Post-photosynthetic Carbon, Oxygen and Hydrogen Isotope Signal Transfer to Tree Rings—How Timing of Cell Formations and Turnover of Stored Carbohydrates Affect Intra-annual Isotope Variations

Abstract

In this chapter, we discuss post-photosynthetic processes that affect intra-annual variation in the stable isotopes of tree rings, such as timing of cell formations and turnover of stored carbohydrates, by combining research findings gained by using either natural-abundance or artificially-enriched carbon, oxygen and hydrogen isotopes. We focus on within-ring variation in stable isotope ratios, with an emphasis on aligning observed ratios in whole wood or extracted cellulose to seasonal dynamics in climate and phenology. We also present a discussion of isotopic fractionation that operates within the scope of observed variations across individual rings. We then introduce a model that traces the seasonal partitioning of photosynthate into tree rings via storage pool, which is based on experimental data gained from labeling studies using artificially enriched ¹³CO₂ gas. Finally, we will describe our current understanding of post-photosynthetic signal transfer processes of oxygen and hydrogen isotopes from leaves to tree rings, such as exchange of oxygen and hydrogen between storage carbohydrates and local cambial water, and possible causes of difference in oxygen and hydrogen isotope fractionations. Finally, we discuss mechanisms behind how oxygen and hydrogen from foliar-absorbed liquid water is then incorporated into wood biomass, by introducing results gained from recent H₂¹⁸O and HDO pulse-labeling experiments.

1 Introduction

Conventional tree-ring analyses have focused on the annual growth ring with a primary aim of inter-annual-to-inter-decadal translation of growth-climate relations. Partitioning of annual rings into earlywood and latewood, in those species that show such differentiation, has been used in some studies to extract early- and late-season growth trends, though the attribution of those trends to more specific dates during the growing season has been impeded by lack of knowledge about the use of stored carbon and seasonal lags in the phases of xylogenesis. As a result, intra-annual dendrochronological perspectives have been considerably fewer in number than inter-annual perspectives, and when developed, they have been relatively coarse in their alignment to patterns of seasonal climate variation. Compared to ring width and density measurement, tree-ring isotope analysis used to be much more costly and time-consuming in 1990s, and the number of tree-ring isotope research has been limited. However, this situation has changed markedly over the past decade as sample preparation and analytical approaches have improved (see Chap. 7).

In the last decade, the labor required for intra-annual analysis of tree-ring isotope ratio analysis has decreased, while the precision associated with these measures has increased (Kagawa et al. 2015, Chaps. 5 and 6). Analysis of intra-annual variations of tree-ring isotope ratio was first conducted by separating single annual rings into sub-sections by means of a knife or chisel under a microscope (Wilson and Grinstead 1977, Leavitt 1993; Kagawa et al. 2003) and isotope analysis of up to 0.1 mm resolution became possible by manually subdividing α-cellulose lath under stereomicroscope, which is prepared directly from thin cross-section (Kagawa et al. 2015; Ohashi et al. 2016; Xu et al. 2016; Chap. 4). Higher spatial resolution is achieved by using a sliding microtome to cut serial tangential sections from wood blocks at thicknesses of 10–240 μm (Ogle and McCormac 1994; Walcroft et al. 1997; Helle and Schleser 2004). Robotic micromilling (Dodd et al. 2008) and UV-laser based dissection (Schollaen et al. 2014, Chap. 7) allow serial isolation of tissue from the millimeter-to-micrometer scale. Although its use is currently limited to carbon isotope analysis and on whole wood, laser ablation can achieve an ultimate spatial resolution of 10 μm, enabling observations at the cellular level (Schulze et al. 2004, Moran et al. 2011; Soudant et al. 2016; Loader et al. 2017). Using these more recent techniques, it is now foreseeable to routinely assess patterns in the seasonal variation of multiple elements, including carbon, oxygen and hydrogen isotope ratios. However, for accurate interpretation, researchers must understand the linkage between an intra-annual sample, and the seasonal timing that the sample represents.

In this chapter, we discuss post-photosynthetic processes that affect intra-annual variation in the stable isotopes of carbon, oxygen and hydrogen in tree rings, such as timing of cell formations and turnover of stored carbohydrates. We discuss past findings and the knowledge-gaps that are most likely to constrain efforts to expand our understanding in the future. In Sect. 15.2, we present a general overview of studies to date that have aimed to assess intra-annual variation in tree ring growth as presented through the anatomical analysis of wood. Scientists have used observable variation in wood density, and cellular differentiation within the wood, to infer seasonal patterns in radial growth rates and general responses to warming and drying as the growing season progresses.

In Sect. 15.3, we focus on within ring variation in carbon and oxygen isotope ratios, with an emphasis on aligning observed ratios in whole wood or extracted cellulose to seasonal dynamics in climate and phenology. We not only present the principal findings from past studies, but also note the likely uncertainties that exist due to a lack of complete knowledge about fractionation processes and seasonal lags in cambial phenology. One of the primary considerations in this topic involves the degree to which stored carbohydrate, which carries isotope signals from antecedent climate conditions is used for xylem maturation, and therefore causes mixing of the isotopic signals recorded during past and present-day climate (Chap. 14). The identification of growth periods when stored carbohydrates are used will be considered in this section, but we will defer to a later section the discussion of experimental studies aimed at revealing the physiology of stored carbohydrates use during xylogenesis.

In Sect. 15.4, we present a discussion of isotopic fractionation that operates within the scope of observed variations across individual rings. We start with consideration of processes at the site of photosynthetic CO₂ assimilation in the chloroplasts of leaves or needles. The processes recorded in leaf-scale sugar isotope ratios vary on the order of fractions of seconds, though by the time the sugars have been transported to intra-leaf storage pools or to the phloem for transport to other parts of the plant, time-averaged isotope fractionation can be well-approximated by the steady-state assumption in observations and models. Following a discussion of leaf-scale photosynthetic dynamics, we progress to 'downstream' influences that have the potential to scramble the association of observed sugar isotope ratios with the short-term physiological and climate controls over leaf physiology, and thus mask efforts to trace tree-ring isotope time series with seasonally-resolved influences.

In Sect. 15.5, we consider xylogenesis itself as a cause in the disruption of the association of observed intra-annual isotope ratios with specific climate and phenological phases during the growing season. The incorporation of photosynthate into cellulose during xylogenesis is affected by seasonal lags in the processes of cell formation and maturation. Thus, the anatomical attributes of variation in the cellular appearance of xylem may not align seasonally with the isotopic attributes of the cellulose that makes up the primary and secondary cell walls of individual xylem elements and tracheids. We discuss new approaches relying on cambial phenology modeling conditioned on high-frequency micro-core sampling to resolve lags in the seasonal phases of xylogenesis.

In Sect. 15.6, we present a discussion of experimental studies that have traced the seasonal partitioning of photosynthate into tree rings. The research developments discussed in this section, when coupled with recent approaches that identify the intra-annual domains of tree rings according to isotope ratios and phases of xylogenesis, hold the greatest promise to provide explanations of intra-annual patterns of isotopic fractionation, especially those fractionations associated with post-assimilation processes. ¹³CO₂ pulse-labeling used in combination with high-resolution analysis of tree-ring isotopes is an effective tool to elucidate the processes that transfer fractionation signals from assimilation processes 'in the current moment' versus those from past processes that are stored in carbon reservoirs and subsequently transferred to the archived cellulose isotope record in tree rings. We also discuss future applications of H₂¹⁸O and HDO pulse labeling for elucidating the post-photosynthetic processes of oxygen and hydrogen isotope signal transfer from leaves to tree rings, such as exchange of oxygen and hydrogen between carbohydrates and local water.

2 Intra-annual Variation in Wood Structure

Wood structure variation within a ring is important to understanding variation in intra-annual isotope ratios, because (1) these differences are caused by seasonal changes in the climate and phenology, and are often visual cues to guide sample analysis, and (2) are related to the seasonal course of storage carbon allocation to wood (e.g. ring-porous wood). Therefore, measures of wood density, width of early- and latewood, and/or chemical composition can be highly useful for helping to interpret the isotope ratio variance.

