The Qinling Mountains form the dividing line between northern and southern China and mark the boundary between the watersheds of the Yellow and Yangtze rivers. Mt. Taibai (Fig. 1; 33.96∘ N, 107.77∘ E) rises to 3767 m a.s.l. (above sea level) and is the peak in the Qinling Mountains; it has a warm temperate ecosystem characterized by a rich diversity of flora and fauna. The mean annual temperature at the bottom of Mt. Taibai is 12.9 ∘C, and the mean annual precipitation is 609.5 mm (Zhang and Liu, 2010). The climate, soil, and vegetation vary significantly along our slope transect, exhibiting a remarkable vertical geo-ecological zonation (Fig. 1). The area contains a variety of climate zones: warm temperate (<1300 m a.s.l.), temperate (1300–2600 m a.s.l.), cool temperate (2600–3350 m a.s.l.), and alpine (>3350 m a.s.l.). The soil types vary from yellow loess soil at low elevations, spectacular rocky outcrops at middle elevations, and glacial remnants at high elevations. Vegetation along the transect is mainly coniferous and broadleaf forests, as well as alpine and subalpine vegetation (Fig. 1; Liu, 2021). The dominant species range from Quercus variabilis, Q. aliena, Betula albosinensis, B. utilis, Abies fargessi, and Larix chinensis forests to Rhododendron clementinae and R. concinnum alpines (Fig. S1 in the Supplement).

Plants and soils were sampled in May, July, and September 2020, and samples were collected from 10 plots (3×3 m) covering all the vegetation zones along the northern slope of Mt. Taibai, extending from 608 to 3533 m a.s.l. (Fig. 1). Among the plots, six sites (i.e., sites 2, 3, 4, 5, 8, and 10; Fig. 1) were selected as being the closest to the weather stations along the elevation transect, and they were used in order to obtain the in situ meteorological data for analysis. For the plants, one or two dominant deciduous and coniferous trees were chosen in each plot across the vegetation zone (Fig. S1). Several large leaves and suberized twigs were collected for each species. Between 3 to 10 large leaves were chosen for sampling, and a small number was collected in broadleaf forests and a large number in coniferous forests, depending on leaf size. The leaf samples were conducted in the context of the intact leaves because of the likely isotopic gradients within a leaf (Helliker and Ehleringer, 2000; Liu et al., 2016). Our sampling period was between 12:00 and 15:00 CST, because maximum diurnal enrichment of the leaf water isotopic composition occurs during this part of the day (Romero and Feakins, 2011; Liu et al., 2021). The twigs were collected simultaneously by cutting suberized twigs, and all of the twigs were cut into samples that were 3–4 cm long. The leaf and twig samples were immediately placed into glass vials with screw caps and sealed with polyethylene parafilm. For the soils, three surface soil samples (less than 10 cm deep) were collected from around the sampled plants using a small metal scoop at each plot. All sampling plots were located on slopes far from rivers and surface water bodies, which ensured that the soil water in each plot was derived exclusively from precipitation. Although the surface soil layers were collected only as the representatives of soil water in this study, these samples could provide a relatively good source of water for the plants, as supported by a prior study conducted along the same elevation transect (Zhang and Liu, 2010). The soil samples were tightly sealed in a polyethylene zipper bag on-site. All plant and soil samples were stored in a cool box (∼4 ∘C) in the field and immediately transported to the laboratory. The altitude of each plot was determined using a handheld GPS unit with an error of ±5 m.

The water in the plant and soil samples was extracted using an automatic cryogenic vacuum extraction system (LI-2100 Pro, LICA United Technology Limited, Beijing, China). The auto-extraction process was set for 3 h, and the extraction rate of water from the samples was more than 98 %. The isotopic composition of the soil water was measured using a Picarro L2130-i isotope water analyzer (Sunnyvale, CA, USA) at the State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences. The analytical accuracies were ±0.1 ‰ for δ18O and ±1 ‰ for δ2H. An isotope ratio mass spectrometer was coupled to a high-temperature conversion elemental analyzer (HT2000 EA-IRMS, Delta V Advantage; Thermo Fisher Scientific, Inc. USA) to take isotopic measurements of twig and leaf water at the Huake Precision Stable Isotope Laboratory on the campus of Tsinghua Shenzhen International Graduate School. The measurement precisions were ±0.2 ‰ and ±1 ‰ for δ18O and δ2H, respectively. The isotopic composition of δ18O and δ2H is expressed as an isotopic ratio: 1δsample(‰)=Rsample-RstandardRstandard×1000, where δsample represents δ18O or δ2H, and Rsample and Rstandard indicate the ratio of 18O/16O or 2H/1H of the sample and standard, respectively. The δ18O and δ2H values are reported relative to the Vienna Standard Mean Ocean Water (VSMOW). In addition, the mean monthly δ18O and δ2H values of precipitation were determined using the Online Isotopes in Precipitation Calculator (Bowen and Revenaugh, 2003).

