Do 2 H and 18 O in leaf water reflect environmental drivers differently?

We compiled hydrogen and oxygen stable isotope compositions (δ2 H and δ18 O) of leaf water from multiple biomes to examine variations with environmental drivers. Leaf water δ2 H was more closely correlated with δ2 H of xylem water or atmospheric vapour, whereas leaf water δ18 O was more closely correlated with air relative humidity. This resulted from the larger proportional range for δ2 H of meteoric waters relative to the extent of leaf water evaporative enrichment compared with δ18 O. We next expressed leaf water as isotopic enrichment above xylem water (Δ2 H and Δ18 O) to remove the impact of xylem water isotopic variation. For Δ2 H, leaf water still correlated with atmospheric vapour, whereas Δ18 O showed no such correlation. This was explained by covariance between air relative humidity and the Δ18 O of atmospheric vapour. This is consistent with a previously observed diurnal correlation between air relative humidity and the deuterium excess of atmospheric vapour across a range of ecosystems. We conclude that 2 H and 18 O in leaf water do indeed reflect the balance of environmental drivers differently; our results have implications for understanding isotopic effects associated with water cycling in terrestrial ecosystems and for inferring environmental change from isotopic biomarkers that act as proxies for leaf water.

Introduction

The stable isotope composition of hydrogen and oxygen in leaf water varies throughout the day, among plants within a site and across environmental gradients (Zundel et al., 1978; Flanagan et al., 1991a; Cernusak et al., 2002, 2016; Lai et al., 2008; West et al., 2008). Leaf water becomes enriched in the heavy isotopes 2H and 18O compared with the water entering the roots as a result of evaporative isotopic fractionation during transpiration (Gonfiantini et al., 1965). There is also isotopic exchange between water vapour in the atmosphere and that in the leaf (Craig & Gordon, 1965); notably, this continues even if transpiration has ceased under a saturated atmosphere (Welp et al., 2008; Kim & Lee, 2011; Helliker, 2014; Goldsmith et al., 2017). Furthermore, the distribution of isotope enrichment within the leaf can vary as a function of leaf anatomy and physiology (Yakir et al., 1989; Gan et al., 2002; Holloway‐Phillips et al., 2016; Barbour et al., 2021). Therefore, the stable isotope composition of leaf water provides an information‐rich isotopic signal that can be applied across a broad range of disciplines (Yakir, 1998). Interest in understanding leaf water stable isotope composition has been further motivated by recognition that leaf water is the starting point for isotope signals in plant organic compounds such as sucrose, starch, cellulose, lignin, leaf waxes (Yakir, 1992; Farquhar et al., 1998; Barbour, 2007; Lehmann et al., 2020). Leaf water isotopic signals can even be reflected in the bones and teeth of herbivores, such as kangaroos (Ayliffe & Chivas, 1990; Faith, 2018).

Models of leaf water stable isotope composition have been developed over several decades and typically perform reasonably well at explaining observed leaf water isotopic variation (Dongmann et al., 1974; Flanagan et al., 1991b; Roden & Ehleringer, 1999; Farquhar & Cernusak, 2005; Cuntz et al., 2007; Ogée et al., 2007). However, some questions about subtler aspects of leaf water isotopic composition remain (Cernusak et al., 2016). One such question is whether stable isotopes of hydrogen and oxygen reflect differently the balance of environmental and physiological drivers that lead to variation in leaf water stable isotopes.

Models of leaf water isotopic composition do not differentiate between hydrogen and oxygen in their general formulation; the major mechanisms that cause leaf water to change isotopically are common to both elements. However, the magnitudes of the fractionation factors associated with the mechanisms do differ. This is also true for meteoric waters, such that the relative extent of variation in the isotopic composition of plant source water and atmospheric vapour across the landscape is different between 2H and 18O; on average, there is a c. 8‰ change in δ2H for a given 1‰ change in δ18O (Craig, 1961; Rozanski et al., 1993). Movement of the two isotopologues H2 18O and 2HHO within the leaf may also vary, for example due to different diffusivities in water (Cuntz et al., 2007), in air (Barbour et al., 2017), and potentially across membranes (Mamonov et al., 2007) and there can be different extents of exchange with organic molecules (Yakir, 1992; Chen et al., 2020). Here, we aimed to assess whether hydrogen and oxygen stable isotopes in leaf water respond differently to the environment, to better understand whether δ2H and δ18O in organic matter proxies capture environmental signals differently.

