates of each tree-ring site from the ITRDB and DEN metadata, we extracted the biomes that each tree-ring site fell within and grouped them into 5 major biomes (Fig. 2A): (i) alpine/boreal (6, 10, 11 and 14); (ii) deserts (13); (iii) Mediterranean (12); (iv) temperate forests (4, 5 and 8); and (v) tropical forests (1, 2, 3 and 7). No trees were sampled in flooded grasslands & savannas (9).

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## Age estimation

All tree-ring data that were submitted to the ITRDB and DEN database have already been cross-dated and should therefore have accurate dates assigned for each ring width⁴¹,⁵². We also scrutinized the cross-dating quality of all individual tree-ring time series by removing poorly cross-dated individuals (e.g., individuals with negative inter-series or segment correlation). Therefore, the resulting tree core data from the ITRDB and DEN database provide reliable dating of individual growth years, from which we estimated tree-age. Most of our study sites were used and published for drought reconstructions at continental scales⁵³,⁵⁴,⁵⁵,⁵⁶,⁵⁷,⁵⁸.

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Researchers attempt to sample tree cores as close to the pith as possible within each of these study sites in order to maximize the time span of the drought reconstruction⁵⁹. Although this sampling approach may produce bias towards more mature canopy-dominant trees⁴¹, there are still substantial variations in individual tree ages for multiple tree species (Extended Data Fig. 1, Extended Data Fig. 2, Extended Data Fig. 3, Extended Data Table 2, Extended Data Table 3).

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To estimate tree age, we summed the number of total rings for each tree individual. Although a minor underestimation of the true tree age is still expected because: samples were likely taken at or near breast height; some samples may miss the pith; and some trees may have a rotten heart⁶⁰,⁶¹. To minimize the underestimation of tree age due to the above reasons, we used the tree core with the most total rings for individuals which have more than one core, allowing us
to have "maximum age" for every tree individual. As such, this underestimation of tree age is
likely the same for all individual trees and thus it should have no directional impact on the main
findings. We also summed the ring widths and multiplied by two to estimate tree size (diameter)
for all individuals. The correlations between tree age and diameter are weak, with values
ranging between 0.37 (angiosperms) and 0.34 (gymnosperms), suggesting that our analysis
using tree age is not just acting as a proxy for tree size but reflects a very different demographic
parameter than tree size.

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460 *Age group classification*

461 Arbitrary age groupings were commonly adopted in previous research to study age-dependent
462 tree growth responses⁶²,⁶³. However, such approaches may ignore species longevity and the
463 age distribution within a population of canopy-dominant trees. Alternatively, previous research
464 also used the mean stand age from canopy-dominant trees as another common
465 approach¹⁶,²⁷,⁶⁴,⁶⁵,⁶⁶, but if a forest stand has a diverse age distribution and structure, mean stand
466 age may also dilute potential age impacts and variations within a stand or across landscape
467 (Extended Data Fig. 5). In practice, calculating mean stand age may also consistently
468 underestimate or overestimate the stand age when exceptionally young or old tree individuals
469 were sampled. Therefore, both arbitrary age groupings and mean stand age are not very
470 accurate approaches for comparing drought responses of multiple tree species across large
471 spatial scales. Instead, *relative age* (i.e., individual tree age relative to longevity and age
472 distribution of the focal species) using a rank-based grouping is more appropriate for
473 comparing interspecific age-dependent responses.
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475 To avoid species-specific longevity and sampling biases influencing our relative age
476 classification, we classified all canopy-dominant tree individuals for a given species into three
477 age cohorts based on the ranking of individual trees along the age distribution of the selected
478 ITRDB and DEN dataset. Tree individuals whose ages fell below the first quartile of the age
479 distribution of the focal species were classified into the younger age cohort, those between the
480 first and the third quartile (i.e., interquartile) were classified as the intermediate age cohort and
481 those above the third quartile were classified as the older age cohort (Extended Data Fig. 1).
482 The maximum, mean, median, and minimum age of each age cohort of angiosperm and
483 gymnosperm are listed in Extended Data Table 3. McDowell et al.¹⁹ defined young trees as
484 being less than 140 years old. Our classification results in a similar definition for young trees,
485 where the mean ages of the youngest cohort for both angiosperm and gymnosperm, that were
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inferred from species-specific age distribution, are also less than 140 years old (Extended Data Table 3). Therefore, our age cohort method provides a precise classification with the flexibility to adjust for species-specific longevity and age-distribution, allowing more accurate interpretations of implications in the context of global demographic shifts in age structure.

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To ensure that the spline-based standardization process did not induce more variability in any of the studied age cohorts (e.g., having more variability within the young age cohort due to the use of a more flexible spline model), we examined empirical probability distributions and estimated standard deviations of standardized ring widths (SRW) for each age cohort of both angiosperms and gymnosperms. The probability distributions and standard deviations between age cohorts were very similar within tree taxa (i.e., angiosperms and gymnosperms, Extended Data Fig. 6), indicating that the standardization did not lead to variability-induced sensitivity differences between age cohorts.

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## Drought data

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To evaluate drought severity and its impacts on tree growth, we used the standardized precipitation evapotranspiration index (SPEI), a measure of the standardized difference between water supply (precipitation) and water demand (potential evapotranspiration)⁶⁷. As a result, SPEI is an ecologically relevant way to control for drought severity across locations and biomes with different baseline macroclimates and thus appropriate for determining the age-dependent drought responses at global scale, with negative and positive SPEI values indicating drier and wetter than usual conditions, respectively¹²,¹⁶,²⁷,⁴⁰,⁶⁵. We accessed the global SPEI dataset on the 1st February 2021 (https://digital.csic.es/handle/10261/202305), using version 2.6 which provides SPEI data at 0.5° spatial resolution globally from 1901 to 2015 on a monthly basis.

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Given that annual radial growth typically takes at least three months to complete at most of our sites (i.e., temperate forests)¹⁶,²⁷, we evaluated all possible 3-month integrations of SPEI (i.e., SPEI03 where, for instance, month-tag “August” represents June-July-August). To allow for variations of drought timing across species and landscapes, we selected the SPEI03 period with the highest correlation to site SRW for each of the 1430 sites (Extended Data Fig. 4). The month-tags of the selected SPEI03 range from January to August for the Northern Hemisphere and October from the preceding year to April for the Southern Hemisphere. We then defined

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drought (SPEI03 ≤ -1.5), non-drought (SPEI03 > -1.5), and normal (-1.5 < SPEI03 < 1.5) conditions at each site for subsequent analyses²⁷.

