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

Lucas Cernusak, Adrià Barbeta, Rosemary Bush, Rebekka Eichstaedt
(bögelein), Juan Ferrio, Lawrence Flanagan, Arthur Gessler, Paula
Martín-Gomez, Regina Hirl, Ansgar Kahmen, et al.

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► To cite this version:

Lucas Cernusak, Adrià Barbeta, Rosemary Bush, Rebekka Eichstaedt (bögelein), Juan Ferrio, et al. Do 2 H and 18 O in leaf water reflect environmental drivers differently?. New Phytologist, 2022, 10.1111/nph.18113. hal-03678147

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Research review

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

Author for correspondence:

Lucas A. Cernusak

Email: lucas.cernusak@jcu.edu.au

Received: 18 November 2021

Accepted: 6 February 2022

Lucas A. Cernusak¹ ID, Adrià Barbeta² ID, Rosemary T. Bush³, Rebekka Eichstaedt (Bögelein)⁴ ID, Juan Pedro Ferrio⁵ ID, Lawrence B. Flanagan⁶ ID, Arthur Gessler⁷ ID, Paula Martín-Gómez⁸,⁹ ID, Regina T. Hirl¹⁰, Ansgar Kahmen¹¹ ID, Claudia Keitel¹² ID, Chun-Ta Lai¹³ ID, Niels C. Munksgaard¹ ID, Daniel B. Nelson¹¹ ID, Jérôme Ogée⁸ ID, John S. Roden¹⁴ ID, Hans Schnyder¹⁰ ID, Steven L. Voelker¹⁵ ID, Lixin Wang¹⁶ ID, Hilary Stuart-Williams¹⁷ ID, Lisa Wingate⁸ ID, Wusheng Yu¹⁸ ID, Liangju Zhao¹⁹ ID and Matthias Cuntz²⁰ ID

¹College of Science and Engineering, James Cook University, Cairns, Qld 4878, Australia; ²BEECA, Department of Evolutionary Biology, Ecology and Environmental Sciences, Universitat de Barcelona, Barcelona, Catalonia 08028, Spain; ³Department of Earth and Planetary Sciences, Northwestern University, Evanston, IL 60208, USA; ⁴Faculty of Regional and Environmental Sciences – Geobotany, University of Trier, Trier 54296, Germany; ⁵ARAID-Departamento de Sistemas Agrícolas, Forestales y Medio Ambiente, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Zaragoza 50059, Spain; ⁶Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; ⁷WSL Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf 8903, Switzerland; ⁸INRAE, Bordeaux Sciences Agro, UMR ISPA, Villenave d'Ornon 33140, France; ⁹Centre Tecnològic Forestal de Catalunya (CTFC), Solsona, Catalonia 25280, Spain; ¹⁰Technische Universität München, Lehrstuhl für Grünlandlehre, Freising-Weihenstephan 85354, Germany; ¹¹Department of Environmental Sciences-Botany, University of Basel, Basel 4056, Switzerland; ¹²School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Camden, NSW 2006, Australia; ¹³Department of Biology, San Diego State University, San Diego, CA 92182, USA; ¹⁴Department of Biology, Southern Oregon University, Ashland, OR 97520, USA; ¹⁵College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA; ¹⁶Department of Earth Sciences, Indiana University-Purdue University Indianapolis, Indianapolis, IND 46202, USA; ¹⁷Research School of Biology, The Australian National University, Canberra, ACT 2601, Australia; ¹⁸Key Laboratory of Tibetan Environmental Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; ¹⁹Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China; ²⁰Université de Lorraine, AgroParisTech, INRAE, UMR Silva, Nancy 54000, France

New Phytologist (2022)

doi: 10.1111/nph.18113

**Key words:** evaporative enrichment, isotopic biomarker, leaf water, relative humidity, stable isotopes.

Summary

We compiled hydrogen and oxygen stable isotope compositions (δ²H and δ¹⁸O) of leaf water from multiple biomes to examine variations with environmental drivers. Leaf water δ²H was more closely correlated with δ²H of xylem water or atmospheric vapour, whereas leaf water δ¹⁸O was more closely correlated with air relative humidity. This resulted from the larger proportional range for δ²H of meteoric waters relative to the extent of leaf water evaporative enrichment compared with δ¹⁸O. We next expressed leaf water as isotopic enrichment above xylem water (Δ²H and Δ¹⁸O) to remove the impact of xylem water isotopic variation. For Δ²H, leaf water still correlated with atmospheric vapour, whereas Δ¹⁸O showed no such correlation. This was explained by covariance between air relative humidity and the Δ¹⁸O 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 ²H and ¹⁸O 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 (Gonfi-antini 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 (Dong-
mann 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 H₂¹⁸O 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 measure-
ments 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.