The earlywood-to-latewood variations in wood anatomy across individual rings are aligned with seasonal phases associated with the earliest and latest portions of the growing season (Brown 1912; Pallardy 2008). Earlywood (EW) consists of larger cells with thinner secondary walls, and is overall less dense than latewood (LW). The anatomical differences of EW and LW reflect differences in the seasonal phases of xylogenesis (Butto et al. 2019). Despite general correlations of EW and LW growth with variations in seasonal climate, detailed alignment of these tissue types with specific dates during the growing season is difficult; the time-dependent growth rate of woody tissues within an annual ring does not occur linearly (Camarero et al. 1998; Rossi et al. 2003; Kagawa et al. 2005; Soudant et al. 2016; Belmecheri et al. 2018). Furthermore, there is ample evidence that antecedent (previous-year) climate conditions and stored water and carbon resources can influence the current-year growth of both EW and LW (e.g. Seargent and Singer 2016; Kerhoulas et al. 2017; Szejner et al. 2018). EW and LW designations in trees from Mediterranean or humid tropical ecosystems is often challenging (Cherubini et al. 2003; Sass et al. 1995), due to stochastic occurrence of droughts (resulting in the formation of false rings) or the lack of seasonality (resulting in a lack of ring boundaries).

In the literature, several classifications of false ring, or double rings, or intra annual density fluctuations (IADF) exist. The appearance of an IADF can create errors in dendrochronological dating as it can be mistaken for a true LW band, and therefore cause a false assumption of annual growth truncation; this is the so-called 'false ring' phenomenon. The formation of IADFs has been related to physiological responses at the leaf scale, including stomatal diffusion restrictions and photosynthetic water-use efficiency (WUE), that are induced by extreme mid-season droughts (Battipaglia et al. 2010, 2013; Balzano et al 2018, 2019; Zalloni et al 2016, 2018a, b, 2019).

The seasonal variations that are evident in the wood structure of annual rings are due to both phenological dynamics in cambial activity and influences from the ambient environment. McCarroll et al. (2003) explored the relationships of ten tree proxies, including several related to wood structure variation, with seasonal climate in Pinus sylvestris from three sites in northern Finland. Clear distinctions were noted in the correlations of EW and LW density and width-increment and seasonal climate variables. EW width was best correlated with late-summer temperature from the previous (antecedent) growing season, and it was hypothesized that overwinter stores of carbohydrate were used for EW construction. No clear climate signal could be associated with EW density. Latewood width and density were best correlated with late-summer (July and August) temperature of the current year, though the correlation was only strong at the southernmost site, and density alone was highly correlated with late-summer temperature at all sites. Similarly, using Quercus robur in Hungary, Kern et al. (2013) showed no significant correlation between EW width-increment and monthly precipitation or temperature, whereas LW width-increment was significantly correlated with July precipitation, but not with temperature. In ring-porous oak species, traits associated with individual EW xylem vessel size, rather than EW ring-width increment, have been shown to respond to site differences in spring moisture availability (Fonti and Garcia-Gonzalez 2008) and appear to be capable of acclimation to changes in spring temperature and moisture conditions (Gea-Izquierdo et al. 2012; Nabeshima et al. 2015). Thus, correlations between anatomical features of tree rings (width/density) and monthly climatic parameters (temperature, precipitation etc.) are closely related to seasonal dynamics of photosynthetic carbon allocation to tree rings, because, typically, latewood is mainly made from current summer and autumn photosynthate and earlywood is made from the mixture of photosynthate from current spring and the previous summer and autumn as we will discuss in Sect. 15.6 (Fig. 15.5, Kagawa 2006; Kagawa et al. 2006a).

3 Theoretical Considerations of Intra-annual Variation in Tree-Ring Isotope Fractionation

The theory regarding fractionation processes that lead to observed ¹³C/¹²C, ¹⁸O/¹⁶O, and ²H/¹H ratios in the cellulose of tree rings has been covered in other chapters of this book (Chaps. 9–11). In this section, we focus on those processes that underlie intra-annual variation in δ¹³C, δ¹⁸O and δ²H. The principle determinant of seasonal variation in the δ¹³C of the photosynthate used in xylogenesis is due to climate variation in the atmospheric environment—specifically, atmospheric humidity and available precipitation. Soil characteristics affect infiltration and drainage of precipitation into and out from soil, and thus alter the availability of source water, locally; but, ultimately the amount of rain and snow deposited to the surface will control soil moisture availability, and thus the physiological states of trees. The ¹³C/¹²C ratio observed at the moment of photosynthetic CO₂ assimilation depends on the atmospheric (source) ratio, the kinetic fractionations associated with diffusion in the air and cellular cytosol between the outer edge of the leaf boundary layer and the site of carboxylation within the chloroplast, and that associated with the active site of the chloroplastic enzyme, riblose 1,5-bisphosphate (RuBP) carboxylase. Each isotopic form of CO₂ can either diffuse to the chloroplast and be assimilated biochemically or it can diffuse back out of the leaf and rejoin the atmospheric source. The relative fates of these molecules can be mathematically predicted by the ratio of stomatal conductance (which controls the diffusive entry and exit from the leaf) to RuBP carboxlation (which controls biochemical removal from the leaf). An equation that has been shown to approximate the interactions of these determining processes is:

$$\Delta_A = a + \left( {b - a} \right)\frac{c_i }{{c_a }}.$$

(15.1)

where Δ_A is the overall fractionation associated with CO₂ assimilation, a is the fractionation of ¹²CO₂ and ¹³CO₂ due to diffusion through air, b is the biochemical fractionation associated with RuBP carboxylase, and c_i and c_a are the CO₂ (¹²CO₂ and ¹³CO₂ combined) concentrations in the intercellular air space and atmosphere, respectively (Farquhar et al. 1989). Equation 15.1 is simplified in that it ignores the liquid-phase diffusion paths within the cell, as well as some additional biochemical processes that can influence the overall fractionation, such as photorespiration. It is important to note that a principal concept underlying the weighting employed in Eq. 15.1 is the requirement for discriminated isotopes to leave (or leak) from a fractionation process. That is, there is no fractionation associated with processes and reactions that are truly closed, and thus capable of producing product from all available reactants. Fractionation requires the 'escape' of discriminated reactants.

Working from Eq. 15.1, seasonal variations in δ¹³C in the cellulose of tree-rings will depend on seasonal variations in c_i/c_a, which in turn reflect dynamics in g_s (stomatal conductance) relative to A (the biochemical capacity for photosynthetic CO₂ assimilation). Although the potential exists for seasonal variation in A through changes in light levels, V_cmax (carboxylation capacity) or J_max (electron transport), which are the biochemical determinants of the maximum photosynthetic rate, the primary influences imposed on Eq. 15.1 will be due to seasonal climate attributes that affect g_s. Thus, precipitation and atmospheric humidity reflect the first-order seasonal climate drivers of intra-annual variation in tree-ring δ¹³C.

The initial control of the atmospheric environment on cellulose δ¹³C is often not straightforward because of the temporal offsets between the time of photosynthate production and its eventual use during xylogenesis (Badeck et al. 2005; Kagawa et al. 2005, 2006a). Offsets in the timing of the production and use of photosynthate are the result of non-structural carbohydrates (NSC) storage and remobilization, and the phenological lags that are imposed on xylogenesis as it proceeds through its phased time course. In both cases, photosynthate that was produced with a unique relation to the atmospheric environment at one point in time, is incorporated into the cellulose of xylem cell walls, where the photosynthetic signals are time-averaged over the period of xylogenesis (as we discuss later in Sect. 15.6.4, Fig.15.5). For example, the offsets in the timing of the production and use of photosynthate for tree-ring formation can range from 9–42 days in Japanese cedar (Kagawa et al. 2005) to 1–2 years in Dahurian larch (Kagawa et al. 2006a). Efforts to reconstruct the atmospheric environment, using theory such as that presented in Eq. 15.1, must include reconciliation of any time gaps between photosynthate production and utilization.

