Cell wall pectin reshapes leaf drought tolerance in dry forests

Domain: chemistry
Published: 2026-01-07
Editorial outcome: accept

Abstract
Cellular and leaf structural traits influence the regulation of leaf water balance, which in turn impacts leaf gas exchange, plant productivity and drought tolerance. Yet, the role of cell wall composition, and especially that of components that render cell walls flexible—such as pectin—in leaf water relations remains elusive. Here, we investigate the linkages among 26 traits, including cell wall composition, anatomy, and drought tolerance (described by pressure-volume curves) across 69 woody species from sub-tropical dry and wet forests. We find that the lower wilting point of species of dry forests relative to wet forests is associated with contrasting anatomy and wall composition. Pressure-volume traits correlate more strongly with wall composition, and particularly pectin concentration, in dry forests, and with anatomy in wet forests. Thus, pectin-enriched cell walls contribute to the ecological specialization of woody plants in dry versus wet forests. Our findings indicate that leaf hydraulic designs diverge according to two strategies: dry forest species vary in elastic and osmotic function via contrasting pectin concentration (“flexible cell wall” strategy), whereas wet forest species do so via contrasting palisade tissue investment (“stable leaf tissue” strategy). Overall, diversity in cell wall properties across species are strongly linked with drought tolerance.

Introduction
Water is essential for the function of leaf tissues, including photosynthesis, and tissue dehydration leads to mortality during drought1,2. The cell wall surrounding plant cells provides structural rigidity and allows for the maintenance of cell volume and hydrostatic pressure necessary for fundamental physiological processes3,4. Thus, variation in cell wall structure has been hypothesized to affect leaf water relations and drought tolerance5–7. Indeed, across species, traits, such as smaller leaf size, smaller cells, and thicker cell walls are associated with greater drought tolerance5,6,8–11. However, the role of cell wall composition as a determinant of cell and leaf drought tolerance remains poorly understood12,13. This knowledge gap hinders an integrated understanding of drought tolerance from the cellular to ecosystem level, limiting the accuracy of predictions regarding plant responses to climate change.

Many leaf-level traits associated with drought tolerance are likely influenced by cell wall composition. The turgor loss point (ψtlp, i.e., the water potential at turgor loss), osmotic potential at full turgor (πo) and bulk modulus of elasticity (εsym, wall elasticity) (measured by pressure-volume (P-V) curves) are key drought tolerance traits driving plant adaptation to diverse habitats14. Specifically, πo is the major determinant of ψtlp; plants from drier habitats exhibit more negative πo and ψtlp14–16. Additionally, these dry-adapted plants might exhibit rigid cell walls (high εsym), which can mechanically constrain shrinkage and water loss in cells at very negative πo or ψtlp6,17. Indeed, some have proposed that wall elasticity is linked to cell wall composition (e.g., concentrations of cellulose, hemicellulose, lignin, and pectin)13,18–21. While cellulose and lignin are recognized as forming the mechanical basis of cell walls18,20, a role for pectin in modulating elastic and osmotic adjustments (εsym and πo) through Ca²⁺ chelation has also been suggested22–25, potentially enhancing environmental responsiveness24,26,27. However, quantitative evidence for the impact of specific cell wall components on physiological traits remains limited and often debated, and these impacts appear to vary depending on the species and environmental context.

The diversification of angiosperms across aridity gradients involved the evolution of diverse leaf drought tolerant strategies. These strategies arise from the coordinated evolution of physiological traits and leaf structural investments14,28–30. When plants first colonized land, they inhabited regions with abundant water availability. Leaf design placed minimal emphasis on water limitations, instead prioritizing investments in structural support and photosynthetic tissues, such as palisade mesophyll31–33. Consequently, species experiencing limited water stress typically maintain relatively stable water relations through the coordination of physiological traits with inherently static anatomical and/or biochemical structure (e.g., cellulose and lignin)34,35. The rigidity provides structural stability but limits flexibility in response to changes in soil water content36.

As plants diversified into drier soils, they evolved more flexible and sensitive water regulation mechanisms35,37. These adaptations involved two alternative cellular modifications: (1) smaller cells with thicker walls, which have reduced cell volume, resulting in higher solute accumulation per unit cell volume, and thus higher osmotic concentration11, and which would have greater reduction in osmotic potentials for a given cell water loss, while the thicker walls provide essential structural reinforcement10; (2) thinner and more elastic cell walls, contributing to increased cell water storage, and improved maintenance of hydration even after stomatal closure triggered by drought stress1,7,29,36,38. The latter is well exemplified by succulents and resurrection plants23,39–41. Empirical evidence suggests that the enhanced cell wall elasticity observed in these species is attributable to abundant pectin23,39, representing a cost-effective matrix with composition that can be adjusted according to environmental conditions22,42. Under water stress, sunflower (Helianthus annuus) exhibits a 60% increase in pectin concentration compared to well-watered controls19. Thus, increasing pectin concentration may serve as an evolutionary solution to increase physiological flexibility with relatively low biomass investment in water-stressed environments. Yet, an important but unresolved question is whether diversification in cell wall composition, such as pectin concentration alters the mechanisms that coordinate physiological and structural traits.

We develop a comprehensive framework integrating leaf water relations, cell wall traits, and leaf morphology to examine the impact of cell wall composition on leaf water relations across species and biomes, thereby clarifying the physiological mechanisms linking cell wall design and soil water availability. We hypothesize that cell wall composition, and particularly pectin concentration, enhances drought tolerance, and that these effects are modulated by water availability. To test this hypothesis, we measure 26 traits using standardized methods across 69 tree species (57 genera, 33 families) in four spatially independent sub-tropical forests. These forests–two wet and two dry–were selected for comparable climate but contrasting edaphic hydrology (Supplementary Fig. 1, Supplementary Table 1). Wet forests develop on deeply weathered lateritic soils (>200 cm) over siliceous rock, characterized by high water retention capacity43,44. Conversely, dry forests occur on karst landscapes with exposed limestone bedrock and shallow calcareous soils (<45 cm), promoting rapid water drainage despite equivalent precipitation inputs45. This hydrological divergence is corroborated by higher leaf predawn water potential (ψpd) in wet forests (mean ± standard error = −0.05 ± 0.01 MPa) versus dry forests (−0.13 ± 0.02 MPa) during sampling in the growing season.

Based on our unique datasets, we specifically predict that: (i) trees in dry forests exhibit more negative ψtlp and πo, and higher εsym than those in wet forests; (ii) dry and wet forests differ primarily in cell wall composition, particularly pectin concentration, rather than in relatively static anatomical structure; (iii) the coordination between cell wall composition, anatomical traits and P-V traits are environment-dependent. Specifically, we predict that in wet forests, εsym, πo, and ψtlp are associated with anatomical traits, while in dry forests, these physiological traits are associated with pectin concentration (Supplementary Fig. 2).

Results
Weakened coordination of osmotic and elastic function in dry forests
Across the 69 species examined, the turgor loss point (ψtlp), osmotic potential at full turgor (πo) and elastic modulus (εsym) showed 2.9-fold, 3.2-fold, and 4.4-fold variation, respectively (Supplementary Table 2). As expected, dry forest species had lower ψtlp than wet forest species (Fig. 1b; Cohen’s d=−0.59, p<0.05, linear mixed-effects model (LMMs)), while other P-V traits, including εsym, πo, apoplastic water fraction (fapo), and capacitance (C*ft), exhibited no significant difference between two forest types (Fig. 1c; Supplementary Table 3). When considering the three species that occur in both forest types, the shift in ψtlp between forest types was stronger (Cohen’s d=1.49, p=0.03) compared to other P-V traits (Cohen’s d=0.11–0.99, p=0.15–0.88) (Supplementary Fig. 3a-e).

The correlation between ψtlp and πo was consistently strong in both wet and dry forests (Fig. 1d; standardized regression coefficients β=0.98 and 0.92, respectively, p<0.001, linear model (LM)). However, the correlation between πo and εsym was significantly weaker in dry forests compared to wet forests (Fig. 1e; β=-0.57 and -0.85, respectively, p<0.001; Fisher’s z=-2.5, p=0.01). Notably, dry forests had a more limited range in πo (Fig. 1e); yet, wet forests maintained a significantly stronger πo versus εsym correlation even when the πo range was standardized to that of dry forests (r=-0.80 and -0.57, respectively, p=0.04; two-sample bootstrap permutation test; Supplementary Fig. 4). After controlling for πo via partial correlation analysis, the initially non-significant or negative εsym-ψtlp relationships (Fig. 1f) became significantly positive in both forest types (partial r=0.43 for wet forests, 0.55 for dry forests; p<0.01).

Enriched cell wall pectin and calcium in dry forests
Wet and dry forests showed comparable variation in six anatomical traits, including cell wall thickness (Tcw), sizes of spongy and palisade cells (Sspongy and Spalisade), thickness of spongy and palisade tissue (Tspongy and Tpalisade), and airspace fraction in the leaf cross-section (AirF), whereas the palisade-to-spongy tissue thickness ratio (PSR) varied significantly more (11-fold) within wet forests (Fig. 2a–d; Fig. 5; Supplementary Table 2). Notably, in dry forests, pectin and Ca2+ concentrations showed both significantly higher means (Cohen’s d=1.66 and 1.46, respectively, p<0.001, LMMs; Supplementary Table 3) and greater variation than in wet forests (Fig. 2f-g; Supplementary Fig. 4, Supplementary Table 2). Analyses of the three species that occurred in both forest types also revealed a markedly higher (approximately 2.4-fold) pectin concentrations in dry than wet forests (Supplementary Fig. 3).

Distinct leaf drought tolerance strategies in dry versus wet forests
Drought tolerance strategies differed significantly between wet and dry forests. In wet forests, the P-V traits were strongly associated with PSR and spongy cell size (Sspongy) (Fig. 3a, PC1, 44%; Supplementary Table 4), and 86% of the explained variation in leaf drought tolerance was attributed to anatomical traits (Fig. 3c). By contrast, among species in dry forests, P-V traits were coupled with cell wall composition (Fig. 3b, PC1 35%; Supplementary Table 4, Supplementary Fig. 6), with cell wall pectin and Ca2+ concentrations explaining 78% of the variation in P-V traits (Fig. 3c).

