# Covariation between oxygen and hydrogen stable isotopes declines along the path from xylem water to wood cellulose across an aridity gradient

Meisha Holloway-phillips, Lucas A Cernusak, Daniel B Nelson, Marco M Lehmann, Guillaume Tcherkez, Ansgar Kahmen

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## To cite this version:

Meisha Holloway-phillips, Lucas A Cernusak, Daniel B Nelson, Marco M Lehmann, Guillaume Tcherkez, et al.. Covariation between oxygen and hydrogen stable isotopes declines along the path from xylem water to wood cellulose across an aridity gradient. New Phytologist, 2023, 240 (5), pp.1758-1773. 10.1111/nph.19248. hal-04494427

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# Covariation between oxygen and hydrogen stable isotopes declines along the path from xylem water to wood cellulose across an aridity gradient

Meisha Holloway-phillips, Lucas A Cernusak, Daniel B Nelson, Marco M Lehmann, Guillaume Tcherkez, Ansgar Kahmen

<figure><img src="image_1.png" /></figure>

## To cite this version:

Meisha Holloway-phillips, Lucas A Cernusak, Daniel B Nelson, Marco M Lehmann, Guillaume Tcherkez, et al.. Covariation between oxygen and hydrogen stable isotopes declines along the path from xylem water to wood cellulose across an aridity gradient. New Phytologist, 2023, 240 (5), pp.1758-1773. 10.1111/nph.19248. hal-04494427

HAL Id: hal-04494427

https://hal.inrae.fr/hal-04494427v1

Submitted on 7 Mar 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

# Covariation between oxygen and hydrogen stable isotopes declines along the path from xylem water to wood cellulose across an aridity gradient

Meisha Holloway-Phillips¹,² ID, Lucas A. Cernusak³ ID, Daniel B. Nelson¹ ID, Marco M. Lehmann² ID,
Guillaume Tcherkez⁴,⁵ ID and Ansgar Kahmen¹ ID

¹Department of Environmental Sciences-Botany, University of Basel, 4056, Basel, Switzerland; ²Research Unit of Forest Dynamics, Research Group of Ecosystem Ecology, Stable Isotope Research Centre, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903, Birmensorf, Switzerland; ³College of Science and Engineering, James Cook University, Cairns, Qld, 4878, Australia; ⁴Research School of Biology, College of Science, Australian National University, Canberra, ACT, 2601, Australia; ⁵Institut de Recherche en Horticulture et Semences, Université d'Angers, INRAe, 42 rue Georges Morel, 49070, Beaucouzé, France

## Summary

* Oxygen and hydrogen isotopes of cellulose in plant biology are commonly used to infer environmental conditions, often from time series measurements of tree rings. However, the covariation (or the lack thereof) between δ¹⁸O and δ²H in plant cellulose is still poorly understood.

* We compared plant water, and leaf and branch cellulose from dominant tree species across an aridity gradient in Northern Australia, to examine how δ¹⁸O and δ²H relate to each other and to mean annual precipitation (MAP).

* We identified a decline in covariation from xylem to leaf water, and onwards from leaf to branch wood cellulose. Covariation in leaf water isotopic enrichment (Δ) was partially preserved in leaf cellulose but not branch wood cellulose. Furthermore, whilst δ²H was well-correlated between leaf and branch, there was an offset in δ¹⁸O between organs that increased with decreasing MAP.

* Our findings strongly suggest that postphotosynthetic isotope exchange with water is more apparent for oxygen isotopes, whereas variable kinetic and nonequilibrium isotope effects add complexity to interpreting metabolic-induced δ²H patterns. Varying oxygen isotope exchange in wood and leaf cellulose must be accounted for when δ¹⁸O is used to reconstruct climatic scenarios. Conversely, comparing δ²H and δ¹⁸O patterns may reveal environmentally induced shifts in metabolism.

Author for correspondence:
Meisha Holloway-Phillips
Email: meisha.holloway@wsl.ch

Received: 17 January 2023

Accepted: 16 August 2023

New Phytologist (2023) 240: 1758–1773

doi: 10.1111/nph.19248

**Key words:** aridity, Australia, cellulose, hydrogen isotopes, isotope tree-ring, oxygen isotopes, paleo-environmental reconstruction.

## Introduction

The potential for hydrogen (H) and oxygen (O) isotope analysis of plant cellulose to yield inferences about past climates and physiological processes has long been recognised (Schiegl & Vogel, 1970; Epstein et al., 1976; Gray & Thompson, 1976; Libby et al., 1976). However, despite its research history, substantial uncertainty remains around the exact biological and environmental information recorded in cellulose, limiting its application to tree-ring archives (e.g. Yakir, 1992; McCarroll & Loader, 2004; Gessler et al., 2014).

The source of all organically bound hydrogen and the majority of oxygen in organic molecules derives from plant water. Compared with root and xylem stem water (i.e. 'source' water), leaf water becomes enriched in the heavier isotopes (¹⁸O and ²H) during transpiration (Dongmann et al., 1974; Leaney et al., 1985; Flanagan et al., 1991; Farquhar & Lloyd, 1993).

The climatic and physiological processes that influence the isotope composition of leaf water are well-understood and lead to predictable relationships between oxygen and hydrogen isotope compositions (δ) of leaf water (Cernusak et al., 2016, 2022). As primary sugars are synthesised in leaves, sugars and by extension plant cellulose should theoretically show similar δ¹⁸O–δ²H covariation as plant water due to the incorporation of O and H sourced both directly and indirectly from water during compound biosynthesis. Across large spatial scales, this appears to be a reasonable assumption (Lehmann et al., 2022). However, at the local scale, analysing temporal variation in tree-ring chronologies, weak relationships between δ²H and δ¹⁸O are often observed (Nabeshima et al., 2018; Nakatsuka et al., 2020; Vitali et al., 2022). This perhaps explains why the climate sensitivity of one element is often found to be different to the other (e.g. Loader et al., 2008; Boettger et al., 2014; Gori et al., 2015) – although not always (e.g. Hafner et al., 2011; An et al., 2014).
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

# Covariation between oxygen and hydrogen stable isotopes declines along the path from xylem water to wood cellulose across an aridity gradient

Meisha Holloway-Phillips¹,² ID, Lucas A. Cernusak³ ID, Daniel B. Nelson¹ ID, Marco M. Lehmann² ID,
Guillaume Tcherkez⁴,⁵ ID and Ansgar Kahmen¹ ID

¹Department of Environmental Sciences-Botany, University of Basel, 4056, Basel, Switzerland; ²Research Unit of Forest Dynamics, Research Group of Ecosystem Ecology, Stable Isotope Research Centre, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903, Birmensorf, Switzerland; ³College of Science and Engineering, James Cook University, Cairns, Qld, 4878, Australia; ⁴Research School of Biology, College of Science, Australian National University, Canberra, ACT, 2601, Australia; ⁵Institut de Recherche en Horticulture et Semences, Université d'Angers, INRAe, 42 rue Georges Morel, 49070, Beaucouzé, France

## Summary

* Oxygen and hydrogen isotopes of cellulose in plant biology are commonly used to infer environmental conditions, often from time series measurements of tree rings. However, the covariation (or the lack thereof) between δ¹⁸O and δ²H in plant cellulose is still poorly understood.

* We compared plant water, and leaf and branch cellulose from dominant tree species across an aridity gradient in Northern Australia, to examine how δ¹⁸O and δ²H relate to each other and to mean annual precipitation (MAP).

* We identified a decline in covariation from xylem to leaf water, and onwards from leaf to branch wood cellulose. Covariation in leaf water isotopic enrichment (Δ) was partially preserved in leaf cellulose but not branch wood cellulose. Furthermore, whilst δ²H was well-correlated between leaf and branch, there was an offset in δ¹⁸O between organs that increased with decreasing MAP.

* Our findings strongly suggest that postphotosynthetic isotope exchange with water is more apparent for oxygen isotopes, whereas variable kinetic and nonequilibrium isotope effects add complexity to interpreting metabolic-induced δ²H patterns. Varying oxygen isotope exchange in wood and leaf cellulose must be accounted for when δ¹⁸O is used to reconstruct climatic scenarios. Conversely, comparing δ²H and δ¹⁸O patterns may reveal environmentally induced shifts in metabolism.

Author for correspondence:
Meisha Holloway-Phillips
Email: meisha.holloway@wsl.ch

Received: 17 January 2023

Accepted: 16 August 2023

New Phytologist (2023) 240: 1758–1773

doi: 10.1111/nph.19248

**Key words:** aridity, Australia, cellulose, hydrogen isotopes, isotope tree-ring, oxygen isotopes, paleo-environmental reconstruction.

## Introduction

The potential for hydrogen (H) and oxygen (O) isotope analysis of plant cellulose to yield inferences about past climates and physiological processes has long been recognised (Schiegl & Vogel, 1970; Epstein et al., 1976; Gray & Thompson, 1976; Libby et al., 1976). However, despite its research history, substantial uncertainty remains around the exact biological and environmental information recorded in cellulose, limiting its application to tree-ring archives (e.g. Yakir, 1992; McCarroll & Loader, 2004; Gessler et al., 2014).

The source of all organically bound hydrogen and the majority of oxygen in organic molecules derives from plant water. Compared with root and xylem stem water (i.e. ‘source’ water), leaf water becomes enriched in the heavier isotopes (¹⁸O and ²H) during transpiration (Dongmann et al., 1974; Leaney et al., 1985; Flanagan et al., 1991; Farquhar & Lloyd, 1993).

The climatic and physiological processes that influence the isotope composition of leaf water are well-understood and lead to predictable relationships between oxygen and hydrogen isotope compositions (δ) of leaf water (Cernusak et al., 2016, 2022). As primary sugars are synthesised in leaves, sugars and by extension plant cellulose should theoretically show similar δ¹⁸O–δ²H covariation as plant water due to the incorporation of O and H sourced both directly and indirectly from water during compound biosynthesis. Across large spatial scales, this appears to be a reasonable assumption (Lehmann et al., 2022). However, at the local scale, analysing temporal variation in tree-ring chronologies, weak relationships between δ²H and δ¹⁸O are often observed (Nabeshima et al., 2018; Nakatsuka et al., 2020; Vitali et al., 2022). This perhaps explains why the climate sensitivity of one element is often found to be different to the other (e.g. Loader et al., 2008; Boettger et al., 2014; Gori et al., 2015) – although not always (e.g. Hafner et al., 2011; An et al., 2014).
Oxygen and hydrogen cellulose isotope models provide a useful framework to understand the drivers of isotopic variation (Yakir & DeNiro, 1990; Barbour & Farquhar, 2000; Roden et al., 2000). They are conceptualised as a two-pool mixing model reflecting the initial synthesis of sugars via photosynthesis in source tissue (i.e. leaves) and its subsequent export to sink tissue (e.g. developing leaves, wood) where some isotope exchange with sink cell water can occur during sugar metabolism. Most applications have assumed that biochemical isotope effects in the models are invariant. However, recent studies have challenged this assumption. For example, the incorporation and exchange of oxygen from water in organic matter, which is an equilibrium isotope process, is thought to vary with temperature (Sternberg & Ellsworth, 2011), and the extent to which isotopic exchange occurs with water during sink cell metabolism has been shown to exhibit considerable variation, particularly in response to the environment (reviewed in Song et al., 2022).

Initial estimates of hydrogen isotope effects during carbohy-
drate biosynthesis suggested that there is a large isotope fractiona-
tion against ²H during the Calvin–Benson–Bassham cycle
(−171‰), whereas the sum of hydrogen isotope fractionation
during postphotosynthetic isotope exchange and metabolism is
in favour of ²H (+158‰) (Yakir & DeNiro, 1990). However,
the constancy of these values has also been challenged on both
theoretical (Cormier et al., 2018; Zhou et al., 2018) and experi-
mental (Holloway-Phillips et al., 2022; Schuler et al., 2023)
grounds. Also, position-specific intramolecular δ²H analysis has
suggested different fractionation factors for individual H-atom
positions, which respond to environmental conditions (Schleu-
cher et al., 1999; Augusti et al., 2006; Wieloch et al., 2022a,b).
Variation in the isotopic offset between cellulose δ²H and plant
water pools also supports the view that fractionation factors can
change with species and growth conditions (Sternberg &
DeNiro, 1983; Cormier et al., 2018, 2019; Baan et al., 2022,
2023; Lehmann et al., 2022).

As long as the extent to which sink cell isotopic exchange
occurs and the values for biosynthetic fractionation remain con-
stant or change proportionally between elements, covariation
between δ¹⁸O and δ²H will persist through biosynthetic transfor-
mations from source water to cellulose. However, there are sev-
eral reasons to explain a potential decoupling between δ¹⁸O and
δ²H values:

(1) First, using the default model parameterisation of the cellulose isotope models and assuming normal leaf and xylem water isotope patterns observed in nature, the cellulose isotope models predict that whilst variation in xylem and leaf water δ values result in positive covariation, variation in the fraction of sugars that undergo isotopic exchange in sink cells (f) results in negative covariation (when all other variables are held constant). This is because an increase in f lowers cellulose δ¹⁸O values (greater proportion of sugars derived from an ¹⁸O-depleted xylem water pool compared to leaf water), and increases δ²H values (in this case, the large positive isotope fractionation during heterotrophic sugar metabolism outweighs the ²H-depleted xylem water pool as f increases). Thus, where cellulose isotopic variation is driven by variability in both plant water and f, the

relationship between δ¹⁸O and δ²H has the potential to weaken
and/or invert.

(2) Second, variability in isotope fractionation during com-
pound biosynthesis would add another mechanism by which the
δ¹⁸O–δ²H covariation may disappear, especially given that
hydrogen is subject to additional kinetic isotope effects (during
enzymatic reactions) that do not impact oxygen (discussed in
Holloway-Phillips *et al.*, 2022 and elaborated on in the *Discussion* section).

(3) Third, tissue-specific patterns have been observed for both cellulose δ¹⁸O (Cheesman & Cernusak, 2016) and δ²H (Ish-Shalom-Gordon et al., 1992; Ruppenthal et al., 2015; Sanchez-Bragado et al., 2019), suggesting that different C sources and metabolism in sink and source tissues can influence the extent to which climatic δ¹⁸O and δ²H signals are recorded by plant cellulose (Barnard et al., 2007; Offermann et al., 2011; Gessler et al., 2013; Kimak et al., 2015; Zhu et al., 2020; Lehmann et al., 2021).

However, there is a lack of studies that have tested whether noncovariation in δ¹⁸O–δ²H is driven by variable biochemical isotope effects in one or both elements, and whether this depends on the tissue type assessed and plant compounds analysed (lipids, cellulose, lignin). For example, δ¹⁸O in wood cellulose from eucalypts across a climatic gradient in north-eastern Australia showed no climatic trend (Cheesman & Cernusak, 2016). By contrast, δ²H in leaf wax *n*-alkanes across another similar Australian transect recorded the leaf-water climatic signal, suggesting that the biochemical isotope effects were invariant with environmental conditions (Kahmen *et al.*, 2013).

To clarify the origin of the potential decoupling between δ¹⁸O and δ²H in organic compounds, we examined both δ¹⁸O and δ²H in leaf and branch-wood cellulose from dominant tree species, including eucalypts and acacia, sampled across a 1500-km climatic gradient in northern Australia (Hutley et al., 2011), where leaf water enrichment was largely driven by both site variation in relative humidity (RH) and RH-independent changes in the isotope composition of xylem water (Kahmen et al., 2013; Cernusak et al., 2016). We addressed the following questions: (1) Is there a δ¹⁸O–δ²H decoupling in cellulose, and if so, is the loss of the climatic isotope signal element dependent? (2) Can we see differences in the delta values between organs and if so, does it relate to expected differences in biochemistry? We then used cellulose isotope models parameterised with commonly accepted values and looked at whether the discrepancy between modelled estimates and observed δ¹⁸O and δ²H values of leaf and branch wood cellulose followed an aridity-dependent isotopic offset in order to suggest predictable shifts in biochemical isotope effects.

Materials and Methods

Field sites, species, and sampling of plant material

We made use of leaf and wood (branch) xylem samples collected within the original sampling campaign published in part by Kahmen *et al.* (2013). The study sampled 11 sites spanning 1500 km from Alice Springs to Darwin in the Northern Territory,
Oxygen and hydrogen cellulose isotope models provide a useful framework to understand the drivers of isotopic variation (Yakir & DeNiro, 1990; Barbour & Farquhar, 2000; Roden et al., 2000). They are conceptualised as a two-pool mixing model reflecting the initial synthesis of sugars via photosynthesis in source tissue (i.e. leaves) and its subsequent export to sink tissue (e.g. developing leaves, wood) where some isotope exchange with sink cell water can occur during sugar metabolism. Most applications have assumed that biochemical isotope effects in the models are invariant. However, recent studies have challenged this assumption. For example, the incorporation and exchange of oxygen from water in organic matter, which is an equilibrium isotope process, is thought to vary with temperature (Sternberg & Ellsworth, 2011), and the extent to which isotopic exchange occurs with water during sink cell metabolism has been shown to exhibit considerable variation, particularly in response to the environment (reviewed in Song et al., 2022).

Initial estimates of hydrogen isotope effects during carbohy-
drate biosynthesis suggested that there is a large isotope fractiona-
tion against ²H during the Calvin–Benson–Bassham cycle
(−171‰), whereas the sum of hydrogen isotope fractionation
during postphotosynthetic isotope exchange and metabolism is
in favour of ²H (+158‰) (Yakir & DeNiro, 1990). However,
the constancy of these values has also been challenged on both
theoretical (Cormier et al., 2018; Zhou et al., 2018) and experi-
mental (Holloway-Phillips et al., 2022; Schuler et al., 2023)
grounds. Also, position-specific intramolecular δ²H analysis has
suggested different fractionation factors for individual H-atom
positions, which respond to environmental conditions (Schleu-
cher et al., 1999; Augusti et al., 2006; Wieloch et al., 2022a,b).
Variation in the isotopic offset between cellulose δ²H and plant
water pools also supports the view that fractionation factors can
change with species and growth conditions (Sternberg &
DeNiro, 1983; Cormier et al., 2018, 2019; Baan et al., 2022,
2023; Lehmann et al., 2022).

As long as the extent to which sink cell isotopic exchange
occurs and the values for biosynthetic fractionation remain con-
stant or change proportionally between elements, covariation
between δ¹⁸O and δ²H will persist through biosynthetic transfor-
mations from source water to cellulose. However, there are sev-
eral reasons to explain a potential decoupling between δ¹⁸O and
δ²H values:
(1) First, using the default model parameterisation of the cellu-
lose isotope models and assuming normal leaf and xylem water
isotope patterns observed in nature, the cellulose isotope models
predict that whilst variation in xylem and leaf water δ values
result in positive covariation, variation in the fraction of sugars
that undergo isotopic exchange in sink cells (f) results in nega-
tive covariation (when all other variables are held constant).
This is because an increase in f lowers cellulose δ¹⁸O values
(greater proportion of sugars derived from an ¹⁸O-depleted
xylem water pool compared to leaf water), and increases δ²H
values (in this case, the large positive isotope fractionation dur-
ing heterotrophic sugar metabolism outweighs the ²H-depleted
xylem water pool as f increases). Thus, where cellulose isotopic
variation is driven by variability in both plant water and f, the

relationship between δ¹⁸O and δ²H has the potential to weaken
and/or invert.

