Revealing a significant isotopic offset between plant water and its sources using a global meta-analysis

Isotope-based approaches to study plant water sources rely on the assumption that root water uptake and withinplant water transport are non-fractionating processes. However, a growing number of studies have reported offsets between 15 plant and source water stable isotope composition, for a wide range of ecosystems. These isotopic offsets can result in the erroneous attribution of source water used by plants and potential overestimations of groundwater uptake by the vegetation. We conducted a global meta-analysis to quantify the magnitude of these plant-source water isotopic offsets and explore whether their variability could be explained by either biotic or abiotic factors. Our database compiled 112 studies, spanning arctic to tropical biomes that reported the dual water isotope composition (δH and δO) of plant (stem) and source water, 20 including soil water. We calculated H offsets in two ways: a line conditioned excess (LC-excess) that describes the H deviation from the local meteoric water line, and a soil water line conditioned excess (SW-excess), that describes the deviation from the soil water line, for each sampling campaign within each study. We tested for the effects of climate (air temperature and soil water content), soil class and plant traits (growth form, leaf habit, wood density and parenchyma fraction and mycorrhizal habit) on LC-excess and SW-excess. Globally, stem water was more depleted in H than soil water (SW25 excess < 0) by 3.02 ± 0.65 ‰. In 95% of the cases where SW-excess was negative, LC-excess was negative, indicating that the uptake of water from mobile pools was unlikely to explain the observed soil-plant water isotopic offsets. SW-excess was more negative in cold and wet sites, whereas it was more positive in warm sites. Soil class and plant traits did not have any significant effect on SW-excess. The climatic effects on SW-excess suggest that methodological artefacts are unlikely to be the sole cause of observed isotopic offsets. Instead, our results support the idea that these offsets are caused by isotopic 30 heterogeneity within plant stems whose relative importance will depend on soil and plant water status and evaporative demand. Our results would imply that plant-source water isotopic offsets may lead to inaccuracies when using the isotopic composition of bulk stem water as a proxy to infer plant water sources.

. Besides parameters inherent to the CVD protocol (mainly extraction time, temperature and vacuum line pressure), soil texture, cation exchange capacity and organic matter content have been shown to affect the isotopic composition of extracted soil water (Chen et al., 2021;Orlowski et al., 2018). Alternatives to CVD exist for soil samples, such as water 85 extraction with suction lysimeters (e.g Carrière et al., 2020) or online measurements of liquid-vapor equilibration (Dubbert et al., 2013), but CVD is still, by far, the most common methodology (Amin et al., 2020). The isotopic composition of stem water could also be altered following CVD, as hydrogen exchange between water and cellulose during extraction should cause a systematic, and potentially significant, depletion of the extracted water in 2 H (Chen et al., 2020). Apparent fractionation could also be caused by within-stem isotopic heterogeneity created by isotopic surface effects in soil (Chen et al., 2016a) and stem 90 (Barbeta et al., 2020b) water pools. In studies where sap water was extracted more directly [taking advantage of positive root pressure (Zhao et al. 2016); or using mechanical squeezing, using a Scholander pressure chamber (Geißler et al., 2019;Magh et al., 2020;Zuecco et al., 2020) or directional centrifugation along the stem main axis, using a Cavitron apparatus (Barbeta et al. 2020b)], no significant isotopic offsets were found between sap and source water. In addition, the CVD-extracted water remaining in non-conductive tissues, as well as bulk stem water, have both been shown to be depleted in 2 H relative to sap 95 water (Barbeta et al., 2020b, Zuecco et al., 2020. These recent findings would suggest that isotopic offsets would be more likely when water contained in non-conductive tissues constituted a larger proportion of bulk stem water, for example under water stress or in species with few small xylem vessels. Most often, detailed measurements of such anatomical traits are only available for discrete study sites (e.g. Cosme et al., 2017), but fortunately other proxies of anatomical traits like wood density or parenchyma fraction are more widely available (Chave et al., 2009;Morris et al., 2018). In addition, isotopic enrichment of 100 stem water above source water can result from evaporative enrichment caused by water loss through the bark under hot and dry conditions (Martín-Gómez et al., 2017). Importantly, none of these mechanisms are mutually exclusive: for example, Barbeta et al. (2020a) found that the isotopic offset between plant and source water in potted saplings disappeared under waterlimited conditions, and argued that this was caused by a combination of surface isotopic effects in soil and stem water pools with evaporative enrichment of stem water as a result of the reduction in sap flow. A systematic characterization of the global 105 patterns of these plant-source isotopic offsets and their correlations with abiotic and biotic drivers would be the first step towards identifying their most likely underlying mechanisms.
Scattered evidence across the literature suggests that the mismatch in isotopic composition between plant and source water could be more widespread than previously assumed, but we still lack a systematic quantification of its extent and variability. 110 In this study, isotopic offsets between plant and source water are quantified by means of the line conditioned excess (LCexcess, Landwehr and Coplen, 2006), and analogous metrics (Barbeta et al., 2019). A negative LC-excess indicates that the plant is accessing water that has undergone evaporative enrichment, for example shallow soil water (Zhao et al., 2020a), Because water stored in the soil is the most likely water source for the vast majority of plants (Amin et al., 2020). To detect isotopic mismatches between plant and source water, we should compute the δ 2 H offset between plant water and its 115 corresponding soil water line (SW-excess, Barbeta et al., 2019), in addition to the LC-excess. Here, we calculated LC-excess and SW-excess values from a compilation of 112 studies reporting the dual water isotopic composition of plant (stem) and source waters and analysed their relationship with environmental and climatic conditions (air temperature and soil moisture content). In addition, we also assessed the influence of ecologically relevant factors, including mycorrhizal habit and plant functional traits, mediating nutrient and water-use strategies Flo et al., 2021), as well as the comparison 120 between taxonomic groups (angiosperms vs. gymnosperms) with known distinct hydraulic architecture and functioning (Johnson et al., 2012). Our aim was to quantify potential isotopic offsets between plant and source water and their relationship with biotic and abiotic drivers. We sought to test whether these offsets were likely driven by methodological, biological or abiotic factors. We expected that in the case where these offsets were the result of methodological artifacts, we would not find any correlation between the magnitude of this offset and environmental or biological variables, whereas significant correlations 125 https://doi.org/10.5194/hess-2021-333 Preprint. Discussion started: 28 June 2021 c Author(s) 2021. CC BY 4.0 License. between the offset and certain drivers would help identify possible underlying mechanisms. Following previous arguments (Barbeta et al. 2019, Poca et al. 2019, we hypothesised that plant-source isotopic offsets: (i) would not be restricted to xeric and saline environments and instead would be found across all biomes, also these offsets (ii) would increase in plants with a higher fraction of stem water in non-conductive tissues (i.e. under low water availability and in species with higher wood density and parenchyma fraction) and (iii) would be more likely in plants that are known to establish mutualistic relationships 130 with mycorrhizal fungi.