The relationships between pectin concentration and P-V traits were environmentally dependent. In the wet forests, species with higher PSR have higher εsym (Fig. 4b; β=0.52, R²=0.29, p<0.001) and lower ψtlp (Fig. 4c; β=-0.70, R²=-0.49, p<0.001). In dry forests, species with greater soluble pectin concentrations (solPectincw) tend to have lower εsym (Fig. 4d; β=-0.70, R²=0.28, p<0.001), while species with greater protopectin concentrations (proPectincw) had higher ψtlp (Fig. 4e; β=0.51, R² = 0.24, p<0.01). These relationships were consistent when using phylogenetic mixed-effect model and phylogenetic independent contrasts (Supplementary Tables 6–8).

Diverse drought tolerance strategy in dry forests linked to cell wall pectin
Underlying the observed distinct drought tolerance strategies, a divergence in optimal leaf hydraulic design was identified (Fig. 5). In wet forests, PSR (a proxy for anatomical organization) was strongly associated with πo and εsym. However, in dry forests, this integration between anatomical traits and water relations differed. Thus, among dry forest species, the diversification in pectin concentration added an additional layer of complexity to the evolution of drought tolerance, as evidenced by a weaker linkage between πo and εsym and decoupling εsym and ψtlp, thereby promoting more diverse tolerance strategies.

Methods
Study Sites and Species Selection
To examine the influence of cell wall structure on leaf water relations and its relationships with soil water availability, we compared two ecologically distinct wet (W1, W2) and two dry (D1, D2) subtropical forests across South China (21°37′N–26°23′N). These forests represent independent ecosystems, separated by hundreds of kilometers, sharing comparable climate conditions (mean annual precipitation, 1397–1532 mm; mean annual temperature, 17°C–21°C) but contrasting geological substrates and water availability (Supplementary Fig. 1, Supplementary Table 1). Two wet forests developed on deeply weathered lateritic soils, whereas two dry forests occupied limestone bedrock with shallow calcareous soils of poor water retention. This edaphic divergence corresponded with distinct ecological traits: wet forest trees were generally taller, and produced leaves with higher carbon but lower Ca concentrations; dry forests exhibited higher soil pH and Ca availability, though soil nitrogen and phosphorus levels remained similar between forest types.

Across wet and dry forests, we studied a total of 69 tree species from 57 genera and 33 families, covering key clades of common species in southern China; the sample included 34 species from dry forests, 35 from wet forests, with three overlapping species (Supplementary Figs. 3, 9); these species were indigenous and dominant in the canopy and sub-canopy layers in the natural communities and vary in their drought tolerance within and between communities45. Data collection within each forest was completed within two weeks during the peak growing season (July and August) between 2019 and 2021 to minimize temporal variation between physiological traits and cell wall composition, although the two-week window varied slightly among forests.

Leaf sampling and preparation
For each species, we selected 5–7 individuals and collected mature, sunlit leaves from their canopies using a combination of crane, climbing, and fishing rod sampling methods. This allowed us to obtain mean trait values for each species. From each selected individual, we collected one leafy branch after sunset to minimize transpiration-induced water potential fluctuations. From each harvested branch, we selected one fully expanded, undamaged leaf for P-V curve measurements. This sampling strategy, common in comparative physiology9, allowed us to balance the need to resolve statistical differences across species with the time-consuming nature of P-V curve measurements. The remaining leaves (approximately 15–30) on each branch were used for morphological/structural trait measurements. Before measurement, branches were re-cut under water and rehydrated overnight. This standard pre-treatment made all measurements comparable at forest sites with different water availability64. Additionally, we collected leaves at predawn (4:30–5:30 am) for three analyses: i) predawn water potential (n=two leaves of five individuals per species); ii) cell wall composition (n=3 individuals per species, immediately placed in a cooler to minimize the effect of leaf starch content on wall composition, Carriquí et al. 2020); iii) and leaf anatomical preparation, stored in FAA solution (37% formaldehyde-glacial acidic acid, 95% ethanol in deionized water) (n=5 individuals per species).

Trait measurements
Pressure-volume curves describe the interplay between leaf water potential (including osmotic and pressure potential) and cell water content and volume, serving as a crucial physiological method for assessing leaf drought tolerance. With the bench drying method65, we measured five critical P-V curve parameters for each species, using 5–7 leaves per species. These parameters were: turgor loss point (ψtlp), osmotic potential at full turgor (πo), bulk modulus of elasticity (εsym), leaf mass-specific capacitance at full turgor (C*ft), and apoplastic fraction (fapo). Water potential was measured in situ using a portable pressure chamber (model 1050D-EXP, PMS Instrument Company). To quantify cell wall composition, we first extracted cell wall dry mass (alcohol-insoluble residue, AIR) following the general protocol outlined by ref. 19, followed by trifluoroacetic acid hydrolysis to obtain a pellet and supernatant fractions for the quantification of cellulose and hemicellulose, respectively, using the anthrone-sulfuric acid colorimetric method66. Lignin was quantified using the acetyl bromide method67. We employed the method provided by ref. 68 to extract and determine soluble pectin and protopectin of cell wall. We also measured cell wall Ca and Mg using inductively coupled plasma atomic emission spectrometry (ICP-AES, Spectro, Germany), as these cations influence cell wall elasticity, cell water exchange and osmosis3,47. Leaf anatomical traits were obtained through photographic measurement of tissue sections69. Transverse sections were made for one leaf from each of 5 individuals (Supplementary Fig. 12). Seven key leaf anatomical traits measured were: palisade and spongy mesophyll tissue thickness (Tpalisade and Tspongy), ratio of palisade to spongy tissue thickness (PSR), fraction of intercellular air spaces (AirF), the volume of palisade and spongy mesophyll cell (approximated as cylinder and sphere, respectively) (Spalisade and Sspongy), and the averaged cell wall thickness of palisade to spongy mesophyll (Tcw). Specific measurement protocols for P-V traits, cell wall composition, and anatomical traits are outlined in Supplementary Method S1.

Species and phylogeny
We constructed phylogenetic trees of our species using the R package ‘V.PHYLOMAKER2’, with the GBOTB phylogeny as the backbone70. Species names in the phylogenetic tree were corrected according to The Plant List (http://www.theplantlist.org/). We calculated parametric Blomberg’s K71 using the ‘Picante’ package in R language and evaluated the strength of the phylogenetic signal for each trait72; a larger K value indicates a greater phylogenetic conservatism for the given trait. We also performed the Pagel’s λ test73 to detect phylogenetic signals of each trait. For the three species that occurred in both forest types, we used mean trait values to calculate total phylogenetic signals. We calculated phylogenetically independent contrast74 using the ‘ape’ package to control for the influence of common ancestry on trait relationships75. Following previous analyses, we defined the divergence time of a plant family using the earliest diverging genus within that family76,77.

Data analyses
Across wet and dry forests, we examined the equality of variance in all leaf traits using Levene′s test and differences in mean leaf traits using linear mixed effects model (lmer function in R package lme4)78. We treated forest type as the fixed effect and species as a random effect to account for non-independence of data points due to three shared species (n=35 and 34 for wet and dry forests, respectively). We examined the normality of residuals using the Shapiro-Wilk test. Data were log-transformed as necessary to meet this assumption; Importantly, after applying log-transformations where necessary, only three out of 26 traits (apoplastic fraction, Airspace fraction and leaf thickness) still failed to fully meet the normality assumption. We therefore performed Mann-Whitney U test for these traits. To determine whether forest type shifts consistently influenced leaf traits within and across species, we conducted paired t-test for each trait-species combination in three species found in both forest types. We reported effect sizes as Cohen’s d for LMMs and t-test, and Cliff’s σ for Mann-Whitney U test79,80.

To examine relationships among P-V traits in wet and dry forests, we performed linear regression and partial correlation analyses. Specifically, we used partial correlation to assess the relationship between ψtlp and εsym, controlling for πo. This isolated the direct relationship between ψtlp and εsym, independent of πo. To test for significant differences in the πo-εsym correlation between forest types, we compared their Pearson correlation coefficients using Fisher’s z81. Finally, to determine if a limited πo range weakened the πo-εsym correlation in dry forests, we performed a bootstrap analysis. We iteratively (1000 iterations) subsampled the wet forest data (n=10 species per iteration) to match the πo range observed in dry forests, calculating the Pearson coefficient for πo-εsym in each subsample. We then compared the resulting distribution of coefficients to the observed dry forest correlation using percentile-based confidence intervals and permutation testing.

We conducted principal component analysis (PCA) to investigate trait coordination and identify the key anatomical and cell wall biochemical traits influencing leaf water relations (Fig. 2). Prior to PCA, all data were standardized using a zero-mean approach to ensure each variable has a unit variance82. To enhance the interpretability of our PCA results, we first retained traits exhibiting significant Pearson correlations between P-V traits and anatomical/biochemical traits in both wet and dry forest (Supplementary Tables 6-7). These traits included PSR, spongy cell size, spongy and palisade tissue thickness, pectin and cell wall Ca. We then used the stepwise approach described by Weigelt et al. (2023)83 to incorporate additional traits, such as cell wall thickness and cellulose, which have direct biological linkage with leaf water relations12,13. Finally, we simplified the analysis by removing variables with high collinearity (e.g., PSR with spongy and palisade tissue thickness) and those with non-significant loadings on the first two principal components using the ‘PCAtest’ package84.

We used multivariate linear models to assess the effects of anatomical traits and cell wall composition on five P-V traits. We first applied a backward stepwise regression (dredge function in ‘MuMIn’ package)85 to screen for the best set of predictors for each P-V trait based on minimizing the corrected Akaike Information Criterion (AICc). Next, we pooled these predictors together and assessed their individual effects on all P-V traits by performing hierarchical partitioning (HP) using the ‘rdacca.hp’ package86. Following Lai et al. (2022), we addressed multicollinearity by removing predictors with negative contributions. The relative importance of each predictor as the ratio of its explained variation to the total explained variation, expressed as a percentage. Finally, to improve visualization, we focused on those predictors with a relative importance of at least 5%. To cross-validate the HP results, random forest analyses with permutation tests, implemented using the ‘rfPermute’ package87, were used to assess the relative importance of anatomical traits and cell wall composition in determining specific P-V traits. We grew 1000 trees and conducted 1000 permutations for each random forest. Predictor importance was quantified as the percentage increase in mean squared error (% IncMSE) after permuting each predictor.

