Determination of respiration and photosynthesis fractionation factors for atmospheric dioxygen inferred from a vegetation–soil–atmosphere analogue of the terrestrial biosphere in closed chambers

. The isotopic composition of dioxygen in the atmosphere is a global tracer which depends on the biosphere ﬂux of dioxygen toward and from the atmosphere (photosynthesis and respiration) as well as exchanges with the stratosphere. When measured in fossil air trapped in ice cores, the relative concentration of 16 O, 17 O, and 18 O of O 2 can be used for several applications such as ice core dating and past global productivity reconstruction


Introduction
The oxygen cycle represents one of the most important biogeochemical cycles on Earth, as oxygen is the second most important gaseous component in the atmosphere. Oxygen is an essential component for life on Earth as it is consumed by all aerobic organisms through respiration and produced by autotrophic organisms through photosynthesis.
The analysis of the oxygen isotopic composition classically expressed as δ 18 O and δ 17 O of O 2 in air bubbles trapped in ice cores is currently used to provide information on the variations of the low-latitude water cycle and the productivity of the biosphere during the Quaternary (Bender et al., 1994;Luz et al., 1999;Malaizé et al., 1999;Severinghaus et al., 2009;Blunier et al., 2002;Landais et al., 2010). δ 18 O of O 2 is also a very useful proxy for ice core dating through the resemblance of its variations with the variations of precession or summer insolation in the Northern Hemisphere (Shackleton, 2000;Dreyfus et al., 2007). These tracers are however complex, and their interpretation relies on the precise knowledge of the various fractionation factors in the oxygen cycle.
First, interpreting the relationship between δ 18 O of O 2 (or δ 18 O atm ) variations in ice core air and the low-latitude water cycle (e.g. Severinghaus et al., 2009;Landais et al., 2010;Seltzer et al., 2017) is still debated because of the multiple processes involved. Dole (1936) reported the relative atomic weight of oxygen in the air and water of Lake Michigan and gave as a measure of the δ 18 O value between both of about 21 ‰.  showed that δ 18 O atm is enriched compared to the δ 18 O of water of the global ocean (taken here as the Vienna Standard Mean Ocean Water, VS-MOW) with a value of 23.88 ‰. With the more recent values of Pack et al. (2017) of 24.15 ‰ and Wostbrock and Sharp (2021) of 24.05 ‰, we can envisage an enrichment of δ 18 O atm with respect to the VSMOW of about ∼ 24 ‰. This Dole effect is the result of several isotopic discriminations caused by biotic processes that enrich the δ 18 O atm relative to the oceanic values of water δ 18 O. First measurements have shown that the photosynthesis itself is not associated with a strong isotopic discrimination and produces oxygen with an isotopic composition which is close to the isotopic composition of the consumed water (Vinogradov et al., 1959;Stevens et al., 1975;Guy et al., 1993;Helman et al., 2005;. This is in contrast to the early results of Dole and Jenks (1944), who proposed a photosynthetic isotopic discrimination for plants and algae of 5 ‰. Vinogradov et al. (1959) challenged the results of Dole and Jenks (1944) by explaining that the 18 O enrichment of O 2 during their photosynthesis experiments is the result of contamination by atmospheric O 2 and respiration. Guy et al. (1993) studied the photosynthetic isotopic discrimination on spinach thylakoids, cyanobacteria (Anacystis nidulans), and diatoms (Phaeodactylum tricornutum) and found on average only a slight isotopic discrimination of 0.3 ‰, which they considered negligible.  also corroborate this idea by studying photosynthetic isotopic discrimination on Philodendron and did not obtain a 18 O enrichment of the O 2 produced. This absence of isotopic discrimination can be theoretically explained by the process of O 2 generation within photosynthesis (photosystem II) involving water oxidation by the oxygen evolving complex (Tcherkez and Farquhar, 2007). For the oceanic biosphere, the isotopic composition of O 2 produced by photosynthesis is very close to the isotopic composition of the ocean. However, in terrestrial biosphere the δ 18 O of water split during photosynthesis (leaf water) is highly variable both spatially and temporally because of the decrease of δ 18 O of meteoric water toward higher latitudes (Dansgaard, 1974) and the enrichment in heavy isotopes in leaf water during evaporation (Dongmann et al., 1974). The mean δ 18 O enrichment of leaf water isotopic composition has been estimated between +4.5 ‰ and +6 ‰ with respect to the isotopic composition of mean global ocean water (Bender et al., 1994;Hoffmann et al., 2004). On top of this enrichment, the terrestrial and oceanic Dole effects are mostly explained by the respiratory isotopic discrimination of the order of magnitude of +18 ‰ (Bender et al., 1994).
Because of the isotopic enrichment in leaf water, the terrestrial Dole effect has been initially estimated to be 5 ‰ higher than the oceanic Dole effect and δ 18 O atm used to estimate changes in the balance between land and marine productivity (Wang et al., 2008;Bender et al., 1994;Hoffmann et al., 2004). However, the evidence by Eisenstadt et al. (2010) of isotopic discrimination up to +6 ‰ for marine phytoplankton photosynthesis rather suggests that the marine and terrestrial Dole effects are of the same order of magnitude. More specifically, Eisenstadt et al. (2010) determined several photosynthetic isotopic discrimination values depending on the phytoplankton studied (Phaeodactylum tricornutum: 4.5 ‰, Nannochloropsis sp.: ‰, Emiliania huxleyi: 5.5 ‰, and Chlamydomonas reinhardtii: 7 ‰). If marine and terrestrial Dole effects are similar, then the past variations of δ 18 O