Theoretical models that aim to couple cellulose oxygen isotope ratios to the surrounding environment begin with consideration of leaf or needle water, which exchanges isotopes with CO₂ during photosynthesis (Chap. 10). The current model assumes leaf surfaces without wetting, as in the case of sunny or cloudy days, and this model does not apply to wet leaf surfaces covered with liquid water, as in the case of rainy days (see Sect. 15.6.5). The isotope composition of leaf water during steady-state transpiration reflects the combined influences of atmospheric vapor pressure deficit (VPD) , the kinetic and equilibrium exchanges between the leaf and atmospheric vapor, and the advective mixing of non-fractionated (xylem) and fractionated (mesophyll) water (the Péclet effect) (Dongmann et al. 1974; Farquhar and Lloyd 1993; Farquhar 1998; Barbour et al. 2004). Modeling of this dynamic interaction starts from the Craig-Gordon model (1965):

$$\Delta_{\text{e}} \approx {\upvarepsilon }^+ + {\upvarepsilon }_{\text{k}} + \left( {\Delta_{\text{v}} - {\upvarepsilon }_{\text{k}} } \right)\frac{{{\text{e}}_{\text{a}} }}{{{\text{e}}_{\text{i}} }}$$

(15.2)

where Δ_e is overall evaporative fractionation, ε⁺ and ε_k are the equilibrium and kinetic fractionations, respectively, Δ_v is the isotope enrichment in atmospheric water vapor relative to source water, e_a is the ambient mole fraction of water vapor, and e_i is the mole fraction of water vapor in the leaf. Equation 15.2 has been modified in some studies to account for the serial diffusive fractionations occurring through the stomatal pore (still-air diffusion) versus the leaf boundary layer (still-air diffusion plus turbulent transfer) (Flanagan et al. 1991).

The observed fractionation of evaporating leaf water is seldom as great as that predicted by Eq. 15.2. Recognition of this discrepancy has led to modification of the model in one of two ways: (1) a transport-based kinetic dilution of Δ_e through advective mixing of fractionated and non-fractionated water in the leaf (the Péclet effect), and (2) a simpler, linear mixing of two pools of leaf water, one enriched (mesophyll) and one not (xylem):

(Péclet modification)

$$\Delta_{{\text{blw}}} \, = \,\Delta_{\text{e}} \left( {1 - e^{ - \wp } } \right)/\wp$$

(15.3)

(Two-pool modification)

$${\updelta }_{{\text{blw}}} = {\updelta }_{\text{e}} \left( {1 - f_u } \right) + {\updelta }_{{\text{wx}}} f_u$$

(15.4)

where Δ_blw refers to fractionation of the bulk leaf water and ℘ is a Péclet number, reflecting the ratio between advective and diffusive transport in the leaf (Farquhar and Lloyd 1993). In Eq. 15.4, the notation has switched from fractionation (Δ) to delta-ratio (δ), and includes the isotope ratio of the unfractionated xylem water pool (δ_wx) and the fraction of leaf water in that pool (f_u) (Roden et al. 2015). Conifer needles are difficult for predicting Péclet modifications due to uncertainties in transport path length, velocity of water movement and degree of xylem suberization (Song et al. 2013; Roden et al. 2015). A simpler 2-pool model may be more appropriate (Roden et al. 2015).

Photosynthates that reflect the isotope effects of transpiration and needle water mixing is modified further during tree-ring cellulose production and deposition. A post-assimilation fractionation occurs when carbonyl oxygens in phloem-transported sucrose exchange with the oxygens of cambial water during cellulose synthesis (Sternberg et al. 1986). This secondary fractionation of cellulose oxygen (Δ_c), can be defined mathematically as:

$$\Delta_{\text{c}} = \Delta_{{\text{blw}}} \left( {1 - {\text{p}}_{\text{x }} {\text{p}}_{{\text{ex}}} } \right) + {\upvarepsilon }^{ + }$$

(15.5)

where p_x is the fraction of cambial cellular water that is not isotopically fractionated (p_x ≈ 1.0), and p_ex is the fraction of carbonyl oxygen atoms that exchange with non-fractionated water (Barbour and Farquhar 2000). Fractionation occurs when mobilized hexoses are broken down to the triose sugar (Hill et al. 1995), dihydroxyacetone phosphate (DHAP), and in the process, exposed to an opportunity to exchange oxygen (and hydrogen) atoms with the surrounding water (Reynolds et al. 1971; Knowles and Albery 1977; Sternberg et al. 1986; Kagawa 2020; Chap. 11). The post-assimilation fraction of cellulose oxygen subjected to exchange with source water (p_ex) has been shown to vary across spatial climate gradients and seasonally (Gessler et al. 2009; Offermann et al. 2011; Cheesman and Cernusak 2016). Cheesman and Cernusak (2016) estimated the exchange fraction as 0.21–0.68 for Australian eucalypts, and observed it to be highest in the most arid sites. Belmecheri et al. (2018) provided indirect evidence that p_ex varies from 0.1–0.4 seasonally in P. ponderosa. Using dual-isotope (H₂¹⁸O and HDO) labeling method, Kagawa (2020) found that Cryptomeria japonica under rainy conditions shows increased exchange (p_ex =0.9). The significance of p_ex to observed intra-annual variations in cellulose δ¹⁸O is a topic that needs more study. As we later discuss in Sect. 15.6.5, incorporation of oxygen and hydrogen originating from foliar-absorbed water (Eller et al. 2013; Goldsmith et al. 2013) into photosynthetic sugars and organic matter (Studer et al. 2015; Lehmann et al. 2018, 2020a) and wood (Kagawa 2020) is interesting phenomenon and might be related to intra-annual oxygen and hydrogen variations in the wood formed during rainy seasons (Nakatsuka et al. 2004; Roden et al. 2009; Managave et al. 2010a; Ohashi et al. 2016; Xu et al. 2016; Nabeshima et al. 2018).

Both in hydrological cycles and inside trees, hydrogen and oxygen isotopes of water behave, more or less, similarly (Dansgaard 1964; Kagawa 2020) and fractionations of hydrogen isotopes in leaf water are explained by the same Eq. (15.2), with specific fractionation factors for hydrogen (Roden et al. 2000). The fraction of carbon-bound hydrogen atoms that exchange with non-fractionated water is also similar to that of oxygen (p_ex) (Yakir and DeNiro 1990; Roden and Ehleringer 1999; Roden et al. 2000; Kagawa 2020). Oxygen and hydrogen show parallel fractionations through hydrological cycles (i.e. non-biological systems) along the meteoric water line (Dansgaard 1964). However, hydrogen isotopes show larger fractionations than oxygen at the cambium, before and during cellulose synthesis (Yakir and DeNiro 1990) and isotopic deviations of hydrogen from oxygen off the meteoric water line (Yakir et al. 1990; Voelker et al. 2014) seem to be related to metabolic activities (Lehmann et al. 2020b, Nakatsuka et al. 2020). One possible cause for such deviations is respiration, because oxygen is eliminated by both carbon dioxide and water released from trees, and hydrogen would be eliminated only by the water. Such deviations are closely related to the amount of respired CO₂ in humans and animals (Schoeller and Van Santen 1982; Speakman 1997), and similar phenomenon might be happening in plants where respiration is high, in such places as symplastic leaf water pool (Kagawa 2020). In support of this hypothesis, Cormier et al. (2018) reports associations between hydrogen isotope fractionations of plants and carbon and energy metabolism under low light conditions.

4 Seasonal Isotope Fractionation Recorded in the Cellulose of Tree Rings

Numerous studies have reported on the isotope composition of wood or cellulose (e.g., δ¹³C and δ¹⁸O) as a means of revealing seasonal dynamics in tree-climate relationships (Wilson and Grinstead 1977; Leavitt and Long 1986, 1991; Kitagawa and Wada 1993; Ogle and McCormac 1994; Li et al. 1996; Livingston and Spittlehouse 1996; Sheu et al. 1996; Jordan and Mariotti 1998; Jäggi et al. 2002; Helle and Schleser 2004; Eglin et al. 2010; An et al. 2012; Kimak and Leuenberger 2015). Variation in δ¹³C across individual rings can be as large as 4‰, though 1–2‰ is most common (Barbour et al. 2002; Kagawa et al. 2003; Helle and Schleser 2004; Li et al. 2005; Kimak and Leuenberger 2015; Fu et al. 2017). This amount of variation is most likely due to the physiological response of trees to climate forcings and/or biochemical and physiological fractionation, and is not related to seasonal variation in the isotopic composition of source CO₂ (Jäggi et al. 2002; Helle and Schleser 2004; Li et al. 2005; Voelker et al. 2016; Fu et al. 2017). Variation in δ¹⁸O across individual rings tends to be greater than that for δ¹³C, ranging as high as 6‰ and often being between 2–4‰ (Wilson and Grinstead 1977; Zeng et al. 2014; Fu et al. 2017; Szejner et al. 2016; Belmecheri et al. 2018). The variation in cellulose δ¹⁸O reflects an important influence of seasonal variation in the isotopic composition of source water (Chap. 18). Because of this variation, intra-annual variations of oxygen isotopes show annual cyclicity in tropical areas and are often used for identifying annual rings in tropical trees that lack anatomically distinct annual rings (Poussart et al. 2004, Managave et al. 2010a, b, Pons and Helle 2011, Ohashi et al. 2016).

