Organ-specific non-structural carbohydrate allocation mediates growth-storage trade-offs and drought resilience in a semiarid forest

Organ-specific non-structural carbohydrate allocation mediates growth-storage trade-offs and drought resilience in a semiarid forest | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Organ-specific non-structural carbohydrate allocation mediates growth-storage trade-offs and drought resilience in a semiarid forest Yang Qi, Hongyan Liu, Jingyu Dai, Wenqi He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9016676/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Non-structural carbohydrates (NSC) are central to plant carbon metabolism, acting as critical buffers that regulate tree growth and survival under environmental stress. In semiarid regions, increasing hydroclimatic variability poses significant challenges to forest stability. However, the mechanistic regulation of how organ-specific NSC allocation mediates metabolic trade-offs between structural growth and carbon storage, and how these partitioning strategies influence drought resilience, remains poorly understood. This study aims to unravel the functional significance of organ-specific carbon partitioning in a natural mixed forest in Northern China. Results Our findings reveal that radial growth and drought responses are governed by organ-specific allocation logic rather than total NSC pools. Radial growth was positively associated with starch concentrations in leaves and fine roots and with soluble sugar concentrations in branches ( P < 0.05). Conversely, a significant negative correlation was found between radial growth and starch concentrations in branches ( P < 0.05), supporting a clear growth–storage trade-off mediated by carbon sequestration in perennial tissues. Furthermore, higher leaf soluble sugar concentrations were the primary physiological determinant of enhanced post-drought growth recovery ( P < 0.05), indicating that the mobilization of short-term, readily available carbon pools is essential for restoring physiological function following extreme water deficit. Conclusions This study demonstrates that drought resilience in semiarid forests depends less on absolute NSC concentrations than on the strategic partitioning of carbon among functional organs and chemical forms. These results provide mechanistic insights into the regulation of plant carbon metabolism and its role in underpinning forest stability. Our work offers a framework for refining vegetation models by integrating organ-specific carbon allocation logic to better predict forest responses to global climate change. Carbon metabolism Non-structural carbohydrates (NSC) Organ-specific allocation Radial growth Drought resilience Source-sink dynamics Semi-arid forests Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Increasing interannual precipitation variability has been widely documented as a consequence of climate change, with its impacts most pronounced in arid and semiarid regions [ 1 , 2 ]. As forests are critical components of terrestrial ecosystems, their resilience—the ability to resist disturbances and maintain or restore structure and function—is of vital importance in the context of global environmental change [ 3 , 4 ]. For trees, resilience is closely linked to growth resistance and recovery. These processes determine the ability of trees to endure extreme climatic events, such as drought, and to resume growth in subsequent years [ 5 ]. At the organismal level, this resilience is fundamentally governed by plant carbon metabolism, specifically the dynamic regulation of carbon partitioning between structural growth and functional storage[ 6 , 7 ]. However, how this metabolic allocation logic mediates tree growth resistance and recovery remains a critical knowledge gap in understanding forest stability. The allocation of carbon assimilates between structural growth and storage compounds plays a fundamental role in shaping plant performance and persistence [ 8 , 9 ]. Non-structural carbohydrates (NSC) serve as dynamic carbon reservoirs, modulated by the balance between carbon supply from photosynthesis and demand for growth (source-sink dynamics) [ 8 ]. NSC storage represents a key adaptive strategy for trees and is a critical factor influencing their growth performance [ 8 ]. Primarily composed of sugars and starch, NSCs fulfill distinct functional roles in transport, energy metabolism, and osmoregulation [ 8 ]. In the semiarid regions, where tree xylem experiences diurnal and seasonal cavitation, soluble sugars are essential role in embolism repair, signaling, and repair, thereby enhancing tree survival under water stress [ 8 , 10 ]. Starch serves as a long-term storage form and can be converted into soluble sugars when needed [ 8 ]. Despite their recognized physiological importance, a growing body of research finds that few tree species die directly from NSC depletion, i.e., carbon starvation. Instead, an alternative perspective posits that carbon allocation to NSC storage competes with structural growth [ 11 ], raising questions about carbon allocation priorities during periods of limited carbon uptake [ 8 , 12 ]. For instance, the mobilization of older NSC reserves to support future growth could mitigate direct trade-offs between NSC storage and structural carbon allocation [ 13 ]. Patterns of NSC allocation among different organs further reflect species-specific drought adaptation strategies. Under drought stress, trees tend to accumulate starch in stems [ 14 ], facilitating its rapid mobilization when needed. In contrast, several studies report declines in root NSC concentrations during drought, suggesting belowground carbon depletion or altered allocation priorities under prolonged stress [ 15 , 16 ]. However, our understanding of how organ-specific NSC allocation relates to growth resistance and recovery remains limited due to insufficient data on interspecific variation, annual growth increments, and NSC storage in belowground organs [ 12 , 17 ]. The forest-steppe ecotone in semiarid regions represents a marginal area of forest distribution characterized by high interannual rainfall variability [ 18 ]. In these regions, increasing precipitation variability is expected to further amplify the effects of drought, posing significant challenges to forest survival and growth [ 19 , 20 ]. Consequently, forests in these areas experience a range of drought-induced stresses and are likely to be highly sensitive to drought events intensified by climate change [ 21 – 23 ]. Reflecting strong environmental filtering, semiarid forests exhibit specific trait–growth relationships [ 24 , 25 ] that may be fundamentally governed by how trees partition carbon between growth and survival. This study was conducted in a natural forest within the Saihanwula Nature Reserve in Inner Mongolia, China. The site supports both coniferous and broad-leaved tree species. Despite significant differences in hydraulic traits and other characteristics between conifers and deciduous trees, they predominantly form mixed forests rather than pure stands in this region. Here, we examine whether organ-specific NSC allocation contributes to interspecific differences in growth resistance and recovery under drought conditions. During the growing season, trees face increased water stress due to higher water demand and elevated vapor pressure deficit (VPD) resulting from warmer temperatures compared to the non-growing season. The interannual variation in NSC storage with tree age is considerably smaller than the intra-annual variation driven by seasonal shifts between growing and dormant periods [ 26 ]. Thus, a single sampling event can effectively represent the NSC concentration during the growing season. By analyzing NSC concentrations across multiple organs (leaves, branches, stems, and roots), we aim to unravel the metabolic logic governing tree resilience. We hypothesize that (1) there is a distinct metabolic trade-off where prioritized starch sequestration in perennial tissues (stems and roots) constrains immediate radial growth (the growth-storage trade-off hypothesis); (2) organ-specific NSC allocation patterns serve as a key regulatory mechanism that determines interspecific differences in growth resistance and recovery, ultimately shaping the long-term stability of semiarid forest ecosystems. Materials and Methods Study site and hydro-climatic conditions Saihanwula National Nature Reserve (43°59'~44°27'N, 118°18'~118°55'E) is located in the mid-temperate semi-humid and warm climate zone of China. The average annual precipitation is 374.9 mm, while the annual evaporation is 2050 mm. Although no significant increasing or decreasing trend in precipitation was observed over the study period, interannual variability was considerable, ranging from approximately 200 mm to 700 mm ( P > 0.05, Fig. 1 ). The Palmer drought severity index (PDSI) was employed as an indicator of soil moisture availability for tree growth, as it incorporates both the cumulative influence of prior drought conditions and the lagged effects of precipitation on tree growth [ 27 ]. In this study, annual PDSI values from 1901 to 2020 were used to represent local drought conditions. The three driest years, 2002, 2007, and 2011, were selected as extreme drought years (Fig. 1 ), with PDSI values below − 3.5 [ 28 ]. Plot survey and species selection Field sampling was carried out within a 6-ha permanent forest plot representing a typical temperate deciduous broadleaved forest. In 2019, comprehensive forest inventories were conducted. We established three 10 m × 10 m representative subplots to record the dendrometric characteristics of all woody individuals with a diameter at breast height (DBH) > 1 cm. Recorded parameters included species identity, tree height, and DBH. Based on the importance value and dominance, four co-occurring tree species in the plot— Betula platyphylla , Populus davidiana , Quercus mongolica , and Larix gmelinii —were selected for field sampling and measurements (Table 1 ). Table 1 Information of the four tree species used in the study. Values of height and DBH are Mean ± 1 SD. Species Height (m) DBH (cm) Wood property Leaf habit Betula platyphylla 11.72 ± 0.53 14.48 ± 1.80 Diffuse-porous Deciduous-broadleaf Populus davidiana 11.04 ± 0.35 9.94 ± 0.94 Diffuse-porous Deciduous-broadleaf Quercus mongolica 4.80 ± 1.52 10.77 ± 2.10 Ring-porous Deciduous-broadleaf Larix gmelinii 13.53 ± 0.23 32.93 ± 4.68 Non-porous Deciduous-conifer Tree-ring data and basal area increment To compare stem growth rates across species, tree-ring cores were sampled in July 2019 using a 5 mm inner diameter increment borer. For each species, two cores were extracted from a minimum of 10 trees at the study site. Cores were taken at breast height (1.3 m) on opposite sides of the trunk, parallel to the contour. After air-drying, the cores were progressively polished with fine-grit sandpaper until tree-ring boundaries were clearly visible. All samples were cross-dated following standard dendrochronological techniques [ 29 ]. Tree-ring widths were measured with a LINTAB measuring system with an accuracy of 0.001 mm. The quality of cross-dating was verified using the COFECHA program [ 30 ], and cores with series intercorrelation below 0.6 were re-examined and re-dated when necessary to ensure measurement reliability. A total of 56 cores passed the quality control and were used for subsequent analysis. Basal area increment (BAI) was calculated from the ring width measurements using the formula: BAI = π (𝑟 𝑡 2 − 𝑟 𝑡−1 2 ) (1) Where r t represents the stem radius at year t and r t−1 represents the stem radius in the previous year t-1 [ 31 ]. Relative BAI ( BAI r , mm/year) was determined by standardizing BAI according to the diameter at breast height (DBH), which was calculated for all species as the slope of the linear regression model of BAI on DBH (Figure S1 ) [ 32 ]. Tree growth variability and drought resistance and recovery The coefficient of variation ( CV ) of BAI was selected to evaluate inter-individual growth variability within each species. The CV is dimensionless and standardizes variation