Intramolecular ¹³C analysis of tree rings provides multiple plant ecophysiology signals covering decades

Abstract

Measurements of carbon isotope contents of plant organic matter provide important information in diverse fields such as plant breeding, ecophysiology, biogeochemistry and paleoclimatology. They are currently based on ¹³C/¹²C ratios of specific, whole metabolites, but we show here that intramolecular ratios provide higher resolution information. In the glucose units of tree-ring cellulose of 12 tree species, we detected large differences in ¹³C/¹²C ratios (>10‰) among carbon atoms, which provide isotopically distinct inputs to major global C pools, including wood and soil organic matter. Thus, considering position-specific differences can improve characterisation of soil-to-atmosphere carbon fluxes and soil metabolism. In a Pinus nigra tree-ring archive formed from 1961 to 1995, we found novel ¹³C signals, and show that intramolecular analysis enables more comprehensive and precise signal extraction from tree rings, and thus higher resolution reconstruction of plants’ responses to climate change. Moreover, we propose an ecophysiological mechanism for the introduction of a ¹³C signal, which links an environmental shift to the triggered metabolic shift and its intramolecular ¹³C signature. In conclusion, intramolecular ¹³C analyses can provide valuable new information about long-term metabolic dynamics for numerous applications.

Introduction

In-depth understanding of the earth system is required to preserve intact ecosystems and protect biodiversity, maintain food supplies and secure other resources in the context of ongoing environmental change. Measurements of stable carbon isotope ratios (¹³C/¹²C ratios, expressed as δ¹³C) have helped to develop such understanding by (inter alia) constraining global C cycle models¹ and illuminating plant-environment interactions². However, there are major uncertainties in earth system models due to incomplete characterisation of soil microbial, biogeochemical, plant physiological, and climatic processes. Notably, estimation of soil-to-atmosphere CO₂ fluxes based on δ¹³C analysis is impeded by (inter alia) lack of knowledge about ¹³C fractionations by soil microbes³. Similarly, simulated C exchange fluxes between the atmosphere and biosphere are insufficiently constrained due to limited understanding of CO₂ fertilization effects⁴, i.e., the increase in plant carbon sequestration associated with rising atmospheric [CO₂].

Natural plant archives, including tree rings, enable ¹³C analyses over decadal to millennial time scales. This is important because covering such timeframes by direct monitoring or manipulative experiments is impossible, but it is essential for robustly constraining vegetation modules of earth system models and predicting changes in plant productivity under climate change. However, the information that can be extracted from archives is currently limited by lack of sufficient understanding of plant ¹³C fractionation. There are well-established differences in ¹³C abundances among intramolecular C positions in various metabolites, including glucose^5–9, but extant studies in plant ecophysiology and the earth sciences report conventional ¹³C/¹²C measurements of whole molecules. These whole-molecule studies rely on the assumption that intramolecular variability is negligible. Here, we test this assumption and investigate the potential of intramolecular ¹³C measurements for extracting information from archives.

To analyse effects of intramolecular ¹³C variation, we distinguish two major ¹³C fractionation systems, diffusion-Rubisco (DR) fractionation and post-Rubisco (PR) fractionation. DR fractionation refers to the ¹³C fractionation by CO₂ diffusion from ambient air into plant chloroplasts and Rubisco-mediated CO₂ fixation², previously called photosynthetic fractionation¹⁰. The rationale for the change in nomenclature is outlined below. Rubisco adds a single carbon from CO₂ to ribulose-1,5-bisphosphate. Therefore, DR fractionation cannot cause intramolecular ¹³C variation, i.e. it is not position-specific. In contrast, PR fractionation denotes ¹³C fractionation by enzymes acting downstream of Rubisco. This type of fractionation is known to occur at individual C positions within metabolites¹¹, i.e. it is position-specific. PR fractionation occurs at metabolic branch points⁹. Theoretically, events such as changes in metabolite allocation at an isotope-sensitive branch point will change the intramolecular ¹³C pattern. Thus, intramolecular ¹³C distributions should carry signals reflecting such shifts in metabolic branching.

To explore the potential of the intramolecular level, we measured intramolecular ¹³C distributions in the glucose units of tree-ring cellulose of an annually resolved Pinus nigra tree-ring series. The samples originate from a moisture limited site, and cover the period 1961–1995. We then conducted a comparative time-series analysis with conventional whole-molecule and intramolecular ¹³C/¹²C ratios. Furthermore, we measured the same distributions in samples of six angiosperm and five additional gymnosperm species from globally distributed sites.

We report six findings. First, ¹³C distributions show intramolecular differences of the order of 10‰. Second, while a signal due to DR fractionation is present at some C positions of Pinus nigra tree-ring glucose, it is attenuated or even absent at other positions. Third, the intramolecular approach enables better description and prediction of environmental variables. Fourth, Hierarchical Cluster Analysis revealed PR signals at several C positions. Fifth, environmental drivers control PR fractionation. Finally, we propose an ecophysiological mechanism for the origin of a PR signal linking an environmental shift with a defined metabolic shift, which leaves its isotopic signature in the tree-ring archive. We conclude that intramolecular ¹³C analysis greatly extends the information that can be extracted from tree-ring archives.

