# Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought

Xiangyi Li, Shilonog Piao, Kai Wang, Xuhui Wang, Tao Wang, Philippe Ciais, Anping Chen, Xu Lian, Shushi Peng, Josep Peñuelas

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Xiangyi Li, Shilonog Piao, Kai Wang, Xuhui Wang, Tao Wang, et al.. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nature Ecology & Evolution, 2020, 4 (8), pp.1075-1083. 10.1038/s41559-020-1217-3. hal-02938429

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# Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought

Xiangyi Li<fnref n="1" />, Shilong Piao<fnref n="1" />,²✉, Kai Wang<fnref n="1" />, Xuhui Wang<fnref n="1" />, Tao Wang², Philippe Ciais¹³, Anping Chen¹⁴, Xu Lian<fnref n="1" />, Shushi Peng<fnref n="1" /> and Josep Peñuelas¹⁵,⁶

The frequency and intensity of droughts have increased over the decades, leading to increased forest decline. The response of forest to drought can be evaluated by both its sensitivity to drought (resistance) and its post-drought recovery rate (resilience). However, it remains uncertain how drought resistance and resilience of forests change over time under climate change. We assessed the spatiotemporal dynamics of forest resistance and resilience to drought over the past century (1901-2015) with global tree ring data records from 2,935 sites, in conjunction with plant trait data. We found that gymnosperms and angiosperms showed different spatial patterns of drought resistance and resilience, driven by variations in eco-physiological traits. Resistance and resilience also varied with drought seasonal timing. Surprisingly, we found that the trade-off between resistance and resilience for gymnosperms, previously reported only spatially, also occurred at the temporal scale. In particular, drought resilience markedly increased, but resistance decreased, for gymnosperms between 1950-1969 and 1990-2009, indicating that previous model simulations assuming invariant resistance may have underestimated the impacts of drought on gymnosperm-dominated forests under future climate change.

The impact of changing climatic extremes is an important area of current research<fnref n="1" />, with increasingly severe droughts being of particular concern². Extreme droughts suppress tree growth and cause mortality from hydraulic failure or carbon starvation³⁻⁷. Previous studies have found that forest responses to drought were spatially heterogeneous and species specific⁸⁻¹⁰. The physiological mechanisms behind forest drought responses are reasonably well understood but poorly quantified at species and regional levels, which prevents the design of better management practices to improve the resistance of forests to drought³,⁵,¹¹. In addition, most models of the global ecosystem do not include processes such as xylem cavitation caused by extreme drought⁵,¹²,¹³. More importantly, it remains controversial whether increasing carbon dioxide (CO₂) concentration can alleviate drought stress against concomitant increases in air temperature and vapour pressure deficits¹⁴⁻¹⁷. Few studies have evaluated long-term variations in forest sensitivity to drought, hindering the prediction of future forest dynamics and the subsequent impacts on ecosystem function and terrestrial carbon balance.

Plant traits describe various phenotypic features of individuals and can be used to assess the effects of species diversity, as well as species-specific plasticity, on ecosystem function. Large trait databases can be used to calibrate ecosystem models¹⁸ and to introduce spatial and temporal variability into their parameterizations¹⁹. Field experiments, meta-analyses and theoretical models indicate that multiple plant traits influence the regulation of gas exchange and water transport in trees under stress, thereby also impacting the risk of tree mortality from hydraulic failure or carbon starvation²⁰⁻²⁴.

However, large-scale links between plant traits and tree responses to drought under global climate change have yet to be established. A recent global study, based on 352 site-year measurements of eddy covariance from 40 sites, found that hydraulic diversity mediated drought resilience in temperate and boreal forests²⁵. However, the effects of multiple plant traits on species-specific resistance (sensitivity to drought) and resilience (post-drought recovery rate) have received limited attention.

Tree ring data provide long-term records of the radial growth of individual trees and can be used to study long-term forest dynamics in response to climate change⁵,¹⁰,¹³,²⁶,²⁷. We used tree ring measurements from 2,935 sites in a global database, the International Tree Ring Data Bank (ITRDB; see details in Methods and Supplementary Table 1) to identify links between multiple plant traits and global forest resistance and resilience. We used a standardized ring width index (RWI) to calculate indices of forest drought resistance and resilience²⁸ (see Methods). We compared these indices between gymnosperms and angiosperms, because they inhabit different environments and differ structurally and physiologically, probably impacting forest drought response²⁹⁻³¹. We also divided drought events into three classes to characterize different drought timings: drought in the dry season (DS), in the wet season (WS) and in both dry and wet seasons (DS + WS)¹⁰ (Fig. 1, Extended Data Figs. 1 and 2 and Supplementary Fig. 1; see Methods).

## Results and discussion

**Spatial variations of drought resistance and resilience.** Figure 1 shows the spatial pattern and data distribution of drought resistance

<footnote n="1">¹Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China. ²Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences, Beijing, China. ³Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ, Gif-sur-Yvette, France. ⁴Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA. ⁵CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Barcelona, Spain. ⁶CREAF, Cerdanyola del Vallès, Barcelona, Spain. e-mail: slpiao@pku.edu.cn</footnote>
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**Fig. 1** | Mean resistance and resilience of gymnosperms and angiosperms in drought years. **a,b,** Spatial pattern of mean resistance (Rt) (a) and mean resilience (Rs) (b) to DS + WS during 1901–2015. Circles and triangles represent gymnosperm and angiosperm tree ring sites, respectively. The same analyses, but for DS and WS, are presented in Extended Data Fig. 3. **c,** Mean ln-transformed resistance (ln(Rt)) for DS, WS and DS + WS for gymnosperm tree ring sites (orange) and angiosperm tree ring sites (blue). **d,** Mean ln(Rs) for DS, WS and DS + WS droughts for gymnosperm and angiosperm tree ring sites. Boxplots in **c,d** show the median (horizontal lines), mean (triangles), 25th, 40th, 60th and 75th quartiles (Q₀.₂₅, Q₀.₄, Q₀.₆ and Q₀.₇₅, respectively) and maximum (Max) and minimum (Min) values (top and bottom of the whiskers). Significant differences (P < 0.05) of ln(Rt) (or ln(Rs)) for sites between gymnosperms and angiosperms in the same drought timing (DS, WS and DS + WS) were identified by ANOVA and are denoted by asterisks. The grey dotted baselines in **c,d** represent the median of ln(Rt) and ln(Rs) across all drought timings and forest groups. Colour bars in **a,b** and **y axes** in **c,d** represent unit-less metrics.

for the two forest groups (gymnosperms and angiosperms) and three drought timings (DS, WS and DS+WS) (Fig. 1a,c and Extended Data Fig. 3a,c). Gymnosperms demonstrate higher drought resistance than angiosperms ($P<0.05$) for DS+WS and WS (Fig. 1a,c). Resistance in gymnosperms also varies significantly among different drought timings ($P<0.05$; Fig. 1a,c and Extended Data Figs. 3a,c and 4). Gymnosperm forests have a stronger capacity to resist WS drought than DS or DS+WS (WS>DS and WS>DS+WS, $P<0.05$; Extended Data Fig. 4), with DS and DS+WS characterized by high severity and long duration, respectively (Extended Data Fig. 5). Drought resistance in angiosperms, however, does not vary markedly with drought timings (Fig. 1a,c and Extended Data Fig. 4).