relative to the mean, facilitating more meaningful comparisons across species compared to variance or standard deviation [ 33 ]. Tree growth resilience was assessed using stability indicators that quantify a tree's ability to resist and recover from disturbance. Resistance ( Rt ) and recovery ( Rc ) were calculated as follows [ 4 ]: Resistance ( Rt ) = Dr/PreDr (2) Recovery ( Rc )=PostDr/Dr (3) Among them, Dr represents the annual ring width index in the year of drought, while PreDr and PostDr represent the average annual ring width index during three years before and after the drought year, respectively. Final resistance and recovery values were calculated as averages across the three extreme drought years. Non-structural carbohydrate measurements While taking the tree ring core sample, we collected plant tissues to measure NSC concentration for each tree in July 2019. Considering that the tree canopy position has no significant effect on the branch NSC concentration in previous studies [ 34 ], this study did not sample according to the canopy position. The first-level branches in the middle of the canopy were randomly selected as standard branches. Branches in healthy growth were cut off with branch shears. Leaves on the branches were picked off, with the leaves without insect eggs collected for NSC measurement. Stem samples were collected using an increment borer, with four tree cores collected at the breast height of each sample tree. During each sampling, the drilling position was slightly moved, and the sampling was in a Z-shape to avoid overlapping with the previous sampling position and decrease the experimental error. Two root samples from each tree were excavated using iron picks, shovels, and were cut from the root canopy at a depth of 5–30 cm. After washing the soil off the root surface, they were divided into thick roots (> 5 mm) and fine roots (< 2 mm), according to the diameters. All collected samples were immediately treated in a microwave oven (600 W) for enzyme deactivation and then dried to constant weight in a drying oven at 65°C. The concentrations of starch and soluble sugar were determined by the anthracene copper-concentrated sulfuric acid method [ 35 , 36 ]. Total nonstructural carbohydrates (TNCs) were then calculated as the sum of soluble sugar and starch concentrations. Anthrone reagent was prepared by dissolving 1 g of purified anthrone in 1000 mL of dilute sulfuric acid. A standard curve was constructed using a 100 µg/mL glucose solution. Approximately 0.05 g of ground plant tissue (exact weight recorded) was repeatedly extracted with 80% ethanol. The supernatant was collected, mixed with anthrone reagent, and absorbance was measured at 620 nm using a spectrophotometer (UV-1800 PC, Shanghai MAPADA Instruments). Sugar concentration was determined from the standard curve (or via linear regression) and expressed as a percentage of sample dry weight. For starch quantification, the residue after soluble sugar extraction was treated with perchloric acid and distilled water, and the extracted solution was similarly analyzed with anthrone reagent. Starch concentration was derived from the same standard curve based on measured absorbance. Statistical analysis Non-parametric Kruskal–Wallis tests were used to assess interspecific differences in NSC storage, tree growth, and drought resistance. A P value < 0.05 was considered to indicate statistically significant differences among species. Pearson correlation analysis was applied to quantify the relationships between tree drought resilience indices and NSC components. Principal component analysis (PCA) was used to examine the relationships between tree growth resistance and recovery, NSC storage patterns, basal area increment (BAI), and BAI variability (CV) across species. All statistical analyses and graphical visualizations were performed using R 4.1.2 and Origin 2020b [ 37 ] . Result Growth and drought response of coniferous and broadleaf trees This study compared the radial growth and interannual growth variation among different coniferous and broadleaf tree species. Drought resilience was quantified using tree-ring indices. Analyses of radial stem growth demonstrated remarkable differences in growth rates among the four species. The BAI r of B. platyphylla (4.08 mm/year) was significantly higher than Q. mongolica (3.05 mm/year) ( P 0.05, Fig. 2 b, c). The recovery of B. platyphylla (2.176) and P. davidiana (1.723) were significantly higher than that of Q. mongolica (1.092) and L. gmelinii (0.847) ( P < 0.05, Fig. 2 d). NSC concentration and allocation in selected tree species The four tree species exhibited distinct organ-specific patterns in NSC allocation. P. davidiana showed notably high soluble sugar concentrations across both aboveground and underground organs, whereas Q. mongolica displayed relatively low soluble sugar levels throughout all tissues (Fig. 3 a). In terms of soluble sugar allocation during the growing season, B. platyphylla (6.58%), P. davidiana (7.53%), and Q. mongolica (5.03%) demonstrated a tendency to accumulate higher concentrations in leaves, while L. gmelinaii (6.59%) had its highest soluble sugar concentration in branches. Regarding starch storage, the concentration in each organ of L. gmelinii was generally high (Fig. 3 b). Specifically, B. platyphylla (10.7%) accumulated more starch in its leaves, whereas P. davidiana (9.84%) and Q. mongolica (14.4%) stored higher starch concentrations in their thick roots. L. gmelinii (17.9%), in contrast, tended to allocate more starch to fine roots. In non-woody leaves, the soluble sugar concentration in P. davidiana (7.5%) was significantly higher than that in Q. mongolica and L. gmelinii (4.33%) ( P < 0.05, Fig.S2a). In contrast, the starch concentration in L. gmelinii leaves (15.2%) markedly exceeded that in P. davidiana (5.6%) and Q. mongolica (9.6%) ( P < 0.05, Fig.S2a). For woody tissues (branches and stems), the soluble sugar concentrations in L. gmelinii branches (6.6%) and stems (3.4%) were significantly higher than those in Betula platyphylla (3.7% in branches; 1.7% in stems) and Q. mongolica (3.1% in branches; 1.8% in stems) ( P < 0.05, Fig.S2b-c). Meanwhile, the starch concentration in Q. mongolica branches (10.4%) was significantly greater than that in B. platyphylla (7.5%) and P. davidiana (7.2%), while the starch in L. gmelinii stems (12.8%) was significantly higher than that in Q. mongolica (8.3%) and P. davidiana (5.9%) ( P < 0.05, Fig.S2b-c). In root systems, the soluble sugar concentration in P. davidiana (6.29%) fine roots was significantly higher than in Q. mongolica (3.56%) and L. gmelinii (2.51%) ( P 0.05, Fig.S2e). The starch concentration in L. gmelinii (17.9%) fine roots was significantly greater than in P. davidiana (9.5%) and Q. mongolica (6.76%), and the starch in thick roots of L. gmelinii (14%) and Q. mongolica (14.4%) was significantly higher than in B. platyphylla (9.13%) ( P < 0.05, Fig.S2d). Regarding total non-structural carbohydrates (TNC), L. gmelinii leaves had significantly higher TNC than P. davidiana and Q. mongolica , and the TNC in L. gmelinii branches (16.8%) was significantly greater than in B. platyphylla (11.2%) and P. davidiana (11.6%) ( P < 0.05, Fig.S2a-e). Additionally, the TNC in L. gmelinii stems (16.2%) and fine roots was significantly higher than in the other three species ( P < 0.05, Fig.S2a-e). Correlation of tree growth and drought resistance with NSC storage Tree radial growth showed a significant positive correlation with starch concentration in leaves (r = 0.41), soluble sugar concentration in branches (r = 0.53), and starch concentration in fine roots (r = 0.79, P < 0.05; Fig. 4 ). In contrast, a significant negative correlation was observed between radial growth and starch concentration in branches (r = -0.42, P 0.05). Regarding drought resilience, tree resistance to extreme drought was negatively associated with starch concentration in stems (r = -0.46, P < 0.05). Post-drought recovery correlated positively with soluble sugar concentration in leaves (r = 0.42) and negatively with leaf starch concentration (r = -0.57, P 1 can be extracted, and the first three principal components can be extracted. The variance contribution rate of principal component 1, principal component 2 and principal component 3 is 31.6%, 18.2% and 14.1%, respectively, and the cumulative contribution rate of the first three principal components is 63.9% (Fig. 5 a). L. gmelinii exhibited significantly distinct patterns compared to other species (Fig. 5 a), necessitating separate analysis. For the three species other than L. gmelinii , the soluble sugars in fine roots (0.405), thick roots (0.325), leaves (0.318), and branches (0.310) had higher positive loadings on PC1, while starch in stems (-0.392), branches (-0.262), and leaves (-0.249) had higher negative loadings on PC1 (Fig. 5 b). The positive direction of PCA1 aligns with an acquisitive strategy for plant functional traits, whereas the negative direction is more consistent with a conservative strategy. On PC2, radial growth (0.434), fine roots (0.405), and starches in leaves (0.345) showed higher positive loadings (Fig. 5 b). PC2 aligns more with the growth rate strategy of trees, where B. platyphylla tends towards a fast-growing acquisitive strategy, P. davidiana favors a slower acquisitive growth strategy, and Q. mongolica adopts a slow-growing conservative strategy. For L. gmelinii , the explained variance of PC1 is 43.8%, and that of PC2 is 25.6% (Fig. 5 c). For the species in this region, radial growth rate is highly positively correlated with drought recovery, but inversely related to resistance. For the species studied in this region, radial growth rate showed a high positive correlation with drought recovery, while exhibiting an inverse relationship with drought resistance indicators (Fig. 5 b-c). Discussion Organ-specific NSC concentrations mediate trade-offs between growth and drought resilience This study demonstrates that organ-specific NSC concentrations play a central role in mediating trade-offs between tree growth and drought resistance and recovery in semi-arid forests. Rather than uniformly promoting growth, NSCs in different organs were associated with contrasting growth and resilience outcomes, indicating that carbon allocation priorities among tissues shape functional strategies under climatic stress [ 8 , 9 ]. Across species, growth was positively associated with NSC concentrations in resource-acquiring organs, including leaf starch, branch soluble sugars, and fine-root starch, consistent with evidence that leaf and root traits reflect plant growth strategies and survival potential [ 38 , 39 ]. In water-limited systems, hydraulic safety strongly constrains growth [ 40 ], and soluble sugars in branches contribute to embolism repair and hydraulic functioning [ 41 – 43 ], thereby supporting cambial activity. Similar growth constraints imposed by hydraulic limitations have been reported in karst ecosystems [ 44 ]. In contrast, higher starch concentrations in branches were negatively associated with radial growth, supporting the growth–storage trade-off hypothesis whereby greater allocation to longer-term carbon forms constrains structural biomass production [ 8 , 11 , 12 ]. These patterns align with the fast–slow plant economics spectrum, in which slower-growing species invest more in NSCs to enhance tolerance to environmental stress [ 45 , 46 ]. Importantly, NSC concentrations were tightly linked to components of growth resilience. Growth resistance to extreme drought was negatively associated with stem starch concentration, whereas recovery was positively associated with leaf soluble sugar concentration and negatively associated with leaf starch concentration. These relationships are consistent with evidence that soluble sugars play a central role in embolism repair and metabolic recovery following drought [ 41 , 43 ]. Drought strongly alters carbon–water relations, and NSC concentrations often recover earlier than photosynthetic carbon assimilation following stress release [ 47 ]. Sugars not immediately required for osmotic regulation or hydraulic