Intramolecular ¹³C fractionation: Concepts and nomenclature

As described above, we distinguish here between diffusion-Rubisco (DR) and post-Rubisco (PR) fractionation. Synonyms for DR and PR fractionation are photosynthetic fractionation, and post-photosynthetic or post-carboxylation fractionation, respectively^10,12. Photosynthesis involves the action of several fractionating enzymes, e.g. Rubisco, transketolase, and aldolase¹³, but the term photosynthetic fractionation usually refers exclusively to fractionation by CO₂ diffusion and Rubisco-catalysed carboxylation. Fractionations occurring downstream of Rubisco carboxylation have been called post-carboxylation fractionation, but other fractionating carboxylases occur in plants. Robust understanding of high-resolution intramolecular ¹³C fractionation requires unambiguous terminology. Therefore, we here introduce the terms DR and PR fractionation, which allow the classification of plant ¹³C fractionations into non-position-specific and position-specific processes.

¹³C discrimination, a measure of the ¹³C fractionation in plants, has been defined¹⁴ as:

$$\mathrm{\Delta }=({\mathrm{R}}_{\mathrm{a}}{/\mathrm{R}}_{\mathrm{p}})-1$$ 1

where R_a and R_p are the ¹³C/¹²C ratios of a carbon source and plant sample, respectively. To screen for intramolecular ¹³C signals, suitable isotope parameters are required. In analogy to Δ, we define positional ¹³C discrimination as:

$${\mathrm{\Delta }}_{\mathrm{i}}=({\mathrm{R}}_{\mathrm{a}}{/\mathrm{R}}_{\mathrm{pi}})-1$$ 2

where R_pi is the ¹³C/¹²C ratio at carbon position i of a plant metabolite (see Fig. 1a for carbon assignments). With R_a and R_pi expressed in terms of the conventional δ scale as δ¹³C_a and δ¹³C_pi, respectively, Δ_i is given as:

$${\mathrm{\Delta }}_{\mathrm{i}}=({\mathrm{\delta }}^{13}{\mathrm{C}}_{\mathrm{a}}{-\mathrm{\delta }}^{13}{\mathrm{C}}_{\mathrm{pi}})/({1+\mathrm{\delta }}^{13}{\mathrm{C}}_{\mathrm{pi}})$$ 3

A process known as triose phosphate cycling (TPC) involves scrambling of substantial proportions (20–25%) of carbon between symmetry-related carbon positions in tree-ring glucose and can potentially confound existing intramolecular ¹³C signals, particularly leaf-level signals. Below, we present a convenient method for removing the effect of TPC from observed intramolecular ¹³C distributions of hexoses and verify its suitability. TPC-free positional ¹³C discrimination, Δ_i′, is then given as:

$${\mathrm{\Delta }}_{\mathrm{i}}\prime =({\mathrm{R}}_{\mathrm{a}}{/\mathrm{R}}_{\mathrm{pi}}\prime )-1$$ 4

and, in terms of δ, as:

$${\mathrm{\Delta }}_{\mathrm{i}}\prime =({\mathrm{\delta }}^{13}{\mathrm{C}}_{\mathrm{a}}{-\mathrm{\delta }}^{13}{\mathrm{C}}_{\mathrm{pi}}\prime )/{(1+\mathrm{\delta }}^{13}{\mathrm{C}}_{\mathrm{pi}}\prime )$$ 5

where isotope parameters marked by a prime are free of TPC-related variation. Δ_i and Δ_i′ each have specific uses: Δ_i, denoting observed ¹³C abundances, is relevant when tree-ring glucose enters microbiological and biogeochemical processes; Δ_i′, denoting TPC-free ¹³C abundances, enables better understanding of ¹³C fractionation systems in plants.

Figure 1.

Intramolecular ¹³C distributions and effects of growing season air vapour pressure deficit (VPD) on ¹³C discrimination. Data were acquired for tree-ring glucose of Pinus nigra laid down from 1961 to 1995 at a site in the Vienna basin. (a) Intramolecular ¹³C distributions (means over 31 years) expressed in terms of intramolecular ¹³C discrimination. Solid line, observed distribution (Δ_i); dashed line, TPC-free distribution (Δ_i′); dotted line, hypothetical distribution without positional ¹³C effects. Insert: Glucose unit of cellulose showing intramolecular locations of carbon positions, i. (b,c) Effects of VPD on whole-molecule ¹³C discrimination, Δ and on positional ¹³C discrimination at C-1 and C-4; Δ₁′ and Δ₄′, respectively. Linear regression demonstrates highly significant negative relationships between VPD and both Δ and Δ₁′, and no detectable relationship between VPD and Δ₄′ (ordinary least squares regressions, n = 31, Δ = −0.011VPD + 20.0, r = −0.72, p = 5.4*10⁻⁶; Δ₁′ = −0.023VPD + 29.1, r = −0.68, p = 3*10⁻⁵; Δ₄′ = 0.002VPD + 12.9, r = 0.09, p = 0.64).