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To ensure that larger variability of SPEI did not induce higher drought sensitivity in any of the three age cohorts of canopy-dominant trees, we examined empirical probability distributions and estimated standard deviations of SPEI for each age cohort of canopy-dominant trees for both angiosperms and gymnosperms. The probability distributions and standard deviations of SPEI between age cohorts are very similar within angiosperms and gymnosperms (Extended Data Fig. 7), indicating that each age cohort experienced similar interannual moisture variability across different locations.

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### Drought sensitivity, resistance, and resilience

We calculated the percentage of growth reduction (PGR) for every individual tree during drought using the differences of the means of standardized radial growth width (SRW) between drought years and non-drought years (i.e., $\overline{\text{SRW}_{\text{drought}}} - \overline{\text{SRW}_{\text{non-drought}}}$)¹⁶:

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$$PGR = \frac{\overline{\text{SRW}_{\text{drought}}} - \overline{\text{SRW}_{\text{non-drought}}}}{\overline{\text{SRW}_{\text{non-drought}}} \times 100}$$

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We also repeated the same procedure with the normal conditions (-1.5 < SPEI03 < 1.5) as the baseline (see sensitivity analysis in Supplementary Information, shown in Fig. S1). Prior to calculating PGR, we set any SRW values larger than 5 or smaller than 0.01 to missing (NA, 0.004% and 0.37% of total observations, respectively) because these SRW values are likely due to measurement errors or non-drought-related missing rings.

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To evaluate the continuous response of tree growth to moisture variability (as measured by SPEI03) across each age cohort, we used a generalized additive mixed-effects model to show and account for the non-linear nature of tree-growth response to moisture variability while controlling for the pseudo-replication in the data due to multiple tree-ring measurements from the same given tree within the same given site (i.e., using both individual tree ID and site ID as random intercept terms in the model: see model formula below). Specifically, we used the *bam* function in the *mgcv* package in R⁶⁸ to predict SRW as a function of age-specific drought
responses for both angiosperms and gymnosperms. We ran two separate models for angiosperms and gymnosperms with the same formula:

$$SRW = f(s(SPEI03_{AgeCohort}) + s(Site_{random}) + s(Individual_{random}))$$

where smooth terms (s) include SPEI03 during months specific to each age cohort at a given site and with random effects for each site and tree individual. The SPEI03 effects on SRW were estimated using three-knot thin plate regression splines with the fast restricted maximum likelihood (fREML) method, which allows flexible responses that vary by age cohort while also avoiding overfitting and undersmoothing⁶⁸,⁶⁹. The regression model was performed on Indiana University's large-memory computer cluster “Carbonate”. The summary output of the regression models for both angiosperms and gymnosperms, separately, is listed in Extended Data Table 4.

To compare how different age cohorts responded during and after a drought event, we also calculated both drought resistance and resilience⁷⁰. We first averaged SRW values five years prior to a drought event (SRW<sub>pre1-5</sub>) to establish a baseline on how an individual behaves before a drought⁷⁰. Drought resistance is the ratio between SRW during drought (SRW<sub>d</sub>) and mean pre-drought SRW (SRW<sub>pre1-5</sub>), therefore representing an individual tree's capacity to endure and function under drought stress:

$$Resistance = \frac{SRW_d}{SRW_{pre1-5}}$$

We then defined drought resilience as the ratio between the SRW in each of the four years after drought (SRW<sub>post1-4</sub>) and SRW<sub>pre1-5</sub>, representing an individual tree's capacity to restore growth rates to the level observed before drought:

$$Resilience = \frac{SRW_{post1-4}}{SRW_{pre1-5}}$$

We calculated resilience for each of the four years following drought⁷⁰ since drought legacies can extend up to four years³⁰. We averaged the four consecutive years after a drought event to
get a mean resilience for each age cohort. We also calculated relative resilience to account for the differences in magnitude of drought resistance between age cohorts<sup>70,71</sup>:

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$$ \text{Relative Resilience} = \text{Resilience} - \text{Resistance} = \frac{\text{SRW}_{\text{post1-4}} - \text{SRW}_{\text{d}}}{\text{SRW}_{\text{pre1-5}}} $$

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We reported the drought resistance, resilience, and relative resilience as a percentage of growth reduction and recovery rate relative to pre-drought growth rate. Drought resistance or resilience greater than 100% means that trees have fully recovered to pre-drought growth rates, while values below 100% indicate that growth remains below pre-drought levels<sup>70,72</sup>. Relative resilience greater than 0 means that trees have recovered from drought with more positive values indicating better recovery, while values below 0 represent that post-drought growth is lower than the growth during drought.

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For all resistance, resilience, and relative resilience analyses, we considered individual trees that have both five consecutive years before a drought event and four consecutive years after a drought event. We also only considered single-year drought events instead of consecutive droughts in both resistance and resilience calculation to avoid including drought effects in either the pre-drought baseline or post-drought recovery estimates. Given that drought legacies can extend up to four years<sup>30</sup>, we defined consecutive droughts as drought events that happened within three years before or after a given drought event. Drought resistance and resilience were considered under two scenarios (i.e., non-drought and normal conditions, see sensitivity analysis in Supplementary Information) to show how water availability before and after a drought event may impact resistance and resilience across age cohorts. Non-drought conditions excluded any drought but included exceptionally wet events (i.e., SPEI03 > 1.5) within three years before or after a given drought event. Normal conditions excluded both drought and exceptionally wet events within three years before or after a given drought event. After considering the above criteria, 21,205 (7810 angiosperms and 13,395 gymnosperms) and 19,482 (7266 angiosperms and 12,216 gymnosperms) tree individuals under non-drought and normal conditions scenarios, respectively were retained for the resistance and resilience analyses. For the tree individuals that experienced multiple single-drought events over their lifetimes, resistance and resilience indices were averaged for that individual.

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We used one-way analysis of variance (ANOVA) and Tukey honest significant differences to examine if the mean percentage growth reduction, resistance, and relative resilience differed between young, intermediate, and old age cohorts within each of the two tree taxonomic groups (angiosperms vs. gymnosperms). We also evaluated the robustness of our findings by repeating the same analyses using log-transformed resistance and relative resilience indices (to make the distribution more symmetric by natural logarithm). We then compared the mean of log-transformed resistance and relative resilience between age cohorts within angiosperms and gymnosperms by ANOVA and Tukey honest significant differences. The results of log-transformed resistance and relative resilience were consistent with the untransformed data for non-drought conditions and normal conditions (Extended Data Table 5).