The C–G equation can be approximated as follows (Cernusak et al., 2022): 2δe=δs+ε++εk+δv-δs-εk×eaei, where δe is the predicted δ18O and δ2H values at the evaporative sites within leaves, δs is the δ18O and δ2H values of source water (equivalent to twig water in our study), ε+ is the equilibrium fractionation between liquid water and vapor, and εk is the kinetic fractionation during the diffusion of vapor through the stomata and the boundary layer.

In our analysis, we calculated Δv (the enrichment of atmospheric vapor relative to source water) as Δv=(δv-δs)/(1+δs), and the values of Δv are often close to -ε+ at the isotopic steady state (Barbour, 2007; Cernusak et al., 2016); therefore, we can calculate δv as δv=-ε++(1-ε+)δs. In addition, eaei is the ratio of the water vapor pressure fraction in the air relative to that in the intercellular spaces and is equal to the relative humidity (RH) in the air at steady state (Cernusak et al., 2022). Thus, Eq. (2) can be derived as 3δe=(1-h)ε++εk+1-ε+hδs, where δs represents the isotopic values of twig water, and h is the mean annual or monthly RH (MARH or MMRH) in this study. The equilibrium fractionation (ε+) varies as a function of temperature (Bottinga and Craig, 1969) and can be equated to δ18O and δ2H, as follows (Majoube, 1971): 4εO+(‰)=exp⁡1.137(273+T)2×103-0.4156273+T-2.0667×10-3-1×1000,5εH+(‰)=exp⁡24.844(273+T)2×103-76.248273+T+52.612×10-3-1×1000. The kinetic fractionation (εk) can be calculated for δ18O and δ2H as (Farquhar et al., 2007; Cernusak et al., 2016) 6εkO(‰)=28rs+19rbrs+rb,7εkH(‰)=25rs+17rbrs+rb, where rs and rb are the resistances of the stomatal and boundary layers, respectively (i.e., the inverse of the conductance of the stomatal and boundary layers). Previous studies have found stomatal and boundary layer conductance values of 0.49 and 2.85 mol m-2 s-1, respectively (Cernusak et al., 2016; Munksgaard et al., 2017), resulting in εkO and εkH values of 26.7 and 23.8, respectively.

Statistical analysis (i.e., the mean, maximum, and minimum values, as well as the standard deviation) of the isotopes extracted from the precipitation, soil, twig, and leaf samples was performed to define the range and distribution of the δ18O and δ2H values across the seasons. The Pearson correlation method was used to assess the correlations between the δ18O and δ2H values among the different water types (i.e., precipitation, soil water, twig water, and leaf water). Hierarchical cluster analysis was used to show the relationships among δ18Oleaf and δ2Hleaf values and potential source water isotopes (δ18O and δ2H values in precipitation, soil water, twig water, and leaf water), and meteorological parameters such as mean annual and monthly precipitation (MAP and MMP), mean annual and monthly temperature (MAT and MMT), and mean annual and monthly relative humidity (MARH and MMRH). A one-way analysis of variance (ANOVA) combined with a post hoc Tukey's least significant difference (LSD) test were performed to identify the significant differences in the isotopic compositions of precipitation, soil, twig, and leaf waters across the months. Comparisons of the relationships of δ18O and δ2H in the soil and leaf water were performed using covariance analysis (ANCOVA) to compare slopes across months. The structural equation model (SEM) was used to explain the respective effects of source waters (i.e., twig water, soil water, and precipitation) and meteorological parameters (i.e., temperature, precipitation, and RH) on δ18Oleaf and δ2Hleaf values. The validated SEMs generated a good model fit, as indicated by a non-significant χ2 test (p>0.05), a high comparative fit index (CFI > 0.95), and a low root mean square error of approximation (RMSEA < 0.05). A special SEM was constructed based on the Mantel R values in AMOS (version 24.0.0). Moreover, we used the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Rolph, 2003) to calculate air mass back trajectory for the central site (34.13∘ N, 107.83∘ E, 2270 m a.s.l.) in the study area. These trajectories were initiated four times daily (at 00:00, 06:00, 12:00, and 18:00 CST), and their air parcel was released at 2300 m a.s.l. for May, July, and September 2020 and moved backwards by winds for 120 h (5 d).