135
To compile a dataset reporting the dual water isotopic composition for plant and soil samples, we first pooled and reviewed studies included in three previous meta-analyses (Amin et al., 2020;Barbeta and Peñuelas, 2017;Evaristo and McDonnell, 2017). We then added studies obtained after a bibliographic search for peer-reviewed papers published after 2016 on Scopus, Web of Science and Google Scholar. The search was performed in December 2020 using the terms: (water AND isotop*) AND (dual OR (hydrogen AND oxygen)) AND (plant OR tree OR vegetat*). After screening the title and abstract, we selected 140 studies that reported: 1) plant (stem) and source water isotopic composition, including the soil; 2) δ 18 O and δ 2 H for both plant (stem) and source water and 3) sufficient simultaneous soil data (n ≥ 3) to fit a soil water line (SWL). Our final database contained 112 studies (Table 1). For each study, the isotopic composition (δ 18 O and δ 2 H) of plant (stem) and source water was obtained from the associated published datasets, provided by the corresponding author/s or extracted from figures in the article using WebPlotDigitizer (Rohatgi, 2020). Plant water included water extracted from wood cores, lignified stems and rhizomes, 145 never leaves or other transpiring tissues and hereafter is referred to as "stem water". Source waters included: soil water, precipitation, groundwater and streamflow. Here, we refer to precipitation, groundwater and streamflow as "mobile waters".
For all studies, we recorded ancillary data including information of the study site, methodology and study species. Information of the study site included: geographic location of the sampling sites (latitude, longitude and elevation), climate (mean annual temperature and precipitation), slope and intercept of the local meteoric water line (LMWL) and study type (experimental 150 studies on potted plants under controlled conditions, observational studies in irrigated urban gardens or agricultural fields and observational studies under natural conditions). For those studies that did not report the slope and intercept of the LMWL, these parameters were calculated from estimates of isotopic composition of precipitation obtained from the Online Isotopes in Precipitation Calculator (OIPC3.0, Bowen, 2017;Bowen et al., 2005;IAEA/WMO, 2015). Information on the methodology included: soil water extraction method (suction lysimeter; direct equilibration; or vacuum distillation, including CVD, 155 azeotropic vacuum distillation, and other similar methodologies), plant water extraction method (direct xylem water extraction, direct vapor equilibration or vacuum distillation) and instrument type used for analyses of water isotopic composition (mass spectrometer or laser spectrometer). From the 112 studies reviewed, 94 studies used vacuum distillation (mainly CVD) to extract both stem and soil water. One study used direct equilibration of liquid-vapor for both soil and stem water (Bertrand et al., 2014). For extraction of soil water, 10 studies combined vacuum distillation and suction lysimeters and four studies used 160 only suction lysimeters. For stem water, 108 studies used vacuum distillation, two studies used mechanical squeezing using a Scholander pressure chamber (Geißler et al., 2019;Magh et al., 2020), one study used hand-pump suction (Jiménez-Rodríguez et al., 2019). Vacuum distillation was the most common methodology for water extraction of soil (93%) and stem water (96%).
Hence, our database did not allow for a robust analysis of the potential effects of water extraction methodology on plant-source isotopic offsets. Information of the study species included: species name, taxonomic group (angiosperm or gymnosperm), leaf 165 habit (deciduous, semi-deciduous or evergreen), leaf shape (broadleaved or narrow-leaved) and growth form (tree, shrub or non-woody). When available, we also recorded the sampling date (month-year) and plot within the study site. For our analyses,