## Linear Mixed Models

We fitted a linear mixed-effects model to examine the impacts of individual tree age as a continuous variable on the percentage of growth reduction (PGR, Fig. 1) with interaction between tree age and tree taxa using the *lme4* package in R⁷³. The linear mixed-effects model controls for the pseudo-replication in the data due to multiple observation from the same given species within the same given site (i.e., using both species ID and site ID as random intercept terms in the model: see model formula below).

$$PGR = A + \beta(Age \times Taxa) + \varepsilon(Site) + \varepsilon(Species)$$

where PGR is the percentage of growth reduction, A is overall intercept, β is the slope for the fixed effects, ε are the random effects for sites and species.

## Examination of potential confounding effects from tree height

To account for potential confounding effect from tree height, we obtained an additional 29 sites from DendroEcological Network with both raw tree-ring width and tree diameter at breast height (DBH) measurement⁴³, which allows us to retrieve a subset data consisting of 540 individual trees from eight species (five angiosperms, three gymnosperms). We then used Tallo, a global tree allometric collection with both tree DBH and height data of nearly 500,000 individual trees from over 5,000 species, to infer the relationship between log-transformed tree height and DBH for those eight species with the following equation²⁵. We then calculated the individual tree height from corresponding DBH measurement within the subset data based on the species-specific height-diameter relationship.
$$ \log(Height) = \log(DBH) $$

The tree age and tree height are poorly correlated ($r$ = 0.21, $n$ = 540), indicating these two variables are not redundant and can be included simultaneously as predictor variables in the same multiple regression model. We first standardized the tree age and tree height by using the `gscale` function provided in the `jtools` package<sup>74</sup> and then employed the following candidate linear mixed-effects models to examine the best variable for explaining the percentage of growth reduction (PGR). We used Akaike information criterion (AIC) to compare the fit of the following models and selected the best candidate model with the lowest AIC scores (i.e., the best model). The first two best models with delta AIC values less than two were reported in the Table S2 and S3. The AIC ranking for the candidate models was done by `aictab` function in `AICcmodavg` package<sup>75</sup>. The candidate models and corresponding abbreviation are as below:

$$ \text{PGR} = A + \beta(\text{Age}) + \varepsilon(\text{Site}) + \varepsilon(\text{Species}) \qquad \text{A0 (Table S6)} $$

$$ \text{PGR} = A + \beta(\text{Age}) + \beta(\text{Taxa}) + \varepsilon(\text{Site}) + \varepsilon(\text{Species}) \qquad \text{A1 (Table S4)} $$

$$ \text{PGR} = A + \beta(\text{Age} \times \text{Taxa}) + \varepsilon(\text{Site}) + \varepsilon(\text{Species}) \qquad \text{A2 (Table S2)} $$

$$ \text{PGR} = A + \beta(\text{Height}) + \varepsilon(\text{Site}) + \varepsilon(\text{Species}) \qquad \text{H0 (Table S10)} $$

$$ \text{PGR} = A + \beta(\text{Height}) + \beta(\text{Taxa}) + \varepsilon(\text{Site}) + \varepsilon(\text{Species}) \qquad \text{H1 (Table S8)} $$

$$ \text{PGR} = A + \beta(\text{Height} \times \text{Taxa}) + \varepsilon(\text{Site}) + \varepsilon(\text{Species}) \qquad \text{H2 (Table S7)} $$

$$ \text{PGR} = A + \beta(\text{Age}) + \beta(\text{Height}) + \varepsilon(\text{Site}) + \varepsilon(\text{Species}) \qquad \text{AH (Table S5)} $$

$$ \text{PGR} = A + \beta(\text{Taxa}) + \varepsilon(\text{Site}) + \varepsilon(\text{Species}) \qquad \text{T0 (Table S9)} $$

$$ \text{PGR} = A + \beta(\text{Age}) + \beta(\text{Height}) + \beta(\text{Taxa}) + \varepsilon(\text{Site}) + \varepsilon(\text{Species}) \qquad \text{All (Table S3)} $$

The best model and the next-best model with less than two delta AIC units were A2 (AIC: 4030.94) and All (AIC: 4032.59), which together carried 80% of the cumulative model weight (Table S1). The models including tree height but excluding tree age as a parameter (i.e., H0, H1, and H2) only contained 1% of the cumulative model weight (Table S1). For the model A2, the growth was reduced by 5.3% per one unit of standardized age (Table S2, $P < 0.01$) while the interaction effect between age and tree taxa is weak probably due to under-representation of gymnosperms, leading to insufficient statistical power. For the model All, the growth was
669 reduced by 4.3% per one unit of standardized age (Table S3, *P* < 0.01) while the growth

670 reduction was increased by 0.7% per one unit of standardized height (Table S3, *P* = 0.72).

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**Extended Data Fig. 1:** Examples of age grouping into young (Y), intermediate (I), and old (O) tree cohorts based on species-specific distribution for (a) an angiosperm species (*Quercus macrocarpa*) and (b) a gymnosperm species (*Pinus jeffreyi*) 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 Extended Data Table 2 and the maximum, mean, median, and minimum ages of each age cohort of angiosperm and gymnosperm are listed in Extended Data Table 3.
<figure><img src="image_25.png" /><figcaption>**Extended Data Fig. 2:** 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 Extended Data Table 3. Note the x-axis scales are different in each panel.</figcaption></figure>

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<figure><img src="image_26.png" /><figcaption>**Extended Data Fig. 3:** 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*. The exact cutoff ages for *L. tulipifera* are listed in Extended Data Table 2.</figcaption></figure>

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<figure><img src="image_27.png" /><figcaption>**Extended Data Fig. 4:** Distribution of correlation coefficients between site-optimized 3-month SPEI and site chronologies for (a) angiosperms and (b) gymnosperms. Numbers in the upper right and the parentheses indicate mean correlation and total number of sites, respectively.</figcaption></figure>

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**Extended Data Fig. 5:** Comparison of *Quercus alba* drought responses at 18 sites between (a) composite chronology with mean stand age and (b) individual series with individual tree age approach using the same dataset as in Au et al. 2020¹⁶. 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 information at the stand level for age-dependent drought responses.

