A high-throughput approach for quantifying turgor loss point in wine grapes

A high-throughput approach for quantifying turgor loss point in wine grapes | 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 A high-throughput approach for quantifying turgor loss point in wine grapes Adam R. Martin, Guangrui Li, Boya Cui, Rachel. O. Mariani, Kale Vicario, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3921663/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Nov, 2024 Read the published version in Plant Methods → Version 1 posted 9 You are reading this latest preprint version Abstract Quantifying drought tolerance in crops is critical for agricultural management under environmental change, and drought response traits in wine grapes have long been the focus of viticultural research. Turgor loss point ( π tlp ) is gaining attention as an indicator of drought tolerance in plants, though estimating π tlp often requires the construction and analysis of pressure-volume (P-V) curves which is time consuming. While P-V curves remain a valuable tool for assessing π tlp and related traits, there is considerable interest in developing high-throughput methods for rapidly estimating π tlp , especially in the context of crop screening. We tested the ability of a dewpoint hygrometer to quantify variation in π tlp across and within 12 varieties of wine grapes ( Vitis vinifera ) and one wild relative ( Vitis riparia ) and compared these results to those derived from P-V curves. At the leaf-level, methodology explained only 4–5% of the variation in π tlp while variety/species identity accounted for 39% of the variation, indicating that both methods are sensitive to detecting intraspecific π tlp variation in wine grapes. Also at the leaf level, π tlp measured using a dewpoint hygrometer significantly approximated π tlp values ( r 2 = 0.254) and conserved π tlp rankings from P-V curves (Spearman’s ρ = 0.459). While the leaf-level datasets differed statistically from one another (paired t -test p = 0.01), average difference in π tlp for a given pair of leaves was small (0.1 ± 0.2 MPa (s.d.)). At the species/variety level, estimates of π tlp measured by the two methods were also statistically correlated ( r 2 = 0.304), did not deviate statistically from a 1:1 relationship, and conserved π tlp rankings across varieties (Spearman’s ρ = 0.692). The dewpoint hygrometer (taking ~ 10–15 minutes on average per measurement) captures fine-scale intraspecific variation in π tlp , with results that approximate those from P-V curves (taking 2–3 hours on average per measurement). The dewpoint hygrometer represents a viable method for rapidly estimating intraspecific variation in π tlp , and potentially greatly increasing replication when estimating this drought tolerance trait in wine grapes and other crops. Drought tolerance traits intraspecific trait variation high throughput phenotyping turgor loss point Vitis vinifera Figures Figure 1 Figure 2 Figure 3 Background Increases in the prevalence and duration of drought events due to climate change are a major concern agricultural production faces, with potentially destabilizing consequences for food security and sustainability at local through to global scales [ 1 – 3 ]. As a result, managing and predicting crop yield under drought is among the most pressing goals for agricultural management under environmental change, and has been for decades [ 4 – 9 ]. In aiming to understand the mechanistic basis of yield change under drought, one primary theme in crop science has been to better understand how plants cope with periods of water stress [ 10 , 11 ]. Several reviews exist that summarize the myriad of short- and long-term drought tolerance mechanisms and strategies in plants globally, which spans from short-term physiological responses such as biochemical signaling that triggers stomatal closure, through to longer-term responses including changes in leaf-, root-, or whole-plant traits [ 2 , 3 , 12 – 15 ]. Indeed, this deep literature demonstrates how and why drought tolerance represents among the most complex and multifaceted plant characteristics, and is dictated by the cumulative expression of multiplebiochemical, anatomical, architectural, and morphological traits [ 13 ]. Owing to its complex nature, studies often use different approaches or metrics to quantify drought tolerance. These metrics utilize various functional traits including instantaneous water-use efficiency (measured as a ratio of photosynthetic carbon gain [ A ] to water loss through transpiration [ E ] [e.g., 16]), stomatal traits including conductance ( g s ) and sensitivity [e.g., 17, 18], traits associated with root phenotypes [e.g., 19], among others [e.g., 20]. One trait employed to characterize drought tolerance in plants is the turgor loss point ( π tlp ), which represents the leaf water potential (Ψ leaf ) at which wilting occurs [ 21 – 23 ]. This trait is widely considered an indicator of drought tolerance, as plants expressing a more negative π tlp are able to maintain leaf turgor and physiological functioning including sustained rates of g s , A , and E , across a wider range of moisture availability [ 23 ]. In the field of comparative plant and functional trait ecology, turgor loss point is considered a “higher-level” drought tolerance trait that can be used to infer leaf- and plant-level environmental tolerances [ 22 ]. As such, π tlp and its plasticity have been employed to characterize drought tolerance across large groups of species [ 21 , 22 , 24 , 25 ]. In turn, in studies on unmanaged ecosystems, inter- and intraspecific variation in π tlp is used to support hypotheses surrounding the environmental determinants of plant species distributions [ 26 , 27 ] and species and community-level responses to changing water availability [e.g., 18, 24, 28]. More recently, and largely as an extension of the literature on “wild” plants, π tlp has received attention as a hypothesized index of drought tolerance in crops. For example, in detecting variation in π tlp within and among eight cultivars of spring wheat ( Triticum aestivum L.), Mart et al. [ 29 ] argued that π tlp represents a trait that could be used to rapidly screen crop drought tolerance. Employing π tlp as a crop drought tolerance trait is relatively new, since prior to this time, there was a widespread assumption that crop physiological functioning ceased below a certain soil-based permanent wilting point [as cited by 29, 30]. Furthermore, researchers have noted that relationships between π tlp and crop growth and yield remain an unresolved area of research, leading to uncertainties as to whether or not this trait represents a viable screening tool in breeding programs that are relevant for agricultural management [ 31 ]. Nonetheless, because π tlp is a metric that integrates multiple aspects of osmotic adjustment in plants, quantifying π tlp within and among crop species and varieties appears an increasingly important component of the wider literature on quantifying crop drought tolerance. To date pressure-volume (P-V) curves have been the classical method for estimating π tlp in plants, species, or genotypes [reviewed by 32], with P-V curve-derived data then supporting literature focused on plant drought tolerance and its ecological implications [ 33 ]. A P-V curve is constructed by progressively drying a fully rehydrated leaf at set pressure or drying intervals, and then assessing the statistical relationship between Ψ leaf and relative water deficit (RWD) [summarized in Fig. 1 of 22]. Data necessary for P-V curve construction and associated π tlp estimation are generated with the use of Scholander-type pressure chambers or “pressure bombs” [ 34 ], generally following either a bench drying method where leaves are air dried on a bench top and both Ψ leaf and RWD are measured at set time intervals, or a “squeeze method” where the mass of expressed sap is weighed at set pressure intervals. Despite being widely applied in both basic and applied plant sciences for decades [ 34 , 35 ], P-V curve generation is associated with major time constraints [ 33 ], often taking hours to complete for a single leaf, and certain assumptions embedded within P-V curve-based methods have motivated critical reviews on their accuracy across studies [ 32 , 33 , 36 ]. For example, Rodriguez-Dominguez et al. (2022) elucidated how key assumptions related to sample preparation, storage, and pressurization techniques (among other factors) may lead to variability in Ψ leaf measurements, and ultimately P-V curve construction from pressure chambers. At the same time, estimating π tlp for a single leaf through a P-V curve can take hours, even when using the relatively fast squeeze method [ 36 ]. So, while P-V curves remain a critical aspect of plant and crop science, there are major limitations associated with this technique for either 1) screening drought tolerance across multiple crops and varieties, or 2) for assessing variation in drought tolerance traits at the individual plant level. The latter is especially important in the fields of crop science and agroecology, where intraspecific trait variation—i.e., variation existing below the species level—is likely a key determinant of agroecosystem processes [ 37 ]. In response to these limitations, researchers have begun developing methods for high-throughput estimation of Ψ leaf and π tlp , which have gained considerable interest in studies of woody and herbaceous species in unmanaged ecosystems [ 33 , 36 , 38 – 40 ], and more recently of crops [ 29 ]. Notably, studies have employed both vapour pressure osmometers [ 33 , 38 , 39 ] and—the focus of our study here—dewpoint hygrometers [ 36 , 40 ] to estimate π tlp in plants by estimating this trait directly as a function of Ψ s at full turgor. Specifically, a dewpoint hygrometer measures the sum of matric potential (Ψ m ) and osmotic potential (Ψ s ) using the chilled-mirror dewpoint technique, while the vapour-pressure osmometer measures osmolality which is then converted into Ψ s following the Van't Hoff equation [ 33 , 36 ]. In both these methods gravitational potential (Ψ g ), matric potential (Ψ m ), and Ψ p are considered negligible when leaves are fully hydrated, and as a result Ψ s in fully hydrated leaf samples can then be correlated to π tlp [ 33 ]. The use of dewpoint hygrometers for measuring leaf bulk water relations dates back decades [ 41 , 42 ] and recent studies using these techniques in high-throughput assessments of plant ecophysiology are promising. Specifically, Petruzellis et al. [ 40 ] found this method could capture interspecific variation in π tlp across 27 Mediterranean woody species (which ranged from ~-4.5 to -0.5 MPa), with dewpoint hygrometer-based measurements of Ψ s at full turgor linearly predicting (adjusted r 2 = 0.46) π tlp values derived from P-V curves. Within species, Banks and Hirons [ 36 ] found this technique was able to quantify fine-scale differences in π tlp that exists among five Acer genotypes, with mean π tlp ranging between roughly − 1.5 to -2.0 MPa: differences that were masked when π tlp was measured using P-V curves alone. While these studies and foundational theory [ 41 , 42 ] point to the dewpoint hygrometer as a viable technique for high-throughput π tlp estimation, no studies have yet tested if this technique is able to quantify variation in π tlp