Plants rely on both newly assimilated carbon and stored reserves of non-structural carbohydrates (NSC) for growth and other physiological functions such as respiration, osmotic regulation and defence1,2. As NSC stores reflect the balance of carbon supply via photosynthesis and its utilisation for plant metabolism, they are highly dynamic in time3. NSC stored during periods when supply exceeds demand are thought to constitute an important buffer during periods of environmental stress when carbon demand outstrips supply2,4. As a result of this, considerable attention has been paid to the potential role of stored NSC in mediating tree tolerance and survival under drought5,6, during which stomatal conductance and assimilation rates are reduced to prevent water loss7. Experimental studies on both temperate8 and tropical9 seedlings have indicated an important role of NSC in the physiological mechanism of mortality, as plants with higher NSC content had higher survivorship under drought. However, the extent to which NSC metabolism moderates tolerance to water deficit in adult trees over large geographical domains remains unclear10.

An understanding of the functional role of NSC in response to water deficit is of particular importance for the Amazon rainforest, the Earth’s largest tropical forest region and a major terrestrial carbon sink, responsible for absorbing 5–10% of global anthropogenic CO2 emissions11. Over the last 15 years, the Amazon has been subject to three large-scale drought events12,13,14 and climate models project an intensification of drought risk over large parts of the Basin in the future15. Recent modelling results suggest that NSC play an important role in regulating the impacts of drought on carbon fluxes in the Amazon by maintaining growth under water deficit16. Moreover, observations of sustained stand-scale net primary productivity during the 2010 drought have also led to suggestions that Amazon trees deplete their NSC reserves during periods of water stress to prioritise growth4. However, empirical studies of NSC dynamics in tropical forests are rare, being limited to a small number of sites and species17,18,19. While a study in Panama found that NSC concentrations increased in the dry season18, the only detailed community-level study in lowland Amazon forests, in a throughfall exclusion experiment in Eastern Amazonia, found that NSC reserves in trees subjected to long-term drought did not differ from those in unstressed trees20. Yet Amazon forests vary greatly in climate21, soils22 and plant life history strategies23,24,25, of which could potentially influence NSC dynamics and forest response to climate change1. The scarcity of empirical data in Amazonia impedes understanding of the significance of NSC in modulating forest responses to water stress and thus limits current vegetation model development efforts to simulate drought impacts on tropical forests16,26.

To address this significant data gap, we conduct a large-scale sampling of NSC across Amazon forests, using fully standardised field and laboratory protocols, performing all NSC analyses in the same lab (see Methods). Such standardisation is critical as differences in sampling and laboratory extraction protocols can yield substantial variation in NSC estimates, obstructing meta-analysis and comparisons across studies27,28. We analyse the concentration of NSC in leaves and branches of 82 canopy tree species in six sites across the Amazon Basin (Fig. 1; Table 1; Supplementary Table 1) effectively spanning the entire Amazon gradient in mean annual precipitation (1167–3155 mm year−1; Fig. 1) and seasonality (0–7 months with rainfall ≤100 mm month−1) and including one site (Man) that experienced an atypically strong drought event14. We collect the plant material for NSC analyses in all sites during wet months (precipitation >100 mm month−1), hereafter referred to as the wet season. For the four sites with more marked seasonality (Ken, Fec, Man and Tam), we also collect plant material in the peak of the dry season, where monthly precipitation is ≤100 mm month−1 (Supplementary Fig. 1). We focus not only on total NSC concentrations (NSCT) but also on the partitioning of NSC into its two major components: soluble sugars (i.e., oligosaccharides such as glucose, sucrose, fructose, etc.) and starch, as these fulfil distinct roles in plants26. Soluble sugars (SS) provide an immediate energy substrate for respiration, defence, plant stress signalling, phloem transport and osmoregulation1. Starch represents a transient or long-term energy store that plants can convert to SS for use when C demand exceeds supply29. To characterise plant water status at the time of NSC sampling, we measure midday leaf water potential (ΨMD) in all sites during the dry season, and in the two sites without a climatological dry season (Alp and Suc). Community-level (mean value of all species in each site) ΨMD ranges from −0.62 ± 0.05 MPa (mean ± SE) in the ever-wet Suc and Alp sites to −2.18 ± 0.30 MPa in the ecotonal Ken site with the longest dry season.

