Increased water availability accelerates C cycling in a dry forest ecosystem

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Minich, Claudia Guidi, Alois Zürcher, Dylan Geissbühler, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8649982/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change is intensifying the frequency and severity of droughts, with profound implications for carbon (C) cycling in forest ecosystems. While progress has been made in understanding how drought alters plant and microbial ecophysiology, it remains unclear how these changes affect the overall magnitude and pace of C cycling within the plant-soil continuum. In this study, we examined the effects of 22 years of experimental irrigation in a naturally drought-prone Scots pine forest. We integrated long-term measurements of C inputs (i.e., litterfall) and outputs (i.e., soil respiration) with radiocarbon (¹⁴C) analysis of soil organic carbon, fine roots, and CO₂ from in situ soil respiration and its autotrophic and heterotrophic components. Our study demonstrates that long-term shifts in water availability enhance both C inputs and outputs, reshaping C cycling within the plant-soil system. Radiocarbon ( 14 C) analysis revealed that irrigation accelerated C cycling within plants and the organic layer, reducing the time that assimilated C remained in the ecosystem before being respired back to the atmosphere. The observed increase in soil respiration under irrigation was largely driven by enhanced autotrophic activity, associated with greater fine root biomass. Concurrently, the decomposition of labile, young organic matter intensified under irrigation, potentially contributing to a net C loss from the organic layer. Despite this increased respiration under irrigation, ¹⁴C contents in bulk SOC indicated greater inputs of young C to the mineral soil and enhanced downward translocation of C from the organic layer to the mineral phase, possibly through rhizodeposition and soil faunal activity. The enhanced input to the mineral soil under irrigation results in net C gains and may promote C stabilization through organo-mineral interactions and aggregate formation. Our findings also indicate that drought conditions limit both the magnitude and rate of C cycling within the plant-soil continuum and potentially reduce long-term C sequestration in mineral soils. Carbon cycling drought irrigation radiocarbon soil respiration heterotrophic respiration autotrophic respiration source contribution Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Climate change is projected to increase the intensity and frequency of drought events (IPCC, 2021), with consequences for carbon (C) cycling in forest ecosystems and in their soils (Gao et al., 2021 ; Guidi et al., 2022 ; Schindlbacher et al., 2012 ). By constraining the metabolic activity of both plants and soil microbes, drought impacts multiple components of the C cycle, reducing both C inputs and outputs. Drought decreases net primary productivity (Ciais et al., 2005 ; Zhou et al., 2016 ), reduces allocation of assimilated C to the soil (Fuchslueger et al., 2016 ; Joseph et al., 2020 ), and alters fine root biomass and production (Brunner et al., 2019 ; Xu, 2023 ). Additionally, drought can reduce the activity and diversity of soil fauna, further disrupting belowground activity and processes (Guidi et al., 2022 ; Kudureti et al., 2023 ). Consequently, drought suppresses C outputs from soils with reduced soil respiration (e.g., Apostolakis et al., 2023 ; Xu, 2023 ). Drought affects the major sources of soil respiration: autotrophic respiration that includes all respiratory processes within the rhizosphere (Trumbore, 2006 ) and which is linked to plant C allocation and metabolism (Gao et al., 2021 ; Hagedorn et al., 2016 ), and heterotrophic respiration, which involves the mineralization of soil organic matter (SOM) by microorganisms (Trumbore, 2006 ). The contribution of autotrophic respiration to total soil respiration is directly affected by drought through reduced allocation and metabolization of assimilates in the rhizosphere (Gao et al., 2024 ; Hagedorn et al., 2016 ) and indirectly through changes in fine root biomass (Apostolakis et al., 2023 ). Limited water availability also impacts heterotrophic respiration by suppressing microbial activity (Guidi et al., 2022 ; Schimel, 2018 ). While it is well known that drought reduces soil respiration in forest ecosystems (e.g., Apostolakis et al., 2023 ; Schindlbacher et al., 2012 ; Xu, 2023 ), it remains uncertain whether this decline is primarily driven by autotrophic (e.g., Hinko-Najera et al., 2015 ; Huang et al., 2018 ) or heterotrophic respiration (Borken et al., 2006 ; Zheng et al., 2021 ). Drought-induced changes in C inputs and outputs lead to a redistribution of C among different pools in forest ecosystems (Bose et al., 2022 ; Guidi et al., 2022 ; Rog et al., 2024 ) and to changes in the rate at which C is transferred between these pools (Gao et al., 2021 ). Long-term irrigation of a forest ecosystem naturally exposed to drought initially increased aboveground biomass, followed by a shift toward greater belowground biomass allocation (Bose et al., 2022 ). Here, drought reduced and delayed the transfer of recently assimilated C from aboveground plant tissues to the rhizosphere (Gao et al., 2021 ; Joseph et al., 2020 ). Responses may be ecosystem specific and depend on the magnitude of drought (Solly et al., 2023 ), e.g. with Norway spruce maintaining high root exudation under drought (Brunn et al., 2022 ). Drought conditions may suppress litter decomposition (Santonja et al., 2017 ; Schindlbacher et al., 2012 ) and limit the transfer of soil organic carbon (SOC) from the organic layer to the mineral soil through reduced soil fauna activity (Guidi et al., 2022 ). As a result, SOC accumulates in the organic layer, while mineral SOC stocks are lower than under higher water availability (Guidi et al., 2022 ). Despite advances in understanding drought effects on C inputs and outputs (e.g., Apostolakis et al., 2023 ; Bose et al., 2022 ; Xu, 2023 ), the extent to which these shifts alter the scale and rate of C cycling, and consequently long-term C storage, within the plant-soil continuum remains poorly understood. The radioactive C isotope ( 14 C) is a powerful tool to investigate C cycling in ecosystems, as it provides insights into the age of C in various pools, informing about the time C has spent in the ecosystem since photosynthesis (Trumbore, 2009 ). Nuclear weapons testing in the mid-20th century caused a sharp increase in atmospheric ¹⁴CO₂ levels – commonly referred to as the “bomb-spike” – which nearly doubled natural background levels in the Northern Hemisphere. Since then, atmospheric ¹⁴CO₂ concentrations have declined enabling researchers to trace C incorporation and cycling across ecosystem compartments on decadal time scales (Graven et al., 2024 ). When combined with stable C isotope (¹³C) analysis, ¹⁴C measurements of respired CO₂ from in situ soil respiration and its autotrophic and heterotrophic sources offer a robust, non-destructive method to assess not only the age of respired C, but also source contributions to total soil respiration (e.g. , Borken et al., 2006 ; Schuur & Trumbore, 2006 ) . Radiocarbon analysis has previously been applied in short-term throughfall exclusion experiments to assess the effects of drought on autotrophic and heterotrophic respiration (e.g., Borken et al., 2006 ; Muhr & Borken, 2009 ; Schindlbacher et al., 2012 ). While such studies offer insights into the short-term responses of plant metabolic processes and microbial activity to changes in water availability, their ability to capture long-term effects on soil C cycling is limited (Bose et al., 2022 ). In this study, we aimed to investigate long-term effects of irrigation on C cycling in a naturally dry Scots pine forest ecosystem. We conducted our study in a large-scale irrigation experiment established in 2003 in a Scots pine forest located in one of the driest inner-alpine regions of the European Alps. The forest in this area is naturally exposed to prolonged and repeated summer droughts, which have intensified in recent decades due to climate warming and the associated increase in water loss through evapotranspiration (Hunziker et al., 2022 ; Rigling et al., 2013 ). The long-term release from natural drought stress through irrigation provides an ideal set-up to investigate C cycling in the plant-soil continuum under persistent and repeated drought, and to compare it with non-water-limited conditions. Here, we combined long-term measurements of soil C inputs (i.e., litterfall) and outputs (i.e., soil respiration) with measurements of belowground ecosystem traits (i.e., fine root biomass), SOC properties (i.e., SOC stocks, C mineralization), and 14 C analysis in bulk SOC, fine roots, and respired CO 2 from in situ soil respiration and from its autotrophic and heterotrophic components. This approach allowed us to identify structural shifts in the ecosystem induced by long-term changes in water availability and to evaluate