across crop varieties. Wine grapes ( Vitis vinifera subsp. vinifera ) represent among the world’s most economically important crops, with the environmental conditions necessary for wine grape production varying widely across the world’s over 6,000 varieties [ 43 – 46 ]. Many varieties require a narrow range of climatic conditions for optimum plant performance, physiological functioning, fruit quality, and yield [ 45 , 46 ]. Indeed, authors have argued that wine grapes are among the most sensitive to climatic shifts with projections indicating that due to alterations in water availability and temperature regimes, wine production will likely shift considerably in the future [ 47 , 48 ]. Wine grape drought tolerance is an integrated characteristic comprised of multiple genetic, morphological, physiological, and phenological traits [ 49 ]. Additionally, the viability of wine grape production under a shifting climate is mediated not just by drought tolerarnce, but also by other agronomic considerations such as berry size, yield, and flavor profiles. No less, high-throughput measurements of π tlp would be valuable towards holistic prediction and modelling of climatic suitability of varieties into the future [ 47 , 50 ]. In this study, we assess whether or not a high-throughput technique based on the use of a dewpoint hygrometer, is able to quantify variation in π tlp both within and among wine grape varieties. To address this, we specifically compare differences in π tlp generated through P-V curves and a dewpoint hygrometer, at both the individual leaf- and variety-scale. While the primary focus of our study is on π tlp variation within and among 12 widely cultivated varieties of wine grapes, our study also includes measurements on a wild relative of wine grapes, namely Vitis riparia , in order to assess the wider application of this technique towards quantifying π tlp in wild crop relatives [ 51 ]. Our study was designed to address the following research questions: 1) do varieties of wine grapes vary significantly in their π tlp , and if so, can these differences be quantified using a high-throughput technique? Then, we asked 2) which varieties are most drought-tolerant as per π tlp values, and if inferences regarding varietal drought tolerance rankings change depending on π tlp methods? Finally, we ask 3) do cultivated wine grapes differ in their π tlp vs. wild relatives? Methods Field site and sample collection Wine grape plant materials for this study were collected at the Niagara College Teaching Vineyard, located in Niagara-on-the-Lake, Ontario, Canada (43.1522° N, 79.1652° W). This 16.2 ha operational vineyard established in 1996 is situated within the Niagara Peninsula Appellation in Southern Ontario, Canada. At the vineyard, branches of 12 cultivated Vitis vinifera L. varieties were sampled, including riesling (varieties 23 and 171), pinot noir (varieties 89 and 828), merlot (varieties 384 and 181), cabernet sauvignon (varieties 29 and varieties 412), cabernet franc (varieties 327 and 314), viognier (variety 642), and sauvignon blanc (variety 906). Our study also sampled a wild relative of cultivated wine grapes V. riparia growing in a forest edge ecosystem situated immediately adjacent to the vineyard (i.e., within ~ 10 m south of the vine rows at the south end of the vineyard). From each variety and wild relative species, one shoot was sampled from three different individual vines. Each shoot was ~ 30–50 cm in length and included a minimum of three pairs of oppositely arranged leaves that were fully expanded, visually healthy, and of similar size and vigor. Once collected, all shoots were immediately recut underwater to avoid desiccation before being transported to the lab at the University of Toronto Scarborough, which occurred within 2 hours of field sample collection. In the lab, shoots were again recut under water at least 1 cm from the base and stored in the dark for 12 hours to fully rehydrate. Following this step, two adjacent leaves in the same growth conditions from each branch were then used for estimating π tlp following two different methods: 1) P-V curves executed using a SAPS II portable plant water status console (Soilmoisture Equipment Corp., CA, USA), and 2) direct measurements of Ψ s at full turgor and π tlp using a WP4C dewpoint hygrometer (METER Environmental, Washington, USA). Estimating π tlp using pressure-volume curves All P-V curves were generated via the pressure chamber method following protocols described by Sack et al. (2022). First, we removed one leaf from each rehydrated branch and recorded leaf area using an LI-3600C leaf area meter (LICOR Bioscience, Lincoln, Nebraska, USA). Then a cut was made at the base of the petiole using a razor blade, and the leaf was immediately weighed and sealed in the pressure chamber. The chamber was then pressurized until an initial balance pressure was reached (≥ 0.2 MPa) which was determined by the first appearance of sap expressed from the cut surface viewed under a digital microscope affixed to the SAP II console. The expressed sap was then collected using pre-weighed low-lint absorbent tissue paper inside a 1.5 ml Eppendorf tube. To prevent evaporation the opening time of the tube was minimized during sap collection. The tube and tissue paper were re-weighed after sap collection to determine the weight of water exuded. The pressure then was increased in 0.2 MPa intervals, and the sap collection procedure was repeated at least 10 times to obtain enough data points to construct a full P-V curve. In sum, this procedure took approximately 2–3 hours for each individual leaf. Based on this data, we used a series of functions in the ‘pvcurveanalysis’ R package [ 52 ] to estimate π tlp for each leaf. First, we used the 'FMSaturated’ function to estimate saturated fresh mass (FM) for each leaf, which is calculated as an extrapolation of a linear regression model fit between leaf mass and Ψ leaf values above the estimated π tlp . Based on this FM estimate, we used the ‘RelativeWaterDeficit” function to calculate the RWD at each pressure interval as: RWD = 100–100 * ((FM – DM) (FMs-DM)-1) (Eq. 1) where DM is dry mass measured at the end of each P-V curve by drying each leaf at 65°C to constant mass, and FMs is FM at water saturation. Then, π tlp was estimated for each leaf using the ‘OsmoticPot’ function in the ‘pvcurveanalysis’ R package [ 52 ]. Estimating π tlp using a dewpoint hygrometer For high throughput estimates of π tlp we used the WP4C dewpoint hygrometer to measure Ψ s at full leaf turgor (with plants being rehydrated as described above), and subsequently convert this value to π tlp estimates. In these analyses, leaves immediately adjacent to those used in P-V curve analyses were selected, and we collected three leaf discs per leaf (or pseudo-replicates) that were 35 mm in diameter from the base of each lobe using a circle cutting blade. Leaf discs were immediately wrapped in tinfoil to avoid water loss, flash frozen in liquid N 2 for five minutes, and then gently abraded using 600 grit sandpaper to remove the cuticle before being placed in the dewpoint hygrometer chamber. Measurements of Ψ s were collected using the WP4C continuous reading mode and recorded after 10–15 minutes when the measuring chamber reached vapor equilibrium. The dewpoint hygrometer was calibrated prior to every 10 measurements using 0.5 mol KCl solution. Based on values of Ψ s at full turgor, we then estimated π tlp following the model described by Bartlett et al. [ 33 ] as: π tlp = (Ψ s * 0.832) – 0.63 (Eq. 2) Statistical analysis Statistical analysis and data visualization were performed using R v. 4.2.2 statistical software (R Foundation for Statistical Computing, Vienna, Austria). Our first analysis made use of our dataset that included a total of n = 39 measurements of π tlp estimated using both P-V curves and the dewpoint hygrometer, such that all leaf-level dewpoint hygrometer π tlp were calculated as the mean of three π tlp pseudo-replicates per leaf. Using this data, we first performed a t -test to evaluate differences in leaf-level π tlp across the two datasets, and followed with a linear regression model (where n = 39 leaves) to test whether π tlp derived from the high-throughput method predicts π tlp derived from P-V curves. Additionally, we also used a linear hypothesis test implemented in the ‘car’ R package [ 53 ] to compare this linear regression model with a 1:1 relationship where π tlp from the high-throughput method perfectly corresponds to π tlp from P-V curves. This linear regression and hypothesis test analysis was augmented by a Spearman rank correlation test, to evaluate the degree to which rankings of π tlp change as a function of methodology. Using this same leaf level dataset, we fitted effects mixed models coupled with variance partitioning techniques to quantify the proportion of variation in π tlp values that were attributable to variety identify (e.g., Riesling vs. Cabernet franc), clone identity (e.g., Riesling 23 vs. 171), and methodology (i.e., dewpoint hygrometer vs. P-V curves). Due to sample size limitations, we performed this analysis by fitting a series of mixed models versus using a single model. Specifically, we used the ‘lme’ function in the ‘nlme’ R package [ 54 ] to fit mixed models that predicted variation in π tlp as a function of: 1) clone identity as a fixed effect and methodology as a random effect; 2) methodology as a fixed effect and clone identity as a random effect; and 3) variety identity as a fixed effect and methodology as a random effect. For each of these models we applied the ‘r.squaredGLMM’ function in the ‘MuMIn’ R package [ 55 ] to estimate the proportion of variation in π tlp associated with both fixed effects (or the marginal r 2 values) and fixed and random effects combined (or the conditional r 2 values) [ 56 ]. For additional analysis, we replicated our leaf-level analysis at the variety/species level. To do so, we calculated mean π tlp values for each variety/species-by-method combination thereby generating a variety/species-level dataset based on n = 3 observations of π tlp in total for each method (except in the case of cab. franc 314, cab. sauv. 412, and riesling 23 where one P-V curve failed). Then, the same t -test, linear regression, linear hypothesis test, and Spearman rank correlation procedures described above were performed on this species/variety-level dataset (where n = 13 for each test). Results Across the leaf-level dataset mean π tlp values were − 1.7 ± 0.3 (s.d.) MPa (interquartile range = 0.3 MPa) when estimated using the P-V curve method, compared to a mean π tlp of -1.8 ± 0.2 MPa (interquartile range = 0.3 MPa) using the high throughput method (Fig. 1 ). Observed π tlp values derived from P-V curves ranged from − 2.4 to -1.2 MPa, and from − 2.2 to -1.2 MPa using the dewpoint hygrometer (Fig. 1 B). Differences in π tlp for individual vines calculated as P-V curve π tlp minus dewpoint hygrometer π tlp values, ranged from − 0.5 MPa to 0.8 MPa and averaged 0.1 ± 0.2 (s.d.) MPa (Fig. 1 ). A paired t -test indicated that these two datasets differed statistically from one another ( t = 2.96, d.f.