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<figure><img src="image_29.png" /><figcaption>**Extended Data Fig. 6:** Empirical probability densities of standardized radial growth (SRW) across young (orange), intermediate (green), and old (blue) cohorts for the (a) angiosperms and (b) gymnosperms, separately. Numbers in the panels indicate standard deviations of the young (Y), intermediate (I), and old (O) age cohort, separately for angiosperms and gymnosperms.</figcaption></figure>

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<figure><img src="image_30.png" /><figcaption>**Extended Data Fig. 7:** Empirical probability densities of the Standardized Precipitation-Evapotranspiration Index (SPEI) across young (orange), intermediate (green), and old (blue) cohorts for (a) angiosperms and (b) gymnosperms, separately. Numbers in the panels indicate standard deviations for the young (Y), intermediate (I), and old (O) age cohort, separately for angiosperms and gymnosperms.</figcaption></figure>

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731 **Extended Data Table 1:** Fixed effect estimates, standard error (SE), and degree of freedom (df) of the linear mixed model for predicting percentage of growth reduction in response to individual tree age.

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<table><thead><tr><th>Variables</th><th>Estimate ± SE</th><th>df</th><th>t value</th><th>p value</th></tr></thead><tbody><tr><td>Intercept</td><td>-27.85 ± 1.18</td><td>120</td><td>-23.68</td><td>&lt; 0.001</td></tr><tr><td>Age</td><td>0.037 ± 0.003</td><td>21570</td><td>14.05</td><td>&lt; 0.001</td></tr><tr><td>Taxa Gymnosperm</td><td>3.50 ± 1.42</td><td>123.8</td><td>2.47</td><td>0.0151</td></tr><tr><td>Age: Taxa Gymnosperm</td><td>-0.033 ± 0.003</td><td>21500</td><td>-11.90</td><td>&lt; 0.001</td></tr></tbody></table>

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<table><caption>**Extended Data Table 2:** Age cutoffs for 119 species of tree taxa angiosperm (A) and gymnosperm (G) based on species-specific age distributions that separate young, intermediate and old age cohorts. Q1 indicates the cutoff age of the first quartile while Q3 indicates the cutoff age of the third quartile. Min and Max indicate the minimum and maximum age of each canopy-dominant species within the dataset. Species codes PISP, JUSP, PCSP represent miscellaneous or unknown species from the *Pinus*, *Juniperus* and *Picea* genera, respectively.</caption><thead><tr><th>Species Code</th><th>Latin Name</th><th>Genus</th><th>Family</th><th>Taxa</th><th>Min</th><th>Q1</th><th>Q3</th><th>Max</th></tr></thead><tbody><tr><td>ABAL</td><td>Abies alba</td><td>Abies</td><td>Pinaceae</td><td>G</td><td>47</td><td>97.75</td><td>166.25</td><td>423</td></tr><tr><td>ABBO</td><td>Abies borisii-regis</td><td>Abies</td><td>Pinaceae</td><td>G</td><td>106</td><td>136</td><td>169</td><td>170</td></tr><tr><td>ABCE</td><td>Abies cephalonica</td><td>Abies</td><td>Pinaceae</td><td>G</td><td>61</td><td>85.75</td><td>133</td><td>303</td></tr><tr><td>ABCI</td><td>Abies cilicica</td><td>Abies</td><td>Pinaceae</td><td>G</td><td>93</td><td>105.5</td><td>202.5</td><td>280</td></tr><tr><td>ABFO</td><td>Abies forestii</td><td>Abies</td><td>Pinaceae</td><td>G</td><td>95</td><td>176</td><td>402.75</td><td>626</td></tr><tr><td>ABPI</td><td>Abies pindrow</td><td>Abies</td><td>Pinaceae</td><td>G</td><td>100</td><td>161.75</td><td>262.75</td><td>373</td></tr><tr><td>ABSB</td><td>Abies spectabilis</td><td>Abies</td><td>Pinaceae</td><td>G</td><td>39</td><td>99</td><td>249.5</td><td>396</td></tr><tr><td>ACRU</td><td>Acer rubrum</td><td>Acer</td><td>Sapindaceae</td><td>A</td><td>65</td><td>85</td><td>99</td><td>111</td></tr><tr><td>ACSH</td><td>Acer saccharum</td><td>Acer</td><td>Sapindaceae</td><td>A</td><td>47</td><td>89.75</td><td>127.5</td><td>207</td></tr><tr><td>ADUS</td><td>Adesmia uspallatensis</td><td>Adesmia</td><td>Fabaceae</td><td>A</td><td>69</td><td>89</td><td>129</td><td>149</td></tr><tr><td>AGAU</td><td>Agathis australis</td><td>Agathis</td><td>Araucariaceae</td><td>G</td><td>67</td><td>147.25</td><td>258.75</td><td>499</td></tr><tr><td>ARAR</td><td>Araucaria araucana</td><td>Araucaria</td><td>Araucariaceae</td><td>G</td><td>78</td><td>192</td><td>305.5</td><td>766</td></tr><tr><td>ATCU</td><td>Athrotaxis cupressoides</td><td>Athrotaxis</td><td>Cupressaceae</td><td>G</td><td>131</td><td>234.75</td><td>424.5</td><td>682</td></tr><tr><td>ATSE</td><td>Athrotaxis selaginoides</td><td>Athrotaxis</td><td>Cupressaceae</td><td>G</td><td>206</td><td>287.75</td><td>419</td><td>630</td></tr><tr><td>AUCH</td><td>Austrocedrus chilensis</td><td>Austrocedrus</td><td>Cupressaceae</td><td>G</td><td>52</td><td>156</td><td>287</td><td>830</td></tr><tr><td>BELE</td><td>Betula lenta</td><td>Betula</td><td>Betulaceae</td><td>A</td><td>73</td><td>115</td><td>146</td><td>182</td></tr><tr><td>BEUT</td><td>Betula utilis</td><td>Betula</td><td>Betulaceae</td><td>A</td><td>56</td><td>69.75</td><td>110.75</td><td>178</td></tr><tr><td>CAPR</td><td>Callitris preissii</td><td>Callitris</td><td>Cupressaceae</td><td>G</td><td>35</td><td>43</td><td>52.25</td><td>64</td></tr><tr><td>CDAT</td><td>Cedrus atlantica</td><td>Cedrus</td><td>Pinaceae</td><td>G</td><td>155</td><td>233</td><td>566.5</td><td>785</td></tr><tr><td>CDBR</td><td>Cedrus brevifolia</td><td>Cedrus</td><td>Pinaceae</td><td>G</td><td>39</td><td>105</td><td>219</td><td>368</td></tr><tr><td>CDDE</td><td>Cedrus deodara</td><td>Cedrus</td><td>Pinaceae</td><td>G</td><td>84</td><td>144.25</td><td>296</td><td>712</td></tr><tr><td>CDLI</td><td>Cedrus libani</td><td>Cedrus</td><td>Pinaceae</td><td>G</td><td>53</td><td>140</td><td>309</td><td>619</td></tr><tr><td>CHLA</td><td>Chamaecyparis<br>lawsoniana</td><td>Chamaecyparis</td><td>Cupressaceae</td><td>G</td><td>91</td><td>170.75</td><td>267.5</td><td>379</td></tr><tr><td>CHOB</td><td>Chamaecyparis obtusa</td><td>Chamaecyparis</td><td>Cupressaceae</td><td>G</td><td>652</td><td>766</td><td>940</td><td>944</td></tr><tr><td>CUCH</td><td>Cupressus chengiana</td><td>Cupressus</td><td>Cupressaceae</td><td>G</td><td>80</td><td>123.5</td><td>262.25</td><td>328</td></tr><tr><td>CYGL</td><td>Carya glabra</td><td>Carya</td><td>Juglandaceae</td><td>A</td><td>68</td><td>97.25</td><td>179.5</td><td>263</td></tr><tr><td>CYOV</td><td>Carya ovata</td><td>Carya</td><td>Juglandaceae</td><td>A</td><td>72</td><td>104.25</td><td>169.75</td><td>354</td></tr><tr><td>FAGR</td><td>Fagus grandifolia</td><td>Fagus</td><td>Fagaceae</td><td>A</td><td>61</td><td>114.25</td><td>185</td><td>293</td></tr><tr><td>FASY</td><td>Fagus sylvatica</td><td>Fagus</td><td>Fagaceae</td><td>A</td><td>57</td><td>120</td><td>167</td><td>242</td></tr><tr><td>FOHO</td><td>Fokienia hodginsii</td><td>Fokienia</td><td>Cupressaceae</td><td>G</td><td>203</td><td>301.25</td><td>499.25</td><td>659</td></tr><tr><td>FRAM</td><td>Fraxinus americans</td><td>Fraxinus</td><td>Oleaceae</td><td>A</td><td>65</td><td>99</td><td>122</td><td>183</td></tr><tr><td>FRNI</td><td>Fraxinus nigra</td><td>Fraxinus</td><td>Oleaceae</td><td>A</td><td>38</td><td>68.5</td><td>101</td><td>198</td></tr><tr><td>JUAU</td><td>Juglans australis</td><td>Juglans</td><td>Juglandaceae</td><td>A</td><td>38</td><td>84.75</td><td>119</td><td>216</td></tr><tr><td>JUEX</td><td>Juniperus excelsa</td><td>Juniperus</td><td>Cupressaceae</td><td>G</td><td>98</td><td>225.5</td><td>460.25</td><td>915</td></tr></tbody></table>