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 environ-
mental drivers of leaf water δ²H and δ¹⁸O. The Craig–Gordon
equation can be approximated as:

$$
\delta_e \approx \delta_s + \epsilon^+ + \epsilon_k + (\delta_v - \delta_s - \epsilon_k)h \qquad \text{Eqn 1}
$$

(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, wᵢ/wᵢ, 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 wᵢ 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 wᵢ is saturated and leaf temperature is equal to air
<table><caption>**Table 1** Datasets and associated site information for the data compilation presented in this paper.</caption><thead><tr><th>Dataset</th><th>Site</th><th>Latitude</th><th>Longitude</th><th>Elevation (m)</th><th>MAP (mm)</th><th>MAT (°C)</th><th>Vegetation type</th><th>References</th></tr></thead><tbody><tr><td rowspan="5">Western_USA_Roden</td><td>Cascade_Heads</td><td>45.03</td><td>-123.91</td><td>14</td><td>2410</td><td>10.7</td><td>Forest</td><td rowspan="5">(Roden & Ehleringer, 2000a,b)</td></tr><tr><td>Bill_Williams_River</td><td>34.26</td><td>-114.03</td><td>150</td><td>97</td><td>23.8</td><td>Woodland</td></tr><tr><td>Weber_River</td><td>41.13</td><td>-111.90</td><td>1450</td><td>510</td><td>10.6</td><td>Woodland</td></tr><tr><td>Red_Butte_Canyon</td><td>40.78</td><td>-111.80</td><td>1790</td><td>700</td><td>10.1</td><td>Woodland</td></tr><tr><td>Big_Cottonwood</td><td>40.62</td><td>-111.73</td><td>1987</td><td>840</td><td>9.4</td><td>Woodland</td></tr><tr><td>Washington_USA_Lai</td><td>Wind_River</td><td>45.82</td><td>-121.95</td><td>371</td><td>2467</td><td>8.7</td><td>Forest</td><td>(Lai & Ehleringer, 2011)</td></tr><tr><td>Utah_USA_Flanagan</td><td>Coral_Pink</td><td>37.04</td><td>-112.72</td><td>1855</td><td>380</td><td>10.5</td><td>Woodland</td><td>(Flanagan et al., 1993)</td></tr><tr><td>Tibetan_Plateau_Yu</td><td>Lhasa</td><td>29.65</td><td>91.03</td><td>3658</td><td>460</td><td>8.4</td><td>Grassland</td><td>W. Yu, unpublished</td></tr><tr><td rowspan="5">Qld_Aus_Munksgaard</td><td>Cairns</td><td>-16.79</td><td>145.69</td><td>30</td><td>2000</td><td>25.0</td><td>Forest/Woodland</td><td rowspan="5">(Munksgaard et al., 2017)</td></tr><tr><td>Tinaroo</td><td>-17.17</td><td>145.54</td><td>680</td><td>1400</td><td>22.0</td><td>Forest/Woodland</td></tr><tr><td>Herberton</td><td>-17.34</td><td>145.42</td><td>918</td><td>1150</td><td>19.0</td><td>Woodland</td></tr><tr><td>Wild_River</td><td>-17.65</td><td>145.28</td><td>860</td><td>950</td><td>21.0</td><td>Woodland</td></tr><tr><td>Mount_Garnet</td><td>-17.67</td><td>145.10</td><td>660</td><td>800</td><td>24.0</td><td>Woodland</td></tr><tr><td rowspan="4">NW_China_Zhao</td><td>Pailugou_2900</td><td>38.54</td><td>100.30</td><td>2900</td><td>369.2</td><td>0.7</td><td>Forest</td><td