DOI: 10.5061/dryad.wh70rxx00
title: Data from: Hydraulic traits are coordinated but decoupled from carbon traits in herbaceous species

description: Plant hydraulic traits primarily define the water regulation strategy,
 thus enabling better understanding of vegetation structure, function and
 dynamics under varying hydro-environments. Despite being intensively
 documented in woody species, variation and correlation of hydraulic traits
 across herbaceous species remain largely understudied. Here, we report on
 the leaf hydraulics of nine herbs with contrasting growth forms (graminoid
 and forb). Traits quantifying drought resistance, including leaf water
 potential thresholds triggering xylem embolism (Px), stomatal closure
 (Pgs) or leaf turgor loss point (Ptlp), and minimum conductance (gmin),
 together with leaf gas exchange, morphological traits and biomass
 allocation, were measured on pot grown plants. In addition, an in situ
 dry-down was imposed on four representative species, with leaf gas
 exchange, water potential and level of xylem embolism, being continuously
 monitored during dehydration to determine the dynamics of stomatal closure
 and leaf xylem embolism. We found that the studied graminoids tended to be
 more drought tolerant than forbs, although the difference in hydraulic
 safety margin for stomatal closure (HSMst) did not differ significantly
 between these growth forms. Across species, Px was coordinated with Pgs
 and Ptlp, but was decoupled from gas exchange traits, including maximum
 photosynthetic rate and stomatal conductance. Furthermore, no correlations
 were found between hydraulic traits and specific leaf area or the ratio of
 aboveground to belowground biomass. For plants that
 experienced in situ dehydration, stomatal closure always preceded
 the onset of xylem embolism in leaves. Moreover, species exhibited a
 distinct stomatal regulation strategy during the dehydration despite
 belonging to the same growth form. Our findings contribute to the
 understanding of herb hydraulics, which will inform prediction on the
 dynamics of grassy ecosystems by providing traits data and guiding the
 classification of plant functional types in “grassy” ecosystems.
description: <strong><em>Plant materials and growth
 condition</em></strong><strong> </strong> Nine herbaceous species representing two growth forms were chosen for the current study (Table 1). Species selected here are representative of understory herbaceous community within forest ecosystems across north China plain, and most species are cosmopolite, with the exception of <em>Crepidiastrum denticulatum</em>, <em>Senna tora,</em> and <em>Elymus dahuricus</em>, which occur sporadically worldwide, according to the occurrence record of Global Biodiversity Information Facility (www.gbif.org). All species are common weeds that have never been artificially selected. The distributional range of these species largely overlaps, yet there is considerable variation in the mean annual precipitation (MAP, mm) and mean annual temperature (MAT, <sup>o</sup>C) of the species climatic envelopes (i.e. bioclimate niche), with the MAP ranging from 434 to 1618 mm and the MAT ranging from 5.5 to 23.9<sup> o</sup>C. Seeds of the selected species were purchased from a commercial supplier that collects seeds from the field annually. Seeds were sown in hiko trays filled with water saturated vermiculite for germination, which typically took 2~3 weeks depending on species. When seedlings reached <em>ca</em>. 2 cm tall, plants were transplanted to square plastic pots (15 cm × 13 cm ×15 cm for length, width and height, respectively) filled with a mixture of multi-purpose compost and vermiculite (2:1), and then thinned to one plant per pot. Nutrients in the compost can sustain the growth of small plants for up to 12 months, thus ensuring the plant materials were not nutrient limited during growth. Each species was represented by 15~24 individuals. The pots were then placed on an indoor vertical growth rack in a well-ventilated, controlled environment lab. The growth rack was modified by installing additional red and white full spectrum LED lights, which enabled approximately 800 μmol m<sup>-2</sup> s<sup>-1</sup> photosynthetic photon flux density (PPFD). The lights were programmed to illuminate 12 h per day from 6 am. Plants were irrigated manually every other day to avoid water limitation during development and were randomly positioned weekly to minimize the heterogeneity in