repair may subsequently be converted into starch for longer-term use [ 48 ] or allocated to leaves to support regrowth [ 49 ]. Together, these results indicate that drought resilience is governed primarily by organ-specific NSC composition rather than by overall carbon reserve levels. Species differences in NSC concentration patterns underpin variation in growth resistance and recovery Marked interspecific differences in NSC concentrations and their organ-specific distribution corresponded with contrasting growth and drought resilience strategies among coexisting species. Previous studies have shown that evergreen species exhibit smaller seasonal fluctuations in NSCs than deciduous species [ 50 , 51 ], and that coniferous and broadleaf species differ substantially in NSC dynamics and allocation strategies [ 52 ]. Our results extend these findings by demonstrating that such differences translate into distinct growth resistance and recovery responses to extreme drought. Ring-porous species typically exhibit higher growth rates and more dynamic NSC concentrations than diffuse-porous species [ 53 , 54 ], reflecting acquisitive carbon-use strategies. In semi-arid environments, this strategy is often accompanied by coordinated above- and belowground allocation, with starch preferentially stored in roots when leaf soluble sugar concentrations are high [ 55 ]. In contrast, the conifer Larix gmelinii maintained consistently high NSC concentrations across organs, consistent with a more conservative carbon-use strategy emphasizing storage over rapid growth. These interspecific differences likely reflect long-term environmental filtering and adaptive responses to drought stress. Angiosperms are often characterized by rapid resource acquisition and growth but comparatively weaker drought resistance, whereas gymnosperms typically exhibit slower growth but enhanced stress tolerance [ 56 ]. Multivariate analyses further revealed clear functional separation among species along gradients of carbon acquisition versus storage investment, consistent with observed differences in growth resistance and recovery. Implications for forest resilience under increasing drought variability Forests in semi-arid regions experience strong environmental filtering and increasing hydroclimatic variability [ 24 , 25 ], and drought is a dominant constraint on tree growth and survival [ 21 – 23 ]. Our results indicate that drought resilience depends less on absolute NSC concentrations than on how carbon is partitioned among organs and chemical forms. Specifically, maintaining soluble sugars in leaves and branches appears critical for sustaining growth resistance and promoting recovery following extreme drought events. These findings highlight the importance of incorporating organ-specific NSC concentration dynamics into mechanistic models of forest carbon cycling and vegetation dynamics to improve predictions of species persistence under intensifying climate extremes. More broadly, our results emphasize carbon allocation strategy—rather than carbon storage quantity—as a key determinant of growth resistance and recovery, providing a mechanistic framework for understanding tree resilience in semi-arid ecosystems. Conclusions This study combined measurements of organ-specific non-structural carbohydrate (NSC) concentrations during the peak growing season with tree-ring derived growth indices to evaluate how carbon metabolic allocation influences radial growth and drought resilience in semi-arid tree species. Rather than uniformly promoting growth, NSC concentrations in different organs exhibited contrasting relationships with growth performance and drought response. Radial growth was positively associated with starch concentrations in leaves and fine roots and with soluble sugar concentrations in branches, but negatively associated with starch concentrations in branches, indicating a growth–storage trade-off mediated by organ-specific carbon allocation. In addition, post-drought recovery was positively related to leaf soluble sugar concentrations and negatively related to leaf starch concentrations, highlighting the functional importance of readily mobilizable carbohydrates for growth recovery following extreme drought. Collectively, these results demonstrate that organ-specific NSC allocation patterns, rather than overall NSC concentrations, underpin interspecific variation in growth resistance and recovery in semi-arid forests. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was granted by National Key Research and Development Program (2022YFF0801803) and National Natural Science Foundation of China (U24A20353). Authors' contributions HL designed the experiment. YQ, JD and WH conducted field surveys and conducted laboratory measurements. YQ analyzed the data and wrote the manuscript. All authors reviewed the manuscript. References Feng X, Porporato A, Rodriguez-Iturbe I. Changes in rainfall seasonality in the tropics. Nat Clim Change. 2013;3(9):811–5. IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Masson-Delmotte VP, Zhai A, Pirani SL, et al., editors. Cambridge: Cambridge University Press; 2021. Gazol A, Camarero JJ, Anderegg WRL, Vicente-Serrano SM. Impacts of droughts on the growth resilience of Northern Hemisphere forests. Global Ecol Biogeogr. 2017;26(2):166–76. Lloret F, Keeling EG, Sala A. Components of tree resilience: effects of successive low-growth episodes in old ponderosa pine forests. Oikos. 2011;120(12):1909–20. Rahman M, Islam M, Bräuning A. Species-specific growth resilience to drought in a mixed semi-deciduous tropical moist forest in South Asia. Forest Ecol Manag. 2019;433:487–96. Dannenberg MP, Wise EK, Smith WK. Reduced tree growth in the semiarid United States due to asymmetric responses to intensifying precipitation extremes. Sci Adv. 2019;5(10):eaaw0667. Yocom L, Ogle K, Peltier D, Szejner P, Liu Y, Monson RK. Tree growth sensitivity to climate varies across a seasonal precipitation gradient. Oecologia. 2022;198(4):933–46. Hartmann H, Trumbore S. Understanding the roles of nonstructural carbohydrates in forest trees – from what we can measure to what we want to know. New Phytol. 2016;211(2):386–403. Mooney HA. The carbon balance of plants. Annu Rev Ecol Evol Syst. 1972;3:315–46. Guo JS, Gear L, Hultine KR, Koch GW, Ogle K. Non-structural carbohydrate dynamics associated with antecedent stem water potential and air temperature in a dominant desert shrub. Plant Cell Environ. 2020;43(6):1467–83. Muller B, Pantin F, Génard M, Turc O, Freixes S, Piques M, Gibon Y. Water deficits uncouple growth from photosynthesis, increase C content, and modify the relationships between C and growth in sink organs. J Exp Bot. 2011;62(6):1715–29. Sala A, Woodruff DR, Meinzer FC. Carbon dynamics in trees: feast or famine? Tree Physiol. 2012;32(6):764–775. Richardson AD, Carbone MS, Huggett BA, Furze ME, Czimczik CI, Walker JC, et al. Distribution and mixing of old and new nonstructural carbon in two temperate trees. New Phytol. 2015;206(2):590–7. Liu H, Shangguan H, Zhou M, Airebule P, Zhao P, He W, et al. Differentiated responses of nonstructural carbohydrate allocation to climatic dryness and drought events in the Inner Asian arid timberline. Agr Forest Meteorol. 2019;271:355–61. Ouyang S, Gessler A, Saurer M, Hagedorn F, Gao D, Wang X, et al. Root carbon and nutrient homeostasis determines downy oak sapling survival and recovery from drought. Tree Physiol. 2021;41(8):1400–12. Yang Y, Ouyang S, Gessler A, Wang X, Na R, He HS, et al. Root carbon resources determine survival and growth of young trees under long drought in combination with fertilization. Front Plant Sci. 2022;13:929855. Wiley E, Helliker B. A re-evaluation of carbon storage in trees lends greater support for carbon limitation to growth. New Phytol. 2012;195(2):285–9. Andreu L, Gutiérrez E, Macias M, Ribas M, Bosch O, Camarero JJ. Climate increases regional tree-growth variability in Iberian pine forests. Global Change Biol. 2007;13(4):804–15. Liu J, Xu X, Zhang Y, Tian Y, Gao Q. Effect of rainfall interannual variability on the biomass and soil water distribution in a semiarid shrub community. Sci China Life Sci. 2010;53(6):729–37. Zuidema PA, Babst F, Groenendijk P, Trouet V, Abiyu A, Acuña-Soto R, et al. Tropical tree growth driven by dry-season climate variability. Nat Geosci. 2022;15(4):269–76. Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecol Manag. 2010;259(4):660–84. Hammond WM, Williams AP, Abatzoglou JT, Adams HD, Klein T, López R, et al. Global field observations of tree die-off reveal hotter-drought fingerprint for Earth’s forests. Nat Commun. 2022;13(1):1761. McDowell NG, Sapes G, Pivovaroff A, Adams HD, Allen CD, Anderegg WRL, et al. Mechanisms of woody-plant mortality under rising drought, CO$_2$ and vapour pressure deficit. Nat Rev Earth Env. 2022;3(5):294–308. Šímová I, Rueda M, Hawkins BA. Stress from cold and drought as drivers of functional trait spectra in North American angiosperm tree assemblages. Ecol Evol. 2017;7(18):7548–59. Dai J, Liu H, Xu C, Qi Y, Zhu X, Zhou M, et al. Divergent hydraulic strategies explain the interspecific associations of co-occurring trees in forest–steppe ecotone. Forests. 2020;11(9):942. Richardson AD, Carbone MS, Keenan TF, Czimczik CI, Hollinger DY, Murakami P, et al. Seasonal dynamics and age of stemwood nonstructural carbohydrates in temperate forest trees. New Phytol. 2013;197(3):850–61. Mika J, Horváth S, Makra L, Dunkel Z. The Palmer Drought Severity Index (PDSI) as an indicator of soil moisture. Phys Chem Earth. 2005;30(1):223–30. Dai A. Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900–2008. J Geophys Res-Atmos. 2011;116:D12106. Schweingruber FH. Tree rings: basics and applications of dendrochronology. Berlin: Springer Science & Business Media; 2012. Holmes RL. Computer-assisted quality control in tree-ring dating and measurement. Tree-Ring Bull. 1983;43:69–78. Monserud RA, Sterba H. A basal area increment model for individual trees growing in even-and uneven-aged forest stands in Austria. Forest Ecol Manag. 1996;80(1-3):57–80. Yin XH, Hao GY, Sterck F. A trade-off between growth and hydraulic resilience against freezing leads to divergent adaptations among temperate tree species. Funct Ecol. 2022;36(3):739–50. Lobry JR, Bel-Venner M, Bogdziewicz M, Hacket-Pain A, Venner S. The CV is dead, long live the CV!. Methods Ecol Evol. 2023;14(11):2780–6. Li MH, Hoch G, Körner C. Spatial variability of mobile carbohydrates within Pinus cembra trees at the alpine treeline. Phyton-Ann Rei Bot A. 2001;41(2):203–13. Osaki M, Shinano T, Tadano T. Redistribution of carbon and nitrogen compounds from the shoot to the harvesting organs during maturation in field crops. Soil Sci Plant Nutr. 1991;37(1):117–28. Trevelyan WE, Harrison JS. Studies on yeast metabolism. I. Fractionation and microdetermination of cell carbohydrates. Biochem J. 1952;50(3):298–303. R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2021. https://www.R-project.org/. Accessed 2 Mar 2026. Da R, He H, Zhang Z, Zhao X, von Gadow K, Zhang C. Leaf and root economics space in Fraxinus mandshurica: A test of the multidimensional trait framework within species. J Ecol. 2025;113(4):1004–17. Freschet GT, Violle C, Bourget MY, Scherer-Lorenzen M, Fort F. Allocation, morphology, physiology, architecture: the multiple facets of plant above- and below-ground responses to resource stress. New Phytol. 2018;219(4):1338–52. Tavares JV, Oliveira RS, Mencuccini M, Signori-Müller C, Pereira L, Diniz FC, et al. Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests. Nature. 2023;617(7959):111–7. O'Brien MJ, Leuzinger S, Philipson CD, Tay J, Hector A. Drought survival of tropical tree seedlings enhanced by non-structural carbohydrate levels. Nat Clim Change. 2014;4(8):710–4. Tomasella M, Petrussa E, Petruzzellis F, Nardini A, Casolo V. The possible role of non-structural carbohydrates in the regulation of tree hydraulics. Int J Mol Sci. 2020;21(1):144. Zwieniecki MA, Holbrook NM. Confronting Maxwell's demon: biophysics of xylem embolism repair. Trends Plant Sci. 2009;14(10):530–4. Aritsara ANA, Ni M, Wang Y, Yan C, Zeng W, Song H, et al. Tree growth is correlated with hydraulic efficiency and safety across 22 tree species in a subtropical karst forest. Tree Physiol. 