Numerous past studies have reported differences in chemical composition between earlywood and latewood in a range of softwoods and hardwoods (Ritter and Fleck 1926; Wilson and Wellwood 1965; Khattak and Mahmood 1986; Bergander 2001; Gindl 2001; Bertaud and Holmbom 2004). Generally, all these authors found that earlywood contained more lignin and less cellulose than latewood. The difference was explained in terms of the structure of the cell wall since earlywood consists of a larger proportion of lignin-rich middle lamella, due to larger tracheid diameters, and thinner cell walls, compared to latewood (Fredriksson et al. 2018). The cellulose and lignin that compose tree rings can be distinguished on the basis of stable isotope ratios (Borella et al. 1998, 1999; Barbour et al. 2001; Loader et al. 2003; Verheyden et al. 2005). Observed δ¹³C values for cellulose are isotopically enriched by approximately 3‰ compared to those for lignin (Loader et al. 2003). δ¹⁸O values are significantly lower in lignin (22.7 ± 2.4‰), compared to whole-wood (27.7 ± 0.5‰) or cellulose (31.4 ± 1.4‰) (Ferrio and Voltas, 2005). In this context, it needs to be considered that lignin composition and percentage is variable, not only between EW and LW, but also among different species and even within a single population of the same plant species depending on age (Campbell and Sederoff 1996).

Several past research efforts have shown that seasonal patterns of δ¹³C differ between EW and LW. Winter deciduous trees must, by phenological constraint, draw on stored carbohydrate (NSC) resources to support their earliest cambial activity. Using this assumption, several studies have interpreted the cellulose δ¹³C values of EW as reflecting fractionation processes from antecedent growing seasons (Jaggi et al. 2002, Helle and Schleser 2004; Li et al. 2005; Eglin et al. 2010; Hafner et al. 2015; Kimak and Leuenberger 2015; Fu et al. 2017; Zeng et al. 2017). Helle and Schleser (2004) described a tri-phasic seasonal response in the δ¹³C from thin sections across individual rings in four deciduous species. In the earliest phase, δ¹³C ratios were at a seasonal high and it was assumed that cellulose deposition was supported by overwintered NSC stores. The enriched δ¹³C values of stored NSC was attributed to post-photosynthetic fractionation related to carbohydrate conversions between free sugars and polymeric starch (Scott et al. 1999). The earliest phase can potentially be short, as current-season autotrophic capacity and NSC export from developing leaves can occur relatively early in the spring (Keel and Schadel 2010). In the second phase, young leaves expand and become net exporters of photosynthates. During this phase, tree-ring δ¹³C was observed to decline and this was attributed to current-season coupling between photosynthetic fractionation during a period of cooler temperature and replete soil moisture. In the third and latest seasonal phase at the end of the growing season, an increase in tree-ring δ¹³C was observed, which is attributed to the late-summer translocation of leaf sugars into storage pools in the stems and bole, and concomitant isotopic enrichment during NSC transformations, as in the start of the growing season. In other studies, the enriched δ¹³C values observed in the EW of temperate-latitude trees has been linked to the use of stored NSCs, but the cause of enrichment was attributed to antecedent climate-induced fractionation, not the fractionation of sugar transformations (Kimak and Leuenberger 2015; Hafner et al. 2015). For example, Hafner et al. (2015) observed that δ¹³C ratios in a 38-year EW chronology of the winter-deciduous species, Quercus robur, were best correlated with climate conditions during the previous year's summer; suggesting a minor role, if any, for non-climatic fractionation effects. Given contrasts within the existing literature, there is a need to better resolve the determinants of EW δ¹³C ratios in deciduous trees.

Evergreen species have opportunities to commence photosynthesis even in winter prior to the growing season where climate is mild, such as temperate European climate, and they may rely less on stored carbon for EW formation (see Zweifel et al. 2006; Kimak and Leuenberger 2015; Soudant et al. 2016). However, the study by Castagneri et al. (2018) showed evidence of significant reliance on stored carbon in the Mediterranean pine, Pinus pinea, for construction of both EW and LW. In contrast, Alvarez et al. (2018) observed no difference in the δ¹³C ratios of LW and whole rings in Canadian black spruce (Picea mariana) trees, and concluded that both EW and LW were constructed from current-year photosynthate. Intra-annual δ¹³C (and δ¹⁸O) variations are closely related to the water status at the time of wood formation (Leavitt and Long 1991; Kagawa et al. 2003; Verheyden et al. 2004; Roden et al. 2009; Li et al. 2011; Sarris et al. 2013; Schubert and Jahren 2015) and Barbour et al. (2002) reported seasonal variation in the δ¹³C of tree-ring cellulose in Pinus radiata that was consistently explained by theory relating intrinsic water-use efficiency (iWUE, Chap. 17) to current-season climatic variation and soil water availability. There was no need to invoke the withdrawal of stored carbohydrate reserves to explain the seasonal variation in δ¹³C. Similarly, Li et al. (2005) attributed the enriched δ¹³C in the EW of Pinus tabulaeformis in the China Loess Plateau as being explained largely by seasonal climate conditions, rather than the reliance on stored NSCs.

Jäggi et al. (2002) conducted a detailed study of δ¹³C ratios in the bulk biomass of needles, needle starch, and the cellulose of EW and LW in Picea abies in an effort to differentiate the effects of stored NSC use from current-year climate fractionation. There was a significant positive correlation between the δ¹³C of current-season starch in 1-year old needles and the current-year's EW, suggesting the use of short-term needle stores to support xylogenesis. There was an additional positive correlation between the δ¹³C of the bulk biomass of current-year needles and the current-year's EW, showing that the ultimate δ¹³C of the EW was likely the result of mixed NSCs from older and newer needles. In either case, however, the NSCs would have resulted from current-year CO₂ assimilation, providing the basis for linkage between EW δ¹³C and current-year spring climate. The starch signals from 1-year old needles and current-year needles did not show correlation with current-year LW δ¹³C. Rather, the LW δ¹³C were best explained by climatically-linked fractionation from mid growing-season of the current year. These mechanistic observations tend to support at least the first two phases of the tri-phasic pattern described by Helle and Schleser (2004), though in this case for an evergreen conifer, and with the caveat of utilization of recent NSC stores.

In the case of δ¹⁸O, seasonal fluctuations as noted in the cellulose of EW and LWwere reflecting variation in source water as well as enrichment fractionation (McCarroll and Loader 2004). The close association of the oxygen isotope ratios with source water variation is exemplified in the study by Miller et al. (2006) in which LW cellulose δ¹⁸O values in southeastern U.S, pines were able to predict inter-annual variation in the highly depleted precipitation water of hurricanes, Li et al. (2011) confirmed this finding at higher time-resolution. Presence of intra-annual isotopic variations are also reported in paleo wood samples and suggests the possibility of reconstructing paleo-hydrological climate at higher time resolution (Jahren and Sternberg 2008). Tree-ring hydrogen isotopes show similar intra-annual variations to oxygen isotopes, however, δ²H was slightly different from δ¹⁸O in that δ²H shows maximum at the beginning of each oak tree ring (Nabeshima et al. 2018). Deviation of hydrogen from oxygen isotope ratios is related to relative humidity (Voelker et al. 2014), and it is also related to plant metabolic activity such as remobilization of storage (Cormier et al. 2018, Chap. 11). The latter might explain observed negative correlation between δ²H and ring widths (Voelker et al. 2014) and positive and negative juvenile effects observed in long-term tree-ring δ¹⁸O and δ²H from central Japan, respectively (Nakatsuka et al. 2020).

An et al. (2012) were able to partially deconstruct the complex influences of precipitation from two separate monsoon systems, and cyclic variability in sea surface temperature, on EW versus LW cellulose δ¹⁸O in a coniferous forest of southwestern China. This knowledge was subsequently used as a basis for concluding that regional coherence exists in the hydrological determinants of EW and LW δ¹⁸O values (An et al. 2012; Fu et al. 2017).