=35, p = 0.006), though these differences owe largely to a π tlp value from P-V curves from one V . riparia leaf that was the most negative π tlp value in our dataset (Fig. 1 A). Spearman rank correlation analysis indicated that π tlp rankings among individual vines was significantly correlated across methodologies (Spearman’s ρ = 0.5, p = 0.002). Similarly, a linear regression analysis found that π tlp estimates generated by the dewpoint hygrometer explained 25.4% of the variation in π tlp generated by P-V curves (regression model p = 0.002, intercept=-0.59 ± 0.32 (s.e.), slope = 0.61 ± 0.18; Fig. 1 A). However, the relationship between paired π tlp measurements did diverge significantly from a 1:1 relationship (linear hypothesis test F = 7.3, p = 0.002; Fig. 1 ). Our mixed effects model analyses found that across the leaf-level dataset, variety/species identity accounted for the largest proportion of variation in π tlp observations. Specifically, a mixed effects model that included variety/species identity as a fixed effect and method as a random effect detected statistically significant differences in π tlp across varieties/species ( F 12, 61 =4.32, p < 0.001; Fig. 2 ). In this model, variety/species identity explained 38.6% of the variation in π tlp across all observations (i.e., based on the marginal r 2 value), while the method (incorporated as a random effect) explained only an additional 6.0% of the variation in π tlp (i.e., based on the conditional r 2 value). These results were largely robust when leaf-level data was analyzed with method as a fixed effect (marginal r 2 = 0.043) and variety as a random effect (conditional r 2 = 0.394), though here method was a statistically significant predictor of π tlp values ( F 1, 61 =5.25, p = 0.025). When data was analyzed at the species/variety level ( n = 13) we did not detect significant differences in mean π tlp across methods ( t = 1.74, d.f.=13, p = 0.107; Fig. 3A). Specifically, across species/varieties mean π tlp values measured using P-V curves were − 1.7 ± 0.2 (s.d.) MPa and ranged from − 2.2 ± 0.3 (s.e.) in cabernet sauvignon 412 to -1.2 ± 0.1 (s.e.) MPa in riesling 23 (Fig. 3A). When π tlp was based on dewpoint hygrometer measurements, mean π tlp across all varieties averaged − 1.8 ± 0.2 (s.d.) MPa, ranging from − 2.1 ± 0.1 (s.e.) in V . riparia to -1.6 ± 0.1 (s.e.) MPa in viognier 642 (Fig. 3). The ranking of species/varieties based on their mean π tlp values did not differ statistically based on methodology (Spearman’s ρ = 0.69, p = 0.011), with the rank of a number of varieties at both the most (e.g., riesling 171) and least negative π tlp values (i.e., pinot noir clone 89) being robust towards methodology (Fig. 3B). And while the ranking of certain varieties did shift across methods (e.g. Cabernet franc 372), most rank changes were relatively limited to two to three positions on a π tlp ranking scheme (Fig. 3B). Finally, a linear regression model found that variety-level mean π tlp values based on dewpoint hygrometer measurements explained 30.4% of the variation in π tlp values from P-V curves (regression model p = 0.05, intercept=-0.18 ± 0.7 (s.e.), slope = 0.84 ± 0.39), and at the species/variety level this relationship did not differ statistically from a 1:1 relationship (linear hypothesis test F = 1.49, p = 0.267; Fig. 3A). Discussion Estimating π tlp using a dewpoint hygrometer presents a promising avenue for high-throughput assessments of wine grape drought tolerance. This technique closely approximates values of the same trait derived from traditional P-V curve techniques (Figs. 1 –3), being able to quantify relatively fine-scale variation that exists among closely related varieties and species. Our results contribute to the literature on high-throughput π tlp estimation, which gained considerable popularity with the development of osmometry-based methods [ 25 , 33 ], extending to recent analyses employing dewpoint hygrometers [ 36 , 40 , 57 ]. Our results align with this previous work indicating that a dewpoint hygrometer, alongside established models correlating π tlp and Ψ s at full turgor [ 33 ], support the generation and analysis of an important drought tolerance trait in plants. However, our work extends this literature to suggest high-throughput methods are equipped to capture the fine-scale variation in π tlp that exists among individual plants or varieties of the same species: a key consideration for studies on crop functional trait variation [ 37 ]. One consistent finding in our results is that at either the leaf- or variety/ species level, π tlp values derived from the dewpoint hygrometer were on average 0.11 or 0.1 MPa lower (more negative) than paired observations from a P-V curve: values corresponding to average declines of π tlp by 5–6% as one moves from traditional to high-throughput methods (Figs. 1 , 3). This trend is similar to results obtained by Banks and Hirons [ 36 ] in their efforts to quantify intraspecific variation in π tlp across five maple ( Acer ) genotypes, where the same hygrometer model generated more negative π tlp values in comparison with those derived from P-V curves. Similarly, one study on a single wine grape variety also found π tlp from a dewpoint hygrometer strongly correlated with values from P-V curves, with more negative values derived from the high-throughput technique [ 57 ]. A proposed explanation for this trend is related to methods associated with P-V curve generation. Specifically, potential water loss during petiole excision, solute accumulation in undamaged tissue, and the possibility that Ψ leaf is lower than water potential in the air (i.e., conditions of high ambient humidity), could all lead to higher π tlp estimates derived from P-V vs. dewpoint hygrometer measurements [ 36 , 57 , 58 ]. In our study though, absolute and relative differences in π tlp across methods were smaller (i.e., 5–6%) compared to observations in other studies (e.g.,, ~ 26.5% on average [ 36 , 57 ]. No less the growing literature on high-throughput approaches to π tlp estimation, including our own results, indicate these methods provide a viable alternative to P-V curves especially in situations where large sample sizes are an important consideration. In relation to issues of sample size, at the leaf-level our high-throughput approach resulted in a slightly narrower range of π tlp values (-1.2 to -2.18 MPa) compared to those generated by P-V curves (-1.2 to -2.41 MPa). This trend then scaled-up to support similar trends at the species/ variety-scale, where the dewpoint hygrometer results supported a more restricted range of mean π tlp from values (-1.56 to -2.1 MPa) vs. P-V curve-based data (-1.28 to -1.16 MPa) (Fig. 3). Here, the (pseudo-)replication afforded by the high-throughput method may provide more robust π tlp for individual varieties. Related, the most notable is the difference in time required to generate data using these different methods. Each of the 39 P-V curves executed in our study required 2–3 hours of processing time, with three of them failing; analytical steps including visual inspection of all P-V curves prior to final curve fitting further added to this time requirement. Comparatively, each of the dewpoint hygrometer values took ~ 12–15 minutes to generate a single π tlp data point. This time consideration is clearly of interest for plant and crop scientists, and indeed is the foundation of multiple high-throughput approaches to screening crop ecophysiological responses to environmental conditions [e.g., 59]. But despite the inherit value of high-throughput screening in terms of time and potential for greater (pseudo-)replication, there remain limitations. Since the WP4C model requires a leaf disc to be 35 mm, plants and crops with leaves smaller than 35 mm diameter cannot be analyzed by this instrument, and species with thicker cuticles or succulents are likely to require specialized preparation methods before measurement. Lastly, P-V curves also return highly informative metrics associated with leaf water relations, including for example estimates of the bulk elastic modulus or the apoplastic fraction: key traits contributing to plant and crop drought tolerarnce and stress responses [ 21 , 60 , 61 ]. Conclusions Crop responses to drought conditions have long been the focus of applied agricultural research, with crops showing complex responses to reduced water availability [ 2 , 3 ]. These responses span above and belowground biophysical processes, and synthetically incorporate phenology, multiple functional traits, biochemical signaling pathways, and genes across leaves, roots, stems, and reproductive structures [ 49 , 62 ]. While drought represents only one part of wider discussions surrounding threats to food security [ 63 ], identifying drought tolerant crops and genotypes is clearly of importance for maintaining yields under a shifting climate [ 64 ]. High-throughput estimation of functional traits associated with drought tolerarnce, both within and among crop species, varieties, and genotypes, services this goal in part. Traditional techniques associated with quantifying plant-water relations remain invaluable in estimating certain traits. Though high-throughput techniques such as those evaluated here, especially when coupled with other techniques, appear well suited and able to rapidly quantify components of drought responses in crops, at data acquisition rates that match the urgency of global change science. Declarations Availability of Data and Materials Upon publication, the dataset supporting the conclusions of this article will be made available upon request to the corresponding author, and in the TRY Functional Trait Database. Acknowledgments Marney Isaac and Eliana Gonzalez-Vigil are thanked for providing constructive comments on earlier drafts of the manuscript. Competing Interests The authors declare they have no competing interests. Funding This work was supported by an NSERC Discovery Grant to A.R.M., and was also supported in part by the University of Toronto Scarborough’s Clusters of Scholarly Prominence Program. Author contributions A.R.M., G.L., and B.C. planned and designed the research; A.R.M., G.L., and B.C. performed field and lab analyses; A.R.M., R.O.M., and K.V. performed statistical analyses; A.R.M. and G.L. wrote the draft manuscript; B.C., R.O.M., K.V., A.F., G.R., and K.C. edited the manuscript; K.C, G.R., and A.R.M. secured funding related to this research. 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Martínez E, Rey B, Fandiño M, Cancela J: Comparison of two techniques for measuring leaf water potential in Vitis vinifera var. Albariño. Ciência e Técnica Vitivinícola 2013, 28(1):29-41. Zhang F-P, Yang Y-J, Yang Q-Y, Zhang W, Brodribb TJ, Hao G-Y, Hu H, Zhang S-B: Floral mass per area and water maintenance traits are correlated with floral longevity in Paphiopedilum (Orchidaceae). Frontiers in Plant Science 2017, 8:501. Van Tassel DL, DeHaan LR, Diaz-Garcia L, Hershberger J, Rubin MJ, Schlautman B, Turner K, Miller AJ: Re-imagining crop domestication in the era of high throughput phenomics. Current Opinion in Plant Biology 2022, 65:102150. Schultz HR, Matthews MA: Growth, osmotic adjustment, and cell‐wall mechanics of expanding grape leaves during water deficits. Crop science 1993, 33(2):287-294. Moutinho-Pereira J, Magalhães N, Gonçalves B, Bacelar E, Brito M, Correia C: Gas exchange and water relations of three Vitis vinifera L. cultivars growing under Mediterranean climate. Photosynthetica 2007, 45:202-207. Trenti M, Lorenzi S, Bianchedi PL, Grossi D, Failla O, Grando MS, Emanuelli F: Candidate genes and SNPs associated with stomatal conductance under drought stress in Vitis . BMC Plant Biology 2021, 21:1-21. Wheeler T, Von Braun J: Climate change impacts on global food security. Science 2013, 341(6145):508-513. Rezaei EE, Webber H, Asseng S, Boote K, Durand JL, Ewert F, Martre P, MacCarthy DS: Climate change impacts on crop yields. Nature Reviews Earth & Environment 2023:1-16. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Nov, 2024 Read the published version in Plant Methods → Version 1 posted Editorial decision: Revision requested 11 Aug, 2024 Reviews received at journal 10 Aug, 2024 Reviewers agreed at journal 20 Jul, 2024 Reviews received at journal 31 May, 2024 Reviewers agreed at journal 30 Apr, 2024 Reviewers invited by journal 17 Mar, 2024 Submission checks completed at journal 03 Feb, 2024 Editor assigned by journal 03 Feb, 2024 First submitted to journal 02 Feb, 2024 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-3921663","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270853988,"identity":"6290dbd1-25c6-4c37-b2fa-ec73bce5b1a0","order_by":0,"name":"Adam R. 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Cathline","email":"","orcid":"","institution":"Niagara College","correspondingAuthor":false,"prefix":"","firstName":"Kimberley","middleName":"A.","lastName":"Cathline","suffix":""},{"id":270853994,"identity":"3f19a2cd-2a36-4ffe-833c-2f3cf4bea9c4","order_by":6,"name":"Allison Findlay","email":"","orcid":"","institution":"Niagara College","correspondingAuthor":false,"prefix":"","firstName":"Allison","middleName":"","lastName":"Findlay","suffix":""},{"id":270853995,"identity":"cc0751fc-228e-4d5c-8ae7-9ac4c80f0b1e","order_by":7,"name":"Gavin Robertson","email":"","orcid":"","institution":"Niagara College","correspondingAuthor":false,"prefix":"","firstName":"Gavin","middleName":"","lastName":"Robertson","suffix":""}],"badges":[],"createdAt":"2024-02-02 16:59:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3921663/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3921663/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13007-024-01304-1","type":"published","date":"2024-11-24T15:56:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50714470,"identity":"4039973b-9cb5-4529-b91c-cd110c9b7759","added_by":"auto","created_at":"2024-02-06 08:21:15","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":203148,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTurgor loss point (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eπ\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cstrong\u003etlp\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e) of leaves from 12 wine grape varieties and one wild relative (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eVitis riparia\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e) estimated through high throughput (dewpoint hygrometer) and traditional (pressure-volume curve) techniques. \u003c/strong\u003ePanel A displays estimates of \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e derived through both methods measured on paired leaves from the same branch of individual vines. Dewpoint hygrometer-based \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e estimates for each individual leaf are derived as the mean of three \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e measurements per leaf (with error bars representing ± 1 s.e.). For clarity, points are colored according to their primary cultivar (or species in the case of the wild relative \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eriparia\u003c/em\u003e), and the solid dashed line represents a 1:1 relationship. Inset box plot in Panel A presents the distribution of pairwise differences in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp \u003c/sub\u003efor all leaves in the dataset (where the average differences across \u003cem\u003en\u003c/em\u003e=36 leaves is 0.1 MPa), such that values above 0 represent leaves where high-throughput \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp \u003c/sub\u003evalues are more negative than paired measurements derived through P-V curves. Panel B displays histograms of \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp \u003c/sub\u003evalues estimated with high-throughput (dewpoint hygrometer) and traditional (P-V curve) techniques. Points below the histograms correspond to the overall mean \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp \u003c/sub\u003evalues (± 1 s.e.) across the two methods.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3921663/v1/468f7ffe5912b9e581afab2e.jpeg"},{"id":50714471,"identity":"c6d92b66-18a2-499d-a42a-0ad029c5e2fd","added_by":"auto","created_at":"2024-02-06 08:21:15","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":528594,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLeaf- and variety-level turgor loss point (\u003c/strong\u003e\u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e\u003cstrong\u003e) estimates for 12 wine grape varieties and one wild relative derived through high throughput and pressure volume curves methodology. \u003c/strong\u003eIndividual points correspond to individual leaf-level observations of \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e. Leaf-level \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp \u003c/sub\u003evalues associated with the dewpoint hygrometer method correspond to the mean of \u003cem\u003en\u003c/em\u003e=3 pseudo-replicates per individual leaf, while leaf-level \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp \u003c/sub\u003evalues associated with pressure-volume curves are derived from one curve per leaf. Bars correspond to variety-level average \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp \u003c/sub\u003evalues (± 1 s.e.) associated with \u003cem\u003en\u003c/em\u003e=3 measurements for each variety-by-method combination.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3921663/v1/391f589d54021a67f04891ea.jpeg"},{"id":50714473,"identity":"5b68c2ac-46d4-45f5-9b6f-3533bc4547fc","added_by":"auto","created_at":"2024-02-06 08:21:16","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":245453,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage turgor loss point (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eπ\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cstrong\u003etlp\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e) of 12 wine grape varieties and one wild relative (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eVitis riparia\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e) estimated through high throughput (dewpoint hygrometer) and traditional (pressure-volume curve) techniques. \u003c/strong\u003ePanel A displays estimates of variety-level mean \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e derived through both methods (with error bars representing ± 1 s.e.). The solid dashed line represents a 1:1 relationship. The inset box plot presents the distribution of pairwise differences in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp \u003c/sub\u003efor all varieties in the dataset, such that values above 0 represent leaves where high throughput \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp \u003c/sub\u003evalues are more negative than paired measurements derived through pressure-volume curves. Panel B represents rank shifts in estimated \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp \u003c/sub\u003efor varieties when estimated using different techniques. Ordering of the left-hand side points denotes \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp \u003c/sub\u003erankings based on pressure-volume curves, while ordering of the right-hand side points denotes \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp \u003c/sub\u003erankings based on high throughput methods.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3921663/v1/0fc45beee11584cc0a24b011.jpeg"},{"id":69834524,"identity":"03611471-40ec-4d16-a498-c25d085baca8","added_by":"auto","created_at":"2024-11-25 16:06:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1658960,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3921663/v1/d3337fc0-e9b5-41c3-8824-378715186d05.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A high-throughput approach for quantifying turgor loss point in wine grapes","fulltext":[{"header":"Background","content":"\u003cp\u003eIncreases in the prevalence and duration of drought events due to climate change are a major concern agricultural production faces, with potentially destabilizing consequences for food security and sustainability at local through to global scales [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As a result, managing and predicting crop yield under drought is among the most pressing goals for agricultural management under environmental change, and has been for decades [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In aiming to understand the mechanistic basis of yield change under drought, one primary theme in crop science has been to better understand how plants cope with periods of water stress [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral reviews exist that summarize the myriad of short- and long-term drought tolerance mechanisms and strategies in plants globally, which spans from short-term physiological responses such as biochemical signaling that triggers stomatal closure, through to longer-term responses including changes in leaf-, root-, or whole-plant traits [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Indeed, this deep literature demonstrates how and why drought tolerance represents among the most complex and multifaceted plant characteristics, and is dictated by the cumulative expression of multiplebiochemical, anatomical, architectural, and morphological traits [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Owing to its complex nature, studies often use different approaches or metrics to quantify drought tolerance. These metrics utilize various functional traits including instantaneous water-use efficiency (measured as a ratio of photosynthetic carbon gain [\u003cem\u003eA\u003c/em\u003e] to water loss through transpiration [\u003cem\u003eE\u003c/em\u003e] [e.g., 16]), stomatal traits including conductance (\u003cem\u003eg\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e) and sensitivity [e.g., 17, 18], traits associated with root phenotypes [e.g., 19], among others [e.g., 20].\u003c/p\u003e \u003cp\u003eOne trait employed to characterize drought tolerance in plants is the turgor loss point (\u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e), which represents the leaf water potential (Ψ\u003csub\u003eleaf\u003c/sub\u003e) at which wilting occurs [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This trait is widely considered an indicator of drought tolerance, as plants expressing a more negative \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e are able to maintain leaf turgor and physiological functioning including sustained rates of \u003cem\u003eg\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e, \u003cem\u003eA\u003c/em\u003e, and \u003cem\u003eE\u003c/em\u003e, across a wider range of moisture availability [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the field of comparative plant and functional trait ecology, turgor loss point is considered a \u0026ldquo;higher-level\u0026rdquo; drought tolerance trait that can be used to infer leaf- and plant-level environmental tolerances [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. As such, \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e and its plasticity have been employed to characterize drought tolerance across large groups of species [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In turn, in studies on unmanaged ecosystems, inter- and intraspecific variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e is used to support hypotheses surrounding the environmental determinants of plant species distributions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and species and community-level responses to changing water availability [e.g., 18, 24, 28].