(2) Second, variability in isotope fractionation during com-
pound biosynthesis would add another mechanism by which the
δ¹⁸O–δ²H covariation may disappear, especially given that
hydrogen is subject to additional kinetic isotope effects (during
enzymatic reactions) that do not impact oxygen (discussed in
Holloway-Phillips et al., 2022 and elaborated on in the Discus-
sion section).

(3) Third, tissue-specific patterns have been observed for both cellulose δ¹⁸O (Cheesman & Cernusak, 2016) and δ²H (Ish-Shalom-Gordon et al., 1992; Ruppenthal et al., 2015; Sanchez-Bragado et al., 2019), suggesting that different C sources and metabolism in sink and source tissues can influence the extent to which climatic δ¹⁸O and δ²H signals are recorded by plant cellulose (Barnard et al., 2007; Offermann et al., 2011; Gessler et al., 2013; Kimak et al., 2015; Zhu et al., 2020; Lehmann et al., 2021).

However, there is a lack of studies that have tested whether noncovariation in δ¹⁸O–δ²H is driven by variable biochemical isotope effects in one or both elements, and whether this depends on the tissue type assessed and plant compounds analysed (lipids, cellulose, lignin). For example, δ¹⁸O in wood cellulose from eucalypts across a climatic gradient in north-eastern Australia showed no climatic trend (Cheesman & Cernusak, 2016). By contrast, δ²H in leaf wax *n*-alkanes across another similar Australian transect recorded the leaf-water climatic signal, suggesting that the biochemical isotope effects were invariant with environmental conditions (Kahmen *et al.*, 2013).

To clarify the origin of the potential decoupling between δ¹⁸O and δ²H in organic compounds, we examined both δ¹⁸O and δ²H in leaf and branch-wood cellulose from dominant tree species, including eucalypts and acacia, sampled across a 1500-km climatic gradient in northern Australia (Hutley et al., 2011), where leaf water enrichment was largely driven by both site variation in relative humidity (RH) and RH-independent changes in the isotope composition of xylem water (Kahmen et al., 2013; Cernusak et al., 2016). We addressed the following questions: (1) Is there a δ¹⁸O–δ²H decoupling in cellulose, and if so, is the loss of the climatic isotope signal element dependent? (2) Can we see differences in the delta values between organs and if so, does it relate to expected differences in biochemistry? We then used cellulose isotope models parameterised with commonly accepted values and looked at whether the discrepancy between modelled estimates and observed δ¹⁸O and δ²H values of leaf and branch wood cellulose followed an aridity-dependent isotopic offset in order to suggest predictable shifts in biochemical isotope effects.

**Materials and Methods**

Field sites, species, and sampling of plant material

We made use of leaf and wood (branch) xylem samples collected within the original sampling campaign published in part by Kahmen *et al.* (2013). The study sampled 11 sites spanning 1500 km from Alice Springs to Darwin in the Northern Territory,
Australia, as part of the North Australian Tropical Transect (Hutley *et al.*, 2011; Fig. 1). For the study of Kahmen *et al.* (2013), only six of the 11 sites were analysed for the δ²H of *n*-alkanes in leaf waxes. The isotopic composition of leaf water was also sampled diurnally (four time points) at five of the 11 sites, and the data set published in full by Cernusak *et al.* (2016). Across all sites, branch xylem was collected for water extraction, and subsequently used for cellulose isotope analyses. Branch sections were *c.* 1 cm diameter, that is 'twigs'. Leaf tissue was also collected at all 11 sites and similarly used for cellulose isotope analyses. Until this study, the cellulose isotope information had not been published. For further details on when the samples were collected and processed, see Supporting Information Dataset S1 – 'Metadata'.

Across the sites, long-term mean annual precipitation (MAP) and mean relative humidity (RH) ranged from 313 mm yr⁻¹ and 34% in Alice Springs to 1739 mm yr⁻¹ and 62% in Darwin. Mean annual temperature across sites was much less variable (see Dataset S1 – 'Metadata' for source information). The two dominant genera, represented across most sites, were *Acacia* and *Eucalyptus*, as well as the genus *Corymbia*, which is phylogenetically close to *Eucalyptus* (see Dataset S1 – 'Isotopic data' for information on which species were represented at each site).

At each site, five to six mature trees of a single acacia or eucalypt
species were sampled for leaf and branch material in April 2010 at
the end of the wet season. Samples were randomly collected from
the sunlit canopy of trees, and bulked, respectively, per replicate
tree. Whole leaves were placed in paper bags and the bark of branch
material was immediately removed, and the samples sealed in Exe-
tainer® vials (Labco Ltd, Lampeter, UK). All material was kept on
ice during transportation before being dried in paper bags at 60°C
for 48 h in a drying oven. In the case of the branch material, xylem
water was first extracted before being oven-dried.

Cellulose purification

After drying, leaves and wood were ground to a fine powder using
a ball mill (Retsch, Düsseldorf, Germany), before cellulose was
purified according to the method of Gaudinski *et al.* (2005).
Briefly, *c*. 200 mg of ground material was sealed into Ankom
bags. The lipids were extracted by reflux in a Soxhlet apparatus
with 2 : 1 toluene : ethanol (95%) mixture for *c*. 24 h under high
heat, and then under ethanol only until the solvent was clear.
Samples were air-dried before being boiled in deionised water for
4 h to remove water-soluble sugars. Lignin was removed with a
bleaching solution of sodium chlorite and acetic acid (pH 4)
under ultrasonic bath at 70°C for *c*. 24 h. Finally, α-cellulose was
purified from holocellulose with a 15% sodium hydroxide solu-
tion at room temperature in an ultrasonic bath. Samples were
oven-dried at 50°C for 3 d before being rehomogenised in 2.0-ml
tubes.

Isotope analysis

Cellulose was analysed for δ¹⁸O and δ²H according to
Holloway-Phillips *et al.* (2022) in the Stable Isotope Ecology Lab
at the University of Basel. Long-term analytical precision for
δ¹⁸O was monitored through repeated analysis of additional
quality control samples and was 0.24‰ (n = 188) for samples
analysed in 2018. An online dual-equilibration technique was
used for analysis of the nonexchangeable hydrogen δ²H
(Wassenaar *et al.*, 2015). This method aims to account for the
exchangeable hydrogen on hydroxy groups by exchanging with
hydrogen from water vapour of known δ²H value. The fraction
of exchangeable hydroxy hydrogen relative to total hydrogen,
expressed as Xe, is in theory 30% for cellulose (Schuler
*et al.*, 2022). We observed values of *c.* 6%, suggesting that we did

Mean annual precipitation (mm)

<figure><img src="image_5.png" /><figcaption>**Fig. 1** Map of Australia showing spatial distribution of long-term mean annual precipitation (1976–2005) at 0.01° resolution (Hutchinson *et al.*, 2014), with site locations for the North Australian Tropical Transect (Hutley *et al.*, 2001) ('This study'), and the comparative aridity gradient of Cheesman & Cernusak (2016) ('C&C').</figcaption></figure>
Australia, as part of the North Australian Tropical Transect (Hutley *et al.*, 2011; Fig. 1). For the study of Kahmen *et al.* (2013), only six of the 11 sites were analysed for the δ²H of *n*-alkanes in leaf waxes. The isotopic composition of leaf water was also sampled diurnally (four time points) at five of the 11 sites, and the data set published in full by Cernusak *et al.* (2016). Across all sites, branch xylem was collected for water extraction, and subsequently used for cellulose isotope analyses. Branch sections were *c.* 1 cm diameter, that is 'twigs'. Leaf tissue was also collected at all 11 sites and similarly used for cellulose isotope analyses. Until this study, the cellulose isotope information had not been published. For further details on when the samples were collected and processed, see Supporting Information Dataset S1 – 'Metadata'.

Across the sites, long-term mean annual precipitation (MAP) and mean relative humidity (RH) ranged from 313 mm yr⁻¹ and 34% in Alice Springs to 1739 mm yr⁻¹ and 62% in Darwin. Mean annual temperature across sites was much less variable (see Dataset S1 – 'Metadata' for source information). The two dominant genera, represented across most sites, were *Acacia* and *Eucalyptus*, as well as the genus *Corymbia*, which is phylogenetically close to *Eucalyptus* (see Dataset S1 – 'Isotopic data' for information on which species were represented at each site).

At each site, five to six mature trees of a single *acacia* or *eucalypt* species were sampled for leaf and branch material in April 2010 at the end of the wet season. Samples were randomly collected from the sunlit canopy of trees, and bulked, respectively, per replicate tree. Whole leaves were placed in paper bags and the bark of branch material was immediately removed, and the samples sealed in Exe-tainer® vials (Labco Ltd, Lampeter, UK). All material was kept on ice during transportation before being dried in paper bags at 60°C for 48 h in a drying oven. In the case of the branch material, xylem water was first extracted before being oven-dried.

## Cellulose purification

After drying, leaves and wood were ground to a fine powder using a ball mill (Retsch, Düsseldorf, Germany), before cellulose was purified according to the method of Gaudinski *et al.* (2005). Briefly, *c.* 200 mg of ground material was sealed into Ankom bags. The lipids were extracted by reflux in a Soxhlet apparatus with 2 : 1 toluene : ethanol (95%) mixture for *c.* 24 h under high heat, and then under ethanol only until the solvent was clear. Samples were air-dried before being boiled in deionised water for 4 h to remove water-soluble sugars. Lignin was removed with a bleaching solution of sodium chlorite and acetic acid (pH 4) under ultrasonic bath at 70°C for *c.* 24 h. Finally, α-cellulose was purified from holocellulose with a 15% sodium hydroxide solution at room temperature in an ultrasonic bath. Samples were oven-dried at 50°C for 3 d before being rehomogenised in 2.0-ml tubes.

## Isotope analysis

Cellulose was analysed for δ¹⁸O and δ²H according to Holloway-Phillips *et al.* (2022) in the Stable Isotope Ecology Lab at the University of Basel. Long-term analytical precision for δ¹⁸O was monitored through repeated analysis of additional quality control samples and was 0.24‰ (*n* = 188) for samples analysed in 2018. An online dual-equilibration technique was used for analysis of the nonexchangeable hydrogen δ²H (Wassenaar *et al.*, 2015). This method aims to account for the exchangeable hydrogen on hydroxy groups by exchanging with hydrogen from water vapour of known δ²H value. The fraction of exchangeable hydroxy hydrogen relative to total hydrogen, expressed as *X*<sub>e</sub>, is in theory 30% for cellulose (Schuler *et al.*, 2022). We observed values of *c.* 6%, suggesting that we did

Mean annual precipitation (mm)

<figure><img src="image_5.png" /><figcaption>**Fig. 1** Map of Australia showing spatial distribution of long-term mean annual precipitation (1976–2005) at 0.01° resolution (Hutchinson *et al.*, 2014), with site locations for the North Australian Tropical Transect (Hutley *et al.*, 2001) ('This study'), and the comparative aridity gradient of Cheesman & Cernusak (2016) ('C&C').</figcaption></figure>
not equilibrate all hydrogen on hydroxy groups; but this is similar to the theoretical fraction of freely exchangeable hydroxy hydrogen under normal conditions (Meier-Augenstein et al., 2014). That said, to determine whether the low Xe values caused systematic bias in our data set, we analysed a subset of leaf and branch wood samples by the same method as Schuler et al. (2022), at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), where higher Xe values were obtained. We found a very strong correlation between the two methods (leaf tissue, R² = 0.92 and branch wood tissue; R² = 0.97; Fig. S1). Despite there being a small statistically significant offset between the two methods (intercept -14.92‰ and -7.10‰ for leaf and branch wood, respectively) and a small statistically significant deviation of the slope from 1 (slope 1.05 and 1.14 for leaf and branch wood, respectively), the slopes of δ²H and δ¹⁸O between analytical methods for each tissue were not significantly different from each other suggesting that inferences made between elements are robust (data not shown). This was confirmed with a sensitivity analysis (Notes S1). Thus, we chose to present the original data where we had good confidence on the internal consistency of the analyses. Analytical performance of the dual-water exchange method at the University of Basel was monitored through routine analysis of bulk cellulose (Sigma-Aldrich) and an in-house quality control standard consisting of powdered Fagus leaves. The δ²H values of the nonexchangeable hydrogen in these materials were -50.7 ± 1.3‰ (n = 242) and -66.0 ± 1.6‰ (n = 224), respectively, during the year over which analyses were conducted.

Isotopic values are reported either as isotopic compositions (δ), defined as:

$$ \delta_{\text{sample}} = \frac{R_{\text{sample}}}{R_{\text{std}}} - 1 \qquad \text{Eqn 1} $$

where, Rsample is the isotope ratio of the measured sample and Rstd is the isotope ratio of VSMOW, or as enrichment above source (xylem) water (Δ), which was calculated as:

$$ \Delta_{\text{compound}} = \frac{\delta_{\text{compound}} - \delta_{\text{XW}}}{1 + \delta_{\text{XW}}} \qquad \text{Eqn 2} $$

where, δXW is the isotopic composition of xylem water and δcompound refers to the compound measured. Isotopic values were multiplied by 1000 to scale to per mil (‰). Isotopic enrichment above source water is particularly useful to isolate the impact of leaf water evaporative enrichment and the extent to which this signal is transferred to cellulose.

## Modelling cellulose isotopic composition: theory and parameters

The seasonally integrated isotope composition of leaf and branch cellulose (δcellulose) can be modelled according to a generalised two-process model: one fraction (1 - f) of O and carbon-bound H comes from leaf water (δLW) during photosynthetic reactions

with biosynthetic fractionation, εA. The other fraction (f) comes from water in developing cells (δsink_water) during postphotosynthetic reactions in developing leaves or stem cambium, with biosynthetic fraction, εH (Yakir & DeNiro, 1990; Roden et al., 2000; Holloway-Phillips et al., 2022):

$$ \delta_{\text{cellulose}} = (1-f)(\delta_{\text{LW}}(1+\varepsilon_{\text{A}}) + \varepsilon_{\text{A}}) + f(\delta_{\text{sink\_water}}(1+\varepsilon_{\text{H}}) + \varepsilon_{\text{H}}) \qquad \text{Eqn 3} $$

The isotope composition of bulk leaf water is generally less enriched in the heavier isotope compared with the estimated isotope composition of water at evaporative sites (δe) (Cernusak et al., 2016). However, in Kahmen et al.'s (2013) study, diurnal observations of δLW were well-predicted by the Craig-Gordon equation for leaf evaporative sites (Fig. S2), δe (Faruquhar & Lloyd, 1993; Cernusak et al., 2016) and there was no evidence of a Péclet effect (Fig. S3). Furthermore, a sensitivity analysis using an empirical correction between the observed diurnally measured values and the Craig-Gordon leaf water estimates, indicated that it did not change the interpretation of the data (Notes S1). Therefore, we also used the Craig-Gordon equation here to estimate seasonally averaged δLW for both elements:

$$ \delta_e = (1 + \varepsilon^+) \left( (1 + \varepsilon_k)(1 + \delta_e) \left(1 - \frac{e_a}{e_i}\right) + \frac{e_a}{e_i}(1 + \delta_v) \right) - 1 \qquad \text{Eqn 4} $$

where ε+ is the isotope fractionation between liquid water and vapour at equilibrium, εk is the fractionation during diffusion of water from the leaf intercellular airspaces to the atmosphere, δe is the isotope composition of transpired vapour, which at steady-state is equal to xylem water (δXW), ea/ei is the ratio of ambient to intercellular saturated vapour pressure, and δv is the isotope composition of atmospheric water vapour.

Enriched water from the leaf mesophyll can be transferred to developing cells by translocation in the phloem. As such, δsink_water can be modelled as a mixture of xylem (δXW) and leaf water, where the proportion of unenriched xylem water is often termed pX. For simplicity, we assumed that pX = 1, that is δsink_water = δXW, which is a good assumption for wood, but for leaves, it may be the case that pX < 1 (Cernusak et al., 2005; Liu et al., 2017).

For modelling the δ¹⁸O value in cellulose, fO (also referred to as pex in the literature) is assumed to be c. 0.4 (Song et al., 2022). We use 0.39 based on studies that also measured fH (Table S1). Isotope fractionation between carbonyl-O and water (εA and εH in Eqn 3) is denoted as εwc and taken to be the same value for photosynthetic and postphotosynthetic reactions. The value of εwc is c. 27‰ (Cernusak et al., 2003), which we corrected for temperature using the empirical relationship in Sternberg & Ellsworth (2011). For modelling the δ²H value in cellulose, we used the commonly applied parameter values for εA = -171‰ and εH = +158‰ (Yakir & DeNiro, 1990; Roden et al., 2000), and the average literature value of fH of 0.38 ± 0.06 (Table S1).
not equilibrate all hydrogen on hydroxy groups; but this is similar to the theoretical fraction of freely exchangeable hydroxy hydrogen under normal conditions (Meier-Augenstein et al., 2014). That said, to determine whether the low Xe values caused systematic bias in our data set, we analysed a subset of leaf and branch wood samples by the same method as Schuler et al. (2022), at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), where higher Xe values were obtained. We found a very strong correlation between the two methods (leaf tissue, R² = 0.92 and branch wood tissue; R² = 0.97; Fig. S1). Despite there being a small statistically significant offset between the two methods (intercept -14.92‰ and -7.10‰ for leaf and branch wood, respectively) and a small statistically significant deviation of the slope from 1 (slope 1.05 and 1.14 for leaf and branch wood, respectively), the slopes of δ²H and δ¹⁸O between analytical methods for each tissue were not significantly different from each other suggesting that inferences made between elements are robust (data not shown). This was confirmed with a sensitivity analysis (Notes S1). Thus, we chose to present the original data where we had good confidence on the internal consistency of the analyses. Analytical performance of the dual-water exchange method at the University of Basel was monitored through routine analysis of bulk cellulose (Sigma-Aldrich) and an in-house quality control standard consisting of powdered Fagus leaves. The δ²H values of the nonexchangeable hydrogen in these materials were -50.7 ± 1.3‰ (n = 242) and -66.0 ± 1.6‰ (n = 224), respectively, during the year over which analyses were conducted.

Isotopic values are reported either as isotopic compositions (δ), defined as:

$$ \delta_{\text{sample}} = \frac{R_{\text{sample}}}{R_{\text{std}}} - 1 \qquad \text{Eqn 1} $$

where, Rsample is the isotope ratio of the measured sample and Rstd is the isotope ratio of VSMOW, or as enrichment above source (xylem) water (Δ), which was calculated as:

$$ \Delta_{\text{compound}} = \frac{\delta_{\text{compound}} - \delta_{\text{XW}}}{1 + \delta_{\text{XW}}} \qquad \text{Eqn 2} $$

where, δXW is the isotopic composition of xylem water and δcompound refers to the compound measured. Isotopic values were multiplied by 1000 to scale to per mil (‰). Isotopic enrichment above source water is particularly useful to isolate the impact of leaf water evaporative enrichment and the extent to which this signal is transferred to cellulose.