A previous study based on tree ring data from the ITRDB documented a trade-off between resistance and resilience—highly resistant forests in the Northern Hemisphere tend to have lower resilience, and vice versa³². This trade-off is supported by our analysis of the variation in resilience with drought timing for gymnosperms (Fig. 1). Gymnosperms are less resilient, but more resistant to, WS than to DS and DS+WS (Fig. 1 and Extended Data Figs. 3 and 4). This is probably because >60% of the growing season overlaps with the dry seasons for >80% of tree ring sites. Severe water stress during DS and DS+WS directly impacts tree radial growth as a result of hydraulic failure and reduction in carbon acquisition and, thereby, lower resistance to drought¹⁰,³³. Gymnosperms also demonstrate quicker recovery (that is, higher resilience) after DS and DS+WS, probably caused by their greater sensitivity to water

availability at those times than during a WS drought (Fig. 1 and Extended Data Fig. 4). Similar to gymnosperms, angiosperms have seasonally variable resilience to drought but their drought resistance is more uniform (Extended Data Fig. 4). Angiosperm and gymnosperm forests demonstrate different resistance during WS and DS+WS (Fig. 1c), but not different resilience to all drought timings (Fig. 1d). These results suggest that all forest groups and drought timings do not necessarily have systematic trade-offs between resistance and resilience.

**Traits and environmental factors determining drought resistance and resilience.** We next identified key plant traits and environmental factors that drive the spatial variation in drought resistance and resilience. We applied the boosted regression trees (BRT) model³⁴, a machine learning approach, to relate drought resistance and resilience as a function of 16 extrinsic and intrinsic factors (Fig. 2 and Supplementary Table 2; see Methods). We used mean annual precipitation (PREC), mean annual temperature (TEMP), the cation-exchange capacity (CEC) of the topsoil and available water-storage capacity (AWC) of the soil to represent the climatic and edaphic conditions of forests. We also included a suite of physiological and functional traits at foliar, stem and root levels. Foliar economic traits included nitrogen concentration per unit dry mass (Nm), phosphorus concentration per unit dry mass (Pm) and specific leaf area (SLA). These traits are closely coupled to photosynthesis and foliar respiration³⁵. Hydraulic traits included wood density (WD), maximum rooting depth (rooting depth), height
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**Fig. 2** | Factors accounting for spatial variations in drought resistance and resilience. a. Relative contributions of 16 controlling factors (see details in Supplementary Table 2) to the spatial variation of drought resistance as estimated by BRT models dependent on drought timings and forest groups: droughts in the dry season for gymnosperms (DS<sub>G</sub>), droughts in the dry season for angiosperms (DS<sub>A</sub>), droughts in the wet season for gymnosperms (WS<sub>G</sub>), droughts in the wet season for angiosperms (WS<sub>A</sub>), droughts in both dry and wet seasons for gymnosperms (DS + WS<sub>G</sub>) and droughts in both dry and wet seasons for angiosperms (DS + WS<sub>A</sub>) (see details in Methods). b. Relative contributions of 16 controlling factors to the spatial variation of drought resilience. Grid size is determined by the contribution (percentage of explained variance) of each factor labelled in the same grids. Only percentages >6% (averaged contribution of all factors) are displayed, and those >9% (relatively high contributions) are highlighted with bold font. Different colours on the y-axis legend represent different types of factors—age (red); severity (blue); tree density (brown); foliage economic traits: Nm, Pm and SLA (purple); hydraulic traits: WD, rooting depth, height, HSM, P50, isohydricity (orange); and environmental factors: AWC, CEC, PREC and TEMP (black).

of the uppermost forest canopy (height), hydraulic safety margin (HSM), xylem water potential at 50% loss of hydraulic conductivity (P50) and degree of isohydricity (isohydricity). These traits are strongly associated with the dynamics of water transport.

Because forest resistance and resilience to drought differed across the forest groups and drought timings (Fig. 1), we built separate BRT models for each of the six classes (two forest groups × three drought timings) of resistance and resilience (see Methods). Our BRT models show acceptable performance in estimating the spatial variations of resistance and resilience, especially considering uncertainties caused by the complexity of the multiple trait-related mechanisms involved in drought responses and the scale mismatch between gridded predictors and tree-level resistance and resilience data (Supplementary Fig. 2; see Methods).

Hydraulic traits generally account for a large fraction of the spatial variation in gymnosperm resistance (51% in DS, 48% in WS and 45% in DS + WS; Fig. 2a). Among these, WD, P50, height and isohydricity account for a substantial proportion of the spatial variation (Fig. 2a). These traits are associated with gymnosperms' conservative water-use strategy of avoiding xylem cavitation and embolism to resist drought. For example, trees with a more negative P50 and a higher WD are less vulnerable to drought-induced cavitation<sup>36,37</sup>, and taller trees are more vulnerable to xylem embolism due to the higher water column tension caused by the greater distance of water transport<sup>38,39</sup>. In addition to xylem cavitation resistance, conifer clades also use stomatal regulation to increase water retention during prolonged drought—gymnosperm resistance to DS + WS is well explained by the degree of isohydricity. For example, Pinaceae (relatively isohydric gymnosperms) are reported to control water loss

through rapid stomatal regulation by a high level of the hormone abscisic acid, while major Cupressaceae (relatively anisohydric gymnosperms) avoid excessive xylem embolism by extreme resistance to xylem cavitation during falling water potential<sup>40,41</sup>.