Calculation of SW-excess and LC-excess 175
We fitted a soil water line (SWL) for isotopic composition of soil water from samples collected within each campaign (observations within a study, sampling date and plot) according to Eq (1) (Sprenger et al., 2016): where 2 and 18 correspond to soil water samples from various depths, locations or pots (in the case of glasshouse experiments); within a site (or plot), sampling date and study. Fitted parameters and are the slope and intercept of the SWL. Parameters and were calculated only for those campaigns where the linear relationship between 2 and 18 were significant (P < 0.05). In this step we discarded 133 campaigns, corresponding to nine studies (Geißler et al., 2019;Huang 185 and Zhang, 2015;Liu et al., 2011;Lovelock et al., 2017;Magh et al., 2020;McKeon et al., 2006;Saha et al., 2015;Su et al., 2020;Twining et al., 2006). Next, we estimated the difference in δ 2 H between each plant water sample and its corresponding soil water line (SW-excess) according to Eq (2) (Barbeta et al., 2019): To measure how well defined SW-excess is for a given species and campaign, we computed the standard error of the mean of SW-excess ( − ) (Taylor, 1997): where and are the standard errors of the slope and intercept of the SWL, respectively and 2 and 18 are the standard errors of the mean (per species and campaign) of 2 and 18 , respectively. To characterize how dispersed SW-excess was for a given campaign, we also calculated the variance of SW-excess: 205 where Var() and Cov() denote the variance and covariance of variables. For studies reporting one value of 2 and 18 per species or campaign, we estimated their variance from the means of all other calculated 2 , 18 and 2 , 18 . 210 Similar statistics were derived for the line conditioned excess (LC-excess) according to Eq (5) (Landwehr and Coplen, 2006):

215
where and are the slope and intercept of the corresponding LMWL. We calculated a value of LC-excess for each plant sample (only for observational studies) and then averaged values within species and campaigns. The standard error of the mean ( − ) and variance [Var(LC-excess)] of LC-excess were calculated as in Eqs (3) and (4), but assuming that and were zero. For each campaign, we considered that either the LC-or SW-excess were different from zero when its estimate plus or minus its standard error was greater or smaller than zero. 220

Climatic, environmental and biological data
Climatic and environmental data were extracted from the ERA5-Land Copernicus data service (Hersbach et al., 2019), downloaded from the Copernicus Climate Change Service (C3S) Climate Data Store. For each study site and sampling date, 225 we extracted: mean monthly and annual air temperature at 2 m above the surface, monthly and annual total precipitation, monthly and annual potential evapotranspiration, mean monthly and annual soil volumetric water content (VWC) at four depth intervals (0-7, 7-28, 28-100 and 100-289 cm) and average soil water content for the upper 100 cm (calculated from soil VWC of the upper soil layers: 0-7, 7-28 and 28-100 cm). In addition, for each study site, we extracted soil class from the ERA5