To do this, we compiled datasets that provided measurements under natural conditions of both δ2H and δ18O in leaf water, xylem water and atmospheric vapour, along with concurrent measurements of air temperature and relative humidity. Table 1 provides a summary of data sources. Within each dataset, we averaged individual observations, such that each row of data in the compiled dataset represents a mean value for a given species by site by time combination. In total, the dataset contained 546 such rows. The geographic range of the combined dataset covered more than 100° of latitude and more than 3000 m of elevation (Table 1). We limited the dataset to daytime observations, as it is primarily during photosynthesis that leaf water signals are incorporated into organic compounds. This also helped to avoid issues of nonsteady state leaf water enrichment at night (Cernusak et al., 2002, 2005; Seibt et al., 2006). We note that it has recently been shown that extraction of stem xylem water for isotopic analysis can be accompanied by an offset in δ2H from the water that is likely to have been taken up by the roots (Zhao et al., 2016; Chen et al., 2020; Barbeta et al., 2022). We did not attempt to apply a correction for this offset as we lacked a basis on which to make the correction that could be applied across the compiled dataset.

Table 1

Datasets and associated site information for the data compilation presented in this paper.

Dataset	Site	Latitude	Longitude	Elevation (m)	MAP (mm)	MAT (°C)	Vegetation type	References
Western_USA_Roden	Cascade_Heads	45.03	−123.91	14	2410	10.7	Forest	(Roden & Ehleringer, 2000a,b)
	Bill_Williams_River	34.26	−114.03	150	97	23.8	Woodland
	Weber_River	41.13	−111.90	1450	510	10.6	Woodland
	Red_Butte_Canyon	40.78	−111.80	1790	700	10.1	Woodland
	Big_Cottonwood	40.62	−111.73	1987	840	9.4	Woodland
Washington_USA_Lai	Wind_River	45.82	−121.95	371	2467	8.7	Forest	(Lai & Ehleringer, 2011)
Utah_USA_Flanagan	Coral_Pink	37.04	−112.72	1855	380	10.5	Woodland	(Flanagan et al., 1993)
Tibetan_Plateau_Yu	Lhasa	29.65	91.03	3658	460	8.4	Grassland	W. Yu, unpublished
Qld_Aus_Munksgaard	Cairns	−16.79	145.69	30	2000	25.0	Forest/Woodland	(Munksgaard et al., 2017)
	Tinaroo	−17.17	145.54	680	1400	22.0	Forest/Woodland
	Herberton	−17.34	145.42	918	1150	19.0	Woodland
	Wild_River	−17.65	145.28	860	950	21.0	Woodland
	Mount_Garnet	−17.67	145.10	660	800	24.0	Woodland
NW_China_Zhao	Pailugou_2900	38.54	100.30	2900	369.2	0.7	Forest	(Zhao et al., 2014)
	Pailugou_2700	38.55	100.29	2780	369.2	0.7	Forest
	Riparian	42.02	101.23	930	34.9	8.9	Woodland
	Gobi	42.27	101.12	906	34.9	8.9	Woodland
NT_Aus_Cernusak	Alice_Springs	−23.70	133.83	598	276	21.0	Woodland	(Kahmen et al., 2013a; Cernusak et al., 2016)
	Tennant_Creek	−19.65	134.16	365	454	25.9	Woodland
	Elliot	−17.50	133.51	234	604	26.8	Woodland
	Katherine	−14.48	132.36	143	1140	27.2	Woodland
	Darwin	−12.44	130.88	33	1736	27.6	Woodland
NSW_Aus_Twining	Tumbarumba	−35.66	148.15	1249	1900	9.6	Forest	(Twining et al., 2006)
Hawaii_USA_Kahmen	MLM_1	19.69	155.20	683	5676	18.4	Forest	(Kahmen et al., 2011)
	MLM_3	19.66	155.47	2061	2000	11.3	Forest/Woodland
	MLM_4	19.59	155.45	2465	1500	9.9	Forest/Woodland
	MLM_5	19.83	155.82	694	500	20.0	Forest/Woodland
Greenland_Bush	Kangerlussuaq	67.02	−50.70	50	140	−5.7	Grassland	(Bush et al., 2017)
Germany_Hirl	Grünschwaige	48.40	11.75	448	743	9.3	Grassland	(Hirl et al., 2019)
Germany_Bögelein	Palatinate	49.28	7.81	550	1067	7.9	Forest	(Bögelein et al., 2017)
France_Wingate	LeBray	44.71	−0.77	62	900	13.0	Forest	L. Wingate & J. Ogée, unpublished
France_Barbeta	Ciron	44.38	−0.31	60	813	12.9	Forest	A. Barbeta, unpublished
Canada_Flanagan	Lethbridge	49.69	−112.83	910	380	5.8	Grassland	(Flanagan et al., 1991a)