Results

Tree-ring glucose exhibits a non-random intramolecular ¹³C distribution

First, we examined intramolecular ¹³C distributions by averaging all 31 annual distributions of the raw and TPC-free datasets (Δ_i and Δ_i′, respectively) of Pinus nigra. Both distributions show non-random patterns with intramolecular differences exceeding 10‰ (Fig. 1a; solid and dashed lines, respectively). Positional differences are more pronounced in Δ_i′ than in Δ_i. This is as expected, given that Δ_i′ is free of the influence of TPC, which causes partial averaging of positional ¹³C abundances (see below). We obtained similar intramolecular ¹³C distributions for six angiosperm and five additional gymnosperm species from different sites with global coverage (Fig. S1, Table S1). Our observations of distinct ¹³C patterns in tree-ring glucose are consistent with observations of glucose derived from other metabolites^15–18. As mentioned above, DR fractionation cannot induce intramolecular ¹³C differences. Thus, observed patterns show that PR fractionations have clearly detectable effects.

The observable DR signal in tree-ring glucose is position-specific

Above, we show that tree-ring glucose exhibits a pronounced intramolecular ¹³C pattern, which can be attributed to PR fractionation effects. If this pattern varies over time, then intramolecular ¹³C abundances may carry unique information about long-term metabolic dynamics. Therefore, all subsequent analyses focus on properties related to temporal variability of the intramolecular ¹³C patterns (i.e. intramolecular ¹³C signals).

A tree ring formed in a particular year may have had significant input of stored glucose monomers from previous years. If so, ¹³C time series would exhibit autocorrelation signals. Therefore, we tested all ¹³C time series (Δ, Δ_i, Δ_i′) for autocorrelation, applying temporal lags of one to three years (see SI). We found no evidence of autocorrelation, showing that interannual carryover of signals is negligible. Thus, all subsequent analyses focused on conditions during the year of tree-ring formation.

DR fractionation may be affected by diverse environmental variables¹⁹. It is routinely evaluated by measurements of whole-molecule ¹³C discrimination, Δ²⁰. An underlying assumption is that DR fractionation controls Δ. To search for the most influential environmental variable, we correlated Δ with air vapour pressure deficit, precipitation, soil moisture, air temperature, and global radiation during the growing season (VPD, PRE, SM, TMP, RAD, respectively; the method used to estimate the growing season is described in SI). VPD was found to be most strongly correlated with Δ (VPD, r = −0.72, p = 5*10⁻⁶; PRE, r = 0.44, p = 0.013; SM, r = 0.38, p = 0.038; TMP, r = −0.38, p = 0.033; RAD, r = −0.58, p = 7*10⁻⁴; n = 31). The strong negative VPD dependency is consistent with expectations for a moisture-limited site, published relationships and the well-established mechanisms underlying DR fractionation^2,19. Thus, the variability of DR fractionation is reflected by the variability of VPD in the first approximation. This establishes VPD as a proxy of DR fractionation under given conditions.

If DR fractionation was the only temporally variable fractionation process in plants, its signal strength should be equal at all positional time series of ¹³C discrimination, Δ_i′ (see above). We tested this by analysing the linear relationships between Δ_i′ and VPD. We found that VPD signal strengths vary among Δ_i′ (Fig. S3). The largest deviations from uniformity were detected in Δ₁′ and Δ₄′ (Figs. 1b,c and S3). While the slope of the Δ₁′~VPD regression is significantly steeper than the slope of the Δ~VPD regression (p = 0.02, see ANCOVA results in Table S4), the slope of the Δ₄′~VPD regression is not significantly different from zero (p = 0.64). Thus, the VPD signal is stronger in Δ₁′ than in Δ, and undetectable in Δ₄′, which implies that the DR signal is transmitted into tree-ring glucose in a position-specific manner.

The intramolecular approach enables better description and prediction of environmental variables

Correlation coefficients for the Δ~VPD and Δ₁′~VPD relationships are similar (r = −0.72 and −0.68, respectively). Thus, simple linear regression modelling provided no indications that Δ_i′ is superior to Δ as a proxy of environmental variables. Therefore, we tested the feasibility of capturing a higher-quality VPD signal using Δ_i′ in a more sophisticated modelling approach. Combining multiple linear regression modelling with automatic model selection, we generated a Δ_i′ model that describes VPD more precisely than the corresponding Δ model (VPD~Δ₁′ + Δ₃′ + Δ₅′, adjR² = 0.60, p = 4*10⁻⁶ vs. VPD~Δ, adjR² = 0.50, p = 5.4*10⁻⁶). In contrast to R², model evaluation by adjR² takes the number of explanatory variables into account, enabling comparison of models with different numbers of explanatory variables. Next, we tested the predictive abilities of both models by 10-fold cross-validation. We found that the Δ_i′ model predicts VPD more precisely (Q² = 0.52 vs. Q² = 0.43, where Q² denotes the cross-validated R²). These findings show that the intramolecular approach enables more precise description and prediction of VPD, and suggests that Δ_i′ might allow for improved climate reconstructions.

Tree-ring glucose contains several distinct intramolecular ¹³C signals

Due to the single carbon addition by Rubisco, DR fractionation equally affects all carbon entering photosynthesis (see above). However, the results presented above show that the DR signal is not equally distributed over all carbon positions of the downstream metabolite tree-ring glucose (Figs. 1b,c and S3), suggesting that PR fractionations influence Δ_i′, and have had varying effects in the 31-year tree-ring series. To confirm this implication, we screened for position-specific signals by hierarchical cluster analysis of Δ_i′. We found four clusters: Δ₁′ to Δ₂′, Δ₃′, Δ₄′, and Δ₅′ to Δ₆′ (Fig. 2a). Cluster formation and separation occur due to common and distinct variability, respectively. For instance, Δ₁′ and Δ₂′ as well as Δ₅′ and Δ₆′ share significantly correlated common signals (r = 0.54, p = 1.65*10⁻³, and r = 0.61, p = 2.36*10⁻⁴, respectively, n = 31). As Δ₁′ and Δ₆′ as well as Δ₂′ and Δ₅′ are uncorrelated (r = 0.08, p = 0.68, and r = 0.11, p = 0.71, respectively, n = 31), detected common signals are independent of each other. Thus, PR fractionations introduce ¹³C signals on top of the DR fractionation signal. Moreover, independence among clusters implies that intramolecular ¹³C patterns of tree-ring glucose vary on interannual timescales.