2023;43(8):1307–18. Herrera-Ramírez D, Sierra CA, Römermann C, Muhr J, Trumbore S, Silvério D, et al. Starch and lipid storage strategies in tropical trees relate to growth and mortality. New Phytol. 2021;230(1):139–54. Signori-Müller C, Oliveira RS, Tavares JV, Diniz FC, Gilpin M, Barros FV, et al. Variation of non-structural carbohydrates across the fast-slow continuum in Amazon forest canopy trees. Funct Ecol. 2022;36(2):341–55. Ruehr NK, Grote R, Mayr S, Arneth A. Beyond the extreme: recovery of carbon and water relations in woody plants following heat and drought stress. Tree Physiol. 2019;39(8):1285–99. Galiano L, Timofeeva G, Saurer M, Siegwolf R, Martínez-Vilalta J, Hommel R, Gessler A. The fate of recently fixed carbon after drought release: towards unravelling C storage regulation in Tilia platyphyllos and Pinus sylvestris. Plant Cell Environ. 2017;40(9):1711–24. Zang U, Goisser M, Grams TEE, Häberle K, Matyssek R, Matzner E, Borken W. Fate of recently fixed carbon in European beech (Fagus sylvatica) saplings during drought and subsequent recovery. Tree Physiol. 2014;34(1):29–38. Palacio S, Camarero JJ, Maestro M, Alla AQ, Lahoz E, Montserrat-Martí G. Are storage and tree growth related? Seasonal nutrient and carbohydrate dynamics in evergreen and deciduous Mediterranean oaks. Trees. 2018;32(3):777–90. Schädel C, Blöchl A, Richter A, Hoch G. Short-term dynamics of nonstructural carbohydrates and hemicelluloses in young branches of temperate forest trees during bud break. Tree Physiol. 2009;29(7):901–11. Lu L, Liu H, Wang J, Zhao K, Miao Y, Li H, et al. Seasonal patterns of nonstructural carbohydrate storage and mobilization in two tree species with distinct life-history traits. Tree Physiol. 2024;44(7):tpae042. Barbaroux C, Bréda N. Contrasting distribution and seasonal dynamics of carbohydrate reserves in stem wood of adult ring-porous sessile oak and diffuse-porous beech trees. Tree Physiol. 2002;22(17):1201–10. Furze ME, Huggett BA, Chamberlain CJ, Wieringa MM, Aubrecht DM, Carbone MS, et al. Seasonal fluctuation of nonstructural carbohydrates reveals the metabolic availability of stemwood reserves in temperate trees with contrasting wood anatomy. Tree Physiol. 2020;40(10):1355–65. Wang Z, Wang C. Dynamics of nonstructural carbohydrates during drought and subsequent recovery: A global meta-analysis. Agr Forest Meteorol. 2025;363:110429. Reich PB. The world-wide ‘fast-slow’ plant economics spectrum: a traits manifesto. J Ecol. 2014;102(2):275–301. Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviews received at journal 31 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviewers agreed at journal 22 Mar, 2026 Reviewers agreed at journal 22 Mar, 2026 Reviewers invited by journal 17 Mar, 2026 Editor invited by journal 13 Mar, 2026 Editor assigned by journal 09 Mar, 2026 Submission checks completed at journal 09 Mar, 2026 First submitted to journal 09 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9016676","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607648156,"identity":"a0735588-87de-456e-9ba1-d5c4a561d8c9","order_by":0,"name":"Yang Qi","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Qi","suffix":""},{"id":607648157,"identity":"db2d5d15-fe40-4b3d-a7d3-43f93a697895","order_by":1,"name":"Hongyan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACPgkGxgcMDAdAbGYQwdhASAubBAOzAclagIg0LdI9ZtW8e+7ImzPwHjbmYbCR3XCA+dkDvFpkzpjd5nn2zHBnA19yMg9DmvGGA2zmBvgdlgPUcuAw44YDPMaHeRgOJwIZIKfi11IM1GIP1fKfOC3MQC0glcZAhx0gRktaseScA8+SNxzmSzacY5BsPPMwmxleLfwSyRs/vDlwx3bD8d7DEm8q7GT7jjc/w6uFgYEDGjzMPEACxGbGrx4I2B9AGTwElY6CUTAKRsEIBQCqlEULfmNZXQAAAABJRU5ErkJggg==","orcid":"","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Liu","suffix":""},{"id":607648158,"identity":"e6a559bd-461a-4a0c-8064-983bfe591aa5","order_by":2,"name":"Jingyu Dai","email":"","orcid":"","institution":"University of Georgia","correspondingAuthor":false,"prefix":"","firstName":"Jingyu","middleName":"","lastName":"Dai","suffix":""},{"id":607648159,"identity":"a046026e-4b42-45f1-949f-9c618035916e","order_by":3,"name":"Wenqi He","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Wenqi","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2026-03-03 06:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9016676/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9016676/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104896079,"identity":"75442d8b-acef-468b-a786-dc288bea1f28","added_by":"auto","created_at":"2026-03-18 12:02:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60903,"visible":true,"origin":"","legend":"\u003cp\u003eInterannual variation in annual precipitation (bar) and PDSI (Palmer drought severity index, solid line) at Saihanwula National Nature Reserve from 1970 to 2020. The dashed line indicates the linear regression trend for annual precipitation, which is not statistically significant (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). The years of extreme drought (2002, 2007, 2011) are highlighted by red open circles\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9016676/v1/ec0284e46e7e057af46157b9.png"},{"id":104896077,"identity":"c846aa0e-bdfa-4153-94c9-99840afd9b8c","added_by":"auto","created_at":"2026-03-18 12:02:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122184,"visible":true,"origin":"","legend":"\u003cp\u003eRadial growth rate and drought responses of four species. Bars represent the mean values, and the error bars show 1 SE. Different lower-case letters above the horizontal lines indicate significant differences between species (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Radial growth is denoted by (a) basal area increment (BAI) calculated as the average value of BAI from 2009 to 2018, and (b) coefficient of variation (CV) of BAI. The adaptive responses of trees to extreme drought events include (c) resistance (Rt), and (d) recovery (Rc)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9016676/v1/376b695323d240b6a8922220.png"},{"id":104896078,"identity":"809bbc76-aa73-44dd-9d3a-ce7369b27df6","added_by":"auto","created_at":"2026-03-18 12:02:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166351,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of different species on NSC concentration in each organ. (a) Soluble sugar concentration and (b) starch concentration\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9016676/v1/708dfb75fa618f3dfb648469.png"},{"id":104896081,"identity":"9b026486-b06a-4658-b065-4a39f1d6f59f","added_by":"auto","created_at":"2026-03-18 12:02:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":194873,"visible":true,"origin":"","legend":"\u003cp\u003eThe spearman correlation analysis between responses to drought events and NSC in tree organs. SS is the abbreviation for soluble sugars, and S is the abbreviation for starch. The orientation of the ellipses represents the direction of correlation (positive or negative), while the color indicates the correlation coefficient. Asterisks indicate significant partial correlations (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9016676/v1/4ada9f1eee02aed4d6791656.png"},{"id":104896082,"identity":"d93fadfd-2338-4fea-a915-fccc48a3c2d3","added_by":"auto","created_at":"2026-03-18 12:02:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":170883,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplots of tree growth traits and NSC variables across species groups. (a) All species with kernel density curves overlaid along the first two principal component axes, (b) broadleaf species, (c)\u003cem\u003e \u003c/em\u003econifer species (\u003cem\u003eL. gmelinii\u003c/em\u003e)\u003cem\u003e \u003c/em\u003eonly\u003cem\u003e.\u003c/em\u003e Different colored dots indicate different species; arrows represent the loadings of different factors on the two main axes\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9016676/v1/dc652dac4af0a882d4853ad9.png"},{"id":105034555,"identity":"1fbafd6e-c769-4a6a-8743-ceb48b2ea3f3","added_by":"auto","created_at":"2026-03-20 07:23:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1187175,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9016676/v1/15a7fd08-92b9-4757-a031-2dafee8ced77.pdf"},{"id":104896080,"identity":"b2be893b-b427-40b9-ad9d-2d19b5e92b1b","added_by":"auto","created_at":"2026-03-18 12:02:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":402926,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9016676/v1/bf59295d73608b40f5ed0350.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Organ-specific non-structural carbohydrate allocation mediates growth-storage trade-offs and drought resilience in a semiarid forest","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIncreasing interannual precipitation variability has been widely documented as a consequence of climate change, with its impacts most pronounced in arid and semiarid regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As forests are critical components of terrestrial ecosystems, their resilience\u0026mdash;the ability to resist disturbances and maintain or restore structure and function\u0026mdash;is of vital importance in the context of global environmental change [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For trees, resilience is closely linked to growth resistance and recovery. These processes determine the ability of trees to endure extreme climatic events, such as drought, and to resume growth in subsequent years [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. At the organismal level, this resilience is fundamentally governed by plant carbon metabolism, specifically the dynamic regulation of carbon partitioning between structural growth and functional storage[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, how this metabolic allocation logic mediates tree growth resistance and recovery remains a critical knowledge gap in understanding forest stability.\u003c/p\u003e \u003cp\u003eThe allocation of carbon assimilates between structural growth and storage compounds plays a fundamental role in shaping plant performance and persistence [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Non-structural carbohydrates (NSC) serve as dynamic carbon reservoirs, modulated by the balance between carbon supply from photosynthesis and demand for growth (source-sink dynamics) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. NSC storage represents a key adaptive strategy for trees and is a critical factor influencing their growth performance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Primarily composed of sugars and starch, NSCs fulfill distinct functional roles in transport, energy metabolism, and osmoregulation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the semiarid regions, where tree xylem experiences diurnal and seasonal cavitation, soluble sugars are essential role in embolism repair, signaling, and repair, thereby enhancing tree survival under water stress [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Starch serves as a long-term storage form and can be converted into soluble sugars when needed [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite their recognized physiological importance, a growing body of research finds that few tree species die directly from NSC depletion, i.e., carbon starvation. Instead, an alternative perspective posits that carbon allocation to NSC storage competes with structural growth [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], raising questions about carbon allocation priorities during periods of limited carbon uptake [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For instance, the mobilization of older NSC reserves to support future growth could mitigate direct trade-offs between NSC storage and structural carbon allocation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Patterns of NSC allocation among different organs further reflect species-specific drought adaptation strategies. Under drought stress, trees tend to accumulate starch in stems [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], facilitating