The measured δ18O and δ2H values of leaf water responded differently to source water isotopes (Fig. 2a) and meteorological parameters (Fig. 2b) across the seasons. The leaf water δ18O and δ2H values (δ18Oleaf and δ2Hleaf) were clustered with those of the twig water (δ18Otwig and δ2Htwig; Fig. 2a) and with MARH, MAT, and MMT (Fig. 2b). The δ2Hleaf values were more closely correlated with isotopes of the potential source waters (e.g., twig water, soil water, and precipitation) than the δ18Oleaf values in different months (Fig. 2a). In contrast, leaf water δ18O and δ2H values were correlated with meteorological parameters (Fig. 2b) throughout the study period. These correlations were more significant in summer (July) and autumn (September) than in spring (May).

The δ18Oleaf and δ2Hleaf values predicted by the C–G model were compared with the measured δ18O and δ2H values across all three months (Fig. 3). The C–G model explained 49 % and 70 % of the observed variations in the δ18Oleaf and δ2Hleaf values, respectively (Fig. 3a and c). The slopes of the relationships for both δ18O and δ2H values of leaf water were less than one, which suggests that part of the bulk leaf water is derived from unenriched vein water. However, there were no significant differences in δ18Oleaf (p=0.54; Fig. 3b) and δ2Hleaf values (p=0.93; Fig. 3d) between the C–G model predicted values and the measured values.

There was a significant correlation between δ18Oleaf and δ2Hleaf values (R2=0.81, p<0.01; Fig. 4), with significant clusters of δ18Oleaf and δ2Hleaf values across the months, and values were higher in May, intermediate in July, and lower in September (Fig. 4). Within each month, the δ18Oleaf and δ2Hleaf values were depleted in 2H and 18O at higher altitudes relative to lower altitudes. Likewise, the potential types of source water (i.e., twig water, soil water, and precipitation) exhibited consistent variations across the months, showing values that were relatively higher in May, intermediate in July, and lower in September (Fig. S1). The correlations between δ18O and δ2H values among the source waters were also significant (Fig. S2). Nevertheless, the slopes and coefficients of determination (R2) between the δ18O and δ2H values showed a decrease for precipitation, soil water, twig water, and leaf water from the three sampling months, except for soil water in May (Fig. S2). In addition, the ANCOVA showed no significant differences for the regression lines for precipitation (df=0.47, F=2.49, p=0.11>0.05), twig water (df=53.2, F=0.42, p=0.66>0.05), and leaf water (df=437.3, F=2.78, p=0.08>0.05) across the study months but a significant difference for soil water across the months (df=308.8, F=10.9, p<0.05).

A recent global meta-analysis indicated that δ18Oleaf and δ2Hleaf values reflect environmental drivers differently and showed that δ2Hleaf values more strongly reflect xylem water and atmospheric vapor δ2H values, whereas δ18Oleaf values more strongly reflect air relative humidity (Cernusak et al., 2022). Seasonal and localized observations along an elevation transect on the Chinese Loess Plateau supported these different responses of δ18Oleaf and δ2Hleaf to the isotopic composition of the source water and meteorological conditions (Fig. 2). This is likely due to the fact that variation in precipitation isotopic values compared with that in leaf water evaporative enrichment is larger for δ2Hleaf than δ18Oleaf (Cernusak et al., 2022). In addition, we found stronger correlations between δ2Hleaf and isotope values of the source water (twig water, soil water, and precipitation) than between δ18Oleaf values and the source water isotope values (Fig. 2a). This is consistent with the global meta-analysis results (Cernusak et al., 2022). However, our localized study did not show a significantly different response of δ18Oleaf and δ2Hleaf values to meteorological parameters, which responded at almost equivalent magnitudes (Fig. 2b). These observations suggest that plant organic isotopic proxies such as leaf wax (Sachse et al., 2012; Liu et al., 2016) and cellulose (Barbour, 2007; Lehman et al., 2017), which originate from δ18Oleaf and δ2Hleaf values, can provide comparative information that indicates climatic signals (e.g., temperature, RH, and precipitation) in natural archives. These results argued with the recent global meta-analysis that δ18Oleaf and δ2Hleaf values reflect climatic parameters (i.e., RH and temperature) differently (Cernusak et al., 2022). The stronger correlations for δ2Hleaf values than δ18Oleaf values with isotopic values of the source water were likely, because the δ2Hleaf values are ultimately determined only by precipitation δ2H (Sachse et al., 2012; Liu et al., 2016), whereas the δ18Oleaf values are affected by a mixture of precipitation δ18O and atmospheric factors (O2 and CO2) (Barbour, 2007; Cernusak et al., 2016). However, the comparative responses of both δ2Hleaf and δ18Oleaf values to climatic parameters were probably due to the same conditions surrounding the leaf.