Fig. 1: Location of sampled sites and monthly precipitation.
figure 1

The map depicts mean annual precipitation in South America (mm year−1). The Amazon basin is bounded by the black outline. Bar plots show the monthly precipitation for each site, blue represents precipitation >100 mm month−1 and red ≤100 mm month−1. Data for the map and bar plots are from WorldClim v2 (1970–2010, 30 s resolution)51.

Table 1 Site information.

We use this multi-site dataset to gain insights into how NSC and water deficit responses are related across Amazonian forests. We evaluate how leaf and branch NSC vary with water availability, both in space and seasonally, and also the relative roles of taxonomy and environment in determining NSC concentrations. Based on ecosystem modelling results16 and observations suggesting prioritisation of aboveground growth under drought in Amazonia4, we hypothesise that the drier sites would experience more seasonality in NSC stores and would also have greater NSC stores in the wet season. Our analyses reveal an important role of non-structural carbohydrates, and soluble sugars in particular, in mediating responses to seasonal water stress in Amazonian forests (Abstract available in Portuguese and Spanish, Supplementary Notes 1, 2).

Results and discussion

Wet season (baseline) NSC: role of environment vs. taxonomy

Despite the wide range of species sampled and the differences in species composition across our study sites, we found little variation in leaf and branch NSCT and its components across sites in the wet season (monthly precipitation > 100 mm month−1). In fact, wet season NSCT and SS in both leaves and branches did not differ significantly across sites (Fig. 2a, e; Supplementary Fig. 2; Supplementary Table 2). Overall, our results support the vegetation modelling assumption of spatially invariant baseline community-level NSCT16, thus potentially simplifying modelling of NSC dynamics in Amazonia. Only leaf starch exhibited significant differences across sites in the wet period, being markedly lower in the moderately seasonal sites (Man and Tam; p = 0.001, Supplementary Fig. 2) than in the driest Ken site and the two wettest sites (Suc and Alp). The higher wet season leaf starch concentrations in these sites may represent important strategies for maintaining function under an extended period of depleted water availability29 in the case of Ken or light limitation30 in the case of Suc and Alp.

Fig. 2: Species-mean total NSC (NSCT) and variance partitioning into site and taxonomic components.
figure 2

Concentrations of NSCT and variance partitioning results are displayed separately for leaves (panels ad) and branches (panels eh) during the wet and dry seasons. Left hand panels: Each box encompasses the 25th to 75th percentiles; the median is indicated by the horizontal line with each box while external horizontal lines indicate the 10th and 90th percentiles; dots indicate outliers. Sites are ordered and colour-coded from left to right from driest to wettest; red to yellow boxes represent the seasonal sites and two blue boxes the aseasonal sites; n indicates the number of species sampled in each site. Differences among sites were tested using Kruskal–Wallis. Sites with different letters are statistically distinguishable (p < 0.05, post hoc Mann–Whitney–Wilcoxon Rank Sum test using Bonferroni correction is indicated by small letters). Right hand panels: Partitioning of total variance of NSCT, starch and soluble sugars into genetic (family/genus/species), environmental (site) and error (residual) components; for the variance partitioning analysis, values were log1p-transformed prior to analysis.

We find that nested family–genus–species identity is very important, explaining much more of the overall variation in wet season NSCT and SS than sampling site for both leaves and branches (Fig. 2b, f). Taxonomy was a particularly important control for leaf NSC, explaining 67% and 72% of the wet season variation in leaf NSCT and SS (Fig. 2b). Within individual sites, there were clear species-level differences in NSCT and SS, which varied by a factor of between 4 and 10 across species (Supplementary Table 3, Supplementary Fig. 3 & 4). Moreover, when species occurred across more than one site, they largely maintained similar wet season levels of NSC and its fractions across sites (Supplementary Figs. 58). Despite the large amount of variance explained by taxonomy, NSCT and its constituent fractions are generally not related to plant traits that are indicative of life history strategies25, such as potential tree size, mean growth, mortality rates and wood density19 (Supplementary Figs. 9 and 10). Although our data on plant phenological strategies were limited, we do find that evergreen species have higher total leaf NSCT (p = 0.023) and SS (p = 0.012) in the wet season than semideciduous/deciduous species (Supplementary Figs. 11 and 12; Supplementary Table 4). The strong taxonomic influence on leaf NSCT and SS likely further relates to physiological attributes for which we have little current information. Leaves have intense metabolic requirements due to their roles in photosynthesis and phloem loading31 and face greater osmoregulatory and defence demands than other plant organs32. Thus, differences across taxa in photosynthetic and respiration rates as well as osmoregulatory and defence mechanisms, although little studied, may help to explain the strong taxonomic signatures we find.