their effects on soil C cycling. We aimed to understand (i) how long-term irrigation affects the magnitude and pathways of C inputs (i.e. litterfall, fine roots) and outputs (i.e. soil respiration and its autotrophic and heterotrophic sources), (ii) if and how irrigation changes the age of C in different compartments of the ecosystem, and (iii) how changes in water availability influence C stabilization in the soil. Materials and methods Pfynwald – long-term irrigation experiment The study site is located in a Scots pine ( Pinus sylvestris ) forest with sporadic occurrences of pubescent oak ( Quercus pubescens ) situated in the Rhone Valley (46° 18' N, 7° 36' E, 615 m a.s.l., Valais, Switzerland). The region is one of the driest inner-alpine valleys of the European Alps, characterized by a mean annual precipitation (MAP) of 576 mm and a mean annual temperature (MAT) of 10.6°C (data from MeteoSwiss station Sion; Bose et al., 2022 ). The study was conducted within the long-term irrigation experiment in the Pfyn forest established in 2003 to investigate the effects of natural drought on ecosystem traits and functions (Bose et al., 2022 ; Hunziker et al., 2022 ). The soil is classified as a shallow Pararendzina developed on an alluvial fan (Gao et al., 2021 ). The experimental setup consists of 8 plots (25 x 40 m), each separated by a buffer zone of 5 m (Herzog et al., 2014 ). Four plots were randomly assigned to an irrigation treatment, while the remaining four serve as naturally dry controls, referred to as “irrigation” and “dry control”, respectively. Irrigation is applied via a sprinkler system during nights from May to October, supplying an additional amount of 600 mm water annually (~ 5 mm per night). The irrigation water is taken from an adjacent but hydrologically disconnected water channel fed by the Rhone River (Bose et al., 2022 ). Soil and root sampling and processing Soil and roots were sampled in November 2019 after the end of the irrigation period. Four soil pits were excavated with a 20 × 20 cm frame in each plot. Soil sampling and processing are described in Guidi et al., 2022 . The organic layer (OL) was separated in the field into litter (Oi) and partially decomposed, humified material (Oe/Oa). Afterwards, the mineral soil was excavated by depth increments (0–2, 2–5, 5–10, 10–20 cm). In the laboratory, the field-moist organic layer and mineral soil samples were hand-homogenized by sieving at 4 mm with stones and roots sorted out. Roots separated from soil sieving were carefully rinsed over a 1-mm sieve with tap water. Fine roots (diameter ≤ 2 mm) were hand-picked and visually separated into Scots pine roots and roots from other species. Fine roots were freeze-dried and weighed. The four subsamples per plot were then pooled into one composite sample per plot and depth increment. In each of the two plots (one irrigated, and one dry control) where in situ CO₂ was sampled for ¹⁴C analysis, one additional soil pit was excavated in 2023 and samples were used for soil incubations (see paragraph: “CO 2 sampling from soil and root incubations” ). For this, mineral soil samples were collected at depth intervals of 0–5, 5–10, and 10–20 cm, while the organic layer was sampled separately into Oi and Oe/Oa horizons. C mineralization Potential C mineralization was measured from samples collected in 2011 as described in Hartmann et al., 2017 . Soil samples were incubated in 300 cm 3 glass jars at 20°C and adjusted to 50% water holding capacity. The amount of soil used varied based on SOC content, ranging from 7 g to 90 g dry weight per sample. Respiration rates were measured immediately after water addition and subsequently once per week over a 28-day period, using a flow-through system connected to a LI-COR gas analyser (LI-8100A, LI-COR INC., Lincoln, NE, USA). Long-term measurements of soil respiration and litterfall Since 2012, in situ soil respiration has been measured on a monthly basis on 32 locations across the experimental site (four replicates per plot). Measurements were less frequent during winter months (December–February) due to snow cover and low temperatures. Soil CO₂ efflux was measured on permanently installed PVC collars (Ø = 20 cm) inserted 2–4 cm into the soil. For the measurements, we used custom-made static flux chambers equipped with diffusion-based infrared gas analyser (IRGAs) and relative humidity/temperature sensors (GMP343 CO 2 probe, HMP75 rH/T probe; Vaisala, Vantaa, Finland), correcting CO 2 measurements for water vapour content. The increase in CO₂ concentration within the chamber headspace was recorded over a 5-minute interval. Respiration rates were calculated as the linear rate of CO₂ accumulation over time, corrected for atmospheric pressure and mean chamber temperature based on the ideal gas law (Butterbach-Bahl et al., 2011 ). Measured soil CO 2 flux with the Vaisala system were validated regularly against a LI-8100A soil CO 2 flux system with a LI 8100 − 102 survey chamber (LI-COR INC., Lincoln, NE, USA). Simultaneously to soil respiration measurements, soil temperature was recorded at a depth of 5–10 cm. To assess gravimetric water content (GWC), soil samples were collected near the collars during each measurement campaign and oven-dried at 105°C for 48 hours. Annual respiration rates were calculated by summing up monthly mean soil respiration rates. Annual litterfall has been monitored since 2016. Litter was collected once or twice a year (December and/or April) from four locations within each plot using rectangular samplers (0.25 m 2 ) placed on the forest floor. Collected samples were dried at 40°C for 72h. CO sampling of in situ soil respiration Soil-respired CO₂ was sampled at single time points in August 2023 and March 2024 from two plots, representing each treatment. In August, CO₂ was collected from one location per plot, whereas in March, samples were taken from three different locations per plot to account for spatial variability. For gas sampling, three cylindrical PVC chambers (Ø = 30 cm; height = 15–25 cm; volume ~ 15 L) were inserted 3–5 cm into the soil surface at a distance of 1–2 m from each other to capture spatial variability in soil respiration. Chambers were installed 1–2 weeks prior to sampling to allow CO 2 fluxes to stabilize following potential installation-induced disturbance. Prior to gas sampling, respiration rates were monitored over a 5- to 10-minute interval using a LI-COR gas analyser (LI-8100A, LI-COR INC., Lincoln, NE, USA). Thereafter, chambers were flushed with CO 2 -free air five times the chamber volume and sealed until CO 2 levels reached ~ 1000 ppm for subsequent 14 C analysis. After flushing the chambers, the gas samples were collected using a flow-through system connected to the LI-COR gas analyser. Two H 2 O traps within the flow-through system prevented moisture from entering the LI-COR pump and samplng bag during the measurement and sample collection. First, all gas tubes and H 2 O traps were scrubbed with a CO 2 scrubber (soda lime) to remove atmospheric background CO 2 in the flow-through system. Air from the three chambers was composited into a 2 L sampling bag (Cali-5-Bond, Calibrated Instruments, LLC, USA). For this, one-third of the bag was sequentially filled from each chamber by connecting the flow-through system and isolating the chambers with valves after each fill. To avoid pressure imbalances during sampling, all three chambers were interconnected via gas tubing, allowing for pressure equilibration across headspaces. For 13 C analysis, gas was sampled from each of the three chambers separately, transferring headspace air into pre-evacuated 12 mL Exetainer® vials using a 60 mL syringe. Soil temperature was monitored before and after the CO 2 sampling. Soil samples were collected from 0–5 cm soil depth for the determination of GWC. CO 2 sampling from soil and root incubations Isotopic signatures of autotrophic and heterotrophic endmembers were determined via short-term root and soil incubations. Root incubations were prepared in situ during the sampling campaigns. Fine roots (< 2 mm), including mycorrhizae, were excavated from 0–10 cm depth within the chambers immediately after gas sampling. After carefully removing soil particles and rinsing the roots in ultrapure water, samples from all three chambers were pooled and incubated promptly to minimize isotopic shifts post-excision (Midwood et al., 2006 ). Approximately 10 g of fresh roots were placed into 2 L glass bottles wrapped in aluminum foil to exclude light, flushed with CO₂-free air, and incubated overnight at ~ 22°C. Gas was collected the next day into a 2 L sampling bag using a flow-through system similar as described for in situ CO 2 sampling. If the CO 2 concentration inside the bottle was high enough for 14 C analysis (> 2000 ppm), we pumped the air from the bottle through a 2 L air bag, which was pre-filled with CO 2 -free air, to maintain the pressure inside the flow-through system. The sampling was completed when the air from the bottle and 2 L air bag was thoroughly mixed within the system (i.e., CO 2 concentration remained constant). δ¹³C of root respiration was sampled in the same way as for in situ soil respiration. Soil incubations followed the same procedure and were conducted for each depth layer, resulting in one heterotrophic ∆ 14 CO 2 value per treatment and depth. Depending on SOC content and layer, 10–140 g dry-equivalent soil was incubated at 22°C and field moisture. Incubation duration ranged from one to seven days, depending on respiration rates. Basal respiration was calculated over the full incubation period and weighted by dry mass per depth. Isotopic analysis of bulk soil samples Radiocarbon (¹⁴C) was analysed in soil (all depths) and root (5–10 cm depth) samples excavated in 2019. Inorganic C was removed from all samples through fumigation with 37% HCl (Komada et al., 2008 ; Walthert et al., 2010 ). Samples were acidified at 60°C for 72 hours and subsequently neutralized using NaOH pellets under the same conditions. Prior to use, all glassware was pre-combusted at 550°C for 5 hours to eliminate potential contaminants. 