atm cannot be attributed to different proportions of terrestrial or marine Dole effects. They would be better related to the low-latitude water cycle influencing the leaf water δ 18 O consumed by photosynthesis and then the δ 18 O of O 2 produced by this process (with a larger flux in the lowlatitude vegetated regions). This is supported by orbital and millennial variations of δ 18 O atm in phase with calcite δ 18 O in Chinese speleothem, a proxy strongly related to the intensity of the hydrological cycle in south-east Asia (Severinghaus et al., 2009;Landais et al., 2010;Extier et al., 2018). The aforementioned studies show that qualitative and quantitative interpretation of δ 18 O atm relies strongly on the estimate of O 2 fractionation factors in the biological cycle, but data to constrain the fractionation factors associated with respiration and photosynthesis for the different ecosystems are sparse.
In addition to the use of δ 18 O atm , the combination of δ 17 O and δ 18 O of O 2 provides a way to quantify variations in past global productivity (Luz et al., 1999). This method relies on the fact that O 2 -fractionating processes in the stratosphere and within the biosphere lead to different relationships between δ 17 O and δ 18 O of O 2 . Oxygen is fractionated in a mass-independent manner in the stratosphere, producing approximately equal 17 O and 18 O enrichments (Luz et al., 1999). On the contrary, the biosphere fractionating processes are mass-dependent such that the 17 O enrichment is about half the 18 O enrichment relative to 16 O. We thus define a 17 O anomaly as (1) 17 O of O 2 is equal to 0 by definition in the present-day troposphere (the standard for isotopic composition of atmospheric oxygen is the present-day atmospheric value). 17 O of O 2 is negative in the stratosphere, and increases in biosphere productivity lead to an increase of 17 O of O 2 . As for the interpretation of δ 18 O atm , the quantitative link between 17 O of O 2 and biosphere productivity depends on the exact fractionation factors associated with biosphere processes (Brandon et al., 2020).
Several studies have been conducted to estimate the fractionation factors during biosphere processes of O 2 production and consumption. These fractionation factors are then implemented in global modelling approaches involving the use of models of global vegetation and oceanic biosphere for interpretation of 17 O of O 2 and δ 18 O atm in terms of environmental parameters (Landais et al., 2007;Blunier et al., 2012;Reutenauer et al., 2015;Brandon et al., 2020). Most of the fractionation factors used in these modelling approaches were obtained from studies conducted at the cell level: cyanobacterium (Helman et al., 2005), E. coli (Stolper et al., 2018), and microalgae (Eisenstadt et al., 2010). In these studies, the underlying assumption is that the fractionation factor associated with O 2 measured at the cell level can be applied at the ecosystem scale. Yet, results from studies conducted at a larger scale, e.g. at the soil scale by Angert et al. (2001) found a global terrestrial respiratory 18 O/ 16 O of O 2 discrimination for soil microorganisms varying between −12 ‰ and −15 ‰. This is lower than the −18 ‰ discrimination classically used for respiration, with diffusion in soil playing a role in addition to the biological respiration isotopic discrimination. Angert et al. (2001) also showed using experiments on roots of Philodendron plants and wheat seedlings that the respiratory discrimination of a soil with roots is lower (about −12 ‰) than the −18 ‰ discrimination associated with dark respiration. This is due to the low O 2 concentration in roots whose presence favours a slower diffusion. Later, Angert et al. (2003) found an even larger spread of O 2 isotopic discrimination in soil and showed that temperate and boreal soils have higher isotopic discrimination, respectively, of −17.8 ‰ and −22.5 ‰.
It has been suggested that the strong discrimination observed for boreal and temperate soils is due to the involvement of the alternative oxidase pathway (AOX, Bendall and Bonner, 1971) in addition to the usual cyclooxygenase (COX) respiratory pathway. In the COX respiration pathway, present in the majority in plants, the cytochrome oxidase enzyme catalyses the oxygen reduction reaction. In the AOX pathway, the oxidation of ubiquinol molecules is directly coupled to the reduction of oxygen. Guy et al. (1989Guy et al. ( , 1992 showed that, for green tissues, the respiratory discrimination of the AOX pathway is much higher (−31 ‰) than the one of the COX pathway (−21 ‰). Similarly, Ribas-Carbo et al. (1995) found a higher respiratory discrimination in phytoplankton that engages the AOX pathway (−31 ‰) relative to bacteria that engage the COX pathway (−24 ‰).
Other studies had attempted to investigate the different respiratory discriminations in the light (dark respiration, Mehler reaction, and photorespiration). As during the light period, dark respiration can be inhibited (70 % inhibition found by Tcherkez et al., 2017, andKeenan et al., 2019), so that the other O 2 consuming processes are important to consider. The Mehler reaction reduces oxygen to form a superoxide ion which is converted to hydrogen peroxide (H 2 O 2 ) in photosystem I and then further converted to water (Mehler, 1951). Photorespiration is the result of the oxygenase activity of Rubisco (Sharkey et al., 1998). This enzyme can oxidise ribulose-1,5-bisphosphate with an oxygen molecule O 2 . This reaction causes a loss of CO 2 incorporation, thus decreasing the photosynthetic yield (Bauwe et al., 2010). Guy et al. (1993) first found a photorespiratory discrimination of −21.7 ‰ and a 18 O/ 16 O discrimination of −15.3 ‰ for the Mehler reaction. Later, on a study performed on peas, Helman et al. (2005) found 18 O/ 16 O discriminations of −21.3 ‰ and −10.8 ‰, respectively, for photorespiration and the Mehler reaction.