\u003c/p\u003e \u003cp\u003eMore recently, and largely as an extension of the literature on \u0026ldquo;wild\u0026rdquo; plants, \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e has received attention as a hypothesized index of drought tolerance in crops. For example, in detecting variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e within and among eight cultivars of spring wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.), Mart et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] argued that \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e represents a trait that could be used to rapidly screen crop drought tolerance. Employing \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e as a crop drought tolerance trait is relatively new, since prior to this time, there was a widespread assumption that crop physiological functioning ceased below a certain soil-based permanent wilting point [as cited by 29, 30]. Furthermore, researchers have noted that relationships between \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e and crop growth and yield remain an unresolved area of research, leading to uncertainties as to whether or not this trait represents a viable screening tool in breeding programs that are relevant for agricultural management [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Nonetheless, because \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e is a metric that integrates multiple aspects of osmotic adjustment in plants, quantifying \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e within and among crop species and varieties appears an increasingly important component of the wider literature on quantifying crop drought tolerance.\u003c/p\u003e \u003cp\u003eTo date pressure-volume (P-V) curves have been the classical method for estimating \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e in plants, species, or genotypes [reviewed by 32], with P-V curve-derived data then supporting literature focused on plant drought tolerance and its ecological implications [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A P-V curve is constructed by progressively drying a fully rehydrated leaf at set pressure or drying intervals, and then assessing the statistical relationship between Ψ\u003csub\u003eleaf\u003c/sub\u003e and relative water deficit (RWD) [summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e of 22]. Data necessary for P-V curve construction and associated \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e estimation are generated with the use of Scholander-type pressure chambers or \u0026ldquo;pressure bombs\u0026rdquo; [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], generally following either a bench drying method where leaves are air dried on a bench top and both Ψ\u003csub\u003eleaf\u003c/sub\u003e and RWD are measured at set time intervals, or a \u0026ldquo;squeeze method\u0026rdquo; where the mass of expressed sap is weighed at set pressure intervals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite being widely applied in both basic and applied plant sciences for decades [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], P-V curve generation is associated with major time constraints [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], often taking hours to complete for a single leaf, and certain assumptions embedded within P-V curve-based methods have motivated critical reviews on their accuracy across studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. For example, Rodriguez-Dominguez et al. (2022) elucidated how key assumptions related to sample preparation, storage, and pressurization techniques (among other factors) may lead to variability in Ψ\u003csub\u003eleaf\u003c/sub\u003e measurements, and ultimately P-V curve construction from pressure chambers. At the same time, estimating \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e for a single leaf through a P-V curve can take hours, even when using the relatively fast squeeze method [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. So, while P-V curves remain a critical aspect of plant and crop science, there are major limitations associated with this technique for either 1) screening drought tolerance across multiple crops and varieties, or 2) for assessing variation in drought tolerance traits at the individual plant level. The latter is especially important in the fields of crop science and agroecology, where intraspecific trait variation\u0026mdash;i.e., variation existing below the species level\u0026mdash;is likely a key determinant of agroecosystem processes [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn response to these limitations, researchers have begun developing methods for high-throughput estimation of Ψ\u003csub\u003eleaf\u003c/sub\u003e and \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e, which have gained considerable interest in studies of woody and herbaceous species in unmanaged ecosystems [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and more recently of crops [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Notably, studies have employed both vapour pressure osmometers [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and\u0026mdash;the focus of our study here\u0026mdash;dewpoint hygrometers [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] to estimate \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e in plants by estimating this trait directly as a function of Ψ\u003csub\u003es\u003c/sub\u003e at full turgor. Specifically, a dewpoint hygrometer measures the sum of matric potential (Ψ\u003csub\u003em\u003c/sub\u003e) and osmotic potential (Ψ\u003csub\u003es\u003c/sub\u003e) using the chilled-mirror dewpoint technique, while the vapour-pressure osmometer measures osmolality which is then converted into Ψ\u003csub\u003es\u003c/sub\u003e following the Van't Hoff equation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In both these methods gravitational potential (Ψ\u003csub\u003eg\u003c/sub\u003e), matric potential (Ψ\u003csub\u003em\u003c/sub\u003e), and Ψ\u003csub\u003ep\u003c/sub\u003e are considered negligible when leaves are fully hydrated, and as a result Ψ\u003csub\u003es\u003c/sub\u003e in fully hydrated leaf samples can then be correlated to \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe use of dewpoint hygrometers for measuring leaf bulk water relations dates back decades [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and recent studies using these techniques in high-throughput assessments of plant ecophysiology are promising. Specifically, Petruzellis et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] found this method could capture interspecific variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e across 27 Mediterranean woody species (which ranged from ~-4.5 to -0.5 MPa), with dewpoint hygrometer-based measurements of Ψ\u003csub\u003es\u003c/sub\u003e at full turgor linearly predicting (adjusted \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.46) \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values derived from P-V curves. Within species, Banks and Hirons [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] found this technique was able to quantify fine-scale differences in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e that exists among five \u003cem\u003eAcer\u003c/em\u003e genotypes, with mean \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e ranging between roughly \u0026minus;\u0026thinsp;1.5 to -2.0 MPa: differences that were masked when \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e was measured using P-V curves alone. While these studies and foundational theory [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] point to the dewpoint hygrometer as a viable technique for high-throughput \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e estimation, no studies have yet tested if this technique is able to quantify variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e across crop varieties.\u003c/p\u003e \u003cp\u003eWine grapes (\u003cem\u003eVitis vinifera\u003c/em\u003e subsp. \u003cem\u003evinifera\u003c/em\u003e) represent among the world\u0026rsquo;s most economically important crops, with the environmental conditions necessary for wine grape production varying widely across the world\u0026rsquo;s over 6,000 varieties [\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Many varieties require a narrow range of climatic conditions for optimum plant performance, physiological functioning, fruit quality, and yield [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Indeed, authors have argued that wine grapes are among the most sensitive to climatic shifts with projections indicating that due to alterations in water availability and temperature regimes, wine production will likely shift considerably in the future [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Wine grape drought tolerance is an integrated characteristic comprised of multiple genetic, morphological, physiological, and phenological traits [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Additionally, the viability of wine grape production under a shifting climate is mediated not just by drought tolerarnce, but also by other agronomic considerations such as berry size, yield, and flavor profiles. No less, high-throughput measurements of \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e would be valuable towards holistic prediction and modelling of climatic suitability of varieties into the future [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we assess whether or not a high-throughput technique based on the use of a dewpoint hygrometer, is able to quantify variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e both within and among wine grape varieties. To address this, we specifically compare differences in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e generated through P-V curves and a dewpoint hygrometer, at both the individual leaf- and variety-scale. While the primary focus of our study is on \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e variation within and among 12 widely cultivated varieties of wine grapes, our study also includes measurements on a wild relative of wine grapes, namely \u003cem\u003eVitis riparia\u003c/em\u003e, in order to assess the wider application of this technique towards quantifying \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e in wild crop relatives [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Our study was designed to address the following research questions: 1) do varieties of wine grapes vary significantly in their \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e, and if so, can these differences be quantified using a high-throughput technique? Then, we asked 2) which varieties are most drought-tolerant as per \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values, and if inferences regarding varietal drought tolerance rankings change depending on \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e methods? Finally, we ask 3) do cultivated wine grapes differ in their \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e vs. wild relatives?