Statistical analyses
Our final database consisted of 656 records of mean values of SW-excess and 642 of LC-excess (the LC-excess was not 240 calculated for glasshouse studies), for 197 species and 407 campaigns gathered from 102 studies. We used linear mixed models (LMMs) to assess the effects of biotic and abiotic variables on the slope of the SWL, SW-excess and LC-excess, including study as a random factor. To assess the global prevalence of isotopic offsets between plant water and its potential sources, first, we ran LMMs without fixed factors (null models). Next, in the fixed part of the model, we included the following potential explanatory variables: mean monthly air temperature, annual potential evapotranspiration, monthly and annual precipitation, 245 mean monthly soil VWC, soil class and methodology used for analyses of water isotopic composition for the slope of the SWL, LC-excess and SW-excess; and wood density, fraction of parenchyma, leaf habit, growth form, leaf shape, mycorrhizal habit and taxonomic group for LC-excess and SW-excess. All explanatory variables were included in our LMMs in standardized form. In addition, for the LMMs assessing potential effects of plant traits measured at the species level (wood density, parenchyma fraction and mycorrhizal habit), species identity was included as a random factor of the model, because 250 some species were measured in multiple studies. We performed individual models for each explanatory variable and those that had significant effects were tested in combination in additive models. Estimated effects for the SWL slope, LC-excess and SW-excess were weighted by the inverse of the variance, to consider the precision of the information given by each study (Koricheva et al., 2013). In meta-analytical models, two potential sources of variation might be accounted: the random sampling variability within each study (i.e. within-study heterogeneity) and the additional variability between studies, caused 255 for instance by different experimental conditions (i.e. between-study heterogeneity).Thus, we calculated a heterogeneity statistical index to test the percentage of variation across studies caused by between-rather than within-study heterogeneity (I 2 , Higgins & Thompson 2002). The 95% confidence intervals of the I 2 indices were 96.60-99.61%, 99.85-99.85% and 99.98-99.98% for the SWL slope, LC-excess and SW-excess, respectively, indicating that most variation corresponded to betweenstudy heterogeneity. We selected the LMMs including random effects (i.e. accounting for both between-and within-study 260 heterogeneity) rather than those with only fixed effects (i.e. only accounting for within-study heterogeneity), as they fitted the data better in terms of the Akaike Information Criterion (AIC) (Burnham and Anderson, 2002). Therefore, both within and between-study heterogeneities were included in the models. In addition, to disentangle direct and indirect effects of environmental variables on the SW-excess, we ran additional mixed models. We aimed to assess whether any observed effect on the SW-excess was caused by a preceding effect of the same variable on the SWL parameters (slope and intercept). Indeed, 265 those linear regression parameters are used to calculate the SW-excess, and they could be potentially affected by environmental variables. Therefore, we extracted the residuals of the correlations of the SW-excess with the SWL parameters, and subsequently introduced them in a model with the relevant environmental variables. This way, only those effects that would be significant in this second model (using residuals as response variable) could be considered as direct environmental effects on the SW-excess. All analyses were performed in R (version 4.0.3, R Core Team, 2020) using packages: lme4 (Bates et al., 270 2015), lmerTest (Kuznetsova et al., 2017), MuMIn (Barton, 2009), standardize (Eager, 2017), emmeans (Searle et al., 1980) and performance (Lüdecke et al., 2020) https://doi.org/10.5194/hess-2021-333 Preprint. Discussion started: 28 June 2021 c Author(s) 2021. CC BY 4.0 License.

Combined analysis of SW-excess and LC-excess
We compared SW-excess and LC-excess within species and campaigns and found that SW-excess was negative in 184 275 campaigns (out of 642 campaigns, glasshouse studies excluded). We found that for 95% of these campaigns (175 out of 184), LC-excess was also negative, (Figure 1). In these 175 cases, there would be a mismatch in isotopic composition between stem and source water, regardless of whether plants were taking up mobile water (precipitation, groundwater or streamflow) or water stored in the soil that had been subject to evaporation enrichment. These 175 cases were distributed across 57 of the 153 study sites with no apparent bias linked to geographical region (Figures 2 and 3). In addition, we found 12 campaigns for 280 which both SW-excess and LC-excess were positive, likely resulting from evaporative enrichment affecting stem water, irrespective of the water source and of potential isotopic offsets.   Table 1 for the corresponding references). Green bars depict study sites where both the line conditioned excess (LC-excess) and SW-excess (plus their corresponding SE) were negative for at least one campaign, grey bars depict study sites where LCexcess and/or SW-excess were not different from zero for all campaigns. Colourless bars depict glasshouse studies.  were negative for at least one campaign. 300