its rapid mobilization when needed. In contrast, several studies report declines in root NSC concentrations during drought, suggesting belowground carbon depletion or altered allocation priorities under prolonged stress [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, our understanding of how organ-specific NSC allocation relates to growth resistance and recovery remains limited due to insufficient data on interspecific variation, annual growth increments, and NSC storage in belowground organs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe forest-steppe ecotone in semiarid regions represents a marginal area of forest distribution characterized by high interannual rainfall variability [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In these regions, increasing precipitation variability is expected to further amplify the effects of drought, posing significant challenges to forest survival and growth [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Consequently, forests in these areas experience a range of drought-induced stresses and are likely to be highly sensitive to drought events intensified by climate change [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Reflecting strong environmental filtering, semiarid forests exhibit specific trait\u0026ndash;growth relationships [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] that may be fundamentally governed by how trees partition carbon between growth and survival. This study was conducted in a natural forest within the Saihanwula Nature Reserve in Inner Mongolia, China. The site supports both coniferous and broad-leaved tree species. Despite significant differences in hydraulic traits and other characteristics between conifers and deciduous trees, they predominantly form mixed forests rather than pure stands in this region. Here, we examine whether organ-specific NSC allocation contributes to interspecific differences in growth resistance and recovery under drought conditions.\u003c/p\u003e \u003cp\u003eDuring the growing season, trees face increased water stress due to higher water demand and elevated vapor pressure deficit (VPD) resulting from warmer temperatures compared to the non-growing season. The interannual variation in NSC storage with tree age is considerably smaller than the intra-annual variation driven by seasonal shifts between growing and dormant periods [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Thus, a single sampling event can effectively represent the NSC concentration during the growing season. By analyzing NSC concentrations across multiple organs (leaves, branches, stems, and roots), we aim to unravel the metabolic logic governing tree resilience. We hypothesize that (1) there is a distinct metabolic trade-off where prioritized starch sequestration in perennial tissues (stems and roots) constrains immediate radial growth (the growth-storage trade-off hypothesis); (2) organ-specific NSC allocation patterns serve as a key regulatory mechanism that determines interspecific differences in growth resistance and recovery, ultimately shaping the long-term stability of semiarid forest ecosystems.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eStudy site and hydro-climatic conditions\u003c/p\u003e\n\u003cp\u003eSaihanwula National Nature Reserve (43\u0026deg;59\u0026apos;~44\u0026deg;27\u0026apos;N, 118\u0026deg;18\u0026apos;~118\u0026deg;55\u0026apos;E) is located in the mid-temperate semi-humid and warm climate zone of China. The average annual precipitation is 374.9 mm, while the annual evaporation is 2050 mm. Although no significant increasing or decreasing trend in precipitation was observed over the study period, interannual variability was considerable, ranging from approximately 200 mm to 700 mm (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe Palmer drought severity index (PDSI) was employed as an indicator of soil moisture availability for tree growth, as it incorporates both the cumulative influence of prior drought conditions and the lagged effects of precipitation on tree growth [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. In this study, annual PDSI values from 1901 to 2020 were used to represent local drought conditions. The three driest years, 2002, 2007, and 2011, were selected as extreme drought years (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), with PDSI values below \u0026minus;\u0026thinsp;3.5 [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003ePlot survey and species selection\u003c/p\u003e\n\u003cp\u003eField sampling was carried out within a 6-ha permanent forest plot representing a typical temperate deciduous broadleaved forest. In 2019, comprehensive forest inventories were conducted. We established three 10 m \u0026times; 10 m representative subplots to record the dendrometric characteristics of all woody individuals with a diameter at breast height (DBH)\u0026thinsp;\u0026gt;\u0026thinsp;1 cm. Recorded parameters included species identity, tree height, and DBH. Based on the importance value and dominance, four co-occurring tree species in the plot\u0026mdash;\u003cem\u003eBetula platyphylla\u003c/em\u003e, \u003cem\u003ePopulus davidiana\u003c/em\u003e, \u003cem\u003eQuercus mongolica\u003c/em\u003e, and \u003cem\u003eLarix gmelinii\u003c/em\u003e\u0026mdash;were selected for field sampling and measurements (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInformation of the four tree species used in the study. Values of height and DBH are Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;1 SD.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHeight (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDBH (cm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWood property\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLeaf habit\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBetula platyphylla\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiffuse-porous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeciduous-broadleaf\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePopulus davidiana\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiffuse-porous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeciduous-broadleaf\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eQuercus mongolica\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRing-porous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeciduous-broadleaf\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLarix gmelinii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-porous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeciduous-conifer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTree-ring data and basal area increment\u003c/p\u003e\n\u003cp\u003eTo compare stem growth rates across species, tree-ring cores were sampled in July 2019 using a 5 mm inner diameter increment borer. For each species, two cores were extracted from a minimum of 10 trees at the study site. Cores were taken at breast height (1.3 m) on opposite sides of the trunk, parallel to the contour. After air-drying, the cores were progressively polished with fine-grit sandpaper until tree-ring boundaries were clearly visible. All samples were cross-dated following standard dendrochronological techniques [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. Tree-ring widths were measured with a LINTAB measuring system with an accuracy of 0.001 mm. The quality of cross-dating was verified using the COFECHA program [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e], and cores with series intercorrelation below 0.6 were re-examined and re-dated when necessary to ensure measurement reliability. A total of 56 cores passed the quality control and were used for subsequent analysis.\u003c/p\u003e\n\u003cp\u003eBasal area increment (BAI) was calculated from the ring width measurements using the formula:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBAI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026pi; (𝑟\u003csub\u003e𝑡\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e \u0026minus; 𝑟\u003csub\u003e𝑡\u0026minus;1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e) (1)\u003c/p\u003e\n\u003cp\u003eWhere r\u003csub\u003et\u003c/sub\u003e represents the stem radius at year t and r\u003csub\u003et\u0026minus;1\u003c/sub\u003e represents the stem radius in the previous year t-1 [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. Relative BAI (\u003cem\u003eBAI\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e, mm/year) was determined by standardizing BAI according to the diameter at breast height (DBH), which was calculated for all species as the slope of the linear regression model of BAI on DBH (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTree growth variability and drought resistance and recovery\u003c/p\u003e\n\u003cp\u003eThe coefficient of variation (\u003cem\u003eCV\u003c/em\u003e) of BAI was selected to evaluate inter-individual growth variability within each species. The \u003cem\u003eCV\u003c/em\u003e is dimensionless and standardizes variation relative to the mean, facilitating more meaningful comparisons across species compared to variance or standard deviation [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTree growth resilience was assessed using stability indicators that quantify a tree\u0026apos;s ability to resist and recover from disturbance. Resistance (\u003cem\u003eRt\u003c/em\u003e) and recovery (\u003cem\u003eRc\u003c/em\u003e) were calculated as follows [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]:\u003c/p\u003e\n\u003cp\u003eResistance (\u003cem\u003eRt\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;Dr/PreDr (2)\u003c/p\u003e\n\u003cp\u003eRecovery (\u003cem\u003eRc\u003c/em\u003e)=PostDr/Dr (3)\u003c/p\u003e\n\u003cp\u003eAmong them, Dr represents the annual ring width index in the year of drought, while PreDr and PostDr represent the average annual ring width index during three years before and after the drought year, respectively. Final resistance and recovery values were calculated as averages across the three extreme drought years.\u003c/p\u003e\n\u003cp\u003eNon-structural carbohydrate measurements\u003c/p\u003e\n\u003cp\u003eWhile taking the tree ring core sample, we collected plant tissues to measure NSC concentration for each tree in July 2019. Considering that the tree canopy position has no significant effect on the branch NSC concentration in previous studies [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], this study did not sample according to the canopy position. The first-level branches in the middle of the canopy were randomly selected as standard branches. Branches in healthy growth were cut off with branch shears. Leaves on the branches were picked off, with the leaves without insect eggs collected for NSC measurement. Stem samples were collected using an increment borer, with four tree cores collected at the breast height of each sample tree. During each sampling, the drilling position was slightly moved, and the sampling was in a Z-shape to avoid overlapping with the previous sampling position and decrease the experimental error. Two root samples from each tree were excavated using iron picks, shovels, and were cut from the root canopy at a depth of 5\u0026ndash;30 cm. After washing the soil off the root surface, they were divided into thick roots (\u0026gt;\u0026thinsp;5 mm) and fine roots (\u0026lt;\u0026thinsp;2 mm), according to the diameters.\u003c/p\u003e\n\u003cp\u003eAll collected samples were immediately treated in a microwave oven (600 W) for enzyme deactivation and then dried to constant weight in a drying oven at 65\u0026deg;C. The concentrations of starch and soluble sugar were determined by the anthracene copper-concentrated sulfuric acid method [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Total nonstructural carbohydrates (TNCs) were then calculated as the sum of soluble sugar and starch concentrations.