## Modelling cellulose isotopic composition: theory and parameters

The seasonally integrated isotope composition of leaf and branch cellulose (δcellulose) can be modelled according to a generalised two-process model: one fraction (1 - f) of O and carbon-bound H comes from leaf water (δLW) during photosynthetic reactions

with biosynthetic fractionation, εA. The other fraction (f) comes from water in developing cells (δsink_water) during postphotosynthetic reactions in developing leaves or stem cambium, with biosynthetic fraction, εH (Yakir & DeNiro, 1990; Roden et al., 2000; Holloway-Phillips et al., 2022):

$$ \delta_{\text{cellulose}} = (1-f)(\delta_{\text{LW}}(1+\varepsilon_{\text{A}}) + \varepsilon_{\text{A}}) + f(\delta_{\text{sink\_water}}(1+\varepsilon_{\text{H}}) + \varepsilon_{\text{H}}) \qquad \text{Eqn 3} $$

The isotope composition of bulk leaf water is generally less enriched in the heavier isotope compared with the estimated isotope composition of water at evaporative sites (δe) (Cernusak et al., 2016). However, in Kahmen et al.'s (2013) study, diurnal observations of δLW were well-predicted by the Craig-Gordon equation for leaf evaporative sites (Fig. S2), δe (Faruquhar & Lloyd, 1993; Cernusak et al., 2016) and there was no evidence of a Péclet effect (Fig. S3). Furthermore, a sensitivity analysis using an empirical correction between the observed diurnally measured values and the Craig-Gordon leaf water estimates, indicated that it did not change the interpretation of the data (Notes S1). Therefore, we also used the Craig-Gordon equation here to estimate seasonally averaged δLW for both elements:

$$ \delta_e = (1 + \varepsilon^+) \left( (1 + \varepsilon_k)(1 + \delta_e) \left(1 - \frac{e_a}{e_i}\right) + \frac{e_a}{e_i}(1 + \delta_v) \right) - 1 \qquad \text{Eqn 4} $$

where ε+ is the isotope fractionation between liquid water and vapour at equilibrium, εk is the fractionation during diffusion of water from the leaf intercellular airspaces to the atmosphere, δe is the isotope composition of transpired vapour, which at steady-state is equal to xylem water (δXW), ea/ei is the ratio of ambient to intercellular saturated vapour pressure, and δv is the isotope composition of atmospheric water vapour.

Enriched water from the leaf mesophyll can be transferred to developing cells by translocation in the phloem. As such, δsink_water can be modelled as a mixture of xylem (δXW) and leaf water, where the proportion of unenriched xylem water is often termed pX. For simplicity, we assumed that pX = 1, that is δsink_water = δXW, which is a good assumption for wood, but for leaves, it may be the case that pX < 1 (Cernusak et al., 2005; Liu et al., 2017).

For modelling the δ¹⁸O value in cellulose, fO (also referred to as pex in the literature) is assumed to be c. 0.4 (Song et al., 2022). We use 0.39 based on studies that also measured fH (Table S1). Isotope fractionation between carbonyl-O and water (εA and εH in Eqn 3) is denoted as εwc and taken to be the same value for photosynthetic and postphotosynthetic reactions. The value of εwc is c. 27‰ (Cernusak et al., 2003), which we corrected for temperature using the empirical relationship in Sternberg & Ellsworth (2011). For modelling the δ²H value in cellulose, we used the commonly applied parameter values for εA = -171‰ and εH = +158‰ (Yakir & DeNiro, 1990; Roden et al., 2000), and the average literature value of fH of 0.38 ± 0.06 (Table S1).
Assumptions to integrate the cellulose isotope signature on a seasonal basis

To estimate seasonally integrated cellulose isotope values, long-
term mean annual 09:00 and 15:00 h observations of RH and air
temperature (T<sub>air</sub>) were obtained from the Australian Bureau of
Meteorology (www.bom.gov.au) (see Dataset S1 for more detail
on weather station number and data collection date range for
each station). As it is not known how variable the phenological
patterns were across sites, we took the pragmatic approach of
using annually averaged information to represent gross differ-
ences in site climate. This approximation is reasonable provided
we do not seek to estimate exact values, but instead aim to repli-
cate any patterns associated with changes in site climatic condi-
tions along the spatial gradient. This is further justified because
leaves were chosen randomly and thus represent an annual cohort
of leaves. In addition, wood growth, whilst peaking at certain
times of the year, can utilise both carbon reserves stored at differ-
ent times of the year and current assimilates. The isotope compo-
sition of water vapour was measured along with leaf water during
the diurnal campaigns. However, as the day of sampling is not
necessarily representative of longer term water vapour isotopic
patterns, we assumed that the water vapour is in isotopic equili-
brium with the measured xylem water, which was sampled in
March and September 2010, and which integrates the meteoric
water isotope signals over longer timescales (see Kahmen
et al., 2013 for more details). The kinetic isotope fractionation
for the leaf water model, ε<sub>k</sub>, was calculated using the average of
all stomatal conductance observations (Cernusak et al., 2016).
Note that the calculation of δ<sub>LW</sub> is fairly insensitive to uncer-
tainty in stomatal conductance (e.g. ±0.2 mol m<sup>-2</sup> s<sup>-1</sup> equates
to less than ±0.04‰ for oxygen and ±0.06‰ for hydrogen).
For a summary of the model inputs and data sources, see
Table S2.

Derived model parameters

There are up to four parameters in the cellulose isotope model that could theoretically vary: *f*, *ε*<sub>A</sub>, *ε*<sub>H</sub> and *δ*<sub>sink_water</sub> as a function of *p*<sub>x</sub> (Eqn 3); but, to model the isotopic composition of cellulose, a value for each parameter must be assumed. However, if the isotope composition of cellulose is known, any discrepancy between modelled and measured isotopic compositions of cellulose can be removed by adjusting the value of one of the model parameters, that is parameter estimation via model inversion. At this stage, hydrogen and oxygen must be considered separately.

For oxygen, the discrepancy between modelled and observed
δcellulose has previously been explained according to either varia-
tion in fO, assuming Tleaf = Tair (e.g. Cheesman & Cernu-
sak, 2016), or decoupling between leaf and air temperature,
assuming fO is constant (e.g. Helliker & Richter, 2008; Song
et al., 2011). As Kahmen et al. (2013) observed minimal differ-
ence between leaf and air temperature during diurnal measure-
ments, we do not consider leaf temperature decoupling to be an
issue in this study. An empirical relationship developed using
Australian published observations of leaf and air temperature

(229 observations) also indicated close agreement (McInerney
et al., 2023), which is the relationship we adopted in this study to
estimate seasonally integrated leaf temperature for modelling the
leaf water isotopic composition (Eqn 4). Similarly, variability in
εwc is assumed to only vary as a function of temperature, which
we accounted for. Thus, rationalising the observed δcellulose values
by solving for fO seems justified, especially for wood where p_x = 1
is a good assumption. Eqn 3 can thus be rearranged to solve for
fO:

$$
f_O = \frac{\delta_{\text{cellulose}} - \delta_{\text{L.W}}(1 + \varepsilon_{\text{wc}}) - \varepsilon_{\text{wc}}}{(\delta_{\text{xylem}} - \delta_{\text{L.W}})(1 + \varepsilon_{\text{wc}})} \quad \text{Eqn 5}
$$

For hydrogen, there is evidence that all biochemical parameters
(f, εA, εH) could vary, and on a mathematical basis, there is nega-
tive compensation between parameters (Holloway-Phillips
et al., 2022). Thus, rather than arbitrarily choosing a variable
to rationalise the discrepancy between observed and modelled
δcellulose values for hydrogen, we instead asked the more general
question of whether any modelled discrepancy related to site arid-
ity, so that a model correction could be used across similar envir-
onmental gradients. For this purpose, we take mean annual
precipitation as representative of site aridity (Hutley et al., 2011;
Nijzink & Schymanski, 2022), which is inversely related to arid-
ity (taken as potential evapotranspiration/mean annual precipita-
tion) to a close approximation (refer to Dataset S1 – ‘Metadata’).

Data analysis

All statistical analyses were conducted in R (v.3.6.1, R Core
Team, 2019) with RSTUDIO (v.1.2.1335, www.rstudio.com).
Fig. 1 was produced using packages NCDF4 (Pierce, 2023), RASTER
(Hijmans, 2023), RGDAL (Bivand et al., 2023), GGPLOT2 (Wick-
ham, 2016), RCOLORBREWER (Neuwirth, 2022), PRISMATIC (Hvit-
feldt, 2022) and VIRIDISLITE (Garnier et al., 2022). Dual isotope
plots (Fig. 2) and covariation between plant tissues (Fig. 3) were
investigated using standard major axis regression due to large nat-
ural variation between individual trees, with the ‘sma’ function in
the package SMATR (Warton et al., 2012). Outliers were consid-
ered with the ‘robust = T’ option, which uses Huber’s M estima-
tion in place of least squares. Relationships where modelled
values were regressed against observations (Fig. 4), or the model
discrepancy (defined as εexp-obs = ((δexp - δobs)/(1 + δobs)))
and fO estimates were regressed against mean annual precipitation
(Figs 5, 6), were investigated using ordinary least squares regres-
sion with the ‘lm’ function in the base R package STATS. Separate
analyses for each genus were not performed as acacia was not
represented across all sites. Also, given species within a genus var-
ied across sites, we had no a priori reason to suggest that species
within a genus were more similar than between genera to warrant
separating the two groups; that is, we did not have the power in
this study to test the significance of species identity on isotope
patterns. Figs 2–6 were graphed in SIGMAPLOT v.11 (SPSS Inc.,
Chicago, IL, USA).

To ensure that the interpretations of the model coefficients of
Figs 4–6 were robust, we assessed alternative parameterisations
Assumptions to integrate the cellulose isotope signature on a seasonal basis

To estimate seasonally integrated cellulose isotope values, long-
term mean annual 09:00 and 15:00 h observations of RH and air
temperature (T<sub>air</sub>) were obtained from the Australian Bureau of
Meteorology (www.bom.gov.au) (see Dataset S1 for more detail
on weather station number and data collection date range for
each station). As it is not known how variable the phenological
patterns were across sites, we took the pragmatic approach of
using annually averaged information to represent gross differ-
ences in site climate. This approximation is reasonable provided
we do not seek to estimate exact values, but instead aim to repli-
cate any patterns associated with changes in site climatic condi-
tions along the spatial gradient. This is further justified because
leaves were chosen randomly and thus represent an annual cohort
of leaves. In addition, wood growth, whilst peaking at certain
times of the year, can utilise both carbon reserves stored at differ-
ent times of the year and current assimilates. The isotope compo-
sition of water vapour was measured along with leaf water during
the diurnal campaigns. However, as the day of sampling is not
necessarily representative of longer term water vapour isotopic
patterns, we assumed that the water vapour is in isotopic equili-
brium with the measured xylem water, which was sampled in
March and September 2010, and which integrates the meteoric
water isotope signals over longer timescales (see Kahmen
et al., 2013 for more details). The kinetic isotope fractionation
for the leaf water model, ε<sub>k</sub>, was calculated using the average of
all stomatal conductance observations (Cernusak et al., 2016).
Note that the calculation of δ<sub>LW</sub> is fairly insensitive to uncer-
tainty in stomatal conductance (e.g. ±0.2 mol m<sup>-2</sup> s<sup>-1</sup> equates
to less than ±0.04‰ for oxygen and ±0.06‰ for hydrogen).
For a summary of the model inputs and data sources, see
Table S2.

Derived model parameters

There are up to four parameters in the cellulose isotope model that could theoretically vary: *f*, *ε*<sub>A</sub>, *ε*<sub>H</sub> and *δ*<sub>sink_water</sub> as a function of *p*<sub>x</sub> (Eqn 3); but, to model the isotopic composition of cellulose, a value for each parameter must be assumed. However, if the isotope composition of cellulose is known, any discrepancy between modelled and measured isotopic compositions of cellulose can be removed by adjusting the value of one of the model parameters, that is parameter estimation via model inversion. At this stage, hydrogen and oxygen must be considered separately.

For oxygen, the discrepancy between modelled and observed
δcellulose has previously been explained according to either varia-
tion in fO, assuming Tleaf = Tair (e.g. Cheesman & Cernu-
sak, 2016), or decoupling between leaf and air temperature,
assuming fO is constant (e.g. Helliker & Richter, 2008; Song
et al., 2011). As Kahmen et al. (2013) observed minimal differ-
ence between leaf and air temperature during diurnal measure-
ments, we do not consider leaf temperature decoupling to be an
issue in this study. An empirical relationship developed using
Australian published observations of leaf and air temperature

(229 observations) also indicated close agreement (McInerney
et al., 2023), which is the relationship we adopted in this study to
estimate seasonally integrated leaf temperature for modelling the
leaf water isotopic composition (Eqn 4). Similarly, variability in
εwc is assumed to only vary as a function of temperature, which
we accounted for. Thus, rationalising the observed δcellulose values
by solving for fO seems justified, especially for wood where p_x = 1
is a good assumption. Eqn 3 can thus be rearranged to solve for
fO:

$$
f_O = \frac{\delta_{\text{cellulose}} - \delta_{\text{L.W}}(1 + \varepsilon_{\text{wc}}) - \varepsilon_{\text{wc}}}{(\delta_{\text{xylem}} - \delta_{\text{L.W}})(1 + \varepsilon_{\text{wc}})} \quad \text{Eqn 5}
$$

For hydrogen, there is evidence that all biochemical parameters
(f, εA, εH) could vary, and on a mathematical basis, there is nega-
tive compensation between parameters (Holloway-Phillips
et al., 2022). Thus, rather than arbitrarily choosing a variable
to rationalise the discrepancy between observed and modelled
δcellulose values for hydrogen, we instead asked the more general
question of whether any modelled discrepancy related to site arid-
ity, so that a model correction could be used across similar envir-
onmental gradients. For this purpose, we take mean annual
precipitation as representative of site aridity (Hutley et al., 2011;
Nijzink & Schymanski, 2022), which is inversely related to arid-
ity (taken as potential evapotranspiration/mean annual precipita-
tion) to a close approximation (refer to Dataset S1 – ‘Metadata’).

Data analysis

All statistical analyses were conducted in R (v.3.6.1, R Core
Team, 2019) with RSTUDIO (v.1.2.1335, www.rstudio.com).
Fig. 1 was produced using packages NCDF4 (Pierce, 2023), RASTER
(Hijmans, 2023), RGDAL (Bivand et al., 2023), GGPLOT2 (Wick-
ham, 2016), RCOLORBREWER (Neuwirth, 2022), PRISMATIC (Hvit-
feldt, 2022) and VIRIDISLITE (Garnier et al., 2022). Dual isotope
plots (Fig. 2) and covariation between plant tissues (Fig. 3) were
investigated using standard major axis regression due to large nat-
ural variation between individual trees, with the ‘sma’ function in
the package SMATR (Warton et al., 2012). Outliers were consid-
ered with the ‘robust = T’ option, which uses Huber’s M estima-
tion in place of least squares. Relationships where modelled
values were regressed against observations (Fig. 4), or the model
discrepancy (defined as εexp-obs = ((δexp - δobs)/(1 + δobs)))
and fO estimates were regressed against mean annual precipitation
(Figs 5, 6), were investigated using ordinary least squares regres-
sion with the ‘lm’ function in the base R package STATS. Separate
analyses for each genus were not performed as acacia was not
represented across all sites. Also, given species within a genus var-
ied across sites, we had no a priori reason to suggest that species
within a genus were more similar than between genera to warrant
separating the two groups; that is, we did not have the power in
this study to test the significance of species identity on isotope
patterns. Figs 2–6 were graphed in SIGMAPLOT v.11 (SPSS Inc.,
Chicago, IL, USA).

To ensure that the interpretations of the model coefficients of
Figs 4–6 were robust, we assessed alternative parameterisations
<figure><img src="image_6.png" /><figcaption>**Fig. 2** Observed covariation between hydrogen and oxygen isotopic compositions on a delta basis (δ) for xylem water, water vapour, leaf water, leaf cellulose and branch wood cellulose (a); and, on a ²H-¹⁸O enrichment basis (Δ) relative to xylem water, for leaf water (b), leaf cellulose (c) and branch wood cellulose (d). Data compiled from Cernusak et al. (2016) and this study. The solid line in (a) refers to the meteoric water line and in (b–d) reflect significant (*P* < 0.05) regression lines fitted by standardised major axis regression.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 2** Observed covariation between hydrogen and oxygen isotopic compositions on a delta basis (δ) for xylem water, water vapour, leaf water, leaf cellulose and branch wood cellulose (a); and, on a ²H-¹⁸O enrichment basis (Δ) relative to xylem water, for leaf water (b), leaf cellulose (c) and branch wood cellulose (d). Data compiled from Cernusak et al. (2016) and this study. The solid line in (a) refers to the meteoric water line and in (b–d) reflect significant (*P* < 0.05) regression lines fitted by standardised major axis regression.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 3** Observed covariation between branch wood and leaves for the oxygen isotope composition, δ¹⁸O (a) and hydrogen isotope composition, δ²H (b) of cellulose. Solid regression lines are significant (*P* < 0.05) and fitted by standardised major axis regression. Slope and intercept significantly different from 1 and 0 for δ¹⁸O. For δ²H, the slope was not significantly different from 1, but intercept significantly different from 0. Colours represent different sites.</figcaption></figure>

of the cellulose isotope model, which can be viewed in Notes S1 – Tables S3–S6. This included testing alternative assumptions for leaf temperature, the isotopic composition of water vapour, and the model used to estimate leaf water delta values, as well as the impact of the analytical method for the δ²H of leaf cellulose. As the sensitivity analysis indicated that there was minimal change to the conclusions, we only present the data based on the model parameterisations given in Table S2.

## Results

### δ¹⁸O–δ²H relationship in water and cellulose

Across the 1500 km aridity transect, the observed range in xylem water δ values sampled from branches varied by 5‰ for δ¹⁸O and 35‰ for δ²H (Table 1). Both xylem and water vapour δ values fell approximately on the global meteoric water line (GMWL; Fig. 2a; Table 2). Leaf water fell below the GMWL reflecting isotope fractionations during transpiration (Fig. 2a). Leaf cellulose had a similar slope to that of leaf water in the dual-isotope plot (3.47 vs 2.93; Table 2), but the δ¹⁸O values were all higher than those of leaf water δ¹⁸O values, whereas the δ²H values were within the range of the leaf water isotope δ²H values (Fig. 2a).

When isotope delta values were converted to enrichment above source water (Δ), leaf water and leaf cellulose also had similar slopes (Fig. 2b,c; slopes not significantly different), suggesting that the evaporative isotopic signal was partially retained within leaf cellulose (strength of the relationship c. 40% of that of leaf water covariation). We consider the comparison between leaf water and leaf and branch wood cellulose isotopic variation across sites (rather than within sites), given the different time-scales of integration (leaf water – diurnal; cellulose – seasonal). The slope of the regression between Δ²H vs Δ¹⁸O of branch wood cellulose (8.57) was much larger than that of leaf water

(3.07) (Fig. 2d vs Fig. 2b), and the δ²H/δ¹⁸O relationship had a similar slope as xylem water (although the relationship was weak, R² = 0.08, Table 2).