δ2H and δ18O of leaf water, xylem water and vapour

The Craig–Gordon equation (Craig & Gordon, 1965) forms the basic building block for models of leaf water isotopic composition and provides a convenient entry point for examining the environmental drivers of leaf water δ2H and δ18O. The Craig–Gordon equation can be approximated as: (δe, predicted δ2H or δ18O at the evaporative sites within leaves; δs, δ2H or δ18O of source water, which we equated in our dataset to xylem water; ε+, equilibrium fractionation between liquid and vapour; εk, kinetic fractionation during diffusion through the stomata and boundary layer; δv, δ2H or δ18O of atmospheric vapour and h, w a/w i, the water vapour mole fraction in the air outside the leaf boundary layer divided by that at the evaporative sites inside the leaf substomatal cavity). The w i is typically assumed to be saturated at leaf temperature, although recent evidence has suggested that it may be less than saturated at times (Cernusak et al., 2018, 2019; Buckley & Sack, 2019; Holloway‐Phillips et al., 2019). If w i is saturated and leaf temperature is equal to air temperature, then w a/w i is equal to the relative humidity of the air surrounding the leaf. Eqn 1 is an approximation of a more precise form of the Craig–Gordon equation (Farquhar et al., 2007); however, it is very useful in that it shows intuitively what the different drivers of δe are expected to be. Therefore, we used the more precise version of the equation for calculations and analyses, but used the approximate version here to guide our discussion. A summary of formulae for calculating the equilibrium and kinetic fractionation factors for δ18O and δ2H and the more precise version of the Craig–Gordon equation can be found in Cernusak et al. (2016) and in the Supporting Information Dataset S1.

Eqn 1 assumes isotopic steady state, in which the water leaving the leaf through transpiration has the same isotopic composition as that entering the leaf from the xylem. Furthermore, it makes a prediction for the evaporative sites, while the unit of measure in our dataset is bulk leaf water (δl), the total sum of water extracted from the leaf. Bulk leaf water can be expected to be somewhat less enriched than the evaporative sites, due to the influx of unenriched xylem water in the veins (Roden & Ehleringer, 1999; Farquhar & Gan, 2003; Farquhar et al., 2007; Holloway‐Phillips et al., 2016). Whereas the mechanisms in the Craig–Gordon equation are identical for δ2H and δ18O, the relative magnitudes of the equilibrium and kinetic fractionation factors differ. For δ2H, the ε+ is relatively large and εk relatively small, whereas the converse is true for δ18O (Merlivat, 1978; Horita & Wesolowski, 1994; Cernusak et al., 2016). The ratio ε+ : εk is c. 3 : 1 for δ2H and 1 : 3 for δ18O (Dataset S1).