NSC seasonality

NSCT exhibited greater differences across sites in the dry season compared to the wet season (Fig. 2c, g). This was especially the case for leaf NSC, where NSCT, SS and starch exhibited significant differences across sites, while in branches, only SS varied across sites (Fig. 2 and Supplementary Fig. 2). In line with this, our variance partitioning analysis also showed that site accounted for more of the overall variation in NSC metrics in the dry season than it did in the wet season (Fig. 2d, h).

Seasonal patterns of NSC across sites diverged markedly (Fig. 3; Supplementary Table 5; Supplementary Figs. 13 and 14). In the two driest sites in our network, Ken and Fec, we find strong evidence of mobilisation of starch reserves to SS. In Ken, the driest site evaluated, leaf starch reserves declined by 81% (p < 0.001) in the dry season while leaf SS concentrations remained unchanged (p = 1), despite a 43% reduction in leaf NSCT (Fig. 3, p = 0.019). In Fec, the second driest site evaluated, leaf starch concentrations also decreased markedly in the dry season (72% reduction; p < 0.001). However, in this site, significant increases in leaf (32% increase; p < 0.001) and branch (48% increase; p < 0.001; Fig. 2) SS were observed, with an overall increase in leaf NSCT in the dry season. The reduction of leaf NSCT in Ken but not in Fec may be attributed to stronger source limitation in the driest Ken site (Supplementary Fig. 15)33. In the two less water-limited sites for which we had data in both seasons (Man and Tam), we interpret the seasonal dynamics of NSCT and its fractions to be driven mainly by growth. In the Man site, productivity is maintained at high levels during the dry season34 (Supplementary Fig. 5), and the observed depletion of branch starch (60% reduction; p < 0.001) accompanied by an increase in SS concentrations (30% increase; p = 0.001) may be associated with enhanced branch growth35. In Tam, however, the dominant pattern was one of greater leaf NSCT and SS in the wet season (Fig. 3), which we attribute to higher productivity in the wet season, as observed by in situ NPP measurements36 and also seen in MODIS-derived Enhanced Vegetation Index (EVI) values (Supplementary Figs. 15 and 16).

Fig. 3: Seasonal variation of total NSC (NSCT), starch and soluble sugars (SS) across Amazon forests.
figure 3

Sites are ordered from left to right, from driest to wettest site. Panels a, c and e represent leaves and panels b, d and f represent branches. Red boxes denote the dry season and blue boxes denote the wet season. Each box encompasses the 25th to 75th percentiles; the median is indicated by the horizontal line with each box while external horizontal lines indicate the 10th and 90th percentiles; dots indicate outliers. Number of species sampled in each site are the same in the dry and wet season and are as follows: Ken = 9, Fec = 14, Man = 13, Tam = 21. To test for differences between season within site we used paired sample Wilcoxon tests.

Relationship between leaf NSC and water potential

Despite the widely varying seasonal patterns across sites, we find a strong relationship between ΨMD measured in the driest period of the year and the proportion of leaf NSCT allocated to SS (SS:NSCT) (Fig. 4a; p = 0.02, R2 = 0.77). This relationship also exists when taking predawn leaf water potential (ΨPD) as a measure of water status and at the species level as well as the community-level (Supplementary Fig. 17) and is underpinned by a strong decline across sites in leaf starch concentrations with increasing water deficit (Fig. 4c; p = 0.001, R2 = 0.93). This relationship points to an notable increase of SS relative to starch in leaves during periods of water stress and is reinforced by the fact that in the two driest sites we found that almost all species increased SS:NSCT in leaves during the dry season (Ken: p = 0.007, Fec: p < 0.001; Fig. 4b; Supplementary Figs. 18 and 19). Indeed, in several species in Ken and Fec, dry season leaf starch reserves were effectively exhausted (zero or very close to zero) while leaf SS levels were unchanged (Ken) or increased (Fec). Furthermore, species co-occurring in both Fec and in the more mesic Tam site almost all had higher dry season foliar SS and lower foliar starch content in the drier site (Supplementary Figs. 20 and 21). However, we do not find evidence of enhanced conversion of foliar starch to SS in Man (Fig. 4b), a mesic site where species are less-adapted to prolonged water deficit, despite sampling during one of the most intense droughts on record at that site (Supplementary Fig. 1).