The results of the cluster analysis showed that the isotope values of leaf water (δ18Oleaf and δ2Hleaf) and twig water (δ18Otwig and δ2Htwig) were clustered into one group, but those of soil water (δ18Osoil and δ2Hsoil) and precipitation (δ18Op and δ2Hp) were clustered into another (Fig. 2a). This indicates that the direct source water of δ18Oleaf and δ2Hleaf should be δ18Otwig and δ2Htwig, providing the source water isotope basis for the C–G model. In the C–G model (see Eq. 2), besides the source water isotopes, the equilibrium fractionation factor (ε+) and atmospheric vapor enrichment (Δv) depend on the temperature at the isotopic steady state. Thus, the δ18Oleaf and δ2Hleaf values were predicted to be associated primarily with temperature, RH, and source water, which is consistent with the results from the cluster analysis that the δ18Oleaf and δ2Hleaf values were clustered with temperature (MAT and MMT) and RH (MARH; Fig. 2b). Based on the C–G model, we plotted the measured and predicted δ18Oleaf and δ2Hleaf values (Fig. 3a and c) and observed no significant differences between them (Fig. 3b and d). This is because our three-repeated samplings occur during the day when leaf water is generally near an isotopic steady state when chloroplasts are mostly located near the evaporative sites (Cernusak et al., 2016). The non-steady-state effects on leaf water isotopes were expected at night because of low stomatal conductance (Cernusak et al., 2005, 2016; Cuntz et al., 2007). Although the slopes of the predicted and measured δ18Oleaf and δ2Hleaf values were less than one, the C–G model still provides a reasonable framework for guiding the analysis of the different controls on δ18Oleaf and δ2Hleaf values.

There was a significant linear correlation between the δ18Oleaf and δ2Hleaf values, with remarkable clusters associated with the three months studied (Fig. 4). As is well known, the local meteoric water line (LMWL), generated by precipitation δ18O and δ2H values at the local scale, serves as an important reference line for intercomparisons of different waters. Furthermore, the regression lines of the δ18O and δ2H values from soil water, twig water, and leaf water (Fig. S2) suggest that the leaf water isotopes could well inherit isotopic signals of source waters that originate from twig water, soil water, and ultimately precipitation. The slopes and intercepts of the δ18O and δ2H values decreased significantly from precipitation, soil water, twig water, and leaf water for each month, except for soil water in May (Fig. S2). Such patterns have been observed in many previous calibration studies (Brooks et al., 2010; Evaristo et al., 2015; Sprenger et al., 2016, 2017; Wang et al., 2017; Benettin et al., 2018; Barbeta et al., 2019; Penna and Van Meerveld, 2019; Liu et al., 2021a, 2022). The slopes of the LMWLs were lower in July (6.79) than in May (7.04) and September (6.85) but were not significantly different (ANCOVA test: df=0.47, F=2.49, p=0.11>0.05). This suggests that the local water vapor from precipitation was derived from the same source across the seasons but was subject to different intensities of evaporation as the temperature changed throughout the seasons (Li et al., 2019; Wu et al., 2019, 2021). The slopes of the δ18O and δ2H values from the soil, twig, and leaf waters were much smaller than the LMWLs across the study months due to the secondary evaporation in the other water types.

