DOI: 10.5061/dryad.p5hqbzkz1
title: The combination of high leaf hydraulic safety and water use efficiency allows alpine shrubs to adapt to high-altitude habitats

description: Leaf hydraulic traits are considered the key determinants of gas exchange
 and therefore affect species distributions along environmental gradients,
 but the patterns of leaf hydraulic traits and their associations with gas
 exchange across altitudinal gradients remain largely unknown. Here, we
 measured leaf hydraulic traits, gas exchange, leaf anatomical traits and
 plant size traits in two dominant alpine shrubs (Caragana jubata and Salix
 gilashanica) across an altitudinal gradient from 3100 to 3700 m. The
 findings indicated that with increasing altitude, both shrub species
 exhibited an increase in leaf hydraulic safety (more negative Kleaf P50),
 a decrease in leaf hydraulic efficiency (Kleaf-max) and an increase in
 intrinsic water use efficiency (WUEi), thus allowing them to adapt to
 higher altitude habitats. The more negative Kleaf P50 was associated with
 a greater ratio of major to minor vein density (VLAmaj/VLAmin), the lower
 Kleaf-max was associated with a lower minor vein density (VLAmin), and
 greater increase in WUEi arose from the small decrease in the
 photosynthetic rate relative to the stomatal conductance. However, C.
 jubata was consistent with the ‘hydraulic safety strategy’ with a great
 decrease in Kleaf P50, a small decrease in Kleaf-max, and a small increase
 in WUEi along with decreasing plant height and leaf area with increasing
 altitude. Whereas S. gilashanica was consistent with the ‘photosynthetic
 efficiency strategy’ with a small decrease in Kleaf P50, a greater
 decrease in Kleaf-max, and a greater increase in WUEi along with an
 increasing plant height and unchanged leaf area with increasing altitude.
 These findings provide new insights to improve understanding how shift in
 leaf hydraulic traits and their associations with gas exchange and plant
 size allow plants to adapt to high-altitude habitats.
description: <strong>Study site and shrub
 species</strong> This study was conducted in the
 Pailugou catchment of the Qilian Mountains (100°17´E, 38°24´N), Gansu
 Province, Northwest China. The altitude of this catchment ranges from 2700
 m to 3700 m. Meteorological variables, including the growing season
 temperature (GST, °C), growing season precipitation (GSP, mm) and relative
 humidity (RH, %) were recorded by an automatic forest ecological station
 (the four monitoring points are at 2700 m, 2900 m, 3300 m and 3700 m) from
 2018-2021. The vapor pressure deficit (VPD, kPa) was calculated via the
 following equations based on the meteorological
 data:VPD=0.61078×e<sup>(17.27×GST)/ (GST+237.3)
 </sup>×(1-RH)(Tang et al., 2023). The GST decreased from 11.5°C to
 6.0°C, the GSP decreased from 337.1 mm to 153.1 mm, the RH ranged from
 57.4% mm to 64.6% and the VPD ranged from 0.58 kPa to 0.33 kPa from 2700 m
 to 3700 m during the growing season (see Figure S1 in Supporting
 Information). The vegetation types are semiarid
 mountain steppe, mountain forest and alpine shrub meadow from 2700 m to
 3700 m. Shrubbery is mainly distributed in the ecologically fragile area
 at an altitude of 3100–3700 m, accounting for two-thirds of the vegetation
 area in the Qilian Mountains. This area plays an important role in water
 conservation and maintaining ecological security and regional high-quality
 development in the Hexi Corridor. The two investigated shrub species
 (<em>C. jubata</em> and<em> S.</em>
 <em>gilashanica</em>) are naturally widespread in the Qilian
 Mountains and are distributed at altitudes ranging from 3100 m to 3600 m
 and 3100 m to 3700 m, respectively.
 <strong>Experimental design</strong>
 Seven sampling sites from 3100 m to 3700 m at 100 m intervals
 were set in July 2021, and three sampling plots (5 m × 5 m) were set up at
 each sampling site. All plots were similar in slope (32.6±1.9) and aspect
 (north slope). In three sampling plots at each sample site, ten fully
 expanded sun leaves were selected from ten individuals of <em>C.