The change in slope between leaf and branch wood cellulose was largely driven by a compression in the δ¹⁸O range going from 16‰ (leaf cellulose) to 8‰ (wood cellulose), whilst the isotopic range was more conserved for δ²H: 90‰ (leaf cellulose) and 80‰ (wood cellulose) (Table 1). Consequently, there was little covariation between leaf and branch wood cellulose for δ¹⁸O (R² = 0.12; Fig. 3a), but strong covariation for δ²H (R² = 0.59; Fig. 3b). Notably, the strength of the correlation between δ²H and δ¹⁸O progressively weakened moving from plant water pools into cellulose: xylem water (R² = 0.83) < leaf water (R² = 0.69) < leaf cellulose (R² = 0.17) < wood cellulose (R² = 0.08) (Table 2). This pattern was also visible with isotope enrichments, Δ (Fig. 2b–d).

### Comparison of observed and predicted isotope compositions

The majority of the variation in cellulose δ¹⁸O–δ²H delta values could not be explained by the cellulose isotope models. In effect, modelled cellulose delta values assuming biochemical parameters are invariant, explained only 21% of leaf and 16% of branch wood cellulose δ¹⁸O values, and 19% of leaf and 35% of branch wood cellulose δ²H values (Fig. 4). There was a significant isotopic offset (εexp-obs) between modelled and observed leaf cellulose delta values for both leaf δ¹⁸O and δ²H (Fig. 5a,c). In contrast, for branch wood cellulose, there was a significant negative isotopic offset with mean annual precipitation (MAP), which explained 60% of the variation in the model discrepancy for the δ¹⁸O of branch wood cellulose (Fig. 5b). In comparison, only 3% of the model discrepancy in branch wood cellulose δ²H values could be explained by MAP (3%; Fig. 5d); in other words,
<figure><img src="image_7.png" /><figcaption>**Fig. 3** Observed covariation between branch wood and leaves for the oxygen isotope composition, δ¹⁸O (a) and hydrogen isotope composition, δ²H (b) of cellulose. Solid regression lines are significant (*P* < 0.05) and fitted by standardised major axis regression. Slope and intercept significantly different from 1 and 0 for δ¹⁸O. For δ²H, the slope was not significantly different from 1, but intercept significantly different from 0. Colours represent different sites.</figcaption></figure>

of the cellulose isotope model, which can be viewed in Notes S1 – Tables S3–S6. This included testing alternative assumptions for leaf temperature, the isotopic composition of water vapour, and the model used to estimate leaf water delta values, as well as the impact of the analytical method for the δ²H of leaf cellulose. As the sensitivity analysis indicated that there was minimal change to the conclusions, we only present the data based on the model parameterisations given in Table S2.

## Results

### δ¹⁸O–δ²H relationship in water and cellulose

Across the 1500 km aridity transect, the observed range in xylem water δ values sampled from branches varied by 5‰ for δ¹⁸O and 35‰ for δ²H (Table 1). Both xylem and water vapour δ values fell approximately on the global meteoric water line (GMWL; Fig. 2a; Table 2). Leaf water fell below the GMWL reflecting isotope fractionations during transpiration (Fig. 2a). Leaf cellulose had a similar slope to that of leaf water in the dual-isotope plot (3.47 vs 2.93; Table 2), but the δ¹⁸O values were all higher than those of leaf water δ¹⁸O values, whereas the δ²H values were within the range of the leaf water isotope δ²H values (Fig. 2a).

When isotope delta values were converted to enrichment above source water (Δ), leaf water and leaf cellulose also had similar slopes (Fig. 2b,c; slopes not significantly different), suggesting that the evaporative isotopic signal was partially retained within leaf cellulose (strength of the relationship c. 40% of that of leaf water covariation). We consider the comparison between leaf water and leaf and branch wood cellulose isotopic variation across sites (rather than within sites), given the different time-scales of integration (leaf water – diurnal; cellulose – seasonal). The slope of the regression between Δ²H vs Δ¹⁸O of branch wood cellulose (8.57) was much larger than that of leaf water

(3.07) (Fig. 2d vs Fig. 2b), and the δ²H/δ¹⁸O relationship had a similar slope as xylem water (although the relationship was weak, R² = 0.08, Table 2).

The change in slope between leaf and branch wood cellulose was largely driven by a compression in the δ¹⁸O range going from 16‰ (leaf cellulose) to 8‰ (wood cellulose), whilst the isotopic range was more conserved for δ²H: 90‰ (leaf cellulose) and 80‰ (wood cellulose) (Table 1). Consequently, there was little covariation between leaf and branch wood cellulose for δ¹⁸O (R² = 0.12; Fig. 3a), but strong covariation for δ²H (R² = 0.59; Fig. 3b). Notably, the strength of the correlation between δ²H and δ¹⁸O progressively weakened moving from plant water pools into cellulose: xylem water (R² = 0.83) < leaf water (R² = 0.69) < leaf cellulose (R² = 0.17) < wood cellulose (R² = 0.08) (Table 2). This pattern was also visible with isotope enrichments, Δ (Fig. 2b–d).

### Comparison of observed and predicted isotope compositions

The majority of the variation in cellulose δ¹⁸O–δ²H delta values could not be explained by the cellulose isotope models. In effect, modelled cellulose delta values assuming biochemical parameters are invariant, explained only 21% of leaf and 16% of branch wood cellulose δ¹⁸O values, and 19% of leaf and 35% of branch wood cellulose δ²H values (Fig. 4). There was a significant isotopic offset (εexp-obs) between modelled and observed leaf cellulose delta values for both leaf δ¹⁸O and δ²H (Fig. 5a,c). In contrast, for branch wood cellulose, there was a significant negative isotopic offset with mean annual precipitation (MAP), which explained 60% of the variation in the model discrepancy for the δ¹⁸O of branch wood cellulose (Fig. 5b). In comparison, only 3% of the model discrepancy in branch wood cellulose δ²H values could be explained by MAP (3%; Fig. 5d); in other words,
<figure><img src="image_8.png" /><figcaption>**Fig. 4** Relationship between expected and observed cellulose delta values for δ¹⁸O (a, b) and δ²H (c, d). Expected values were modelled according to Eqn 3, assuming biosynthetic isotope parameters are invariant (Supporting Information Table S2). Regression lines are significant (*P* < 0.05) and fitted with ordinary least squares regression. 1 : 1 line also shown. Colours represent different sites.</figcaption></figure>

biochemical parameters ($f$, $\varepsilon_H$, and $\varepsilon_A$) varied across sites inde-
pendently of MAP.

Rationalising the discrepancy between modelled and observed
values by varying the contribution of oxygen derived from
xylem water ($f_O$) is justified for oxygen since it is believed that
changes in isotope fractionation ($\varepsilon_{wc}$) are minimal (see 'Derived
model parameters' in the Materials and Methods section). $f_O$
(calculated with Eqn 5 using temperature corrected $\varepsilon_{wc}$) in
branch wood cellulose was always higher than that of leaf cellu-
lose (Fig. 6a). With respect to mean annual precipitation, a
negative correlation was found, which was particularly strong
for branch wood cellulose. In fact, $f_O$ decreased by 0.02 for
every 100 mm increase in mean annual precipitation, compared
with 0.01 for leaf cellulose (Fig. 6a). Interestingly, the relation-
ship between $f_O$ values associated with branch wood cellulose

and mean annual precipitation was not significantly different
between this study and that obtained by Cheesman & Cernu-
sak (2016) (Fig. 6b). However, the absolute values were found
to be sensitive to the model parameterisation used (Fig. S4).
For the interested reader, we provide (but do not interpret or
discuss) $f_H$ estimates, and alternatively rationalise the discre-
tancy according to $p_x$. A summary of site averaged $f_O$, $f_H$ and $p_x$
values can be found in Table S7.

Discussion

δ¹⁸O–δ²H covariation depends on the plant organ

The extent to which climatic information is recorded by the
δ¹⁸O and δ²H of plant cellulose has been mostly assessed in tree-
<figure><img src="image_8.png" /><figcaption>**Fig. 4** Relationship between expected and observed cellulose delta values for δ¹⁸O (a, b) and δ²H (c, d). Expected values were modelled according to Eqn 3, assuming biosynthetic isotope parameters are invariant (Supporting Information Table S2). Regression lines are significant (*P* < 0.05) and fitted with ordinary least squares regression. 1 : 1 line also shown. Colours represent different sites.</figcaption></figure>

biochemical parameters ($f$, $\varepsilon_H$, and $\varepsilon_A$) varied across sites inde-
pendently of MAP.

Rationalising the discrepancy between modelled and observed
values by varying the contribution of oxygen derived from
xylem water ($f_O$) is justified for oxygen since it is believed that
changes in isotope fractionation ($\varepsilon_{wc}$) are minimal (see 'Derived
model parameters' in the Materials and Methods section). $f_O$
(calculated with Eqn 5 using temperature corrected $\varepsilon_{wc}$) in
branch wood cellulose was always higher than that of leaf cellu-
lose (Fig. 6a). With respect to mean annual precipitation, a
negative correlation was found, which was particularly strong
for branch wood cellulose. In fact, $f_O$ decreased by 0.02 for
every 100 mm increase in mean annual precipitation, compared
with 0.01 for leaf cellulose (Fig. 6a). Interestingly, the relation-
ship between $f_O$ values associated with branch wood cellulose

and mean annual precipitation was not significantly different
between this study and that obtained by Cheesman & Cernu-
sak (2016) (Fig. 6b). However, the absolute values were found
to be sensitive to the model parameterisation used (Fig. S4).
For the interested reader, we provide (but do not interpret or
discuss) $f_H$ estimates, and alternatively rationalise the discre-
tancy according to $p_x$. A summary of site averaged $f_O$, $f_H$ and $p_x$
values can be found in Table S7.

Discussion

δ¹⁸O–δ²H covariation depends on the plant organ

The extent to which climatic information is recorded by the
δ¹⁸O and δ²H of plant cellulose has been mostly assessed in tree-
<figure><img src="image_9.png" /><figcaption>**Fig. 5** Isotopic offset ($\epsilon_{exp-obs}$) between modelled and observed oxygen, $\delta^{18}O$ (a, b) and hydrogen, $\delta^2H$ (c, d) isotopic compositions ($\delta$) as dependent on long-term mean annual precipitation, for leaf (a, c) or branch wood (b, d) cellulose. Individual plant values are shown by the grey circles; means are shown by the black circles with SD used for the error bars. Regression (solid) lines are significant ($P < 0.05$) and fitted with ordinary least squares regression. Dashed lines indicate the intercept is significantly different from 0 ($P < 0.05$).</figcaption></figure>

rings where time provides the source of environmental variation. Using European-wide century-long timeseries, the climatic associations were found to be more consistent for O than for H, reflecting weak covariation between the two elements (Vitali et al., 2022). Here, we also found that covariation was weakened in cellulose compared with plant water pools, despite the expected hydrologic isotope signals induced by the environmental gradient being larger in this study than is typically found within a timeseries of a given site. Moreover, covariation declined to a greater extent in branch wood cellulose compared with leaf cellulose (Fig. 2). Whilst the leaf water enrichment signal captured across the environmental gradient was partially retained within leaf cellulose $\Delta$ values (Fig. 2c), the loss of covariation between $\Delta^2H$ and $\Delta^{18}O$ in branch wood cellulose (Fig. 2d) appeared to be driven by an aridity-dependent isotope effect that impacted oxygen but not hydrogen (Fig. 5). This is supported by the fact that the slope of the regression between leaf and wood cellulose $\delta$ values was significantly different from unity for oxygen but not for hydrogen (Fig. 3). However, $\delta^2H$ variation remained dominated by biochemical isotope effects based on the variability between expected and observed $\delta$ values (Fig. 4c,d). The lack of simple (e.g. linear) environmental predictability to the modelled discrepancy (Fig. 5) adds increased complexity in accounting for biochemical-induced variation within cellulose isotope models. Below, we take advantage of the differential isotope patterns for oxygen and hydrogen between leaf and branch

wood cellulose to examine the biochemical origin of the $\delta^{18}O-\delta^2H$ decoupling.

### Principle of biochemistry impacting on $\delta^{18}O$ and $\delta^2H$

The main mechanism by which carbon metabolism has been hypothesised to enrich sugars in $^2H$ and deplete in $^{18}O$ in sink cells is isotopic exchange with source water, with corresponding equilibrium isotope effects. As highlighted by Holloway-Phillips et al. (2022), mechanisms for oxygen and carbon-bound hydrogen isotope exchange with solvent water are rather different, despite the reactions associated with isotope exchange being similar between the two elements (see Fig. S5 for a simplified schematic of the reactions involved). On the one hand, oxygen exchange with water occurs in carbonyl groups of sugars. This can occur during hydrolysis (e.g. aldolase reaction) or spontaneously, that is without a reaction needing to proceed (e.g. in carbonyl O of triose-phosphates), but the probability of exchange likely increases during enzymatic reactions that facilitate covalent bond breakage (e.g. reactions involving triosephosphate isomerase; TPI, and phosphoglucose isomerase; PGI) (Farquhar et al., 1998). Based on studies that have compared the isotopic composition of leaf sucrose from terrestrial plants (Cernusak et al., 2003), or cellulose of aquatic plants with isotopic measures of the leaf water pool or growth media (DeNiro & Epstein, 1981), respectively, isotopic equilibrium seems to be
<figure><img src="image_9.png" /><figcaption>**Fig. 5** Isotopic offset ($\epsilon_{exp-obs}$) between modelled and observed oxygen, $\delta^{18}O$ (a, b) and hydrogen, $\delta^2H$ (c, d) isotopic compositions ($\delta$) as dependent on long-term mean annual precipitation, for leaf (a, c) or branch wood (b, d) cellulose. Individual plant values are shown by the grey circles; means are shown by the black circles with SD used for the error bars. Regression (solid) lines are significant ($P < 0.05$) and fitted with ordinary least squares regression. Dashed lines indicate the intercept is significantly different from 0 ($P < 0.05$).</figcaption></figure>

rings where time provides the source of environmental variation. Using European-wide century-long timeseries, the climatic associations were found to be more consistent for O than for H, reflecting weak covariation between the two elements (Vitali et al., 2022). Here, we also found that covariation was weakened in cellulose compared with plant water pools, despite the expected hydrologic isotope signals induced by the environmental gradient being larger in this study than is typically found within a timeseries of a given site. Moreover, covariation declined to a greater extent in branch wood cellulose compared with leaf cellulose (Fig. 2). Whilst the leaf water enrichment signal captured across the environmental gradient was partially retained within leaf cellulose $\Delta$ values (Fig. 2c), the loss of covariation between $\Delta^2H$ and $\Delta^{18}O$ in branch wood cellulose (Fig. 2d) appeared to be driven by an aridity-dependent isotope effect that impacted oxygen but not hydrogen (Fig. 5). This is supported by the fact that the slope of the regression between leaf and wood cellulose $\delta$ values was significantly different from unity for oxygen but not for hydrogen (Fig. 3). However, $\delta^2H$ variation remained dominated by biochemical isotope effects based on the variability between expected and observed $\delta$ values (Fig. 4c,d). The lack of simple (e.g. linear) environmental predictability to the modelled discrepancy (Fig. 5) adds increased complexity in accounting for biochemical-induced variation within cellulose isotope models. Below, we take advantage of the differential isotope patterns for oxygen and hydrogen between leaf and branch

wood cellulose to examine the biochemical origin of the $\delta^{18}O-\delta^2H$ decoupling.

## Principle of biochemistry impacting on $\delta^{18}O$ and $\delta^2H$

The main mechanism by which carbon metabolism has been hypothesised to enrich sugars in $^2H$ and deplete in $^{18}O$ in sink cells is isotopic exchange with source water, with corresponding equilibrium isotope effects. As highlighted by Holloway-Phillips et al. (2022), mechanisms for oxygen and carbon-bound hydrogen isotope exchange with solvent water are rather different, despite the reactions associated with isotope exchange being similar between the two elements (see Fig. S5 for a simplified schematic of the reactions involved). On the one hand, oxygen exchange with water occurs in carbonyl groups of sugars. This can occur during hydrolysis (e.g. aldolase reaction) or spontaneously, that is without a reaction needing to proceed (e.g. in carbonyl O of triose-phosphates), but the probability of exchange likely increases during enzymatic reactions that facilitate covalent bond breakage (e.g. reactions involving triosephosphate isomerase; TPI, and phosphoglucose isomerase; PGI) (Farquhar et al., 1998). Based on studies that have compared the isotopic composition of leaf sucrose from terrestrial plants (Cernusak et al., 2003), or cellulose of aquatic plants with isotopic measures of the leaf water pool or growth media (DeNiro & Epstein, 1981), respectively, isotopic equilibrium seems to be
<figure><img src="image_10.png" /><figcaption>**Fig. 6** Relationship between the fraction of oxygen exchanged during sugar processing in sink cells ($f_O$) with respect to long-term mean annual precipitation in leaf and branch wood cellulose (a) and with wood cellulose between studies (b). Note in (b), for this study values include those of both acacia and eucalypts combined, whereas the values of Cheesman & Cernusak (2016) relate only to eucalypt species. Mean values are shown in (a) with SD used for the error bars. Regression lines are significant ($P < 0.05$) and fitted with ordinary least squares regression.</figcaption></figure>

mostly achieved at the whole compound level for oxygen (Waterhouse *et al.*, 2013). On the other hand, exchange of C-bound hydrogen atoms with water always requires the facilitation of enzymes to break covalent bonds. Water can be incorporated directly during hydrolysis (e.g. aldolase), or indirectly whilst the abstracted hydrogen resides on the enzyme residue (e.g. TPI and PGI). In the latter case, isotopic equilibrium is not guaranteed with a high degree of intramolecular transfer noted during uni-directional assessment of PGI (Rose & O'Connell, 1961; Noltman, 1972; Malaisse *et al.*, 1991) and TPI (especially in

the direction glyceraldehyde-3-phosphate to dihydroxyacetone phosphate; O'Donoghue *et al.*, 2005a,b). In other words, even if a reaction proceeds, hydrogen exchange with water is not systematic. Additionally, for hydrogen, there are also opportunities for large kinetic isotope effects (KIE) to occur that do not involve water (e.g. glucose-6-phosphate dehydrogenase, KIE = 2.97; Hermes & Cleland, 1984; glyceraldehyde-3-phosphate dehydrogenase, KIE = 1.89; Canellas & Cleland, 1991), which were not considered in the original interpretation of isotope effects associated with cellulose production in the Yakir & DeNiro model (1990).

Therefore, there are two important differences in the mechanisms shaping isotope variability between hydrogen and oxygen: (1) First, $f_O$ and $f_H$ are unlikely to covary given that isotopic exchange with water can occur spontaneously for oxygen (independent of C fluxes), and intramolecular transfer for hydrogen (with $^1H/^2H$ isotope effects) may introduce variable isotope fractionations (dependent on carbon fluxes and metabolic pathways).