Building on the random forest results, which highlighted the importance of PSR and pectin, we used linear regression to specifically analyze their relationships with three P-V traits (εsym, πo, and ψtlp) (Supplementary Fig. 7). We also implemented phylogenetic mixed effect models with the phylogenetic relatedness matrix as a random effect, to further assessing the same relationships using the relmatLmer function in ‘lme4qtl’ package88. Given the strong phylogenetic signal and high variation observed in pectin content, we tested the relationship between pectin and divergence time of major taxa using linear regression analysis. All LM and LMMs standardized regression coefficients (β), confidential interval (CI) and p value were reported.

We employed piecewise structural equation modeling (SEM), combined with multivariate linear regression, to explore the relationships among anatomical, biochemical, and P-V traits in wet and dry forests separately, using the ‘piecewiseSEM’ package89. A basic model was constructed with all possible paths (Supplementary Fig. 2). Following Bartlett et al. (2012), we allowed εsym and πo to be interdependent in this model. Recognizing that including both πo as a predictor of the ψtlp can lead to inflated path coefficients, we chose to fit this relationship using standardized major axis (SMA) regression instead of including a direct path in the SEM. We used all-subsets regression (dredge function in ‘MuMIn’ package) to both select relevant traits guided by lowest AICc and remove non-significant paths. This iterative procedure continued until a parsimonious model with acceptable was achieved. Model fit was evaluated using Fisher’C, P value and Akaike Information Criterion (AIC)89. We reported the standardized path coefficients and R2 for the response variables (εsym and πo).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript entitled “Cell wall pectin reshapes leaf drought tolerance in dry forests” tests differences in a variety of plant\nleaf traits and drought tolerance-related physiological measurements across a range of plant species from dry and wet\nsubtropical forests. The authors employ a variety of modelling and ordination approaches to identify leaf-traits common to\ndry- or wet-forest species, and link these to two evolutionary strategies to drought tolerance.\nThe manuscript is generally very well written, and the methods and interpretations of data appear to be robust to the best of\nmy knowledge. In my view, the key improvements to the manuscript that I’d suggest is to make the delineation of wet and dry\nforests clearer – I did not fully appreciate that this had little to do with precipitation and was instead more a function of\nunderlying geology until the methods section (at the end of the manuscript). Linked to this, I’d strongly suggest the authors\nadd further details of the sites – in particular their geological context and how distant / close they are to each other, as these\nappear to be key details that are not well described in the manuscript currently. Finally, there are some opportunities in the\nmethods section to improve the description of statistical methods.\nI enjoyed reading the manuscript overall, and will defer to more-qualified reviewers on the technical aspects of measuring\nplant leaf-traits and the novelty of the manuscript, as I am not a plant phyisiologist. Most of my comments focus on aspects of\nthe data analysis which is more closely related to my particular field. I hope my comments will be useful to the authors and\nwish them luck in publishing their manuscript.\nSpecific comments\nLine 45 – The line about pectin needs to be linked to a particular result, otherwise it is difficult to understand why the authors\nsuddenly mention pectin and its importance.\nLine 146 – I appreciate the transparent nature of the figure (1b), but I’m not necessarily sure it is visually clear that a\ndifference in means exists. It could be worth adding a point or dashed line to denote the means + confidence intervals of wet\nvs dry so that the figure better supports the statistical finding.\nL161 – 165 – From the figure legend it is unclear to me whether the authors have performed separate regression analyses\nfor wet and dry species, or if this is a single regression with an interaction between the x-axis variable and the dry/wet status\nof each species. Please could the authors clarify this point?\nL164 – replace category with categorical\nFigure 2 – it is worthwhile to add a legend to remind the reader of which colour is associated with which forest type. Also, to\nbe clear, within lines 167 – 174, are we referring exclusively to differences in variance here, or means?\nL197 – Are these only the significant vectors that are shown, or were some other criteria used to filter out other trait vectors?\nL214 and throughout – it would be good to have some idea of effect sizes here. P values and R2 both help, but neither\nmeasure strength of effects…\nL268 – two sentences starting with “Our findings …”\nLine 321 – it’d be good to be more specific here – ‘succulents’ is a broad term encompassing great phylogenetic diversity, so\ndoes this statement apply to all succulents, or major succulent taxa (e.g. Crassulaceae)?\nL403 – correct spelling of soil\nL406 – This seems like a really key point on how wet-dry forests are defined but it remains unclear until this point in the\nmethods section – I think it may be worth clarifying this earlier as, until this point, I was under the impression that wet-dry\nreferred to precipitation conditions, whereas it is really just an indicator of soil drainage / water retention time.\nL409 – geological differences between the forests appear to be a key driver of soil moisture availability and indirectly, plant\ntraits. Therefore, it would be helpful to add some details of the geology of these sites, as this seems to be missing (apologies\nif I have missed it). A map of the sampling sites would also be helpful, to show how geographically distant the sites are.\nL429 – to be clear, did the authors calculate mean trait values for each individual? If so, please add details of the number of\nleaves used to do this.\nL487 – for clarity, please add the sample sizes here.\nL473 (and throughout) – please provide citations for R packages (Picante, ape, lme4, and check for others).\nL488 – how was the normality of residuals assumption checked? And did the authors re-check this assumption after log-\ntransformation of the data to see whether the transformation worked?\nL509 – More details are needed on the random forest regression hyperparameters – for example, how many trees were\nused, were trees pruned at all, etc.\nL527 – Which criterion was used to assess goodness of fit?", "reviewer_2": "General comments\nDrought tolerance may be achieved through different traits, yet the impact of cell wall composition remains barely addressed.\nHere, Duan et al. report the linkages among 26 traits, including cell wall compositions, anatomies, and drought tolerance\n(described by pressure-volume curves) across 69 woody species from sub-tropical wet and dry forests. They pinpoint a role\nof pectins in shaping the drought tolerance strategy in dry forests. It is shown that leaf traits associated with drought\ntolerance (measured from classical P-V curves) are coupled with anatomical organization (palisade-to-spongy tissue\nthickness ratio) in the wet forest, but with cell wall composition (pectins and Ca2+) in the dry forest – leading the authors to\nconclude that drought tolerance strategies differ between wet and dry forests. Based on structural equation modelling, it is\nproposed that in dry forest species, the diversification in pectin content promoted the emergence of more diverse drought\ntolerance strategies compared to wet forest species.\nOverall, the dataset is very original and valuable for the community. The idea that leaf pectins may contribute to drought\nresistance is not novel but had never been thoroughly investigated in relationship with other leaf traits (P-V and anatomy).\nThe figure layout is very pleasant and consistent throughout the text. The manuscript is well-written and inspiring, yet some\nof the conclusions are overstated:\n1/ The conclusions about the key role of pectin enrichment for land colonization by trees are overstated. In turn, the concept\nof ancestral ‘stable wet leaf tissue’ strategy in wet forests that would have evolved into a ‘flexible dry cell wall’ strategy in dry\nforests is interesting but still lacks strong biological evidence. The argument about the phylogenetic signal is quite poor. It is\nnot clear how the authors think that plants acquired pectin functionality over evolutionary timescales. When has pectin\nmetabolism appeared and diversified? How does it relate to the evolution of lignin and stomata? Has it evolved several\ntimes independently? What about non-woody plants? A review on the evolution of pectin biosynthesis says that even the\nmost basal plant lineages have pectins, yet in different forms, suggesting that pectin composition rather than abundance has\ndriven evolution in their functionality (Atmodjo et al, 2013, Annu Rev Plant Biol).\n2/ It seems that there was only one wet forest and one dry forest under investigation. If this is correct, then the use of the word\n‘forests’ in the plural throughout the text (including the title) is a quite exaggerated generalization.\nMoreover:\n3/ A review of whether and how drought conditions may affect pectin content and composition (within a single plant) is\nmissing in the introduction or discussion. That would clarify what is meant by ‘dynamic wall compositions’.\n4/ Three species were shared between the dry and wet forest. It would be great to show a focus on these species so that we\ncan evaluate if biome consistently influences the key traits (e.g. jitterplots + relevant statistical tests to assess the effect of dry\nvs. wet forest on pectins, PSR, εsym, ψtlp… for each of the three species).\nI have also listed specific, minor comments below.\nSpecific comments\n- Line 108-109: why smaller cells and thicker cell walls would enhance solute accumulation per unit volume?\n- Line 136: ‘dynamic wall compositions’. The time scale considered here is unclear: instantaneous response to stress? leaf\ndevelopment? long-term acclimation to drought?\n- Line 137-141: the difference between iii) and iv) is not obvious\n- Line 146: the abbreviation ‘LMMs’ (linear mixed models) is not explicitly defined. The effect looks very small. This could\nexplain why no significant difference is found for πo despite very strong correlation with ψtlp (Fig 1d).\n- Line 150-152: ‘the correlation between πo and εsym was weaker in dry forests, leading to a decoupling between εsym and\nψtlp compared to wet forests (Fig. 1e, f)’. Low statistical support. One could argue that R2 is lower in the dry forest simply\nbecause the range explored in the x-axis is narrower.\n- Line 167: ‘size sponge and palisade cell’ -> ‘size of spongy and palisade cells’\n- Line 214-216: The sentence ‘Pectin, … time (Fig. 4c; R2 = -0.13, p < 0.001).’ seems disconnected from the next one.\n- Line 268-270: ‘Our findings indicate that pectin enrichment in cell walls has been central to the evolutionary radiation of\nwoody plants from wet to dry habitats.’ Much overstated.\n- Line 298-301: Why would more variable concentrations (between species?) confer greater flexibility and responsiveness to\ndrought?