The above-presented state of the art shows contrasting results for the determination of fractionation factors for the different photosynthesis and O 2 uptake processes, thus underlining the importance of performing new measurements to correctly interpret global variations of the isotopic composition of oxygen. Moreover, because there may be a difference between the fractionation factors at the cell level and at a broader level as shown for dark respiration in soil, we will favour here an approach at the scale of a terrarium including plant and soil.
In this study we developed a simplified vegetation-soilatmosphere analogue of the terrestrial biosphere in a closed chamber of 120 dm 3 with the aim of estimating the fractionation factors of atmospheric dioxygen due to soil respiration, plant respiration, and photosynthesis. With this set-up we carried out several experimental runs with soil only and soil with plants in order to estimate the isotopic discrimination of the different compartments and check values obtained at the cell level. The implications for our interpretation of the Dole effect are also discussed. Seeds of Festuca arundinacea(Schreb.), also commonly called tall fescue, were first sown in a commercial potting soil (Terreau universel, Botanic, France; composition: black and blond peat, wood fibre, green compost, and vermicompost manure, organic and organo-mineral fertilisers and micronutrient fertilisers). During 15 to 20 d, they were then placed in a growth chamber of the Microcosms experimental platform of the European Ecotron of Montpellier (https://www.ecotron.cnrs.fr, last access: 9 March 2023) under diurnal light-dark cycles (Table S1 in the Supplement), air temperature set at 20 • C (T air ), air relative humidity (RH) at 80 %, and CO 2 atmospheric concentration close to ambient air (concentration of CO 2 = 400 ppm).
Twelve pots (8 cm × 8 cm × 12 cm with 180 to 200 g of dry soil) containing approximately 25 to 30 mature fescue plants were used for each experimental run. All plants were placed in a plastic tray filled with tap water inside an airtight transparent chamber manufactured from welded polycarbonate (10 mm wall thickness and 120 L volume) similar to the chambers used by Milcu et al. (2013) (Fig. 1). The sealing Figure 1. A vegetation-soil-atmosphere analogue of the terrestrial biosphere in a closed chamber. (a) Schematic of the closed chamber set-up used for the terrestrial biosphere model. The 120 dm 3 gas tight closed chamber containing a terrestrial biosphere analogue is enclosed in a larger growth chamber from the Ecotron Microcosms platform. Main environmental parameters inside the closed chamber are actively controlled and monitored: temperature (T ), light intensity, CO 2 , relative humidity (RH), and pressure differential ( P ). The water cycle in the closed chamber is shown in blue. (b) Photograph of the closed chamber used in the experiment with Festuca arundinacea. of the closed chamber was checked before each experiment using helium.
To control temperature and light intensity inside the closed chamber, this smaller chamber was placed in a larger controlled-environment growth chamber. Light was provided by two plasma lamps (GAVITA Pro 300 LEP02; GAVITA) with PAR = 200 µmol m −2 s −1 , and air temperature inside the closed chamber was regulated at 19 ± 1 • C by adjusting the growth chamber temperature.
The closed chamber ( Fig. 1) was used as a closed gasexchange system with controlled, and continuously monitored, environmental parameters. Air and soil temperature (CTN 35, Carel), air relative humidity (PFmini72, Michell instrument, USA), and CO 2 atmospheric concentration (GMP343, Vaisala, Finland) were measured and recorded using the growth chamber datalogger (sampling rate = 1 min). O 2 concentration was continuously monitored using an optical sensor (Oxy1-SMA, Presens, Germany). Because precise O 2 concentrations are determined in our samples by mass spectrometry (see next section), the measurements of the Oxy1-SMA were only used as a control during the experiment. The measured O 2 value for atmospheric air was adjusted to 20.9 % before each sequence of experiments, and the same adjustment (offset) was then applied to the O 2 record during the following sequence.
Air relative humidity was regulated between 80 % and 90 % using a heat exchanger (acting as a condenser) connected to a closed-cycle water cooling system. The condenser was positioned in a way to create a closed water cycle in the biological chamber (water vapour from evapotranspiration was condensed back into irrigation water). In order to keep the CO 2 mixing ratio close to 400 ppm during the light periods, photosynthetic CO 2 uptake was compensated with injections of pure CO 2 using a mass flow controller (F200CV, Bronkhorst, the Netherlands). During the dark periods, a soda lime trap connected to a micro-pump (NMS 020B, KNF, Germany) was used to remove the excess CO 2 coming from respiration. CO 2 atmospheric concentration during the night was kept below 200 ppm.
To ensure atmospheric pressure stability in the closed chamber, a pressure compensation system, made of two connected 10 L gas tight bags (Restek multi-layer polyvinyl fluoride foil gas sampling bag, USA), was installed. Each bag was half full of atmospheric air; the first one was installed in the closed chamber while the second one was outside the chamber. This way, each bag inflated or deflated in response to pressure variations caused either by O 2 or CO 2 uptake or release. The pressure difference between the closed chamber and the atmosphere was regularly measured using a differential sensor (FD A602-S1K Almemo, Ahlborn, Germany).
Finally, the enclosed air was mixed using seven brushless fans and considered homogeneous.