(2) Second, $^{16}$O/$^{18}$O isotope effects are mostly equilibrium isotope effects (dependent on temperature but otherwise considered constant; Sternberg & Ellsworth, 2011), whereas hydrogen metabolism involves additional kinetic isotope effects (observed isotope effect dependent on metabolite partitioning; Hayes, 2001).

Therefore, it is conceivable that oxygen and hydrogen could show different environmentally induced metabolic dependencies. These metabolic effects thus impact on key parameters describing cellulose isotope variability (Eqn 3): $f_O$ and $f_H$, and fractionation factors, $\epsilon$. They are anticipated to depend on organs, since leaves and branches have contrasting carbohydrate metabolisms, as explained below.

## Metabolic differences at the origin of leaf-branch wood isotope patterns

As found here and elsewhere (e.g. Kahmen *et al.*, 2011; Cheesman & Cernusak, 2016), wood cellulose $\delta^{18}$O values tend to be lower than leaf cellulose $\delta^{18}$O values. A number of previously proposed mechanisms have rationalised the relative $^{18}$O-depletion of wood compared with leaf cellulose according to either (1) that 'metabolic' water in which sugars and cellulose are synthesised is $^{18}$O-depleted in heterotrophic tissues compared with leaves (Cernusak *et al.*, 2005; Gessler *et al.*, 2013; Cheesman & Cernusak, 2016; Zhu *et al.*, 2020); and/or, (2) that the extent of isotopic exchange in heterotrophic tissues is greater than that in leaf tissue (Cheesman & Cernusak, 2016). Indeed, when we eliminated the discrepancy between observed and modelled $\delta^{18}$O values by allowing $f_O$ estimates to vary, our results indicated that $f_O$ differed between tissues (estimates in branch wood > leaves; Fig. 6a) and was negatively related to mean annual precipitation (slope of $f_O$ vs MAP for branch wood > leaves; Fig. 6a). Strikingly, the environmental sensitivity of $f_O$ in branch wood cellulose with MAP was statistically the same as determined for branch wood cellulose of eucalypts across a similar aridity gradient in Queensland, Australia (Fig. 6b; Cheesman & Cernusak, 2016).
<figure><img src="image_10.png" /><figcaption>**Fig. 6** Relationship between the fraction of oxygen exchanged during sugar processing in sink cells ($f_O$) with respect to long-term mean annual precipitation in leaf and branch wood cellulose (a) and with wood cellulose between studies (b). Note in (b), for this study values include those of both acacia and eucalypts combined, whereas the values of Cheesman & Cernusak (2016) relate only to eucalypt species. Mean values are shown in (a) with SD used for the error bars. Regression lines are significant ($P < 0.05$) and fitted with ordinary least squares regression.</figcaption></figure>

mostly achieved at the whole compound level for oxygen (Water-
house *et al.*, 2013). On the other hand, exchange of C-bound
hydrogen atoms with water always requires the facilitation of
enzymes to break covalent bonds. Water can be incorporated
directly during hydrolysis (e.g. aldolase), or indirectly whilst the
abstracted hydrogen resides on the enzyme residue (e.g. TPI and
PGI). In the latter case, isotopic equilibrium is not guaranteed
with a high degree of intramolecular transfer noted during uni-
directional assessment of PGI (Rose & O'Connell, 1961; Nolt-
man, 1972; Malaisse *et al.*, 1991) and TPI (especially in

the direction glyceraldehyde-3-phosphate to dihydroxyacetone
phosphate; O'Donoghue *et al.*, 2005a,b). In other words, even if
a reaction proceeds, hydrogen exchange with water is not sys-
tematic. Additionally, for hydrogen, there are also opportunities
for large kinetic isotope effects (KIE) to occur that do not involve
water (e.g. glucose-6-phosphate dehydrogenase, KIE = 2.97;
Hermes & Cleland, 1984; glyceraldehyde-3-phosphate dehydro-
genase, KIE = 1.89; Canellas & Cleland, 1991), which were not
considered in the original interpretation of isotope effects asso-
ciated with cellulose production in the Yakir & DeNiro
model (1990).

Therefore, there are two important differences in the mechan-
isms shaping isotope variability between hydrogen and oxygen:
(1) First, $f_O$ and $f_H$ are unlikely to covary given that isotopic
exchange with water can occur spontaneously for oxygen
(independent of C fluxes), and intramolecular transfer for
hydrogen (with $^1H/^2H$ isotope effects) may introduce variable
isotope fractionations (dependent on carbon fluxes and metabolic
pathways).

(2) Second, $^{16}$O/$^{18}$O isotope effects are mostly equilibrium iso-
tope effects (dependent on temperature but otherwise considered
constant; Sternberg & Ellsworth, 2011), whereas hydrogen meta-
bolism involves additional kinetic isotope effects (observed iso-
tope effect dependent on metabolite partitioning; Hayes, 2001).

Therefore, it is conceivable that oxygen and hydrogen could
show different environmentally induced metabolic dependencies.
These metabolic effects thus impact on key parameters describing
cellulose isotope variability (Eqn 3): $f_O$ and $f_H$, and fractionation
factors, $\epsilon$. They are anticipated to depend on organs, since leaves
and branches have contrasting carbohydrate metabolisms, as
explained below.

Metabolic differences at the origin of leaf-branch wood
isotope patterns

As found here and elsewhere (e.g. Kahmen *et al.*, 2011; Chees-
man & Cernusak, 2016), wood cellulose $\delta^{18}$O values tend to be
lower than leaf cellulose $\delta^{18}$O values. A number of previously
proposed mechanisms have rationalised the relative $^{18}$O-
depletion of wood compared with leaf cellulose according to
either (1) that ‘metabolic’ water in which sugars and cellulose are
synthesised is $^{18}$O-depleted in heterotrophic tissues compared
with leaves (Cernusak *et al.*, 2005; Gessler *et al.*, 2013; Cheesman
& Cernusak, 2016; Zhu *et al.*, 2020); and/or, (2) that the extent
of isotopic exchange in heterotrophic tissues is greater than that
in leaf tissue (Cheesman & Cernusak, 2016). Indeed, when we
eliminated the discrepancy between observed and modelled $\delta^{18}$O
values by allowing $f_O$ estimates to vary, our results indicated that
$f_O$ differed between tissues (estimates in branch wood > leaves;
Fig. 6a) and was negatively related to mean annual precipitation
(slope of $f_O$ vs MAP for branch wood > leaves; Fig. 6a). Strik-
ingly, the environmental sensitivity of $f_O$ in branch wood cellu-
lose with MAP was statistically the same as determined
for branch wood cellulose of eucalypts across a similar aridity
gradient in Queensland, Australia (Fig. 6b; Cheesman &
Cernusak, 2016).
<table><caption>**Table 1** Isotopic measurements (‰) for oxygen (δ¹⁸O) and hydrogen (δ²H) of xylem water, cellulose and modelled seasonally integrated leaf water, averaged (with standard deviation) by site and for all species along the north Australian aridity gradient.</caption><thead><tr><th rowspan="2">Location</th><th rowspan="2">Site code<sup>a</sup></th><th colspan="2">Observed xylem water<sup>b</sup></th><th colspan="2">Observed leaf cellulose</th><th colspan="2">Observed wood cellulose</th><th colspan="2">Seasonally integrated ave. modelled leaf water<sup>c</sup></th></tr><tr><th>δ¹⁸O</th><th>δ²H</th><th>δ¹⁸O</th><th>δ²H</th><th>δ¹⁸O</th><th>δ²H</th><th>δ¹⁸O</th><th>δ²H</th></tr></thead><tbody><tr><td>Darwin</td><td>1</td><td>-6.50 ± 0.45</td><td>-52.31 ± 2.25</td><td>36.96 ± 2.15</td><td>-9.68 ± 20.02</td><td>29.60 ± 2.05</td><td>4.43 ± 12.03</td><td>17.92 ± 0.46</td><td>14.53 ± 2.41</td></tr><tr><td>Adelaide River</td><td>2</td><td>-7.08 ± 0.36</td><td>-58.47 ± 4.73</td><td>37.40 ± 1.69</td><td>-26.25 ± 5.38</td><td>30.45 ± 0.96</td><td>-7.08 ± 8.86</td><td>17.66 ± 0.37</td><td>7.96 ± 5.07</td></tr><tr><td>Katherine</td><td>3</td><td>8.45 ± 0.74</td><td>-69.52 ± 4.52</td><td>37.58 ± 3.04</td><td>-31.99 ± 13.36</td><td>29.63 ± 1.25</td><td>-24.32 ± 9.55</td><td>16.52 ± 0.76</td><td>-3.19 ± 4.84</td></tr><tr><td>Larrimah</td><td>4</td><td>-7.76 ± 0.53</td><td>-66.78 ± 3.32</td><td>33.03 ± 3.23</td><td>-35.61 ± 10.46</td><td>29.70 ± 0.83</td><td>-23.88 ± 11.67</td><td>16.94 ± 0.54</td><td>-1.82 ± 3.55</td></tr><tr><td>Daly Waters</td><td>5</td><td>-7.67 ± 0.38</td><td>-64.25 ± 2.25</td><td>30.05 ± 2.67</td><td>-45.57 ± 5.86</td><td>28.42 ± 1.09</td><td>-37.30 ± 7.21</td><td>15.41 ± 0.39</td><td>-3.85 ± 2.40</td></tr><tr><td>Elliott</td><td>6</td><td>-7.39 ± 0.56</td><td>-64.14 ± 1.94</td><td>34.31 ± 3.43</td><td>-39.22 ± 10.16</td><td>28.54 ± 1.57</td><td>-28.68 ± 15.14</td><td>14.06 ± 0.57</td><td>-8.43 ± 2.06</td></tr><tr><td>Hellen Springs</td><td>7</td><td>-9.73 ± 0.33</td><td>-78.41 ± 1.42</td><td>34.66 ± 2.48</td><td>-43.16 ± 13.42</td><td>28.40 ± 0.78</td><td>-38.00 ± 6.23</td><td>10.68 ± 0.34</td><td>-25.93 ± 1.50</td></tr><tr><td>Tennant Creek</td><td>8</td><td>-7.89 ± 0.59</td><td>-70.18 ± 1.99</td><td>33.91 ± 3.27</td><td>-40.44 ± 10.33</td><td>27.90 ± 1.27</td><td>-33.56 ± 8.68</td><td>10.03 ± 0.60</td><td>-23.82 ± 2.09</td></tr><tr><td>Barrow Creek</td><td>9</td><td>-7.70 ± 0.16</td><td>-64.78 ± 1.59</td><td>32.87 ± 3.64</td><td>-21.55 ± 7.96</td><td>27.31 ± 1.78</td><td>-12.00 ± 9.07</td><td>8.65 ± 0.16</td><td>-21.56 ± 1.67</td></tr><tr><td>Territory Grape Farm</td><td>10</td><td>-6.27 ± 0.55</td><td>-48.66 ± 1.68</td><td>33.80 ± 1.31</td><td>-17.98 ± 10.58</td><td>30.02 ± 1.23</td><td>-4.21 ± 6.78</td><td>8.50 ± 0.56</td><td>-9.62 ± 1.75</td></tr><tr><td>Alice Springs</td><td>11</td><td>-6.72 ± 0.47</td><td>-52.87 ± 3.02</td><td>28.17 ± 1.00</td><td>-37.28 ± 8.33</td><td>28.01 ± 1.08</td><td>-23.69 ± 7.46</td><td>6.14 ± 0.47</td><td>-18.91 ± 3.13</td></tr><tr><td>Observed range<sup>d</sup> δ</td><td></td><td>4.53</td><td>34.75</td><td>16.42</td><td>89.66</td><td>7.81</td><td>79.17</td><td></td><td></td></tr><tr><td>Site range<sup>e</sup> Δ</td><td></td><td></td><td></td><td>8.61</td><td>28.52</td><td>1.37</td><td>29.06</td><td>11.63</td><td>34.67</td></tr></tbody></table>

Samples are taken from dominant acacia and eucalypt tree species.

<sup>a</sup>Sites in bold correspond to the sites represented in Kahmen et al. (2013). Site coordinates are given in Supporting Information Dataset S1.

<sup>b</sup>Source: Cernusak et al. (2016) – data averaged from both the March/April and September sampling campaigns.

<sup>c</sup>Refer to Table S2 for model inputs. Variation is a result of sampled xylem water used as direct input and estimated water vapour δ, which was assumed in equilibrium with xylem water. All other model inputs are site averaged.

<sup>d</sup>Calculated as maximum–minimum observed δ values from individual data points (see Dataset S1 for individual data points).

<sup>e</sup>Δ calculated from site averages as (δ<sub>variable</sub> – δ<sub>xylem water</sub>)/(1 + δ<sub>xylem water</sub>) and then the range computed as Site 1–Site 11 to reflect the expected gradient in leaf water enrichment values.
<table><caption>**Table 1** Isotopic measurements (‰) for oxygen (δ¹⁸O) and hydrogen (δ²H) of xylem water, cellulose and modelled seasonally integrated leaf water, averaged (with standard deviation) by site and for all species along the north Australian aridity gradient.</caption><thead><tr><th rowspan="2">Location</th><th rowspan="2">Site code<sup>a</sup></th><th colspan="2">Observed xylem water<sup>b</sup></th><th colspan="2">Observed leaf cellulose</th><th colspan="2">Observed wood cellulose</th><th colspan="2">Seasonally integrated ave.<br>modelled leaf water<sup>c</sup></th></tr><tr><th>δ<sup>18</sup>O</th><th>δ<sup>2</sup>H</th><th>δ<sup>18</sup>O</th><th>δ<sup>2</sup>H</th><th>δ<sup>18</sup>O</th><th>δ<sup>2</sup>H</th><th>δ<sup>18</sup>O</th><th>δ<sup>2</sup>H</th></tr></thead><tbody><tr><td>Darwin</td><td>1</td><td>-6.50 ± 0.45</td><td>-52.31 ± 2.25</td><td>36.96 ± 2.15</td><td>-9.68 ± 20.02</td><td>29.60 ± 2.05</td><td>4.43 ± 12.03</td><td>17.92 ± 0.46</td><td>14.53 ± 2.41</td></tr><tr><td>Adelaide River</td><td>2</td><td>-7.08 ± 0.36</td><td>-58.47 ± 4.73</td><td>37.40 ± 1.69</td><td>-26.25 ± 5.38</td><td>30.45 ± 0.96</td><td>-7.08 ± 8.86</td><td>17.66 ± 0.37</td><td>7.96 ± 5.07</td></tr><tr><td>Katherine</td><td>3</td><td>8.45 ± 0.74</td><td>-69.52 ± 4.52</td><td>37.58 ± 3.04</td><td>-31.99 ± 13.36</td><td>29.63 ± 1.25</td><td>-24.32 ± 9.55</td><td>16.52 ± 0.76</td><td>-3.19 ± 4.84</td></tr><tr><td>Larrimah</td><td>4</td><td>-7.76 ± 0.53</td><td>-66.78 ± 3.32</td><td>33.03 ± 3.23</td><td>-35.61 ± 10.46</td><td>29.70 ± 0.83</td><td>-23.88 ± 11.67</td><td>16.94 ± 0.54</td><td>-1.82 ± 3.55</td></tr><tr><td>Daly Waters</td><td>5</td><td>-7.67 ± 0.38</td><td>-64.25 ± 2.25</td><td>30.05 ± 2.67</td><td>-45.57 ± 5.86</td><td>28.42 ± 1.09</td><td>-37.30 ± 7.21</td><td>15.41 ± 0.39</td><td>-3.85 ± 2.40</td></tr><tr><td>Elliott</td><td>6</td><td>-7.39 ± 0.56</td><td>-64.14 ± 1.94</td><td>34.31 ± 3.43</td><td>-39.22 ± 10.16</td><td>28.54 ± 1.57</td><td>-28.68 ± 15.14</td><td>14.06 ± 0.57</td><td>-8.43 ± 2.06</td></tr><tr><td>Hellen Springs</td><td>7</td><td>-9.73 ± 0.33</td><td>-78.41 ± 1.42</td><td>34.66 ± 2.48</td><td>-43.16 ± 13.42</td><td>28.40 ± 0.78</td><td>-38.00 ± 6.23</td><td>10.68 ± 0.34</td><td>-25.93 ± 1.50</td></tr><tr><td>Tennant Creek</td><td>8</td><td>-7.89 ± 0.59</td><td>-70.18 ± 1.99</td><td>33.91 ± 3.27</td><td>-40.44 ± 10.33</td><td>27.90 ± 1.27</td><td>-33.56 ± 8.68</td><td>10.03 ± 0.60</td><td>-23.82 ± 2.09</td></tr><tr><td>Barrow Creek</td><td>9</td><td>-7.70 ± 0.16</td><td>-64.78 ± 1.59</td><td>32.87 ± 3.64</td><td>-21.55 ± 7.96</td><td>27.31 ± 1.78</td><td>-12.00 ± 9.07</td><td>8.65 ± 0.16</td><td>-21.56 ± 1.67</td></tr><tr><td>Territory Grape Farm</td><td>10</td><td>-6.27 ± 0.55</td><td>-48.66 ± 1.68</td><td>33.80 ± 1.31</td><td>-17.98 ± 10.58</td><td>30.02 ± 1.23</td><td>-4.21 ± 6.78</td><td>8.50 ± 0.56</td><td>-9.62 ± 1.75</td></tr><tr><td>Alice Springs</td><td>11</td><td>-6.72 ± 0.47</td><td>-52.87 ± 3.02</td><td>28.17 ± 1.00</td><td>-37.28 ± 8.33</td><td>28.01 ± 1.08</td><td>-23.69 ± 7.46</td><td>6.14 ± 0.47</td><td>-18.91 ± 3.13</td></tr><tr><td>Observed range<sup>d</sup> δ</td><td></td><td>4.53</td><td>34.75</td><td>16.42</td><td>89.66</td><td>7.81</td><td>79.17</td><td></td><td></td></tr><tr><td>Site range<sup>e</sup> Δ</td><td></td><td></td><td></td><td>8.61</td><td>28.52</td><td>1.37</td><td>29.06</td><td>11.63</td><td>34.67</td></tr></tbody></table>

Samples are taken from dominant acacia and eucalypt tree species.

<sup>a</sup>Sites in bold correspond to the sites represented in Kahmen et al. (2013). Site coordinates are given in Supporting Information Dataset S1.

<sup>b</sup>Source: Cernusak et al. (2016) – data averaged from both the March/April and September sampling campaigns.

<sup>c</sup>Refer to Table S2 for model inputs. Variation is a result of sampled xylem water used as direct input and estimated water vapour δ, which was assumed in equilibrium with xylem water. All other model inputs are site averaged.

<sup>d</sup>Calculated as maximum–minimum observed δ values from individual data points (see Dataset S1 for individual data points).