growth environment. During the experimental period, lab temperature was maintained at 25 <sup>o</sup>C, and relative humidity (RH, %) fluctuated between 45%~50 %. Measurements commenced 4~6 weeks after transplanting. <strong><em>Experimental design</em></strong> Two complementary experiments were designed to test our hypotheses. The first experiment aimed to address the variation of key hydraulic traits across species, as well as their correlations, with carbon and morphological traits. For each species, we measured xylem vulnerability to embolism, leaf pressure volume (PV) traits, leaf minimum conductance and morphological traits, including specific leaf area as well as the ratio of aboveground to belowground biomass, from well-watered individuals. Then, a subset of individuals (6~8) of each species were exposed to progressive dry-down by withholding irrigation. Subsequently, leaf gas exchange characteristics and leaf water potential (Ψ<sub>leaf</sub>, -MPa) were assessed periodically during dry-down until leaf stomatal conductance approached zero, thereby obtaining the response of leaf gas exchange to Ψ<sub>leaf</sub>. For all species, measurements were conducted during the vegetative phase and no flowering was observed by the end of the experiment.    The objective of our second experiment was to resolve the sequence of stomatal closure and initiation of xylem embolism. To this end, we monitored the dynamics of leaf gas exchange and development of xylem embolism <em>in situ</em> on slowly dehydrated plants. Four representative species were selected for the experiment (i.e., <em>Eleusine indica</em>, <em>Cynodon dactylon</em>, <em>Crepidiastrum denticulatum</em> and <em>Medicago sativa</em>; Table 1). Each species involved three replicas except for M.<em> sativa</em>, which was represented by two individuals. <strong><em>Experiment I: Variation of hydraulic, gas exchange and morphology traits across species</em></strong> <strong><em>Leaf xylem vulnerability curves</em></strong> For each species, 3~5 individuals of similar size were used for leaf xylem vulnerability curves (VCs) assessment. Prior to the day of measurement, soil of targeted plants was watered to field capacity and plants were covered with an opaque black plastic bag to ensure full rehydration and complete stomatal closure. Leaf embolism level was measured with the optical visualization (OV) technique https://www.opensourceov.org/) (Brodribb et al., 2016). This method was based on the variation of xylem optical attributes before and after the occurrence of embolism. The technique allows for the spatiotemporal pattern of embolism propagation within the vein network to be revealed, and is particularly suitable for plant material characterized by tender and fragile tissues such as herbs (Jacob et al. 2022); embolism thresholds generated by OV have been validated elsewhere (Brodribb et al., 2016; Chen et al., 2021; Gauthey et al., 2020). For each individual plant, one recently mature and fully expanded leaf was placed into custom-built leaf clamps equipped with a digital camera (Cavicam, https://cavicams.com/), which was controlled by a single board computer (Raspberry Pi, Raspberry Pi Foundation), and photographed upon illumination. Glass microscope slides were used to flatten the leaf when necessary. A neighboring leaf was used for measuring Ψ<sub>leaf</sub> using a psychrometer (ICT International, Armidale, NSW, Australia). Before measurement, the leaf was gently polished using an 800-grit sanding stick on the adaxial side to remove the epidermis. A custom-made holder was employed to fix the psychrometer, thus ensuring the chamber made full contact with the leaf. High vacuum grease was applied to seal the possible gaps between the chamber and the leaf. Following installation of these devices, the plant was cut above the soil level and allowed to desiccate until crispy. During dehydration, Ψ<sub>leaf </sub>and images of the leaf were taken at 10 min intervals. Images of leaves were processed using Image J software (National Institutes of Health, Bethesda, MD, USA). The protocol for image processing has been elaborated elsewhere (Li et al., 2019; Skelton et al., 2021). In short, images obtained from an independent leaf were stacked, and the difference between two consecutive images was revealed by the “Image difference” function in the OSOV toolbox. Pixels indicating the embolized area on the conduits were highlighted and then analyzed after noise was removed. Percentage