14 C measurements of SOC in bulk soil were conducted using a MIni radioCArbon DAting System (MICADAS, ETH Zurich, Switzerland; Synal et al., 2007 ) equipped with a gas ion source and coupled to an Elemental Analyzer (EA vario MICRO cube, Elementar, Germany; Ruff et al., 2007 ) at the Laboratory of Ion Beam Physics, ETH Zurich. Measurement uncertainties ranged from 6 to 8‰. Isotopic analysis of CO samples The 13 CO 2 content of all gas samples was measured using an isotope-ratio mass spectrometer (IRMS Gas-Bench II coupled with a Delta-V Advanced IRMS, Thermo GmbH, Germany) at the Swiss Federal Research Institute of Forest, Snow and Landscape WSL. For 14 C analysis, gas samples were graphitized using an Air Loading Facility (ALF; Gautschi, 2017 ) coupled to an Automated Graphitization Equipment (AGE3, ETH Zurich, Switzerland; Wacker, Němec, et al., 2010) with an integrated zeolite trap to adsorb CO 2 from the sampling bag. The 14 CO 2 content of all gas samples were measured using a MICADAS (ETH Zurich, Switzerland) or a Low Energy AMS (LEA, ETH Zurich & IonPlus AG, Switzerland; Ramsperger et al., 2024 ). Measurement uncertainties were < 2‰. For data evaluation, the standard Oxalic Acid II (Mann, 1983 ) and blank material from the 14 C-free phthalic anhydride (PhA) were measured alongside the samples and evaluated with the BATS software (Wacker, Christl, et al., 2010). Graphitization and 14 C measurements were performed at the Laboratory of Ion Beam Physics, ETH Zurich, Switzerland. In samples from 10–20 cm soil depth, ẟ 13 CO 2 values were less depleted than ẟ 13 C values of bulk SOC, suggesting a potential contribution of carbonate weathering to the soil-emitted CO 2 . To estimate the potential contribution of this process, we applied a two-endmember mass balance model, assuming a ẟ 13 C of 0‰ for carbonate and ẟ 13 CO 2 = ẟ 13 C-SOC. The estimated contribution of carbonate weathering to soil respiration and its isotopic signatures was minimal (< 1–2%), consistent with findings from other forest sites (e.g., Schindlbacher et al., 2012 ). Also, their contribution would not affect the share of autotrophic respiration and therefore would not affect our result interpretation. Source partitioning of soil respiration Soil-respired CO₂ collected in situ from the chamber headspace was partitioned into autotrophic, heterotrophic, and residual atmospheric sources using Bayesian mixing models. Source apportionment was performed with the MixSIAR package (R version 3.1.12; Moore & Semmens, 2008 ; Stock et al., 2018 ) and with a custom implementation in Python. A detailed description of the approach is presented in Minich et al., 2025 . For source partitioning in March, we used heterotrophic Δ¹⁴CO₂ values from incubated soil samples collected in August. Statistical analysis Linear mixed-effect models (LME) were fitted to investigate the effect of treatment and soil depths on Δ 14 C values of heterotrophically respired CO 2 and SOC in bulk soil, as well as on SOC stocks, fine root biomass, and C mineralization with plots included as random effects (R package lmerTest, version 3.1.3; Kuznetsova et al., 2017 ) to account for spatial heterogeneity. Transformations of the response variables were conducted in case the assumption of normal distribution (tested with Shapiro-Wilk normality test) or homoscedasticity (tested with Levene’s test and plots of fitted vs. residuals) was violated. Analysis of variance (ANOVA) was conducted on the fixed effects (treatment, depth) of the models to assess their significance and interaction. Following the main effects, post hoc pairwise comparisons were conducted using estimated marginal means (EMMs) (R package emmeans, version 1.10.4; Lenth, 2024 ) to assess significant differences between treatment within each depth and vice versa, adjusted for multiple comparisons using Tukey’s method. Generalized least square models (GLS) were fitted to investigate the effect of treatment on long-term soil respiration rates, soil moisture, and soil temperature (R package nlme, version 3.1.166; Pinheiro et al., 2025 ). The model included seasonal cycling terms – sin(2π x month / 12) and cos(2π x month / 12) – to capture intra-annual variation in the soil respiration, temperature, and moisture. An interaction term between treatment and these seasonal predictors was included to assess whether seasonal responses differed across treatments. To account for temporal autocorrelation a first-order autoregressive structure was specified within each year. The response variables were log-transformed to meet model assumptions regarding normality and homoscedasticity of residuals. ANOVA was performed on the fixed effects (treatment, seasonality terms) to assess their significance. We further fitted a GLS to investigate how soil temperature and moisture affected soil respiration under dry control and irrigated conditions. The seasonal cycling terms were excluded as the seasonal variation is already reflected in soil temperature and moisture. Interaction terms between all predictor variables (soil temperature, soil moisture, treatment) allowed us to assess whether relationships between environmental drivers and respiration rates varied between treatments. Results Long-term irrigation effects on soil respiration Long-term measurements revealed that irrigation significantly increased soil respiration (SR; p < 0.001; Table S2), by ~ 66% on an annual basis with an overall mean respiration rate of 111 ± 64 mg CO 2 -C m − 2 h − 1 in dry control and 185 ± 111 mg CO 2 -C m − 2 h − 1 in irrigation plots (averaged across 2012–2024; Table S1). The increase in SR in the irrigation treatment was most pronounced during the irrigation period (May-October) (+ 79%; p < 0.001; Fig. 1 b, Table S1, Table S2). Although not significant, SR increased by 18% in the non-irrigation period (November-April) (Fig. 1 b, Table S1). While the increase of SR under irrigation relative to the dry control treatment (i.e., drought effect: SR dry control / SR irrigation ) exhibited seasonal variation with lower values in summer than in winter, there was no trend across the entire period between 2012 and 2024 (Figure S2). However, soil respiration in the dry control closely corresponded to precipitation during the irrigation period, reducing the apparent drought effect in years with higher precipitation (Fig. 1 , Figure S2). The relationship between the drought effect and soil moisture in the dry control treatment was well described by a Boltzmann fit, showing that SR in the dry control remained lower than in the irrigation treatment even at soil moisture levels above 35% (w-% of moist soil; Fig. 2 b). While soil moisture significantly differed between treatments during the irrigation period ( p < 0.001, Figure S1, Table S2), soil temperature was almost identical in dry control and irrigated plots (Figure S1, Table S2). In the irrigation treatment, soil temperature had a significant effect ( p = 0.020, Table S3) on respiration rates which increased exponentially with increasing temperature (Fig. 2 a). In the dry control treatment, soil moisture was the main driving factor of respiration rates ( p = 0.003, Table S3), while the effect of soil temperature was not significant ( p = 0.23, Fig. 2 a). The interaction between soil temperature and soil moisture was highly significant in the dry control treatment ( p < 0.001), but less pronounced under irrigation ( p = 0.015, Table S3). Irrigation significantly increased the effect of soil temperature on soil respiration ( p = 0.010, Table S3, Fig. 2 a). Long-term irrigation effects on source contribution Δ 14 CO 2 values of in situ soil respiration were consistently lower in the irrigation (August: 1‰, March: -5‰) compared to the dry control treatment (August: 19‰, March: 4‰) in both seasons (Figure S4). The difference between treatments was more pronounced in August than in March. Overall, in situ Δ¹⁴CO₂ values were lower in March than in August but exceeded the atmospheric background levels measured in both months (March: +32‰; August: − 14‰). Δ 14 CO 2 values of autotrophic respiration