Unlike gymnosperm resistance, the spatial variation of angiosperm resistance can be attributed to both intrinsic and extrinsic factors (Fig. 2a). Isohydricity, a hydraulic trait, explains the spatial variation in angiosperm resistance to DS + WS (11.6%) while Nm, a foliar economic trait, is important for the spatial variation of angiosperm resistance under DS (8.4%) and WS (13.4%) (Fig. 2a). Multiple extrinsic factors (for example, drought severity and AWC in DS, PREC in WS and CEC in DS + WS) are also well related to angiosperm resistance (Fig. 2a). Angiosperms probably have a riskier embolism-tolerance strategy to resist drought stress<sup>30</sup>. They maintain a high photosynthetic rate under drought, with the impact of several intrinsic and extrinsic factors associated with plant nutritional status<sup>42</sup>, such as Nm and CEC. However, it is costly to maintain photosynthesis—it can cause a rapid decrease in water potential under drought, leading to hydraulic dysfunction in severe or persistent droughts<sup>8,17,31</sup>. This maintenance cost means that angiosperm resistance is greatly impacted by extrinsic and intrinsic factors associated with the water supply network in different drought timings. Our results suggest that the status of external water deficit (that is, drought severity) is more important to angiosperm resistance under DS, while internal water regulation (that is, isohydricity) has a stronger link to their resistance under DS + WS. Gymnosperms are significantly more resistant than angiosperms in WS and DS + WS (Fig. 1, *P* < 0.05), indicating that the strategy adopted by the former may be more advantageous for drought resistance.
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**Fig. 3** | Temporal changes in resistance and resilience for gymnosperm and angiosperm forests, 1950–2009. **a–d**, Temporal changes in gymnosperm resistance (**a**), angiosperm resistance (**b**), gymnosperm resilience (**c**) and angiosperm resilience (**d**). Three hundred and thirty-two gymnosperm sites in **a,c** and seventy-two angiosperm sites in **b,d** experiencing droughts in all three periods (1950–1969, 1970–1989 and 1990–2009) were used for temporal analysis. The x axes represent the range of ln(Rt + 1) and ln(Rs + 1) at each standardized RWI site during the three periods. Histograms (left y axes) show the distributions of ln(Rt + 1) and ln(Rs + 1), and lines (right y axes) show the kernel density distributions of ln(Rt + 1) and ln(Rs + 1) during 1950–1969, 1970–1989 and 1990–2009. The boxplots in the insets show the distribution of temporal change in 1970–1989 and 1990–2009 relative to 1950–1969 for all sites. The yellow lines represent median values, and points represent mean values in the boxes. The percentages of sites showing positive (+) and negative change (−) are displayed. Significant differences (P < 0.05) for all sites between 1970–1989 and 1950–1969, 1990–2009 and 1950–1969 were identified by Kruskal–Wallis testing and are denoted by asterisks.

Hydraulic traits explain a larger portion of spatial variation in drought resilience (that is, post-drought recovery rate) of gymnosperms (~39–45%) than of angiosperms (~29–38%) (Fig. 2b). Isohydricity accounts for 9.2 and 10.7% of the spatial variation in gymnosperm resilience under WS and DS+WS, respectively. This strong relationship between isohydricity and gymnosperm resilience arises from the fact that isohydricity represents a trade-off between water loss and photosynthesis<sup>43</sup>, which are both associated with the ability of a tree to recover<sup>44</sup>. Previous studies suggest that anisohydric trees recover faster from moderate droughts because they continue to assimilate carbon under water stress, yet they may recover less successfully from severe drought due to excessive hydraulic damages<sup>44,45</sup>. Rooting depth and canopy height are also associated with variation in gymnosperm resilience to droughtings. These traits are important for rebuilding the soil–tree hydraulic connection—taller trees with deeper roots can access deep soil moisture, effectively transporting water from the soil to the canopy along larger water-potential gradients<sup>39</sup>.

In addition to hydraulic traits (isohydricity, height and root-
ing depth), foliar economic traits (for example, Nm and Pm) also
account for spatial variation in angiosperm resilience (Fig. 2b),
suggesting an important role of photosynthesis rates in drought
recovery of angiosperms. Plants with high foliar nutrient concen-
trations tend to have high photosynthetic rates¹⁶, thus facilitating
the re-establishment of physiological and structural functions that
assist with rapid recovery after drought²⁹,⁴⁶,⁴⁷.

Extrinsic factors also have a notable impact on the spatial varia-
tion of resilience (Fig. 2b). For example, tree density accounts for
more spatial variation in gymnosperm resilience in DS than do
any of the intrinsic plant traits (Fig. 2b). Due to competitive pres-
sure among trees<sup>48,49</sup>, gymnosperms grown in a dense community
recover at a slower rate after severe drought. Drought severity and
CEC account for much of the variation in resilience after WS for
angiosperms, highlighting the impact of soil moisture and fertility
during post-drought recovery.

**Temporal trade-off of gymnosperm resistance and resilience.**
Anthropogenic emissions have increased atmospheric CO₂ con-
centrations by approximately 100 µmol mol⁻¹ since 1950, when
fossil fuels became the dominant source of emissions¹,⁵⁰. Both the-
ory and observation suggest that such increases in CO₂ have major
impacts on plant physiology (for example, increasing water-use
efficiency)⁵¹⁻⁵³, which may affect plant sensitivity to drought⁶,⁵⁴.
Previous model simulations found that rising CO₂ concentra-
tions can alleviate drought stress in plants, both by shortening
the time required to recover productivity⁵⁵ and by alleviating the
risk of drought-induced tree mortality¹⁷,⁴⁷. We analysed varia-
tions in drought resistance and resilience for both gymnosperms
and angiosperms in 20-year increments (periods), from 1950 to
2009 (Fig. 3 and Extended Data Fig. 6), to determine whether
rising CO₂ concentrations affected tree radial growth (see
Methods).
<figure><img src="image_13.png" /><figcaption>**Fig. 4** | Trade-off of gymnosperm resistance and resilience to drought over time. **a,** Frequency of temporal correlation (R) between resistance and resilience for gymnosperm tree ring sites that experienced at least five droughts during 1950–2009. **b,** Frequency of P values for the temporal correlation between resistance and resilience. **c,** Percentage of negative (blue bars) and positive (purple bars) correlations between resistance and resilience. The percentage of significantly negative correlations (25%, *P* < 0.05) is marked by dark blue, and the percentage of significantly positive correlations (1%, *P* < 0.05) is marked by dark purple. **d,** Spatial relationship of the differences in resistance (ΔRt) and resilience (ΔRs) between 1950–1969 and 1970–1989 across 332 gymnosperm sites included in Fig. 3. **e,** Analysis as in **d,** but for the difference between 1970–1989 and 1990–2009. **f,** Analysis as in **d,** but for the difference between 1950–1969 and 1990–2009. **d–f,** Insets denote the percentages of sites with the opposite (filled bars) and the same directions (open bars).</figcaption></figure>

Consistent with model simulations⁴⁷, gymnosperm resilience to drought increased (56% of 332 gymnosperm sites), particularly between 1950–1969 and 1990–2009 (*P* < 0.05; Fig. 3c). Surprisingly, gymnosperm resistance decreased (59% of 332 gymnosperm sites) over the same time period (*P* < 0.05), suggesting a temporal trade-off in the evolution of resistance and resilience (that is, an 11% decrease in resistance and a 16% increase in resilience between the two periods above, *P* < 0.05), as illustrated in Fig. 3a,c. Expanding the analysis period to 1910–2009, we found a similar temporal trade-off between these metrics (Extended Data Fig. 6a,c and Supplementary Fig. 3a).