We plotted the Craig–Gordon predicted leaf water isotopic compositions against observations for our dataset, to determine whether the Craig–Gordon equation could provide a reasonable framework for guiding analyses of different drivers. Fig. 1 shows the observed bulk leaf water δ2H and δ18O plotted against that predicted by the Craig–Gordon equation, using the measured air temperature, relative humidity, isotopic composition of xylem water and atmospheric vapour. Overall, the Craig–Gordon equation explained 89% of observed variation in leaf water δ2H and 67% of observed variation in leaf water δ18O. As anticipated, the slopes of the relationships were less than unity, as would be the case if some fraction of bulk leaf water represented unenriched xylem water. The generally good predictive ability of the Craig–Gordon equation for daytime leaf water isotopic composition suggests that it can provide a framework for evaluating whether different environmental drivers predominate for hydrogen vs oxygen.

Fig. 1

Observed leaf water isotopic composition for δ2H (a) and δ18O (b) plotted against values predicted by the Craig–Gordon equation. Symbols with different colours refer to the different datasets that have been compiled for this paper. The dotted lines show one‐to‐one lines and solid lines show least‐squares linear regressions with fitted coefficients shown in the panels along with the coefficient of determination, R 2.

Some additional sources of unexplained variation in Fig. 1 could include departures from isotopic steady state (Dongmann et al., 1974; Farquhar & Cernusak, 2005), variation in the fraction of unenriched water in leaves associated with differences in leaf anatomy and physiology (Holloway‐Phillips et al., 2016; Barbour et al., 2021) and unaccounted for variation in boundary layer conductance (Buhay et al., 1996). The detailed data required to test for each of these possibilities were not available across the compiled dataset. However, we did repeat our analyses with observations limited to the middle of the day (from 11:00 h to 14:00 h), when isotopic steady state is most likely to be achieved (Harwood et al., 1998). This yielded very similar results to those shown in Fig. 1. The same was also true for subsequent figures and we therefore present analyses with all daytime observations included.

The environmental drivers that are used in the Craig–Gordon equation are air temperature, which impacts ε+ (Horita & Wesolowski, 1994); relative humidity, which is assumed equal to w a/w i if leaf temperature has not deviated from air temperature and w i is saturated; isotopic composition of source water entering the leaf, assumed equal to the measured xylem water in our analyses; and the isotopic composition of atmospheric water vapour. Fig. 2 shows the observed leaf water δ2H and δ18O plotted against each of these four environmental drivers. For δ2Hl, xylem water δ2H and atmospheric vapour δ2H were much more strongly correlated with it than air temperature or relative humidity. For δ18Ol, conversely, air relative humidity was much more strongly correlated than any of the other drivers. For δ2Hl, either xylem water or atmospheric vapour δ2H explained more than two‐thirds of its variation, whereas for δ18Ol the air relative humidity explained about half of its variation.

Fig. 2

Observed isotopic composition for leaf water δ2H (a–d) and δ18O (e–h) plotted against the four environmental drivers in the Craig–Gordon equation: air temperature (a, e), air relative humidity (b, f), the corresponding isotopic composition of atmospheric vapour (c, g) and the corresponding isotopic composition of xylem water (d, h). The symbol colours show the different datasets compiled for this paper. Solid lines are least‐squares linear regressions, with fitted coefficients shown in the panels, along with the coefficient of determination, R 2.

The reason that xylem water is a much stronger driver of leaf water for δ2H than for δ18O is because the range of variation in meteoric water isotopic composition compared with that in leaf water evaporative enrichment is larger for δ2H than for δ18O. This can be seen through inspection of Fig. 3, which shows the evaporation lines for leaf water for each site in the dataset and their extrapolation to the meteoric water line. The range in the y‐axis over which the evaporation lines intersect the meteoric water line for δ2H is c. 120‰ and the corresponding range on the x‐axis for δ18O is c. 15‰, for a ratio of c. 8 : 1, consistent with the slope of the meteoric water line. Conversely, the range for leaf water isotopic composition beginning at the meteoric water line and moving right along the evaporation lines is c. 100‰ on the y‐axis for δ2H and c. 30‰ on the x‐axis for δ18O, for a ratio of c. 3 : 1. Therefore the point at which the evaporation line intersects the meteoric water line can exert a much stronger influence on leaf water for δ2H than for δ18O, because its range is relatively large compared with the range over which evaporation can enrich the leaf water above source water. Another way to understand this conceptually is to consider that the slope of the meteoric water line, defining source water variation in δ2H–δ18O space, corresponds approximately to the ratio of the equilibrium fractionations for δ2H and δ18O (mean = 8.6 in our dataset). Conversely, the slopes of the evaporation lines corresponded approximately to the ratio of the sum of equilibrium and kinetic fractionations (mean = 2.9 in our dataset).