\n- Line 306-308: maybe state explicitly that these processes would provide cells with a reservoir of solutes and water upon\ndehydration? Because one might argue that chelating solutes such as Ca2+ in the cell wall through pectin accumulation\ntherein is not likely to improve water retention within the cell, as water may follow solutes by osmosis in the cell wall.\n- Line 312: ‘demonstrate’ -> ‘suggest’\n- Line 402, Study Sites and Species Selection: not entirely clear how many forests of each type were analyzed. I understand\nthat they were only 1 dry forest and 1 wet forest, yet with two sampling sites in each.\n- Line 403: ‘soli’ -> ‘soil’\n- Line 476: ‘traits’ -> ‘trait’\n- Line 492: ‘composition’ -> ‘component’\n- Line 539: ‘Before calculation, we used the standard correction method to deal with the ‘plateau’ effect triggered by the\noversaturation of cellular symplast .’ Unclear. Was the plateau similar between species of the two forest types?\n- Line 589: Leaf anatomical trait measurements. Please provide the area imaged and used for anatomical trait determination.\n- Line 984: ‘susceptibility’ -> ‘exposure’\n- Line 1004: ‘Extended Data Figure 1’ -> ‘Extended Data Figure 3’\n- Figures 1 and 2: please specify the statistical test used here; I guess in Fig 1 the significance reported corresponds to that\nof the pvalue of the variable ‘biome’ in the linear mixed model, but in Fig 2 it is unclear whether you compare the mean or\nvariance.\n- Extended Data Table 1: why showing only Levene’s test for variance equality and not the test used for mean comparison?\n- Extended Data Table 1: capital P in MPa for turgor loss point; unit parenthesis not in exponent for cell wall dry mass;\n‘sponge’ -> ‘spongy’"}, "author_response": "Response to Reviewer #1:\n6. The manuscript entitled “Cell wall pectin reshapes leaf drought tolerance in dry\nforests” tests differences in a variety of plant leaf traits and drought tolerance-related\nphysiological measurements across a range of plant species from dry and wet\nsubtropical forests. The authors employ a variety of modelling and ordination\napproaches to identify leaf-traits common to dry- or wet-forest species, and link these\nto two evolutionary strategies to drought tolerance.\nWe are grateful to the reviewer for their positive assessment of our work, and their\nsummary of its novelty and strengths.\n7. The manuscript is generally very well written, and the methods and interpretations\nof data appear to be robust to the best of my knowledge.\nWe greatly appreciate the reviewer’s comments and are pleased that our quantitative\nanalysis was clear.\n8. In my view, the key improvements to the manuscript that I’d suggest is to make the\ndelineation of wet and dry forests clearer – I did not fully appreciate that this had little\nto do with precipitation and was instead more a function of underlying geology until\nthe methods section (at the end of the manuscript). Linked to this, I’d strongly suggest\nthe authors add further details of the sites – in particular their geological context and\nhow distant / close they are to each other, as these appear to be key details that are not\nwell described in the manuscript currently.\nWe appreciated these specific suggestions for improvement. We have now clearly\ndefined wet and dry forests in the Introduction (Lines 138–147), including a\ndescription of the soil water characteristics that distinguish these forest types, and\nfurther expanded this definition in the Methods section. Additionally, we have\nprovided detailed geological context and spatial information in the Methods section\n(Lines 453–465), and included a map of sampling sites (Extended Data Fig. 1) to\nillustrate their geographic distribution. Please also see our response to comment 22\nand 23.\n9. Finally, there are some opportunities in the methods section to improve the\ndescription of statistical methods.\nWe appreciated the constructive comments regarding our statistical analysis. We have\ncarefully reviewed and updated the statistical descriptions in the figures accordingly.\n10. I enjoyed reading the manuscript overall, and will defer to more-qualified\nreviewers on the technical aspects of measuring plant leaf-traits and the novelty of the\nmanuscript, as I am not a plant physiologist.\nWe are very pleased that the reviewer enjoyed the writing and sincerely appreciate\nthese constructive comments.\n1\n11. Most of my comments focus on aspects of the data analysis which is more closely\nrelated to my particular field. I hope my comments will be useful to the authors and\nwish them luck in publishing their manuscript.\nAgain, we thank the reviewer for their work and encouraging assessment of the paper.\nSpecific comments\n12. Line 45 – The line about pectin needs to be linked to a particular result, otherwise\nit is difficult to understand why the authors suddenly mention pectin and its\nimportance.\nThank you for pointing this out. We agree it’s important to clearly link this to our\nfindings. We revised Line 45 to briefly mention:\n‘Pressure-volume traits correlated more strongly with anatomy in wet\nforests, but with wall composition, and particularly pectin concentration,\nin dry forests. Thus, pectin-enriched cell walls contribute to the\necological specialization of woody plants in dry versus wet forests.’\n13. Line 146 – I appreciate the transparent nature of the figure (1b), but I’m not\nnecessarily sure it is visually clear that a difference in means exists. It could be worth\nadding a point or dashed line to denote the means + confidence intervals of wet vs dry\nso that the figure better supports the statistical finding.\nWe appreciated this helpful comment on Figure 1b. We agree the difference between\nwet and dry forests was not clear previously. We have added marginal mean points\nand 95% confidence intervals to Figures 1b and 1c, and updated the figure legend in\nLines 190–193.\n‘These results were derived using linear mixed model with forest types\nas a fixed effect and species as a random effect (Methods). Solid points\nand lines represent marginal means and their 95% confidence intervals,\nrespectively.’\nThis modification clearly highlights the difference in means and better supports the\nstatistical finding (as shown in the revised Figure 1b and c).\n14. L161 – 165 – From the figure legend it is unclear to me whether the authors have\nperformed separate regression analyses for wet and dry species, or if this is a single\nregression with an interaction between the x-axis variable and the dry/wet status of\neach species. Please could the authors clarify this point?\nThank you. We have rephrased the sentence in Lines 196–197 to make it clearer:\n‘Separate linear regression analyses were performed for wet and dry\nspecies (panels d-f).’\n15. L164 – replace category with categorical\n16. Figure 2 – it is worthwhile to add a legend to remind the reader of which colour is\nassociated with which forest type. Also, to be clear, within lines 167 – 174, are we\nreferring exclusively to differences in variance here, or means?\nWe have added a legend in Figure 2a to clearly indicate the color associated with each\nforest type. In Lines 199–209, we refer specifically to differences in variance between\nwet and dry forest types for anatomical traits. We have revised the text to clearly\ndistinguish when we are referring to differences in means versus differences in\nvariances.\n‘Wet and dry forests showed comparable variation in six anatomical\ntraits, including cell wall thickness (T ), sizes of spongy and palisade\ncw\ncells (S and S ), thickness of spongy and palisade tissue\nspongy palisade\n(T and T ), and airspace fraction in leaf cross-section area\nspongy palisade\n(AirF), though the palisade-to-spongy tissue thickness ratio (PSR),\nwhich varied significantly more (11-fold) within wet forests (Fig. 2a-d;\nExtended Data Table 1, Fig. 5). Notably, pectin and Ca2+concentrations\nshowed both significantly higher means (Cohen's d = 1.66 and 1.46,\nrespectively, p < 0.001, LMMs; Extended Data Table 2) and greater\nvariation in dry forests (Fig. 2f-g; Extended Data Fig.4, Table 2).’\n17. L197 – Are these only the significant vectors that are shown, or were some other\ncriteria used to filter out other trait vectors?\nWe measured a total of 26 traits, and 11 are shown in Figure 3. Traits with non-\nsignificant vectors were not displayed.\nWhen conducting the PCAs (Figure 3a; b), we filtered traits based on the following\nthree steps:\nStep 1: We selected leaf anatomical and cell wall compositional traits that showed\nsignificant Pearson correlations with PV traits in both wet and dry forests (Extended\nData Tables 5 and 6).\nStep 2: We selected additional cell wall traits, such as cell wall thickness and\ncellulose content, following the stepwise approach of Weigelt et al. (2023).\nStep 3: We then conducted the PCA based on the selected traits from Steps 1 and 2.\nTraits with high collinearity or non-significant loadings on the first two principal\ncomponents were excluded using the ‘PCAtest’ package, resulting in the final PCA\nmodel.\nWhen conducting variance partitioning (Figure 3c), we selected predictor variables\nusing a two-step process.\nFirst, for each PV trait, we identified the optimal set of predictors using backward\nstepwise regression with the dredge function in the ‘MuMIn’ package (Bartoń, 2009).\nCriterion (AICc), favoring parsimony.\nSecond, we combined the predictors selected for each PV trait and applied\nhierarchical partitioning (HP) to quantify the variance in all PV traits explained by the\ncombined predictors. Following Lai et al. (2022), we removed predictors with\nnegative contributions, as these indicate potential multicollinearity. To emphasize the\nmost influential factors, we retained only those predictors that independently\nexplained at least 5% of the variance in PV traits.\nThus, we selected key predictor variables to improve readability and visualization of\nthe results. We have added this information to the Methods section (Lines 576-586,\n588-604) for clarity.\n‘To enhance the interpretability of our PCA results, we first retained\ntraits exhibiting significant Pearson correlations between PV traits and\nanatomical/biochemical traits in both wet and dry forest (Extended Data\nTable 5, 6). These traits included PSR, sponge cell size, spongy and\npalisade tissue thickness, pectin and cell wall Ca. We then used the\nstepwise approach described by Weigelt et al. (2023) to incorporate\nadditional traits, such as cell wall thickness and cellulose, which have\ndirect biological linkages with leaf water relations (Flexas et al. 2021;\nCosgrove 2022). Finally, we simplified the analysis by removing\nvariables with high collinearity (e.g., PSR with spongy and palisade\ntissue thickness) and those with non-significant loadings on the first\ntwo principal components using the ‘PCAtest’ package (Camargo\n2022).’\n‘We used multivariate linear models to assess the effects of anatomical\ntraits and cell wall compositions on five PV traits. We first applied a\nbackward stepwise regression (dredge function in ‘MuMIn’ package;\nBartoń 2009) to screen for the best set of predictors for each\nPVtrait )based on minimizing the corrected Akaike Information\nCriterion (AICc). Next, we pooled these predictors together and\nassessed their individual effects on all PV traits by performing\nhierarchical partitioning (HP) using the ‘rdacca.hp’ package (Lai et al.