<sup>e</sup>Δ calculated from site averages as (δ<sub>variable</sub> – δ<sub>xylem water</sub>)/(1 + δ<sub>xylem water</sub>) and then the range computed as Site 1–Site 11 to reflect the expected gradient in leaf water enrichment values.
<table><caption>**Table 2** Parameter values for slope and intercepts of significant (*P* < 0.05) correlations of δ²H vs δ¹⁸O for water and cellulose data presented in Fig. 2(a), fitted using standard major axis regression.</caption><thead><tr><th>Sample</th><th>Slope</th><th>Intercept</th><th>R<sup>2</sup></th><th>n</th></tr></thead><tbody><tr><td>Water vapour<sup>a</sup></td><td>7.41</td><td>6.37</td><td>0.99</td><td>332</td></tr><tr><td>Xylem water<sup>a</sup></td><td>9.17</td><td>7.94</td><td>0.83</td><td>132</td></tr><tr><td>Leaf water</td><td>2.93</td><td>-43.25</td><td>0.69</td><td>332</td></tr><tr><td>Leaf cellulose<sup>b</sup></td><td>3.47</td><td>-148.58</td><td>0.17</td><td>132</td></tr><tr><td>Branch wood cellulose<sup>c</sup></td><td>10.78</td><td>-332.99</td><td>0.08</td><td>132</td></tr></tbody></table>

<sup>a</sup>Slope significantly different from global meteoric water line of 8.

<sup>b</sup>Slope significantly different (*P* = 0.045) from that of leaf water correlation but within 95% CI.

<sup>c</sup>Slope not significantly different from that of xylem water correlation.

In principle, mechanisms driving δ¹⁸O tissue patterns should also be applicable to hydrogen. In the first case (mechanism 1 above), ²H-depleted water in heterotrophic tissues should similarly result in ²H-depleted branch wood cellulose relative to leaf cellulose (all else being equal). In the second case (mechanism 2), greater isotopic exchange should result in the opposite pattern with ²H-enriched branch wood relative to leaf cellulose (due to the particularly large isotopic fractionations during heterotrophic metabolism as discussed in the Introduction section). There are only two studies that we could find that compared leaf/needle and wood cellulose δ²H values to assess the likelihood of either mechanism: in piñon pine (Pinus edulis and Pinus monophylla) sampled intra-annually, needle and stem wood δ²H values were similar (Pendall et al., 2005); whereas in avocado (Persea americana) grown from seed in terrariums, there was a variable δ²H offset between tissues, even within a relative humidity treatment (Terwilliger & Deniro, 1995). Nonetheless, both studies concluded that whilst leaf cellulose δ²H values retained the leaf water climatic signal, the strength of the signal retention in stem cellulose varied. To this end, Terwilliger & Deniro (1995) hypothesised that the extent to which stem cellulose reflected leaf water isotope signals depended on internal metabolic drivers, which impact on postphotosynthetic processes, including the reliance on stored C vs current assimilates. Although direct evidence is still lacking, the idea that different internal C sources may have different δ²H values was also recently proposed by Lehmann et al. (2021) and may be another mechanism that differentiates O and H in terms of isotopic effects leading to weakened covariance.

In comparison with the other two studies, we found that branch wood cellulose was ²H-enriched compared with leaf cellulose; although, the offset was statistically nonsignificant according to the intercept of Fig. 3. The simplest interpretation of the consistency in δ²H values between organs is that hydrogen is less sensitive to isotopic exchange during sink cell metabolism. This may be the consequence of either intramolecular transfer and/or because equilibrium fractionation is smaller than the estimated value of 158‰, resulting in an insensitivity to isotopic exchange. Given the large kinetic isotope effects associated with dehydrogenase reactions (discussed in the 'Principle of biochemistry impacting on δ¹⁸O and δ²H' in the Discussion section), it is

plausible that the value estimated for isotope fractionation during exchange with water in Yakir & DeNiro (1990) is a composite of equilibrium and kinetic isotope effects. Furthermore, it was recently shown that the majority of leaf cellulose δ²H variation across species could be explained by isotope fractionation during sucrose synthesis (Holloway-Phillips et al., 2022), and at the position-specific level, ²H-enrichment in wood could be explained by isotope effects on carbon-bound positions H-1 and H-2 of glucose, which was assumed to originate from source leaves (Wieloch et al., 2022b). Therefore, we hypothesise that the similarity in the δ²H values between organs may reflect biosynthetic variability induced during photosynthesis and before sucrose export ('source-cell isotope effects'). Consequently, we suggest that it is the different metabolic isotope signals captured by each element that led to a loss of covariation, particularly in branch wood material where isotopic exchange of oxygen with water appeared to be particularly responsive to metabolic-induced responses to aridity.

## Interpreting the metabolic isotope signals

The lack of environmental predictability to the hydrogen metabolic signal may reflect the shift in species identity across the aridity gradient, since species differences in cellulose δ²H values and biosynthetic isotope effects associated with source-cell metabolism have recently been noted (Holloway-Phillips et al., 2022; Lehmann et al., 2022; Baan et al., 2023; Schuler et al., 2023). Conversely, the trend towards increasing isotopic exchange of oxygen with water with increasing aridity may be supported by ecological knowledge of the sites, as suggested below.

Isotopic exchange has been experimentally linked to the cycling of triose-hexose phosphates (Hill et al., 1995), which led to the suggestion by Barbour & Farquhar (2000) and experimentally supported by Song et al. (2014), that the longer the residence time of sugars within sink cells, the greater the opportunity for metabolite cycling and hence isotopic exchange. This cycle often interacts with a cycle of sucrose synthesis and degradation (Dancer et al., 1990; Hatzfeld & Stitt, 1990). Altogether, these cycles are thought to allow for large and rapid changes in the net rate of sucrose breakdown in response to the demand in the cell (e.g. during stress events), whilst minimising changes in steady-state concentrations of metabolites (Geigenberger, 2003). In applying this interpretation to fO changes, it would therefore suggest that metabolite cycling (and/or the flux through the cycle) increases with increasing aridity, particularly in heterotrophic tissues.

Despite significant periods of absent rain throughout the transect and decreasing predictability of rainfall patterns (Rogers & Beringer, 2017), plant water stress of the woody overstory is minimal based on measures of leaf water potential, water use (O'Grady et al., 1999; Eamus et al., 2000; Hutley et al., 2001) and δ¹³C (Schulze et al., 1998; Miller et al., 2001). Furthermore, modelling suggests shifts in growth strategies and C allocation to capitalise on small, isolated rain events (Cook & Heerdegen, 2001; Cook et al., 2002). As such, the capacity to respond opportunistically to rainfall may be facilitated by
<table><caption>**Table 2** Parameter values for slope and intercepts of significant (*P* < 0.05) correlations of δ²H vs δ¹⁸O for water and cellulose data presented in Fig. 2(a), fitted using standard major axis regression.</caption><thead><tr><th>Sample</th><th>Slope</th><th>Intercept</th><th>R<sup>2</sup></th><th>n</th></tr></thead><tbody><tr><td>Water vapour<sup>a</sup></td><td>7.41</td><td>6.37</td><td>0.99</td><td>332</td></tr><tr><td>Xylem water<sup>a</sup></td><td>9.17</td><td>7.94</td><td>0.83</td><td>132</td></tr><tr><td>Leaf water</td><td>2.93</td><td>-43.25</td><td>0.69</td><td>332</td></tr><tr><td>Leaf cellulose<sup>b</sup></td><td>3.47</td><td>-148.58</td><td>0.17</td><td>132</td></tr><tr><td>Branch wood cellulose<sup>c</sup></td><td>10.78</td><td>-332.99</td><td>0.08</td><td>132</td></tr></tbody></table>

In principle, mechanisms driving δ¹⁸O tissue patterns should also be applicable to hydrogen. In the first case (mechanism 1 above), ²H-depleted water in heterotrophic tissues should similarly result in ²H-depleted branch wood cellulose relative to leaf cellulose (all else being equal). In the second case (mechanism 2), greater isotopic exchange should result in the opposite pattern with ²H-enriched branch wood relative to leaf cellulose (due to the particularly large isotopic fractionations during heterotrophic metabolism as discussed in the Introduction section). There are only two studies that we could find that compared leaf/needle and wood cellulose δ²H values to assess the likelihood of either mechanism: in piñon pine (Pinus edulis and Pinus monophylla) sampled intra-annually, needle and stem wood δ²H values were similar (Pendall et al., 2005); whereas in avocado (Persea americana) grown from seed in terrariums, there was a variable δ²H offset between tissues, even within a relative humidity treatment (Terwilliger & Deniro, 1995). Nonetheless, both studies concluded that whilst leaf cellulose δ²H values retained the leaf water climatic signal, the strength of the signal retention in stem cellulose varied. To this end, Terwilliger & Deniro (1995) hypothesised that the extent to which stem cellulose reflected leaf water isotope signals depended on internal metabolic drivers, which impact on postphotosynthetic processes, including the reliance on stored C vs current assimilates. Although direct evidence is still lacking, the idea that different internal C sources may have different δ²H values was also recently proposed by Lehmann et al. (2021) and may be another mechanism that differentiates O and H in terms of isotopic effects leading to weakened covariance.

In comparison with the other two studies, we found that branch wood cellulose was ²H-enriched compared with leaf cellulose; although, the offset was statistically nonsignificant according to the intercept of Fig. 3. The simplest interpretation of the consistency in δ²H values between organs is that hydrogen is less sensitive to isotopic exchange during sink cell metabolism. This may be the consequence of either intramolecular transfer and/or because equilibrium fractionation is smaller than the estimated value of 158‰, resulting in an insensitivity to isotopic exchange. Given the large kinetic isotope effects associated with dehydrogenase reactions (discussed in the 'Principle of biochemistry impacting on δ¹⁸O and δ²H' in the Discussion section), it is

plausible that the value estimated for isotope fractionation during exchange with water in Yakir & DeNiro (1990) is a composite of equilibrium and kinetic isotope effects. Furthermore, it was recently shown that the majority of leaf cellulose δ²H variation across species could be explained by isotope fractionation during sucrose synthesis (Holloway-Phillips et al., 2022), and at the position-specific level, ²H-enrichment in wood could be explained by isotope effects on carbon-bound positions H-1 and H-2 of glucose, which was assumed to originate from source leaves (Wieloch et al., 2022b). Therefore, we hypothesise that the similarity in the δ²H values between organs may reflect biosynthetic variability induced during photosynthesis and before sucrose export ('source-cell isotope effects'). Consequently, we suggest that it is the different metabolic isotope signals captured by each element that led to a loss of covariation, particularly in branch wood material where isotopic exchange of oxygen with water appeared to be particularly responsive to metabolic-induced responses to aridity.

Interpreting the metabolic isotope signals

The lack of environmental predictability to the hydrogen metabolic signal may reflect the shift in species identity across the aridity gradient, since species differences in cellulose δ²H values and biosynthetic isotope effects associated with source-cell metabolism have recently been noted (Holloway-Phillips et al., 2022; Lehmann et al., 2022; Baan et al., 2023; Schuler et al., 2023). Conversely, the trend towards increasing isotopic exchange of oxygen with water with increasing aridity may be supported by ecological knowledge of the sites, as suggested below.

Isotopic exchange has been experimentally linked to the cycling of triose-hexose phosphates (Hill et al., 1995), which led to the suggestion by Barbour & Farquhar (2000) and experimentally supported by Song et al. (2014), that the longer the residence time of sugars within sink cells, the greater the opportunity for metabolite cycling and hence isotopic exchange. This cycle often interacts with a cycle of sucrose synthesis and degradation (Dancer et al., 1990; Hatzfeld & Stitt, 1990). Altogether, these cycles are thought to allow for large and rapid changes in the net rate of sucrose breakdown in response to the demand in the cell (e.g. during stress events), whilst minimising changes in steady-state concentrations of metabolites (Geigenberger, 2003). In applying this interpretation to fO changes, it would therefore suggest that metabolite cycling (and/or the flux through the cycle) increases with increasing aridity, particularly in heterotrophic tissues.

Despite significant periods of absent rain throughout the transect and decreasing predictability of rainfall patterns (Rogers & Beringer, 2017), plant water stress of the woody overstory is minimal based on measures of leaf water potential, water use (O'Grady et al., 1999; Eamus et al., 2000; Hutley et al., 2001) and δ¹³C (Schulze et al., 1998; Miller et al., 2001). Furthermore, modelling suggests shifts in growth strategies and C allocation to capitalise on small, isolated rain events (Cook & Heerdegen, 2001; Cook et al., 2002). As such, the capacity to respond opportunistically to rainfall may be facilitated by

<footnote>aSlope significantly different from global meteoric water line of 8.</footnote>

<footnote>bSlope significantly different (*P* = 0.045) from that of leaf water correlation but within 95% CI.</footnote>

<footnote>cSlope not significantly different from that of xylem water correlation.</footnote>
increased metabolite cycling. To test this hypothesis, it would be
interesting to use the approach of Hill *et al.* (1995) to determine
whether there are shifts in metabolite cycling along the transect,
aligned with shifts in *f*<sub>O</sub> estimates. Similarly, to investigate the
relative importance of intramolecular hydrogen transfer during
cytosolic metabolism as the reason for why shifts in *f*<sub>O</sub> and *f*<sub>H</sub>
may not covary, analysing the position-specific δ<sup>2</sup>H of glucosyl
and fructosyl moieties of sucrose may be useful, particularly when
combined with <sup>13</sup>C labelling to quantify flux changes and cycling
rates (e.g. Krook *et al.*, 2000; van der Merwe *et al.*, 2010).

Limitations and implications

Whilst δ¹⁸O has generally been found to retain climatic signals
in tree-ring series, we did not observe that here. In fact, the cellu-
lose isotope model predicted the observed isotopic variability bet-
ter for δ²H in wood (R² = 0.35; Fig. 4). This perhaps reflects the
larger range in isotopic variability associated with plant water
pools in hydrogen compared with oxygen, relative to that
induced by metabolism (inferred from the range in expected
values vs the scatter around the line in Fig. 4). Thus, our data
have demonstrated that metabolic-induced isotopic variation is
not only an issue that needs to be considered for hydrogen, but
also for oxygen, especially over large spatial gradients where plant
adaptation has occurred. In this regard, due to confounding
changes in environmental parameters across sites and species
shifts, the transfer of climatic isotope signals from plant water
pools to leaf and wood cellulose should be isolated and experi-
mentally tested before space-for-time experimental results are
applied to understand temporal variation in isotopic data (e.g. to
tree-ring time series). This similarly applies to understanding
(and thus modelling) the drivers of metabolic-induced isotope
variability. Specifically, the link between aridity and isotopic
exchange with water needs to be independently tested within a
given species, where all other environmental factors remain
the same.

In addition, model inversion to estimate model parameters
from cellulose δ values is only as good as the input data, as was
recently highlighted by Lin *et al.* (2022). In the current study,
this meant modelling leaf water δ values. Whilst the leaf water
model successfully predicted diurnally measured δ values (Kah-
men *et al.*, 2013), a number of simplifying assumptions were
made to provide seasonal averages applicable to the time integra-
tion of cellulose synthesis (Table S2). Determining the relevant
climate period captured within the sugar used for cellulose synth-
esis depends on the synchrony of assimilation and growth and
sugar pool mixing processes including the use of previously stored
C. Determining this period remains a major challenge for the
field, but progress is being made with the development of
process-based modelling solutions (e.g. Hirl *et al.*, 2021).

Critically, our data have demonstrated that the metabolic
information captured by each element and plant tissue differs.
Whilst this drives the decoupling between δ¹⁸O and δ²H and
therefore complicates efforts to combine isotopic information for
estimating climatic parameters (e.g. Voelker *et al.*, 2014), it

points to new opportunities to explore different aspects of plant
metabolic responses to the environment.

Acknowledgements

We thank Stefan Arndt at University of Melbourne, Australia, for his assistance in the organisation and sample collection during the original field campaign, and Svenja Förster at the University of Basel, Switzerland, for assistance with the cellulose and lipid extractions. This project was made possible by funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 724750). This grant employed MH-P, with additional funding assistance by the Swiss National Science Foundation (no. 205492) during the revision and publication stages of this manuscript. MML was funded by the SNF Ambizione grant (no. 179978). GT was funded by the *Région Pays de la Loire* via the Connect Talent grant (Isoseed). Open access funding provided by Universitat Basel.

Competing interests

None declared.

Author contributions

MH-P, AK and LAC conceived the study. MH-P conducted and analysed the isotopic data. MML and DBN provided lab assistance. MH-P interpreted the results with feedback and input from all authors. MH-P developed the isotopic hypotheses in consultation with GT. MH-P wrote the paper with contributions from all authors.

ORCID

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

Meisha Holloway-Phillips https://orcid.org/0000-0002-8353-3536

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

Marco M. Lehmann https://orcid.org/0000-0003-2962-3351

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

Guillaume Tcherkez https://orcid.org/0000-0002-3339-956X

Data availability

Measured isotope values and site information used in the analyses presented in this paper are available in Dataset S1. Equations required to estimate modelled cellulose isotope values and derived parameters (e.g. *f*<sub>O</sub>) are given in Table S2.

References

An W, Liu X, Leavitt SW, Xu G, Zeng X, Wang W, Qin D, Ren J. 2014. Relative humidity history on the Batang-Litang Plateau of western China since 1755 reconstructed from tree-ring δ¹⁸O and δD. *Climate Dynamics* 42: 2639–2654.
increased metabolite cycling. To test this hypothesis, it would be
interesting to use the approach of Hill *et al.* (1995) to determine
whether there are shifts in metabolite cycling along the transect,
aligned with shifts in *f*<sub>O</sub> estimates. Similarly, to investigate the
relative importance of intramolecular hydrogen transfer during
cytosolic metabolism as the reason for why shifts in *f*<sub>O</sub> and *f*<sub>H</sub>
may not covary, analysing the position-specific δ<sup>2</sup>H of glucosyl
and fructosyl moieties of sucrose may be useful, particularly when
combined with <sup>13</sup>C labelling to quantify flux changes and cycling
rates (e.g. Krook *et al.*, 2000; van der Merwe *et al.*, 2010).

Limitations and implications

Whilst δ¹⁸O has generally been found to retain climatic signals
in tree-ring series, we did not observe that here. In fact, the cellu-
lose isotope model predicted the observed isotopic variability bet-
ter for δ²H in wood (R² = 0.35; Fig. 4). This perhaps reflects the
larger range in isotopic variability associated with plant water
pools in hydrogen compared with oxygen, relative to that
induced by metabolism (inferred from the range in expected
values vs the scatter around the line in Fig. 4). Thus, our data
have demonstrated that metabolic-induced isotopic variation is
not only an issue that needs to be considered for hydrogen, but
also for oxygen, especially over large spatial gradients where plant
adaptation has occurred. In this regard, due to confounding
changes in environmental parameters across sites and species
shifts, the transfer of climatic isotope signals from plant water
pools to leaf and wood cellulose should be isolated and experi-
mentally tested before space-for-time experimental results are
applied to understand temporal variation in isotopic data (e.g. to
tree-ring time series). This similarly applies to understanding
(and thus modelling) the drivers of metabolic-induced isotope
variability. Specifically, the link between aridity and isotopic
exchange with water needs to be independently tested within a
given species, where all other environmental factors remain
the same.