736 737 738 739 740 741 742
<table>
  <tr>
    <td>JUFO</td>
    <td>Juniperus foetidissima</td>
    <td>Juniperus</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>239</td>
    <td>315</td>
    <td>514</td>
    <td>601</td>
  </tr>
  <tr>
    <td>JUNI</td>
    <td>Juglans nigra</td>
    <td>Juglans</td>
    <td>Juglandaceae</td>
    <td>A</td>
    <td>125</td>
    <td>175.5</td>
    <td>206</td>
    <td>233</td>
  </tr>
  <tr>
    <td>JUOC</td>
    <td>Juniperus occidentalis</td>
    <td>Juniperus</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>82</td>
    <td>222</td>
    <td>354.5</td>
    <td>1025</td>
  </tr>
  <tr>
    <td>JUOS</td>
    <td>Juniperus osteosperma</td>
    <td>Juniperus</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>79</td>
    <td>243.5</td>
    <td>398.5</td>
    <td>573</td>
  </tr>
  <tr>
    <td>JUPH</td>
    <td>Juniperus phoenicea</td>
    <td>Juniperus</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>200</td>
    <td>290.5</td>
    <td>427.5</td>
    <td>526</td>
  </tr>
  <tr>
    <td>JUPR</td>
    <td>Juniperus przewalskii</td>
    <td>Juniperus</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>121</td>
    <td>490.25</td>
    <td>953.25</td>
    <td>1621</td>
  </tr>
  <tr>
    <td>JUSC</td>
    <td>Juniperus scopulorum</td>
    <td>Juniperus</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>84</td>
    <td>231.25</td>
    <td>397</td>
    <td>777</td>
  </tr>
  <tr>
    <td>JUSE</td>
    <td>Juniperus serayschanica</td>
    <td>Juniperus</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>33</td>
    <td>100.75</td>
    <td>168.5</td>
    <td>223</td>
  </tr>
  <tr>
    <td>JUSP</td>
    <td></td>
    <td>Juniperus</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>43</td>
    <td>130</td>
    <td>418</td>
    <td>1257</td>
  </tr>
  <tr>
    <td>JUTI</td>
    <td>Juniperus tibetica</td>
    <td>Juniperus</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>82</td>
    <td>283</td>
    <td>543.75</td>
    <td>969</td>
  </tr>
  <tr>
    <td>JUTU</td>
    <td>Juniperus turkestanica</td>
    <td>Juniperus</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>186</td>
    <td>294.75</td>
    <td>516.5</td>
    <td>979</td>
  </tr>
  <tr>
    <td>JUVI</td>
    <td>Juniperus virginiana</td>
    <td>Juniperus</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>35</td>
    <td>112.75</td>
    <td>203.5</td>
    <td>506</td>
  </tr>
  <tr>
    <td>LASI</td>
    <td>Larix sibirica</td>
    <td>Larix</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>86</td>
    <td>244</td>
    <td>393</td>
    <td>713</td>
  </tr>
  <tr>
    <td>LITU</td>
    <td>Liriodendron tulipifera</td>
    <td>Liriodendron</td>
    <td>Magnoliaceae</td>
    <td>A</td>
    <td>42</td>
    <td>110</td>
    <td>202.5</td>
    <td>418</td>
  </tr>
  <tr>
    <td>NOBE</td>
    <td>Nothofagus betuloides</td>
    <td>Nothofagus</td>
    <td>Nothofagaceae</td>
    <td>A</td>
    <td>87</td>
    <td>120.5</td>
    <td>182.75</td>
    <td>251</td>
  </tr>
  <tr>
    <td>NOPU</td>
    <td>Nothofagus pumilio</td>
    <td>Nothofagus</td>
    <td>Nothofagaceae</td>
    <td>A</td>
    <td>34</td>
    <td>129.5</td>
    <td>208.5</td>
    <td>384</td>
  </tr>
  <tr>
    <td>PCAB</td>
    <td>Picea abies</td>
    <td>Picea</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>37</td>
    <td>78</td>
    <td>120</td>
    <td>202</td>
  </tr>
  <tr>
    <td>PCGL</td>
    <td>Picea glauca</td>
    <td>Picea</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>64</td>
    <td>114.75</td>
    <td>198</td>
    <td>379</td>
  </tr>
  <tr>
    <td>PCLI</td>
    <td>Picea likiangensis</td>
    <td>Picea</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>156</td>
    <td>177</td>
    <td>238.25</td>
    <td>345</td>
  </tr>
  <tr>
    <td>PCSH</td>
    <td>Picea schrenkiana</td>
    <td>Picea</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>64</td>
    <td>143.25</td>
    <td>262</td>
    <td>432</td>
  </tr>
  <tr>
    <td>PCSM</td>
    <td>Picea smithiana</td>
    <td>Picea</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>51</td>
    <td>163.5</td>
    <td>285</td>
    <td>619</td>
  </tr>
  <tr>
    <td>PCSP</td>
    <td></td>
    <td>Picea</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>220</td>
    <td>243.5</td>
    <td>450</td>
    <td>529</td>
  </tr>
  <tr>
    <td>PCTI</td>
    <td>Picea tienschanica</td>
    <td>Picea</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>110</td>
    <td>188</td>
    <td>249.5</td>
    <td>279</td>
  </tr>
  <tr>
    <td>PHAS</td>
    <td>Phyllocladus<br>aspleniifolius</td>
    <td>Phyllocladus</td>
    <td>Podocarpaceae</td>
    <td>G</td>
    <td>66</td>
    <td>161</td>
    <td>280</td>
    <td>528</td>
  </tr>
  <tr>
    <td>PHGL</td>
    <td>Phyllocladus glaucus</td>
    <td>Phyllocladus</td>
    <td>Podocarpaceae</td>
    <td>G</td>
    <td>100</td>
    <td>221.5</td>
    <td>331</td>
    <td>435</td>
  </tr>
  <tr>
    <td>PHTR</td>
    <td>Phyllocladus<br>trichomanoides</td>
    <td>Phyllocladus</td>
    <td>Podocarpaceae</td>
    <td>G</td>
    <td>157</td>
    <td>197</td>
    <td>257.5</td>
    <td>313</td>
  </tr>
  <tr>
    <td>PIAM</td>
    <td>Pinus armandii</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>158</td>