were slightly lower (August: -12‰, March: -16‰) and thus closer to atmospheric Δ 14 CO 2 in the irrigation treatment than in the dry control treatment (August: -8‰, March: -13‰). Although Δ 14 CO 2 values of heterotrophic respiration were slightly higher in the irrigation treatment (mean across the soil profile: 18‰) than in the dry control treatment (mean across the soil profile: 12‰; Table 1 , Figure S4), this difference was not significant (Table S4, Table S5). Overall, source partitioning showed that the relative contribution of heterotrophic respiration was higher in August than in March (Fig. 3a). Heterotrophic respiration dominated total soil respiration in the dry control, with relative contributions of 86% in August and 58% in March. In the irrigated soils, autotrophic and heterotrophic respiration contributed equally to total soil respiration in August, while autotrophic respiration accounted for 64% in March (Fig. 3). While absolute heterotrophic respiration from the mineral soil was similar between treatments, respired CO₂ originating from the organic layer and autotrophic sources was higher from irrigated soils. During August, these two sources respired 10 to 13 times more CO₂ in irrigated soils compared to the dry control (Fig. 3b, Figure S3). Long-term irrigation effects on C inputs, SOC distribution, and C mineralization Both aboveground (i.e., litterfall) and belowground (i.e., fine root biomass) C inputs increased under irrigation. On average across the years 2016–2023, litterfall increased by 49% from 333 ± 47 g m − 2 yr − 1 in the dry control treatment to 496 ± 98 g m − 2 yr − 1 under irrigation. Total fine root biomass (Scots pine + other species in OL + 0–20 cm soil depth) increased by 25% under irrigation, with mean values rising from 285 g m⁻² in the dry control to 356 g m⁻² in the irrigation treatment (Table 1 , Fig. 4). While fine root biomass increased significantly in the mineral soil under irrigation (+ 44%; Table S4), especially at 2–5 cm (+ 78%) and 5–10 cm depth (+ 72%), it was reduced in the organic layer (-66%; Fig. 4), also considering the reduced organic layer mass compared to the dry control (− 36%; Guidi et al., 2022 ). In the organic layer, SOC stocks decreased by 1.0 kg m − 2 , whereas in the mineral soil, SOC stocks increased by 0.8 kg m − 2 . Cumulative C mineralization across the soil profile measured upon one month incubation at 20°C was 44% higher in the irrigation treatment (45.1 ± 8.1 mg CO₂–C g SOC⁻¹ month⁻¹) compared to the dry control treatment (65.0 ± 18.7 mg CO₂–C g SOC ⁻¹ month⁻¹; Fig. 4). This higher C mineralization was most pronounced in the organic layer and upper 0–2 cm of the mineral soil (Fig. 4, Table 4.1, Table S5). Table 1 Mean values of SOC stocks, fine root biomass, C mineralization, Δ¹⁴C values of bulk SOC and heterotrophically respired CO 2 across soil depth profiles under dry control and irrigated conditions. Soil depth SOC stock Fine root biomass C mineralization Δ 14 C-SOC Δ 14 CO 2 cm kg m − 2 g m − 2 mg CO 2 -C g SOC − 1 month − 1 ‰ ‰ Dry control Oi 0.42 ± 0.12 71.1 ± 18.6 35.2 ± 26.2 -13.0 Oe/Oa 1.21 ± 0.67 48.1 ± 33.7 28.9 ± 7.3 86.6 ± 29.7 -2.9 0–2 1.17 ± 0.15 85.2 ± 26.7 28.9 ± 10.7 107.6 ± 19.4 27.0 2–5 0.94 ± 0.23 58.6 ± 16.4 51.7 ± 4.9 98.2 ± 23.8 5–10 0.85 ± 0.25 47.9 ± 15.8 68.3 ± 4.1 83.2 ± 19.0 27.6 10–20 1.04 ± 0.26 44.7 ± 14.1 46.9 ± 8.1 69.1 ± 23.6 19.5 Profile 5.6 285 45 80 12 Irrigation Oi 0.31 ± 0.09 90.1 ± 27.9 28.2 ± 8.4 -0.3 Oe/Oa 0.30 ± 0.26 16.5 ± 15.9 54.8 ± 10.2 41.6 ± 11.4 1.6 0–2 1.09 ± 0.21 98.8 ± 7.7 55.7 ± 18.2 80.1 ± 25.5 38.9 2–5 1.26 ± 0.24 104.5 ± 19.8 62.8 ± 24.9 107.5 ± 31.6 5–10 1.18 ± 0.14 81.9 ± 18.7 77.4 ± 13.0 86.7 ± 12.9 31.6 10–20 1.26 ± 0.17 54.6 ± 13.0 59.7 ± 18.1 72.5 ± 14.7 18.2 Profile 5.4 356 65 69 18 Radiocarbon contents in SOC, respired CO 2 , and fine roots Δ¹⁴C-SOC values mirrored the depth-dependent differences in SOC stocks between the two treatments. In the organic layer and 0–2 cm of the mineral soil, mean Δ¹⁴C-SOC values were significantly lower under irrigation compared to the dry control (~ 50‰ vs. ~76‰; p FH < 0.0001, p 0 − 2 = 0.098; Table S5). At greater depths (2–20 cm), this pattern reversed, with slightly higher Δ¹⁴C-SOC values in the irrigation treatment than in the dry control (~ 89‰ vs. ~84‰), although this difference was statistically not significant. Mean Δ¹⁴C values of fine roots were lower and closer to atmospheric 14 CO 2 levels in 2019 in the irrigation treatment (Scots pine: 37 ± 14‰, other species: 38 ± 14‰) than in the dry control treatment (Scots pine: 48 ± 25‰, other species: 46 ± 14‰). Compared with the decline in atmospheric 14 CO 2 , this corresponds to ages of 9 and 11 years, respectively. Discussion Our study showed that long-term irrigation in a dry pine forest led to a fundamental change in C cycling in the plant-soil continuum, increasing the in- and outputs, altering the Δ 14 C values of belowground C pools and fluxes, and thus changing the time that C spends in the ecosystem. Although irrigation enhanced the redistribution of SOC stocks and Δ 14 C values from the organic layer to the mineral soil, effects on total SOC stocks remained negligible after 22 treatment years. Long-term increases and shifting sources of soil respiration under irrigation Annual soil respiration was increased by 66% in the irrigation treatment, indicating that irrigation removed the moisture limitation of respiratory processes. This is also reflected in the tight positive relationship between soil respiration and soil temperature in the irrigation treatment, while soil moisture was the main regulating factor in dry control conditions (Table S3). However, soil respiration in the irrigation treatment was not only enhanced during the irrigation period – when soil moisture differences were most pronounced – but also during non-irrigated periods in winter, despite similar moisture levels in both treatments (Fig. 1 b, Fig. 2 ). This suggests that in addition to immediate plant and microbial responses to soil moisture, ecosystem adaptation must have contributed to the increased respiration rates in the irrigation treatment. In the following, we will discuss three ecosystem adaptations which likely affected responses of soil respiration to irrigation: (i) increased belowground carbon allocation by plants, (ii) increased root growth due to elevated plant water demand, and (iii) increased decomposition in the organic layer. Our analysis of Δ¹⁴C values of in situ soil respiration and its sources revealed that the observed increase in total soil respiration in the irrigation treatment is primarily driven by increased autotrophic respiration as well as increased heterotrophic respiration in the organic layer (Fig. 3). We attribute higher autotrophic contributions in the irrigation treatment mainly to significant increases in fine root biomass (Fig. 4), enhancing autotrophic respiration (e.g., Apostolakis et al., 2023 ; Liu et al., 2016 e & Buchmann, 2005; Zhang et al., 2021 ). Additionally, both faster and significantly enhanced allocation of newly assimilated C from plants to the rhizosphere (Gao et al., 2021 ; Hartmann et al., 2017 ; Joseph et al., 2020 ) likely contributed to higher autotrophic respiration under irrigation. In a ¹³C pulse-labelling study conducted in this forest, Gao et al., 2021 observed that irrigation resulted in a three- to four-fold increase in the amount of C assimilates transferred to and respired from the soil, relative to the smaller increase observed in total soil respiration – suggesting that irrigation disproportionately enhanced autotrophic processes. This aligns with findings of reduced allocation and metabolization of recent C assimilates under dry conditions in grasslands and young beech forests, which reduced autotrophic respiration more strongly than total soil respiration (Fuchslueger et al., 2014 ; Hagedorn et al., 2016 ). The magnitude of the increase in soil respiration in the irrigation treatment compared to dry control conditions varied with the amount of summer precipitation but generally remained relatively stable between 2012 and 2024 (Fig. 1 , Figure S2). This suggests that most structural changes in the ecosystem which were responsible for the increase in soil respiration must have occurred during the initial decade of the experiment (2003–2012), prior to the beginning of continuous soil respiration measurements. Bose et al., 2022 reported that trees initially responded to increased water availability in irrigated plots with increased aboveground growth, evidenced by wider tree rings and expanded crown areas. While the enhancement in aboveground growth remained constant (Bose et al., 2022 ) and was even followed by a slight regression to pre-irrigation growth rates of individual trees (Vitali et al., 2024 ), it likely induced a greater water demand (Zweifel et al., 2020 ). This may have triggered a shift toward belowground biomass allocation (Poorter & Nagel, 2000 ), which is supported by the observed continuous increase in Scots pine fine root biomass from the start of the experiment in 2003 through 2016 (Brunner et al., 