Barbour et al. (2002) made observations of δ¹⁸O and δ¹³C in thin slices across two annual rings in Pinus radiata growing in three sites with different hydrology regimes. Generally, the two ratios were positively correlated across the growing season, showing that as evaporative enrichment of the isotopic content of leaf water increases, the iWUE of the leaf also increases. Furthermore, both δ¹⁸O and δ¹³C reflected increases in seasonally-averaged values among the three sites according to increased tendency for drought. These relationships are best explained by the correlated, but opposing, effects of increasing atmospheric VPD on stomatal conductance (decreases as VPD increases) and transpiration (increases as VPD increases). Thus, at the scale of selected trees growing across an individual season, ecophysiological responses to climate were a relatively accurate reflection of leaf gas-exchange theory (sensu Farquhar et al. 1982), however, a complete understanding of the tree-ring record of seasonal climate requires knowledge of the phenological gaps that occur in the process of xylogenesis.

5 Seasonal Lags in Cambial Phenology and Isotope Variation in Tree Rings

As interest in the intra-annual record of stable-isotope fractionation in tree rings has increased, it has become clear that an accurate alignment of that record with seasonal weather events requires knowledge about the timing and progression of xylogenesis (Rathgeber et al. 2016, Chap. 3). The progression of xylogenesis from incipient cell formation by the cambial meristem to eventual maturation (when carbon deposition to cell walls is complete) can take weeks to months (Kagawa et al. 2005), meaning that cell-wall cellulose that is extracted and analyzed for δ¹³C, δ¹⁸O, and δ²H reflects fractionation that occurred across a broad seasonal climate gradient. At the onset of seasonal cambial activity, the production of radial files of newly-formed cells occurs, and continues until the cessation of xylogenesis, either due to temperature or water stress prior to the end of the growing season, or to programmed phenology that is determined by day-length and/or temperature at the end of the growing season. Following their respective production, cells will begin the processes of enlargement, secondary wall formation and programmed death, in turn, according to interactions between environmental constraints on metabolic processes and intrinsic controls by plant growth regulators. This creates a continuous intra-annual record of cellular isotope fractionation, but one that is ultimately determined by environment-phenology interactions. The rate of cell division is relatively fast early in the growing season, and the cell enlargement phase is relatively long, creating EW cells. As the season progresses, the rate of cell division will slow and the enlargement phase will shorten, producing the smaller, denser cells that characterize LW. Conventionally, the duration of the wall thickening phase was thought to be the primary cause of thicker cell walls with more stored cellulose in LW tissues, especially in conifers (Skene 1969; Wodzicki 1971; Denne 1972). Cuny et al. (2014) have challenged this view with support from detailed measurements of each phase of xylogenesis in several temperate-latitude species. Their observations showed that it is the duration of cell enlargement, early in the process of xylogenesis, combined with a constant deposition of cellulose to secondary cell wall formation, that ultimately determines cell wall thickness in LW. According to this view, the amount of cellulose per cell, and thus carbon per cell, stored in both EW and LW is roughly equal, but it is spread across the surface area of smaller cells in the case of LW. This provides the visible appearance of thicker cell walls in LW. Recent ¹³CO₂ pulse-labeling experiment of evergreen conifer (Chamaecyparis obtusa) found that early-spring photosynthate appears in the previous late-latewood cells. In fact, late-latewood cells stay alive during winter, because these cells retain nucleus during winter (Ino et al. 2018).

Cuny et al. (2015) reported that the phased nature of xylogenesis sub-processes underlies a phenology gap between the time of cell enlargement, which controls increases in stem diameter, and deposition of cellulose in secondary cell walls, which controls the storage of carbon in temperate-latitude coniferous species. This observation provides a basis for temporal offsets in the seasonal analysis of ring-width increment and cellulose stable isotope fractionation; with the latter reflecting seasonal climate conditions that exist days to weeks later in the season, compared to the former. These results have great importance for efforts to align intra-annual tree-ring stable isotope records with the climate regime that existed during the time of initial photosynthetic fractionation (Post-assimilation fractionation is a separate, but potentially as important a factor, in determining the relation of observed tree-ring cellulose isotope ratios to the seasonal climate during the time of initial photosynthetic fractionation).

In a follow-up study, Cuny et al. (2019) observed variance in xylogenesis sub-processes across an altitudinal temperature gradient. They found that cooler temperature regimes slow the rates of cell enlargement and cell-wall thickening during xylogenesis, but that the tree compensates through longer duration of the enlargement and thickening phases. Thus, there potentially exists an interaction between extrinsic (environmental) control over metabolic factors, such as the enzyme activities that enable cell differentiation, and intrinsic (hormonal) factors that control the duration of phenological phases. The temperature-dependent compensatory relation of these processes appears to breakdown in the final LW tissues produced, late in the growing season. In Mediterranean environments, studies of xylogenesis have shown that cambium in Pinus pinea (Balzano et al. 2018) and P. helepensis (De Micco et al 2016) were productive throughout the calendar year, while in Arbutus unedo, a double pause in cell production was observed, in summer and winter with the formation on more than one IADF.

6 The Transport and Utilization of Stored Photosynthate for Xylogenesis

Post-photosynthetic metabolic processes leading to wood formation, such as translocation, storage and remobilization of photoassimilate are closely related to intra-annual variation of stable isotope ratios in tree rings. Although the seasonal course of leaf δ¹³C is well reflected in intra-annual variation of tree-ring δ¹³C in some cases (Leavitt and Long 1991; Leavitt 1993), the signal transfer is non-linear, rather than direct. For example, there are seasonally variable time lags between photosynthetic carbon incorporation and its use for wood formation (Schleser et al. 1999; Helle and Schleser 2004), due to phloem transport and storage processes lying between initial assimilation and the eventual use of photosynthate for xylogenesis. Here, we are referring to interannual time lags on the scale of years, not the intra-annual time lags of cambial phenology. Partly because of the time lag, which can last up to 1–2 years, first- and second order interannual auto-correlations are frequently observed in isotope dendroclimatological studies (Monserud and Marshall 2001; Szejner et al. 2018).

A model that takes into account the relative contribution of current photosynthate directly translocated from the leaves (new carbon) and older photosynthate remobilized from the storage pool (old carbon) was developed in 2001 to explain intra-annual variation of tree-ring δ¹³C (Hemming et al. 2001). This carryover phenomenon has since been verified by the use of an artificially enriched ¹³C tracer and it is now clear that the EW is made of the mixture of new and old carbon (Kagawa et al. 2006a; von Felten et al. 2007). Recent labeling studies have revealed a wide range of mixing ratios of new to old carbon among different tree species (Keel et al. 2007; He et al. 2020), and mobile carbon pools of trees have been classified into fast- and slow-turnover pools (Keel et al. 2006). However, such carryover phenomenon was absent, at least for root formation of pulse-labeled trees with H₂¹⁸O and HDO (Kagawa 2020) and future investigations are necessary, into the use of carried-over O and H signals in carbohydrate pools, if any, for tree ring formation.

6.1 The Experimental Design of Isotope-Labeling Experiments

During the 1960s and continuing into the 1980s, numerous experiments on tree carbon allocation were conducted with a radioactive ¹⁴CO₂ tracer (Hansen et al. 1997), notably in field studies. Due largely to the development of new analytical capabilities with stable carbon isotopes, researchers started and have been using ¹³CO₂ (>98 atom %) as a substitute for ¹⁴CO₂ in tree carbon allocation studies since the late 1990s (Simard et al. 1997a, b; Lacointe et al. 2004; Kagawa et al. 2005; Keel et al. 2007; Talhelm et al. 2007).

In this chapter, we define “pulse-labeling” as a short-term isotope labeling where fumigation of leaves with labeled CO₂ (¹³CO₂ or ¹⁴CO₂) lasts not more than a few days. The length of such a short input signal can be regarded as a pulse compared to the length of the tree’s growing season. In pulse-labeling experiments, either a whole tree or a branch is enclosed in a sealed chamber into which labeled CO₂ is injected. Fumigating a tree for one day with ¹³CO₂ at atmospheric concentration (380 ppm) provides a ¹³C signal sufficiently greater than the natural abundance ratio in each tree body part (Kagawa et al. 2005, 2006b). Strong ¹³CO₂ signal is efficiently incorporated into the tree within a short period and loss of labeled gas to the atmosphere is minimal. Efficient incorporation is especially important when using expensive ¹³CO₂ gas.