    <td>352</td>
    <td>437</td>
    <td>535</td>
  </tr>
  <tr>
    <td>PIBA</td>
    <td>Pinus balfouriana</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>404</td>
    <td>571</td>
    <td>875</td>
    <td>1230</td>
  </tr>
  <tr>
    <td>PIBR</td>
    <td>Pinus brutia</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>65</td>
    <td>120</td>
    <td>233.75</td>
    <td>279</td>
  </tr>
  <tr>
    <td>PICE</td>
    <td>Pinus cembra</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>110</td>
    <td>185</td>
    <td>260.25</td>
    <td>265</td>
  </tr>
  <tr>
    <td>PICM</td>
    <td>Pinus cembroides</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>57</td>
    <td>90</td>
    <td>135</td>
    <td>389</td>
  </tr>
  <tr>
    <td>PICO</td>
    <td>Pinus contorta</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>120</td>
    <td>169</td>
    <td>288</td>
    <td>489</td>
  </tr>
  <tr>
    <td>PIEC</td>
    <td>Pinus echinata</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>45</td>
    <td>74</td>
    <td>174</td>
    <td>272</td>
  </tr>
  <tr>
    <td>PIED</td>
    <td>Pinus edulis</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>95</td>
    <td>272.5</td>
    <td>426.5</td>
    <td>962</td>
  </tr>
  <tr>
    <td>PIFL</td>
    <td>Pinus flexilis</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>101</td>
    <td>274.5</td>
    <td>513</td>
    <td>995</td>
  </tr>
  <tr>
    <td>PIGE</td>
    <td>Pinus gerardiana</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>115</td>
    <td>186</td>
    <td>370</td>
    <td>747</td>
  </tr>
  <tr>
    <td>PIHA</td>
    <td>Pinus halepensis</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>39</td>
    <td>58</td>
    <td>131.5</td>
    <td>153</td>
  </tr>
  <tr>
    <td>PIHR</td>
    <td>Pinus hartwegii</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>88</td>
    <td>109.5</td>
    <td>307.5</td>
    <td>399</td>
  </tr>
  <tr>
    <td>PIJE</td>
    <td>Pinus jeffreyi</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>49</td>
    <td>223</td>
    <td>374</td>
    <td>661</td>
  </tr>
  <tr>
    <td>PIMK</td>
    <td>Pinus merkusii</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>112</td>
    <td>151.75</td>
    <td>200.25</td>
    <td>257</td>
  </tr>
  <tr>
    <td>PIMO</td>
    <td>Pinus monophylla</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>96</td>
    <td>190</td>
    <td>350</td>
    <td>613</td>
  </tr>
</table>
<table>
  <tr>
    <td>PIMZ</td>
    <td>Pinus montezumae</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>69</td>
    <td>84.75</td>
    <td>180.5</td>
    <td>203</td>
  </tr>
  <tr>
    <td>PINI</td>
    <td>Pinus nigra</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>35</td>
    <td>133.5</td>
    <td>301.5</td>
    <td>674</td>
  </tr>
  <tr>
    <td>PIPA</td>
    <td>Pinus palustris</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>25</td>
    <td>68.25</td>
    <td>213</td>
    <td>424</td>
  </tr>
  <tr>
    <td>PIPN</td>
    <td>Pinus pinea</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>22</td>
    <td>56.25</td>
    <td>94.5</td>
    <td>126</td>
  </tr>
  <tr>
    <td>PIPO</td>
    <td>Pinus ponderosa</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>48</td>
    <td>185.75</td>
    <td>326</td>
    <td>704</td>
  </tr>
  <tr>
    <td>PIPU</td>
    <td>Pinus pungens</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>111</td>
    <td>119</td>
    <td>126</td>
    <td>171</td>
  </tr>
  <tr>
    <td>PIRE</td>
    <td>Pinus resinosa</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>50</td>
    <td>135</td>
    <td>227</td>
    <td>430</td>
  </tr>
  <tr>
    <td>PIRI</td>
    <td>Pinus rigida</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>59</td>
    <td>97</td>
    <td>147</td>
    <td>379</td>
  </tr>
  <tr>
    <td>PIRO</td>
    <td>Pinus roxburghii</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>67</td>
    <td>108</td>
    <td>185.5</td>
    <td>326</td>
  </tr>
  <tr>
    <td>PISF</td>
    <td>Pinus strobiformis</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>93</td>
    <td>145</td>
    <td>251.5</td>
    <td>514</td>
  </tr>
  <tr>
    <td>PISI</td>
    <td>Pinus sibirica</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>87</td>
    <td>205.25</td>
    <td>397.75</td>
    <td>841</td>
  </tr>
  <tr>
    <td>PISP</td>
    <td></td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>63</td>
    <td>116</td>
    <td>143</td>
    <td>161</td>
  </tr>
  <tr>
    <td>PIST</td>
    <td>Pinus strobus</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>28</td>
    <td>76</td>
    <td>147</td>
    <td>340</td>
  </tr>
  <tr>
    <td>PISY</td>
    <td>Pinus sylvestris</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>49</td>
    <td>117</td>
    <td>211</td>
    <td>381</td>
  </tr>
  <tr>
    <td>PITB</td>
    <td>Pinus tabulaeformis</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>106</td>
    <td>122.25</td>
    <td>176</td>
    <td>234</td>
  </tr>
  <tr>
    <td>PIVI</td>
    <td>Pinus virginiana</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>43</td>
    <td>59</td>
    <td>101.25</td>
    <td>176</td>
  </tr>
  <tr>
    <td>PIWA</td>
    <td>Pinus wallichiana</td>