Fig. 3

The isotopic composition of xylem water, leaf water and atmospheric vapour plotted in δ2H–δ18O dual‐isotope space. The black line shows the meteoric water line, defined as δ2H = 8 × δ18O + 10. The coloured lines show the evaporation lines for leaf water, in which the intercept with the meteoric water line is the mean for each site and the slope is calculated as (δ2Hl − δ2Hx)/(δ18Ol − δ18Ox) using the mean quantities for each site, where subscript ‘l’ refers to leaf water and ‘x’ to xylem water. The colours of the lines refer to the individual datasets compiled for this paper. The range of δ18Ol observed for each site defines the length of the coloured lines.

This difference between leaf water dynamics for δ2H and δ18O, driven by source water isotopic composition, is important for interpreting organic material signals. For example, leaf water proxies based on δ2H, such as the δ2H of n‐alkanes derived from leaf waxes, if sampled across a large geographic range, could be expected to be strongly influenced by a widely varying δ2H of source water (Liu & Yang, 2008; Sachse et al., 2012; Ladd et al., 2021). Conversely, only if there is little variation in source water δ2H, will the variation in n‐alkane δ2H of leaf waxes reliably record the extent of leaf water evaporative enrichment (Kahmen et al., 2013b). For an organic matter proxy such as cellulose δ18O, we would expect the geographic variation in source water isotopic composition to have less influence compared with the dynamics of leaf water enrichment above source water, driven primarily by relative humidity (Barbour & Farquhar, 2000; Kahmen et al., 2011). To the extent that such geographic variation can provide a space for time substitution, our results also have implications for interpreting changes through time within a site. For example, δ2H of n‐alkanes from leaf waxes has been combined with δ18O of hemicellulose sugars for reconstructing paleoclimate from sedimentary records (Zech et al., 2013; Hepp et al., 2021). Our results suggest that δ2H of n‐alkanes should be better suited to detecting changes in δ2H of precipitation and δ18O of hemicellulose sugars to detecting changes in relative humidity. We note, however, that the extent of transfer of the leaf water signal to the biomarker will also be important; for example, for cellulose δ18O, our analysis of leaf water δ18O may be more relevant to leaf cellulose than to stem wood cellulose, as the latter is subject to partial exchange with unenriched xylem water (Roden et al., 2000; Kahmen et al., 2011; Cheesman & Cernusak, 2017), with the same caveat also applicable for grasses (Helliker & Ehleringer, 2000; Liu et al., 2017).

Isotopic enrichment of leaf water above xylem water

In addition to xylem water δ2H having a close correlation with leaf water δ2H, atmospheric vapour δ2H also had a close correlation. The next question we asked in our analysis was whether the relationship between leaf water and vapour for δ2H would still remain stronger than that for δ18O when variation in xylem water isotopic composition was removed. To answer this question, we expressed leaf water as enrichment above xylem water (Δl), calculated as Δl = (δl − δx)/(1 + δx), where the subscript ‘l’ refers to leaf water and ‘x’ to xylem water. Again, we use the approximate form of the Craig–Gordon equation here for ease of interpretation to guide our analysis, but used the more precise form in our calculations. With leaf water expressed as enrichment above source water, the Craig–Gordon equation becomes (Farquhar et al., 1989): (Δe, predicted enrichment at the evaporative sites in leaves and Δv, enrichment of atmospheric vapour relative to source water). Note that this latter term is generally negative; that is, atmospheric vapour is generally depleted in heavier isotopes compared with source water. In our analysis, we calculated Δv as Δv = (δv − δx)/(1 + δx), where δv is δ2H or δ18O of atmospheric vapour and δx is that of xylem water. In Fig. 4, we show the observed bulk leaf water enrichment plotted against the three environmental drivers remaining in Eqn 2.