In the dual isotope plot of leaf water, there were well-defined clusters of δ18Oleaf and δ2Hleaf values across the three months: 18O and 2H were depleted in September, there were intermediate values in July, and 18O and 2H were enriched in May (Fig. 4). When focusing on each month, relatively higher isotopic values occurred at low elevations, but lower isotopic values were present at high elevations despite there being no, or only weak, correlations between the δ18Oleaf and δ2Hleaf values and altitude (Fig. S3). The correlations between the δ18Oleaf and δ2Hleaf values and altitude, and between the δ18Otwig and δ2Htwig values and altitude, were not significant across the three months; however, the δ18Op and δ2Hp, and also the δ18Osoil and δ2Hsoil values, were significantly correlated with altitude (Fig. S3), indicating that besides source water (precipitation and soil water), other factors associated with plants also affect δ18Oleaf and δ2Hleaf values.

The dual isotope plot of δ18Oleaf and δ2Hleaf values shows a significant isotope line, y=4.52x-50.7 (R2=0.81, p<0.01; Fig. 4), but shallower slopes (3.53, 1.86, and 2.81 in May, July, and September, respectively) of δ18Oleaf and δ2Hleaf values were observed across the seasons (Fig. S2). Such a correlation was supported by a recent study that conducted consecutive measurements of δ18O and δ2H values in xylem and leaf water in Switzerland and indicated that leaf water provided great potential to determine the source water of plants (Benettin et al., 2021). Our study showed remarkable clusters in the measured (Fig. 4) and the C–G model-predicted (Fig. 3) δ18Oleaf and δ2Hleaf values across the months and the consistencies of respective δ18Oleaf and δ2Hleaf values with potential source water isotopes across months (Fig. S1). These findings of temporally consistent dynamics among the water types (i.e., precipitation, soil water, twig/stem water, and leaf water) have been observed in a number of previous studies (Phillips and Ehleringer, 1995; Cernusak et al., 2005; Sprenger et al., 2016; Berry et al., 2017; Liu et al., 2021a).

The isotopic inheritance from precipitation to leaf water indicates that seasonal variations of δ18Op and δ2Hp values are the first-order control on the temporal patterns observed in leaf water. The seasonal dynamics of the δ18Op and δ2Hp values reflect the combined effects of factors such as temperature, altitude, and precipitation amount, which are associated with orographic conditions, as well as sub-cloud evaporation, moisture recycling, and differences in the vapor source (Dansgaard, 1964; McGuire and McDonnell, 2007; Li et al., 2016; Penna and Van Meerveld, 2019; Wu et al., 2019). In this study, we used the HYSPLIT model to demonstrate the ultimate cause of the seasonal variations of δ18Oleaf and δ2Hleaf values, that is, the monthly dynamics of the δ18Op and δ2Hp values. The monthly variations of the δ18Op and δ2Hp values from the Global Network for Isotopes in Precipitation (GNIP, http://www.iaea.org/, last access: 18 April 2021) at Xi'an station (AD 1985–1992), which is ∼100 km from our study transect, were enriched in 18O and 2H in May relative to July and September (Fig. 5a and b). The cluster mean of the moisture transport routes from HYSPLIT (Draxler and Rolph, 2003) and the climatological 850 hPa wind vectors showed that the primary moisture sources were from western China and central Asia in May, the China–India Peninsula and the Bay of Bengal, and local moisture recycling and convection (Fig. 5c–e). The seasonal variations in δ18Op and δ2Hp values are consistently related to the onset, advancement, and retreat of the Asian summer monsoon and associated changes in the large-scale monsoon circulation (e.g., Zhang et al., 2020, 2021). As the summer monsoon starts in mid-May, the rainfall season starts in southern China; however, our study area is controlled mainly by moisture from the westerlies (Chiang et al., 2015) with relatively higher vapor, δ18Op, and δ2Hp values (Fig. 5a–c). In July, the summer monsoon reaches its strongest phase, and the rainfall belt shifts to central and northern China, where the southerly wind brings plenty of moisture from the China–India Peninsula and the Bay of Bengal with lower vapor, δ18Op, and δ2Hp values (Fig. 5a, b, and d). When the summer monsoon withdraws in September, the study area is controlled mainly by local moisture recycling and convection (Fig. 5e). Soil water, stored after the June–August monsoon rainfall with its lower δ18O and δ2H values, results in even lower δ18Op and δ2Hp values in September than in July (Fig. S1), causing significantly lower δ18O and δ2H values of leaf water (Fig. 4).