Alternatively, cheaper fossil CO₂ gas depleted in ¹³C (δ¹³C = −29.7‰ according to Körner et al. 2005) is available in large quantities and can be used for long-term web-FACE labeling experiments (Pepin and Körner 2002) lasting more than one growing season (Helle and Panferov 2004; Körner et al. 2005; Keel et al. 2006). The main advantage of web-face labeling is that artificial changes to the photosynthetic environment caused by the labeling experiment are minimal.

In order to study phloem translocation pathways of Japanese cedar, Kagawa et al. (2005) injected ¹³CO₂ to a branch on the upper side of the stem in June, during the earlywood formation period. Since Japanese cedar has straight grain, ¹³C was translocated in parallel to the phloem grain, and confined to the specific side of the stem showing little tangential diffusion. ¹³CO₂ was injected again to the same branch in September, during latewood formation, and detected a stronger ¹³C signal at a slightly different location within latewood (Fig. 15.1, Kagawa et al. 2005). At another experiment, ¹³CO₂ was injected to a branch of Dahurian larch, which has spiral grain, in August during latewood formation, and spiral translocation of ¹³C was observed (Fig. 15.1, Kagawa et al. 2006a).

Fig. 15.1

Phloem translocation pathways for straight-grained wood (Japanese Cedar, Cryptomeria japonica) and spiral-grained wood (Dahurian Larch, Larix gmelinii). In three different experiments, a piece of branch was enclosed in a transparent bag and ¹³CO₂ (> 98%) gas was injected over a few sunny days. Shaded areas on the stem disks illustrate the locations where ¹³C label was detected in the wood of the stem. After sampling trees, the outer bark on each stems was removed, then slivers were pulled off from the inner bark and outermost tracheids originating from the base of the ¹³C-fed branch. Since slivers are pulled off in parallel to the grain direction, a cell alignment line was continuously drawn following the vertical cell direction from the branch base to the disk below. Open circles indicate cell alignment line for Japanese cedar in May, and filled circles for Japanese cedar in September, or Dahurian larch in August

Kagawa et al. (2005, 2006a) drew lines in parallel to the straight and spiral grain of Japanese cedar and Dahurian larch, originating from the base of the pulse-labeled branch (Fig. 15.1), and ¹³C was detected along the line in both cases. This means, straight and spiral phloem translocation happens in parallel to the straight and spiral grain of Japanese cedar and Dahurian larch, respectively. These results supported the pipe model theory, where the stem and branches are considered as the assemblage of unit pipes and photosynthetic organs connected to each unit pipe provide carbon to each connected side of the stem (Shinozaki et al. 1964).

6.2 Evidence from Isotope-Labeling Studies for the Use of Stored Carbon in Tree-Ring Production

Directions of phloem translocation and the allocation ratio of photosynthetic products to storage versus net production show seasonal changes, and such changes are governed by the source (photosynthetic activity, amount of available storage)-sink (consumption of photosynthetic products and storage for growth and respiration) relationship (Hansen et al. 1997). Cambium-derived sinks (e.g. EW and LW) gain carbon from nearby sources (e.g. storage in xylem and phloem parenchyma cells or transport from phloem sieve-tube members). In spring-time, when EW formation happens, acropetal translocation prevails because newly forming leaves, and shoots becomes a strong sink (Hansen and Beck 1994; Gordon and Larson 1968). On the other hand, part of the photosynthetic products assimilated at the lower part of the crown is translocated and used for tree ring formation at breast height. When shoot elongation slows down in the early summer, basipetal translocation becomes prevailing (Gordon and Larson 1968). There is a time lag between carbon assimilation via photosynthesis and use of the assimilated carbon for tree-ring formation (Figs. 15.1, 15.2 and 15.3). Part of photosynthetic product is instantly used for tree-ring formation (new carbon), and part of the new carbon (sucrose) is converted to storage substance such as starch, before being stored in parenchyma cells of the bole or branches. For evergreen conifers, a significant amount of carbohydrate can be stored also in needles, then used later for tree-ring formation (Jäggi et al 2002).

Fig. 15.2

Timing of cell formation and intra-annual isotopic depositions of pulse-labeled photosynthetic carbon. Use of spring (May 29th) and autumn (September 18th) photosynthetic carbon for tree-ring formation of Japanese cedar in Tsukuba, Japan. In this region, tree-ring formation starts around April 13th and ends around November 9th. A shift from earlywood to latewood formation happens around July 22nd (Kagawa et al. 2005). Solid circles indicate ¹³C concentrations in wholewood, and solid triangles indicate that in holocellulose. Horizontal axis at the bottom indicate relative position within the tree ring formed, and the axis at the top indicate estimated date of cell formation

Fig. 15.3

Deposition of spring and summer photoassimilates into tree rings. ¹³CO₂ pulse-labeling was conducted in June, 2001 (left) and July, 2000 (right) in eastern Siberia. Horizontal dashed lines with larger spacing show ¹³C concentrations of starch. Thin solid line and thick dashed line with smaller spacing in the right subfigure show ¹³C concentrations of tree rings from two individual trees (Kagawa et al. 2006a). The vertical dotted line indicates the tree-ring boundary between rings formed within the two years

Kagawa et al. (2006b) enclosed whole saplings of Dahurian larch in transparent bags and conducted pulse-labeling over two sunny days in summer (July), and also in the spring of the following year (June) and tracked allocation of ¹³C to the different parts of each tree. In spring, sink strength at the apical parts were strong and spring photosynthetic products were allocated mainly to the apex; a lesser amount went to the below-ground parts of the tree. Spring photosynthetic products were also used for EW formation right after the labeling (Fig. 15.3 left, Kagawa et al. 2006a). When new carbon is directly used for tree-ring formation, the ¹³C concentration of wood shows a higher concentration than that of starch (horizontal dotted line in Fig. 15.3 left). On the other hand, if ¹³C-labeled photosynthetic products are used for tree-ring formation via the storage pool, the ¹³C concentrations of wood become lower than those of starch (Fig. 15.3 right). Photosynthetic products labeled during the previous summer are directed to the storage pool, where ¹³C concentrations are diluted with non-labeled carbon, then carried over to the current year. It is further diluted with newly assimilated spring photosynthetic products (new carbon) as shown in the EW of Fig. 15.3 right. Based on these results, we believe that EW is made of the mixture of “new and old carbon”. “New carbon” is newly assimilated spring photosynthate, translocated in phloem from the crown, and “old carbon” is storage carbon that is carried over from previous years and transferred from xylem and phloem parenchyma from above- or belowground (von Felten et al. 2007).

On the other hand, the EW of broad-leaved species that form ring-porous wood, such as oak, should be mostly made of storage, because formation of EW vessels of ring-porous trees precedes the onset of leaf flushing. In ring-porous oak, not only carbon but also oxygen and hydrogen isotope ratios show unusual values in EW (Helle and Schleser 2004; Nabeshima et al. 2018), which cannot be explained by climate at the time of formation. Carbon isotope ratios of storage carbohydrates, such as starch, shows higher carbon isotope ratios than newly assimilated sucrose. Initially, oak EW is made only from storage, then, as photosynthetic production from leaves becomes available, the proportion of newly-assimilated carbon for wood formation is increased. In summer, sink strength at the apical tissues becomes weak, and basipetal translocation of photosynthetic products prevails. In the LW formation of Dahurian larch in eastern Siberia, production is driven by both spring and summer photoassimilates from the same year and it relies less on storage carried over from previous years. However, if there is an unusually strong carbon demand, such as compression wood formation, a larger remobilization of storage resources is observed (Kagawa et al. 2006a), suggesting that storage can be an important source for wood formation, whenever an unusual demand arises.