Gas sampling
To measure the isotopic composition along the experiment, small samples of gas were collected in 5 mL glass flasks, made of two Louwers HV glass valves (1-way bore 9 mm Ref. LH10402008, Louwers Hanique, the Netherlands) welded together. Those flasks, previously evacuated, were mounted on PFA tubing (1/4th) using two 1/4th UltraTorr fittings (SS-4-UT-9, Swagelok, USA). Two manual valves (SS-4H, Swagelok, USA) were also installed on the PFA tubes to open or close the circuit. A micro-pump (NMS 20B, KNF, Germany) was finally turned on during air sampling to ensure closed chamber atmosphere circulation through the flask. The flow rate was equal to 1.6 L min −1 .

Water extraction from leaf and isotopic analysis
After each experiment, the plant leaves were collected, placed in airtight flasks, and immediately frozen at −20 • C for at least 24 h to make sure there was minimal loss of water through vaporisation when the vial was opened later. The extraction of water from leaves was done according to the procedure detailed in Alexandre et al. (2018). The vial was fixed onto a cryogenic extraction line and was first immersed in a liquid nitrogen Dewar to prevent any sublimation of the water. The water extraction line was emptied of most of its air (< 10 −5 Pa). Once this pressure was reached, the pump was turned off and a valve was closed in order to keep a constant static void within the system. The "reception" vial was then immerged in a liquid nitrogen Dewar which will act as a water trap, whilst the sample vial for the water was then transferred to a water bath maintained at 75 • C. The system was kept in these conditions for no less than 6 h, so that all the water present in the leaf and stems was extracted. Afterwards, in order to remove all of the organic compounds of the extracted water, an active charcoal was placed in the extracted water and left under agitation for the night.
For analysis of δ 17 O and δ 18 O of water, leaf water was converted to O 2 using a fluorination line for reaction of H 2 O with CoF 3 heated to 370 • C at LSCE. The isotopic composition of the dioxygen was measured with an IRMS equipped with a dual inlet (Thermo Scientific MAT253 mass spectrometer). The standard that was chosen was an O 2 standard calibrated against VSMOW. The precision was 0.015 ‰ for δ 17 O, 0.010 ‰ for δ 18 O, and 6 ppm for 17 O (Eq. 1); for more details, refer to Landais et al. (2006).  (Yeung et al., 2018;Wostbrock et al., 2021). In our case, because we use a similar set-up to the one developed by Barkan and Luz (2003) for the analyses of the triple isotopic composition of O 2 in the air (cf. next section), we have chosen to base our calculation on their estimates. In this study, we have thus chosen the value of 23.88 ‰ for δ 18 O of O 2 values with respect to VSMOW following Barkan and Luz, 2005. As for the δ 17 O of O 2 value with respect to the VSMOW value, we use two different possible estimates from these authors, either 12.03 ‰ (Luz and Barkan, 2011)

O 2 purification and isotopic analysis
The air samples collected in the closed chambers were transported to LSCE for analyses of the isotopic composition of O 2 . The flasks were connected on a semi-automatic separation line inspired from Barkan and Luz (2003) which was made up of eight ports in which two standards (outside air) and six samples were analysed daily (Brandon et al., 2020). After pumping the whole line, the air was circulated through a water trap (ethanol at −100 • C) and then through a carbon dioxide trap immersed in liquid nitrogen at −196 • C. After collection of the gas samples on a molecular sieve trap cooled at −196 • C, a helium flow carried it through a chromatographic column which was immersed in a water reservoir at 0 • C to separate the dioxygen and the argon from the dinitrogen. After separation of the dioxygen and argon from helium, the gas was collected in a stainless steel manifold immersed in liquid helium at −269 • C.
After collection, the samples were analysed by the IRMS, previously mentioned for leaf water analyses. The following ratios were measured: 18 O/ 16 O, 17 O/ 16 O, and O 2 /Ar (as an indicator of the O 2 concentration because Ar is an inert gas). δ 17 O and δ 18 O of O 2 each sample were obtained through three series of 24 dual inlet measurements against a standard made of O 2 and Ar. This sequence was followed by two peak jumping analyses of the O 2 /Ar ratio, including separate measurements of the O 2 and Ar signals for both the standard and the sample. The uncertainty associated with each measurement was obtained from the standard deviation of the three runs and from the repeated peak jumping measurement for δO 2 /Ar which was defined by Each day, we performed measurements of the dioxygen isotopic composition and O 2 /Ar ratio on two samples of outside air, which is the standard for the isotopic composition of O 2 (Hillaire-Marcel et al., 2021), so that the calibrated δ 18 O value for our sample was calculated as in Eq. (2). (2) 2.3 Experimental runs 2.3.1 General strategy Our goal was to calculate the fractionation factor associated with δ 17 O and δ 18 O for soil respiration, dark leaf respiration, and photosynthesis using the microcosm described above. In order to quantify the fractionation factors, we needed to work in closed and controlled conditions. Given the volume of the closed chamber (120 dm 3 , hence about 1.12 mol of O 2 ) and the order of magnitude of dark respiration (order of magnitude of 0.08 µmol O 2 s −1 for soil respiration) and net photosynthetic fluxes (order of magnitude of 0.45 µmol O 2 s −1 ) inside the chamber, we calculated that experiments should last from 3 d to more than 2 weeks so that more than 1 10 of the O 2 in the chamber can be recycled by the plant and soil. This recycling allows the creation of sufficiently large isotopic signals (especially 17 O of O 2 ) to be detected and measured. We set up two different experiments in the closed chamber, each experiment being repeated three or four times to characterise the experimental repeatability of the system. The first experiment (repeated four times, i.e. in four sequences) aimed at studying the fractionation factors during soil respiration. The second experiment (repeated three times, i.e. in three sequences, each sequence being divided into several periods with or without light) aimed at studying the fractionation factors during dark respiration and photosynthesis of plants.
Prior to the aforementioned experiments, measurements were carried out on a closed empty chamber to check the absence of leaks as well as the absence of isotopic fractionation (Table S2).