\u003c/p\u003e\n\u003cp\u003eAnthrone reagent was prepared by dissolving 1 g of purified anthrone in 1000 mL of dilute sulfuric acid. A standard curve was constructed using a 100 \u0026micro;g/mL glucose solution. Approximately 0.05 g of ground plant tissue (exact weight recorded) was repeatedly extracted with 80% ethanol. The supernatant was collected, mixed with anthrone reagent, and absorbance was measured at 620 nm using a spectrophotometer (UV-1800 PC, Shanghai MAPADA Instruments). Sugar concentration was determined from the standard curve (or via linear regression) and expressed as a percentage of sample dry weight. For starch quantification, the residue after soluble sugar extraction was treated with perchloric acid and distilled water, and the extracted solution was similarly analyzed with anthrone reagent. Starch concentration was derived from the same standard curve based on measured absorbance.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eNon-parametric Kruskal\u0026ndash;Wallis tests were used to assess interspecific differences in NSC storage, tree growth, and drought resistance. A \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistically significant differences among species. Pearson correlation analysis was applied to quantify the relationships between tree drought resilience indices and NSC components. Principal component analysis (PCA) was used to examine the relationships between tree growth resistance and recovery, NSC storage patterns, basal area increment (BAI), and BAI variability (CV) across species. All statistical analyses and graphical visualizations were performed using R 4.1.2 and Origin 2020b [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] .\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003eGrowth and drought response of coniferous and broadleaf trees\u003c/p\u003e \u003cp\u003eThis study compared the radial growth and interannual growth variation among different coniferous and broadleaf tree species. Drought resilience was quantified using tree-ring indices. Analyses of radial stem growth demonstrated remarkable differences in growth rates among the four species. The \u003cem\u003eBAI\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e of \u003cem\u003eB. platyphylla\u003c/em\u003e (4.08 mm/year) was significantly higher than \u003cem\u003eQ. mongolica\u003c/em\u003e (3.05 mm/year) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). No significant interspecific differences were observed in terms of tree growth variation (coefficient of variation) or resistance to extreme drought events (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c). The recovery of \u003cem\u003eB. platyphylla\u003c/em\u003e (2.176) and \u003cem\u003eP. davidiana\u003c/em\u003e (1.723) were significantly higher than that of \u003cem\u003eQ. mongolica\u003c/em\u003e (1.092) and \u003cem\u003eL. gmelinii\u003c/em\u003e (0.847) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNSC concentration and allocation in selected tree species\u003c/p\u003e \u003cp\u003eThe four tree species exhibited distinct organ-specific patterns in NSC allocation. \u003cem\u003eP. davidiana\u003c/em\u003e showed notably high soluble sugar concentrations across both aboveground and underground organs, whereas \u003cem\u003eQ. mongolica\u003c/em\u003e displayed relatively low soluble sugar levels throughout all tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In terms of soluble sugar allocation during the growing season, \u003cem\u003eB. platyphylla\u003c/em\u003e (6.58%), \u003cem\u003eP. davidiana\u003c/em\u003e (7.53%), and \u003cem\u003eQ. mongolica\u003c/em\u003e (5.03%) demonstrated a tendency to accumulate higher concentrations in leaves, while \u003cem\u003eL. gmelinaii\u003c/em\u003e (6.59%) had its highest soluble sugar concentration in branches.\u003c/p\u003e \u003cp\u003eRegarding starch storage, the concentration in each organ of \u003cem\u003eL. gmelinii\u003c/em\u003e was generally high (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Specifically, \u003cem\u003eB. platyphylla\u003c/em\u003e (10.7%) accumulated more starch in its leaves, whereas \u003cem\u003eP. davidiana\u003c/em\u003e (9.84%) and \u003cem\u003eQ. mongolica\u003c/em\u003e (14.4%) stored higher starch concentrations in their thick roots. \u003cem\u003eL. gmelinii\u003c/em\u003e (17.9%), in contrast, tended to allocate more starch to fine roots.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn non-woody leaves, the soluble sugar concentration in \u003cem\u003eP. davidiana\u003c/em\u003e (7.5%) was significantly higher than that in \u003cem\u003eQ. mongolica\u003c/em\u003e and \u003cem\u003eL. gmelinii\u003c/em\u003e (4.33%) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.S2a). In contrast, the starch concentration in \u003cem\u003eL. gmelinii\u003c/em\u003e leaves (15.2%) markedly exceeded that in \u003cem\u003eP. davidiana\u003c/em\u003e (5.6%) and \u003cem\u003eQ. mongolica\u003c/em\u003e (9.6%) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.S2a).\u003c/p\u003e \u003cp\u003eFor woody tissues (branches and stems), the soluble sugar concentrations in \u003cem\u003eL. gmelinii\u003c/em\u003e branches (6.6%) and stems (3.4%) were significantly higher than those in \u003cem\u003eBetula platyphylla\u003c/em\u003e (3.7% in branches; 1.7% in stems) and \u003cem\u003eQ. mongolica\u003c/em\u003e (3.1% in branches; 1.8% in stems) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.S2b-c). Meanwhile, the starch concentration in \u003cem\u003eQ. mongolica\u003c/em\u003e branches (10.4%) was significantly greater than that in \u003cem\u003eB. platyphylla\u003c/em\u003e (7.5%) and \u003cem\u003eP. davidiana\u003c/em\u003e (7.2%), while the starch in \u003cem\u003eL. gmelinii\u003c/em\u003e stems (12.8%) was significantly higher than that in \u003cem\u003eQ. mongolica\u003c/em\u003e (8.3%) and \u003cem\u003eP. davidiana\u003c/em\u003e (5.9%) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.S2b-c).\u003c/p\u003e \u003cp\u003eIn root systems, the soluble sugar concentration in \u003cem\u003eP. davidiana\u003c/em\u003e (6.29%) fine roots was significantly higher than in \u003cem\u003eQ. mongolica\u003c/em\u003e (3.56%) and \u003cem\u003eL. gmelinii\u003c/em\u003e (2.51%) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.S2d), though no significant interspecific difference was detected in thick roots (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.S2e). The starch concentration in \u003cem\u003eL. gmelinii\u003c/em\u003e (17.9%) fine roots was significantly greater than in \u003cem\u003eP. davidiana\u003c/em\u003e (9.5%) and \u003cem\u003eQ. mongolica\u003c/em\u003e (6.76%), and the starch in thick roots of \u003cem\u003eL. gmelinii\u003c/em\u003e (14%) and \u003cem\u003eQ. mongolica\u003c/em\u003e (14.4%) was significantly higher than in \u003cem\u003eB. platyphylla\u003c/em\u003e (9.13%) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.S2d).\u003c/p\u003e \u003cp\u003eRegarding total non-structural carbohydrates (TNC), \u003cem\u003eL. gmelinii\u003c/em\u003e leaves had significantly higher TNC than \u003cem\u003eP. davidiana\u003c/em\u003e and \u003cem\u003eQ. mongolica\u003c/em\u003e, and the TNC in \u003cem\u003eL. gmelinii\u003c/em\u003e branches (16.8%) was significantly greater than in \u003cem\u003eB. platyphylla\u003c/em\u003e (11.2%) and \u003cem\u003eP. davidiana\u003c/em\u003e (11.6%) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.S2a-e). Additionally, the TNC in \u003cem\u003eL. gmelinii\u003c/em\u003e stems (16.2%) and fine roots was significantly higher than in the other three species (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.S2a-e).\u003c/p\u003e \u003cp\u003eCorrelation of tree growth and drought resistance with NSC storage\u003c/p\u003e \u003cp\u003eTree radial growth showed a significant positive correlation with starch concentration in leaves (r\u0026thinsp;=\u0026thinsp;0.41), soluble sugar concentration in branches (r\u0026thinsp;=\u0026thinsp;0.53), and starch concentration in fine roots (r\u0026thinsp;=\u0026thinsp;0.79, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, a significant negative correlation was observed between radial growth and starch concentration in branches (r = -0.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant association was detected between interannual growth variation (\u003cem\u003eBAI CV\u003c/em\u003e) and NSC concentrations across any organ (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eRegarding drought resilience, tree resistance to extreme drought was negatively associated with starch concentration in stems (r = -0.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Post-drought recovery correlated positively with soluble sugar concentration in leaves (r\u0026thinsp;=\u0026thinsp;0.42) and negatively with leaf starch concentration (r = -0.57, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterspecific variation in growth and carbon allocation\u003c/p\u003e \u003cp\u003eAccording to the principal component analysis (PCA) results, the principal component with eigenvalue\u0026thinsp;\u0026gt;\u0026thinsp;1 can be extracted, and the first three principal components can be extracted. The variance contribution rate of principal component 1, principal component 2 and principal component 3 is 31.6%, 18.2% and 14.1%, respectively, and the cumulative contribution rate of the first three principal components is 63.9% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). \u003cem\u003eL. gmelinii\u003c/em\u003e exhibited significantly distinct patterns compared to other species (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), necessitating separate analysis.\u003c/p\u003e \u003cp\u003eFor the three species other than \u003cem\u003eL. gmelinii\u003c/em\u003e, the soluble sugars in fine roots (0.405), thick roots (0.325), leaves (0.318), and branches (0.310) had higher positive loadings on PC1, while starch in stems (-0.392), branches (-0.262), and leaves (-0.249) had higher negative loadings on PC1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The positive direction of PCA1 aligns with an acquisitive strategy for plant functional traits, whereas the negative direction is more consistent with a conservative strategy.\u003c/p\u003e \u003cp\u003eOn PC2, radial growth (0.434), fine roots (0.405), and starches in leaves (0.345) showed higher positive loadings (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). PC2 aligns more with the growth rate strategy of trees, where \u003cem\u003eB. platyphylla\u003c/em\u003e tends towards a fast-growing acquisitive strategy, \u003cem\u003eP. davidiana\u003c/em\u003e favors a slower acquisitive growth strategy, and \u003cem\u003eQ. mongolica\u003c/em\u003e adopts a slow-growing conservative strategy. For \u003cem\u003eL. gmelinii\u003c/em\u003e, the explained variance of PC1 is 43.8%, and that of PC2 is 25.6% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). For the species in this region, radial growth rate is highly positively correlated with drought recovery, but inversely related to resistance.