Figure 2.

Common variability among and components of variance in time-series of ¹³C discrimination. Data were acquired for tree-ring glucose of Pinus nigra laid down from 1961 to 1995 at a site in the Vienna basin. (a) Dendrogram showing clustering of time series of the TPC-free intramolecular ¹³C discrimination, Δ_i′. Asterisks denote the significance of correlation between Δ_i′ forming a cluster (*p ≤ 0.05; **p ≤ 10⁻²; ***p ≤ 10⁻³, n = 31). (b) De-convolution of the explainable component of variance in Δ, and Δ_i′ into an explained and an unexplained component of variance according to previous authors²¹. Explainable variance denotes the total variance minus estimated error variance. Explained variance denotes the component of variance accounted for by growing season air vapour pressure deficit. Unexplained varaince denotes the component of variance not accounted for by independent variables.

Ecophysiological information is better resolved on the intramolecular level

Observation of multiple intramolecular ¹³C signals implies that Δ is a composite of several signals with distinct physiological origins, and raises questions about the relative importance of DR and PR fractionation for Δ and Δ_i′. To address these questions, we first estimated the error variances in Δ and Δ_i′, which reflect random components of variance caused by finite measurement precision, which differs strongly between Δ and Δ_i′. Then, we calculated explainable components of variance, which may theoretically be linked to specific ecophysiological processes through modelling²¹. We then de-convoluted the explainable variance into a component explained by growing season air VPD and an unexplained component. With VPD as a proxy of DR fractionation (see above), this approach enables estimation of the relative importance of DR versus PR fractionation.

The explainable variance differs substantially among Δ_i′, from 0.45‰ for Δ₄′ to 3.37‰ for Δ₁′ (Fig. 2b). High values indicate substantial fractionation effects. From this perspective, Δ₁′, Δ₂′, Δ₅′ have high potential, and Δ₄′ has low potential for extracting ecophysiological information. In most Δ_i′, the unexplained component of variance exceeds the explained component. In Δ, both components of variance are similar. These findings suggest that PR fractionation has non-negligible effects on Δ and all Δ_i′. Moreover, they emphasise the generally high potential for extracting multiple ecophysiological signals from intramolecular-level ¹³C data, particularly novel signals reflecting dynamic regulation of enzyme reactions downstream of Rubisco.

Discussion

Intramolecular ¹³C distributions of tree-ring glucose are generally non-random (Fig. 1a and S1). This finding is consistent with previous observations of glucose derived from other species, tissues, and metabolites^15–18. Detected intramolecular ¹³C differences exceed 10‰. Thus, they are an order of magnitude larger than intra-annual ¹³C variations of atmospheric CO₂²². Moreover, their magnitude is similar to ¹³C differences reported for distinct plant metabolites²³, and to the whole ¹³C range reported for bulk plant materials, including C3 and C4 plants²⁴.

Wood cellulose (composed of glucose units) is one of the largest global C pools²⁵ and thus may strongly influence responses of the global C cycle to climatic changes. More specifically, wood cellulose is a major contributor to soil organic matter and, hence, subject to numerous biogeochemical transformations. These transformations are incompletely understood with respect to contributions of different microbial communities, turnover times of soil organic matter components, and responses to climatic changes²⁶.

Isotopes are powerful tools for analysing soil C turnover and associated phenomena. However, their use requires information about both fractionation effects of microbial communities³ and the isotopic composition of soil substrates. For instance, soil cellulose decomposition occurs under both aerobic and anaerobic conditions via several metabolic pathways²⁷. Because of the non-random ¹³C distribution of wood glucose (Fig. 1a, solid line and Fig. S1), different breakdown pathways will liberate CO₂ with distinct δ¹³C fingerprints. The δ¹³C of liberated CO₂ will equal the δ¹³C of substrate glucose, if glucose molecules are completely respired. If glucose is fermented (liberating C-3 and C-4), CO₂ with approximately 2.5‰ more positive δ¹³C values will be released (Fig. S1). Although this reasoning neglects fractionation effects of decarboxylation reactions, it illustrates the association of distinct breakdown pathways with substantial ¹³C differences in respired CO₂. Thus, it shows that considering positional ¹³C differences in soil organic matter will enable better characterisation of C turnover pathways and quantification of heterotrophic soil respiration. This, in turn, will help reduce uncertainties in regional- to global-scale models of terrestrial productivity, and earth system models²⁸.

Our data provide the first proof of temporal variability in intramolecular ¹³C patterns; more specifically, interannual variation in the ¹³C patterns of glucose derived from Pinus nigra tree rings (Fig. 2a). As non-random intramolecular ¹³C patterns result from specific isotopic effects of enzymes acting downstream of Rubisco²⁹, these observations establish a clear link between ¹³C abundances of plant organic matter and temporal variability in metabolic dynamics.