Soil respiration experiment
To conduct the soil respiration experiment, 2.6 kg of soil (Terreau universel, Botanic) were placed in 12 different pots. The light was turned off during this experimental run (Table S1). We decided not to apply any diurnal cycles during dark respiration experimentations for two reasons. First, we wanted to prevent the development of algae, mosses, or any photosynthetic organisms in the chamber. Secondly, it was easier to optimise temperature control as the light radiation could increase the temperature inside the closed chamber. During this dark period, CO 2 from soil respiration accumulates in the biological closed chamber. To have a stable concentration of CO 2 during the whole dark period, the CO 2 was trapped using soda lime. Four sequences were performed with respective durations of 53, 51, 43, and 36 d.

Photosynthesis and dark respiration experiment
We used the same soil with plants (Festuca arundinacea) grown before the start of the three sequences of the photosynthesis and dark respiration experiment. In order to obtain a significant change of the 17 O of O 2 signal in our closed 120 dm 3 chambers, the three experiments were run for 1 to 2 months. CO 2 level was controlled to 400 ppm by a CO 2 trap and CO 2 injections. This was done to ensure that the CO 2 in the chamber did not reach levels too far from the atmospheric composition as this could have affected the physiology of the plant. The light cycle was controlled to alternate between day (photosynthesis and respiration) and night conditions (respiration) ( Table S1).
The values of the leaf water measurements are presented in Table S3. Because the experiments had to be carried in a closed chamber, we could not sample leaves during the experiment and only got a value at the end of each sequence. Nevertheless, we could compare the isotopic composition of the irrigation and soil water at the start and at the end of the experiment.

Quantification of fractionation factors
We detail below how we used the results from our experiments to quantify the associated fractionation factors. Notations used below are gathered in Table 1.
The isotopic fractionation factor of oxygen is expressed through the fractionation factor α.
where α is the fractionation factor and 18 R is the ratio of the concentration 18 R = n( 18 O) n( 16 O) with n the number of moles of O 2 containing 18 O or 16 O. 18 R is linked to the δ 18 O value through The isotopic discrimination is related to the isotopic fractionation factor through 18 ε = 18 α − 1.
The same Eqs.
In some studies, referred to later, the notation γ is also used with γ = 17 ε 18 ε .

Soil respiration
Respiration is associated with isotopic fractionation. The light isotopes, 16 O, are more easily integrated by microorganisms than the heavy isotopes, 18 O, which hence remain in the atmosphere. We express the fractionation factor for soil respiration as In our experiment, the respiratory process took place in a closed reservoir so that we could calculate the fractionation factors from the evolution of the concentration and isotopic composition of dioxygen in the chamber. The number of molecules of dioxygen in the air of the closed chamber, n (O 2 ), between time t and time t + dt can be written as The evolution of the isotopic ratio of oxygen, 18 R, between time t and time t + dt can be written as: Combining equations Eqs. (8), (9), and (10); neglecting the second order term d 18 R t × dn(O 2 ) t ; and integrating from t 0 (starting time of the experiment when the chamber is closed) to t leads to Because argon is an inert gas, we can link n(O 2 ) t

Dark respiration
In order to calculate the isotopic fractionation associated with soil and plant respiration during the dark period, we followed the same calculation as for the soil respiration (Sect. 2.4.1).
In this case, we selected only night periods from each sequence of the photosynthesis and dark respiration experiment. Determined by our study * α soil_respi Fractionation factor associated with soil respiration Determined by our study * α total_respi Fractionation factor associated with total respiration during light period Determined by our study * ε Isotopic discrimination * ε dark_respi Isotopic discrimination of soil and plant respiration during night periods Determined by our study * ε dark_leaf_respi Isotopic discrimination of leaf respiration during night periods Determined by our study * ε photosynthesis Isotopic discrimination associated with photosynthesis Determined by our study * ε soil_respi Isotopic discrimination of soil respiration associated with soil respiration experiment Determined by our study