Fig. 4

Leaf water isotopic enrichment above xylem water for hydrogen, Δ2H (a–c), and for oxygen, Δ18O (d–f), plotted against air temperature (a, d), air relative humidity (b, e) and the corresponding isotopic enrichment of atmospheric vapour above xylem water (c, f). Solid lines show least‐squares linear regressions, along with fitted coefficients and the coefficient of determination, R 2. Symbol colours refer to individual datasets compiled for this paper.

Fig. 4 shows that the correlation between leaf water and relative humidity for hydrogen has been markedly improved by removing source water variation, with relative humidity now explaining 41% of the variation in Δ2Hl. Therefore, after calculating Δ2Hl to remove the source water signal, the sensitivity to relative humidity became more apparent. For oxygen, there was also a strengthening of the correlation between leaf water enrichment and relative humidity, with the R 2 increasing from 0.49 to 0.57. Interestingly, however, the correlation between leaf water enrichment and atmospheric vapour enrichment was still relatively strong for hydrogen, but weak for oxygen, which stands out as a point of difference between Δ2H and Δ18O in Fig. 4. Stronger relationships with atmospheric vapour for Δ2Hl than for Δ18Ol have also been observed previously in some of the individual datasets that have now been compiled for this paper (Cernusak et al., 2016; Bögelein et al., 2017; Munksgaard et al., 2017).

Role of atmospheric vapour isotopic composition

Can we identify further the underlying cause of the stronger correlation between leaf water and atmospheric vapour for Δ2H compared with Δ18O? To explore this, we turned again to the Craig–Gordon equation, taking the derivative of Eqn 2 with respect to Δv. This provides a mathematical description of predicted drivers of the change in Δl for a given change in Δv:

We used our dataset to estimate the terms in Eqn 3 by taking regression slopes for the derivative terms and mean values for h and (Δv − εk). These estimates are shown in Table 2 for Δ2H and Δ18O. From Table 2, it can be seen that the first two terms on the right side of Eqn 3 are small in magnitude for both Δ2H and Δ18O and unlikely to have a strong influence on dΔl/dΔv for either. Because dεk/dΔv is small, it means that the third term on the right side will approach the value of h, which is larger by comparison, having a mean value in our dataset of 0.5, or an air relative humidity of c. 50%. The largest term in Eqn 3 by far for both Δ2H and Δ18O is (Δv − εk), having mean values of −79‰ and −35‰ for Δ2H and Δ18O, respectively. This is then multiplied by a much smaller term, dh/dΔv. Importantly, (Δv − εk) is negative, setting up the possibility that the interplay between the third and fourth terms on the right side of the equation could be important, with the third term, h(1 − dεk/dΔv), being positive and the fourth term, (Δv − εk)dh/dΔv, potentially counteracting it with a negative value.

Table 2

Values for the terms in Eqn 3 calculated from the combined dataset.

	dε^+dΔ_v	dε_kdΔ_v	h	Δ_v‐ε_k	dhdΔ_v	h1‐dε_kdΔ_v	Δ_v‐ε_kdhdΔ_v	Sum of shaded columns
Δ2H	−0.09	−0.01	0.51	−79.2	−0.0004	0.51	0.03	0.45
Δ18O	0.01	−0.02	0.51	−35.2	0.0102	0.52	−0.36	0.15

Derivative terms (dy/dx) were calculated as the slope of a linear regression of the two parameters y and x, whereas nonderivative terms were calculated as the mean of the given parameter. According to Eqn 3, the dependence of leaf water isotopic enrichment on the atmospheric vapour isotopic composition, dΔL/dΔv, is equal to the sum of the shaded columns, which is shown in the final column. As can be seen, the primary difference for Δ2H compared with Δ18O results from the term dh/dΔv; that is, a correlation between atmospheric humidity and the isotopic composition of atmospheric vapour, which is much stronger for Δ18O than for Δ2H.