2019 ) and in 2019 measured here (Fig. 4). The irrigation effect on fine root biomass became not significant before 2012. One reason for the retarded belowground response could be the slow fine root growth, evidenced by the Δ 14 C-derived fine root ages of several years (Fig. 4). Taken together, we propose that both the higher contribution of autotrophic respiration as well as consistently higher total soil respiration rates in the irrigation treatment reflect a long-term adjustment in belowground biomass driven by sustained water availability and demand. In addition to enhanced autotrophic respiration, the increase in soil respiration in the irrigation treatment was also driven by accelerated heterotrophic respiration in the organic layer, which contributed to as much as 60% to total heterotrophic respiration in irrigated soils and showed a 10-fold higher rate than dry soils in August (Fig. 3). The pronounced sensitivity of the organic layer to both drought and irrigation likely reflects its high reactivity to changes in soil moisture, its disconnection from the underlying mineral soil (Keith et al., 2010 ), its high content of labile SOM, and its role as a hotspot of biological activity (Schindlbacher et al., 2010 ). Additionally, Hartmann et al., 2017 observed a shift from oligotrophic to copiotrophic microbial life strategies under irrigation in our experiment. While oligotrophic life strategies are dominant in soils of low C availability, copiotrophic organisms dominate in soils with higher net C mineralization rate (Fierer et al., 2007 ). This shift might have enhanced microbial C use efficiency and microbial turnover rates (Fierer et al., 2007 ), thereby contributing to the observed increases in C mineralization (Fig. 4) and respiration from the organic layer (Fig. 3). The increase in both autotrophic and heterotrophic respiration from the organic layer under irrigation was more pronounced in August than in March (Fig. 3). In March, similar soil temperature and moisture conditions between treatments likely led to more comparable respiration rates and source contributions. The small effect sizes of heterotrophic respiration in the mineral soil could be explained by long-term decreases of the irrigation effect on soil water contents, due to the greater water uptake by the larger trees (Herzog et al., 2014 ; Shakas et al., 2025 ). This effect is so pronounced that in spring, before the onset of the irrigation period, soil water potentials in irrigated treatment were even lower than in the control plots (Shakas et al., 2024). In contrast, heterotrophic respiration from the minerals soil remained largely unaffected by irrigation (Fig. 3). This is in line with previous studies, observing that reductions in heterotrophic respiration under drought are primarily related to decreased decomposition activity in the organic layer (Borken et al., 2006 ; Cisneros-Dozal et al., 2007 ; Huang et al., 2018 ; Schindlbacher et al., 2012 ). Autotrophic contributions to total soil respiration were generally higher in March than in August which is unexpected as autotrophic respiration in forest ecosystems typically peaks during the summer months (Borken et al., 2006 ; Schindlbacher et al., 2012 ). Since autotrophic respiration is more strongly influenced by plant phenology than by soil temperature (Atarashi-Andoh et al., 2012 ) it seems likely that measurements in March coincided with an already active phenological phase. Additionally, air temperatures exceeded soil temperatures by up to 9.5°C during in situ CO 2 sampling. While low soil temperatures of 9°C likely suppressed heterotrophic microbial activity, the evergreen pine trees might have been metabolically active in the warmer air (Ferrari et al., 2018 ), resulting in a relatively greater proportion of autotrophic respiration during the measurement. Irrigation-induced C losses in the organic layer and gains in the mineral soil In addition to the shifted soil CO 2 fluxes, long-term irrigation led to a SOC redistribution in the soil profile, with decreasing SOC stocks in the organic layer but gains in the mineral soil, with minimal net effect on total SOC stocks (Fig. 4; Guidi et al., 2022 ). The altered SOC depth distribution by irrigation is paralleled by lower Δ¹⁴C values in the organic layer and higher values in the mineral soil. Multiple mechanisms may contribute to this redistribution at higher soil moisture conditions, including an accelerated decomposition, an increased rhizodeposition (Gao et al., 2021 ), and an enhanced translocation and incorporation of litter materials into the mineral soils (Guidi et al., 2022 ). The Δ 14 C-SOC depth pattern followed historical atmospheric Δ¹⁴CO₂ trends, and differences in the pattern between treatments thereby allow to infer on the importance of redistribution mechanisms (Fig. 4). Δ 14 C values were closest to recent atmospheric levels in the organic layer, peaked in 0–5 cm depth (reflecting the bomb-spike with decadal old C), followed by a decline with further depth, indicating an increasing presence of older, pre-bomb C in deeper mineral layers. Δ¹⁴C-SOC values closer to atmospheric levels in the organic layer and 0–2 cm depth of irrigated soils indicate a faster SOC cycling and a higher input of younger litter- and root-derived inputs compared to the dry control soil. As SOC stocks in the organic layer decreased under irrigation (Fig. 4), the acceleration of decomposition must have been stronger than the enhancement of litter inputs. A possible underlying mechanism is that irrigation enhanced the activity of the soil fauna, which is particularly drought-sensitive and transforms the organic layer (Guidi et al., 2022 ). In the mineral soil below 2 cm depth, Δ¹⁴C-SOC values were higher in the irrigation treatment as compared to the control treatment, indicating higher inputs of decadal old, bomb-derived C. This pattern strongly indicates that increased SOC stocks in the mineral soil of the irrigation treatment were not caused by increased belowground plant inputs (in which case one would expect lower Δ¹⁴C-SOC values). Instead, this pattern suggests increased downward translocation of bomb-derived C from the organic layer and upper mineral soil (0–2 cm) to deeper soil layers. One main vector appears to be faunal-mediated transfer and incorporation of litter and organic layer into the mineral soil (Guidi et al., 2022 ). Higher water availability was found to increase the abundance, composition and diversity of micro-, meso-, and macro-fauna (e.g. Guidi et al., 2022 ; Kudureti et al., 2023 ; Singh et al., 2019 ; Tan et al., 2021 ), which promotes both litter decomposition and C translocation to the mineral soil (G. Angst et al., 2024 ). Enhanced DOC leaching under irrigation might additionally increase C transfer from the surface layers to the mineral soil. However, this contribution is likely minor as indicated by low DOC fluxes of 13 to 26 g DOC m − 2 yr − 1 observed in Swiss forests with shallow organic layers and comparable mean annual precipitation (Graf Pannatier et al., 2012 ). Lastly, the interpretation that increased vertical translocation of organic material was the dominant mechanism behind the irrigation-induced shifts in SOC stocks is further supported by shifts in stable isotope values. In the mineral soil under irrigation, 13 C and 15 N values resembled the less processed SOC from the organic layer more closely as compared to the dry control soils (Guidi et al., 2022 ). Translocation of C from surface soil layers, together with increased C inputs to the mineral phase through increased litter and rhizodeposition likely promote SOC sequestration and stabilization in deeper soil layers of the mineral soil in the irrigation treatment. Although Δ¹⁴C-SOC values indicate that SOC is younger under irrigation, increased C inputs likely increase the persistence of SOC through several processes. The translocation of C into deeper soil layers likely increases the potential of this translocated C to become stabilized through mineral protection as the sorption capacity for organo-mineral associations increases with soil depth (Ahrens et al., 2020 ). In addition, the shift from oligotrophic to copiotrophic microbial life strategies under irrigation (Hartmann et al., 2017 ) and subsequent enhanced turnover of microbial biomass (Gao et al., 2021 ) might increase the formation of microbial products that can be stabilized through organo-mineral associations (Cotrufo et al., 2015 ). Furthermore, litter ingestion of soil fauna, especially earthworms, promote the occlusion of SOM in soil aggregates, which leads to the physical protection of POM and MAOM from decomposition (e.g., G. Angst et al., 2024 ; Š. Angst et al., 2017 ; Bossuyt et al., 2004 ). Conclusion Our study shows that long-term changes in soil moisture regimes enhanced both C inputs as well as outputs and reshaped C cycling within the plant-soil system, primarily driven by structural changes in the forest ecosystem. We found that irrigation increased soil respiration and accelerated C cycling within the plant system and organic layer, shortening the