Our analyses show that intramolecular ¹³C abundances of tree-ring glucose contain information about the dynamics of both primary CO₂ fixation and downstream metabolic processes. While DR fractionation explains much of the interannual variability of Δ, PR fractionations are clearly not negligible (Figs. 2a,b). This may explain why the sensitivity of whole-molecule δ¹³C values in tree rings to ecophysiological parameters is highly variable³⁰, and why coefficients of determination (R²) obtained by attempts to model Δ rarely exceed 50%. This, in turn, suggests that multiple intramolecular signals are generally present in ¹³C datasets, and that intramolecular ¹³C analysis offers considerable scope to improve the resolution and robustness of ¹³C analyses.

While the mechanisms behind observed PR fractionation signals require further attention, intramolecular ¹³C ratios clearly offer more information than whole-molecule ratios (Figs. 2a,b). This will likely facilitate retrospective assessment of plant ecophysiological and environmental traits unrelated to the diffusion-Rubisco mechanism. To illustrate this point, we relate the magnitudes of observed Δ_i′~VPD dependencies to published magnitudes of enzyme isotope effects, and derive a hypothesis for the physiological origin of PR fractionations at glucose C-1 and C-2.

Δ₁′ and Δ₂′ exhibit higher degrees of explainable variance than any other Δ_i′, and are highly correlated with each other (Figs. 2a,b). In comparison, the correlation between Δ₃′ and the average over Δ₁′ and Δ₂′ is less significant. Above, we established VPD as proxy of DR fractionation under given conditions, and we found significant VPD correlations with Δ₁′ (r = −0.68, p = 3*10⁻⁵), Δ₂′ (r = −0.49, p = 5.5*10⁻³) and Δ₃′ (r = −0.51, p = 3.5*10⁻³). However, as shown in Figure S3, regression slopes between VPD and Δ_i′ decline in the order Δ₁′ (b₁′ = −0.0226 ± 0.0046SE ‰ Pa⁻¹), Δ₂′ (b₂′ = −0.0156 ± 0.0052SE ‰ Pa⁻¹) and Δ₃′ (b₃′ = −0.0116 ± 0.0037SE ‰ Pa⁻¹). DR fractionation is not position-specific, and can therefore only introduce regression slopes of equal size. Significant VPD correlations suggest that the DR signal is present at Δ₁′ to Δ₃′. Above-average explainable variance, a strong common signal, and steeper VPD slopes indicate that Δ₁′ and Δ₂′ contain additional VPD-dependent PR signals. Thus, assuming that b₃′ represents the common DR signal, the PR contributions to the Δ₁′~VPD and Δ₂′~VPD slopes are b_1PR′ = b₁′–b₃′ and b_2PR′ = b₂′–b₃′, respectively.

Phosphoglucose isomerase (PGI, EC 5.3.1.9) catalyses conversion of fructose-6-phosphate to glucose-6-phosphate (G6P), which is used in formation of starch or tree-ring cellulose. It is the only enzyme that simultaneously modifies C-1 and C-2 bonds of G6P and hence glucose units in tree-ring cellulose, and can therefore introduce isotope effects of substantial size at these positions (primary isotope effects). Hence, PGI is the most likely generator of the correlated PR signals in Δ₁′ and Δ₂′. Bacterial glucose isomerase (EC 5.3.1.5) has substantial equilibrium and kinetic isotope effects at glucose C-1 and C-2 (EIE_C-1 = −13‰, EIE_C-2 = 7‰, KIE_C-1 = 5‰, KIE_C-2 = 15‰), according to previous authors¹⁸, who hypothesis that plant PGI should have similar effects, as it operates by the same reaction mechanism. Hence, shifts of the PGI reaction from irreversible to equilibrium conditions will be accompanied by correlated ¹³C shifts at C-1 and C-2 of the reaction product, G6P. Magnitudes of these ¹³C shifts are proportional to the differences between corresponding kinetic and equilibrium isotope effects, and ¹³C shifts at C-1 and C-2 of G6P are linearly related as (KIE_C-1-EIE_C-1)/(KIE_C-2-EIE_C-2) = 2.25. That is, a given shift at G6P C-2 will be accompanied by a 2.25-fold larger shift at C-1. This ratio should equal the ratio of the PR contributions to the Δ₁′~VPD and Δ₂′~VPD regression slopes, b_1PR′/b_2PR′. We found that b_1PR′/b_2PR′ = 2.74 (+1.35SE, −0.60SE), which is consistent with a PGI-related mechanism introducing a ¹³C signal in Δ₁′ and Δ₂′.