To delineate the mechanisms that control the δ18Oleaf and δ2Hleaf values, we used the SEMs to quantify the complex interactions among δ18Oleaf or δ2Hleaf values, source waters, and meteorological parameters (Fig. 6). The coefficients of determination (R2) were 0.48 and 0.71 for the δ18Oleaf and δ2Hleaf values, respectively, indicating that the models explained more variance for δ2Hleaf values than δ18Oleaf values (Fig. 6). The SEMs showed that potential source waters (i.e., twig water, soil water, and precipitation) had stronger effects on δ2Hleaf relative to δ18Oleaf values, while the meteorological parameters showed weak effects on both δ18Oleaf and δ2Hleaf values (a little larger for δ2Hleaf than δ18Oleaf values). This is consistent with our above correlation analysis (Fig. 2). Surprisingly, the MMT had significant effects on δ18Op and δ2Hp values, suggesting that temperature plays a key role in determining δ18Op and δ2Hp values, but this finding is not discussed further here. Collectively, the SEMs also showed that source water exerts the first-order control but affects δ18Oleaf and δ2Hleaf differently; the meteorological parameters had a weak control on δ18Oleaf and δ2Hleaf, with a more substantial effect on δ2Hleaf than δ18Oleaf values.

A schematic representation of the controls on δ18Oleaf and δ2Hleaf values (respective and dual) is shown in Fig. 7. It involves multiple processes associated with the hydroclimatic and biochemical factors that affect δ18Oleaf and δ2Hleaf values. The meteorological parameters (temperature, RH, and precipitation) exerted distinct effects on the δ18O and δ2H values of the source water and, thus, on the δ18Oleaf and δ2Hleaf values, as demonstrated above by the SEM. Significant isotopic fractionation occurred mainly at two key locations across the vertical soil profiles and leaf architectures from precipitation to leaf water. First, an isotopic gradient across the vertical soil profile appeared because of evaporation from the surface soil layers (Ehleringer and Dawson, 1992; Goldsmith et al., 2012; Evaristo et al., 2015). This evaporative isotopic fractionation causes a linearly isotopic trajectory down the soil profile (Goldsmith et al., 2012; Rothfuss and Javaux, 2017; Wu et al., 2018; Wang et al., 2019; Amin et al., 2020; Zhao et al., 2020; Liu et al., 2021a). Second, there were significant isotopic heterogeneities because of transpiration associated with the δ18Oleaf (Helliker and Ehleringer, 2000; Farquhar and Gan, 2003; Gan et al., 2003; Song et al., 2015) and δ2Hleaf values (Šantrůček et al., 2007; Liu et al., 2016, 2021b) within a leaf, which depends substantially on veinal structures (Liu et al., 2021b). The within-leaf heterogeneity of the δ18Oleaf and δ2Hleaf values can be explained using the Péclet-modified C–G model (Gan et al., 2003; Farquhar and Gan, 2003; Cernusak et al., 2005, 2016). Collectively, the soil evaporation and leaf transpiration produce isotopic enrichment above source water (precipitation or soil water). Soil evaporation leads to an isotopic gradient across the vertical soil profile, providing water sources for plant root uptake without isotope fractionation during the process (Dawson and Ehleringer, 1991; Ehleringer and Dawson, 1992; Chen et al., 2020). During water transport between roots and leaf petioles, isotopic compositions of xylem water remain unaltered from those in soils (i.e., soil immobile water), until it reaches the leaf, which undergoes water loss (Ehleringer and Dawson, 1992). Within the leaf, transpiration leads to significant isotopic enrichment (Helliker and Ehleinger, 2000; Liu et al., 2015; Cernusak et al., 2016), which is highly dependent on meteorological parameters (e.g., temperature and relative humidity). However, the meteorological parameters varied with altitude and seasonality, yielding an isotopic leaf water line (LWL) in the dual-isotope plot (Fig. 4). The LWL provides an important baseline for leaf-derived organic matter such as cellulose (e.g., Barbour, 2007; Lehman et al., 2017) and leaf wax (Liu et al., 2016, 2021). Overall, the LWL is controlled primarily by altitude and seasonality, as these are the main influencers of the hydroclimatic factors.