We conducted a focused analysis of the relationship between temporal changes in resistance and resilience for the period 1950–2009 at 161 gymnosperm sites that had experienced at least five droughts (Fig. 4; see Methods). Temporal changes in resistance and resilience are negatively correlated at 91% of gymnosperm sites (25% with significant correlations, *P* < 0.05; see Methods) (Fig. 4a–c). Changes in resistance and resilience between early and late time periods are in opposite directions around 70% of the tree ring sites, with correlation coefficients ranging between –0.23 and –0.34 (Fig. 4d–f). This suggests the existence of a temporal trade-off between gymnosperm resistance and resilience, in addition to the previously reported spatial trade-off between these metrics³².

The increase in drought sensitivity (that is, decrease in resistance) in gymnosperms may arise from their strong response to rising CO₂ concentrations. Increasing CO₂ causes gymnosperms to reduce stomatal conductance through physiological adjustments and hydraulic acclimation, and it limits CO₂ diffusion by mesophyll conductance³¹,⁵⁶,⁵⁷. Gymnosperms have a wider hydraulic safety margin than angiosperms (Extended Data Fig. 7a), so they tend to have early stomatal closure³¹, which induces an increase in foliar temperature driven by the decrease in latent heat loss through transpiration. Several studies suggest that high foliar temperature due to rising CO₂ concentrations increases foliar respiratory costs

and decreases photosynthesis²⁰,²²,⁴⁷. Because of the more isohydric behaviour of gymnosperms, their hydraulic architecture has a greater potential to make temporal adjustments to increasing CO₂ using stomatal regulation in response to environmental change⁵⁸. The reduction in leaf hydraulic conductivity under elevated CO₂, as reported in free air CO₂ experiment studies²⁰,⁵⁷,⁵⁹⁻⁶¹, increases leaf sensitivity to cavitation under drought stress. In addition, gymnosperm photosynthetic activity and intrinsic water-use efficiency are expected to be more sensitive to rising CO₂ concentrations because of their greater mesophyll limitation to CO₂ diffusion than angiosperms (which have higher SLA)⁵⁶ (Extended Data Fig. 7b). The increasing drought sensitivity of gymnosperms, therefore, suggests greater impacts of foliar stress associated with excessive foliar temperatures and physiological limitations, which override the benefit of reduced transpiration during drought attributable to elevated CO₂.

Further statistical analyses also highlight the strong association of changes in tree growth with resistance (Extended Data Fig. 8a). For example, the difference in resistance is significantly positively correlated (*P* < 0.001) with the difference in tree growth during drought years for gymnosperms between each two decades over the period 1950–2009. In contrast, the difference in gymnosperm resistance is not significantly (*P* > 0.05), or only poorly (*R* < 0.2), correlated with the differences in drought severity (indicated by the standardized precipitation–evapotranspiration index (SPEI), annual precipitation and annual temperature, respectively, of drought years between the corresponding periods (Extended Data Fig. 8b–d)). This indicates that forests experiencing more severe droughts over the past six decades do not necessarily have higher drought sensitivities. We conjecture that there is a critical threshold of drought severity that causes a decrease in gymnosperm resistance, which may not have been reached between 1950 and 2009. Below this critical threshold of drought severity, drought sensitivity of gymnosperms does not concomitantly change with drought severity. However, once the
drought severity threshold is surpassed, gymnosperms suffer from severe drought damage and gradually lose capacity to survive the drought<sup>55,62–65</sup> (see the tree ring site in Extended Data Fig. 9).

In contrast, angiosperm resistance and resilience show no marked decadal changes over the last century (1910–2009) (Fig. 3b,d and Extended Data Fig. 6b,d). Although angiosperm resistance decreased and resilience increased between 1950–1969 and 1970–1989, the change was not significant (*P* > 0.05) and was reversed by 1990–2009. This suggests that the faster drought recovery predicted by tree physiological models<sup>47</sup> does not occur in angiosperms, but rather that forest responses to drought under rising CO<sub>2</sub> concentrations may depend more on the species. Some studies of tree ring chronologies have also indicated that rising CO<sub>2</sub> concentrations do not notably affect tree growth, because increasing water-use efficiency compensates insufficiently for the negative effects of stress due to water deficiency<sup>14,15,66,67</sup>. Furthermore, unlike that in gymnosperms, angiosperm resistance to drought is significantly correlated with self-growth condition (*P* < 0.05) and depends partly on drought severity (Extended Data Fig. 8).

Limitations must be considered when interpreting these findings. Tree ring-based resistance and resilience data from tropical forests are very limited, due to the reduced formation of reliable tree rings and the grand challenge of tropical dendrochronology<sup>68</sup>. As an alternative to tree ring data, we used measurements of aboveground forest biomass to analyse the spatial variation in resistance and resilience of tropical forests such as the Amazonian forests (Extended Data Fig. 10). However, even with the biomass data, the temporal sensitivity of Amazonian forests remains unclear due to the lack of long-term continuous records. Furthermore, the limited spatiotemporal coverage of trait measurements does not allow us to have collocated data at each tree ring site. Such limited coverage could be a source of uncertainty in regard to the assessment of trait acclimations to spatial and temporal change across a wide range of environmental conditions. This limitation warrants further studies to validate the results presented here.

## Conclusion

To our knowledge, this study of long-term tree ring data is the first to report a trade-off between the recent trends of gymnosperm resistance and resilience to drought. Reduced drought resistance, with increased drought resilience, may indicate a progressive shift of the strategy for gymnosperms in coping with intensified drought stress due to climate change. This theory requires validation by further studies. Increase in gymnosperm sensitivity to drought suggests that their elevated intrinsic water-use efficiency does not alleviate the impacts of drought stress. Even though gymnosperms tend to have a faster recovery rate, drought damages may still occur from the cumulative effects of subsequent intense and frequent droughts, thereby increasing the risk of tree mortality<sup>3,47,69</sup>. The enhanced risk of tree mortality will probably result in biomass loss and redistribution of tree growth, and might even lead to ecosystem shifts<sup>7,70–72</sup>. We suggest that altered ecosystem carbon cycle processes should be considered in the next generation of forest models, to improve their predictive capacity of future ecosystem functioning and terrestrial carbon balance. The projections of Coupled Model Intercomparison Project Phase 5 models<sup>1</sup> indicate that more severe, frequent and longer droughts are expected in the future, further intensifying the challenge for sustainable forest management. We explored the spatial link between multiple plant traits and the resistance and resilience of forests to drought, but did not investigate their associations at a temporal scale due to the paucity of long-term plant trait measurements. This highlights the need to establish a network of long-term, synchronized observations of plant growth conditions and traits to support further studies.