The interaction between the third and fourth terms in Eqn 3 does indeed appear to be pivotal in explaining why leaf water correlates more strongly with atmospheric vapour for Δ2H than for Δ18O. For Δ2H, the linear regression between h and Δ2Hv was not significant (P = 0.32, n = 546) and had a slope of −0.0004. This gives a value for the fourth term in Eqn 3 for Δ2H of 0.03, which therefore adds slightly to the positive value of the third term, again having a value of c. 0.5. Conversely, for Δ18O, the regression between h and Δ18Ov was significant (P < 0.001, n = 546) and had a positive slope of 0.0102. Because this slope was positive, the fourth term on the right side of Eqn 3 for Δ18O takes on an overall negative value of −0.36. Therefore, for Δ18O, the fourth term on the right side of Eqn 3 largely cancels the influence of the third term and, as a result, there is little change in Δ18Ol for a given change in Δ18Ov. This manifests in our dataset as a weakened correlation between leaf water and atmospheric vapour for Δ18O as seen in Fig. 4f whereas, for Δ2H, there is a stronger correlation between leaf water and atmospheric vapour, as seen in Fig. 4c.

The analysis above shows that there is a positive correlation between relative humidity and the Δ18O of atmospheric vapour in our dataset, whereas such a correlation does not exist between relative humidity and the Δ2H of atmospheric vapour. Through application of Eqn 3, we showed that this difference partly explains why leaf water δ2H more strongly correlates with atmospheric vapour δ2H than is the case for δ18O. Another way to approach the underlying issue of this apparent difference in behaviour of atmospheric vapour for the two isotopologues with respect to relative humidity is to calculate the deuterium excess or the departure from an expectation of the relationship between δ2H and δ18O based on the meteoric water line. The global meteoric water line is described by δ2H = 8 × δ18O + 10 (Craig, 1961). We therefore calculated the deuterium excess of water vapour, d v, as d v = δ2Hv – 8 × δ18Ov (Dansgaard, 1964). We also made the same calculation for atmospheric vapour composition with respect to xylem water, Δdv = Δ2Hv – 8 × Δ18Ov. We then tested for correlations between these parameters and the air relative humidity in our dataset. Both showed significant negative correlations with relative humidity, with the relationship stronger for Δdv (R 2 = 0.17, P < 0.001, n = 546) than for d v (R 2 = 0.08, P < 0.001, n = 546).

A relationship between the deuterium excess of atmospheric vapour and relative humidity has also been observed on diurnal timescales at six sites in the northern hemisphere (Welp et al., 2012) and at a tropical site in Cairns, Australia (Munksgaard et al., 2020). This pronounced, general pattern is thought to be driven by the diurnal pattern of plant transpiration and the contribution of transpired water to atmospheric vapour and by entrainment of the free atmosphere into the planetary boundary layer with increased convective mixing during the day. The result is a general midday decrease in the δ18O of atmospheric vapour, but little to no change in δ2H. This leads to the diurnal variability of d v, which is anticorrelated with the diurnal pattern of relative humidity (Welp et al., 2012; Munksgaard et al., 2020). A shorter time series of 3 d at the Wind River Experimental Forest (Washington, USA) showed a similar pattern (Lai & Ehleringer, 2011). The strength of this pattern suggests that such diurnal variation could be driving the overall relationship between d v and relative humidity in our dataset. Welp et al. (2012) also observed negative correlations between day‐to‐day variation in d v and relative humidity throughout the summer months at sites located near large bodies of water, with such patterns also previously reported for sites in marine‐type settings (Uemura et al., 2008; Gat et al., 2011). Such dynamics related to marine air sources may also have been relevant at some sites within our dataset. When we restricted our analysis from daytime observations to only midday observations (between 11:00 h and 13:00 h) to minimise diurnality, we observed a weak, but still significant, relationship between d v and relative humidity (R 2 = 0.02, P < 0.05, n = 200), showing the importance of diurnal effects. What is clear overall is that covariation between Δ18Ov and relative humidity, but not Δ2Hv and relative humidity, plays an important role in modulating leaf water isotope dynamics, leading to the result highlighted in our dataset of Δ2H of leaf water showing stronger correlation with Δ2H of atmospheric vapour than is the case for Δ18O.

Are two isotopes better than one?