Overall value and effects of abiotic variables on the slope of the soil water line (SWL)
The linear regression between δ 2 H and δ 18 O of soil water samples was significant for 422 of the 555 campaigns compiled. Of all campaigns for which the SWL regression was significant, the mean SWL slope was 5.52 ± 0.17 (±SE) and the mean intercept was -16.0 ± 2.4 ‰. According to the results of the null LMM (Table 2) and considering the weight and the random 305 variability across studies, the overall mean SWL slope was significantly positive and lower than that of the global meteoric water line (P < 0.001). The LMMs including climate variables in the fixed part of the model indicated that the SWL slope decreased (i.e. greater evaporative enrichment) with warmer temperatures (P < 0.001), increased with monthly and annual soil VWC of the upper soil layers (P < 0.001 for monthly values of 0-7, 7-28 and 28-100 cm; P = 0.014, 0.016 and 0.034 for annual values of 0-7, 7-28 and 28-100 cm, respectively), with integrated soil water content (P < 0.001 for monthly values for soil 310 depths 0-100 and 0-289 cm, P = 0.024 and P = 0.034 for annual values for soil depths 0-100 and 0-289 cm, respectively) and with annual (P = 0.013) and monthly precipitation (P < 0.001). We did not find any significant differences in the SWL slope among soil classes (Table S2). The methodology for measuring soil water isotopic composition (mass-vs laser-spectrometers) did not have any significant effect on the estimated SWL slope (P = 0.327 Table S2).

Overall estimates of line conditioned-excess (LC-excess) and effects of biotic and abiotic variables
We calculated 642 mean values of LC-excess, from 400 campaigns, 98 studies and 194 species (glasshouse experiments excluded). The overall mean value of LC-excess was significantly negative (-12.2 ± 1.3 ‰, P < 0.001, for the LMM with no fixed effects), indicating that, overall, plant water samples fell below their corresponding LMWL in the dual isotope space. 320 The annual potential evapotranspiration had a positive effect on LC-excess (P < 0.001). There were some differences in LCexcess among soil classes (P = 0.028): LC-excess was less negative in organic than in medium texture soils, although our database only included five observations for the soil class "organic" (Table S2) (Table S2), while no differences were found between ectomycorrhizal or 325 arbuscular associations (P = 0.999). We did not find significant differences between LC-excess values calculated from measurements of stem water isotopic composition measured with mass-or laser-spectrometers (P = 0.421).

Overall estimates of soil water excess (SW-excess) and effects of biotic and abiotic variables
We calculated 656 mean values of SW-excess, from 407 campaigns and 197 species, using observations of stem water isotopic 330 composition and the slope and intercept of their corresponding SWL. The overall mean estimate of SW-excess was significantly negative (-3.02 ± 0.65 ‰, P < 0.001, according to the LMM with no fixed effects), indicating that there was an overall significant isotopic offset between stem and soil water.
We found that there was a significantly positive relationship between SW-excess and monthly air temperature (P < 0.001; 335 Figure 4a) and monthly potential evapotranspiration (PET; P = 0.002), and a significantly negative relationship between SWexcess and mean monthly soil VWC of the upper soil layers (0-7, 7-28 and 28-100 cm; P < 0.001), but not with soil VWC from deeper soil layers (Table S1). SW-excess was also significantly and negatively correlated with integrated soil water content for the upper (0-100 cm) soil (P < 0.001; Figure 4b). Neither monthly, nor annual precipitation were significantly correlated with SW-excess (Table S1). When assessed in combination, we found that monthly air temperature still had a 340 significantly positive correlation with SW-excess (P < 0.001), but soil water content did not (P = 0.083). Importantly, a more detailed analysis of the residuals of the relationship between SW-excess and the SWL parameters (slope and intercept) revealed that only the temperature effects had a direct effect on SW-excess (Table S3). On the other hand, the observed effects of soil VWC on SW-excess appeared to be a consequence of the direct effect of soil VWC on the SWL slope and intercept (Table   S3) Table 2. Results (t and P), sample size (n) and parameter estimates according to the linear mixed models (including the null models without any predictor variables) to assess the effects of temperature, soil volumetric water content (VWC) and potential evapotranspiration (PET) on the slope of the soil water line (SWL slope), line-conditioned excess (LC-excess) and soil water excess (SW-excess). All parameter estimates have been standardized. Only models with significant results are shown. According to our results, the mean SW-excess did not differ among soil classes (Table S2). We did not find any significant difference between plant groups (Table S2): mean values of SW-excess did not differ between angiosperms and gymnosperms (P = 0.73), nor among growth forms (trees, shrubs and non-woody plants, P = 0.07), or leaf habit (deciduous, evergreen or semi-deciduous, P = 0.63) or leaf shape (broad or narrow, P = 0.51). Also, we did not find significant differences among plant 365 groups according to their presumed mycorrhizal habit (P = 0.64). For those species for which we had estimates of wood density and/or parenchyma fraction, the LMMs (including species identity in the random part of the model) did not reveal any significant relationship of any of these wood anatomical variables with SW-excess (Table S1). Mean (± SE) SW-excess values estimated from studies using either mass-or laser-spectrometers to measure stem water isotopic composition were both significantly negative: -2.1 ± 0.9 ‰ and -5.0 ± 1.1 ‰, for mass-and laser-spectrometers, respectively, abut there was a 370 significant difference between instrument types (P = 0.048). Similarly, the type of instrument used to measure soil water isotopic composition also had a significant effect on SW-excess (P = 0.015, -2.05 ± 0.95 and -5.03 ± 1.11, for mass-and laserspectrometers, respectively)