time that C spent in the system from C assimilation through photosynthesis until it was respired back to the atmosphere. This is primarily driven by increased autotrophic respiration, resulting from higher fine root biomass and belowground C allocation under irrigation. Additionally, 14 C data revealed an accelerated SOC turnover in the organic layer and uppermost mineral soil. However, while a larger proportion of C is quickly released back to the atmosphere without entering the mineral soil, vertical changes of SOC stock and Δ 14 C values document a SOC redistribution under irrigation. Possibly, this results from the combined effects of greater rhizosphere inputs and translocation from upper soil layers through soil faunal activity. The enhanced translocation of C to the mineral soil resulted in a net SOC gain in the soil from 0–20 cm depth, which potentially promotes C stabilization. Taken together, our findings indicate that current drought conditions limit both the magnitude and pace of C cycling within the plant-soil continuum, and potentially hinder long-term C sequestration in mineral soils. Declarations Acknowledgements This study is based on data from the long-term experimental research platform Pfynwald, part of the Swiss Forest Lab and the eLTER research infrastructures. We are especially grateful to the Pfynwald core team for their support and for providing access to the research infrastructure. We also thank Petra d’Orico and Fluppi Sutter for sharing aerial images and site maps of the experimental area, and MeteoSwiss for supplying essential climate data. We gratefully acknowledge Michael Guggenbühl, Stefan Tobler, Clara Juliette Gund, Jan Ziegler, Logan James Banner, Thomas Laemmel, and Mathias Mayer for their valuable assistance during fieldwork. Our thanks also go to Daniel Christen, Daniel Wasner, Roger Köchli, and Marco Walser for their dedicated help with laboratory work. We further appreciate the expertise of André Albrecht and Urs Ramsperger in sample preparation and ¹⁴C measurements, as well as the support of Alessandro Schlumpf and Ursula Graf with ¹³C analyses. This research was funded by the Radiocarbon Inventories of Switzerland project (Grant No. 193770) of the Swiss National Science Foundation. We also gratefully acknowledge the Marie Curie funding program for supporting the PostDoc position of Claudia Guidi, whose data contributed to this study. 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Agric Ecosyst Environ 228:70–81. https://doi.org/10.1016/j.agee.2016.04.030 Zweifel R, Etzold S, Sterck F, Gessler A, Anfodillo T, Mencuccini M, Von Arx G, Lazzarin M, Haeni M, Feichtinger L, Meusburger K, Knuesel S, Walthert L, Salmon Y, Bose AK, Schoenbeck L, Hug C, De Girardi N, Giuggiola A, Rigling A (2020) Determinants of legacy effects in pine trees – implications from an irrigation-stop experiment. New Phytol 227(4):1081–1096. https://doi.org/10.1111/nph.16582 Additional Declarations The authors declare no competing interests. Supplementary Files image1.png Graphical abstract 03manuscriptLTDpreprintsupplement.docx Supporting Information Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8649982","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577523646,"identity":"666436a4-0d03-4b77-8950-ac83555ae35a","order_by":0,"name":"Luisa I. 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11:45:14","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":226939,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8649982/v1/dfbec4ad2865c48a08f721f1.html"},{"id":100782060,"identity":"fd09e244-370d-4751-bc17-8010130a3fd9","added_by":"auto","created_at":"2026-01-21 11:44:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":260771,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) \u003c/strong\u003eMonthly and total precipitation [mm] during the irrigation period, measured at the nearest MeteoSwiss Station in Sion from 2012 to 2024.\u003cstrong\u003e b) \u003c/strong\u003eMean soil respiration rates (mg CO\u003csub\u003e2\u003c/sub\u003e-C m\u003csup\u003e-2\u003c/sup\u003e h\u003csup\u003e-1\u003c/sup\u003e) in the dry control and irrigation treatment from winter 2012 to summer 2024. Irrigation period: May-October.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8649982/v1/878afa71fb77670032700f89.png"},{"id":100782055,"identity":"0ac176ca-3962-42e4-b9ae-c6995ff8fb43","added_by":"auto","created_at":"2026-01-21 11:44:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":229106,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea)\u003c/strong\u003e Exponential relationship between soil respiration rate (mg CO\u003csub\u003e2\u003c/sub\u003e-C m\u003csup\u003e-2\u003c/sup\u003e h\u003csup\u003e-1\u003c/sup\u003e) and soil temperature (°C) in the dry control and irrigation treatment. \u003cstrong\u003eb)\u003c/strong\u003e Relationship between drought effect (SR\u003csub\u003edry control\u003c/sub\u003e/SR\u003csub\u003eirrigation\u003c/sub\u003e) and soil moisture (w-% of moist soil) in the dry control treatment. The data were fitted using a Boltzmann function.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8649982/v1/1c974129614918d20fd300d2.png"},{"id":100782366,"identity":"4b4896fb-14a5-404b-9774-bf458177971c","added_by":"auto","created_at":"2026-01-21 11:46:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea):\u003c/strong\u003e Relative contributions of heterotrophic respiration (%) in the dry control and irrigation treatments during August and March. \u003cstrong\u003eb):\u003c/strong\u003e Absolute contributions of autotrophic respiration and heterotrophic respiration (mg CO₂-C m⁻² h\u003csup\u003e-1\u003c/sup\u003e) from the organic layer (OL = Oi + Oe/Oa), topsoil (0-10 cm), and subsoil (10-20 cm) in both treatments and seasons. Mean values are shown for March. Values for replicates are presented in Figure S3.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8649982/v1/5970a80d9232855fbe058917.png"},{"id":100782085,"identity":"463b32dd-ddb5-4f63-83ab-24e483d85f98","added_by":"auto","created_at":"2026-01-21 11:45:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":160663,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of Δ\u003csup\u003e14\u003c/sup\u003eC values of SOC (‰), SOC stocks (kg m\u003csup\u003e-2\u003c/sup\u003e), fine root biomass (g m\u003csup\u003e-2\u003c/sup\u003e), and C mineralization (mg CO\u003csub\u003e2\u003c/sub\u003e-C g SOC\u003csup\u003e-1\u003c/sup\u003e month\u003csup\u003e-1\u003c/sup\u003e) in dry control and irrigation treatments. Also shown are mean Δ¹⁴C values (‰) of fine roots from Scots pine and other species. Significant treatment differences are highlighted in red. The black dashed line indicates atmospheric Δ\u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e levels in 2019.\u0026nbsp;\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8649982/v1/e4050a0830ee013161afc10c.png"},{"id":100785704,"identity":"1a36c721-fa67-45aa-8ee8-00e49f2cd226","added_by":"auto","created_at":"2026-01-21 11:58:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1765200,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8649982/v1/ad6dc88e-cc5c-4961-8ff0-10e7f8818ad0.pdf"},{"id":100782226,"identity":"d9784004-becd-4175-a5cd-49268d4d5138","added_by":"auto","created_at":"2026-01-21 11:45:36","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":802242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8649982/v1/1d3f09284fcdad7549855a22.png"},{"id":100782230,"identity":"d3d5ca5d-a1de-49f9-94b6-d1e93f4de2b3","added_by":"auto","created_at":"2026-01-21 11:45:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":584266,"visible":true,"origin":"","legend":"\u003cp\u003eSupporting Information\u003c/p\u003e","description":"","filename":"03manuscriptLTDpreprintsupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-8649982/v1/1cafec4152b8a95ad80241a7.