From an ecophysiological perspective, the occurrence of PGI-driven fractionation is plausible for the following reasons. In isohydric plants like Pinus nigra, strong negative relationships between VPD and both stomatal conductance and intercellular [CO₂] can be expected³¹. At high intercellular [CO₂], plants photosynthesise at high rates, and stromal PGI is strongly displaced from equilibrium^32–34. As intercellular [CO₂] declines, plants photosynthesise at lower rates, and stromal PGI shifts towards equilibrium³². According to published isotope effects¹⁸, a shift towards equilibrium results in ¹³C enrichments at C-1 and C-2 of stromal G6P. From G6P, the signal is transmitted to transitory starch and the glucose units of tree-ring cellulose derived therefrom. Low intercellular [CO₂], as induced by stomatal closure due to high VPD, is associated with ¹³C enrichment by the DR fractionation system. Consequently, DR and PR fractionation at C-1 and C-2 have synergistic effects, and lead to steeper Δ₁′ ~ VPD and Δ₂′ ~ VPD regression slopes. A regulated shift towards PGI equilibrium may putatively facilitate stabilisation of the Calvin-Benson cycle³⁵, which is probably most important when intercellular [CO₂] is low. Thus, a PGI-related mechanism can explain enhanced Δ₁′ and Δ₂′ fractionations and is ecophysiologically plausible.

Analysis of intramolecular variation in isotope ratios is intended to resolve multiple ecophysiological signals using several information channels. In that sense, it is conceptually related to the so-called “dual-isotope approach”; the independent, but simultaneous, examination of stomatal conductance and carbon assimilation through combined analysis of whole-molecule δ¹³C and δ¹⁸O of plant organic matter³⁶. In its current form, however, application of such dual-isotope analysis depends on several assumptions, which impedes its widespread implementation³⁷. One problem noted by the cited authors is that stomatal conductance and carbon assimilation are not the only processes that modulate isotope ratios. Our observation of PR fractionation, which the dual-isotope concept neglects, highlights this challenge.

The sensitivity of Δ to multiple ecophysiological variables (Figs. 2a,b) hinders attempts to model the ¹³C fractionation system of plants and to derive ecophysiological and environmental information from Δ measurements. Generally, deconvolution of several signals with only one observable variable is not feasible. In contrast, resolution of six partly independent intramolecular ¹³C variables (Fig. 2a) offers a conceptual shift from underdetermined towards fully or even overdetermined model systems. This development can potentially reduce numbers of confounding factors and (hence) model uncertainty. The most powerful approaches may combine intramolecular and multi-isotope techniques, which would offer the highest number of independent isotope information channels. In future, estimations of physiological and environmental parameters including source isotope compositions will most likely rely on such “multichannel” approaches.

Intrinsic water-use efficiency (iWUE) is defined as the ratio between the rates of carbon assimilation and transpiration. It is a major determinant of plant performance at water-limited sites². DR fractionation is correlated with iWUE, and Δ is often used as proxy of iWUE¹⁹. Our results indicate that a purer DR signal can be obtained on the level of intramolecular ¹³C abundances. Thus, models based on Δ_i′ may provide better estimates of iWUE.

We found that a statistical model of VPD based on Δ_i′ has greater descriptive and predictive capacities than the corresponding Δ model. This finding is especially noteworthy given the lower achievable accuracy of Δ_i′ measurements compared to Δ measurements (SD ± 1‰ vs. SD ± 0.1‰, respectively). Currently, Δ_i′ measurements are time consuming and thus limited to small sample sets. We expect that Δ_i′ applications will improve markedly with anticipated analytical advancements and with the further elucidation of PR fractionation effects, which might allow more sophisticated mechanistic modelling.

Intramolecular ¹³C abundances are functions of environmental and related physiological variables, studied here at annual resolution. The approach is generally suitable for analysis of samples covering much longer timeframes³⁸, far exceeding the scope of manipulation experiments or direct observation. However, upscaling to these timeframes requires an assessment of the temporal robustness of ¹³C signals. In nature, wood cellulose often persists for long periods, and is datable with high accuracy. Several tree-ring chronologies with annual resolution and calendric exactness encompass the entire Holocene³⁹. Subfossil wood samples date back to the last interglacial period, ≈130,000 to 115,000 BP^40,41. Thus, intramolecular ¹³C distributions in wood are promising archives of information about physiological and environmental conditions in past decades, centuries, and millennia. Position-specific isotope abundances may be particularly valuable for acquiring information (which is difficult to acquire by any other available technology) about the capacity of different plant species to acclimatise and adapt to long-term environmental changes. This, in turn, might aid attempts to identify suitable plants, cultivars and genotypes for changing environments.

We anticipate that intramolecular ¹³C measurements will complement whole-molecule stable isotope measurements and multi-isotope approaches in several applications. These include: prediction of ¹³C abundances of CO₂ formed by different respiratory pathways; characterisation of the C metabolism of soil microbial communities; analyses of soil carbon turnover; elucidation of plants’ physiological responses to environmental changes and their long-term acclimatisation (in periods and conditions covered by calibrating data); and reconstructions of plant physiological and environmental traits based on mechanistic models (outside periods and conditions covered by calibrating data).

Methods

Additional information is provided under Supporting Information.

Site and samples

We used samples of annual rings of 19 Pinus nigra Arnold trees (two cores per tree) from the Bierhäuselberg site (Vienna region, Austria), which has shallow, very dry soil. Both the site and samples have been previously described in detail⁴². In addition, we used dated tree-ring samples, pooling 5–10 annual rings of 11 angiosperm and gymnosperm species from ecologically different sites with global coverage (Table S1).