    <td>Pinus</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>72</td>
    <td>138.5</td>
    <td>255.25</td>
    <td>379</td>
  </tr>
  <tr>
    <td>PSMA</td>
    <td>Pseudotsuga macrocarpa</td>
    <td>Pseudotsuga</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>28</td>
    <td>149</td>
    <td>288.5</td>
    <td>658</td>
  </tr>
  <tr>
    <td>PSME</td>
    <td>Pseudotsuga menziesii</td>
    <td>Pseudotsuga</td>
    <td>Pinaceae</td>
    <td>G</td>
    <td>44</td>
    <td>160</td>
    <td>338</td>
    <td>862</td>
  </tr>
  <tr>
    <td>QUAL</td>
    <td>Quercus alba</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>33</td>
    <td>122</td>
    <td>238</td>
    <td>414</td>
  </tr>
  <tr>
    <td>QUCE</td>
    <td>Quercus cerris</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>59</td>
    <td>102</td>
    <td>136</td>
    <td>142</td>
  </tr>
  <tr>
    <td>QUCO</td>
    <td>Quercus coccinea</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>37</td>
    <td>57</td>
    <td>81</td>
    <td>118</td>
  </tr>
  <tr>
    <td>QUDG</td>
    <td>Quercus douglasii</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>28</td>
    <td>98</td>
    <td>199</td>
    <td>448</td>
  </tr>
  <tr>
    <td>QUFA</td>
    <td>Quercus falcata</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>62</td>
    <td>70.5</td>
    <td>79.5</td>
    <td>108</td>
  </tr>
  <tr>
    <td>QUHA</td>
    <td>Quercus hartwissiana</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>89</td>
    <td>151</td>
    <td>226.5</td>
    <td>362</td>
  </tr>
  <tr>
    <td>QULO</td>
    <td>Quercus lobata</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>57</td>
    <td>90.25</td>
    <td>190.5</td>
    <td>314</td>
  </tr>
  <tr>
    <td>QULY</td>
    <td>Quercus lyrata</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>73</td>
    <td>154.75</td>
    <td>228.5</td>
    <td>329</td>
  </tr>
  <tr>
    <td>QUMA</td>
    <td>Quercus macrocarpa</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>33</td>
    <td>96</td>
    <td>141</td>
    <td>362</td>
  </tr>
  <tr>
    <td>QUMC</td>
    <td>Quercus macrolepis</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>59</td>
    <td>66.5</td>
    <td>73.5</td>
    <td>89</td>
  </tr>
  <tr>
    <td>QUMG</td>
    <td>Quercus mongolica</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>46</td>
    <td>59</td>
    <td>116.75</td>
    <td>178</td>
  </tr>
  <tr>
    <td>QUMO</td>
    <td>Quercus montana</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>48</td>
    <td>125</td>
    <td>228.75</td>
    <td>425</td>
  </tr>
  <tr>
    <td>QUMU</td>
    <td>Quercus muehlenbergii</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>47</td>
    <td>136</td>
    <td>191.5</td>
    <td>425</td>
  </tr>
  <tr>
    <td>QUPA</td>
    <td>Quercus palustris</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>84</td>
    <td>94.25</td>
    <td>103.5</td>
    <td>108</td>
  </tr>
  <tr>
    <td>QUPE</td>
    <td>Quercus petraea</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>44</td>
    <td>118</td>
    <td>165</td>
    <td>441</td>
  </tr>
  <tr>
    <td>QURO</td>
    <td>Quercus robur</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>38</td>
    <td>119</td>
    <td>172.5</td>
    <td>449</td>
  </tr>
  <tr>
    <td>QURU</td>
    <td>Quercus rubra</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>33</td>
    <td>98.25</td>
    <td>150.25</td>
    <td>217</td>
  </tr>
  <tr>
    <td>QUST</td>
    <td>Quercus stellata</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>46</td>
    <td>145</td>
    <td>230</td>
    <td>374</td>
  </tr>
  <tr>
    <td>QUVE</td>
    <td>Quercus velutina</td>
    <td>Quercus</td>
    <td>Fagaceae</td>
    <td>A</td>
    <td>36</td>
    <td>70</td>
    <td>121</td>
    <td>259</td>
  </tr>
  <tr>
    <td>SAAL</td>
    <td>Salix alba</td>
    <td>Salix</td>
    <td>Salicaceae</td>
    <td>A</td>
    <td>65</td>
    <td>65</td>
    <td>79</td>
    <td>91</td>
  </tr>
  <tr>
    <td>TADI</td>
    <td>Taxodium distichum</td>
    <td>Taxodium</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>54</td>
    <td>154.5</td>
    <td>394.5</td>
    <td>1621</td>
  </tr>
  <tr>
    <td>TAMU</td>
    <td>Taxodium mucronatum</td>
    <td>Taxodium</td>
    <td>Cupressaceae</td>
    <td>G</td>
    <td>48</td>
    <td>115</td>
    <td>300.5</td>
    <td>751</td>
  </tr>
  <tr>
    <td>TEGR</td>
    <td>Tectona grandis</td>
    <td>Tectona</td>
    <td>Lamiaceae</td>
    <td>A</td>
    <td>61</td>
    <td>109</td>
    <td>217.5</td>
    <td>391</td>
  </tr>
</table>
<table><tr><td>THOC</td><td>Thuja occidentalis</td><td>Thuja</td><td>Cupressaceae</td><td>G</td><td>54</td><td>115.25</td><td>478</td><td>745</td></tr><tr><td>TOCI</td><td>Toona ciliata</td><td>Toona</td><td>Meliaceae</td><td>A</td><td>33</td><td>49.25</td><td>101</td><td>147</td></tr><tr><td>TSCA</td><td>Tsuga canadensis</td><td>Tsuga</td><td>Pinaceae</td><td>G</td><td>30</td><td>134</td><td>289</td><td>540</td></tr><tr><td>TSCR</td><td>Tsuga caroliniana</td><td>Tsuga</td><td>Pinaceae</td><td>G</td><td>232</td><td>244.5</td><td>280.5</td><td>307</td></tr><tr><td>TSME</td><td>Tsuga mertensiana</td><td>Tsuga</td><td>Pinaceae</td><td>G</td><td>93</td><td>187.75</td><td>251</td><td>443</td></tr></table>
745