Discussion 375
Our meta-analysis revealed that the isotopic composition of stem water does not generally overlap with that of the corresponding soil water in the dual-isotope space (average SW-excess: -3.02 ± 0.65 ‰). The isotopic depletion of stem water relative to its source was originally thought to be restricted to arid or saline environments (e.g. Ellsworth and Williams, 2007;Lin and Sternberg, 1993). However, we show here that sites depicting significantly negative SW-excess (i.e. where plant water 380 is depleted with respect to its most likely source: the soil) are more ubiquitous, and span temperate, boreal and tropical ecosystems. The combined analysis of SW-excess and LC-excess showed that for the majority of cases where SW-excess was negative (95%), LC-excess was also negative. This result indicates that plant water uptake from sources other than soil water that have not undergone evaporative enrichment (such as groundwater), cannot explain the observed mismatch in isotopic composition between plant and soil water. Instead, our results call into question the general assumption that plant water 385 faithfully reflects the isotopic composition of its source.
We compiled 112 studies reporting dual isotopic composition of plant water and its sources and we estimated values of LCexcess and SW-excess for 102 of them. These estimates were widely distributed across the globe, encompassing boreal, tropical, temperate, Mediterranean, arid and semi-arid ecosystems. However, overall there is a literature bias towards data 390 collection in temperate forests, consequently these ecosystems were overrepresented in our database. In contrast, observations from tropical ecosystems were the scarcest, in line with the observations of previous meta-analyses of stable isotope data of plant water and its sources (Amin et al., 2020;Barbeta and Peñuelas, 2017;Evaristo and McDonnell, 2017). Here, we aimed to partially overcome the limited climatic variability represented by biome type and geographic location by incorporating seasonal climatic variability, specific to each study site when available. To do so, we gathered monthly values of air 395 temperature and soil water availability for each study plot and sampling campaign, encompassing a large breadth of climatic values spanning from -10 up to 35 °C for mean monthly air temperature and from 1 to 50% for soil VWC. Our analyses from this data compilation agreed with predictions from classic theory, for example, we found that, globally, the slope of the soil water line (SWL) is smaller as temperature increases and water availability reduces, because of increased evaporative enrichment (Craig and Gordon, 1965;Sprenger et al., 2016) At the global scale, we found positive effects of monthly air temperature and negative effects of soil VWC on the SW-excess.
One of the main results from our analysis was that the SW-excess was clearly most negative in cooler and wetter environments.
This result is in agreement with recent observations from an array of boreal forests, where significant offsets (i.e. negative SW-excess) were found in all study sites, with the two coldest sites depicting the most negative values of SW-excess (Tetzlaff 405 et al., 2021). The SW-excess is calculated with the slope and intercept of the soil water line, but in turn, those parameters correlate with soil VWC and air temperature (see above). Therefore, we run a subsequent analysis of the residuals to tease apart direct and indirect environmental effects on SW-excess (Table S3). This analysis revealed that the negative effect of soil VWC on the SW-excess was mediated by the variability in the parameters of the SWL. In moist sites, the slope of the SWL was steeper and the intercept was larger, which resulted in more negative estimates of SW-excess. On the other hand, air 410 temperature appeared to affect SW-excess more directly (Figure 4a). In cold sites, the effect of low soil water evaporative enrichment could have resulted in steeper SWL slopes, and hence more negative SW-excess (see Eq. 2). Meanwhile, on the opposite end of the temperature range, warm temperatures could be causing greater evaporative enrichment of stem water (Martín-Gómez et al., 2017), and hence partially or completely compensate the negative values of SW-excess (Barbeta et al. 2019). Taken together, these results show that isotopic offsets between plant water and its sources are largest in cold and wet 415 places and suggest that besides evaporative enrichment, other temperature-sensitive processes could be causing these offsets in the field, such as transport or water exchange among pools within the stem.
Isotopic mismatches between sources and plant water have been identified in glasshouse and field experiments (e.g. Tetzlaff et al., 2021;Barbeta et al., 2020a;Chen et al., 2020;Vargas et al., 2017). Previously, these have been attributed to fractionation 420 processes occurring along the soil-plant-atmosphere continuum, mostly related to hydrogen isotopes. Our meta-analysis confirms this pattern at the global scale but cannot pinpoint a definitive mechanistic explanation A recent study suggested that methodological artifacts related to 2 H exchange with cellulose during cryogenic vacuum extraction could be at the origin of these negative SW-excess (Chen et al., 2020), at least in studies where plant stem water was extracted using CVD. Following the latter mechanistic explanation, the relative depletion in 2 H of stem water should be associated with stem water content: 425 plants with a lower stem water content should show more negative SW-excess values (Chen et al. 2020). If this was the case, one would expect that plants growing in drier sites should have lower stem water content and thus more negative SW-excess.
This was not what we found here, and in fact drier sites tended to have less negative (smaller) SW-excess values. In the database compiled here, cryogenic vacuum distillation (CVD) was the most common methodology used for extraction of both stem and soil water, as in Amin et al., 2020. Additional recent studies under controlled conditions also suggest that during 430 CVD, isotopic exchange both within the soil and the stem could cause apparent isotopic fractionation (Adams et al., 2020;Chen et al., 2021;Orlowski et al., 2018). However, our meta-analysis did not allow to test for the effects of extraction methodology on plant-source isotopic offsets due to the paucity of studies applying alternative methodologies to CVD, since alternative methodologies based on centrifugation (Barbeta et al., 2020b) or low-suction (Geißler et al., 2019Magh et al., 2020;Zuecco et al., 2021) have only emerged recently. To help identify apparent fractionation caused by artifacts associated 435 with CVD, future studies applying these novel methodologies should consider combining them with analyses of cryogenically extracted water from a concurrent subset of their samples (Geißler et al., 2019;Marshall et al., 2020). In addition, isotopic offsets between plant water and its sources are often attributed to soil properties underlying methodological artifacts, particularly also during CVD. Soil properties that can affect water isotopic composition measured following CVD include organic matter, texture, and cationic exchange capacity (Adams et al., 2020;Araguás-Araguás et al., 1995;Chen et al., 2021). 440 In our meta-analysis, we compiled all the soil properties provided in the studies revised, but there were large inconsistencies across studies in the type of data used to describe soil properties. Hence, we opted to use soil classes derived from the Digital Soil Map of the World (FAO and UNESCO, 2003), downloaded from the ERA5 service (Hersbach et al., 2019). Here, we did https://doi.org/10.5194/hess-2021-333 Preprint. Discussion started: 28 June 2021 c Author(s) 2021. CC BY 4.0 License. not find significant differences among soil classes in either the slope of the SWL, LC-or SW-excess. However, we acknowledge that the soil classes used here may not be representative of the actual soil properties at each study site, due to 445 their coarse spatial resolution (~10 km). In the future, it would be desirable that studies analysing soil water isotopic composition systematically reported at least the following soil properties: soil texture (preferably by providing percentages of sand, silt and clay), cationic exchange capacity and organic matter or total carbon; instead of merely stating the soil type or texture. Finally, our study does not completely discard potential biases in water isotopic composition associated to the type of instrument used for measuring water isotopic composition (mass-vs. laser-spectrometers). However, instrument type cannot 450 explain the negative overall estimate of SW-excess, as estimates from either type were significantly negative . , Overall, our results showed that plant-source water isotopic offsets depict significant relationships with climatic drivers and suggest that methodological artifacts associated to isotopic measurements and cryogenic vacuum extractions are highly unlikely to be the sole mechanisms explaining the observed source-stem water isotopic offset.