\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eField site and sample collection\u003c/h2\u003e \u003cp\u003eWine grape plant materials for this study were collected at the Niagara College Teaching Vineyard, located in Niagara-on-the-Lake, Ontario, Canada (43.1522\u0026deg; N, 79.1652\u0026deg; W). This 16.2 ha operational vineyard established in 1996 is situated within the Niagara Peninsula Appellation in Southern Ontario, Canada. At the vineyard, branches of 12 cultivated \u003cem\u003eVitis vinifera\u003c/em\u003e L. varieties were sampled, including riesling (varieties 23 and 171), pinot noir (varieties 89 and 828), merlot (varieties 384 and 181), cabernet sauvignon (varieties 29 and varieties 412), cabernet franc (varieties 327 and 314), viognier (variety 642), and sauvignon blanc (variety 906). Our study also sampled a wild relative of cultivated wine grapes \u003cem\u003eV. riparia\u003c/em\u003e growing in a forest edge ecosystem situated immediately adjacent to the vineyard (i.e., within ~\u0026thinsp;10 m south of the vine rows at the south end of the vineyard).\u003c/p\u003e \u003cp\u003eFrom each variety and wild relative species, one shoot was sampled from three different individual vines. Each shoot was ~\u0026thinsp;30\u0026ndash;50 cm in length and included a minimum of three pairs of oppositely arranged leaves that were fully expanded, visually healthy, and of similar size and vigor. Once collected, all shoots were immediately recut underwater to avoid desiccation before being transported to the lab at the University of Toronto Scarborough, which occurred within 2 hours of field sample collection. In the lab, shoots were again recut under water at least 1 cm from the base and stored in the dark for 12 hours to fully rehydrate. Following this step, two adjacent leaves in the same growth conditions from each branch were then used for estimating \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e following two different methods: 1) P-V curves executed using a SAPS II portable plant water status console (Soilmoisture Equipment Corp., CA, USA), and 2) direct measurements of Ψ\u003csub\u003es\u003c/sub\u003e at full turgor and \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e using a WP4C dewpoint hygrometer (METER Environmental, Washington, USA).\u003c/p\u003e \u003cp\u003e \u003cem\u003eEstimating π\u003c/em\u003e \u003csub\u003etlp\u003c/sub\u003e \u003cem\u003eusing pressure-volume curves\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAll P-V curves were generated via the pressure chamber method following protocols described by Sack et al. (2022). First, we removed one leaf from each rehydrated branch and recorded leaf area using an LI-3600C leaf area meter (LICOR Bioscience, Lincoln, Nebraska, USA). Then a cut was made at the base of the petiole using a razor blade, and the leaf was immediately weighed and sealed in the pressure chamber. The chamber was then pressurized until an initial balance pressure was reached (\u0026ge;\u0026thinsp;0.2 MPa) which was determined by the first appearance of sap expressed from the cut surface viewed under a digital microscope affixed to the SAP II console. The expressed sap was then collected using pre-weighed low-lint absorbent tissue paper inside a 1.5 ml Eppendorf tube. To prevent evaporation the opening time of the tube was minimized during sap collection. The tube and tissue paper were re-weighed after sap collection to determine the weight of water exuded. The pressure then was increased in 0.2 MPa intervals, and the sap collection procedure was repeated at least 10 times to obtain enough data points to construct a full P-V curve. In sum, this procedure took approximately 2\u0026ndash;3 hours for each individual leaf.\u003c/p\u003e \u003cp\u003eBased on this data, we used a series of functions in the \u0026lsquo;pvcurveanalysis\u0026rsquo; R package [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] to estimate \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e for each leaf. First, we used the 'FMSaturated\u0026rsquo; function to estimate saturated fresh mass (FM) for each leaf, which is calculated as an extrapolation of a linear regression model fit between leaf mass and Ψ\u003csub\u003eleaf\u003c/sub\u003e values above the estimated \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e. Based on this FM estimate, we used the \u0026lsquo;RelativeWaterDeficit\u0026rdquo; function to calculate the RWD at each pressure interval as:\u003c/p\u003e \u003cp\u003eRWD\u0026thinsp;=\u0026thinsp;100\u0026ndash;100 * ((FM \u0026ndash; DM) (FMs-DM)-1) (Eq.\u0026nbsp;1)\u003c/p\u003e \u003cp\u003ewhere DM is dry mass measured at the end of each P-V curve by drying each leaf at 65\u0026deg;C to constant mass, and FMs is FM at water saturation. Then, \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e was estimated for each leaf using the \u0026lsquo;OsmoticPot\u0026rsquo; function in the \u0026lsquo;pvcurveanalysis\u0026rsquo; R package [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eEstimating π\u003c/em\u003e \u003csub\u003etlp\u003c/sub\u003e \u003cem\u003eusing a dewpoint hygrometer\u003c/em\u003e\u003c/p\u003e \u003cp\u003eFor high throughput estimates of \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e we used the WP4C dewpoint hygrometer to measure Ψ\u003csub\u003es\u003c/sub\u003e at full leaf turgor (with plants being rehydrated as described above), and subsequently convert this value to \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e estimates. In these analyses, leaves immediately adjacent to those used in P-V curve analyses were selected, and we collected three leaf discs per leaf (or pseudo-replicates) that were 35 mm in diameter from the base of each lobe using a circle cutting blade. Leaf discs were immediately wrapped in tinfoil to avoid water loss, flash frozen in liquid N\u003csub\u003e2\u003c/sub\u003e for five minutes, and then gently abraded using 600 grit sandpaper to remove the cuticle before being placed in the dewpoint hygrometer chamber. Measurements of Ψ\u003csub\u003es\u003c/sub\u003e were collected using the WP4C continuous reading mode and recorded after 10\u0026ndash;15 minutes when the measuring chamber reached vapor equilibrium. The dewpoint hygrometer was calibrated prior to every 10 measurements using 0.5 mol KCl solution. Based on values of Ψ\u003csub\u003es\u003c/sub\u003e at full turgor, we then estimated \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e following the model described by Bartlett et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] as:\u003c/p\u003e \u003cp\u003e \u003cem\u003eπ\u003c/em\u003e \u003csub\u003etlp\u003c/sub\u003e = (Ψ\u003csub\u003es\u003c/sub\u003e * 0.832) \u0026ndash; 0.63 (Eq.\u0026nbsp;2)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis and data visualization were performed using R v. 4.2.2 statistical software (R Foundation for Statistical Computing, Vienna, Austria). Our first analysis made use of our dataset that included a total of \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;39 measurements of \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e estimated using both P-V curves and the dewpoint hygrometer, such that all leaf-level dewpoint hygrometer \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e were calculated as the mean of three \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e pseudo-replicates per leaf. Using this data, we first performed a \u003cem\u003et\u003c/em\u003e-test to evaluate differences in leaf-level \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e across the two datasets, and followed with a linear regression model (where \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;39 leaves) to test whether \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e derived from the high-throughput method predicts \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e derived from P-V curves. Additionally, we also used a linear hypothesis test implemented in the \u0026lsquo;car\u0026rsquo; R package [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] to compare this linear regression model with a 1:1 relationship where \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e from the high-throughput method perfectly corresponds to \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e from P-V curves. This linear regression and hypothesis test analysis was augmented by a Spearman rank correlation test, to evaluate the degree to which rankings of \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e change as a function of methodology.\u003c/p\u003e \u003cp\u003eUsing this same leaf level dataset, we fitted effects mixed models coupled with variance partitioning techniques to quantify the proportion of variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values that were attributable to variety identify (e.g., Riesling vs. Cabernet franc), clone identity (e.g., Riesling 23 vs. 171), and methodology (i.e., dewpoint hygrometer vs. P-V curves). Due to sample size limitations, we performed this analysis by fitting a series of mixed models versus using a single model. Specifically, we used the \u0026lsquo;lme\u0026rsquo; function in the \u0026lsquo;nlme\u0026rsquo; R package [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] to fit mixed models that predicted variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e as a function of: 1) clone identity as a fixed effect and methodology as a random effect; 2) methodology as a fixed effect and clone identity as a random effect; and 3) variety identity as a fixed effect and methodology as a random effect. For each of these models we applied the \u0026lsquo;r.squaredGLMM\u0026rsquo; function in the \u0026lsquo;MuMIn\u0026rsquo; R package [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] to estimate the proportion of variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e associated with both fixed effects (or the marginal \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e values) and fixed and random effects combined (or the conditional \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e values) [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor additional analysis, we replicated our leaf-level analysis at the variety/species level. To do so, we calculated mean \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values for each variety/species-by-method combination thereby generating a variety/species-level dataset based on \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 observations of \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e in total for each method (except in the case of cab. franc 314, cab. sauv. 412, and riesling 23 where one P-V curve failed). Then, the same \u003cem\u003et\u003c/em\u003e-test, linear regression, linear hypothesis test, and Spearman rank correlation procedures described above were performed on this species/variety-level dataset (where \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13 for each test).