## Methods

**Tree ring data.** Tree ring chronologies provide information about long-term tree growth, ecological processes and climatic signals. Raw data of tree ring widths were obtained from the International Tree-Ring Data Bank v.7.15 (https://www1.ncdc.noaa.gov/pub/data/paleo/treering/) in September 2018. We derived standardized site-level chronologies by detrending, standardizing and averaging growth series of tree samples collected at the same site using the dpIR package (v.1.6.8) in R<sup>73</sup>. Tree ring width series were detrended based on the 'cubic smoothing spline' approach, with a 50% frequency-response cut-off at 30 years to remove non-climatic signals from low-frequency fluctuations in ring width<sup>74</sup>. Site-level standard chronologies were derived from an averaged tree-level standardized tree ring index using a bi-weight robust mean<sup>74</sup>. Chronologies of tree ring widths were selected at 2,935 sites based on the following criteria: availability of metadata (longitude, latitude, elevation and species code), tree ring series covering at least 25 years between 1901 and 2015, and availability of tree ring measurements including total ring widths.

Among the 2,935 tree ring sites there were 141 gymnosperm species and 66 angiosperm species, with 28 gymnosperm species and 10 angiosperm species occurring in at least 20 sites.

These 38 main species were spread across 2,159 tree ring sites, belonging to 11 genera (*Pseudotsuga*, *Pinus*, *Picea*, *Tsuga*, *Fagus*, *Abies*, *Larix*, *Quercus*, *Nothofus*, *Juniperus* and *Libocedrus*) and 4 families (Pinaceae, Fagaceae, Nothofagaceae and Cupressaceae; Supplementary Table 1).

**Climatic metrics.** The difference between precipitation (P) and potential evapotranspiration (PET) (P-PET) can be used as a metric of climatic water deficit. Datasets of monthly precipitation and potential evapotranspiration with a spatial resolution of 0.5° for 1901–2015 were obtained from the Climatic Research Unit (CRU), v.TS 4.01 (ref. 75). We identified the dry and wet seasons for each calendar year using the P-PET index.

Data for SPEI were used to identify extreme droughts. Gridded SPEI (SPEIbase v.2.5) data with a spatial resolution of 0.5° were obtained from the Global SPEI dataset (http://digital.csic.es/handle/10261/153475)<sup>76</sup>. We used SPEIs with timescales of 12 months to represent the annual water balance<sup>28</sup>. We extracted P, PET and SPEI with a window of 3 × 3 pixels (1.5 × 1.5°) for all tree ring sites.

**Variables for the BRT models.** Sixteen variables from different sources were used as predictive factors in the BRT models, including stand age, drought severity, topsoil, soil AWC (mm m⁻¹), PREC, TEMP, tree density, P50, HSM, canopy height, maximum rooting depth, WD, isohydricity, SLA and foliar Nm and Pm concentrations (see details in Supplementary Table 2).

Mean stand age at each site was averaged by forest stand age in drought years. Drought severity was measured by the mean SPEI value for drought years. Drought severity in DS, WS and DS + WS was defined by averaged monthly SPEI in the dry season, the wet season and annually, respectively. CEC is a measure of soil fertility, indicating the total capacity of soil to fix nutrients, which was obtained from the Regripped Harmonized World Soil Database v.1.2 (ref. 77) (https://daac.ornl.gov/SOILS/guides/HWSD.html). AWC represents the available water-storage capacity by accounting for the textural class of the topsoil<sup>77</sup>. AWC classes 1–7 represent capacities of 150, 125, 100, 75, 50, 15 and 0 mm m⁻¹, respectively<sup>77</sup>. Mean annual precipitation and temperature were derived from the CRU TS 4.01 dataset, and represent the long-term climatic norms for 1901–2015. Tree density was used to represent the competitive pressure among individual trees, derived from a global map of tree density<sup>78</sup>.

Nine plant traits were obtained from published data. P50 and HSM were obtained from Choat et al.<sup>30</sup>. Canopy height was obtained from the Global 1 km Forest Canopy Height dataset<sup>79</sup> (https://webmap.ornl.gov/ogc/dataset.jsp?dg_id=10023_1). Maximum rooting depth was derived from the project of Global Earth Observation for Integrated Water Resource Assessment (Earth2Observe, https://wci.earth2observe.eu/thredds/catalog/usc/root-depth/catalog.html). WD was obtained from the Global Wood Density Database<sup>80,81</sup>, calculated as dry mass divided by fresh volume. Isohydricity was derived from Konings et al.<sup>15</sup> (https://github.com/agkonings/isohydricity). SLA, Nm and Pm were derived from the trait maps developed by Butler et al.<sup>35</sup> based on the TRY database (www try-db.org). We extracted data from these datasets for the 2,935 RWI sites based on latitude and longitude.

**Identification of season categories.** The dry and wet seasons were defined following the method used by Huang et al.<sup>10</sup>. We first calculated monthly P-PET anomalies relative to the means for 1901–2015 using the P-PET index and then tracked the changes in a time series of P-PET anomalies (see examples in Extended Data Fig. 1). The dry and wet seasons were defined as P-PET anomalies below and above zero, respectively, in at least two consecutive months.

**Drought classification.** We classified drought timings as DS (drought in dry seasons of a year), WS (droughts in wet seasons of a year) and DS + WS (droughts in both wet and dry seasons of a year). Mean monthly SPEIs in dry and wet seasons for all years for the period 1901–2015 were sorted in ascending order, and the drought threshold was defined as the tenth percentile of the data distribution
(Extended Data Fig. 2a,b). We identified three classes of drought years based on the drought thresholds in the dry and wet seasons: (1) DS drought year, with mean SPEI below the threshold in the dry season; (2) WS drought year, with mean SPEI below the threshold in the wet season; and (3) DS + WS drought year, with mean SPEIs below the thresholds in both the dry and wet seasons (Extended Data Fig. 2c-e).

We focused only on individual droughts, ignoring consecutive drought years and any droughts within 2 years of an extreme drought. We retained only drought years when both the SPEI and tree ring data had valid records. We identified 2,576 RWI sites that experienced DS, 2,333 that experienced WS and 2,649 that experienced DS + WS (Supplementary Fig. 1).

**Resistance and resilience indices.** Rt and Rs were calculated for each drought year and site based on the definitions proposed by Isbell et al.²⁸:

$$Rt = \frac{\bar{Y}_n}{|Y_e - \bar{Y}_n|} \qquad (1)$$

$$Rs = \frac{|Y_e - \bar{Y}_n|}{Y_{e+2} - \bar{Y}_n} \qquad (2)$$

where $\bar{Y}_n$ is mean RWI during years with no droughts, $Y_e$ is RWI in a drought year and $Y_{e+2}$ is RWI 2 years after a drought. We selected 2 years of unperturbed forest recovery to measure resilience, because previous studies²⁸,²⁹ have indicated that the effects of drought on forest growth last for 2 years, and Schwalm et al.⁵⁵ reported that most vegetation will recover from drought within 2 years.