rowspan="4">(Zhao et al., 2014)</td></tr><tr><td>Pailugou_2700</td><td>38.55</td><td>100.29</td><td>2780</td><td>369.2</td><td>0.7</td><td>Forest</td></tr><tr><td>Riparian</td><td>42.02</td><td>101.23</td><td>930</td><td>34.9</td><td>8.9</td><td>Woodland</td></tr><tr><td>Gobi</td><td>42.27</td><td>101.12</td><td>906</td><td>34.9</td><td>8.9</td><td>Woodland</td></tr><tr><td rowspan="5">NT_Aus_Cernusak</td><td>Alice_Springs</td><td>-23.70</td><td>133.83</td><td>598</td><td>276</td><td>21.0</td><td>Woodland</td><td rowspan="5">(Kahmen et al., 2013a; Cernusak et al., 2016)</td></tr><tr><td>Tennant_Creek</td><td>-19.65</td><td>134.16</td><td>365</td><td>454</td><td>25.9</td><td>Woodland</td></tr><tr><td>Elliott</td><td>-17.50</td><td>133.51</td><td>234</td><td>604</td><td>26.8</td><td>Woodland</td></tr><tr><td>Katherine</td><td>-14.48</td><td>132.36</td><td>143</td><td>1140</td><td>27.2</td><td>Woodland</td></tr><tr><td>Darwin</td><td>-12.44</td><td>130.88</td><td>33</td><td>1736</td><td>27.6</td><td>Woodland</td></tr><tr><td>NSW_Aus_Twining</td><td>Tumbarumba</td><td>-35.66</td><td>148.15</td><td>1249</td><td>1900</td><td>9.6</td><td>Forest</td><td>(Twining et al., 2006)</td></tr><tr><td rowspan="4">Hawaii_USA_Kahmen</td><td>MLM_1</td><td>19.69</td><td>155.20</td><td>683</td><td>5676</td><td>18.4</td><td>Forest</td><td rowspan="4">(Kahmen et al., 2011)</td></tr><tr><td>MLM_3</td><td>19.66</td><td>155.47</td><td>2061</td><td>2000</td><td>11.3</td><td>Forest/Woodland</td></tr><tr><td>MLM_4</td><td>19.59</td><td>155.45</td><td>2465</td><td>1500</td><td>9.9</td><td>Forest/Woodland</td></tr><tr><td>MLM_5</td><td>19.83</td><td>155.82</td><td>694</td><td>500</td><td>20.0</td><td>Forest/Woodland</td></tr><tr><td>Greenland_Bush</td><td>Kangerlussuaq</td><td>67.02</td><td>-50.70</td><td>50</td><td>140</td><td>-5.7</td><td>Grassland</td><td>(Bush et al., 2017)</td></tr><tr><td>Germany_Hirl</td><td>Grünschwaiige</td><td>48.40</td><td>11.75</td><td>448</td><td>743</td><td>9.3</td><td>Grassland</td><td>(Hirl et al., 2019)</td></tr><tr><td>Germany_Bögelein</td><td>Palatinate</td><td>49.28</td><td>7.81</td><td>550</td><td>1067</td><td>7.9</td><td>Forest</td><td>(Bögelein et al., 2017)</td></tr><tr><td>France_Wingate</td><td>LeBray</td><td>44.71</td><td>-0.77</td><td>62</td><td>900</td><td>13.0</td><td>Forest</td><td>L. Wingate & J. Ogée, unpublished</td></tr><tr><td>France_Harbeta</td><td>Ciron</td><td>44.38</td><td>-0.31</td><td>60</td><td>813</td><td>12.9</td><td>Forest</td><td>A. Barbeta, unpublished</td></tr><tr><td>Canada_Flanagan</td><td>Lethbridge</td><td>49.69</td><td>-112.83</td><td>910</td><td>380</td><td>5.8</td><td>Grassland</td><td>(Flanagan et al., 1991a)</td></tr></tbody></table>