In addition, model inversion to estimate model parameters
from cellulose δ values is only as good as the input data, as was
recently highlighted by Lin *et al.* (2022). In the current study,
this meant modelling leaf water δ values. Whilst the leaf water
model successfully predicted diurnally measured δ values (Kah-
men *et al.*, 2013), a number of simplifying assumptions were
made to provide seasonal averages applicable to the time integra-
tion of cellulose synthesis (Table S2). Determining the relevant
climate period captured within the sugar used for cellulose synth-
esis depends on the synchrony of assimilation and growth and
sugar pool mixing processes including the use of previously stored
C. Determining this period remains a major challenge for the
field, but progress is being made with the development of
process-based modelling solutions (e.g. Hirl *et al.*, 2021).

Critically, our data have demonstrated that the metabolic
information captured by each element and plant tissue differs.
Whilst this drives the decoupling between δ¹⁸O and δ²H and
therefore complicates efforts to combine isotopic information for
estimating climatic parameters (e.g. Voelker *et al.*, 2014), it

points to new opportunities to explore different aspects of plant
metabolic responses to the environment.

Acknowledgements

We thank Stefan Arndt at University of Melbourne, Australia, for his assistance in the organisation and sample collection during the original field campaign, and Svenja Förster at the University of Basel, Switzerland, for assistance with the cellulose and lipid extractions. This project was made possible by funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 724750). This grant employed MH-P, with additional funding assistance by the Swiss National Science Foundation (no. 205492) during the revision and publication stages of this manuscript. MML was funded by the SNF Ambizione grant (no. 179978). GT was funded by the *Région Pays de la Loire* via the Connect Talent grant (Isoseed). Open access funding provided by Universitat Basel.

Competing interests

None declared.

Author contributions

MH-P, AK and LAC conceived the study. MH-P conducted and analysed the isotopic data. MML and DBN provided lab assistance. MH-P interpreted the results with feedback and input from all authors. MH-P developed the isotopic hypotheses in consultation with GT. MH-P wrote the paper with contributions from all authors.

ORCID

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

Meisha Holloway-Phillips https://orcid.org/0000-0002-8353-3536

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

Marco M. Lehmann https://orcid.org/0000-0003-2962-3351

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

Guillaume Tcherkez https://orcid.org/0000-0002-3339-956X

Data availability

Measured isotope values and site information used in the analyses presented in this paper are available in Dataset S1. Equations required to estimate modelled cellulose isotope values and derived parameters (e.g. *f*<sub>O</sub>) are given in Table S2.

References

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Cernusak L, Farquhar G, Pate J. 2005. Environmental and physiological controls over oxygen and carbon isotope composition of Tasmanian blue gum, *Eucalyptus globulus*. *Tree Physiology* 25: 129-146.

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Cheesman AW, Cernusak LA. 2016. Infidelity in the outback: climate signal recorded in Δ¹⁸O of leaf but not branch cellulose of eucalypts across an Australian aridity gradient. *Tree Physiology* 37: 554-564.

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Cormier M-A, Werner RA, Sauer PE, Gröcke DR, Leuenberger MC, Wieloch T, Schleucher J, Kahmen A. 2018. ²H-fractionations during the biosynthesis of carbohydrates and lipids imprint a metabolic signal on the δ²H values of plant organic compounds. *New Phytologist* 218: 479-491.

Dancer J, Hatzfeld W-D, Stitt M. 1990. Cytosolic cycles regulate the turnover of sucrose in heterotrophic cell-suspension cultures of *Chenopodium rubrum* L. *Planta* 182: 223-231.

DeNiro MJ, Epstein S. 1981. Isotopic composition of cellulose from aquatic organisms. *Geochimica et Cosmochimica Acta* 45: 1885-1894.

Dongmann G, Nürnberg HW, Förstel H, Wagener K. 1974. On the enrichment of H₂¹⁸O in the leaves of transpiring plants. *Radiation and Environmental Biophysics* 11: 41-52.

Eamus D, O'Grady AP, Hutley L. 2000. Dry season conditions determine wet season water use in the wet-tropical savannas of northern Australia. *Tree Physiology* 20: 1219-1226.

Epstein S, Yapp CJ, Hall JH. 1976. The determination of the D/H ratio of non-exchangeable hydrogen in cellulose extracted from aquatic and land plants. *Earth and Planetary Science Letters* 30: 241-251.

Farquhar GD, Barbour MM, Henry BK. 1998. Interpretation of oxygen isotope composition of leaf material. In: Griffiths H, ed. *Stable isotopes: integration of biological, ecological, and geochemical processes*. Oxford, UK: BIOS Scientific, 27-48.

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Flanagan LB, Comstock JP, Ehleringer JR. 1991. Comparison of modeled and observed environmental influences on the stable oxygen and hydrogen isotope composition of leaf water in *Phaseolis vulgaris* L. *Plant Physiology* 96: 588-596.

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Geigenberger P. 2003. Response of plant metabolism to too little oxygen. *Current Opinion in Plant Biology* 6: 247-256.

Gessler A, Brandes E, Keitel C, Boda S, Kayler ZE, Granier A, Barbour M, Farquhar GD, Treydde K. 2013. The oxygen isotope enrichment of leaf-exported assimilates – does it always reflect lamina leaf water enrichment? *New Phytologist* 200: 144-157.

Gessler A, Ferrio JP, Hommel R, Treydde K, Werner RA, Monson RK. 2014. Stable isotopes in tree rings: towards a mechanistic understanding of isotope fractionation and mixing processes from the leaves to the wood. *Tree Physiology* 34: 796-818.

Gori Y, Wehrens R, La Porta N, Camin F. 2015. Oxygen and hydrogen stable isotope ratios of bulk needles reveal the geographic origin of Norway spruce in the European Alps. *PLoS ONE* 10: e0118941.

Gray J, Thompson P. 1976. Climatic information from ¹⁸O/¹⁶O ratios of cellulose in tree rings. *Nature* 262: 481-482.

Hafner P, Robertson I, McCarroll D, Loader NJ, Gagen M, Bale RJ, Jungner H, Sonninen E, Hilasuviuri E, Levanič T. 2011. Climate signals in the ring widths and stable carbon, hydrogen and oxygen isotopic composition of *Larix decidua* growing at the forest limit in the southeastern European Alps. *Trees* 25: 1141-1154.

Hatzfeld W-D, Stitt M. 1990. A study of the rate of recycling of triose phosphates in heterotrophic *Chenopodium rubrum* cells, potato tubers, and maize endosperm. *Planta* 180: 198-204.

Hayes JM. 2001. Fractionation of carbon and hydrogen isotopes in biosynthetic processes. In: Valley JW, Cole DR, eds. *Stable isotope geochemistry*. Berlin, Germany: De Gruyter, 225-278.

Helliker BR, Richter SL. 2008. Subtropical to boreal convergence of tree-leaf temperatures. *Nature* 454: 511-514.

Hermes JD, Cleland WW. 1984. Evidence from multiple isotope effect determinations for coupled hydrogen motion and tunneling in the reaction catalyzed by glucose-6-phosphate dehydrogenase. *Journal of the American Chemical Society* 106: 7263-7264.

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Hill SA, Waterhouse JS, Field EM, Switsur VR, Ap Rees T. 1995. Rapid recycling of triose phosphates in oak stem tissue. *Plant, Cell & Environment* 18: 931-936.

Hirl RT, Ogee J, Ostler U, Schäufele R, Baca Cabrera JC, Zhu J, Schleip I, Wingate L, Schnyder H. 2021. Temperature-sensitive biochemical ¹⁸O-fractionation and humidity-dependent attenuation factor are needed to predict δ¹⁸O of cellulose from leaf water in a grassland ecosystem. *New Phytologist* 229: 3156-3171.

Holloway-Phillips M, Baan J, Nelson DB, Lehmann MM, Tcherkez G, Kahmen A. 2022. Species variation in the hydrogen isotope composition of
Augusti A, Betson TR, Schleucher J. 2006. Hydrogen exchange during cellulose synthesis distinguishes climatic and biochemical isotope fractionations in tree rings. *New Phytologist* 172: 490-499.

Baan J, Holloway-Phillips M, Nelson DB, Kahmen A. 2022. The metabolic sensitivity of hydrogen isotope fractionation differs between plant compounds. *Phytochemistry* 207: 113563.

Baan J, Holloway-Phillips M, Nelson DB, Kahmen A. 2023. Species and biosynthetic effects cause uncorrelated variation in oxygen and hydrogen isotope compositions of plant organic compounds. *Geochimica et Cosmochimica Acta* 352: 1-13.

Barbour MM, Farquhar GD. 2000. Relative humidity- and ABA-induced variation in carbon and oxygen isotope ratios of cotton leaves. *Plant, Cell & Environment* 23: 473-485.

Barnard R, Salmon Y, Kodama N, Sörgel K, Holst J, Rennenberg H, Gessler A, Buchmann N. 2007. Evaporative enrichment and time lags between δ¹⁸O of leaf water and organic pools in a pine stand. *Plant, Cell & Environment* 30: 539-550.

Bivand R, Keitt T, Rowlingson B. 2023. RGDAL: bindings for the 'Geospatial' data abstraction library. [WWW document] URL https://CRAN.R-project.org/package=rgdal [accessed 22 May 2023].

Boettger T, Haupt M, Friedrich M, Waterhouse JS. 2014. Reduced climate sensitivity of carbon, oxygen and hydrogen stable isotope ratios in tree-ring cellulose of silver fir (Abies alba Mill.) influenced by background SO₂ in Franconia (Germany, Central Europe). *Environmental Pollution* 185: 281-294.

Canellas PF, Cleland WW. 1991. Carbon-13 and deuterium isotope effects on the reaction catalyzed by glyceraldehyde-3-phosphate dehydrogenase. *Biochemistry* 30: 8871-8876.

Cernusak L, Farquhar G, Pate J. 2005. Environmental and physiological controls over oxygen and carbon isotope composition of Tasmanian blue gum, *Eucalyptus globulus*. *Tree Physiology* 25: 129-146.

Cernusak LA, Barbeta A, Bush RT, Eichstaedt R, Ferrio JP, Flanagan LB, Gessler A, Martín-Gómez P, Hirl RT, Kahmen A et al. 2022. Do ²H and ¹⁸O in leaf water reflect environmental drivers differently? *New Phytologist* 235: 41-51.

Cernusak LA, Barbour MM, Arndt SK, Cheesman AW, English NB, Feild TS, Helliker BR, Holloway-Phillips MM, Holtum JAM, Kahmen A et al. 2016. Stable isotopes in leaf water of terrestrial plants. *Plant, Cell & Environment* 39: 1087-1102.

Cernusak LA, Wong S-C, Farquhar GD. 2003. Oxygen isotope composition of phloem sap in relation to leaf water in *Ricinus communis*. *Functional Plant Biology* 30: 1059-1070.

Cheesman AW, Cernusak LA. 2016. Infidelity in the outback: climate signal recorded in Δ¹⁸O of leaf but not branch cellulose of eucalypts across an Australian aridity gradient. *Tree Physiology* 37: 554-564.

Cook GD, Heerdegen RG. 2001. Spatial variation in the duration of the rainy season in monsoonal Australia. *International Journal of Climatology* 21: 1723-1732.

Cook GD, Williams RJ, Hutley LB, O'Grady AP, Liedloff AC. 2002. Variation in vegetative water use in the savannas of the North Australian Tropical Transect. *Journal of Vegetation Science* 13: 413-418.

Cormier MA, Werner RA, Leuenberger MC, Kahmen A. 2019. ²H-enrichment of cellulose and n-alkanes in heterotrophic plants. *Oecologia* 189: 365-373.

Cormier M-A, Werner RA, Sauer PE, Gröcke DR, Leuenberger MC, Wieloch T, Schleucher J, Kahmen A. 2018. ²H-fractionations during the biosynthesis of carbohydrates and lipids imprint a metabolic signal on the δ²H values of plant organic compounds. *New Phytologist* 218: 479-491.

Dancer J, Hatzfeld W-D, Stitt M. 1990. Cytosolic cycles regulate the turnover of sucrose in heterotrophic cell-suspension cultures of *Chenopodium rubrum* L. *Planta* 182: 223-231.

DeNiro MJ, Epstein S. 1981. Isotopic composition of cellulose from aquatic organisms. *Geochimica et Cosmochimica Acta* 45: 1885-1894.

Dongmann G, Nürnberg HW, Förstel H, Wagener K. 1974. On the enrichment of H₂¹⁸O in the leaves of transpiring plants. *Radiation and Environmental Biophysics* 11: 41-52.

Eamus D, O'Grady AP, Hutley L. 2000. Dry season conditions determine wet season water use in the wet-tropical savannas of northern Australia. *Tree Physiology* 20: 1219-1226.

Epstein S, Yapp CJ, Hall JH. 1976. The determination of the D/H ratio of non-exchangeable hydrogen in cellulose extracted from aquatic and land plants. *Earth and Planetary Science Letters* 30: 241-251.

Farquhar GD, Barbour MM, Henry BK. 1998. Interpretation of oxygen isotope composition of leaf material. In: Griffiths H, ed. *Stable isotopes: integration of biological, ecological, and geochemical processes*. Oxford, UK: BIOS Scientific, 27-48.

Farquhar GD, Lloyd J. 1993. Carbon and oxygen isotope effects in the exchange of carbon dioxide between terrestrial plants and the atmosphere. In: Ehleringer JR, Hall AE, Farquhar GD, eds. *Stable isotopes and plant carbon-water relations*. San Diego, CA, USA: Academic Press, 47-70.

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Geigenberger P. 2003. Response of plant metabolism to too little oxygen. *Current Opinion in Plant Biology* 6: 247-256.

Gessler A, Brandes E, Keitel C, Boda S, Kayler ZE, Granier A, Barbour M, Farquhar GD, Treydde K. 2013. The oxygen isotope enrichment of leaf-exported assimilates – does it always reflect lamina leaf water enrichment? *New Phytologist* 200: 144-157.

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Malaisse WJ, Liemans V, Malaisse-Lagae F, Ottinger R, Willem R. 1991. Phosphoglucoisomerase-catalyzed interconversion of hexose phosphates. *Molecular and Cellular Biochemistry* 103: 131–140.

McCarroll D, Loader NJ. 2004. Stable isotopes in tree rings. *Quaternary Science Reviews* 23: 771–801.

McInerney FA, Gerber C, Dangerfield E, Cernusak LA, Puccini A, Szarvas S, Singh T, Welti N. 2023. Leaf water δ¹⁸O, δ²H and *d*-excess isoscapes for Australia using region-specific plant parameters and non-equilibrium vapour. *Hydrological Processes* 37: e14878.

Meier-Augenstein W, Kemp HF, Schenk ER, Almirall JR. 2014. Discrimination of unprocessed cotton on the basis of geographic origin using multi-element stable isotope signatures. *Rapid Communications in Mass Spectrometry* 28: 545–552.

van der Merwe MJ, Groenewald JH, Stitt M, Kossmann J, Botha FC. 2010. Downregulation of pyrophosphate: D-fructose-6-phosphate 1-phosphotransferase activity in sugarcane culms enhances sucrose accumulation due to elevated hexose-phosphate levels. *Planta* 231: 595–608.

Miller JM, Williams RJ, Farquhar GD. 2001. Carbon isotope discrimination by a sequence of *Eucalyptus* species along a subcontinental rainfall gradient in Australia. *Functional Ecology* 15: 222–232.

Nabeshima E, Nakatsuka T, Kagawa A, Hiura T, Funada R. 2018. Seasonal changes of δD and δ¹⁸O in tree-ring cellulose of *Quercus crispula* suggest a change in post-photosynthetic processes during earlywood growth. *Tree Physiology* 38: 1829–1840.

Nakatsuka T, Sano M, Li Z, Xu C, Tsushima A, Shigeoka Y, Sho K, Ohnishi K, Sakamoto M, Ozaki H *et al*. 2020. A 2600-year summer climate reconstruction in central Japan by integrating tree-ring stable oxygen and hydrogen isotopes. *Climate of the Past* 16: 2153–2172.

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Nijzink RC, Schymanski SJ. 2022. Vegetation optimality explains the convergence of catchments on the Budyko curve. *Hydrology and Earth System Sciences* 26: 6289–6309.

Noltman EA. 1972. Aldose-ketose isomerases. In: Boyer PD, ed. *The enzymes*. San Diego, CA, USA: Academic Press, 271–354.

O'Donoghue AC, Amyes TL, Richard JP. 2005a. Hydron transfer catalyzed by triosephosphate isomerase. Products of isomerization of (r)-glyceraldehyde 3-phosphate in D₂O. *Biochemistry* 44: 2610–2621.

O'Donoghue AC, Amyes TL, Richard JP. 2005b. Hydron transfer catalyzed by triosephosphate isomerase. Products of isomerization of dihydroxyacetone phosphate in D₂O. *Biochemistry* 44: 2622–2631.

Offermann C, Ferrio JP, Holst J, Grote R, Siegwolf R, Kayler Z, Gessler A. 2011. The long way down – are carbon and oxygen isotope signals in the tree ring uncoupled from canopy physiological processes? *Tree Physiology* 31: 1088–1102.

O'Grady AP, Eamus D, Hutley LB. 1999. Transpiration increases during the dry season: patterns of tree water use in eucalypt open-forests of northern Australia. *Tree Physiology* 19: 591–597.

Pendall E, Williams DG, Leavitt SW. 2005. Comparison of measured and modeled variations in piñon pine leaf water isotopic enrichment across a summer moisture gradient. *Oecologia* 145: 605–618.

Pierce D. 2023. *NCDF4: interface to Unidata netCDF (v.4 or earlier) format data files*. [WWW document] URL https://CRAN.R-project.org/package=ncdf4 [accessed 22 May 2023].

R Core Team. 2019. *R: a language and environment for statistical computing*. Vienna, Austria: R Foundation for Statistical Computing.

Roden JS, Lin G, Ehleringer JR. 2000. A mechanistic model for interpretation of hydrogen and oxygen isotope ratios in tree-ring cellulose. *Geochimica et Cosmochimica Acta* 64: 21–35.

Rogers CDW, Beringer J. 2017. Describing rainfall in northern Australia using multiple climate indices. *Biogeosciences* 14: 597–615.

Rose IA, O'Connell EL. 1961. Intramolecular hydrogen transfer in the phosphoglucose isomerase reaction. *The Journal of Biological Chemistry* 236: 3086–3092.

Ruppenthal M, Oelmann Y, del Valle HF, Wilcke W. 2015. Stable isotope ratios of nonexchangeable hydrogen in organic matter of soils and plants along a 2100-km climb sequence in Argentina: new insights into soil organic matter sources and transformations? *Geochimica et Cosmochimica Acta* 152: 54–71.

Sanchez-Bragado R, Serret MD, Marimon RM, Bort J, Araus JL. 2019. The hydrogen isotope composition δ²H reflects plant performance. *Plant Physiology* 180: 793–812.

Schiegl WE, Vogel JC. 1970. Deuterium content of organic matter. *Earth and Planetary Science Letters* 7: 307–313.

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Loader NJ, Santillo PM, Woodman-Ralph JP, Rolfe JE, Hall MA, Gagen M, Robertson I, Wilson R, Froyd CA, McCarroll D. 2008. Multiple stable isotopes from oak trees in southwestern Scotland and the potential for stable isotope dendroclimatology in maritime climatic regions. *Chemical Geology* 252: 62–71.