Sample preparation

We carefully separated dated Pinus nigra tree rings (from 1961 to 1995) using a binocular microscope and a scalpel, and combined rings in annual pools. Thus, our data represent properties of the tree species at the site rather than individual trees. Pooled samples were ground (Retsch® MM400, Haan, Germany) and their glucose contents were converted into 1,2-O-isopropylidene-α-D-glucofuranose following a published protocol⁴³. Samples of 11 additional angiosperm and gymnosperm species were processed in the same way, but in a final step their glucose contents were converted into 3,6-anhydro-1,2-O-isopropylidene-α-D-glucofuranose⁴³. Checks by ¹H NMR showed that sample purity was ≥99.9%.

¹³C EA-IRMS and ¹³C NMR spectroscopy

Conventional δ¹³C_VPDB measurements of the glucose derivative were acquired for Pinus nigra samples. Quantitative 1D ¹³C NMR spectra were collected⁴⁴ using a Bruker 400 MHz AVANCE III instrument equipped with a 5 mm BBFO SmartProbe^TM (Bruker BioSpin GmbH, Rheinstetten, Germany). We recorded and processed 30 spectra per Pinus nigra sample and eight spectra per sample of the additional species using TopSpin^TM 3.1 (Bruker BioSpin GmbH, Rheinstetten, Germany). We excluded Pinus nigra samples from 1977, 1978, 1981, and 1982 because they were too small.

Calculation of Δ_i and Δ_i′

Integration of ¹³C NMR spectra resulted in average signal integrals, S_i, of specific carbon positions of the glucose derivatives, i = {C-1,…, C-6, C-q, C-Me1, C-Me2}. Each carbon is directly bound to one or two neighbouring carbons. Calculation of ¹³C molar equivalents, S_i(c), considered corresponding signal satellites⁴⁵. Removal of ¹³C variation related to TPC followed methods described below, eq. (8), and resulted in TPC-free ¹³C molar equivalents, S_i(c)′. Calculation of positional ¹³C/¹²C ratios, expressed as δ¹³C_pi and δ¹³C_pi′ followed published procedures⁴⁶. Calculation of positional discrimination, Δ_i, and the TPC-free positional discrimination, Δ_i′, followed eqs. (3) and (5), and incorporated reconstructed annual atmospheric δ¹³CO₂ (=δ¹³C_a) for the northern hemisphere⁴⁷. As the open canopy at our site presumably allows rapid mixing of biogenic and atmospheric CO₂, errors in Δ_i and Δ_i′ due to the contribution of isotopically distinct biogenic CO₂ should be minimal. Positional ¹³C deviations from the molecular average were calculated as Δδ¹³C_i = (S_i(c)/(ΣS_i(c)/n)−1)*10³ with i = {C-1, …, C-6}.

Fractional redistribution of ¹³C signals between symmetry-related glucose carbon positions by heterotrophic triose phosphate cycling

When cellulose is synthesized, translocated sucrose is first broken down to hexoses, which are converted to UDP-glucose. During these reactions, 40 to 50% of the hexose phosphates generated are further broken down to triose phosphates, before use in cellulose synthesis. This is known as triose phosphate cycling (TPC). Triose phosphate isomerase equilibrates glyceraldehyde 3-phosphate (G3P) with dihydroxyacetone phosphate (DHAP), respectively derived from C4–6 and C1–3 portions of hexoses. Their equilibration causes carbon exchange between C1–3 and C4–6 portions of hexoses. Thus, in comparison to the hexose units of sucrose, approximately 20 to 25% of carbons in the UDP-glucose pool have been effectively redistributed between symmetry-related carbon positions, i.e. between C-1 and C-6, C-2 and C-5, C-3 and C-4. This implies that intramolecular ¹³C differences between these symmetry-related positions are partially levelled out by TPC. In the following text, we derive equations to back-calculate the intramolecular ¹³C distribution before TPC. Please note that the resulting TPC-free distribution does not represent the ¹³C distribution of any naturally occurring hexose. This is because both parts of sucrose, i.e. glucose and fructose, are used for cellulose synthesis, but differ with respect to their ¹³C distributions⁸.

Equation for removing the averaging effect of heterotrophic TPC

With y denoting the fraction of hexose phosphates cycling through triose phosphates, and with complete triose phosphate equilibration, the fraction of carbon redistributed between symmetry-related carbon positions is given by y/2. Then, the observed ¹³C abundance at a specific hexose carbon position, C_i, is given by:

$${}^{13}\mathrm{C}_{\mathrm{i}}=\left(1-\mathrm{y}/2\right){}^{13}\mathrm{C}_{\mathrm{i}}\prime +\left(\mathrm{y}/2\right){}^{13}\mathrm{C}_{\mathrm{s}}\prime$$ 6

and the observed ¹³C abundance of the symmetry-related carbon position, C_s, is given by:

$${}^{13}\mathrm{C}_{\mathrm{s}}=\left(1-\mathrm{y}/2\right){}^{13}\mathrm{C}_{\mathrm{s}}\prime +\left(\mathrm{y}/2\right){}^{13}\mathrm{C}_{\mathrm{i}}\prime$$ 7

Here, ¹³C_i′ and ¹³C_s′ denote TPC-free ¹³C abundances. Solving eqs (6) and (7) for ¹³C_i′, TPC-free ¹³C abundances are given by:

$${}^{13}\mathrm{C}_{\mathrm{i}}\text{'}=((2/\mathrm{y}-1){}^{13}\mathrm{C}_{\mathrm{i}}-{}^{13}\mathrm{C}_{\mathrm{s}})/(2/\mathrm{y}-2)$$ 8

Validation of the procedure

Reported estimates of proportions of carbon redistributed by TPC include 20–25% in Quercus robur⁴⁸, 25% and 19% in Quercus petraea and Picea abies, respectively⁴⁹, and 19% in various riparian tree species⁵⁰. Thus, the fraction of carbons redistributed by TPC seems to fall within a quite narrow range in all investigated species. Both phylogenetically and in terms of wood anatomy, Pinus nigra is closer to Picea abies than to Quercus species. Therefore, we chose y = 0.4 as a TPC factor for calculating TPC-free ¹³C abundances (¹³C_i′). Δ_i and Δ_i′ were then calculated as described above.