Photosynthesis
During photosynthesis, the oxygen atoms in the dioxygen produced by the plant come from the oxygen atom of water consumed by photosynthesis in the leaves, so that the fractionation factor during photosynthesis can be expressed as where lw stands for leaf water. For our study of Festuca arundinacea, we consider that the water in the mesophyll layer can be represented by bulk leaf water.
Photosynthesis occurs during the light periods. However, it should be noted that dark respiration, photorespiration, and the Mehler reaction occur at the same time. In a first approach, we made the assumption that respiration rates remain the same during the light and dark periods. This assumption is probably true for soil respiration since flux of heterotrophic dark respiration is not expected to change for different light conditions if the other environmental drivers (e.g. humidity, temperature, soil organic matter) are constant. However, autotrophic dark respiration is expected to decrease during light periods compared to dark periods. As a consequence, we present sensitivity tests to the dependence of a vanishing dark respiration of leaves during the dark period in Table S4.
Thus, at each stage, dioxygen is both produced by photosynthesis and consumed by the aforementioned O 2 uptake processes (hereafter total_respi) by the plant according to the mass conservation equation where dn total_respi is the number of molecules of O 2 consumed by dark respiration, photorespiration, and the Mehler reaction between time t and t + dt, and dn photosynthesis is the number of molecules of O 2 produced by photosynthesis between t and t + dt. The budget for 18 O of O 2 can be written as where 18 α total_respi is the fractionation factors associated with each O 2 consuming process period throughout the whole experiment.
We introduced the normalised fluxes of photosynthesis and total respiration as This led to the following expression of 18 α photosynthesis : This equation can be simplified at t = 0 for 18 R t = 18 R t0 = 1 and n(O 2 ) t = n(O 2 ) t0 : 18 α photosynthesis depends on the values of 18 α total_respi and of F total_respi , themselves dependent on the values of 18 α Mehler (fractionation factor associated with Mehler reaction), F Mehler (flux of oxygen related to Mehler reaction), 18 α dark_respi , F dark_respi , 18 α photorespi (fractionation factor associated with photorespiration), and F photorespi (photorespiration flux of oxygen). These last four parameters could not be determined in our global experiment. Our determination of 18 α photosynthesis will thus rely on assumptions for the estimations of 18 α Mehler , F Mehler , 18 α photorespi , and F photorespi .
To separate the 18 α dark_respi from the other fractionation factors, we defined 18 α total_respi = 18 α photorespi × f photorespi f indicates the fraction of the total oxygen uptake flux corresponding to each process (dark respiration, photorespiration, and Mehler reaction) so that In the absence of further constraints, we used here as first approximation the global values from Landais et al. (2007) for f dark_respi (0.6), f photorespi (0. 3) and f Mehler (0.1). Values for α photorespi and α Mehler were based on the most recent estimates of Helman et al. (2005).

Experimental data
During the four sequences, the respiration activity led to a decreasing level of the O 2 concentration measured by the optical sensor or through the δO 2 /Ar evolution from IRMS measurements (Fig. S1 in the Supplement). The comparison of the evolution of the O 2 concentration during the different sequences showed that respiratory fluxes were different with a maximum factor of 4 between the different sequences (Fig. S1). In parallel to the decrease in O 2 concentration, the δ 18 O increased as expected because respiration preferentially consumes the lightest isotopes: over the 51 d of the second soil respiration sequence, we observed a linear decrease of oxygen concentration by more than 5 % while δ 18 O increased by 8 ‰ (Fig. 2). A Mann-Kendall trend test showed that the 17 O of O 2 does not show any statistically significant trend over the four sequences ( Fig. S2) (p values were equal to 0.40, 0.08, 0.58, and 0.47, respectively).

Fractionation factors
We used the 15 to 20 samples obtained during each sequence of the soil respiration experiment to draw the relative evolution of ln ( 18 R t / 18 R t0 ) vs. ln ((δ O 2 Ar t /1000 +

1)/(δ O 2
Ar t0 /1000 + 1)) following Eq. (11) (Fig. 3). The slope of the corresponding regression line provided the isotopic discrimination 18 ε soil_respi and hence the fractionation factor 18 α soil_respi for each sequence (Table S5). It could be observed that despite differences in respiratory fluxes for the different sequences (the standard deviation is equal to 50 % of the average flux across sequences; see Table S5), the relationship between δ 18 O of O 2 and O 2 concentration (or δO 2 /Ar), and hence the calculated fractionation factor associated with respiration, is not much affected.

Experimental data
During the night periods, when only respiration occurred, we observed a decrease in O 2 concentration by 1 % within 3 d and a δ 18 O increase by 1 ‰ during the same period (Fig. 4). The evolution was qualitatively similar to that of soil respiration experiments with higher fluxes. We observed the same trends for the evolution of δO 2 /Ar during the night periods as for the respiration experiment. During light periods, there was a marked decrease in δ 18 O (2 ‰) and a marked increase in the flux of oxygen released (1 %) during 1 d. We observed the same trends for the evolution of δO 2 /Ar during the night periods as for the respiration experiment.
The Mann-Kendall test (95 %) showed a significative increasing trend of the 17 O of O 2 over sequences 1 and 2 (Fig. S3) ( 100 ppm in 31 d for sequence 1, 100 ppm in 40 d for sequence 2), while no significant increase of 17 O of O 2 is observed over sequence 3 (Fig. S3).