Fig. 4: Relationship between leaf NSC and water status, and distributions of species-level seasonal shifts in leaf NSC allocation.
figure 4

Relationship between a leaf SS:NSCT (proportion of leaf NSCT in the form of soluble sugars) and midday leaf water potential (ΨMD); c leaf starch and ΨMD. Distributions of species-level seasonal shifts in b leaf SS:NSCT allocation (SS:NSCT Wet − SS:NSCT Dry) and d starch allocation (starch wet − starch dry). In the panel a and c the SS:NSCT, starch and ΨMD represent mean of all species sampled in each site in the dry season, except in the two sites where there is no dry season (monthly precipitation ≤ 100 mm month−1). Vertical and horizontal bars denote one standard error of the mean. The relationship between SS:NSCT, starch and ΨMD was fitted using standardised major axis (SMA) regression. In the panel b and d sites are ordered from top to bottom from drier to wettest. Long vertical black line denotate the mean ΔSS:NSCT and starch, each dot represents one species. Values to the left of the blue line denote species that increased SS:NSCT and starch in the dry season while those to the right of the line increased SS:NSCT and starch in the wet season.

The increase of foliar SS relative to starch in the driest sites (Fig. 4b) and its strong relationship with community-level water status (Fig. 4a) suggest an important role of foliar SS in mediating responses to water deficit in Amazonian forests. The increased allocation of leaf NSC to SS in these sites is further independent of the seasonal behaviour of NSCT (Supplementary Fig. 22) and thus does not simply result from changes in source-sink dynamics. Studies mainly on agricultural crop systems37,38,39 and on a small number of shrubs40 and trees41 have shown that foliar SS can be very important for osmotic adjustment, actively accumulating in response to declining water potentials and thus helping plants to avoid dessication42. Our study suggests this phenomenon is widespread and that leaf SS contributes to the maintenance of hydraulic function in adult tropical trees across a broad range of taxa. A key future focus of research should be the identification of the specific sugars involved in osmoregulation in tropical plants. Studies on herbaceous species and temperate tree species suggest an important osmoregulatory role of oligosaccharides such as raffinose and pinitol43,44,45, but their role in osmoregulation in tropical forest trees is unknown.

The ability of species to rapidly mobilise leaf starch into SS under water deficit is likely an important mechanism for tolerating water stress46. While our study shows that species found in drier forests of the Amazon almost all have this capacity, it is far from clear that species in less seasonal regions of the Amazon are able to adjust their SS balance to the same extent. Indeed, the lack of seasonal adjustment in leaf NSC allocation to SS under exceptional water stress in the mesic Man site suggests that tree species that are less adapted to strong seasonal drought may not have the capacity to rapidly adjust foliar SS under water stress. This capacity may ultimately be an important determinant of future Amazon forest composition under continued climate change.


Sites and species

Plant material for NSC analysis was collected in six Amazonian sites (Fig. 1 and Table 1). These sites were selected from the RAINFOR network of permanent and well-identified forest plots12,47,48 as representing the Amazon-wide gradient in mean annual precipitation (MAP) and seasonality (Supplementary Fig. 22 and 23). The study sites also encompass a broad range of soil types and forest plant communities22,49. Our sites included aseasonal forests with no climatological dry season (no months with rainfall ≤100 mm50; Alp, Suc), forests with a moderate dry season (1–4 months with rainfall ≤100 mm; Fec, Man, Tam) and a transitional forest in the most seasonal site at the southern margin of the Amazon biome (7 months with rainfall <100 mm; Ken)51. Sampling took place during what were climatologically normal years in most sites, with the exception of Man, where sampling took place during the strong 2015 El Niño drought event (Supplementary Fig. 1). In total we sampled 82 canopy tree species, from 63 genera and 29 families (Supplementary Table 1). In each site, sampling was focused on the most dominant canopy species in terms of basal area, with the total number of species sampled at each site ranging from 9 to 31 (Table 1). Species-level identification of all trees sampled is based on botanical vouchers previously collected and deposited in Amazon state herbaria (AMAZ, CUZ, HOXA, INPA, UFACPZ, USZ) by RAINFOR partners. All plot trees are tagged and identifications were obtained from the database48 (; Ken, Fec, Tam, Alp and Suc) and from collaborator databases for Man24. All branches collected had fully expanded leaves, no evidence of liana infestation or injuries and were not shaded. Species collected in the wet season that did not keep their leaves during the dry season were excluded from all analysis to avoid potential biases due to different phenological strategies1.