**Spatial analysis.** We first calculated mean resistance and resilience for DS, WS and DS + WS for each tree ring site and mapped their spatial distributions (Fig. 1a,b and Extended Data Fig. 3). We then compared the data distributions of the natural logarithms of resistance and resilience (ln(Rt) and ln(Rs)) for gymnosperms and angiosperms in the drought timings (DS, WS and DS + WS) (Fig. 1c,d and Extended Data Fig. 1). Significant differences (*P* < 0.05) were identified using one-way analysis of variance (ANOVA).

We applied the BRT model to quantify the relative contributions of 16 factors to resistance and resilience, to explore the links between plant traits and spatial variations in resistance and resilience. The BRT model is a machine learning approach used to obtain regression and boosting³⁴. It improves predictive performance by introducing randomness into the model, and achieves optimal prediction by controlling the parameters of learning rate and tree complexity. The advantages of this approach are that it can cope with different types of predictor variables (both numeric and categorical), accommodate missing data and handle interactive effects between predictors³⁴.

We fitted BRT models for the response variables (resistance and resilience) for all three drought timings and forest groups (gymnosperms and angiosperms). The BRT models were fitted for the gymnosperm and angiosperm tree ring sites that experienced DS (DS<sub>G</sub>, DS<sub>A</sub>), WS (WS<sub>G</sub>, WS<sub>A</sub>) and DS + WS (DS + WS<sub>G</sub>, DS + WS<sub>A</sub>) (Fig. 2). All numeric factors were first standardized (z-score). Response variables means of drought years for each site were natural-log transformed (ln(Rt) and ln(Rs)) to meet the assumption of normality in the BRT models. We defined a tree complexity of 5, a learning rate of 0.001 and a bag fraction of 0.5 when building the BRT models using the gbm R package⁸². Tree ring-based resistance (or resilience) is significantly correlated with BRT-predicted resistance (or resilience), with $R^2$ values ranging from 0.27 to 0.53 (Supplementary Fig. 2, *P* < 0.05). Our results showed the co-contribution of multiple factors, rather than the leading contribution of a single factor, to the spatial variation of resistance and resilience for gymnosperms and angiosperms in different drought timings (Fig. 2). Given that, we explored whether groupings of similar factors, such as hydraulic traits and foliar economic traits (see factors with the same colour in Fig. 2), can account for a large proportion of the variation in resistance and resilience. According to the relative contributions of specific factors shown in Fig. 2, we discussed the mechanisms linking the three most important factors to the strategies of gymnosperms and angiosperms in different drought timings.

**Temporal analysis.** To detect variations in resistance and resilience over time, we selected (1) 290 gymnosperm sites and 6 angiosperm sites that experienced droughts in all four periods (before 1950, 1950–1969, 1970–1989 and 1990–2009; Extended Data Fig. 6 and Supplementary Fig. 3a); and (2) 332 gymnosperm sites and 72 angiosperm sites that experienced droughts in all three periods after 1950 (1950–1969, 1970–1989 and 1990–2009; Fig. 3 and Supplementary Fig. 3b). For each period, mean resistance and resilience of all drought years for each site were derived and transformed into ln(Rt + 1) and ln(Rs + 1). The data distribution and kernel density curves of resistance and resilience were not normally distributed, so significant differences (*P* < 0.05) among the periods were identified using the Kruskal–Wallis non-parametric test⁸³. Because we found significant differences (*P* < 0.05) between the periods after 1950, we compared the changes in resistance and resilience for all gymnosperm and angiosperm sites for the periods 1970–1989 and 1990–2009 to 1950–1969, and displayed the percentages of sites with positive or negative change (Fig. 3 and Extended Data Fig. 6).

We further selected 161 sites that had experienced at least five droughts over the period 1950–2009 from the 332 gymnosperm sites, and temporally correlated the changes in resistance and resilience (Fig. 4a–c) to find evidence of a trade-off between the two metrics after 1950. We also examined the spatial correlation of changes in resistance and resilience across all 332 gymnosperm sites (Supplementary Fig. 3b) for 1950–1969 versus 1970–1989, 1970–1989 versus 1990–2009 and 1950–1969 versus 1990–2009 (Fig. 4d,e).

Several regional studies have demonstrated that temporal variation in forest responses can be driven by multiple factors, including climatic conditions, drought severity and internal growth consistency³⁴,³⁵. Therefore, we attempted to identify the factors associated with temporal changes in resistance and resilience for gymnosperms and angiosperms. We spatially correlated the changes in resistance and resilience with those in averaged standardized chronologies, annual temperature, annual precipitation and drought severity between two consecutive periods (the differences between 1950–1969 and 1970–1989, and between 1970–1989 and 1990–2009) across sites (Extended Data Fig. 8).

Precipitation, temperature and RWI means were calculated differently for use in correlation with resistance versus resilience (Extended Data Fig. 8). To make correlations with resistance, we derived mean RWI, temperature and precipitation of the corresponding period for each site by averaging the values of drought years. For correlations with resilience, we derived mean RWI, temperature and precipitation of the corresponding period for each site by averaging the values of drought years and two consecutive years after drought. We also derived mean drought severity of the corresponding period for each site, by averaging SPEI of drought years for correlations with both resistance and resilience. The differences in mean RWI, SPEI, temperature and precipitation between two periods were derived and correlated with changes in resistance and resilience.

**Tropical forest plots.** Given that tropical forests are poorly represented by tree ring data, we followed the same method to calculate resistance and resilience indices in DS, WS and DS + WS using the aboveground biomass data from Amazonian forest plots measured since the 1980s⁸⁴,⁸⁵ (Extended Data Fig. 10). We excluded plot sites having either long census interval length or no drought events in their available data. A critical constraint when addressing temporal changes in drought sensitivity over the past 50 years from these tropical forest plot data is that biomass measurements are available only for periods of about 10–20 years with two or more biomass census intervals³⁴. Similar to angiosperm sites, biomass-based resistance and resilience in Amazonian forests show that resistance is not markedly different among drought timings but that resilience is lower in WS than in DS and DS + WS (Extended Data Figs. 1 and 10).

**Reporting Summary.** Further information on research design is available in the Nature Research Reporting Summary linked to this article.

## Data availability

All data from public sources used in this study can be obtained from https://figshare.com/articles/Trade-off Between gymnosperm resistance and resilience increases forest sensitivity to extreme drought/12047241.

## Code availability

Computer codes for the analysis of the data are available from https://github.com/LilyXiangyi/ForestResponse.