Dark respiration
The average of the isotopic discrimination for dark respiration 18 ε dark_respi and 17 ε dark_respi were calculated over the nine night periods, and we obtained values of, respectively, −17.0 ± 2.0 ‰ and −8.5 ± 0.8 ‰. The average of θ dark_respi during the experiment was equal to 0.5124 ± 0.0084 (details in Table S6).
The dark respiration of this experiment includes respiration of both soil and leaves. Because soil respiration fractionation factor has been determined above, it is possible to estimate here the fractionation factor for the dark leaf respiration, and we consider that respiration rate during dark and light periods do not vary: with F dark_leaf_respi the flux of leaf respiration during the night, f soil_respi the fraction of soil respiration during night periods (F soil_respi /F dark_respi ), and f dark_leaf_respi the fraction of dark leaf respiration during night periods (F dark_leaf_respi /F dark_respi ).

Photosynthesis
In order to calculate an average value for the fractionation factor associated with photosynthesis from Eq. (19), we first calculated the averages of the flux of the O 2 consuming processes and of the fractionation factors associated with each  sequence: F total_respi and 18 α total_respi . We also calculated the net O 2 flux during light periods, aN = F photosynthesis − F total_respi , as the linear regression, aN , of n(O 2 ) t n(O 2 ) t0 with time. a 18 R is also obtained as a linear regression of 18 R with time over each light period. Our data support our assumption that the regime was stationary over time and n(O 2 ) t /n(O 2 ) t0 evolved linearly over time, which is why we were able to do linear regressions.
The results of the eight individual α photosynthesis values are given in Table S10. The value of isotopic fractionation associated with the light period of period 1 of sequence 1 appeared clearly out of range. Following the Dixon's outlier detection test (Dixon, 1960), this value was considered an anomaly (likelihood > 99 %) and was removed from further analysis. We finally estimated the values of 18 ε photosynthesis and 17 ε photosynthesis as +3.7 ± 1.3 ‰ and +1.9 ± 0.6 ‰, respectively. The average of θ photosynthesis was equal to 0.5207 ± 0.0537, a value which depends on the value taken for the δ 17 O value of atmospheric O 2 vs. VSMOW (Sharp and Wostbrock, 2021), see Table 2.
We performed different sensitivity tests (Sects. S1 and S2 in the Supplement). Sensitivity test 1 (Table S4) Table 2. Summary of the mean values of the isotopic discriminations and θ values for Festuca arundinacea of all sequences of (1) the soil respiration experiment and of (2) the respiration and photosynthesis experiment and the number of data on which they were calculated ( * is the value for θ photosynthesis that depends on the determination of the δ 17 O of atmospheric O 2 vs. δ 17 O of VSMOW). We provide here the two different possible estimates using either 12.03 ‰ (Luz andBarkan, 2011) or 12.08 ‰ (Barkan andLuz, 2005) Sensitivity tests 2 (Tables S7, S8 and S9) were performed on values of the O 2 flux and associated fractionation factors for photorespiration and the Mehler reaction. They resulted in additional uncertainties of 0.0007 and 0.0005 for the values of 18 α photosynthesis and 17 α photosynthesis (Table S10). Sensitivity tests 3 concerned the possible evolution of the isotopic composition of leaf water over the course of an experiment. The comparison of the δ 18 O of irrigation water and soil water at the end of the experiment shows a possible increase up to 2 ‰ (Table S3). We thus estimate that our values of leaf water δ 18 O measured at the end of the experiment may be overestimated by 1 ‰ compared to the mean value of leaf water δ 18 O during the course of the experiment. Taking this possible effect into account would lead to a fractionation factor for photosynthesis higher by 1 ‰ compared to the presented one of 3.7 ± 1.3 ‰, hence a higher isotopic discrimination associated with photosynthesis.
Finally, we evaluated by a Monte Carlo calculation how the different uncertainties listed in the three sensitivity tests described above influence the final uncertainty on the photosynthesis isotopic discrimination. We found a final standard deviations (1σ ) equal to 0.3 ‰ for 18 ε photosynthesis and 0.15 ‰ for 17 ε photosynthesis .  1). This result is in good agreement and within the uncertainties given by Helman et al. (2005) with the γ value of 0.5174 (equivalent to a θ of 0.515 ± 0.0003) obtained with respiration experiments on several microorganisms.
During the experiment involving both oxygen uptake and photosynthesis, the 17 O of O 2 has a globally increasing trend with values reaching about 100 ppm after 1 month. Such behaviour is expected and was already observed by Luz et al. (1999)  . Within the water cycle, the slopes of ln(1 + δ 17 O) vs. ln(1 + δ 18 O) for the meteoric, evaporation, and evapotranspiration lines are larger than 0.516 Li, 1998 Landais et al., 2006) so that 17 O of water consumed by the plants during photosynthesis should be slightly lower than the 17 O of VSMOW expressed vs. isotopic composition of atmospheric O 2 but still higher than the 17 O of atmospheric O 2 . Photosynthesis can thus explain the 17 O of O 2 increase in the closed chamber.