6.3 The Time Resolution of Intra-annual Variation in Tree Ring Isotopes

Analogous to the peaks observed in chromatography, a signal output is observed as a broadened ¹³C peak in the tree ring after pulse-labeling with ¹³CO₂ (Fig. 15.4, Kagawa et al. 2005). The pulse signal is transferred as ¹³C labeled sugars dissolved in phloem sap. If we make an analogy of a tree’s sieve cells to a liquid chromatographic column and tree rings to a chromatogram recorder (D), then we can expect to observe a Gaussian peak in the rings as a function of time-since-labeling as expressed in the formula,

$${\text{D}}\left( {\text{t}} \right) = {\text{exp}}\left( { - \left( {\frac{t - t_c }{{H_{{\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-\nulldelimiterspace}\!\lower0.7ex\hbox{$2$}}} }}} \right)^2 \cdot ln2} \right)$$

(15.6)

where t_c is the time when labeled carbon reaches the cambium for tree ring formation. H_1/2, which is a measure of time resolution, is the half width at half maximum of the peak observed in the tree ring. Apart from a small tail to the right, this Gaussian peak matches the observed peak shape from a pulse-labeling experiment with fast-growing Japanese cedar (Figs. 15.2 and 15.4d, Kagawa et al. 2005).

Fig. 15.4

Time resolution of tree-ring archive and residence time of storage carbon. Time resolutions can be estimated from half-widths at the half height of the peaks (H_1/2). Storage pool with longer residence time (T_1/2) can generate peak tail to the right of each peak. a two discrete pulses of label given to a tree. Solid vertical lines represent the start and end of the first pulse, and dashed lines for the second pulse. b, c expected tree-ring output signal from the two pulses in (a). d predicted Gaussian peak from Eq. 15.6 (dashed line) and the observed peak shape from a pulse-labeling experiment with fast-growing Japanese cedar (solid line). r and 1–r are the ratios of the carbon contribution from new and old carbon pools, respectively

The time delay t_c is the time needed for new carbon to be transported from leaves to the cambium for tree-ring formation. According to estimates based on the natural fluctuation of the δ¹³C values of soluble organic matter in leaves and in the stem-base phloem, the time of phloem transport of new carbon from leaves to stem base has been estimated to be 1–2 days for adult beech trees of 25–27 m of height (Keitel et al. 2003) and the velocity of phloem transport has been estimated to be ca. 0.2–1.2 m h⁻¹ (Brandes et al. 2006; Dannoura et al 2011). However, carbon in the starch pool turns over slower than carbon in the soluble organic matter pool in leaves (Brugnoli et al. 1988). Residence time, or time required to replace old carbon pools in leaves with new carbon, is 9 days in deciduous species and 39–63 days in evergreen conifer species (Keel et al. 2007). It thus takes much longer than 1–2 days for non-structural carbohydrates in leaves to be exported, and the averaged time lag (t_c) for the carbon used for wood formation may be accordingly longer.

If we give two discrete pulses to a tree (Fig. 15.4a), then one would observe two small overlapping peaks instead of one large peak (Fig.15.4c). Thus, time resolution of a time series can be estimated by a half-width at half height of a peak, as two same-sized peaks separated by an interval of more than twice the half-width (2*H_1/2) are discernible as separate peaks (thick line in the Fig. 15.4c, IUPAC 1997). By fitting a Gaussian curve to the observed ¹³C peaks, we have estimated the time resolution of tree-ring δ¹³C proxy to be on a weekly to monthly level for a fast-growing ever-green conifer (Cryptomeria japonica) in temperate area (Tsukuba, Japan; Kagawa et al. 2005) and on a seasonal to yearly level for a slow-growing deciduous conifer (Larix gmelinii) in boreal area (Yakutsk, Russia; Kagawa et al. 2006a).

However, to explain the ¹³C peak shape more precisely, the peak’s tail has to be accounted for by an additional correction term. Tails with larger areas and longer durations are observed with boreal Larix gmelinii. The size of the tail of such slow-growing species depends primarily on the turnover of ¹³C tracer in the storage pool near the site of wood formation, e.g. sugars and starch in parenchyma cells. This is especially the case when a peak’s tail extends beyond the ring boundary into the following year’s ring (“Summer” and “Autumn” peaks in Fig. 15.5, Kagawa et al. 2006a). In recent pulse-labeling experiments with Japanese cypress (Chamaecyparis obtusa), pulse-labeled photoassimilates in early spring was used for formation of late-latewood cells of previous year (Ino et al. 2018). Overwintering cells of late-latewood can stay alive, because when overwintering latewood cells are observed under microscope, the last few latewood cells still keep their nucleus (Keiji Takabe, unpublished). They probably stay alive until early spring to finish the last part of latewood formation, hence carbon deposition of hemicellulose and lignin observed at the late-latewood of previous year. Such phenomenon, less clear though, was also observed in Dahurian larch. Early-spring photoassimilates in June 2001 was actually used for making the last few latewood cells of tree ring formed in 2000 (Fig. 15.3 left), giving another explanation for auto-correlation in addition to carry-over of storage from previous year. To summarize, photoassimilates of a given year can be used for tree ring of previous, current and the following year.

Fig. 15.5

A model for carbon isotope signal transfer from leaves to tree rings of deciduous conifer. The colored bars in the top panel represent the amount of ¹³C label fixed during 4 different labeling events, representing early spring (E-spring, green), late spring (L-spring, red), summer (blue) and autumn (brown), which are distributed through the current year. Dotted lines in the upper panel represent seasonal variation of net photosynthetic rate. In the lower panel, the colored lines represent the proportion carbon from each labeling event to form the tree rings. Shape of the signal transfer function from leaves to tree rings, LT(m,t), differs between early-spring, late-spring, summer and autumn. Therefore, LT(m,t) depends on the month when ¹³C is assimilated by leaves (Drawn based on the results of Kagawa et al. 2006a). Dashed black lines in the bottom panel on the right side represent signal transfer function for ¹³C assimilated in the following year

We observed an exponential decrease of ¹³C concentrations in the storage pool of Dahurian larch over time (Kagawa et al. 2006b), therefore, the isotope signal transfer model from leaves to tree rings should involve a storage term (S),

$${\text{S}}\left( {\text{t}} \right) = {\text{exp}}\left( { - \left( {\frac{t - t_r }{{T_{{\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-\nulldelimiterspace}\!\lower0.7ex\hbox{$2$}}} }}} \right)^2 \cdot ln2} \right)$$

(15.7)

where T_1/2 is residence time, the time needed for half of the old carbon in the storage pool to be replaced with new carbon. In other words, the time required for ¹³C concentrations in the storage pool to be reduced by half (Fig. 15.4c, d), and t_r is the time when the storage pool is remobilized. Residence time can be calculated by fitting an exponential curve to the tail of the ¹³C peak. For example, residence time has been calculated in this way to be 10.5 days for storage within a branch of Japanese cedar (Fig.15.4d, Kagawa et al. 2005). The calculation for the boreal Dahurian larch, revealed a much longer residence time of ca. 1 year (Kagawa et al. 2006a). According to labeling experiments of mature trees with fossil CO₂, a residence time (T_1/2) of NSC pool within wood was estimated to be ca. 50–200 days (Keel et al. 2007).

6.4 A Preliminary Model for Carbon Isotope Signal Transfer from Leaves to Tree Rings

The concept of a non-linear signal transfer function was introduced to explain the transfer of environmental signals (temperature, VPD) to tree-ring δ¹³C (Schleser et al. 1999). Here we present another signal transfer function, to explain the carbon isotope signal transfer from leaves to tree rings. In order to explain how the temporal carbon isotopic signal of photosynthetic products assimilated on a given day is transferred to tree rings, we have developed a signal transfer function which is a function of the month (m) when photosynthetic fixation of carbon (or ¹³C label) takes place at certain month (m) and the time thereafter (t) when the carbon (or the ¹³C label) is used for wood formation (Fig. 15.4). For example, the EW of boreal Larix gmelinii is made of a mixture of photoassimilate from the current June-July and the previous July–August (Fig. 15.3, Kagawa et al. 2006a). The total carbon used for tree-ring formation (LT) is therefore expected to be derived from both new (current photosynthate) and old (storage pool) carbon pools (Hemming et al. 2001; Kagawa et al. 2006a; Keel et al. 2007) as follows,

$${\text{LT}}\left( {\text{t}} \right) = {\text{r}} \cdot {\text{exp}}\left( { - \left( {\frac{t - t_c }{{H_{{\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-\nulldelimiterspace}\!\lower0.7ex\hbox{$2$}}} }}} \right)^2 \cdot ln2} \right) + \left( {1 - r} \right) \cdot exp\left( { - \left( {\frac{t - t_r }{{T_{{\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-\nulldelimiterspace}\!\lower0.7ex\hbox{$2$}}} }}} \right)^2 \cdot ln2} \right)$$

(15.8)

where r and 1–r are the ratios of the carbon (or ¹³C) contribution from new and old carbon pools, respectively (Fig. 15.4).