Non-structural carbohydrate (NSC) sampling and analysis

Two to six individuals were sampled per species, with all individuals being >20 cm in diameter at breast height (DBH). To minimise effects of diurnal changes in NSC concentrations, samples were obtained before sunrise in all sites, except Man. In Man, branches and leaves were obtained just after sunrise and always before 8 a.m. Leaf and branch samples were obtained by a tree climber from first order fully sunlit branches with fully expanded leaves and kept in ice during sampling and transported to the laboratory. Upon arrival at the laboratory, samples were microwaved for 90 s at 700 W to stop enzymatic activity that would otherwise affect NSC levels, and oven-dried at ~60 °C for at least 48 h or until they were completely dry (no >72 h). All NSC sample preparation and analyses were performed at the University of Campinas, in the laboratory of plant ecophysiology. Prior to NSC quantification, samples were ground to a fine powder (Geno/Grinder® SPEX SamplePrep mill). Branch samples had their bark removed before being ground.

Non-structural carbohydrates (NSC) are defined here as free, low molecular weight sugars (i.e., oligosaccharides such as glucose, fructose, sucrose, etc.) plus starch. NSC was analysed as described in Hoch et al.52 with minor modifications. Two replicates of each sample were analysed and the mean of the two replicates used as the sample NSC value. First, we diluted ~15 mg of the ground plant material with 1.6 mL of distilled water and then incubated in a water bath at 90–100 °C for 60 min to solubilise sugars. Then we took an aliquot of 700 μL from each sample (700 μL). We use the remaining aliquot volumes (900 μL) to determine the SS concentration using invertase from Saccharomyces cerevisiae (Sigma-Aldrich, St. Louis, MO, USA) to break down sucrose and fructose to glucose20. Additionally, for both reaction routines, we used GAHK (Glucose Assay Hexokinase Kit - Sigma-Aldrich, St. Louis, MO, USA) together with phosphoglucose isomerase from Saccharomyces cerevisiae (Sigma-Aldrich, St. Louis, MO, USA). The concentration of free glucose was measured photometrically in a 96-well microplate spectrophotometer at 340 μm (EPOCH-Biotek Instruments INC-Winooski, VT-USA). The aliquot that we initially separated was incubated overnight to react with Amyloglucosidase from Aspergillus niger (Sigma-Aldrich, St. Louis, MO, USA) to breakdown the total NSC to glucose. Thereafter total glucose (corresponding to NSC) was determined as described above and starch was calculated as total NSC minus soluble sugars. All NSC values are expressed in mg/g dry mass.

Predawn (ΨPD) and midday (ΨMD) leaf water potential

In situ predawn and midday leaf water potential (ΨPD and ΨMD respectively) measurements were made in the same day on the same trees for which we obtained samples for determination of NSC concentrations. We sampled 2–6 trees per species and measured the ΨPD and ΨMD in 2–5 canopy fully expanded leaves using a pressure chamber (PMS 1505D and PMS 1000, PMS instruments) and the values were then averaged per individual. ΨPD measurements were taken before sunrise from 3:30–5:30 and ΨMD from 11:00 am –2:30 pm. Water potential data collection took place in what is typically the driest time of the year in each sampling plot except in the Alp and Suc sites where there is no climatological dry season (months with precipitation ≤100 mm) and little seasonality in rainfall. Owing to logistical limitations, we did not measure ΨPD in the Man site.