As TPC averages ¹³C abundances at symmetry-related hexose positions, it should lead to correlation between symmetry-related Δ_i values, and these correlations should be removed by the calculation of TPC-free Δ_i′ values. As expected for ¹³C abundances affected by TPC, Δ_i time series of symmetry-related glucose carbon positions correlate significantly (Table 1, values in boldface). In contrast, the TPC-free dataset, Δ_i′, does not exhibit such a correlation pattern, indicating that co-variation introduced by TPC was removed (Table 2). In mathematical terms, TPC causes weighted averaging of carbon abundances, eqs. (6) and (7). Like any averaging, it reduces variability. Accordingly, Δ_i′ exhibits more pronounced variation in its intramolecular distribution than Δ_i (Fig. 1a). Generally, averaging only has a net effect if differences are present, and the effect increases with the magnitude of the differences. This is reflected by the larger impact of removing TPC effects on Δ₂ and Δ₅ than on Δ₁ and Δ₆ (Fig. 1a).

Table 1.

Correlation coefficients and significance levels (*p ≤ 0.05; **p ≤ 10⁻²; ***p ≤ 10⁻³; ****p ≤ 10⁻⁴) obtained from the Δ_i cross-correlation analysis (n = 31).

	Δ1	Δ2	Δ3	Δ4	Δ5	Δ6
Δ1	1
Δ2	0.60***	1
Δ3	0.31	0.52**	1
Δ4	0.00	0.31	0.38*	1
Δ5	0.37*	0.42*	0.24	0.39*	1
Δ6	0.55**	0.48**	0.31	0.11	0.69****	1

Table 2.

Correlation coefficients and significance levels (*p ≤ 0.05; **p ≤ 10⁻²; ***p ≤ 10⁻³) obtained from the Δ_i′ cross-correlation analysis (n = 31).

	Δ1′	Δ2′	Δ3′	Δ4′	Δ5′	Δ6′
Δ1′	1
Δ2′	0.54**	1
Δ3′	0.31	0.48**	1
Δ4′	−0.12	0.10	−0.12	1
Δ5′	0.11	−0.07	0.03	0.32	1
Δ6′	0.08	0.19	0.21	0.06	0.61***	1

Environmental data

We acquired monthly means of precipitation, air temperature and global radiation from the Hohe Warte climate station (Central Institution for Meteorology and Geodynamics, Vienna, Austria, 16°22′ E, 48°15′ N, 203 m AMSL, WMO ID: 1103500). Deficits in air vapour pressure, VPD [Pa], were calculated following published procedures⁵¹. We acquired monthly means of soil moisture from a global grid dataset (CPC Soil Moisture V2, NOAA, OAR, ESRL, PSD, Boulder, Colorado, USA) for 16°15′ E, 48°15′ N. Both the climate station and the selected grid point are no more than a horizontal distance of 15 km from the sampling site with a negligible vertical offset. Thus, all data should represent site conditions well. In conifers, tracheids form over several months⁵². Thus, we calculated climate averages and sums of the growing season, which we estimated to extend from March to November (Fig. S2).

Statistical analyses

Statistical analyses were performed in R 1.0.143. We compared regression slopes by ANCOVA using two categories and type II sum of squares. For statistical description of VPD, we first fitted the maximal model, VPD~Δ₁′ + Δ₂′ + Δ₃′ + Δ₄′ + Δ₅′ + Δ₆′. We arrived at the minimal adequate model by stepwise model simplification based on Akaike’s information criterion using the step() function of the Stats package with default settings. To test the predictive abilities of the simple linear regression model, VPD~Δ, and the minimal adequate model from multiple linear regression modelling, VPD~Δ₁′ + Δ₃′ + Δ₅′, we performed 10-fold cross-validation using cv.lm(m = 10) and CVlm(m = 10) functions of the DAAG package. We performed Hierarchical Cluster Analysis on z-scores of Δ_i′ using Euclidean distances and Ward’s fusion criterion for cluster formation⁵³.

Data availability

The datasets generated and analysed during the current study are available from the corresponding authors on reasonable request.

Author Contributions

T.W. and J.S. conceived the study. T.W., I.E., M.G. and J.S. prepared samples and acquired data. T.W. and J.S. contributed new analytical tools. T.W., J.Y., D.F. and J.S. performed statistical analyses. T.W., J.Y., A.G. and J.S. interpreted statistical results. T.W. developed a method for removing isotope redistribution effects by triose phosphate cycling, and introduced an ecophysiological mechanism explaining fractionation effects at GLC C-1 and C-2. T.W., A.G., D.F. and J.S. wrote the manuscript.

Competing Interests

The authors declare no competing interests.