\n2022). Following Lai et al. (2022), we addressed multicollinearity by\nremoving predictors with negative contributions. The relative\nimportance of each predictor as the ratio of its explained variation to the\ntotal explained variation, expressed as a percentage. Finally, to improve\nvisualization, we focused on those predictors with a relative importance\nof at least 5%.’\n18. L214 and throughout – it would be good to have some idea of effect sizes here. P\nvalues and R2 both help, but neither measure strength of effects…\nWe thank the reviewer for this excellent suggestion. We now include standardized\nregression coefficients (β) for continuous variables, Cohen’s d for categorical groups\n4\nanalyzed with LMMs (linear mixed-effects model), and Cliff’s δ for categorical\ngroups analyzed with non-parametric tests.\nThese values are shown in Extended Data Tables 2 and 3, described in the Methods\n(Lines 555–557, 613–614), and reported in the Results (Lines 164, 169, 173, 175,\n205, 252, and 255).\n19. L268 – two sentences starting with “Our findings …”\nWe have revised these words:\n‘We propose that higher pectin in cell walls may have acted as an\nimportant evolutionary mechanism for the adaptation of woody in dry\nhabitats.’\n20. Line 321 – it’d be good to be more specific here – ‘succulents’ is a broad term\nencompassing great phylogenetic diversity, so does this statement apply to all\nsucculents, or major succulent taxa (e.g. Crassulaceae)?\nThank you for this constructive comment. We have specified examples of succulent\nspecies from the Caryophyllales and Crassulaceae clades in Lines 360–361:\n‘Succulents from phylogenetically diverse clades (e.g., Caryophyllales,\nCrassulaceae) and resurrection plants, which have leaves rich in soluble\npectin, are good examples of this’.\n21. L403 – correct spelling of soil\nCorrected.\n22. L406 – This seems like a really key point on how wet-dry forests are defined but it\nremains unclear until this point in the methods section – I think it may be worth\nclarifying this earlier as, until this point, I was under the impression that wet-dry\nreferred to precipitation conditions, whereas it is really just an indicator of soil\ndrainage / water retention time.\nWe are grateful for this excellent suggestion. We realize that the definition of ‘wet-\ndry forests’ needs to be clearly stated. We have now defined these forest types based\non soil water characteristics in the revised paragraph. The specific addition to the\nIntroduction (Lines 138–147) is as follows:\n‘These forests–two wet and two dry–were selected for comparable\nclimate but contrasting edaphic hydrology (Extended Data Fig.1). Wet\nforests developed on deeply weathered lateritic soils (> 200 cm) over\nsiliceous rock, characterized by high water retention capacity (Chen et\nal. 2015; Jiang et al. 2021). Conversely, dry forests occurred on karst\nlandscapes with exposed limestone bedrock and shallow calcareous\nsoils (< 45 cm), promoting rapid water drainage despite equivalent\nprecipitation inputs (Cao et al. 2014). This hydrological divergence was\ncorroborated by higher leaf predawn water potential (ψ ) in wet forests\npd\n5\n(mean ± standard error = -0.05 ± 0.01 MPa) versus dry forests (-0.13 ±\n0.02 MPa), during sampling in the wet growing season’\n23. L409 – geological differences between the forests appear to be a key driver of soil\nmoisture availability and indirectly, plant traits. Therefore, it would be helpful to add\nsome details of the geology of these sites, as this seems to be missing (apologies\nif I have missed it). A map of the sampling sites would also be helpful, to show how\ngeographically distant the sites are.\nWe have added a discussion about the influence of geological differences on plant\nhydraulic traits in Lines 310. In particular, we have added geological information for\nthe wet and dry forest sites in the Methods section (Lines 453–465) and included a\nmap of the sampling sites (Extended Data Fig. 1).\n‘To examine the influence of cell wall structure on leaf water relations\nand its relationships with soil water availability, we compared two\necologically distinct wet (W1, W2) and two dry (D1, D2) subtropical\nforests across South China (21°37'N-26°23'N). These forests represent\nindependent ecosystems, separated by hundreds of kilometers, sharing\ncomparable climate conditions (mean annual precipitation, 1397-1532\nmm; mean annual temperature, 17-21 °C) but contrasting geological\nsubstrates and water availability (Extended Data Fig. 1, Table 1). The\ntwo wet forests developed on deeply weathered lateritic soils, whereas\nthe two dry forests occupied limestone bedrock with shallow calcareous\nsoils of poor water retention. This edaphic divergence corresponded\nwith distinct ecological traits: wet forest trees are generally taller, and\nproduce leaves with lower Ca concentrations, and dry forests exhibit\nhigher soil pH and Ca availability, though soil nitrogen and phosphorus\nlevels remain similar between forest types.’\nExtended Data Figure 1 | Comparison of dry and wet forest in community\nstructure, leaf nutrients and soil properties. Two distinct forest landscapes have\nformed in (sub)tropical regions, despite abundant hydrothermal resources, due to\n6\nvariation in soil water availability caused by differences in geological parent material\nand its weathering. The soil in dry forests is typically very thin (< 45 cm), with poor\nwater retention capacity, and tree height is generally lower compared to wet forests.\nPlants in dry forests experience more negative water potentials compared to those in\nwet forests; in the wet season, mean ± standard error of predawn leaf water potentials\nwere -0.13 ± 0.02 MPa versus -0.05 ± 0.01 MPa respectively. Furthermore, leaves in\ndry forests have higher calcium (Ca) concentration than those in wet forests.\nCompared to wet forests, dry forests show no significant differences in soil nitrogen\n(N) and phosphorus (P), but have higher soil pH and Ca (Extended Data Table 1).\nExtended Data Table 1 | Comparison of dry and wet forest in leaf nutrients and\nsoil properties.\nThough sharing comparable climate conditions, the wet and dry forests differ in their leaf traits and soil\nproperties.\nPredawn water potential was reported as mean ± standard error at each sampling site.\nLeaf C and Ca derived from: #our unpublished data; ※Xu et al. (2016); ‡Cao et al. (2014); §Xing et al. (2021)\nSoil properties were retrieved from China Ecosystem Research Network (CERN), publicly available for approval\nthrough their website (http://www.cnern.org.cn).\n24. L429 – to be clear, did the authors calculate mean trait values for each individual?\nIf so, please add details of the number of leaves used to do this.\nWe have added the sample sizes used for different trait measurements.\nFor physiological traits—specifically pressure–volume traits—we selected 5–7\nindividuals per species, and measured one mature leaf from each individual, such that\nthe species-level mean for these traits represents the average across 5–7 individuals.\nAccordingly, we have revised the description of the measurement process in Lines\n481–487:\n‘From each selected tree, we harvested one branch after sunset to\nminimize transpiration-induced variation in water potential. From that\nbranch, we selected one fully expanded, undamaged leaf to measure the\npressure–volume (P–V) curve. This approach, commonly used in\ncomparative plant physiology (Baird et al., 2021), balances the need for\nspecies-level replication with the time-intensive nature of P–V\nmeasurements. The remaining 15–30 leaves were used for\nmorphological trait measurements.’\n25. L487 – for clarity, please add the sample sizes here.\n‘We treated forest type as the fixed effect and species as a random\neffect to account for non-independence of data points due to three\nshared species (n = 35 and 34 for wet and dry forests, respectively).’\n26. L473 (and throughout) – please provide citations for R packages (Picante, ape,\nlme4, and check for others).\nWe have now added citations for R packages, including Picante, ape, lme4, PCAtest,\nMuMIn, rfPermute, to the Methods section in Lines 533, 539, 545, 586, 590, and 600.\nWe added citations for the R packages in the References.\n27. L488 – how was the normality of residuals assumption checked? And did the\nauthors re-check this assumption after log-transformation of the data to see whether\nthe transformation worked?\nWe appreciated the reviewer’s questions about the normality of data distributions. We\nhave added details about the residual distributions in Lines 548–555.\nFirst, we assessed the normality of residuals using the Shapiro–Wilk test on the\noriginal data. In cases where non-normality was detected, we applied a log-\ntransformation and re-ran the Shapiro–Wilk test to verify that this improved the\ndistribution of residuals.\nSecond, after applying log-transformations where necessary, only 3 out of 26 traits\n(apoplastic fraction, airspace fraction, and leaf thickness) still failed to fully meet the\nassumption of normality. Thus, we conducted a Mann–Whitney U test for these three\ntraits, which yielded similar results are similar to the previous ones. We believe this\nanalysis again confirms the key findings from the linear mixed-effects model.\n28. L509 – More details are needed on the random forest regression hyperparameters\n– for example, how many trees were used, were trees pruned at all, etc.\nWe have added details about the hyperparameters used in the random forest\nregression in Line 598-604.\n‘To cross-validate the HP results, random forest analyses with\npermutation tests were implemented using the ‘rfPermute’ package\n(Archer 2025) to assess the relative importance of anatomical traits and\ncell wall composition in determining specific PV traits. We grew 1000\ntrees and used 1000 permutations (nrep = 1000) for each random forest.\nPredictor importance was quantified as the percentage increase in mean\nsquared error (%IncMSE) after permuting each predictor.’\n29. L527 – Which criterion was used to assess goodness of fit?\nWe evaluated SEM fit using Fisher’s C statistic, p-value, and the Akaike Information\nCriterion, as shown in Figure 5 (Shipley, 2013; Lefcheck, 2016). In this revision, we\n8\nhave added details on variable selection and goodness-of-fit metrics in Lines 627–\n630.\nGeneral comments\n30. Drought tolerance may be achieved through different traits, yet the impact of cell\nwall composition remains barely addressed. Here, Duan et al. report the linkages\namong 26 traits, including cell wall compositions, anatomies, and drought tolerance\n(described by pressure-volume curves) across 69 woody species from sub-tropical wet\nand dry forests. They pinpoint a role of pectins in shaping the drought tolerance\nstrategy in dry forests. It is shown that leaf traits associated with drought tolerance\n(measured from classical P-V curves) are coupled with anatomical organization\n(palisade-to-spongy tissue thickness ratio) in the wet forest, but with cell wall\ncomposition (pectins and Ca2+) in the dry forest – leading the authors to conclude that\ndrought tolerance strategies differ between wet and dry forests. Based on structural\nequation modelling, it is proposed that in dry forest species, the diversification in\npectin content promoted the emergence of more diverse drought tolerance strategies\ncompared to wet forest species.