 jubata</em> and <em>S. gilashanica</em> for gas exchange
 measurements at each altitude. The five leaves close to the measured leaf
 gas exchange were collected and placed in a formaldehyde-acetic acid-90%
 ethanol fixative (5:5:90, vol: vol: vol) for morphological and anatomical
 structure measurements. Five to ten leafy branches from each sampling plot
 were randomly selected and cut under pure water at nightfall, wrapped with
 black plastic bags to avoid leaf transpiration, and immediately
 transported to the forest ecological station, it is a national station,
 allowing field investigation and sampling. All excised branches were
 rehydrated for 2–4 hours and then used to construct leaf hydraulic
 vulnerability curves. <strong>Gas exchange
 </strong><strong>and leaf water potential
 </strong><strong>measurements</strong>
 The saturated photosynthesis
 (<em>A</em><sub>sat</sub>) and stomatal
 conductance<em>
 </em>(<em>g</em><sub>s</sub>) were measured
 between 09:00 and 11:00 a.m. in ten replicate individuals per sample site
 with a portable open gas exchange system (LI-6800; LI-COR, Lincoln, NE,
 USA). The leaf chamber was supplied with 1200 μmol
 m<sup>-2</sup> s<sup>-1</sup> photosynthetic
 photon flux density, the leaf temperature was set at 25°C, the
 CO<sub>2</sub> concentration was set at 400 μmol
 mol<sup>−1</sup>, the VPD was set at approximately 1.1–1.4
 kPa, and the relative humidity was maintained at 50%. The intrinsic water
 use efficiency (WUEi) was calculated as the ratio between
 <em>A</em><sub>sat</sub> and
 <em>g</em><sub>s</sub> according to previous
 studies (Klein et al., 2013; Yang et al., 2021). Leaf
 water potential (<em>ψ</em><sub>gas</sub>) was
 measured on the same individuals from leaves close to those used for the
 photosynthetic measurement between 09:00 and 11:00 via a pressure chamber
 (Model 1000, PMS Instrument Company, Albany, OR). Briefly, leaves were
 collected and immediately placed in a sealed black plastic bag, previously
 wrapped in soaked paper towel to create a humid environment. Following 20
 min of equilibration, <em>ψ</em><sub>gas</sub> was
 measured (Rodriguez-Dominguez et al., 2022).
 <strong>Leaf hydraulic trait
 measurements</strong> Leaf hydraulic conductance
 (<em>K</em><sub>leaf</sub>)<em>
 </em>was measured via the two-point rehydration method (Nardini et
 al., 2012; Ocheltree et al., 2016), which is based on the following
 formula:
 <em>K</em><sub>leaf</sub>=<em>C</em>ln(<em>ψ</em><sub>0/</sub><em>ψ</em><sub>f</sub>)/t where <em>C</em> is the leaf capacitance, <em>ψ</em><sub>0</sub> is the initial leaf water potential, <em>ψ</em><sub>f</sub> is the final leaf water potential and t is the duration of rehydration. The air temperature and relative humidity were 22–26℃ and 42%–55%, respectively. Briefly, the leafy branches were cut into segments with at least three leaves under ultrapure water and allowed to desiccate slowly before being carefully bagged to prevent water loss and maintain water potential equilibrium throughout the segments. The initial leaf water potential (<em>ψ</em><sub>0</sub>) was determined by measuring leaves neighboring the sample leaf with a Scholander pressure chamber (PMS, Albany, OR, USA). The sample leaf was then cut under water and allowed to rehydrate for a period of between 15 and 300 s depending on <em>ψ</em><sub>0</sub>, after which their petioles were immediately dabbed dry, and the leaf was wrapped in moist paper towel and double bagged for 10 min to allow water potentials to equilibrate throughout the leaf before <em>ψ</em><sub>f</sub> determination. For each sample, <em>K</em><sub>leaf </sub>was calculated via the corresponding formula. Leaf capacitance (<em>C</em>) was measured from five leaves of each species via the slope of the pressure–volume relationship for each species. The leaf hydraulic vulnerability curve was generated by plotting <em>K</em><sub>leaf</sub> against <em>ψ</em><sub>0</sub>, <em>K</em><sub>leaf–max </sub>was calculated when <em>ψ</em><sub>0</sub><sub> </sub>was equal to -0.3 MPa, and <em>K</em><sub>leaf </sub><em>P</em><sub>50</sub> was calculated from the leaf hydraulic vulnerability curve as the <em>ψ</em><sub>leaf</sub><em> </em>value at which 50% of <em>K</em><sub>leaf</sub> was lost from <em>K</em><sub>leaf–max</sub> (Nardini et al., 2012; Scoffoni et al., 2016). <strong>Leaf vein density </strong><strong>and stomatal density</strong><strong> measurements</strong> The leaf vein density (VLA) was measured according to the conventional method (Xiong & Flexas, 2022). The densities