\u003c/p\u003e \u003cp\u003eFor the species studied in this region, radial growth rate showed a high positive correlation with drought recovery, while exhibiting an inverse relationship with drought resistance indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb-c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOrgan-specific NSC concentrations mediate trade-offs between growth and drought resilience\u003c/p\u003e \u003cp\u003eThis study demonstrates that organ-specific NSC concentrations play a central role in mediating trade-offs between tree growth and drought resistance and recovery in semi-arid forests. Rather than uniformly promoting growth, NSCs in different organs were associated with contrasting growth and resilience outcomes, indicating that carbon allocation priorities among tissues shape functional strategies under climatic stress [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAcross species, growth was positively associated with NSC concentrations in resource-acquiring organs, including leaf starch, branch soluble sugars, and fine-root starch, consistent with evidence that leaf and root traits reflect plant growth strategies and survival potential [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In water-limited systems, hydraulic safety strongly constrains growth [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and soluble sugars in branches contribute to embolism repair and hydraulic functioning [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], thereby supporting cambial activity. Similar growth constraints imposed by hydraulic limitations have been reported in karst ecosystems [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, higher starch concentrations in branches were negatively associated with radial growth, supporting the growth\u0026ndash;storage trade-off hypothesis whereby greater allocation to longer-term carbon forms constrains structural biomass production [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These patterns align with the fast\u0026ndash;slow plant economics spectrum, in which slower-growing species invest more in NSCs to enhance tolerance to environmental stress [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImportantly, NSC concentrations were tightly linked to components of growth resilience. Growth resistance to extreme drought was negatively associated with stem starch concentration, whereas recovery was positively associated with leaf soluble sugar concentration and negatively associated with leaf starch concentration. These relationships are consistent with evidence that soluble sugars play a central role in embolism repair and metabolic recovery following drought [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Drought strongly alters carbon\u0026ndash;water relations, and NSC concentrations often recover earlier than photosynthetic carbon assimilation following stress release [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Sugars not immediately required for osmotic regulation or hydraulic repair may subsequently be converted into starch for longer-term use [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] or allocated to leaves to support regrowth [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Together, these results indicate that drought resilience is governed primarily by organ-specific NSC composition rather than by overall carbon reserve levels.\u003c/p\u003e \u003cp\u003eSpecies differences in NSC concentration patterns underpin variation in growth resistance and recovery\u003c/p\u003e \u003cp\u003eMarked interspecific differences in NSC concentrations and their organ-specific distribution corresponded with contrasting growth and drought resilience strategies among coexisting species. Previous studies have shown that evergreen species exhibit smaller seasonal fluctuations in NSCs than deciduous species [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and that coniferous and broadleaf species differ substantially in NSC dynamics and allocation strategies [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Our results extend these findings by demonstrating that such differences translate into distinct growth resistance and recovery responses to extreme drought.\u003c/p\u003e \u003cp\u003eRing-porous species typically exhibit higher growth rates and more dynamic NSC concentrations than diffuse-porous species [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], reflecting acquisitive carbon-use strategies. In semi-arid environments, this strategy is often accompanied by coordinated above- and belowground allocation, with starch preferentially stored in roots when leaf soluble sugar concentrations are high [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In contrast, the conifer \u003cem\u003eLarix gmelinii\u003c/em\u003e maintained consistently high NSC concentrations across organs, consistent with a more conservative carbon-use strategy emphasizing storage over rapid growth.\u003c/p\u003e \u003cp\u003eThese interspecific differences likely reflect long-term environmental filtering and adaptive responses to drought stress. Angiosperms are often characterized by rapid resource acquisition and growth but comparatively weaker drought resistance, whereas gymnosperms typically exhibit slower growth but enhanced stress tolerance [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Multivariate analyses further revealed clear functional separation among species along gradients of carbon acquisition versus storage investment, consistent with observed differences in growth resistance and recovery.\u003c/p\u003e \u003cp\u003eImplications for forest resilience under increasing drought variability\u003c/p\u003e \u003cp\u003eForests in semi-arid regions experience strong environmental filtering and increasing hydroclimatic variability [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and drought is a dominant constraint on tree growth and survival [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our results indicate that drought resilience depends less on absolute NSC concentrations than on how carbon is partitioned among organs and chemical forms. Specifically, maintaining soluble sugars in leaves and branches appears critical for sustaining growth resistance and promoting recovery following extreme drought events.\u003c/p\u003e \u003cp\u003eThese findings highlight the importance of incorporating organ-specific NSC concentration dynamics into mechanistic models of forest carbon cycling and vegetation dynamics to improve predictions of species persistence under intensifying climate extremes. More broadly, our results emphasize carbon allocation strategy\u0026mdash;rather than carbon storage quantity\u0026mdash;as a key determinant of growth resistance and recovery, providing a mechanistic framework for understanding tree resilience in semi-arid ecosystems.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study combined measurements of organ-specific non-structural carbohydrate (NSC) concentrations during the peak growing season with tree-ring derived growth indices to evaluate how carbon metabolic allocation influences radial growth and drought resilience in semi-arid tree species. Rather than uniformly promoting growth, NSC concentrations in different organs exhibited contrasting relationships with growth performance and drought response. Radial growth was positively associated with starch concentrations in leaves and fine roots and with soluble sugar concentrations in branches, but negatively associated with starch concentrations in branches, indicating a growth\u0026ndash;storage trade-off mediated by organ-specific carbon allocation. In addition, post-drought recovery was positively related to leaf soluble sugar concentrations and negatively related to leaf starch concentrations, highlighting the functional importance of readily mobilizable carbohydrates for growth recovery following extreme drought. Collectively, these results demonstrate that organ-specific NSC allocation patterns, rather than overall NSC concentrations, underpin interspecific variation in growth resistance and recovery in semi-arid forests.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eapproval\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efor\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was granted by National Key Research and Development Program (2022YFF0801803) and National Natural Science Foundation of China (U24A20353).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHL designed the experiment. YQ, JD and WH conducted field surveys and conducted laboratory measurements. YQ analyzed the data and wrote the manuscript. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFeng X, Porporato A, Rodriguez-Iturbe I. Changes in rainfall seasonality in the tropics. Nat Clim Change. 2013;3(9):811\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eIPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Masson-Delmotte VP, Zhai A, Pirani SL, et al., editors. Cambridge: Cambridge University Press; 2021.\u003c/li\u003e\n\u003cli\u003eGazol A, Camarero JJ, Anderegg WRL, Vicente-Serrano SM. Impacts of droughts on the growth resilience of Northern Hemisphere forests. Global Ecol Biogeogr. 2017;26(2):166\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eLloret F, Keeling EG, Sala A. Components of tree resilience: effects of successive low-growth episodes in old ponderosa pine forests. Oikos. 2011;120(12):1909\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eRahman M, Islam M, Br\u0026auml;uning A. Species-specific growth resilience to drought in a mixed semi-deciduous tropical moist forest in South Asia. Forest Ecol Manag. 2019;433:487\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003eDannenberg MP, Wise EK, Smith WK. Reduced tree growth in the semiarid United States due to asymmetric responses to intensifying precipitation extremes. Sci Adv. 2019;5(10):eaaw0667.\u003c/li\u003e\n\u003cli\u003eYocom L, Ogle K, Peltier D, Szejner P, Liu Y, Monson RK. Tree growth sensitivity to climate varies across a seasonal precipitation gradient. Oecologia. 2022;198(4):933\u0026ndash;46.\u003c/li\u003e\n\u003cli\u003eHartmann H, Trumbore S. Understanding the roles of nonstructural carbohydrates in forest trees \u0026ndash; from what we can measure to what we want to know. New Phytol. 2016;211(2):386\u0026ndash;403.\u003c/li\u003e\n\u003cli\u003eMooney HA. The carbon balance of plants. Annu Rev Ecol Evol Syst. 1972;3:315\u0026ndash;46.\u003c/li\u003e\n\u003cli\u003eGuo JS, Gear L, Hultine KR, Koch GW, Ogle K. Non-structural carbohydrate dynamics associated with antecedent stem water potential and air temperature in a dominant desert shrub. Plant Cell Environ. 2020;43(6):1467\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eMuller B, Pantin F, G\u0026eacute;nard M, Turc O, Freixes S, Piques M, Gibon Y. Water deficits uncouple growth from photosynthesis, increase C content, and modify the relationships between C and growth in sink organs. J Exp Bot. 2011;62(6):1715\u0026ndash;29.\u003c/li\u003e\n\u003cli\u003eSala A, Woodruff DR, Meinzer FC. Carbon dynamics in trees: feast or famine? Tree Physiol. 2012;32(6):764\u0026ndash;775.\u003c/li\u003e\n\u003cli\u003eRichardson AD, Carbone MS, Huggett BA, Furze ME, Czimczik CI, Walker JC, et al. Distribution and mixing of old and new nonstructural carbon in two temperate trees. New Phytol. 2015;206(2):590\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eLiu H, Shangguan H, Zhou M, Airebule P, Zhao P, He W, et al. Differentiated responses of nonstructural carbohydrate allocation to climatic dryness and drought events in the Inner Asian arid timberline. Agr Forest Meteorol. 2019;271:355\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eOuyang S, Gessler A, Saurer M, Hagedorn F, Gao D, Wang X, et al. Root carbon and nutrient homeostasis determines downy oak sapling survival and recovery from drought. Tree Physiol. 2021;41(8):1400\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eYang Y, Ouyang S, Gessler A, Wang X, Na R, He HS, et al. Root carbon resources determine survival and growth of young trees under long drought in combination with fertilization. Front Plant Sci. 2022;13:929855.\u003c/li\u003e\n\u003cli\u003eWiley E, Helliker B. A re-evaluation of carbon storage in trees lends greater support for carbon limitation to growth. New Phytol. 2012;195(2):285\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eAndreu L, Guti\u0026eacute;rrez E, Macias M, Ribas M, Bosch O, Camarero JJ. Climate increases regional tree-growth variability in Iberian pine forests. Global Change Biol. 2007;13(4):804\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eLiu J, Xu X, Zhang Y, Tian Y, Gao Q. Effect of rainfall interannual variability on the biomass and soil water distribution in a semiarid shrub community. Sci China Life Sci. 2010;53(6):729\u0026ndash;37.\u003c/li\u003e\n\u003cli\u003eZuidema PA, Babst F, Groenendijk P, Trouet V, Abiyu A, Acu\u0026ntilde;a-Soto R, et al. Tropical tree growth driven by dry-season climate variability. Nat Geosci. 2022;15(4):269\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eAllen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecol Manag. 2010;259(4):660\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eHammond WM, Williams AP, Abatzoglou JT, Adams HD, Klein T, L\u0026oacute;pez R, et al. Global field observations of tree die-off reveal hotter-drought fingerprint for Earth\u0026rsquo;s forests. Nat Commun. 