\nWe greatly appreciated summary, recognizing the underexplored role of cell wall\ncomposition in drought tolerance. We are grateful to the reviewer for their thoughtful\nreview and evaluation of our work.\n31. Overall, the dataset is very original and valuable for the community.\nWe greatly appreciated this comment and the recognition of the value and originality\nof our dataset.\n32. The idea that leaf pectins may contribute to drought resistance is not novel but had\nnever been thoroughly investigated in relationship with other leaf traits (P-V and\nanatomy).\nWe are grateful for this assessment of the value of our work. We agree that the\nimportance of leaf cell wall composition in physiological and ecological processes\nneeds to be more widely studied and acknowledged. Such a comment is very\nrewarding given our past five years of committed work on this question.\n33. The figure layout is very pleasant and consistent throughout the text.\nWe greatly appreciate these generous comments.\n34. The manuscript is well-written and inspiring, yet some of the conclusions are\noverstated:\nWe also appreciated this comment and are grateful for the constructive advice.\n35. The conclusions about the key role of pectin enrichment for land colonization by\ntrees are overstated. In turn, the concept of ancestral ‘stable wet leaf tissue’ strategy in\nwet forests that would have evolved into a ‘flexible dry cell wall’ strategy in dry\nforests is interesting but still lacks strong biological evidence. The argument about the\n10\nphylogenetic signal is quite poor. It is not clear how the authors think that plants\nacquired pectin functionality over evolutionary timescales. When has pectin\nmetabolism appeared and diversified? How does it relate to the evolution of lignin\nand stomata? Has it evolved several times independently? What about non-woody\nplants? A review on the evolution of pectin biosynthesis says that even the most basal\nplant lineages have pectins, yet in different forms, suggesting that pectin composition\nrather than abundance has driven evolution in their functionality (Atmodjo et al, 2013,\nAnnu Rev Plant Biol).\nWe appreciate these questions and agree broadly with these points, and have revised\nthe text to reduce the overstatements regarding the evolution of pectin, given the\nmultiple environmental drivers and complexity of evolutionary history. Thus, we have\nsimplified our conclusions, to emphasize the main focus on drought tolerance and cell\nwall composition across diverse species.\nWe agree that the phylogenetic signal for pectin requires nuanced interpretation,\nbecause it shows a moderate conservation (Pagel’s λ = 0.69, p < 0.001). We now\ndiscuss this as potential evidence of rapid evolutionary sensitivity to environmental\npressures, possibly driven by regulatory changes, and suggest this as an area for future\nresearch.\nAdditionally, we have removed speculative statements about the singular role of\npectin in plant terrestrial colonization. Our conclusions now focus specifically on the\ninvolvement of pectin in drought adaptation strategies observed in extant woody\nspecies within wet and dry forests. We have also carefully reframed the wet/dry forest\nstrategy concept as contemporary adaptation rather than evolutionary trajectory.\nFurther, we have incorporated the evolutionary perspective of Atmodjo et al. (2013)\nand others, recognizing the ancient origins of pectin and its presence across diverse\nplant lineages, including basal groups (e.g., mosses, Sarkar et al. 2009). Our revised\ndiscussion now emphasizes that functional diversification in drought adaptation likely\ninvolves modifications in pectin composition and its abundance regulation across\ndiverse plant lineages, as the reviewer suggested.\nOverall, we agree with the reviewer that pectin composition is crucial and have\nadjusted our discussion to highlight both abundance and composition. Although\nspecific land plants lineages vary in their pectin composition, abundance is also an\nimportant functional dimension, as evidenced by intraspecific plasticity and\ninterspecific variation in our study. Further, we have highlighted recent evidence that\nshowcases the wide variation in pectin abundance across plant phylogeny (e.g.,\nmosses, ferns, gymnosperms, and angiosperms) and its correlation with\nphotosynthesis in non-crop angiosperms (Roig-Oliver et al. 2025, New Phytologist).\nWe have added the key points in Discussion section (Lines 420-432):\n‘This biochemical flexibility may, in turn, facilitate functional\ndiversification of drought tolerance across aridity gradients. Moderate\nphylogenetic conservation of pectin abundance (Fig. 4c) indicates that\nits evolutionary advantage may contribute to its persistence in dry\n11\nhabitats, or during radiations of species between wet and dry\nenvironments (Donoghue 2008).’\n‘Recent evidence confirms wide variation in pectin abundance across\ndiverse plant lineages (e.g., mosses, ferns, gymnosperms, and\nangiosperms) and its correlation with photosynthetic capacity in non-\ncrop angiosperms (Roig-Oliver et al. 2025).While pectin composition\nexhibits lineage-specific diversification (Sarkar et al. 2009; Atmodjo et\nal. 2013), our observations of strong interspecific divergences among\nangiosperms suggests that pectin abundance may represent a crucial\nfunctional dimension.’\n36. It seems that there was only one wet forest and one dry forest under investigation.\nIf this is correct, then the use of the word ‘forests’ in the plural throughout the text\n(including the title) is a quite exaggerated generalization.\nWe appreciate the opportunity to clarify that each forest type (dry/wet) was\nrepresented by two distinct forests (not one forest with two subsites). Please see reply\nto comment 23 above.\nOur study design captured forests with similar subtropical climates but distinct soil\nwater conditions. We have provided the delineation of the wet and dry forest types\nand the rationale for our site replications within the (sub)tropical forest type in the\nIntroduction section (as suggested by reviewer #1). (Lines 138-147)\nFurther, we have expanded the Methods section to include detailed descriptions of the\nunderlying geology of each wet and dry forest site (Lines 453–465). We have also\nadded a map of the sampling locations (new Extended Data Fig. 1, as suggested by\nreviewer #1).\nAdditionally, we have added data on both soil water content and leaf predawn water\npotential in Extended Data Table 1 to further demonstrate the differences in soil water\navailability between wet and dry forests, reinforcing the distinct nature of these forest\ntypes.\n37. A review of whether and how drought conditions may affect pectin content and\ncomposition (within a single plant) is missing in the introduction or discussion. That\nwould clarify what is meant by ‘dynamic wall compositions’.\nWe thank the reviewer for this suggestion. To clarify how drought “dynamically”\nmodulates pectin at the intraspecific level, we have added the key points:\nIn Introduction (Lines 122-124):\n‘Under water stress, sunflower (Helianthus annuus) exhibited a 60%\nincrease in pectin concentration compared to well-watered controls\n(Roig-Oliver et al. 2021)’\nIn Discussion (Lines 334-337):\n12\n‘Furthermore, the strong adjustment of pectin concentration for species\nof wet and dry forest through plasticity and/or ecotypic differentiation\n(2.4-fold increase in dry forests; Extended Data Fig. 3) supports its role\nas a dynamically responsive matrix.’\n38. Three species were shared between the dry and wet forest. It would be great to\nshow a focus on these species so that we can evaluate if biome consistently influences\nthe key traits (e.g. jitterplots + relevant statistical tests to assess the effect of dry vs.\nwet forest on pectins, PSR, ε , ψ … for each of the three species).\nsym tlp\nWe appreciated this valuable suggestion. We added a new analysis for three species\n(Camptotheca acuminata, Cleidion brevipetiolatum, and Lasiococca comberi)\noccurring in both dry and wet forests. We have generated species-specific jitter plots\ncomparing dry vs. wet forests for 20 traits (Extended Data Figure 3). We have also\nperformed paired t-tests for each trait-species combination, with effect sizes (Cohen's\nd) in the figure.\nThis analysis confirms that soluble pectin significantly increased in all three species\nfrom wet to dry forest (Extended Data Figure. 3i); additionally, two of the three\nspecies showed a significant increase in both total pectin and protopectin (Extended\nData Figure. 3g, h). These findings confirm the regulation of cell wall pectin as a key\nmechanism for plant adjustment to dry environments, consistent with our key\nconclusions. Notably, cell wall dry mass and cell wall pectin (Extended Data Figure 3\nf-h) were similar among species in the wet forest, but showed clear divergence in the\ndry forest, suggesting that cell wall regulation plays a limited role in driving variation\nin\nvariation in leaf water relations under wet conditions.\nWe added this analysis to the Results (Lines 167–170, and 207–209), and hope it will\nencourage more similar studies.\n‘When considering the three species that occur in both forest types, the\nshift in ψ between forest types was stronger (Cohen's d = 1.49)\ntlp\ncompared to other pressure-volume traits (Cohen's d = 0.11–0.99)\n(Extended Data Fig. 3a-e).’\n‘Analyses of the three species that occurred in both forest types\nanalyses also revealed a marked increase (approximately 2.4-fold) in\npectin concentrations from wet to dry forests’\nWe are grateful to the reviewer for suggesting these analyses, which helped to\ndisentangle adjustment of given species between forest types from adaptations across\nspecies.\nshared in both wet and dry forests. Leaf functional traits of Camptotheca\nacuminata (squares), Cleidion brevipetiolatum (circles), and Lasiococca comberi\n(triangles) were compared between wet (green) and dry (orange) forests. Traits are\ngrouped as follows: (a-e) pressure-volume traits; (f-j) cell wall composition;\n(k-o) anatomical traits; (p-t) morphological traits. Data points represent individual leaf\nmeasurements; solid symbols denote species mean values within each forest type.\nEffect sizes (Cohen's d) from paired t-test quantify the difference in trait values between\nforest types.\nI have also listed specific, minor comments below.\nSpecific comments\n39. Line 108-109: why smaller cells and thicker cell walls would enhance solute\naccumulation per unit volume?\nWe agree that this point required clearer explanation. As proposed by Cutler et al.\n(1977), there are important relationships between cell size, cell wall thickness, solute\nconcentration, and osmotic potential. Their model shows that, even when solute\ncontent per unit dry leaf mass is the same, smaller cells have higher solute\nconcentration per unit cell volume than larger cells. This is because smaller cells\ncontain less volume, concentrating the same amount of solute into a smaller space,\nresulting in higher cellular solute concentrations, which in turn influences cell and\nbulk leaf osmotic potential (which is the cell volume-weighted average of all cells in\nthe leaf).