of xylem embolism level (%) was calculated as the pixels on each image divided by the total embolized area during dehydration. Leaf VCs were then generated by plotting PLC against Ψ<sub>leaf</sub>. <strong><em>Gas exchange and water potential measurement</em></strong> Gas exchange characteristics were measured between 9 am to 11 am on fully expanded, mature leaves of each plant. Leaf gas exchange was measured using a portable photosynthesis system (Model 6800, Li‐Cor, Lincoln, NE, USA) equipped with a red-blue LED light source and external CO<sub>2</sub> injector. The selected leaf was placed into a chamber supplied with 800 μmol m<sup>-2</sup> s<sup>-1</sup> PPFD and 420 μmol mol<sup>-1</sup> CO<sub>2</sub> in the air, with the temperature and vapor pressure deficit inside the cuvette being maintained at 25 <sup>o</sup>C and 1.2~1.5 kPa, respectively. Variables including maximum photosynthetic rate (A, μmol m<sup>-2</sup> s<sup>-1</sup>), stomatal conductance (g<sub>s</sub>, mol m<sup>-2</sup> s<sup>-1</sup>), and transpiration rate (E, mmol m<sup>-2</sup> s<sup>-1</sup>) were logged when these readings were visually stable, which typically took 10~15 mins depending on leaf water status. Leaves were removed from the plant after gas exchange measurements and Ψ<sub>leaf </sub>was then immediately determined using a Scholander‐type pressure chamber (PMS Instrument, Corvalis, OR, USA). Maximum leaf photosynthetic rate (A<sub>max</sub>), stomatal conductance (g<sub>smax</sub>) and transpiration rate (E<sub>max</sub>) were defined as the corresponding values recorded under well-watered conditions and averaged across datapoints measured at Ψ<sub>leaf</sub>&gt;-1 MPa. Measurements were taken once a day, starting from the well-watered state until the stomata were completely closed. <strong><em>Pressure-volume traits</em></strong> For each species, 4~6 leaves from 3~5 individuals were collected for pressure-volume (PV) curve measurements. Selected individuals were irrigated and placed in darkness overnight to allow for full rehydration. Leaf samples were detached from plants the next morning, covered with para-film if necessary, and then used for PV curve measurements. Leaf PV curves were generated following the standard method. Briefly, leaf samples were slowly dehydrated under laboratory conditions, and then leaf water potential (Ψ<sub>leaf</sub>) and leaf fresh weight (FW, g) were regularly measured by pressure chamber and analytical balance (weighted to 0.001 g), respectively, until Ψ<sub>leaf</sub> reached -2.0 MPa to -3.0 MPa and leaves were visually wilting, depending on the species. PV curves were analyzed following Lenz et al. (2006) to obtain leaf turgor loss point (P<sub>tlp</sub>, -MPa) and osmotic potential at full turgor (P<sub>0</sub>, -MPa). <strong><em>Leaf minimum conductance</em></strong> For each species, two leaves from each of the three randomly chosen plants were used to determine leaf minimum conductance (g<sub>min</sub>, mmol m<sup>-2</sup> s<sup>-1</sup>). Prior to the measurement, plants were rehydrated in darkness overnight, similar to material used for VC assessments. Then, leaf samples were cut from a plant and bench dried under 25<sup>o</sup>C and 50% RH for up to 10 hours, until the relationship between leaf FW and time became linear. Leaf FW was measured periodically during dehydration. The g<sub>min</sub> was calculated following Blackman et al. (2019). <em><strong>Leaf morphology and biomass allocation</strong></em> Specific leaf area (SLA, m<sup>2</sup> kg<sup>-1</sup>) was determined from the samples used for g<sub>min</sub> measurements. Prior to bench dehydration, leaves were flattened and photographed with a known scale, and leaf area was then estimated using the Image J software. Following the g<sub>min</sub> measurement, leaf samples were oven-dried to gain the dry weight (DW, g). Leaf SLA was calculated as the ratio of leaf area to DM. The ratio of aboveground to belowground biomass (ABR) was measured on the plant material used for constructing VCs. Following the measurement of VCs, the aboveground portion of plants were collected, and the corresponding root was carefully rinsed free of soil. Samples were then oven-dried to obtain DW. For all plant material, oven drying was performed at 70 <sup>o</sup>C and lasted for at least 72 hours or until no more weight change was detected. <strong><em>Experiment II: In situ dry-down of intact plants</em></strong> For each individual subjected