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAcross the leaf-level dataset mean \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values were \u0026minus;\u0026thinsp;1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 (s.d.) MPa (interquartile range\u0026thinsp;=\u0026thinsp;0.3 MPa) when estimated using the P-V curve method, compared to a mean \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e of -1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 MPa (interquartile range\u0026thinsp;=\u0026thinsp;0.3 MPa) using the high throughput method (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Observed \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values derived from P-V curves ranged from \u0026minus;\u0026thinsp;2.4 to -1.2 MPa, and from \u0026minus;\u0026thinsp;2.2 to -1.2 MPa using the dewpoint hygrometer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Differences in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e for individual vines calculated as P-V curve \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e minus dewpoint hygrometer \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values, ranged from \u0026minus;\u0026thinsp;0.5 MPa to 0.8 MPa and averaged 0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 (s.d.) MPa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A paired \u003cem\u003et\u003c/em\u003e-test indicated that these two datasets differed statistically from one another (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.96, d.f.=35, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), though these differences owe largely to a \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e value from P-V curves from one \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eriparia\u003c/em\u003e leaf that was the most negative \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e value in our dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eSpearman rank correlation analysis indicated that \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e rankings among individual vines was significantly correlated across methodologies (Spearman\u0026rsquo;s \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Similarly, a linear regression analysis found that \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e estimates generated by the dewpoint hygrometer explained 25.4% of the variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e generated by P-V curves (regression model \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, intercept=-0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 (s.e.), slope\u0026thinsp;=\u0026thinsp;0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). However, the relationship between paired \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e measurements did diverge significantly from a 1:1 relationship (linear hypothesis test \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur mixed effects model analyses found that across the leaf-level dataset, variety/species identity accounted for the largest proportion of variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e observations. Specifically, a mixed effects model that included variety/species identity as a fixed effect and method as a random effect detected statistically significant differences in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e across varieties/species (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e12, 61\u003c/sub\u003e=4.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In this model, variety/species identity explained 38.6% of the variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e across all observations (i.e., based on the marginal \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e value), while the method (incorporated as a random effect) explained only an additional 6.0% of the variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e (i.e., based on the conditional \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e value). These results were largely robust when leaf-level data was analyzed with method as a fixed effect (marginal \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.043) and variety as a random effect (conditional \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.394), though here method was a statistically significant predictor of \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e1, 61\u003c/sub\u003e=5.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen data was analyzed at the species/variety level (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13) we did not detect significant differences in mean \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e across methods (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.74, d.f.=13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.107; Fig.\u0026nbsp;3A). Specifically, across species/varieties mean \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values measured using P-V curves were \u0026minus;\u0026thinsp;1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 (s.d.) MPa and ranged from \u0026minus;\u0026thinsp;2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 (s.e.) in cabernet sauvignon 412 to -1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 (s.e.) MPa in riesling 23 (Fig.\u0026nbsp;3A). When \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e was based on dewpoint hygrometer measurements, mean \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e across all varieties averaged \u0026minus;\u0026thinsp;1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 (s.d.) MPa, ranging from \u0026minus;\u0026thinsp;2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 (s.e.) in \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eriparia\u003c/em\u003e to -1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 (s.e.) MPa in viognier 642 (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eThe ranking of species/varieties based on their mean \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values did not differ statistically based on methodology (Spearman\u0026rsquo;s \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.69, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), with the rank of a number of varieties at both the most (e.g., riesling 171) and least negative \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values (i.e., pinot noir clone 89) being robust towards methodology (Fig.\u0026nbsp;3B). And while the ranking of certain varieties did shift across methods (e.g. Cabernet franc 372), most rank changes were relatively limited to two to three positions on a \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e ranking scheme (Fig.\u0026nbsp;3B). Finally, a linear regression model found that variety-level mean \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values based on dewpoint hygrometer measurements explained 30.4% of the variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values from P-V curves (regression model \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, intercept=-0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 (s.e.), slope\u0026thinsp;=\u0026thinsp;0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39), and at the species/variety level this relationship did not differ statistically from a 1:1 relationship (linear hypothesis test \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.267; Fig.\u0026nbsp;3A).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEstimating \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e using a dewpoint hygrometer presents a promising avenue for high-throughput assessments of wine grape drought tolerance. This technique closely approximates values of the same trait derived from traditional P-V curve techniques (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;3), being able to quantify relatively fine-scale variation that exists among closely related varieties and species. Our results contribute to the literature on high-throughput \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e estimation, which gained considerable popularity with the development of osmometry-based methods [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], extending to recent analyses employing dewpoint hygrometers [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Our results align with this previous work indicating that a dewpoint hygrometer, alongside established models correlating \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e and Ψ\u003csub\u003es\u003c/sub\u003e at full turgor [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], support the generation and analysis of an important drought tolerance trait in plants. However, our work extends this literature to suggest high-throughput methods are equipped to capture the fine-scale variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e that exists among individual plants or varieties of the same species: a key consideration for studies on crop functional trait variation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne consistent finding in our results is that at either the leaf- or variety/ species level, \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values derived from the dewpoint hygrometer were on average 0.11 or 0.1 MPa lower (more negative) than paired observations from a P-V curve: values corresponding to average declines of \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e by 5\u0026ndash;6% as one moves from traditional to high-throughput methods (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 3). This trend is similar to results obtained by Banks and Hirons [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] in their efforts to quantify intraspecific variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e across five maple (\u003cem\u003eAcer\u003c/em\u003e) genotypes, where the same hygrometer model generated more negative \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values in comparison with those derived from P-V curves. Similarly, one study on a single wine grape variety also found \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e from a dewpoint hygrometer strongly correlated with values from P-V curves, with more negative values derived from the high-throughput technique [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA proposed explanation for this trend is related to methods associated with P-V curve generation. Specifically, potential water loss during petiole excision, solute accumulation in undamaged tissue, and the possibility that Ψ\u003csub\u003eleaf\u003c/sub\u003e is lower than water potential in the air (i.e., conditions of high ambient humidity), could all lead to higher \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e estimates derived from P-V vs. dewpoint hygrometer measurements [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In our study though, absolute and relative differences in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e across methods were smaller (i.e., 5\u0026ndash;6%) compared to observations in other studies (e.g.,, ~\u0026thinsp;26.5% on average [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. No less the growing literature on high-throughput approaches to \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e estimation, including our own results, indicate these methods provide a viable alternative to P-V curves especially in situations where large sample sizes are an important consideration.