\n‘These adaptations involved two alternative cellular modifications: (1)\nsmaller cells with thicker walls, which have reduced cell volume,\nresulting in higher solute accumulation per unit cell volume, and thus\nhigher osmotic concentration (Cutler et al., 1977), and which would\nhave greater reduction in osmotic potentials for a given cell water loss,\nwhile the thicker walls provide essential structural reinforcement\n(Peguero-Pina et al. 2017); (2) thinner and more elastic cell walls,\ncontributing to increased cell water storage, and improved maintenance\nof hydration even after stomatal closure triggered by drought stress’\n40. Line 136: ‘dynamic wall compositions’. The time scale considered here is unclear:\ninstantaneous response to stress? leaf development? long-term acclimation to\ndrought?\nWe agree with the reviewer that cell wall compositions can change during leaf\ndevelopment, in response to environmental stress, and through long-term acclimation\nto drought. We revised the sentence to emphasize the functional implications of\nvariation in cell wall composition across species in response to environmental cues,\nrather than its temporal dynamics. Please see our response to comment 37.\n41. Line 137-141: the difference between iii) and iv) is not obvious\nWe agree and have combined and rephrased Hypotheses (iii) and (iv) into a single,\noverarching hypothesis:\n‘iii) the coordination between cell wall composition, anatomical traits\nand PV traits is environment-dependent. Specifically, in wet forests,\nε , π , and ψ would be associated with anatomical traits, while in\nsym o tlp\ndry forests, these physiological traits would be associated with pectin\nconcentration.’\n42. Line 146: the abbreviation ‘LMMs’ (linear mixed models) is not explicitly\ndefined. The effect looks very small. This could explain why no significant difference\nis found for π despite very strong correlation with ψ (Fig 1d).\no tlp\nWe have now defined the abbreviation ‘LMMs’ as ‘linear mixed-effects model’ in\nLine 164 to ensure clarity for the readers.\nWe agree with the reviewer that the observed effect sizes for ψ and π are modest\ntlp o\n(Cohen's d = -0.59 and -0.46, respectively) in our dataset. We now report the effect\nsizes for these parameters in the Results. We agree that could result in the lack of\nsignificant difference for π , despite its strong correlation with ψ , which is the more\no tlp\nimportant functional variable, and which depends on both π and ε (Fig. 1d).\no\n43. Line 150-152: ‘the correlation between π and ε was weaker in dry forests,\no sym\nleading to a decoupling between ε and ψ compared to wet forests (Fig. 1e, f)’.\nsym tlp\nLow statistical support. One could argue that R2 is lower in the dry forest simply\nbecause the range explored in the x-axis is narrower.\nWe greatly appreciated this comments and have added three new analyses to confirm\nour key findings. The potential influence of the narrower π range in dry forests on the\no\nobserved correlation with ε . We describe these in the Methods (Lines 559-570):\nsym\n Partial Correlation Analysis. When controlling for π o , the relationship between\nε and ψ shifted from negative in wet forests to positive in both forest types\nsym tlp\n(partial r = 0.43 for wet forests, 0.55 for dry forests; p < 0.01). This indicates that\nthe negative ε -ψ correlation is primarily mediated by π , consistent with\nsym tlp o\nglobal patterns reported by Bartlett et al. (2012a).\n Fisher's z Transformation Test. The comparison of correlation coefficients\nconfirmed that the π -ε relationship is significantly stronger in wet forests (r =\no sym\n-0.78) than in dry forests (r = -0.57) (Fisher's z = -2.5, p = 0.01).\n Bootstrap Analysis. To explicitly test the range effect of π o , we subsampled wet\nforest data (1000 iterations) to match the π range of dry forests.\no\nThe mean\ncorrelation remained strong (r = -0.80, 95% CI: [-0.86, -0.73]), significantly\nhigher than the dry forest correlation (r = -0.57; p = 0.04). This demonstrates that\nthe weaker correlation in dry forests cannot be explained solely by narrower π\no\nrange.\nThese additional analyses confirm that the weaker correlation in dry forests is not\nmerely an artifact of the narrower range but reflects a biological decoupling\nmechanism. We have updated the Results section and added details to the Methods\nsection.\nIn Results (Lines 172-182):\n16\n‘The correlation between ψ and π was consistently strong in both wet\ntlp o\nand dry forests (Fig. 1d; standardized regression coefficients β = 0.98\nand 0.92, respectively, p < 0.001, linear model (LM)). However, the\ncorrelation between π and ε was significantly weaker in dry forests\no sym\ncompared to wet forests (Fig. 1e; β = -0.57 and -0.85, respectively, p <\n0.001; Fisher's z = -2.5, p = 0.01). Notably, dry forests had a more\nlimited range in π (Fig. 1f); yet, wet forests maintained a significantly\no\nstronger π -ε correlation even when the π range was standardized to\no sym o\nthat of dry forests (r = -0.80 and -0.57, respectively, p = 0.04; two-\nsample bootstrap permutation test; Extended Data Fig. 4). When we\ntested the relationship between ε and ψ after statistically\nsym tlp\ncontrolling for π , the relationship reversed to positive in both forest\no\ntypes (partial r = 0.43 for wet forests, 0.55 for dry forests; p < 0.01).’\nIn Methods (Lines 559-570):\n‘To examine relationships among PV traits in wet and dry forests,\nwe\nperformed linear regression and partial correlation analyses.\nSpecifically, we used partial correlation to assess the relationship\nbetweenψ and ɛ , controlling for π . This isolated the direct\ntlp sym o\nrelationship between ψ and ɛ , independent of π . To test for\ntlp sym o\nsignificant differences in the π -ɛ correlation between forest types,\no sym\nwe compared their Pearson correlation coefficients using Fisher’s z\n(David 1949). Finally, to determine if a limited π range weakened the\no\nπ -ɛ correlation in dry forests, we performed a bootstrap analysis. We\no sym\niteratively (1000 iterations) subsampled the wet forest data (n=10\nspecies per iteration) to match the π range observed in dry forests,\no\ncalculating the Pearson coefficient for π -ɛ in each subsample. We\no sym\nthen compared the resulting distribution of coefficients to the observed\ndry forest correlation using percentile-based confidence intervals and\npermutation testing.’\nExtended Data Figure 4. Range-matching bootstrap analysis validated the\nbiological basis for the weakened π -ε correlation in dry forests. a, comparison\no sym\nof trait correlations after range standardization showed that wet forests maintained a\nsignificantly stronger π -ε correlation even when the πo range was standardized to\no sym\nthat of dry forests (r = -0.80 and -0.57, respectively, p = 0.04; two-sample bootstrap\npermutation test). This indicates that the observed reduction in correlation in dry\n17\nforests primarily reflects biological decoupling rather than a statistical artifact. b,\nschematic diagram illustrating π range matching, with orange and green representing\no\nthe full π range in dry and wet forests, respectively, and gray representing the\no\nreduced π range in wet forests after matching the dry forest range.\no\n44. Line 167: ‘size sponge and palisade cell’ -> ‘size of spongy and palisade cells’\nCorrected.\n45. Line 214-216: The sentence ‘Pectin, … time (Fig. 4c; R2 = -0.13, p < 0.001).’\nseems disconnected from the next one.\nWe agree that the transition was not clear. To address this, we first removed the\noriginal sentence associated with evolutionary history, as it detracted from the main\nfocus. We then added a transition sentence to emphasize the environmental\ndependence of pectin relationships in Lines 251–252:\n‘The relationships between pectin concentration and PV traits were\nenvironmentally dependent.’\n46. Line 268-270: ‘Our findings indicate that pectin enrichment in cell walls has been\ncentral to the evolutionary radiation of woody plants from wet to dry habitats.’ Much\noverstated.\nWe appreciate this question. We agree that the term ‘central’ may overstate the role of\npectin. We have revised this sentence (Lines 302–304) in a soft manner to more\naccurately reflect the scope of our findings.\n‘We propose that higher pectin in cell walls may have acted as an\nimportant evolutionary mechanism for the adaptation of woody in dry\nhabitats.’\nWe aimed to avoid overstating our study while still highlighting the potential\nimportance of pectin enrichment during evolution.\n47. Line 298-301: Why would more variable concentrations (between species?)\nconfer greater flexibility and responsiveness to drought?\nHere, we considered that higher pectin concentrations in dry forest than wet forest\nspecies (not variability) may enhance drought responsiveness for two key reasons.\nFirst, pectin can provide readily accessible domains for rapid cross-linking with\nsignaling molecules like Ca²⁺, facilitating faster stress perception (Vaahtera et al.\n2019). Second, pectin exhibits high plasticity within species (supported by Extended\nData Fig 3), further underscoring its role as a responsive matrix.\nWe have revised the text to eliminate ambiguity and emphasize this potential\nmechanism (Lines 332–338):\n‘This elevated pectin concentration may enhance drought\nresponsiveness through chemically accessible domains that facilitate\n18\nrapid cross-linking with signaling molecules (Vaahtera et al. 2019).\nFurthermore, the strong adjustment of pectin concentration for species\nof wet and dry forest through plasticity and/or ecotypic differentiation\n(2.4-fold increase in dry forests; Extended Data Fig. 3) supports its role\nas a dynamically responsive matrix.’\n48. Line 306-308: maybe state explicitly that these processes would provide cells with\na reservoir of solutes and water upon dehydration? Because one might argue that\nchelating solutes such as Ca2+ in the cell wall through pectin accumulation therein is\nnot likely to improve water retention within the cell, as water may follow solutes by\nosmosis in the cell wall.\nThis is an insightful comment. We agree that it is important to explicitly state how\nthese processes contribute to cell survival during dehydration. We also understand the\nreviewer’s concern that pectin accumulation in the cell wall—particularly through\nCa²⁺ chelation—may not intuitively appear to enhance intracellular water retention,\ngiven the potential for water movement within the apoplast. To address this, we have\nclarified (Lines 342–350) to emphasize the multi-functional role of pectin in drought\ntolerance and to more clearly explain its contribution to maintaining cell integrity\nunder water stress.\n‘Protopectin would exert its influence on these physiological traits\nthrough several mechanisms:(1) enhanced apoplastic cation exchange\ncapacity (CEC) via preferential Ca²⁺ chelation, establishing a dynamic\nionic reservoir that enables rapid osmotic adjustment during stress (Taiz\net al. 2015; White & Broadley 2003; White et al. 2018); (2) increased\nwater retention capacity through hydrogel formation in the cell wall\nmatrix, where bound water molecules create a hydration buffer (Moore\net al. 2006); and (3) activated aquaporin activity via pectin-Ca²⁺\nsignaling pathways to optimize membrane water permeability (Tyerman\net al. 2002; Zhao et al. 1987).’\n49. Line 312: ‘demonstrate’ -> ‘suggest’\nCorrected.\n50. Line 402, Study Sites and Species Selection: not entirely clear how many forests\nof each type were analyzed. I understand that they were only 1 dry forest and 1 wet\nforest, yet with two sampling sites in each.