Enhanced vegetation index (EVI)

To gain further insights into how seasonality in canopy productivity might affect our observations, we extracted Enhanced Vegetation Index (EVI) values for each site derived from the MODIS-MAIAC product, using data from 2003 to 201853. The surface reflectance data were normalised to nadir target and 45-degree solar zenith angle through the Bidirectional Reflectance Distribution function, at a spatial resolution of 1 km and aggregated to biweekly (16-day) composites using the median values in this product, before EVI calculation54,55. The EVI was calculated using Eq. 1:

$${\mathrm{EVI}} = 2.5 \ast \frac{{\rho {\mathrm{NIR}} - \rho {\mathrm{Red}}}}{{\rho {\mathrm{NIR}} + \left( {6 \ast \rho {\mathrm{Red}} - 7.5 \ast \rho {\mathrm{Blue}}} \right) + 1}}$$

where ρNIR is infrared reflectance, ρRed is red reflectance, and ρBlue is blue reflectance. The constants (6, 7.5, 1, and 2.5) in the divisor represent the aerosol coefficient adjustment of the atmosphere for the red and blue band, the adjustment factor for the soil and the gain factor, respectively55,56.

The composites were retrieved considering only cloud-free and low atmospheric turbidity according to MAIAC quality flags. Further information on image processing and correction are described in Dalagnol et al.54. MODIS pixel values were extracted considering the coordinate system of each site using raster57 and rgdal58 R packages.

Statistical analysis

We performed all statistical analysis in R (R Core Team 2018, version 3.6.2)59. Preliminary tests included: analysis of normality (Shapiro–Wilk), and homogeneity of variances (Fligner–Killeen) for each NSC fraction (NSCT, SS and starch) in each organ (branches and leaves). As NSCT, starch and SS were not normally distributed, these parameters were log1p-transformed60 prior to variance partitioning. For comparison of means across sites and seasons, data were not log1p-transformed and non-parametric tests were used.

To evaluate differences across sites in NSCT, SS and starch concentrations, a Kruskal–Wallis test was used (R base function). We conducted statistical analyses separately for each plant organ (leaves and branches) and season (wet and dry). When a significant site effect was found, a post hoc Mann–Whitney–Wilcoxon Rank Sum test using Bonferroni correction (from “Agricolae package in R61) was performed to evaluate which sites were significantly different. To evaluate whether there were significant differences in NSCT, starch and SS between seasons, we performed paired sample Wilcoxon tests (R base package) for each site and plant organ separately. The ALP and SUC sites were excluded from the seasonal analysis, as these sites were only sampled in one point in time. Figures were constructed using the ggplot2 package62 and to display the p-values in the figures we used the “ggpubr package (stat_compare_means function)63.

We conducted standardised major axis (SMA) regression, using the “smatr” package in R64 to assess relationships between NSCT, SS and starch and site level water-status (Ψmin and ΨPD) at both the community-level (mean species value per site) and species level. To account for differences across sites in seasonal source limitation we also tested for relationships between the ratio of SS and NSCT (SS:NSCT) and ΨMD and ΨPD. SMA regressions were conducted for each organ separately, using NSC values corresponding to the same season Ψ was measured. We further tested for bivariate relationships between species-mean NSC metrics and plant attributes indicative of species life history strategies, including branch wood density (Tavares et al. in prep and Barros et al. unpublished data), mean growth rate, potential maximum size and mean mortality rate25,65 using the lm function (R base package).

To determine the relative importance of taxonomy vs. measurement site in determining NSCT and its fractions, we undertook a variance partitioning analysis as described by Fyllas et al.23, where a multilevel model was first fitted for each NSC fraction, organ and season according to Eq. 2:

$$T = {\mathrm{\mu }} + p + {\mathrm{f}}/{\mathrm{g}}/{\mathrm{s}} + {\upvarepsilon}$$

where µ is the overall mean species value of each NSC fraction (T), p is the random site effect, i.e., the effect of the location at which each individual was found (soil and climate), f/g/s represents the random effect caused by the genetic structure of the data, i.e., that each individual belongs to a species (s), nested in a genus (g), nested in a family (f), and ε is the residual term, which includes both the within-species variability not explained by site, as well as any measurement error. All parameters were estimated by the Residual or Restricted Maximum Likelihood (REML) method with the “lme4” package in R66.

Reporting summary

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