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIncreased water availability accelerates C cycling in a dry forest ecosystem\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change is projected to increase the intensity and frequency of drought events (IPCC, 2021), with consequences for carbon (C) cycling in forest ecosystems and in their soils (Gao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Schindlbacher et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). By constraining the metabolic activity of both plants and soil microbes, drought impacts multiple components of the C cycle, reducing both C inputs and outputs. Drought decreases net primary productivity (Ciais et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), reduces allocation of assimilated C to the soil (Fuchslueger et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Joseph et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and alters fine root biomass and production (Brunner et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xu, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, drought can reduce the activity and diversity of soil fauna, further disrupting belowground activity and processes (Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kudureti et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, drought suppresses C outputs from soils with reduced soil respiration (e.g., Apostolakis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xu, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Drought affects the major sources of soil respiration: autotrophic respiration that includes all respiratory processes within the rhizosphere (Trumbore, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and which is linked to plant C allocation and metabolism (Gao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hagedorn et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and heterotrophic respiration, which involves the mineralization of soil organic matter (SOM) by microorganisms (Trumbore, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The contribution of autotrophic respiration to total soil respiration is directly affected by drought through reduced allocation and metabolization of assimilates in the rhizosphere (Gao et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hagedorn et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and indirectly through changes in fine root biomass (Apostolakis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Limited water availability also impacts heterotrophic respiration by suppressing microbial activity (Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Schimel, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While it is well known that drought reduces soil respiration in forest ecosystems (e.g., Apostolakis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Schindlbacher et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Xu, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), it remains uncertain whether this decline is primarily driven by autotrophic (e.g., Hinko-Najera et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) or heterotrophic respiration (Borken et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDrought-induced changes in C inputs and outputs lead to a redistribution of C among different pools in forest ecosystems (Bose et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rog et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and to changes in the rate at which C is transferred between these pools (Gao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Long-term irrigation of a forest ecosystem naturally exposed to drought initially increased aboveground biomass, followed by a shift toward greater belowground biomass allocation (Bose et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Here, drought reduced and delayed the transfer of recently assimilated C from aboveground plant tissues to the rhizosphere (Gao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Joseph et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Responses may be ecosystem specific and depend on the magnitude of drought (Solly et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), e.g. with Norway spruce maintaining high root exudation under drought (Brunn et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Drought conditions may suppress litter decomposition (Santonja et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schindlbacher et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and limit the transfer of soil organic carbon (SOC) from the organic layer to the mineral soil through reduced soil fauna activity (Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a result, SOC accumulates in the organic layer, while mineral SOC stocks are lower than under higher water availability (Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite advances in understanding drought effects on C inputs and outputs (e.g., Apostolakis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bose et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xu, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the extent to which these shifts alter the scale and rate of C cycling, and consequently long-term C storage, within the plant-soil continuum remains poorly understood.\u003c/p\u003e \u003cp\u003eThe radioactive C isotope (\u003csup\u003e14\u003c/sup\u003eC) is a powerful tool to investigate C cycling in ecosystems, as it provides insights into the age of C in various pools, informing about the time C has spent in the ecosystem since photosynthesis (Trumbore, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Nuclear weapons testing in the mid-20th century caused a sharp increase in atmospheric \u0026sup1;⁴CO₂ levels \u0026ndash; commonly referred to as the \u0026ldquo;bomb-spike\u0026rdquo; \u0026ndash; which nearly doubled natural background levels in the Northern Hemisphere. Since then, atmospheric \u0026sup1;⁴CO₂ concentrations have declined enabling researchers to trace C incorporation and cycling across ecosystem compartments on decadal time scales (Graven et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). When combined with stable C isotope (\u0026sup1;\u0026sup3;C) analysis, \u0026sup1;⁴C measurements of respired CO₂ from \u003cem\u003ein situ\u003c/em\u003e soil respiration and its autotrophic and heterotrophic sources offer a robust, non-destructive method to assess not only the age of respired C, but also source contributions to total soil respiration \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(e.g.\u003c/span\u003e, Borken et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Schuur \u0026amp; Trumbore, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e. Radiocarbon analysis has previously been applied in short-term throughfall exclusion experiments to assess the effects of drought on autotrophic and heterotrophic respiration (e.g., Borken et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Muhr \u0026amp; Borken, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Schindlbacher et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). While such studies offer insights into the short-term responses of plant metabolic processes and microbial activity to changes in water availability, their ability to capture long-term effects on soil C cycling is limited (Bose et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we aimed to investigate long-term effects of irrigation on C cycling in a naturally dry Scots pine forest ecosystem. We conducted our study in a large-scale irrigation experiment established in 2003 in a Scots pine forest located in one of the driest inner-alpine regions of the European Alps. The forest in this area is naturally exposed to prolonged and repeated summer droughts, which have intensified in recent decades due to climate warming and the associated increase in water loss through evapotranspiration (Hunziker et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rigling et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The long-term release from natural drought stress through irrigation provides an ideal set-up to investigate C cycling in the plant-soil continuum under persistent and repeated drought, and to compare it with non-water-limited conditions. Here, we combined long-term measurements of soil C inputs (i.e., litterfall) and outputs (i.e., soil respiration) with measurements of belowground ecosystem traits (i.e., fine root biomass), SOC properties (i.e., SOC stocks, C mineralization), and \u003csup\u003e14\u003c/sup\u003eC analysis in bulk SOC, fine roots, and respired CO\u003csub\u003e2\u003c/sub\u003e from \u003cem\u003ein situ\u003c/em\u003e soil respiration and from its autotrophic and heterotrophic components. This approach allowed us to identify structural shifts in the ecosystem induced by long-term changes in water availability and to evaluate their effects on soil C cycling. We aimed to understand (i) how long-term irrigation affects the magnitude and pathways of C inputs (i.e. litterfall, fine roots) and outputs (i.e. soil respiration and its autotrophic and heterotrophic sources), (ii) if and how irrigation changes the age of C in different compartments of the ecosystem, and (iii) how changes in water availability influence C stabilization in the soil.