\u003c/p\u003e \u003cp\u003eIn relation to issues of sample size, at the leaf-level our high-throughput approach resulted in a slightly narrower range of \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values (-1.2 to -2.18 MPa) compared to those generated by P-V curves (-1.2 to -2.41 MPa). This trend then scaled-up to support similar trends at the species/ variety-scale, where the dewpoint hygrometer results supported a more restricted range of mean \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e from values (-1.56 to -2.1 MPa) vs. P-V curve-based data (-1.28 to -1.16 MPa) (Fig.\u0026nbsp;3). Here, the (pseudo-)replication afforded by the high-throughput method may provide more robust \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e for individual varieties.\u003c/p\u003e \u003cp\u003eRelated, the most notable is the difference in time required to generate data using these different methods. Each of the 39 P-V curves executed in our study required 2\u0026ndash;3 hours of processing time, with three of them failing; analytical steps including visual inspection of all P-V curves prior to final curve fitting further added to this time requirement. Comparatively, each of the dewpoint hygrometer values took\u0026thinsp;~\u0026thinsp;12\u0026ndash;15 minutes to generate a single \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e data point. This time consideration is clearly of interest for plant and crop scientists, and indeed is the foundation of multiple high-throughput approaches to screening crop ecophysiological responses to environmental conditions [e.g., 59]. But despite the inherit value of high-throughput screening in terms of time and potential for greater (pseudo-)replication, there remain limitations. Since the WP4C model requires a leaf disc to be 35 mm, plants and crops with leaves smaller than 35 mm diameter cannot be analyzed by this instrument, and species with thicker cuticles or succulents are likely to require specialized preparation methods before measurement. Lastly, P-V curves also return highly informative metrics associated with leaf water relations, including for example estimates of the bulk elastic modulus or the apoplastic fraction: key traits contributing to plant and crop drought tolerarnce and stress responses [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCrop responses to drought conditions have long been the focus of applied agricultural research, with crops showing complex responses to reduced water availability [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These responses span above and belowground biophysical processes, and synthetically incorporate phenology, multiple functional traits, biochemical signaling pathways, and genes across leaves, roots, stems, and reproductive structures [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. While drought represents only one part of wider discussions surrounding threats to food security [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], identifying drought tolerant crops and genotypes is clearly of importance for maintaining yields under a shifting climate [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. High-throughput estimation of functional traits associated with drought tolerarnce, both within and among crop species, varieties, and genotypes, services this goal in part. Traditional techniques associated with quantifying plant-water relations remain invaluable in estimating certain traits. Though high-throughput techniques such as those evaluated here, especially when coupled with other techniques, appear well suited and able to rapidly quantify components of drought responses in crops, at data acquisition rates that match the urgency of global change science.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eAvailability of Data and Materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUpon publication, the dataset supporting the conclusions of this article will be made available upon request to the corresponding author, and in the TRY Functional Trait Database.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMarney Isaac and Eliana Gonzalez-Vigil are thanked for providing constructive comments on earlier drafts of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by an NSERC Discovery Grant to A.R.M., and was also supported in part by the University of Toronto Scarborough\u0026rsquo;s Clusters of Scholarly Prominence Program.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA.R.M., G.L., and B.C. planned and designed the research; A.R.M., G.L., and B.C. performed field and lab analyses; A.R.M., R.O.M., and K.V. performed statistical analyses; A.R.M. and G.L. wrote the draft manuscript; B.C., R.O.M., K.V., A.F., G.R., and K.C. edited the manuscript; K.C, G.R., and A.R.M. secured funding related to this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCohen I, Zandalinas SI, Huck C, Fritschi FB, Mittler R: Meta‐analysis of drought and heat stress combination impact on crop yield and yield components. \u003cem\u003ePhysiologia Plantarum \u003c/em\u003e2021, 171(1):66-76.\u003c/li\u003e\n\u003cli\u003eDietz KJ, Z\u0026ouml;rb C, Geilfus CM: Drought and crop yield. \u003cem\u003ePlant Biology \u003c/em\u003e2021, 23(6):881-893.\u003c/li\u003e\n\u003cli\u003eGupta A, Rico-Medina A, Ca\u0026ntilde;o-Delgado AI: The physiology of plant responses to drought. \u003cem\u003eScience \u003c/em\u003e2020, 368(6488):266-269.\u003c/li\u003e\n\u003cli\u003eBodner G, Nakhforoosh A, Kaul H-P: Management of crop water under drought: a review. \u003cem\u003eAgronomy for Sustainable Development \u003c/em\u003e2015, 35:401-442.\u003c/li\u003e\n\u003cli\u003eChai Q, Gan Y, Zhao C, Xu H-L, Waskom RM, Niu Y, Siddique KH: Regulated deficit irrigation for crop production under drought stress. 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Turgor loss point (\u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e) is gaining attention as an indicator of drought tolerance in plants, though estimating \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e often requires the construction and analysis of pressure-volume (P-V) curves which is time consuming. While P-V curves remain a valuable tool for assessing \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e and related traits, there is considerable interest in developing high-throughput methods for rapidly estimating \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e, especially in the context of crop screening. We tested the ability of a dewpoint hygrometer to quantify variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e across and within 12 varieties of wine grapes (\u003cem\u003eVitis vinifera\u003c/em\u003e) and one wild relative (\u003cem\u003eVitis riparia\u003c/em\u003e) and compared these results to those derived from P-V curves. At the leaf-level, methodology explained only 4\u0026ndash;5% of the variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e while variety/species identity accounted for 39% of the variation, indicating that both methods are sensitive to detecting intraspecific \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e variation in wine grapes. Also at the leaf level, \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e measured using a dewpoint hygrometer significantly approximated \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e values (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.254) and conserved \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e rankings from P-V curves (Spearman\u0026rsquo;s \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.459). While the leaf-level datasets differed statistically from one another (paired \u003cem\u003et\u003c/em\u003e-test \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), average difference in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e for a given pair of leaves was small (0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 MPa (s.d.)). At the species/variety level, estimates of \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e measured by the two methods were also statistically correlated (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.304), did not deviate statistically from a 1:1 relationship, and conserved \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e rankings across varieties (Spearman\u0026rsquo;s \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.692). The dewpoint hygrometer (taking\u0026thinsp;~\u0026thinsp;10\u0026ndash;15 minutes on average per measurement) captures fine-scale intraspecific variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e, with results that approximate those from P-V curves (taking 2\u0026ndash;3 hours on average per measurement). The dewpoint hygrometer represents a viable method for rapidly estimating intraspecific variation in \u003cem\u003eπ\u003c/em\u003e\u003csub\u003etlp\u003c/sub\u003e, and potentially greatly increasing replication when estimating this drought tolerance trait in wine grapes and other crops.\u003c/p\u003e","manuscriptTitle":"A high-throughput approach for quantifying turgor loss point in wine grapes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 08:21:11","doi":"10.21203/rs.3.rs-3921663/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-12T02:28:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-10T15:35:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260081475919123450337524702398510557906","date":"2024-07-20T23:23:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-31T18:20:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7c526b62-6672-4fd2-9259-f0250f135b3e","date":"2024-04-30T04:31:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-17T05:20:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-03T05:10:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-03T05:10:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Methods","date":"2024-02-02T16:55:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plme","sideBox":"Learn more about [Plant Methods](http://plantmethods.biomedcentral.com/)","snPcode":"13007","submissionUrl":"https://submission.nature.com/new-submission/13007/3","title":"Plant Methods","twitterHandle":"@PlantMethods","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17507020-8f89-440c-8e8e-abc04a84613b","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T15:59:03+00:00","versionOfRecord":{"articleIdentity":"rs-3921663","link":"https://doi.org/10.1186/s13007-024-01304-1","journal":{"identity":"plant-methods","isVorOnly":false,"title":"Plant Methods"},"publishedOn":"2024-11-24 15:56:59","publishedOnDateReadable":"November 24th, 2024"},"versionCreatedAt":"2024-02-06 08:21:11","video":"","vorDoi":"10.1186/s13007-024-01304-1","vorDoiUrl":"https://doi.org/10.1186/s13007-024-01304-1","workflowStages":[]},"version":"v1","identity":"rs-3921663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3921663","identity":"rs-3921663","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}