If we make an analogy of phloem sieve cells to a liquid chromatographic column, then an input ¹³C pulse (solid line peak in Fig. 15.4b) would appear on a tree ring as a Gaussian peak (Fig. 15.4d). If the two input pulses are far apart from each other more than twice the half width at the half height of the peaks (2*H_1/2), then summation of the two peaks (thick solid line peaks in Fig. 15.4c) can be recognized as two separate peaks on the tree ring. Thus, half-width at the half height (HWHF or H_1/2) can be used as an indicator for time resolution of tree-ring isotope archives. The observed ¹³C peak has a tail due to retention in the storage pool, and a correction term must be added to adequately model this shape. The δ¹³C of photosynthate in leaves Δ_A is given by Eq. 15.1 (Farquhar et al. 1989), and Δ_A varies according to the month of photosynthetic production. Then the carbon isotope ratio of tree-ring cell layer, δ¹³_TR, formed at the time t can be expressed as,

$$\begin{aligned} \Delta _{TR} \left( t \right) = & \int_{}^t {A\left( m \right) \cdot \Delta _A \left( m \right) \cdot \left( {LT\left( {m,t} \right) + \delta _{PP} } \right) \cdot dm} \\ = & \int_{}^t {A\left( m \right) \cdot \Delta _A \left( m \right) \cdot \left\{ \begin{gathered} r\left( m \right) \cdot \left( {D\left( {m,t} \right) + \delta _D } \right) \hfill \\ + \left( {1 - r\left( m \right)} \right) \cdot \left( {S\left( {m,t} \right) + \delta _S } \right) \hfill \\ \end{gathered} \right\} \cdot dm} \\ \end{aligned}$$

(15.9)

where A(m) is the photosynthetic assimilation rate of all carbon used for tree-ring formation (Fig.15.5). The term r is known to change seasonally and is therefore written as r(m). For example, the EW of ring-porous species relies less on current photoassimilate and more on storage photosynthate, yielding a small r value. The ratio r also varies widely among tree species (Keel et al. 2007) and under different weather conditions (He et al. 2020). We added the terms δ_pp, δ_D, and δ_S to account for the post-photosynthetic fractionation of total, new and old carbon, respectively.

6.5 Future Prospects

Since our current knowledge of post-photosynthetic carbon isotope signal transfer processes is not yet sufficient to fully explain intra-annual tree-ring δ¹³C, the preliminary model described here is based on limited experimental data. We could quantify important parameters such as the time resolution of tree-ring δ¹³C (2*H_1/2) (Kagawa et al. 2005) or the residence time of carbon in the storage pool (T_1/2) (Kagawa et al. 2006b), the model is too simplistic. In order to provide an experimental basis for validation and further refinement of this preliminary model, we need to conduct pulse-labeling experiments with mature, large trees as opposed to the young trees used in many of our own studies. By pulse-labeling trees in different seasons and later analyzing intra-annual δ¹³C of the tree rings subsequently formed, exact shape of the signal transfer function (LT(m,t)) can be determined. We also need to measure seasonal variation of natural carbon isotope ratios of leaf photosynthate (δ¹³L(m)) and tree rings (δ¹³TR(t)) at the same site to check if the model can precisely predict intra-annual tree-ring δ¹³C variation (δ¹³TR(t)) from δ¹³L(m). In fact, natural earlywood δ¹³C of boreal larch is correlated to the temperature of the current June-July and the previous August (Kagawa 2006), reflecting a seasonal lag between photosynthetic carbon assimilation and xylogenesis as we discussed in previous Sects. 15.3–5. The shape of the signal transfer function (Fig. 15.5) is expected to differ between different types of trees, such as coniferous vs. broad-leaved, ring porous vs. diffuse porous, deciduous vs. evergreen, and fat vs. starch tree species. A variety of such representative tree species frequently used for isotope dendroclimatology studies should be chosen for future pulse-labeling experiments.

Although single-substrate model exists to explain intra-annual oxygen isotope variation of tree rings (Ogée et al. 2009), post-photosynthetic oxygen and hydrogen isotope signal transfer processes are much less explored compared to those of carbon isotopes, partly because effective pulse-labeling method with oxygen and hydrogen isotopes has been lacking. However, recent development of pulse-labeling techniques with enriched or depleted H₂¹⁸O and HDO (Lehman et al. 2018, 2020a, Kagawa 2020) might contribute to the improvement of such model. For example, new roots formed in the post-labeling period (within six months of heavy-water labeling) did not contain significant oxygen and hydrogen labeling signals, possibly due to post-labeling exchange of oxygen and hydrogen in carbohydrate pool with those of non-labeled water (Kagawa 2020). In a previous study, Kagawa et al. (2006b) successfully detected signals from photosynthate labeled with ¹³CO₂ in July in roots formed later that year. Therefore, turnover rates of oxygen and hydrogen in carbohydrate pool may turn out to be much faster than those of carbon. Future analysis of intra-annual oxygen and hydrogen isotope analysis of the pulse-labeled trees in Kagawa (2020) will answer how pulse-labeled ¹⁸O and ²H signals in storage carbohydrates, if any, will affect intra-annual oxygen and hydrogen isotope signals of tree rings. Furthermore, pulse-labeling trees with three different isotopes (¹³CO₂, H₂¹⁸O, and HDO) at the same time should highlight similarities and differences of carbon, oxygen and hydrogen in post-fixation and exchange processes in carbohydrate pools.

Another interesting discovery from recent dual labeling experiments with H₂¹⁸O and HDO is the use of foliar-absorbed water for wood formation. Recent studies have identified foliar water uptake as a significant net water source for terrestrial plants (Eller et al. 2013; Goldsmith et al. 2013; Dawson and Goldsmith 2018; Berry et al. 2019; Schreel and Steppe 2020) and not only is foliar-absorbed water incorporated into leaf water, it is also assimilated into leaf sugar and organic matter (Studer et al. 2015; Lehman et al. 2018, 2020a) and cellulose of leaves, wood and roots (Kagawa 2020). Surprisingly, approximately half of oxygen and hydrogen in branch wood of Japanese cedar formed during simulated rain event originated from foliar-absorbed water, and the other half from root-absorbed water, which was caused by an increased oxygen and hydrogen exchange between sugars and local cambial water under rainy conditions. These results suggest foliar water uptake as a significant oxygen and hydrogen source of tree rings formed during rainy seasons (Kagawa 2020), and current mechanistic models explaining oxygen and hydrogen isotope ratios of leaf water and tree rings might need to be revised in future to account for contributions of oxygen and hydrogen from foliar-absorbed liquid water.

7 Conclusions

In this chapter, we discussed state-of-understanding of intra-annual variation in the stable isotopes of tree rings. Isotope signal transfer processes from leaves to tree rings include phloem translocation of photosynthetic products and storage of carbohydrates. There are seasonally variable time lags between photosynthetic carbon incorporation at leaves and its use for wood formation, and the time lags range widely from a week to years (Kagawa et al. 2005, 2006a). Incorporation of photosynthetic carbon into cellulose during xylogenesis is further affected by the processes of cell formation and maturation. There are larger volume of work on intra-annual tree-ring δ¹³C than for δ¹⁸O and δ²H, both on natural and pulse-labeled trees. Therefore, our current understanding of oxygen and hydrogen isotope signal transfer processes are limited compared to that of carbon. Our understanding is especially limited on oxygen and hydrogen isotopic exchange processes between carbohydrates and local cambial water before xylogenesis. However, recently developed H₂¹⁸O and HDO pulse-labeling techniques are beginning to uncover such processes. For example, recent experiments with heavy (or depleted) H₂¹⁸O, and HDO water identified foliar water uptake as a significant water source for trees (Eller et al. 2013; Goldsmith et al. 2013; Lehmann et al. 2018) and hence a significant source of oxygen and hydrogen in sugars, organic matter, and wood cellulose (Studer et al. 2015; Lehmann et al. 2018, 2020a; Kagawa 2020).