of the 1st–3rd veins are considered major VLAs (VLA<sub>maj</sub>), and veins higher than the third order are considered minor VLAs (VLA<sub>min</sub>) (Kawai & Okada, 2016). Briefly, the leaf samples were boiled for 20 min and then placed in 5% NaOH in aqueous solution with water bath heating (keeping the leaf in it for 40 min) until the leaf cuticle had dissolved. The leaves were then flushed with water until NaOH was vacuumed off until they appeared transparent. If the leaves exhibited any color, 5% NaClO was added to the leaves to bleach them for 20 min, after which they were rinsed twice with water. These leaves were brought into EtOH dilution series solutions (30%, 50%, 70%, 100%). Each series lasted 3–5 min. After the 100% EtOH stage, the leaves were soaked in 1% safranin (1 g safranin/100 ml of 100% EtOH) for 15–30 min and gently rinsed with 100% EtOH. The stained leaves were scanned with a scanner (Epson-V800, Japan) at 9,600–12,800 dpi to measure the length of the 1st–3rd veins. Next, the leaves were imaged with a light microscope (Zeiss LSM 880, Germany) at 40× magnification to measure the length of higher-order veins. Leaf areas were measured from leaf images, and the lengths of all veins were measured via ImageJ (version 1.42q; National Institutes of Health, USA) to calculate the VLA<sub>min</sub>. Stomatal density (SD) was analyzed from images obtained via light microscopy at 40× magnification of five leaves per species at each sampling site following methods described by Zhao et al., (2016). <strong>Plant and leaf morphology measurements</strong> Thirty leaves of each species in each altitude were randomly selected to measure lead area and leaf mass per area (LMA). Leaves were scanned with a digital scanner at 300 DPI, and leaf area was calculated with ImageJ. Leaf dry mass was determined after dehydration at 80°C for 72 h, and LMA was calculated as the ratio of dry mass to its leaf area (Chen et al., 2021). Plant height was recorded in ten replicates at each altitude from the base of the plant to the tip of the tallest leaf (Lehrer & Hawkins, 2023). <strong>Turgor loss point measurement</strong> The turgor loss point (π<sub>tlp</sub>) was measured following the osmometer method (Bartlett et al., 2012). In short, five branches were randomly sampled from each sampling plot and kept humid in opaque bags, recut underwater and then rehydrated overnight to ensure that all measurements were made at full hydration. Several exposed, healthy, fully expanded leaves were randomly sampled from five excised branches and immediately stored in liquid nitrogen for measurement of the leaf osmotic potential at full turgor (<em>ψ</em><sub>o</sub>). <em>ψ</em><sub>0 </sub>was measured at room temperature with a vapor pressure osmometer (VAPRO 5520, Wescor Inc., Logan, Utah) and used to calculate π<sub>tlp</sub> with the following equation: π<sub>tlp</sub>=0.832<em>ψ</em><sub>0</sub>-0.631(Fuchs et al., 2021). The residual turgor (RT) was calculated with the following equation: RT=<em>ψ</em><sub>gas</sub>-π<sub>tlp </sub>(Nardini et al., 2014)<sub>.</sub> <strong>Data analysis</strong> The functions fitplc and fitcond were used to extract <em>K</em><sub>leaf-max</sub><em> </em>and<em> K</em><sub>leaf </sub><em>P</em><sub>50</sub> from leaf vulnerability curves and to calculate their associated 95% confidence intervals (CIs). The package “fitplc” was used to assess differences in <em>K</em><sub>leaf-max</sub> and <em>K</em><sub>leaf </sub><em>P</em><sub>50</sub> along the altitudinal gradient (Duursma & Choat, 2017). Differences in plant height (10 individuals at each altitude), leaf area (30 leaves from ten individuals at each altitude), as well as leaf physiological and structural traits (5-10 leaves from five to ten individuals at each altitude) were analyzed using one-way ANOVA (Duncan's test). Prior to this analysis, data were tested for normal distribution and homoscedasticity using SPSS 19.0 (SPSS Inc., Chicago, IL, USA). Trait–altitude correlations were tested to examine how the traits shift with increasing altitude, and then trait–trait correlations were tested to examine the trade-offs between <em>K</em><sub>leaf-max</sub> and <em>K</em><sub>leaf </sub><em>P</em><sub>50</sub>, and also to determine the structural traits behind the shift of <em>K</em><sub>leaf</sub><sub>-max</sub> and <em>K</em><sub>leaf </sub><em>P</em><sub>50</sub> via simple linear functions (SigmaPlot; version 14.0; Systat Software, San Jose, CA, USA). For those analysis, each best regression function was used with the