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 $\delta_e$ 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 $\delta^{18}\text{O}$ and $\delta^{2}\text{H}$ 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 ($\delta_l$), 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 $\delta^{2}\text{H}$ and $\delta^{18}\text{O}$, the relative magnitudes of the equilibrium and kinetic fractionation factors differ. For $\delta^{2}\text{H}$, the $\epsilon^+$ is relatively large and $\epsilon_k$ relatively small, whereas the converse is true for $\delta^{18}\text{O}$ (Merlivat, 1978; Horita & Wesolowski, 1994; Cernusak et al., 2016). The ratio $\epsilon^+ : \epsilon_k$ is c. 3 : 1 for $\delta^{2}\text{H}$ and 1 : 3 for $\delta^{18}\text{O}$ (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 $\delta^{2}\text{H}$ and $\delta^{18}\text{O}$ 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 $\delta^{2}\text{H}$ and 67% of observed variation in leaf water $\delta^{18}\text{O}$. 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
<figure><img src="image_5.png" /><figcaption>**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, R².</figcaption></figure>

different environmental drivers predominate for hydrogen vs oxygen.

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 $\epsilon^+$ (Horita &

Wesolowski, 1994); relative humidity, which is assumed equal to $w_a/w_l$ if leaf temperature has not deviated from air temperature and $w_l$ 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 δ²H, xylem water δ²H and atmospheric vapour δ²H were much more strongly correlated with it than air temperature or relative humidity. For δ¹⁸O, conversely, air relative humidity was much more strongly correlated than any of the other drivers. For δ²H, either xylem water or atmospheric vapour δ²H explained more than two-thirds of its variation, whereas for δ¹⁸O, the air relative humidity explained about half of its variation.

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

<figure><img src="image_6.png" /><figcaption>**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, R².</figcaption></figure>
**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 (δ²Hᵢ – δ²Hₓ)/ (δ¹⁸Oᵢ – δ¹⁸Oₓ) using the mean quantities for each site, where subscript 'i' 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 δ¹⁸Oᵢ observed for each site defines the length of the coloured lines.

<figure><img src="image_7.png" /></figure>

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).

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 (Δ₁), calculated as Δ₁ = (δ₁ – 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):
<figure><img src="image_8.png" /><figcaption>**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, R². Symbol colours refer to individual datasets compiled for this paper.</figcaption></figure>

$$ \Delta_e \approx \epsilon^+ + \epsilon_k + (\Delta_v - \epsilon_k)b \qquad \text{Eqn 2} $$

($\Delta_e$, predicted enrichment at the evaporative sites in leaves and $\Delta_v$, 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 $\Delta_v$ as $\Delta_v = (\delta_v - \delta_x)/(1 + \delta_x)$, where $\delta_v$ is $\delta^2$H or $\delta^{18}$O of atmospheric vapour and $\delta_x$ 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 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 $\Delta^2$H. Therefore, after calculating $\Delta^2$H 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 R² 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 $\Delta^2$H and $\Delta^{18}$O in Fig. 4. Stronger relationships with atmospheric vapour for $\Delta^2$H than for $\Delta^{18}$O 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 $\Delta^2$H compared with $\Delta^{18}$O? To explore this, we turned again to the Craig-Gordon equation, taking the derivative of Eqn 2 with respect to $\Delta_v$. This provides a mathematical description of predicted drivers of the change in $\Delta_l$ for a given change in $\Delta_v$:

$$ \frac{d\Delta_l}{d\Delta_v} = \frac{d\epsilon^+}{d\Delta_v} + \frac{d\epsilon_k}{d\Delta_v} + h\left(1 - \frac{d\epsilon_k}{d\Delta_v}\right) + (\Delta_v - \epsilon_k)\frac{dh}{d\Delta_v} \qquad \text{Eqn 3} $$