Some organic matter proxies, such as plant cellulose, allow both δ18O and δ2H to be measured. In such cases, it is possible to estimate leaf water isotopic composition for both δ2H and δ18O and therefore to reconstruct d l, the deuterium excess for leaf water. As seen in Fig. 3, the slopes of the evaporation lines tend to be uniform and independent of the source water and the position along the evaporation line is determined mainly by relative humidity. Therefore, d l is expected to show a relationship with relative humidity that is largely independent of source water isotope composition. For this reason, it has been suggested that such a dual‐isotope approach could provide a stronger basis for reconstructing relative humidity than either isotope alone when the source water isotopic composition is not known (Zech et al., 2013; Voelker et al., 2014). Our dataset gave us an opportunity to test this idea across a diverse range of sites and conditions.

Fig. 5 shows d l for the dataset, calculated as d l = δ2Hl – 8 × δ18Ol, plotted against potential drivers, including relative humidity. Because d l was calculated from δ18Ol and δ2Hl, we can compare the relationships with relative humidity among the three. The d l does indeed have the strongest relationship; however, it is not very much stronger than that for δ18Ol. The R 2 for d l vs relative humidity is 0.54 (Fig. 5b), whereas that for δ18Ol is 0.49 (Fig. 2f), and that for δ2Hl is 0.07 (Fig. 2b). Therefore, surprisingly, leaf water δ18O on its own would be nearly as good a predictor of air relative humidity as d l calculated from both the δ18O and δ2H of leaf water. The explanation for this is the relatively constrained variation in δ18O of xylem water, especially in comparison with δ2H (Fig. 3) and the relative insensitivity of Δ18Ol to atmospheric vapour Δ18Ov, as discussed above. Given these considerations, we suggest that if applying a dual‐isotope approach in this way, one should weigh up carefully the uncertainty associated with estimating leaf water δ2H from cellulose δ2H, given the relative complexity in the signal transfer pathway from leaf water to cellulose for δ2H (Cormier et al., 2018; Lehmann et al., 2021). The cost in uncertainty associated with this may not be worth the relatively modest improvement in strength of the correlation between d l and relative humidity compared with that for δ18O alone.

Fig. 5

Deuterium excess in leaf water plotted against (a) air temperature, (b) air relative humidity, (c) the deuterium excess in atmospheric water vapour and (d) the deuterium excess in xylem water. The deuterium excess, d, was calculated as d = δ2H – 8 × δ18O. Solid lines show least‐squares linear regressions, along with fitted coefficients and the coefficient of determination, R 2. Symbol colours refer to individual datasets compiled for this paper.

Conclusions

We compiled a dataset for δ2H and δ18O of leaf water, xylem water, atmospheric vapour, air temperature and relative humidity from a diverse range of sites and used the dataset to test for differences in how leaf water δ2H and δ18O reflect environmental drivers. We conducted the analysis in the context of asking which drivers could be best reconstructed from leaf water proxies based on organic material. Xylem water δ2H was a much stronger driver of variation in leaf water δ2H than was the case for xylem water δ18O as a driver of variation in leaf water δ18O. Conversely, relative humidity showed a considerably stronger relationship with leaf water δ18O than it did with leaf water δ2H. This pattern persisted when we removed xylem water isotopic variation from the leaf water signal by expressing the leaf water isotopic composition as an enrichment above xylem water. We identified the underlying reason for this pattern as a correlation between relative humidity and the δ18O of atmospheric vapour. Such a correlation has also been observed in time series of vapour isotopic composition measurements, and manifests most clearly as an anticorrelation between the deuterium excess of atmospheric vapour and relative humidity on diurnal timescales. While we did not have sufficient resolution of sampling within sites to tease this apart in our dataset, we suspect that this diurnal pattern is likely to underlie the correlation between relative humidity and atmospheric vapour Δ18Ov that we observed. We conclude that leaf water δ2H and δ18O do indeed reflect the balance of potential environmental drivers differently: leaf water δ2H reflects more strongly xylem water δ2H and atmospheric vapour δ2H, whereas leaf water δ18O reflects more strongly air relative humidity.

Author contributions

LAC and MC initiated the review and conducted initial analyses. All authors contributed to further development of ideas and writing of the manuscript.

Dataset S1 An Excel file containing the compiled dataset that was analysed for this paper.

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