Received: 27 August 2019; Accepted: 5 May 2020; Published online: 15 June 2020

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## Acknowledgements

This study was supported by the National Natural Science Foundation of China (nos. 41530528 and 41988101) and the Second Tibetan Plateau Scientific Expedition and Research Program (no. 2019QZKK0208). P.C. and J.P. acknowledge support from European Research Council Synergy project SyG-2013-610028 IMBALANCE-P and P.C. acknowledges support from the ANR CLAND Convergence Institute. We gratefully acknowledge all voluntary researchers who have contributed to and maintained the ITRDB. We also thank A. K. Post for proofreading the article.

## Author contributions

S.P. designed the research. X.L. performed analysis and created all the figures. X.L. and S.P. drafted the paper. All authors contributed to interpretation of the results and to the text.

## Competing interests

The authors declare no competing interests.

## Additional information

*Extended data* is available for this paper at https://doi.org/10.1038/s41559-020-1217-3.

*Supplementary information* is available for this paper at https://doi.org/10.1038/s41559-020-1217-3.

*Correspondence and requests for materials* should be addressed to S.P.

*Reprints and permissions information* is available at www.nature.com/reprints.

*Publisher's note* Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

© The Author(s), under exclusive licence to Springer Nature Limited 2020
a

<figure><img src="image_14.png" /></figure>

b

<figure><img src="image_15.png" /></figure>

c

<figure><img src="image_16.png" /></figure>

d

<figure><img src="image_17.png" /></figure>

e

<figure><img src="image_18.png" /></figure>

**Extended Data Fig. 1** | Examples for identifying season categories. (a) Location of the ring-width index (RWI) at four sites, co551 (40.05°N, 105.43°W), chin048 (30.30°N, 91.52°E), zimb001 (18.67°S, 26.68°E), and ausl004 (32.00°S, 115.65°E). The variation of monthly precipitation minus potential evapotranspiration (P-PET) anomalies divided each drought year into a dry season and a wet season (see details of identification of season categories in Methods) for RWI sites (b) co551 in North America with an elevation of 2560m, (c) chin048 in Asia (elevation missing), (d) zimb001 in Africa with an elevation of 1085m, and (e) ausl004 in Australia with an elevation of 328m. The shaded areas in (b-e) indicate the dry season. The names of the four RWI sites are taken from the ITRDB tree-ring database.
<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

**Extended Data Fig. 2** | Examples for identifying drought classification for a drought year using RWI site co551. The drought threshold in (a) the dry season (Threshold<sub>DS</sub>) and (b) the wet season (Threshold<sub>WS</sub>), derived from the tenth percentile of the data distribution (see details of drought classification in Methods). c, 1966, a DS drought year with a drought only in the dry season, indicated by a mean SPEI in the dry season lower than the drought threshold in the dry season (SPEI<sub>DS</sub> < Threshold<sub>DS</sub>) but a mean SPEI in the wet season higher than the drought threshold in the wet season (SPEI<sub>WS</sub> > Threshold<sub>WS</sub>). d, 1981, a WS drought year with a drought only in the wet season, indicated by SPEI<sub>DS</sub> > Threshold<sub>DS</sub> but SPEI<sub>WS</sub> < Threshold<sub>WS</sub>. e, 1977, a DS+WS drought year with a drought in both the dry and wet seasons, indicated by SPEI<sub>DS</sub> < Threshold<sub>DS</sub> and SPEI<sub>WS</sub> < Threshold<sub>WS</sub>.
<figure><img src="image_24.png" /><figcaption>b Mean resilience in DS drought</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>c Mean resistance in WS drought</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>d Mean resilience in WS drought</figcaption></figure>

<figure><img src="image_27.png" /></figure>

**Extended Data Fig. 3** | Mean resistance and resilience of gymnosperms and angiosperms in drought years. **a**, resistance to drought in the dry season (DS droughts), **(b)** resilience to drought in the dry season (DS droughts), **(c)** resistance to drought in the wet season (WS droughts), **(d)** resilience to drought in the wet season (WS droughts). Circles represent the gymnosperm tree-ring sites, and triangles represent the angiosperm tree-ring sites. The color bars denote the range of magnitude of (a, c) mean resistance (Rt) and (b, d) mean resilience (Rs), respectively.
<table><thead><tr><th></th><th></th><th>DS+WS drought</th><th>DS drought</th><th>WS drought</th></tr></thead><tbody><tr><td rowspan="2">Resistance</td><td>Gymnosperms</td><td>2.40<sup>b</sup></td><td>2.33<sup>b</sup></td><td>2.50<sup>a</sup></td></tr><tr><td>Angiosperms</td><td>2.26<sup>a</sup></td><td>2.22<sup>a</sup></td><td>2.34<sup>a</sup></td></tr><tr><td rowspan="2">Resilience</td><td>Gymnosperms</td><td>0.72<sup>a</sup></td><td>0.58<sup>b</sup></td><td>0.42<sup>c</sup></td></tr><tr><td>Angiosperms</td><td>0.76<sup>a</sup></td><td>0.69<sup>a</sup></td><td>0.49<sup>b</sup></td></tr></tbody></table>

Extended Data Fig. 4 | Comparison of ln-transformed resistance (ln(Rt)) and resilience (ln(Rs)) for drought years in both the dry and wet seasons (DS+WS drought), the dry season (DS drought), and the wet season (WS drought) for gymnosperms and angiosperms. For each row, different letters within a row represent significant differences (p < 0.05) among the drought timings. Values shown in the table are all unitless.
<figure><img src="image_28.png" /><figcaption>**Extended Data Fig. 5** | Drought severity (indicated by SPEI) of WS, DS and DS+WS droughts at gymnosperm and angiosperm tree-ring sites.</figcaption></figure>

Significant differences (*p* < 0.05) among the drought timings were identified by an analysis of variance (one-way ANOVA) and are marked by different letters for gymnosperms (denoted by filled boxplots) and for angiosperms (denoted by open boxplots). The boxes indicate the 25th and 75th percentiles, and the lines in the boxes indicate the medians.
<figure><img src="image_29.png" /><figcaption>b Temporal change of angiosperm resistance</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>c Temporal change of gymnosperm resilience</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>d Temporal change of angiosperm resilience</figcaption></figure>