highest <em>r</em><sup>2</sup> determined from the original data plots. All data analyses were considered highly significant if<em> P </em>&lt;0.05. <strong>References </strong> Bartlett, M. K., Scoffoni, C., Ardy, R., Zhang, Y., Sun, S., Cao, K., & Sack, L. (2012). Rapid determination of comparative drought tolerance traits: Using an osmometer to predict turgor loss point. <em>Methods in Ecology and Evolution</em>, <em>3</em>(5), 880–888. https://doi.org/10.1111/j.2041-210X.2012.00230.x Chen, Y. J., Choat, B., Sterck, F., Maenpuen, P., Katabuchi, M., Zhang, S. Bin, Tomlinson, K. W., Oliveira, R. S., Zhang, Y. J., Shen, J. X., Cao, K. F., & Jansen, S. (2021). Hydraulic prediction of drought-induced plant dieback and top-kill depends on leaf habit and growth form. <em>Ecology Letters</em>, <em>24</em>(11), 2350–2363. https://doi.org/10.1111/ele.13856 Duursma, R., & Choat, B. (2017). fitplc - an R package to fit hydraulic vulnerability curves. <em>Journal of Plant Hydraulics</em>, <em>4</em>, e002. https://doi.org/10.20870/jph.2017.e002 Fuchs, S., Leuschner, C., Mathias Link, R., & Schuldt, B. (2021). Hydraulic variability of three temperate broadleaf tree species along a water availability gradient in central Europe. <em>New Phytologist</em>, <em>231</em>(4), 1387–1400. https://doi.org/10.1111/nph.17448 Kawai, K., & Okada, N. (2016). How are leaf mechanical properties and water-use traits coordinated by vein traits? A case study in Fagaceae. <em>Functional Ecology</em>, <em>30</em>(4), 527–536. https://doi.org/10.1111/1365-2435.12526 Klein, T., Shpringer, I., Fikler, B., Elbaz, G., Cohen, S., & Yakir, D. (2013). Relationships between stomatal regulation, water-use, and water-use efficiency of two coexisting key Mediterranean tree species. <em>Forest Ecology and Management</em>, <em>302</em>, 34–42. https://doi.org/10.1016/j.foreco.2013.03.044 Lehrer, M. A., & Hawkins, J. S. (2023). Plant height shapes hydraulic architecture but does not predict metaxylem area under drought in Sorghum bicolor. <em>Plant Direct</em>, <em>7</em>(5), 1–10. https://doi.org/10.1002/pld3.498 Nardini, A., & Luglio, J. (2014). Leaf hydraulic capacity and drought vulnerability: Possible trade-offs and correlations with climate across three major biomes. <em>Functional Ecology</em>, <em>28</em>(4), 810–818. https://doi.org/10.1111/1365-2435.12246 Nardini, A., Pedà, G., & Rocca, N. La. (2012). Trade-offs between leaf hydraulic capacity and drought vulnerability: Morpho-anatomical bases, carbon costs and ecological consequences. <em>New Phytologist</em>, <em>196</em>(3), 788–798. https://doi.org/10.1111/j.1469-8137.2012.04294.x Ocheltree, T. W., Nippert, J. B., & Prasad, P. V. V. (2016). 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description: # The combination of high leaf hydraulic safety and water use efficiency
 allows alpine shrubs to adapt to high-altitude habitats
 [https://doi.org/10.5061/dryad.p5hqbzkz1](https://doi.org/10.5061/dryad.p5hqbzkz1) ## Description of the data and file structure We have submitted our raw data, including the leaf hydraulic traits, gas exchange, leaf structural and plant size traits from two dominant alpine shrubs (*Caragana jubata* and* Salix* *gilashanica*) across a broad altitudinal range (3100–3700 m) in the Qilian Mountains to reveal the patterns of leaf hydraulic traits and their relationships with gas exchange and to advance our understanding of the mechanisms of species distribution along altitudinal gradients. Data_table contains 18 morphological, anatomical and physiological traits and results of analysis of variance for the difference across altitude (one-way ANOVAs) in Caragana jubata and Salix gilashanica PH: Plant height (cm) LA: Leaf area (cm2) VLAmaj: Major vein length per area (mm mm-2) VLAmin: Minor vein length per area  (mm mm-2) SD: Stomatal density (pores mm-2 ) VLAmaj/VLAmin: the ratio between major vein density and minor vein density (no units) VLAmin/SD: the ratio between minor vein density and stomatal density (mm pores-1 ) Kleaf-max: Maximum values for leaf hydraulic conductance (mmol m-2 s-1 MPa-1) Kleaf P50: The water potential at 50% loss of leaf hydraulic conductance (MPa) Asat: The saturated photosynthesis (umol m-2 s-1) gs: Stomatal conductance (mol m-2 s-1) Kleaf-max/gs: Leaf hydrualic supply to demand (MPa-1) WUEi: The intrinsic water use efficiency (MPa-1) LMA: Leaf mass per area (g m-2) πo: Osmotic potential at full turgor (MPa) πtlp: Turgor loss point (MPa) ψgas: Leaf water potential corresponding to leaf photosynthesis (MPa) RT: Residual turgor (MPa)