2022;13(1):1761.\u003c/li\u003e\n\u003cli\u003eMcDowell NG, Sapes G, Pivovaroff A, Adams HD, Allen CD, Anderegg WRL, et al. Mechanisms of woody-plant mortality under rising drought, CO$_2$ and vapour pressure deficit. Nat Rev Earth Env. 2022;3(5):294\u0026ndash;308.\u003c/li\u003e\n\u003cli\u003e\u0026Scaron;\u0026iacute;mov\u0026aacute; I, Rueda M, Hawkins BA. Stress from cold and drought as drivers of functional trait spectra in North American angiosperm tree assemblages. Ecol Evol. 2017;7(18):7548\u0026ndash;59.\u003c/li\u003e\n\u003cli\u003eDai J, Liu H, Xu C, Qi Y, Zhu X, Zhou M, et al. Divergent hydraulic strategies explain the interspecific associations of co-occurring trees in forest\u0026ndash;steppe ecotone. Forests. 2020;11(9):942.\u003c/li\u003e\n\u003cli\u003eRichardson AD, Carbone MS, Keenan TF, Czimczik CI, Hollinger DY, Murakami P, et al. Seasonal dynamics and age of stemwood nonstructural carbohydrates in temperate forest trees. New Phytol. 2013;197(3):850\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eMika J, Horv\u0026aacute;th S, Makra L, Dunkel Z. The Palmer Drought Severity Index (PDSI) as an indicator of soil moisture. Phys Chem Earth. 2005;30(1):223\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eDai A. Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900\u0026ndash;2008. J Geophys Res-Atmos. 2011;116:D12106.\u003c/li\u003e\n\u003cli\u003eSchweingruber FH. Tree rings: basics and applications of dendrochronology. Berlin: Springer Science \u0026amp; Business Media; 2012.\u003c/li\u003e\n\u003cli\u003eHolmes RL. Computer-assisted quality control in tree-ring dating and measurement. Tree-Ring Bull. 1983;43:69\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eMonserud RA, Sterba H. A basal area increment model for individual trees growing in even-and uneven-aged forest stands in Austria. Forest Ecol Manag. 1996;80(1-3):57\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eYin XH, Hao GY, Sterck F. A trade-off between growth and hydraulic resilience against freezing leads to divergent adaptations among temperate tree species. Funct Ecol. 2022;36(3):739\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eLobry JR, Bel-Venner M, Bogdziewicz M, Hacket-Pain A, Venner S. The CV is dead, long live the CV!. Methods Ecol Evol. 2023;14(11):2780\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eLi MH, Hoch G, K\u0026ouml;rner C. Spatial variability of mobile carbohydrates within Pinus cembra trees at the alpine treeline. Phyton-Ann Rei Bot A. 2001;41(2):203\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eOsaki M, Shinano T, Tadano T. Redistribution of carbon and nitrogen compounds from the shoot to the harvesting organs during maturation in field crops. Soil Sci Plant Nutr. 1991;37(1):117\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eTrevelyan WE, Harrison JS. Studies on yeast metabolism. I. Fractionation and microdetermination of cell carbohydrates. Biochem J. 1952;50(3):298\u0026ndash;303.\u003c/li\u003e\n\u003cli\u003eR Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2021. https://www.R-project.org/. Accessed 2 Mar 2026.\u003c/li\u003e\n\u003cli\u003eDa R, He H, Zhang Z, Zhao X, von Gadow K, Zhang C. Leaf and root economics space in Fraxinus mandshurica: A test of the multidimensional trait framework within species. J Ecol. 2025;113(4):1004\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eFreschet GT, Violle C, Bourget MY, Scherer-Lorenzen M, Fort F. Allocation, morphology, physiology, architecture: the multiple facets of plant above- and below-ground responses to resource stress. New Phytol. 2018;219(4):1338\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eTavares JV, Oliveira RS, Mencuccini M, Signori-M\u0026uuml;ller C, Pereira L, Diniz FC, et al. Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests. Nature. 2023;617(7959):111\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Brien MJ, Leuzinger S, Philipson CD, Tay J, Hector A. Drought survival of tropical tree seedlings enhanced by non-structural carbohydrate levels. Nat Clim Change. 2014;4(8):710\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eTomasella M, Petrussa E, Petruzzellis F, Nardini A, Casolo V. The possible role of non-structural carbohydrates in the regulation of tree hydraulics. Int J Mol Sci. 2020;21(1):144.\u003c/li\u003e\n\u003cli\u003eZwieniecki MA, Holbrook NM. Confronting Maxwell\u0026apos;s demon: biophysics of xylem embolism repair. Trends Plant Sci. 2009;14(10):530\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eAritsara ANA, Ni M, Wang Y, Yan C, Zeng W, Song H, et al. Tree growth is correlated with hydraulic efficiency and safety across 22 tree species in a subtropical karst forest. Tree Physiol. 2023;43(8):1307\u0026ndash;18.\u003c/li\u003e\n\u003cli\u003eHerrera-Ram\u0026iacute;rez D, Sierra CA, R\u0026ouml;mermann C, Muhr J, Trumbore S, Silv\u0026eacute;rio D, et al. Starch and lipid storage strategies in tropical trees relate to growth and mortality. New Phytol. 2021;230(1):139\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eSignori-M\u0026uuml;ller C, Oliveira RS, Tavares JV, Diniz FC, Gilpin M, Barros FV, et al. Variation of non-structural carbohydrates across the fast-slow continuum in Amazon forest canopy trees. Funct Ecol. 2022;36(2):341\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eRuehr NK, Grote R, Mayr S, Arneth A. Beyond the extreme: recovery of carbon and water relations in woody plants following heat and drought stress. Tree Physiol. 2019;39(8):1285\u0026ndash;99.\u003c/li\u003e\n\u003cli\u003eGaliano L, Timofeeva G, Saurer M, Siegwolf R, Mart\u0026iacute;nez-Vilalta J, Hommel R, Gessler A. The fate of recently fixed carbon after drought release: towards unravelling C storage regulation in Tilia platyphyllos and Pinus sylvestris. Plant Cell Environ. 2017;40(9):1711\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eZang U, Goisser M, Grams TEE, H\u0026auml;berle K, Matyssek R, Matzner E, Borken W. Fate of recently fixed carbon in European beech (Fagus sylvatica) saplings during drought and subsequent recovery. Tree Physiol. 2014;34(1):29\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003ePalacio S, Camarero JJ, Maestro M, Alla AQ, Lahoz E, Montserrat-Mart\u0026iacute; G. Are storage and tree growth related? Seasonal nutrient and carbohydrate dynamics in evergreen and deciduous Mediterranean oaks. Trees. 2018;32(3):777\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eSch\u0026auml;del C, Bl\u0026ouml;chl A, Richter A, Hoch G. Short-term dynamics of nonstructural carbohydrates and hemicelluloses in young branches of temperate forest trees during bud break. Tree Physiol. 2009;29(7):901\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eLu L, Liu H, Wang J, Zhao K, Miao Y, Li H, et al. Seasonal patterns of nonstructural carbohydrate storage and mobilization in two tree species with distinct life-history traits. Tree Physiol. 2024;44(7):tpae042.\u003c/li\u003e\n\u003cli\u003eBarbaroux C, Br\u0026eacute;da N. Contrasting distribution and seasonal dynamics of carbohydrate reserves in stem wood of adult ring-porous sessile oak and diffuse-porous beech trees. Tree Physiol. 2002;22(17):1201\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eFurze ME, Huggett BA, Chamberlain CJ, Wieringa MM, Aubrecht DM, Carbone MS, et al. Seasonal fluctuation of nonstructural carbohydrates reveals the metabolic availability of stemwood reserves in temperate trees with contrasting wood anatomy. Tree Physiol. 2020;40(10):1355\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eWang Z, Wang C. Dynamics of nonstructural carbohydrates during drought and subsequent recovery: A global meta-analysis. Agr Forest Meteorol. 2025;363:110429.\u003c/li\u003e\n\u003cli\u003eReich PB. The world-wide \u0026lsquo;fast-slow\u0026rsquo; plant economics spectrum: a traits manifesto. J Ecol. 2014;102(2):275\u0026ndash;301.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Carbon metabolism, Non-structural carbohydrates (NSC), Organ-specific allocation, Radial growth, Drought resilience, Source-sink dynamics, Semi-arid forests","lastPublishedDoi":"10.21203/rs.3.rs-9016676/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9016676/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNon-structural carbohydrates (NSC) are central to plant carbon metabolism, acting as critical buffers that regulate tree growth and survival under environmental stress. In semiarid regions, increasing hydroclimatic variability poses significant challenges to forest stability. However, the mechanistic regulation of how organ-specific NSC allocation mediates metabolic trade-offs between structural growth and carbon storage, and how these partitioning strategies influence drought resilience, remains poorly understood. This study aims to unravel the functional significance of organ-specific carbon partitioning in a natural mixed forest in Northern China.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur findings reveal that radial growth and drought responses are governed by organ-specific allocation logic rather than total NSC pools. Radial growth was positively associated with starch concentrations in leaves and fine roots and with soluble sugar concentrations in branches (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, a significant negative correlation was found between radial growth and starch concentrations in branches (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), supporting a clear growth\u0026ndash;storage trade-off mediated by carbon sequestration in perennial tissues. Furthermore, higher leaf soluble sugar concentrations were the primary physiological determinant of enhanced post-drought growth recovery (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that the mobilization of short-term, readily available carbon pools is essential for restoring physiological function following extreme water deficit.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study demonstrates that drought resilience in semiarid forests depends less on absolute NSC concentrations than on the strategic partitioning of carbon among functional organs and chemical forms. These results provide mechanistic insights into the regulation of plant carbon metabolism and its role in underpinning forest stability. Our work offers a framework for refining vegetation models by integrating organ-specific carbon allocation logic to better predict forest responses to global climate change.\u003c/p\u003e","manuscriptTitle":"Organ-specific non-structural carbohydrate allocation mediates growth-storage trade-offs and drought resilience in a semiarid forest","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 12:01:51","doi":"10.21203/rs.3.rs-9016676/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-20T06:21:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T16:58:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166308953808512960350328784499036582131","date":"2026-04-08T05:22:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T08:44:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T06:15:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265513580971017669634584809795705687220","date":"2026-03-23T00:27:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201854691890248743213236072283767846447","date":"2026-03-22T10:09:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T05:09:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-13T04:59:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-09T13:11:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-09T08:19:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2026-03-09T07:36:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf2ba320-7c9b-4582-893e-09e4a47da405","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T08:39:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 12:01:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9016676","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9016676","identity":"rs-9016676","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}