Malaisse WJ, Liemans V, Malaisse-Lagae F, Ottinger R, Willem R. 1991. Phosphoglucoisomerase-catalyzed interconversion of hexose phosphates. *Molecular and Cellular Biochemistry* 103: 131–140.

McCarroll D, Loader NJ. 2004. Stable isotopes in tree rings. *Quaternary Science Reviews* 23: 771–801.

McInerney FA, Gerber C, Dangerfield E, Cernusak LA, Puccini A, Szarvas S, Singh T, Welti N. 2023. Leaf water δ¹⁸O, δ²H and *d*-excess isoscapes for Australia using region-specific plant parameters and non-equilibrium vapour. *Hydrological Processes* 37: e14878.

Meier-Augenstein W, Kemp HF, Schenk ER, Almirall JR. 2014. Discrimination of unprocessed cotton on the basis of geographic origin using multi-element stable isotope signatures. *Rapid Communications in Mass Spectrometry* 28: 545–552.

van der Merwe MJ, Groenewald JH, Stitt M, Kossmann J, Botha FC. 2010. Downregulation of pyrophosphate: D-fructose-6-phosphate 1-phosphotransferase activity in sugarcane culms enhances sucrose accumulation due to elevated hexose-phosphate levels. *Planta* 231: 595–608.

Miller JM, Williams RJ, Farquhar GD. 2001. Carbon isotope discrimination by a sequence of *Eucalyptus* species along a subcontinental rainfall gradient in Australia. *Functional Ecology* 15: 222–232.

Nabeshima E, Nakatsuka T, Kagawa A, Hiura T, Funada R. 2018. Seasonal changes of δD and δ¹⁸O in tree-ring cellulose of *Quercus crispula* suggest a change in post-photosynthetic processes during earlywood growth. *Tree Physiology* 38: 1829–1840.

Nakatsuka T, Sano M, Li Z, Xu C, Tsushima A, Shigeoka Y, Sho K, Ohnishi K, Sakamoto M, Ozaki H *et al*. 2020. A 2600-year summer climate reconstruction in central Japan by integrating tree-ring stable oxygen and hydrogen isotopes. *Climate of the Past* 16: 2153–2172.

Neuwirth E. 2022. *RCOLORBREWER: ColorBrewer palettes*. [WWW document] URL https://CRAN.R-project.org/package=RColorBrewer [accessed 22 May 2023].

Nijzink RC, Schymanski SJ. 2022. Vegetation optimality explains the convergence of catchments on the Budyko curve. *Hydrology and Earth System Sciences* 26: 6289–6309.

Noltman EA. 1972. Aldose-ketose isomerases. In: Boyer PD, ed. *The enzymes*. San Diego, CA, USA: Academic Press, 271–354.

O'Donoghue AC, Amyes TL, Richard JP. 2005a. Hydron transfer catalyzed by triosephosphate isomerase. Products of isomerization of (r)-glyceraldehyde 3-phosphate in D₂O. *Biochemistry* 44: 2610–2621.

O'Donoghue AC, Amyes TL, Richard JP. 2005b. Hydron transfer catalyzed by triosephosphate isomerase. Products of isomerization of dihydroxyacetone phosphate in D₂O. *Biochemistry* 44: 2622–2631.

Offermann C, Ferrio JP, Holst J, Grote R, Siegwolf R, Kayler Z, Gessler A. 2011. The long way down – are carbon and oxygen isotope signals in the tree ring uncoupled from canopy physiological processes? *Tree Physiology* 31: 1088–1102.

O'Grady AP, Eamus D, Hutley LB. 1999. Transpiration increases during the dry season: patterns of tree water use in eucalypt open-forests of northern Australia. *Tree Physiology* 19: 591–597.

Pendall E, Williams DG, Leavitt SW. 2005. Comparison of measured and modeled variations in piñon pine leaf water isotopic enrichment across a summer moisture gradient. *Oecologia* 145: 605–618.

Pierce D. 2023. *NCDF4: interface to Unidata netCDF (v.4 or earlier) format data files*. [WWW document] URL https://CRAN.R-project.org/package=ncdf4 [accessed 22 May 2023].

R Core Team. 2019. *R: a language and environment for statistical computing*. Vienna, Austria: R Foundation for Statistical Computing.

Roden JS, Lin G, Ehleringer JR. 2000. A mechanistic model for interpretation of hydrogen and oxygen isotope ratios in tree-ring cellulose. *Geochimica et Cosmochimica Acta* 64: 21–35.

Rogers CDW, Beringer J. 2017. Describing rainfall in northern Australia using multiple climate indices. *Biogeosciences* 14: 597–615.

Rose IA, O'Connell EL. 1961. Intramolecular hydrogen transfer in the phosphoglucose isomerase reaction. *The Journal of Biological Chemistry* 236: 3086–3092.

Ruppenthal M, Oelmann Y, del Valle HF, Wilcke W. 2015. Stable isotope ratios of nonexchangeable hydrogen in organic matter of soils and plants along a 2100-km climb sequence in Argentina: new insights into soil organic matter sources and transformations? *Geochimica et Cosmochimica Acta* 152: 54–71.

Sanchez-Bragado R, Serret MD, Marimon RM, Bort J, Araus JL. 2019. The hydrogen isotope composition δ²H reflects plant performance. *Plant Physiology* 180: 793–812.

Schiegl WE, Vogel JC. 1970. Deuterium content of organic matter. *Earth and Planetary Science Letters* 7: 307–313.

Schleucher J, Vanderveer P, Markley JL, Sharkey TD. 1999. Intramolecular deuterium distributions reveal disequilibrium of chloroplast phosphoglucose isomerase. *Plant, Cell & Environment* 22: 525–533.
Schuler P, Cormier M-A, Werner RA, Buchmann N, Gessler A, Vitali V, Saurer M, Lehmann MM. 2022. A high-temperature water vapor equilibration method to determine non-exchangeable hydrogen isotope ratios of sugar, starch and cellulose. *Plant, Cell & Environment* 45: 12–22.

Schuler P, Vitali V, Saurer M, Gessler A, Buchmann N, Lehmann MM. 2023. Hydrogen isotope fractionation in carbohydrates of leaves and xylem tissues follows distinct phylogenetic patterns: a common garden experiment with 73 tree and shrub species. *New Phytologist* 239: 547–561.

Schulze ED, Williams RJ, Farquhar GD, Schulze W, Langridge J, Miller JM, Walker BH. 1998. Carbon and nitrogen isotope discrimination and nitrogen nutrition of trees along a rainfall gradient in northern Australia. *Functional Plant Biology* 25: 413–425.

Song X, Barbour MM, Saurer M, Helliker BR. 2011. Examining the large-scale convergence of photosynthesis-weighted tree leaf temperatures through stable oxygen isotope analysis of multiple data sets. *New Phytologist* 192: 912–924.

Song X, Farquhar GD, Gessler A, Barbour MM. 2014. Turnover time of the non-structural carbohydrate pool influences δ¹⁸O of leaf cellulose. *Plant, Cell & Environment* 37: 2500–2507.

Song X, Lorrey A, Barbour MM. 2022. Environmental, physiological and biochemical processes determining the oxygen isotope ratio of tree-ring cellulose. In: Siegwolf RTW, Brooks JR, Roden J, Saurer M, eds. *Stable isotopes in tree rings: inferring physiological, climatic and environmental responses*. Cham, Switzerland: Springer International, 311–329.

Sternberg L, Deniro MJ. 1983. Isotopic composition of cellulose from C₃, C₄, and CAM plants growing near one another. *Science* 220: 947–949.

Sternberg L, Ellsworth PFV. 2011. Divergent biochemical fractionation, not convergent temperature, explains cellulose oxygen isotope enrichment across latitudes. *PLoS ONE* 6: e28040.

Terwilliger VJ, Deniro MJ. 1995. Hydrogen isotope fractionation in wood-producing avocado seedlings: biological constraints to paleoclimatic interpretations of δD values in tree ring cellulose nitrate. *Geochimica et Cosmochimica Acta* 59: 5199–5207.

Vitali V, Martínez-Sancho E, Treydte K, Andreu-Hayles L, Dorado-Liñán I, Gutierrez E, Helle G, Leuenberger M, Loader NJ, Rinne-Garmston KT *et al.* 2022. The unknown third – hydrogen isotopes in tree-ring cellulose across Europe. *Science of the Total Environment* 813: 152281.

Voelker SL, Brooks JR, Meinzer FC, Roden J, Pazdur A, Pawelczyk S, Hartsough P, Snyder K, Plavcová L, Šantrůček J. 2014. Reconstructing relative humidity from plant δ¹⁸O and δD as deuterium deviations from the global meteoric water line. *Ecological Applications* 24: 960–975.

Warton DI, Duursma RA, Falster DS, Taskinen S. 2012. *SMatr 3* – an R package for estimation and inference about allometric lines. *Methods in Ecology and Evolution* 3: 257–259.

Wassenaar LI, Hobson KA, Sisti L. 2015. An online temperature-controlled vacuum-equilibration preparation system for the measurement of δ²H values of non-exchangeable-H and of δ¹⁸O values in organic materials by isotope-ratio mass spectrometry. *Rapid Communications in Mass Spectrometry* 29: 397–407.

Waterhouse JS, Cheng S, Juchelka D, Loader NJ, McCarroll D, Switsur VR, Gautam L. 2013. Position-specific measurement of oxygen isotope ratios in cellulose: isotopic exchange during heterotrophic cellulose synthesis. *Geochimica et Cosmochimica Acta* 112: 178–191.

Wickham H. 2016. *GGPLOT2: elegant graphics for data anlaysis*. New York, NY, USA: Springer-Verlag.

Wieloch T, Augusti A, Schleucher J. 2022a. Anaplerotic flux into the Calvin–Benson cycle: hydrogen isotope evidence for *in vivo* occurrence in C₃ metabolism. *New Phytologist* 234: 405–411.

Wieloch T, Grabner M, Augusti A, Serk H, Ehlers I, Yu J, Schleucher J. 2022b. Metabolism is a major driver of hydrogen isotope fractionation recorded in tree-ring glucose of *Pinus nigra*. *New Phytologist* 234: 449–461.

Yakir D. 1992. Variations in the natural abundance of oxygen-18 and deuterium in plant carbohydrates. *Plant, Cell & Environment* 15: 1005–1020.

Yakir D, DeNiro MJ. 1990. Oxygen and hydrogen isotope fractionation during cellulose metabolism in *Lemna gibba* L. *Plant Physiology* 93: 325–332.

Zhou Y, Zhang B, Stuart-Williams H, Grice K, Hocart CH, Gessler A, Kayler ZE, Farquhar GD. 2018. On the contributions of photorespiration and compartmentation to the contrasting intramolecular ²H profiles of C₃ and C₄ plant sugars. *Phytochemistry* 145: 197–206.

Zhu Z, Yin X, Song X, Wang B, Ma R, Zhao Y, Rani A, Wang Y, Yan Q, Jing S *et al.* 2020. Leaf transition from heterotrophy to autotrophy is recorded in the intraleaf C, H and O isotope patterns of leaf organic matter. *Rapid Communications in Mass Spectrometry* 34: e8840.

## Supporting Information

Additional Supporting Information may be found online in the Supporting Information section at the end of the article.

**Dataset S1** Metadata and isotopic data used within this study.

**Fig. S1** Comparison of δ²H values between analytical methods for a subset of cellulose samples.

**Fig. S2** Relationship between observed and estimated leaf water delta values.

**Fig. S3** Fractional difference between predicted evaporative site isotopic enrichments.

**Fig. S4** *f*<sub>O</sub> estimated with different model assumptions.

**Fig. S5** Simplified schematic of key reactions with isotope effects.

**Notes S1** Description and results of the sensitivity analysis.

**Table S1** Data sources for *f*<sub>O</sub> and *f*<sub>H</sub>.

**Table S2** Data inputs and model parameterisation of the cellulose isotope model.

**Table S3** Sensitivity analyses performed on cellulose isotope model parameters.

**Table S4** Coefficients from the regression of expected vs observed cellulose isotope values.

**Table S5** Slope values of the regression between ε<sub>exp-obs</sub> and MAP.

**Table S6** Coefficients of the regression between *f*<sub>O</sub> and MAP for branch wood cellulose.

**Table S7** Summary of site averaged *p*<sub>ex</sub>, *f*<sub>H</sub> and *p*<sub>x</sub> estimates.

Please note: Wiley is not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the *New Phytologist* Central Office.
Schuler P, Cormier M-A, Werner RA, Buchmann N, Gessler A, Vitali V, Saurer M, Lehmann MM. 2022. A high-temperature water vapor equilibration method to determine non-exchangeable hydrogen isotope ratios of sugar, starch and cellulose. *Plant, Cell & Environment* 45: 12–22.

Schuler P, Vitali V, Saurer M, Gessler A, Buchmann N, Lehmann MM. 2023. Hydrogen isotope fractionation in carbohydrates of leaves and xylem tissues follows distinct phylogenetic patterns: a common garden experiment with 73 tree and shrub species. *New Phytologist* 239: 547–561.

Schulze ED, Williams RJ, Farquhar GD, Schulze W, Langridge J, Miller JM, Walker BH. 1998. Carbon and nitrogen isotope discrimination and nitrogen nutrition of trees along a rainfall gradient in northern Australia. *Functional Plant Biology* 25: 413–425.

Song X, Barbour MM, Saurer M, Helliker BR. 2011. Examining the large-scale convergence of photosynthesis-weighted tree leaf temperatures through stable oxygen isotope analysis of multiple data sets. *New Phytologist* 192: 912–924.

Song X, Farquhar GD, Gessler A, Barbour MM. 2014. Turnover time of the non-structural carbohydrate pool influences δ¹⁸O of leaf cellulose. *Plant, Cell & Environment* 37: 2500–2507.

Song X, Lorrey A, Barbour MM. 2022. Environmental, physiological and biochemical processes determining the oxygen isotope ratio of tree-ring cellulose. In: Siegwolf RTW, Brooks JR, Roden J, Saurer M, eds. *Stable isotopes in tree rings: inferring physiological, climatic and environmental responses*. Cham, Switzerland: Springer International, 311–329.

Sternberg L, Deniro MJ. 1983. Isotopic composition of cellulose from C₃, C₄, and CAM plants growing near one another. *Science* 220: 947–949.

Sternberg L, Ellsworth PFV. 2011. Divergent biochemical fractionation, not convergent temperature, explains cellulose oxygen isotope enrichment across latitudes. *PLoS ONE* 6: e28040.

Terwilliger VJ, Deniro MJ. 1995. Hydrogen isotope fractionation in wood-producing avocado seedlings: biological constraints to paleoclimatic interpretations of δD values in tree ring cellulose nitrate. *Geochimica et Cosmochimica Acta* 59: 5199–5207.

Vitali V, Martínez-Sancho E, Treydte K, Andreu-Hayles L, Dorado-Liñán I, Gutierrez E, Helle G, Leuenberger M, Loader NJ, Rinne-Garmston KT *et al.* 2022. The unknown third – hydrogen isotopes in tree-ring cellulose across Europe. *Science of the Total Environment* 813: 152281.

Voelker SL, Brooks JR, Meinzer FC, Roden J, Pazdur A, Pawelczyk S, Hartsough P, Snyder K, Plavcová L, Šantrůček J. 2014. Reconstructing relative humidity from plant δ¹⁸O and δD as deuterium deviations from the global meteoric water line. *Ecological Applications* 24: 960–975.

Warton DI, Duursma RA, Falster DS, Taskinen S. 2012. *SMatr 3* – an R package for estimation and inference about allometric lines. *Methods in Ecology and Evolution* 3: 257–259.

Wassenaar LI, Hobson KA, Sisti L. 2015. An online temperature-controlled vacuum-equilibration preparation system for the measurement of δ²H values of non-exchangeable-H and of δ¹⁸O values in organic materials by isotope-ratio mass spectrometry. *Rapid Communications in Mass Spectrometry* 29: 397–407.

Waterhouse JS, Cheng S, Juchelka D, Loader NJ, McCarroll D, Switsur VR, Gautam L. 2013. Position-specific measurement of oxygen isotope ratios in cellulose: isotopic exchange during heterotrophic cellulose synthesis. *Geochimica et Cosmochimica Acta* 112: 178–191.

Wickham H. 2016. *GGPLOT2: elegant graphics for data anlaysis*. New York, NY, USA: Springer-Verlag.

Wieloch T, Augusti A, Schleucher J. 2022a. Anaplerotic flux into the Calvin–Benson cycle: hydrogen isotope evidence for *in vivo* occurrence in C₃ metabolism. *New Phytologist* 234: 405–411.

Wieloch T, Grabner M, Augusti A, Serk H, Ehlers I, Yu J, Schleucher J. 2022b. Metabolism is a major driver of hydrogen isotope fractionation recorded in tree-ring glucose of *Pinus nigra*. *New Phytologist* 234: 449–461.

Yakir D. 1992. Variations in the natural abundance of oxygen-18 and deuterium in plant carbohydrates. *Plant, Cell & Environment* 15: 1005–1020.

Yakir D, DeNiro MJ. 1990. Oxygen and hydrogen isotope fractionation during cellulose metabolism in *Lemna gibba* L. *Plant Physiology* 93: 325–332.

Zhou Y, Zhang B, Stuart-Williams H, Grice K, Hocart CH, Gessler A, Kayler ZE, Farquhar GD. 2018. On the contributions of photorespiration and compartmentation to the contrasting intramolecular ²H profiles of C₃ and C₄ plant sugars. *Phytochemistry* 145: 197–206.

Zhu Z, Yin X, Song X, Wang B, Ma R, Zhao Y, Rani A, Wang Y, Yan Q, Jing S *et al.* 2020. Leaf transition from heterotrophy to autotrophy is recorded in the intraleaf C, H and O isotope patterns of leaf organic matter. *Rapid Communications in Mass Spectrometry* 34: e8840.

## Supporting Information

Additional Supporting Information may be found online in the Supporting Information section at the end of the article.

**Dataset S1** Metadata and isotopic data used within this study.

**Fig. S1** Comparison of δ²H values between analytical methods for a subset of cellulose samples.

**Fig. S2** Relationship between observed and estimated leaf water delta values.

**Fig. S3** Fractional difference between predicted evaporative site isotopic enrichments.

**Fig. S4** *f*<sub>O</sub> estimated with different model assumptions.

**Fig. S5** Simplified schematic of key reactions with isotope effects.

**Notes S1** Description and results of the sensitivity analysis.

**Table S1** Data sources for *f*<sub>O</sub> and *f*<sub>H</sub>.

**Table S2** Data inputs and model parameterisation of the cellulose isotope model.

**Table S3** Sensitivity analyses performed on cellulose isotope model parameters.

**Table S4** Coefficients from the regression of expected vs observed cellulose isotope values.

**Table S5** Slope values of the regression between ε<sub>exp-obs</sub> and MAP.

**Table S6** Coefficients of the regression between *f*<sub>O</sub> and MAP for branch wood cellulose.

**Table S7** Summary of site averaged *p*<sub>ex</sub>, *f*<sub>H</sub> and *p*<sub>x</sub> estimates.

Please note: Wiley is not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the *New Phytologist* Central Office.