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The combined analyses of LC-excess and SW-excess can help identify the type of ecosystems where we could expect larger biases on the attribution of plant water sources from water isotopic composition. For example, in cold and/or very wet climates, where soil water is subject to very little evaporative enrichment and the slopes of LWML and SWL are similar, when neither LC-excess nor SW-excess were different from zero, variations in plant water isotopic composition would likely track that of its most likely source: precipitation water (e.g. Geris et al., 2015). In arid or semi-arid ecosystems where deep-rooted vegetation 460 has access to groundwater, when SW-excess is different from zero, but LC-excess is not, we would infer that the vegetation was taking up groundwater that had not undergone evaporative enrichment (e.g. Miller et al., 2010). Conversely, in temperate ecosystems, when SW-excess was zero, whereas LC-excess was negative, most likely, isotopic composition of plant water would also match the most likely source: water stored in the upper soil, subject to evaporative enrichment (Brinkmann et al., 2018). However, in hot climates, evaporative enrichment could affect plant water isotopic composition too, irrespective of the 465 water source and of potential isotopic offsets (Martín-Gómez et al., 2017) and partially or completely compensate for potential isotopic offsets. In any case, the various possible mechanisms underlying isotopic mismatches are not mutually exclusive and multiple mechanisms exerting opposing effects could coexist, but these can only be disentangled in experiments under controlled conditions (Barbeta et al., 2020a;Chen et al., 2020;Vargas et al., 2017). Still, in field studies, potential errors in the attribution of plant water sources could be avoided, or at least identified, by including analyses of both LC-excess and SW-470 excess. Yet, here, we emphasize that null values for either SW-excess and/or LC-excess might not necessarily imply absence of offsets in isotopic composition between plant water and its sources, since these offsets can be masked by mechanisms with opposite effects acting simultaneously (Barbeta et al., 2020a).
We expected differences among plant groups in plant-source isotopic offsets based on their anatomical traits and mycorrhizal 475 partnership. For example, water in non-conducting tissues has been shown to be depleted in 2 H with respect to sap water (Zhao et al., 2016, Barbeta et al. 2020b) and, thus, a greater fraction of water stored in non-conductive stem compartments could cause larger isotopic offsets between plant and source water. Our analyses did not reveal any significant difference in either LC-excess or SW-excess among plant groups according to their evolutionary history and hydraulic strategy (angiosperms vs. gymnosperms), growth form (trees, shrubs or non-woody), leaf habit or morphology. Our dataset, however, did not encompass 480 a balanced representation of all plant groups, for example, nearly three quarters (141 out of 197) of the species included in our meta-analysis were trees, whereas less than 20% of our observations corresponded to non-woody species (36 out of 197).
There were some differences among plant groups according to presumed mycorrhizal partnership on LC-excess. Arbuscular mycorrhizal associations have been hypothesised to cause isotopic fractionation during root water uptake (Poca et al., 2019) and therefore we expected larger isotopic offsets in plants forming associations with arbuscular mycorrhizae, but our results 485 did not support this hypothesis. We found that LC-excess, but not SW-excess, was more negative for plants that have been https://doi.org/10.5194/hess-2021-333 Preprint. Discussion started: 28 June 2021 c Author(s) 2021. CC BY 4.0 License.
shown to form mycorrhizal associations with either arbuscular or ectomycorrhizal fungi. Mycorrhizal associations are beneficial for the host plant because they increase nutrient and water availability for the plant and in return the host plant supplies carbohydrates to their mycorrhizal partner (Antunes and Koyama, 2017). Given the carbon costs of these associations for the plant, to maximise their investment return, we would expect that plants forming mycorrhizal associations would allocate 490 larger proportions of their root and fungal hyphal biomass to the shallower soil layers, where nutrient concentrations are higher (Esteban and Robert, 2001). This could explain the more negative LC-excess observed in plants forming mycorrhizal associations, as their main water source would be shallow soil water (subject to evaporative enrichment), instead of deep mobile water pools.