to in situ dry-down, three fully expanded leaves with similar size were labeled for leaf gas exchange, water potential and leaf xylem embolism level measurements, respectively. Plants were well-watered prior to the day of dehydration, and received no irrigation thereafter. For each plant, a Cavicam and psychrometer were installed on two labelled, neighboring leaves following the same protocol described above, with images and the water potential of leaves recorded every 10 mins. Leaf gas exchange was measured between 9 am to 11 am daily on the remaining labelled leaf, with the cuvette environment, including PPFD, vapor pressure deficit, temperature and CO<sub>2</sub> concentration, being set as detailed above. <em>In situ</em> dehydration ceased until the aboveground portion of plant became completely dry and fragile, with Ψ<sub>leaf</sub> being no longer measurable by the psychrometer. Then, the leaf used for imaging was excised from the base, still allowing the Cavicam to continuously photograph until no embolism events were observed over the next 24 hours. Obtained images were processed according to the aforementioned protocol to estimate the dynamics of xylem embolism during dehydration.     <strong><em>Data analysis</em></strong> <strong>Scale of inference</strong> <strong>Scale at which the factor of interest is applied</strong> <strong>Number of replicates at the appropriate scale</strong> experimental units Species 3-5 Leaf VCs were fitted by a three-parameter sigmoidal-exponential model to estimate leaf water potential inducing 12% and 50 % xylem embolism (P<sub>12</sub> and P<sub>50</sub>, -MPa), respectively. Measured g<sub>s</sub> during dehydration was plotted against Ψ<sub>leaf</sub>, and the relationship was fitted with a nonlinear model with a local polynomial regression (loess) approach, to extract water potential thresholds at 50% and 90% loss of g<sub>smax</sub> (P<sub>gs50</sub> and P<sub>gs90</sub>; -MPa), respectively. All curve fittings were performed using the <em>fitplc</em> package. Hydraulic safety margin for stomatal closure (HSM<sub>st</sub>, -MPa) was defined as P<sub>12</sub>-P<sub>gs90</sub>. To compare the P<sub>50</sub> of herbs with that of woody species, P<sub>50</sub> data measured from leaves of woody species, exclusively using the optical visualization technique (P50 OV), were sourced from Cardoso et al. (2022) (n=98). Additional P50 OV data for herbs were extracted from the data compiled in Huang et al. (2023) (n=6), as well as by Cardoso et al. (2022) (n=8). Note that such analysis was to compare the overall magnitude of variation in P50 between growth forms (e.g. Lens et al. 2016), instead of contrasting the difference in embolism resistance among coexisting woody species and herbs. The difference in gas exchange traits, xylem embolism thresholds, pressure volume traits, g<sub>min</sub> and morphological traits across species were compared using a linear model with the <em>lm()</em> function, followed by a Turkey’s HSD <em>post hoc</em> when needed. The difference in stomatal closure thresholds (i.e. P<sub>gs50</sub> and P<sub>gs90</sub>) across species was assessed by comparing the overlap of confidence interval. Also, the linear model was used to test the difference in hydraulic traits (i.e. P<sub>50</sub>, P<sub>gs90</sub>, P<sub>tlp,</sub> and HSM<sub>st</sub>) between graminids and forbs with averaged trait values. Leaf P50 OV data of woody species and herbs was contrasted using the Welch’ t-test, which assumes unequal variance because of the uneven sample size within each group. Furthermore, to avoid the potential influence of phylogenetic relatedness on comparison, phylogenetic information of the compared species was obtained using the V.PhyloMaker2 package based on the “The Plant List” nomenclature system under scenario3 (Jin & Qian 2022; Figure S2), and were incorporated in the analysis. Thereafter, a phylogenetic independent contrast was conducted for P50 OV data using pic() function of the ape package. Data were tested with Kolmogorov-Smirnov and Levene’s test, for normality, equality, and consistency in distribution, and were log-transformed, if necessary. Correlative relationships among traits were fitted using linear regression with the lm() function, while multivariate analysis (i.e. principal component analysis, PCA) was performed using the procomp() function. All statistical analyses were conducted within the R computing environment (version 4.3.2, R Core Team, 2023).