<figure><img src="image_32.png" /></figure>

**Extended Data Fig. 6** | Temporal changes of resistance and resilience for gymnosperm and angiosperm forests during 1910-2009. Temporal change of (a) gymnosperm resistance, (b) angiosperm resistance, (c) gymnosperm resilience, (d) angiosperm resilience. 290 gymnosperm sites in (a) and (c) and 65 angiosperm sites in (b) and (d) experiencing droughts in all four periods, i.e. before 1950, 1950-1969, 1970-1989, and 1990-2009 were used for temporal analysis. The x-axis represents the range of ln-transformed resistance (ln(Rt + 1)) and resilience (ln(Rs + 1)) at each RWI site during three periods. Histograms (left y-axis) show the distribution of ln (Rt + 1) and ln(Rs + 1) and lines (right y-axis) show the kernel density distribution of ln(Rt + 1) and ln(Rs + 1) during the period of before 1950 (gray), 1950-1969 (blue), 1970-1989 (orange), and 1990-2009 (purple). The boxplots in the inset of each panel show the distribution of temporal change in 1970-1989 (orange) and 1990-2009 (purple) relative to 1950-1969 for all sites. The yellow lines represent median values and points represent mean values in the boxes. The percentages of sites showing positive change (+) and negative change (-) are displayed in the panels. Significant differences (p < 0.05) of all sites between 1970-1989 and 1950-1969, and between 1990-2009 and 1950-1969 were identified by Kruskal-Wallis test and denoted by asterisks (+).
<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

**Extended Data Fig. 7** | Comparison of hydraulic safety margin (HSM) and specific leaf area (SLA) between gymnosperms and angiosperms. a, Empirical cumulative distribution function (CDF) of HSM between gymnosperms (black) and angiosperms (blue). b, Empirical CDF of SLA between gymnosperms and angiosperms.
a

Correlation with RWI

<figure><img src="image_35.png" /></figure>

b

Correlation with drought severity

<figure><img src="image_36.png" /></figure>

c

Correlation with temperature

<figure><img src="image_37.png" /></figure>

d

Correlation with precipitation

<figure><img src="image_38.png" /></figure>

**Extended Data Fig. 8** | Spatial correlations of differences in resistance and resilience with differences in tree growth, temperature, precipitation, and drought severity between two consecutive periods. **a**, Correlations between resistance and RWI (upper panel), and between resilience and RWI (lower panel) across gymnosperm (blue bars) and angiosperm (orange bars) forest sites. **b**, Correlations between resistance and drought severity (upper panel) and between resilience and drought severity (lower panel) across gymnosperm and angiosperm forest sites. **c**, Correlations between resistance and temperature (upper panel) and between resilience and temperature (lower panel) across gymnosperm and angiosperm forest sites. **d**, Correlations between resistance and precipitation (upper panel) and between resilience and precipitation (lower panel) across gymnosperm and angiosperm forest sites. I, differences between 1950-1969 and 1970-1989; II, differences between 1970-1989 and 1990-2009. Significance correlations are denoted by *** (*p* < 0.001), ** (*p* < 0.01), and * (*p* < 0.05).
<figure><img src="image_39.png" /></figure>

**Extended Data Fig. 9 | An example of decrease in drought resistance with increase in drought severity.** A tree-ring site (32.43°N, 110.79°W, *Ponderosa pine*) in Arizona, USA presents a decrease in drought resistance with increase in drought severity during three periods (i.e. 1950-1969, 1970-1989, and 1990-2009). The name of the tree-ring site is az598 in the ITRDB tree-ring database. Blue bars (upper panel) denote the mean drought severity during each period and orange bars (lower panel) denote the mean resistance during each period. Arrows show the directions of changes in drought severity (blue) and resistance (orange) over time.
<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

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<figure><img src="image_47.png" /></figure>

**Extended Data Fig. 10** | Biomass-based resistance and resilience in Amazon forests. **a**, The spatial pattern of mean resistance derived from the measured above ground biomass in trees for 5 plots in DS drought in tropical South America, derived from Brienen et al., 2015 (ref 84,85). **b**, The spatial pattern of mean resistance derived from the measured aboveground biomass in trees for 18 plots in WS drought in tropical South America. **c**, The spatial pattern of mean resistance derived from the measured aboveground biomass in trees for 15 plots in DS+WS drought in tropical South America. **(d-f)** The same analysis as (a-c) but for biomass-based resilience in (d) DS, (e) WS, and (f) DS+WS drought. **g**, Mean In-transformed resistance (ln(Rt)) for plots that experienced DS, WS, and DS+WS droughts in tropical South America. **(h)** Mean In-transformed resilience (ln(Rs)) for plots experienced DS, WS, and DS+WS droughts in tropical South America. Boxplots in **(g)** and **(h)** show the median (horizontal lines), mean (triangle), 25th, 40th, 60th, and 75th percentiles (i.e. Q0.25, Q0.4, Q0.6, Q0.75; boxes), and maximum and minimum values (i.e. Max and Min; the top and bottom of the whiskers). Significant differences (p < 0.05) among DS, WS, and DS+WS droughts are denoted by different letters for multiple comparisons. The numbers in the bottom of the panels in **(g)** and **(h)** represent the magnitude of plots that experienced DS, WS, and DS+WS droughts, respectively.
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# Field-specific reporting

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<figure><img src="image_48.png" /></figure>

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<figure><img src="image_49.png" /></figure>

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# Ecological, evolutionary & environmental sciences study design

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**Study description**

We assessed the spatial and temporal trade-off of resistance and resilience for gymnosperms and angiosperms to extreme drought using a global tree ring records from 2935 sites over the past century.

**Research sample**

The data we use are all obtained from publicly available sources.

**Sampling strategy**

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**Data collection**

Existing datasets are downloaded from the sources. All datasets include: raw data of tree-ring widths from https://www1.ncdc.noaa.gov/pub/data/paleo/treering/; the Climatic Research Unit (CRU), version TS 4.01 from https:// crudata.uea.ac.uk/cru/data/hrg/; the gridded SPEI data (SPEIbase v2.5) from http:// digital.csic.es/handle/10261/153475; the Regripped Harmonized World Soil Database v1.2 from https:// daac.ornl.gov/SOILS/guides/HWSD.html; canopy height from https:// webmap.ornl.gov/ogc/dataset.jsp?dg_id=10023_1; maximum rooting depth from https:// wci.earth2observe.eu/thredds/catalog/usc/root-depth/catalog.html; wood density from https:// datadryad.org/handle/10255/dryad.235; SLA, Nm, and Pm from https:// github.com/abhirupdatta/globalMapsOfPlantTraits; HSM and P50 from https:// www.nature.com/articles/nature11688; isohydricity data from https:// github.com/agkonings/isohydricity; tree density from https:// elischolar.library.yale.edu/yale_fes_data/1/; above ground biomass of tropical forest plots from http:// www.forestplots.net/data-packages/brienen-et-al-2015.

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We used global tree ring sites and climate data from 1901-2015.

**Data exclusions**

We excluded chronologies of tree-ring widths not following criteria: availability of metadata (longitude, latitude, elevation, and species code), tree-ring series covering at least 25 years during 1901-2015, and types of tree-ring measurements providing total ring widths.

**Reproducibility**

N/A

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N/A

**Did the study involve field work?**

<figure><img src="image_51.png" /></figure>

Yes

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No

# Reporting for specific materials, systems and methods

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<figure><img src="image_53.png" /></figure>

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