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We also explored correlations between plant-source isotopic offsets and two wood anatomical traits: wood density and parenchyma fraction, at least in angiosperms (Morris et al., 2018). If isotopic heterogeneities within stem water pools underlie isotopic offsets (e.g. Barbeta et al., 2020b;Zhao et al., 2016), then we should observe larger isotopic offsets in species where sap water constitutes a smaller fraction of total stem water, i.e. species with narrower conduits, higher parenchyma fraction and denser wood. Our results did not agree with this prediction and suggest that anatomical traits might not be good predictors 500 of plant-source isotopic offsets. Nonetheless, our results do not discard isotopic heterogeneity within the stem as a plausible mechanism driving observed offsets. Isotopic heterogeneity between water pools within the stem can still result in isotopic offsets between bulk stem water and source water, but the extent of this offset would be determined by the actual plant relative water content at the time of measurement (Barbeta et al., 2020a;Chen et al., 2020), more than wood anatomical traits alone.

Conclusions
We calculated LC-excess and SW-excess from more than a hundred studies distributed globally and found that overall, plant water did not isotopically match the considered source waters. This isotopic offset was largest in cold and wet sites, where plant water plotted below and/or to the right of source water in the dual isotope space, whereas plant water generally plotted 510 closer to the soil water line in hot climates. Our results call into question the long-standing assumption that plant water isotopic composition faithfully reflects that of its source. Based on the recent literature, this does not seem to be the case for δ 2 H, at least. The significant correlations found between the magnitude of these plant-source isotopic offsets with temperature and with soil moisture suggest that these offsets are unlikely caused by purely methodological artifacts. However, the ultimate mechanisms driving these isotopic offsets and their ecological significance can only be unveiled with experiments under 515 controlled conditions. The results from our meta-analysis suggest that these experiments should include comparisons of contrasting soil properties, plant species with varying wood traits and encompass gradients of plant relative water content and storage. These experiments would shed light on the most plausible mechanisms underlying these isotopic offsets and contribute to avoid erroneous attributions of source water from analyses of water isotopic composition. 520

Code availability
The code used for all statistical analyses is available upon request.

Data availability
All the data will be made available on a publicly accessible online repository upon acceptance for publication. 525