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 ($\Delta_v - \epsilon_k$). These estimates are shown in Table 2 for $\Delta^2$H and $\Delta^{18}$O. 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 $\Delta^2$H and $\Delta^{18}$O and unlikely to have a strong influence on $d\Delta_l/d\Delta_v$ for either. Because $d\epsilon_k/d\Delta_v$ 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 $\Delta^2$H and $\Delta^{18}$O is ($\Delta_v - \epsilon_k$), having mean values of -79‰ and -35‰ for $\Delta^2$H and $\Delta^{18}$O, respectively. This is then multiplied by a much smaller term, $dh/d\Delta_v$. Importantly, ($\Delta_v - \epsilon_k$) 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, $h(1 - d\epsilon_k/d\Delta_v)$, being positive and the fourth term,
<table><caption>**Table 2** Values for the terms in Eqn 3 calculated from the combined dataset.</caption><thead><tr><th></th><th>d<i>c</i><sub>v</sub><sup>1</sup> / d<i>Δ</i><sub>v</sub></th><th>d<i>c</i><sub>k</sub> / d<i>Δ</i><sub>v</sub></th><th><i>h</i></th><th>(Δ<sub>v</sub> - <i>ε</i><sub>k</sub>)</th><th>d<i>h</i> / d<i>Δ</i><sub>v</sub></th><th><i>h</i> (1 - d<i>c</i><sub>k</sub> / d<i>Δ</i><sub>v</sub>)</th><th>(Δ<sub>v</sub> - <i>ε</i><sub>k</sub>) d<i>h</i> / d<i>Δ</i><sub>v</sub></th><th>Sum of shaded columns</th></tr></thead><tbody><tr><td>Δ<sup>2</sup>H</td><td>-0.09</td><td>-0.01</td><td>0.51</td><td>-79.2</td><td>-0.0004</td><td>0.51</td><td>0.03</td><td>0.45</td></tr><tr><td>Δ<sup>18</sup>O</td><td>0.01</td><td>-0.02</td><td>0.51</td><td>-35.2</td><td>0.0102</td><td>0.52</td><td>-0.36</td><td>0.15</td></tr></tbody></table>

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, $d\Delta_L/d\Delta_v$, is equal to the sum of the shaded columns, which is shown in the final column. As can be seen, the primary difference for $\Delta^2$H compared with $\Delta^{18}$O results from the term $d\text{h}/d\Delta_v$; that is, a correlation between atmospheric humidity and the isotopic composition of atmospheric vapour, which is much stronger for $\Delta^{18}$O than for $\Delta^2$H.

($\Delta_v - \varepsilon_k$)d$\text{h}/d\Delta_v$, potentially counteracting it with a negative value.

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 $\Delta^2$H than for $\Delta^{18}$O. For $\Delta^2$H, the linear regression between h and $\Delta^2$H$_v$ 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 $\Delta^2$H 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 $\Delta^{18}$O, the regression between h and $\Delta^{18}$O$_v$ 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 $\Delta^{18}$O takes on an overall negative value of -0.36. Therefore, for $\Delta^{18}$O, 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 $\Delta^{18}$O$_l$ for a given change in $\Delta^{18}$O$_v$. This manifests in our dataset as a weakened correlation between leaf water and atmospheric vapour for $\Delta^{18}$O as seen in Fig. 4f whereas, for $\Delta^2$H, 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 $\Delta^{18}$O of atmospheric vapour in our dataset, whereas such a correlation does not exist between relative humidity and the $\Delta^2$H of atmospheric vapour. Through application of Eqn 3, we showed that this difference partly explains why leaf water $\delta^2$H more strongly correlates with atmospheric vapour $\delta^2$H than is the case for $\delta^{18}$O. 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 $\delta^2$H and $\delta^{18}$O based on the meteoric water line. The global meteoric water line is described by $\delta^2$H = 8 × $\delta^{18}$O + 10 (Craig, 1961). We therefore calculated the deuterium excess of water vapour, $d_v$, as $d_v = \delta^2H_v - 8 \times \delta^{18}O_v$ (Dansgaard, 1964). We also made the same calculation for atmospheric vapour composition with respect to xylem water, $\Delta_{dv} = \Delta^2H_v - 8 \times \Delta^{18}O_v$. 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 $\Delta_{dv}$ ($R^2 = 0.17, P < 0.001, n = 546$) 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 $\delta^{18}$O of atmospheric vapour, but little to no change in $\delta^2$H. 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 $\Delta^{18}O_v$ and relative humidity, but not $\Delta^2H_v$ and relative humidity, plays an important role in modulating leaf water isotope dynamics, leading to the result highlighted in our dataset of $\Delta^2$H of leaf water showing stronger correlation with $\Delta^2$H of atmospheric vapour than is the case for $\Delta^{18}$O.