\nWe added a new figure to show four forests of each two types were analyzed. We\ncompared subtropical forests with similar subtropical climates, precipitation, and\nmean temperature. We have clarified that two independent forests (not subsites within\na forest) of each type were studied, as detailed in the Introduction and Methods.\nPlease see our response to comment 23.\n51. Line 403: ‘soli’ -> ‘soil’\nCorrected.\n19\n52. Line 476: ‘traits’ -> ‘trait’\nCorrected.\n53. Line 492: ‘composition’ -> ‘component’\nCorrected.\n54. Line 539: ‘Before calculation, we used the standard correction method to deal\nwith the ‘plateau’ effect triggered by the oversaturation of cellular symplast .’\nUnclear. Was the plateau similar between species of the two forest types?\nWe appreciated this question, and have added a more detailed explanation of the\n‘plateau’ effect. The ‘plateau’ effect refers to water infiltration of leaf airspaces\nduring rehydration, leading to a biased estimation of P-V traits if uncorrected. This\nsaturation manifests as a flattening of the P-V curve at high water potentials,\nobscuring the true relationship between water potential and symplastic water content.\nTo address this, we rigorously followed the well-established protocol of Sack et al.\n(2011), i.e., visually identifying the ‘plateau’ region of each P-V curve and removing\ndata points representing this infiltration prior to further analysis, ensuring that our P-V\ntrait estimations accurately reflect leaf water relations. We have now added a detailed\ndescription of P-V curve correction procedure on Lines 647–653.\nTo address the reviewer’s concern regarding plateau similarity, we visually inspected\nthe raw pressure-volume curves and found no apparent differences in plateau effects\nbetween forest types. We observed a plateau in 7 of 35 species in wet forests and 6 of\n34 species in dry forests.\n‘A ‘plateau effect’ was observed in 7 of 35 species from wet forests and\n6 of 34 species from dry forests. To mitigate the ‘plateau effect’ caused\nby leaf airspace infiltration during the rehydration pre-treatment, which\ncan bias P-V trait estimation, we applied a standard correction method\nprior to analysis. Following the well-established protocol of Sack et al.\n(2011), we visually identified the ‘plateau’ region on each P-V curve.\nData points within this plateau were then removed to ensure a more\naccurate and unbiased assessment of P-V traits.’\n55. Line 589: Leaf anatomical trait measurements. Please provide the area imaged and\nused for anatomical trait determination.\nWe added detail of our methodological process. Our measurements followed the\nprotocol of John et al. (2017, Ecology Letters). Anatomical traits were quantified\nfrom 3–5 representative fields of view per leaf, each covering 0.5–1.0 mm² of the\nlamina, avoiding major veins. Representative images of these sampling regions are\nprovided in Extended Data Figure 12 (200× magnification; scale bars = 100 μm).\nforest species. We measured leaf anatomical traits across species, with images\ncaptured at 200× magnification under a microscope. The upper panels show wet forest\nspecies (a–c), and lower panels show dry forest species (d–f). Scale bars = 100 μm. A\nsummary of anatomical traits is provided in Extended Data Table 1.”\n56. Line 984: ‘susceptibility’ -> ‘exposure’\nDone.\n57. Line 1004: ‘Extended Data Figure 1’ -> ‘Extended Data Figure 3’\nCorrected.\n58. Figures 1 and 2: please specify the statistical test used here; I guess in Fig 1 the\nsignificance reported corresponds to that of the p value of the variable ‘biome’ in the\nlinear mixed model, but in Fig 2 it is unclear whether you compare the mean or\nvariance.\nIn Figure 1b and c, we examined differences in mean P-V traits between the two\nforest types. To do this, we used a Linear Mixed-effects Model (LMMs) with forest\ntype as a fixed effect and P-V trait as the dependent variable. We included species as\na random effect to account for the non-independence of data points derived from the\nsame species.\nIn Figure 2, we investigated whether the variance in leaf anatomical traits and cell\nwall compositions differed significantly across different forest types. To assess this,\nwe employed the Levene's test for homogeneity of variances. The p-value reported in\nFigure 2 corresponds to the significance level of Levene's test.\nWe have updated the figure legends to clarify the statistical tests in the revised\nmanuscript.\n59. Extended Data Table 1: why showing only Levene’s test for variance equality and\nnot the test used for mean comparison?\nthe homogeneity of variances between the two forest types, and thus applied Levene's\ntest. We have used Linear Mixed-Effects Model (LMMs) for mean comparisons in\nExtended Data Table 3.\n60. Extended Data Table 1: capital P in MPa for turgor loss point; unit parenthesis not\nin exponent for cell wall dry mass; ‘sponge’ -> ‘spongy’\nCorrected. Thank you.\n1. I appreciate the authors' positive response to my comments. I am satisfied that they\nhave given my suggestions thorough consideration and their modifications have\nimproved the manuscript accordingly. I have only two further minor suggested edits\nrelated to the visual clarity of figures.\nWe thank the reviewer for their positive feedback and confirmation that our revisions\nhave improved the manuscript. We are happy to incorporate their two minor\nsuggested edits to enhance the visual clarity of the figures.\n2. First, in figure 3 the percentage values can be slightly tricky to read due to the\nunderlying ribbons - it could be worth nudging the percentages further to the left /\nright so that they have a white background behind them.\nWe appreciate this helpful comment. The change to Figure 3 has been made as\nsuggested to improve visual clarity.\nFigure 3. | Distinct leaf drought tolerance strategies between dry and wet forest tree species.\na, in wet forests, five pressure–volume (P–V) traits (gray) showed a decoupling from cell wall\ncompositions (orange) but a strong coupling with leaf anatomical traits (green). b, in contrast, leaf\ndrought tolerance in dry forests was associated with three cell wall compositions and was less\ninfluenced by leaf anatomical traits. Arrow length represents trait loadings (Table S5). Gray\n1\narrow represents P-V traits: turgor loss point (ψ ), osmotic potential at full turgor (π ), modulus\ntlp 0\nof elasticity (ɛ ), capacitance (C* ), and apoplastic fraction (f ). Orange arrows are cell wall\nsym ft apo\ncompositions: cellulose (Cellulose ), protopectin (proPectin ), and soluble pectin (solPectin ).\ncw cw cw\nGreen arrows represent the ratio of palisade to spongy thickness (PSR), cell wall thickness (T ),\ncw\nand spongy cell size (S ). c, variation of P-V traits in wet forests was primarily explained by\nspongy\nleaf anatomical traits, while in dry forests, drought tolerance was primarily explained by cell wall\ncompositions such as proPectin , solPectin , and Ca (Supplementary Fig. 7, 8). Values represent\ncw cw\nthe relative importance of each explanatory variable. T , palisade tissue thickness; T ,\npalisade spongy\nspongy tissue thickness; S , palisade cell size; AirF, airspace fraction in leaf cross-section\npalisade\narea; hemiCellulose , the hemicellulose content.\ncw\n3. Finally, on extended data figure 1, it would be good if the authors could enlarge the\nphotos a little as they are a bit small as they stand.\nThank you for this suggestion. We have enlarged the photos in Extended Data Figure\n1 to improve their visibility.\nExtended Data Figure 1 | Comparison of dry and wet forest in community structure, leaf\nnutrients and soil properties. Two distinct forest landscapes have formed in (sub)tropical\nregions, despite abundant hydrothermal resources, due to variations in soil water availability\ncaused by differences in geological parent material and its weathering. The soil in dry forests is\ntypically very thin (< 45 cm), with poor water retention capacity, with poor water retention\ncapacity, and tree height is generally lower compared to wet forests. Plants in dry forests\nexperience more negative water potentials compared to those in wet forests; in the wet season,\nmean ± standard error of predawn leaf water potentials were -0.13 ± 0.02 MPa versus -0.05 ± 0.01\n(Ca) concentrations than those in wet forests. Compared to wet forests, dry forests show no\nsignificant differences in soil nitrogen (N) and phosphorus (P), but have higher soil pH and Ca\n(Supplementary Table 1).\n4. Otherwise, I thank the authors for making the suggested changes and wish them\nluck in the publication of this work.\nWe thank the reviewer once again for their constructive and insightful feedback\nthroughout the review process, which has significantly strengthened our manuscript.\n5. I am grateful to the authors for their thorough response to reviewers, which\naddressed all my concerns convincingly. I much appreciated the new figures,\nincluding the map on Extended Fig 1 and the species comparison on Extended Fig 3.\nPrevious overstatements, about the central role of pectin in land colonization during\nthe evolution of plants, have been polished into the much more balanced conclusion\nthat pectin concentration is a relevant drought-tolerance trait across species of dry\nforests.\nWe greatly appreciated the reviewer for their thoughtful engagement with our work\nand their generous assessment of our responses. We are delighted that the new figures\nwere effective and that our refined conclusions on the role of pectin now strikes the\nappropriate scientific balance.\n6. Overall, this solid article provides fresh insight into the contribution of pectin to\ndrought tolerance. Integrating leaf water relations, anatomy and cell wall biochemistry\nwithin a framework to analyze habitat specialization is very insightful.\nCongratulations to the authors for this original piece of work.\nWe are grateful for the reviewer's positive and generous comments. It is very\nrewarding to know that our integrated approach provided fresh insight, and we\nsincerely appreciate their congratulations.\n7. I would suggest providing vector figures for the publication – here figure quality is\nquite low when zooming in.\nThank you for this important technical point. For the final version, we will replace all\nfigures with high-resolution vector graphics (in .pdf format) as required by the\njournal's production standards.\nBelow are some typos I noticed.\n8. Line 163: add space between “turgor” and “(”\nCorrected.\n9. Line 178: “Fig. 1f” -> “Fig. 1e”\nCorrected.\n10. Line 181-183: “When we tested the relationship…” -> unclear sentence; Fig 1f is\nnot mentioned in the text anymore in this version\nhave explicitly integrated the figure citation.\n‘After controlling for π via partial correlation analysis, the initially\no\nnon-significant or negative ε -ψ relationships (Fig. 1f) became\nsym tlp\nsignificantly positive in both forest types (partial r = 0.43 for wet\nforests, 0.55 for dry forests; p < 0.01).’\n11. Line 228: 37% (text) or 44% (Fig 3a) for PC1?\nCorrected. The text on Line 228 has been updated to 44%.\n12. Line 231: 34% (text) or 35% (Fig 3b) for PC1?\nCorrected. The text on Line 231 has been updated to 35%.\n13. Line 304: “woody in” -> “woody plants in”\nCorrected. Thank you.\n14. Line 583: “al. (2023) incorporate” -> “al. (2023) to incorporate”\nCorrected.\n15. Extended Data Figure 12: remove red lines (spell checker) in panels a, b, e, f\nDone.\n16. Extended Data Table 1: “Soli properties” -> “Soil properties”\nCorrected."}]

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