746

747

**Extended Data Table 3:** The maximum, mean, median, and minimum age of young, intermediate, and old age cohort of angiosperm and gymnosperm.

<table><thead><tr><th rowspan="2">Age Cohort</th><th colspan="4">Angiosperms</th><th colspan="4">Gymnosperms</th></tr><tr><th>Max</th><th>Mean</th><th>Median</th><th>Min</th><th>Max</th><th>Mean</th><th>Median</th><th>Min</th></tr></thead><tbody><tr><td>Young</td><td>172</td><td>89</td><td>89</td><td>28</td><td>766</td><td>130</td><td>117</td><td>22</td></tr><tr><td>Intermediate</td><td>237</td><td>151</td><td>148</td><td>53</td><td>953</td><td>242</td><td>228</td><td>45</td></tr><tr><td>Old</td><td>449</td><td>237</td><td>242</td><td>74</td><td>1621</td><td>406</td><td>376</td><td>53</td></tr></tbody></table>

748
749

750

751

752

<table><caption>**Extended Data Table 4:** Generalized additive mixed-effect model summary for angiosperms and gymnosperms, including effective degrees of freedom (edf), referenced degrees of freedom (Ref. df), F statistics, and corresponding p-values.</caption><thead><tr><th rowspan="2">Smooth terms</th><th colspan="4">Angiosperms</th><th colspan="4">Gymnosperms</th></tr><tr><th>edf</th><th>Ref. df</th><th>F</th><th>p value</th><th>edf</th><th>Ref. df</th><th>F</th><th>p value</th></tr></thead><tbody><tr><td>s(SPEI): Young</td><td>1.998</td><td>2</td><td>11132</td><td>&lt; 0.001</td><td>1.994</td><td>2</td><td>13841</td><td>&lt; 0.001</td></tr><tr><td>s(SPEI): Inter</td><td>1.999</td><td>2</td><td>21742</td><td>&lt; 0.001</td><td>1.997</td><td>2</td><td>29788</td><td>&lt; 0.001</td></tr><tr><td>s(SPEI): Old</td><td>1.995</td><td>2</td><td>8330</td><td>&lt; 0.001</td><td>1.992</td><td>2</td><td>14859</td><td>&lt; 0.001</td></tr><tr><td>s/site)</td><td>300</td><td>501</td><td>1.839</td><td>&lt; 0.001</td><td>717.8</td><td>927</td><td>4.31</td><td>&lt; 0.001</td></tr><tr><td>s(individual)</td><td>0.004</td><td>8088</td><td>0</td><td>1</td><td>0.089</td><td>13870</td><td>0</td><td>1</td></tr></tbody></table>

753
<table><caption>**Extended Data Table 5:** Difference of mean estimate of log-transformed resistance and relative resilience between age cohorts within angiosperms and gymnosperms for non-drought and normal conditions by Tukey Honest Significant Differences. The numbers with parentheses are the mean estimate under normal conditions. Numbers in bold indicate significant difference in mean estimate ($\alpha$ = 0.05).</caption><thead><tr><th></th><th>Age Cohort</th><th>Angiosperm</th><th>Gymnosperm</th></tr></thead><tbody><tr><td rowspan="3">Resistance</td><td>Young-Inter</td><td><strong>0.051 (0.061)</strong></td><td><strong>0.025 (0.034)</strong></td></tr><tr><td>Inter-Old</td><td><strong>0.056 (0.066)</strong></td><td>0.013 (0.012)</td></tr><tr><td>Young-Old</td><td><strong>0.106 (0.127)</strong></td><td><strong>0.039 (0.046)</strong></td></tr><tr><td rowspan="3">Resilience</td><td>Young-Inter</td><td><strong>-0.023 (-0.025)</strong></td><td><strong>-0.016 (-0.014)</strong></td></tr><tr><td>Inter-Old</td><td><strong>-0.036 (-0.030)</strong></td><td>-0.007 (-0.005)</td></tr><tr><td>Young-Old</td><td><strong>-0.059 (-0.056)</strong></td><td><strong>-0.023 (-0.019)</strong></td></tr></tbody></table>

**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).

**Code availability**

The code used to calculate the results for this work is available from the corresponding author on reasonable request.

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1049 Acknowledgments

1050 We thank Neil Pederson, Ying Zhao, Yuting Jin, and Suet Lin Kong for providing feedback 1051 and data. We thank all contributors to the ITRDB and DEN to make this analysis feasible. The 1052 research is supported by Indiana University College of Arts and Sciences Dissertation Research 1053 Fellowship.

1054

1055 Author Contributions

1056 T.F.A. and J.L. conceived and designed the study with all authors providing input. J.T.M., J.L., 1057 M.P.D., T.L., and Z.C. contributed data. T.F.A. performed analyses with contribution from 1058 S.M.R. and J.L. All authors discussed, interpreted results, drew conclusions and wrote the 1059 paper.

1060

1061 Competing interests

1062 The authors declare no competing interests.

1063

1064 Corresponding author

1065 Correspondence and requests for materials should be addressed to Tsun Fung Au.