description: # Data from: Hydraulic traits are coordinated but decoupled from carbon
 traits in herbaceous species Dataset DOI:
 [10.5061/dryad.wh70rxx00](10.5061/dryad.wh70rxx00) ## Description of the
 data and file structure **Decoupling of hydraulic and carbon traits in
 herbs** The “Huang_et_al_2025_Data” zip file contains two folders:
 “Raw_data” and “Integrated_data.” The “Raw_data” folder includes eight
 files, which were used for fitting curves and conducting part of the
 statistical analysis (such as ANOVA). Specifically, the “ABR” file
 contains data on the ratio of aboveground to belowground biomass, the
 “SLA” file contains specific leaf area data, the “Epidermal_conductance”
 file contains leaf minimum conductance data, the “gs_wp_experimental_1”
 file contains gas exchange traits data measured in experiment one, the
 “gs_wp_experimental_2” file contains gas exchange traits data measured in
 experiment two, the “PV” file contains turgor loss point data and water
 potential at full turgor data calculated from leaf pressure-volume curves
 (PVs), the “VC_experimental_1” file contains data used for fitting
 vulnerability curves measured in experiment one, and the
 “VC_experimental_2” file contains data used for fitting vulnerability
 curves measured in experiment two. Additionally, the “Integrated_data”
 folder includes the “Data_of_herb_hydraulics” file and the “species_list”
 file. The former integrates the mean values and standard errors of the
 traits data we measured and used in our paper, thus can be used to compare
 drought resistance differences between graminoids and forbs, as well as to
 explore the variation and correlation between key hydraulic traits. The
 latter provides basic information about the species used in the current
 study. Note: "NA" in these files indicates missing data, as the
 limited number of replications prevents the calculation of the standard
 error. Since “NA” would affect curve fitting in R, we left those cells
 empty in the “VC_experimental_2” file. Plant traits incorporated in the
 dataset include key hydraulic traits, gas exchange traits, morphological
 traits as well as biomass allocation traits measured from nine herbaceous
 species belonging to two distinct growth forms (graminoid and forb). The
 abbreviation, units and definition for each trait are shown in Table 1.
 Table 1 Acronym of traits, units, definitions as well as climatic
 variables | **File name** | **Traits** | **Units** | **Definition** | |
 :--------------------------------------------------------------- |
 :---------------------- | :------------- |
 :------------------------------------------------------------------------------------------------------------- | | Data\_of\_herb\_hydraulics | P12mean | -MPa | Mean value of water potential threshold triggering 12% loss of xylem hydraulic conductivity | | Data\_of\_herb\_hydraulics | P12se | -MPa | Standard error of water potential threshold triggering 12% loss of xylem hydraulic conductivity | | Data\_of\_herb\_hydraulics | P50mean | -MPa | Mean value of water potential threshold triggering 50% loss of xylem hydraulic conductivity | | Data\_of\_herb\_hydraulics | P50se | -MPa | Standard error of water potential threshold triggering 50% loss of xylem hydraulic conductivity | | PV | TLP | -MPa | Value of leaf turgor loss point | | Data\_of\_herb\_hydraulics | TLPmean | -MPa | Mean value of leaf turgor loss point | | Data\_of\_herb\_hydraulics | TLPse | -MPa | Standard error of leaf turgor loss point | | PV | Po | -MPa | Value of osmotic potential at full turgor | | Data\_of\_herb\_hydraulics | Pomean | -MPa | Mean value of osmotic potential at full turgor | | Data\_of\_herb\_hydraulics | Pose | -MPa | Standard error of osmotic potential at full turgor | | Data\_of\_herb\_hydraulics | Pgs50 | -MPa | Water potential thresholds at 50% loss of maximum stomata conductance | | Data\_of\_herb\_hydraulics | Pgs50\[lower] | -MPa | Lower confidence limit of water potential thresholds at 50% loss of maximum stomata conductance | | Data\_of\_herb\_hydraulics | Pgs50\[upper] | -MPa | Upper confidence limit of water potential thresholds at 50% loss of maximum stomata conductance | | Data\_of\_herb\_hydraulics | Pgs90 | -MPa | Water potential thresholds at 90% loss of maximum stomata conductance | | Data\_of\_herb\_hydraulics | Pgs90\[lower] | -MPa | Lower confidence limit of water potential thresholds at 90% loss of maximum stomata conductance | | Data\_of\_herb\_hydraulics | Pgs90\[upper] | -MPa | Upper confidence limit of water potential thresholds at 90% loss of maximum stomata conductance | | gs\_wp\_experimental\_1& gs\_wp\_experimental\_2 | gsw | mol m-2 s-1 | Stomatal conductance | | Data\_of\_herb\_hydraulics | gswmean | mol m-2 s-1 | Mean value of stomatal conductance | | Data\_of\_herb\_hydraulics | gswse | mol m-2 s-1 | Standard error of stomatal conductance | | Epidermal\_conductance | gmin | mmol m-2 s-1 | Leaf minimum conductance | | Data\_of\_herb\_hydraulics | gminmean | mmol m-2 s-1 | Mean value of leaf minimum conductance | | Data\_of\_herb\_hydraulics | gminse | mmol m-2 s-1 | Standard error of leaf minimum conductance | | gs\_wp\_experimental\_1& gs\_wp\_experimental\_2 | A | μmol m-2 s-1 | Maximum leaf carbon assimilation rate | | Data\_of\_herb\_hydraulics | Amean | μmol m-2 s-1 | Mean value of maximum leaf carbon assimilation rate | | Data\_of\_herb\_hydraulics | Ase | μmol m-2 s-1 | Standard error of maximum leaf carbon assimilation rate | | gs\_wp\_experimental\_1& gs\_wp\_experimental\_2 | E | mmol m-2 s-1 | Transpiration rate | | Data\_of\_herb\_hydraulics | Emean | mmol m-2 s-1 | Mean value of transpiration rate | | Data\_of\_herb\_hydraulics | Ese | mmol m-2 s-1 | Standard error of transpiration rate | | SLA | SLA | m2 kg-1 | Value of specific leaf area | | Data\_of\_herb\_hydraulics | SLAmean | m2 kg-1 | Mean value of specific leaf area | | Data\_of\_herb\_hydraulics | SLAse | m2 kg-1 | Standard error of specific leaf area | | ABR | ABR | unitless | Ratio of plant aboveground to belowground biomass. | | Data\_of\_herb\_hydraulics | ABRmean | unitless | Mean value of ratio of aboveground to belowground biomass | | Data\_of\_herb\_hydraulics | ABRse | unitless | Standard error of aboveground to belowground biomass | | Data\_of\_herb\_hydraulics | MAT\_mean | degree Celsius | Climatic variables include mean annual temperature | | Data\_of\_herb\_hydraulics | MAT\_q05 | degree Celsius | 5th percentile of mean annual temperature | | Data\_of\_herb\_hydraulics | MAT\_q95 | degree Celsius | 95th percentile of mean annual temperature | | Data\_of\_herb\_hydraulics | MAP\_mean | mm | Mean annual precipitation | | Data\_of\_herb\_hydraulics | MAP\_q05 | mm | 5th percentile of mean annual precipitation | | Data\_of\_herb\_hydraulics | MAP\_q95 | mm | 95th percentile of mean annual precipitation | | Data\_of\_herb\_hydraulics | latitude | oN | Mean latitude of species distribution | | Data\_of\_herb\_hydraulics | longitude | oE | Mean longitude of species distribution | | ABR | aboveground mass | g | Total biomass of plant material above the soil surface | | ABR | belowground mass | g | Total biomass of plant material below the soil surface | | gs\_wp\_experimental\_1、VC\_experimental\_1& VC\_experimental\_2 | water potential | -MPa | Matrix quantifying the water status of plant | | VC\_experimental\_1& VC\_experimental\_2 | embolism level | % | The ratio of embolized leaf area at a given water potential to the embolized leaf area at complete dehydration | | VC\_experimental\_2 | time of water potential | unitless | Time of water potential measurement | | All files in “Raw\_data” file folder | replication | unitless | Identification number of the plant sample used for measurement |