Fractionation factors associated with δ 18 O of O 2 and implications for the Dole effect
The isotopic discrimination 18 ε soil_respi = −12.3 ± 1.7 ‰ for the soil respiration experiments is comparable to the average terrestrial soil respiration isotopic discrimination found by Angert et al. (2001) of −12 ‰. Still, among the diversity of soils studied by Angert et al. (2001), the soils showing the 18 ε values closest to our values are clay soil ( 18 ε = −13 ‰) and sandy soil ( 18 ε = −11 ‰). Soil respiration isotopic discriminations are less strong than isotopic discrimination due to dark respiration alone (−18 ‰, Bender et al., 1994). These lower values for soil respiration isotopic discrimination are due to the roles of root diffusion in the soil (Angert et al., 2001). The soils studied by Angert et al. (2001) are, however, different from our soil, which was enriched in organic matter. Further experiments are then needed to understand the variability in 18 ε associated with soil respiration. The isotopic discrimination for dark leaf respiration, 18 ε dark_leaf_respi = −19.1 ± 2.4 ‰ is associated with a large uncertainty and would benefit from additional experiments with a higher sampling and measurement rate. Still, even if it was obtained on different organisms and experimental set-ups, this value is in agreement with the values for isotopic discrimination for dark respiration determined by Helman et al. (2005) on bacteria from the Lake Kinneret ( 18 ε = −17.1 ‰) and Synechocystis ( 18 ε = −19.4 ‰ and −19.5 ‰) and Guy et al. (1989Guy et al. ( , 1992 on Phaeodactylum tricornutum and on terrestrial plants (−17 ‰ to −19 ‰ for COX respiration).
The average 18 ε photosynthesis is +3.7 ± 1.3 ‰ for Festuca arundinacea species, which goes against the classical assumption that terrestrial photosynthesis does not fractionate (Vinogradov et al., 1959;Guy et al., 1993;Helman et al., 2005;. Vinogradov explains that the low photosynthetic isotopic discrimination that can occur is due to contamination by atmospheric O 2 or by respiration. Guy et al. (1993) corroborate this idea by finding a photosynthetic isotopic discrimination of 0.3 ‰ in cyanobacteria (Anacystis nidulans) and diatoms (Phaeodactylum tricornutum) that they consider negligible.  in their study on Philodendron, consider that there is no photosynthetic isotopic discrimination. Our value suggests that there is a terrestrial photosynthetic isotopic discrimination, and the value found for Festuca arundinacea is slightly smaller than the photosynthetic isotopic discrimination in the marine environment 18 ε photosynthesis = +6 ‰ found by Eisenstadt et al. (2010). More specifically, Eisenstadt et al. (2010) determined several photosynthetic isotopic discrimination values depending on the phytoplankton studied (Phaeodactylum tricornutum: 4.5 ‰, Nannochloropsis sp.: 3 ‰, Emiliania huxleyi: 5.5 ‰, and Chlamydomonas reinhardtii: 7 ‰). One of the conclusions given by Eisenstadt et al. (2010) is that eukaryotic organisms enrich their produced oxygen more in 18 O than the prokaryotic organisms. Our conclusion based on experiments performed with the Festuca arundinacea species is in agreement with these conclusions.
Our experiments were performed at the scale of the plants, which is different to previous studies performed at the scale of the chloroplast (e.g. Guy et al., 1993) where no evidence of oxygen fractionation has been found. We can thus not exclude that this fractionation attributed here to photosynthesis is due to oxygen-consuming processes not taken into account in our approach. Our main goal however is to interpret the global δ 18 O of atmospheric O 2 using the fractionation observed at the scale of the plants. As a consequence, we believe that if there is a light-dependent oxygen fractionation process that we did not identify in our approach, it will also be present at the global scale. It should thus be taken into account in our future interpretation of the Dole effect. We thus keep our estimate of the photosynthesis 18 O discrimination described above but name it as an effective photosynthesis 18 O discrimination at the scale of the plants because the details of the processes at play is not fully elucidated.
Finally, we should however note that we tested only one species. Additional experiments with different plants are needed to check if the positive effective fractionation factor should be applied for global Dole effect calculation. Still, this positive effective 18 O discriminations during photosynthesis suggests that the terrestrial Dole effect may be higher than currently assumed and challenge the assumption that terrestrial and oceanic Dole effects have the same values (Luz and Barkan, 2011).

Conclusion
Using a simplified analogue of the terrestrial biosphere in a closed chamber, we found that the fractionation factors of soil respiration and dark leaf respiration at the biological chamber level agree with the previous estimates derived from studies at microorganism level. This is an important confirmatory step for the fractionation factors previously used to estimate the global Dole effect. More importantly, we document for the first time a significant effective 18 O discrimination at the scale of the plant during terrestrial photosynthesis with the Festuca arundinacea species (+3.7 ± 1.3 ‰). If confirmed by future studies, this can have a substantial impact on the calculation of the Dole effect, with important consequences for our estimates of the past global primary production.
Our study showed the usefulness of closed chamber systems to quantify the fractionation factors associated with biological processes in the oxygen cycle at the plant level. The main limitation of our present study was the low sampling rate during our experiments which hampers the precision of the determined fractionation factors. Future work should use this validated set-up to multiply such experiments to improve the precision of fractionation factors and to explore the variability of fractionation factors for different plants and hence different metabolisms. A good application would be to study the difference between C 3 and C 4 plants, because C 4 plants do not photorespire. C 4 plants, adapted to dry environments, have their own strategy and make very little photorespiration through specialised cells. This allows them to produce their own energy in an optimal way without the waste produced by photorespiration.
Data availability. All individual fractionation factors for each experiment are given in the Supplement.
Author contributions. AL and CPi designed the project. CPi, JS, and SD carried out experiments at ECOTRON of Montpellier and FP, CPa, RJ, AD, and OJ at LSCE. CPa, NP, and AL analysed the data. CPa and AL prepared the paper with contributions from NP, CPi, JS, and AM.