## Are two isotopes better than one?

Some organic matter proxies, such as plant cellulose, allow both $\delta^{18}$O and $\delta^2$H to be measured. In such cases, it is possible to estimate leaf water isotopic composition for both $\delta^2$H and $\delta^{18}$O and therefore to reconstruct $d_v$, 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
<figure><img src="image_9.png" /><figcaption>**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 $d = \delta^2H - 8 \times \delta^{18}O$. 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.</figcaption></figure>

along the evaporation line is determined mainly by relative humidity. Therefore, *d*ᵢ 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*ᵢ for the dataset, calculated as *d*ᵢ = δ²Hᵢ - 8 × δ¹⁸Oᵢ, plotted against potential drivers, including relative humidity. Because *d*ᵢ was calculated from δ¹⁸Oᵢ and δ²Hᵢ, we can compare the relationships with relative humidity among the three. The *d*ᵢ does indeed have the strongest relationship; however, it is not very much stronger than that for δ¹⁸Oᵢ. The *R*² for *d*ᵢ vs relative humidity is 0.54 (Fig. 5b), whereas that for δ¹⁸Oᵢ is 0.49 (Fig. 2f), and that for δ²Hᵢ is 0.07 (Fig. 2b). Therefore, surprisingly, leaf water δ¹⁸Oᵢ on its own would be nearly as good a predictor of air relative humidity as *d*ᵢ calculated from both the δ¹⁸Oᵢ and δ²Hᵢ of leaf water. The explanation for this is the relatively constrained variation in δ¹⁸Oᵢ of xylem water, especially in comparison with δ²Hᵢ (Fig. 3) and the relative insensitivity of Δ¹⁸Oᵢ to atmospheric vapour Δ¹⁸Oᵣ, 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 δ²Hᵢ from cellulose δ²Hᵢ, given the relative complexity in the signal transfer pathway from leaf water to cellulose for δ²Hᵢ (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*ᵢ and relative humidity compared with that for δ¹⁸Oᵢ alone.

## 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 Δ¹⁸Oᵣ 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.

## Acknowledgements

LAC acknowledges the support of a Make Our Planet Great Again Fellowship through Campus France to visit MC at INRAE, UMR Silva, Nancy to work on this project. MC was supported by a grant overseen by the French National Research Agency (ANR) as part of the ‘Investissements d’Avenir’ programme (ANR-11-LABX-0002-01, Laboratory of Excellence ARBRE) and by a grant from the German Research Foundation (DFG, CU 173/4-1). L. Wang acknowledges partial support from the Division of Earth Sciences of the US National Science Foundation (NSF EAR-1554894).

## 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.
ORCID

Adrià Barbeta https://orcid.org/0000-0002-8357-1719

Lucas A. Cernusak https://orcid.org/0000-0002-7575-5526

Matthias Cuntz https://orcid.org/0000-0002-5966-1829

Rebekka Eichstaedt (Bögelein) https://orcid.org/0000-0002-4905-7994

Juan Pedro Ferrio https://orcid.org/0000-0001-5904-7821

Lawrence B. Flanagan https://orcid.org/0000-0003-1748-0306

Arthur Gessler https://orcid.org/0000-0002-1910-9589

Ansgar Kahmen https://orcid.org/0000-0002-7823-5163

Claudia Keitel https://orcid.org/0000-0003-3101-7529

Chun-Ta Lai https://orcid.org/0000-0001-9662-8545

Paula Martín-Gómez https://orcid.org/0000-0001-5810-6762

Niels C. Munksgaard https://orcid.org/0000-0003-4906-1544

Daniel B. Nelson https://orcid.org/0000-0002-2716-7770

Jérôme Ogée https://orcid.org/0000-0002-3365-8584

John S. Roden https://orcid.org/0000-0001-8641-1625

Hans Schnyder https://orcid.org/0000-0002-0139-7535

Hilary Stuart-Williams https://orcid.org/0000-0002-9272-9178

Steven L. Voelker https://orcid.org/0000-0002-0110-3381

Lixin Wang https://orcid.org/0000-0003-0968-1247

Lisa Wingate https://orcid.org/0000-0003-1921-1556

Wusheng Yu https://orcid.org/0000-0002-3089-0820

Liangju Zhao https://orcid.org/0000-0001-7445-3916

Data availability

All data used in the analyses presented in this paper are available in
Supporting Information Dataset S1.

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**Supporting Information**

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

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