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePfynwald \u0026ndash; long-term irrigation experiment\u003c/h2\u003e \u003cp\u003eThe study site is located in a Scots pine (\u003cem\u003ePinus sylvestris\u003c/em\u003e) forest with sporadic occurrences of pubescent oak (\u003cem\u003eQuercus pubescens\u003c/em\u003e) situated in the Rhone Valley (46\u0026deg; 18' N, 7\u0026deg; 36' E, 615 m a.s.l., Valais, Switzerland). The region is one of the driest inner-alpine valleys of the European Alps, characterized by a mean annual precipitation (MAP) of 576 mm and a mean annual temperature (MAT) of 10.6\u0026deg;C (data from MeteoSwiss station Sion; Bose et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The study was conducted within the long-term irrigation experiment in the Pfyn forest established in 2003 to investigate the effects of natural drought on ecosystem traits and functions (Bose et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hunziker et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The soil is classified as a shallow Pararendzina developed on an alluvial fan (Gao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The experimental setup consists of 8 plots (25 x 40 m), each separated by a buffer zone of 5 m (Herzog et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Four plots were randomly assigned to an irrigation treatment, while the remaining four serve as naturally dry controls, referred to as \u0026ldquo;irrigation\u0026rdquo; and \u0026ldquo;dry control\u0026rdquo;, respectively. Irrigation is applied via a sprinkler system during nights from May to October, supplying an additional amount of 600 mm water annually (~\u0026thinsp;5 mm per night). The irrigation water is taken from an adjacent but hydrologically disconnected water channel fed by the Rhone River (Bose et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSoil and root sampling and processing\u003c/h3\u003e\n\u003cp\u003eSoil and roots were sampled in November 2019 after the end of the irrigation period. Four soil pits were excavated with a 20 \u0026times; 20 cm frame in each plot. Soil sampling and processing are described in Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e. The organic layer (OL) was separated in the field into litter (Oi) and partially decomposed, humified material (Oe/Oa). Afterwards, the mineral soil was excavated by depth increments (0\u0026ndash;2, 2\u0026ndash;5, 5\u0026ndash;10, 10\u0026ndash;20 cm). In the laboratory, the field-moist organic layer and mineral soil samples were hand-homogenized by sieving at 4 mm with stones and roots sorted out. Roots separated from soil sieving were carefully rinsed over a 1-mm sieve with tap water. Fine roots (diameter\u0026thinsp;\u0026le;\u0026thinsp;2 mm) were hand-picked and visually separated into Scots pine roots and roots from other species. Fine roots were freeze-dried and weighed. The four subsamples per plot were then pooled into one composite sample per plot and depth increment. In each of the two plots (one irrigated, and one dry control) where \u003cem\u003ein situ\u003c/em\u003e CO₂ was sampled for \u0026sup1;⁴C analysis, one additional soil pit was excavated in 2023 and samples were used for soil incubations (see paragraph: \u003cem\u003e\u0026ldquo;CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003esampling from soil and root incubations\u0026rdquo;\u003c/em\u003e). For this, mineral soil samples were collected at depth intervals of 0\u0026ndash;5, 5\u0026ndash;10, and 10\u0026ndash;20 cm, while the organic layer was sampled separately into Oi and Oe/Oa horizons.\u003c/p\u003e\n\u003ch3\u003eC mineralization\u003c/h3\u003e\n\u003cp\u003ePotential C mineralization was measured from samples collected in 2011 as described in Hartmann et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e. Soil samples were incubated in 300 cm\u003csup\u003e3\u003c/sup\u003e glass jars at 20\u0026deg;C and adjusted to 50% water holding capacity. The amount of soil used varied based on SOC content, ranging from 7 g to 90 g dry weight per sample. Respiration rates were measured immediately after water addition and subsequently once per week over a 28-day period, using a flow-through system connected to a LI-COR gas analyser (LI-8100A, LI-COR INC., Lincoln, NE, USA).\u003c/p\u003e\n\u003ch3\u003eLong-term measurements of soil respiration and litterfall\u003c/h3\u003e\n\u003cp\u003eSince 2012, \u003cem\u003ein situ\u003c/em\u003e soil respiration has been measured on a monthly basis on 32 locations across the experimental site (four replicates per plot). Measurements were less frequent during winter months (December\u0026ndash;February) due to snow cover and low temperatures. Soil CO₂ efflux was measured on permanently installed PVC collars (\u0026Oslash; = 20 cm) inserted 2\u0026ndash;4 cm into the soil. For the measurements, we used custom-made static flux chambers equipped with diffusion-based infrared gas analyser (IRGAs) and relative humidity/temperature sensors (GMP343 CO\u003csub\u003e2\u003c/sub\u003e probe, HMP75 rH/T probe; Vaisala, Vantaa, Finland), correcting CO\u003csub\u003e2\u003c/sub\u003e measurements for water vapour content. The increase in CO₂ concentration within the chamber headspace was recorded over a 5-minute interval. Respiration rates were calculated as the linear rate of CO₂ accumulation over time, corrected for atmospheric pressure and mean chamber temperature based on the ideal gas law (Butterbach-Bahl et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Measured soil CO\u003csub\u003e2\u003c/sub\u003e flux with the Vaisala system were validated regularly against a LI-8100A soil CO\u003csub\u003e2\u003c/sub\u003e flux system with a LI 8100\u0026thinsp;\u0026minus;\u0026thinsp;102 survey chamber (LI-COR INC., Lincoln, NE, USA). Simultaneously to soil respiration measurements, soil temperature was recorded at a depth of 5\u0026ndash;10 cm. To assess gravimetric water content (GWC), soil samples were collected near the collars during each measurement campaign and oven-dried at 105\u0026deg;C for 48 hours. Annual respiration rates were calculated by summing up monthly mean soil respiration rates. Annual litterfall has been monitored since 2016. Litter was collected once or twice a year (December and/or April) from four locations within each plot using rectangular samplers (0.25 m\u003csup\u003e2\u003c/sup\u003e) placed on the forest floor. Collected samples were dried at 40\u0026deg;C for 72h.\u003c/p\u003e\n\u003ch3\u003eCO sampling of in situ soil respiration\u003c/h3\u003e\n\u003cp\u003eSoil-respired CO₂ was sampled at single time points in August 2023 and March 2024 from two plots, representing each treatment. In August, CO₂ was collected from one location per plot, whereas in March, samples were taken from three different locations per plot to account for spatial variability. For gas sampling, three cylindrical PVC chambers (\u0026Oslash; = 30 cm; height\u0026thinsp;=\u0026thinsp;15\u0026ndash;25 cm; volume\u0026thinsp;~\u0026thinsp;15 L) were inserted 3\u0026ndash;5 cm into the soil surface at a distance of 1\u0026ndash;2 m from each other to capture spatial variability in soil respiration. Chambers were installed 1\u0026ndash;2 weeks prior to sampling to allow CO\u003csub\u003e2\u003c/sub\u003e fluxes to stabilize following potential installation-induced disturbance. Prior to gas sampling, respiration rates were monitored over a 5- to 10-minute interval using a LI-COR gas analyser (LI-8100A, LI-COR INC., Lincoln, NE, USA). Thereafter, chambers were flushed with CO\u003csub\u003e2\u003c/sub\u003e-free air five times the chamber volume and sealed until CO\u003csub\u003e2\u003c/sub\u003e levels reached\u0026thinsp;~\u0026thinsp;1000 ppm for subsequent \u003csup\u003e14\u003c/sup\u003eC analysis. After flushing the chambers, the gas samples were collected using a flow-through system connected to the LI-COR gas analyser. Two H\u003csub\u003e2\u003c/sub\u003eO traps within the flow-through system prevented moisture from entering the LI-COR pump and samplng bag during the measurement and sample collection. First, all gas tubes and H\u003csub\u003e2\u003c/sub\u003eO traps were scrubbed with a CO\u003csub\u003e2\u003c/sub\u003e scrubber (soda lime) to remove atmospheric background CO\u003csub\u003e2\u003c/sub\u003e in the flow-through system. Air from the three chambers was composited into a 2 L sampling bag (Cali-5-Bond, Calibrated Instruments, LLC, USA). For this, one-third of the bag was sequentially filled from each chamber by connecting the flow-through system and isolating the chambers with valves after each fill. To avoid pressure imbalances during sampling, all three chambers were interconnected via gas tubing, allowing for pressure equilibration across headspaces. For \u003csup\u003e13\u003c/sup\u003eC analysis, gas was sampled from each of the three chambers separately, transferring headspace air into pre-evacuated 12 mL Exetainer\u0026reg; vials using a 60 mL syringe. Soil temperature was monitored before and after the CO\u003csub\u003e2\u003c/sub\u003e sampling. Soil samples were collected from 0\u0026ndash;5 cm soil depth for the determination of GWC.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCO\u003csub\u003e2\u003c/sub\u003e sampling from soil and root incubations\u003c/h2\u003e \u003cp\u003eIsotopic signatures of autotrophic and heterotrophic endmembers were determined via short-term root and soil incubations. Root incubations were prepared \u003cem\u003ein situ\u003c/em\u003e during the sampling campaigns. Fine roots (\u0026lt;\u0026thinsp;2 mm), including mycorrhizae, were excavated from 0\u0026ndash;10 cm depth within the chambers immediately after gas sampling. After carefully removing soil particles and rinsing the roots in ultrapure water, samples from all three chambers were pooled and incubated promptly to minimize isotopic shifts post-excision (Midwood et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Approximately 10 g of fresh roots were placed into 2 L glass bottles wrapped in aluminum foil to exclude light, flushed with CO₂-free air, and incubated overnight at ~\u0026thinsp;22\u0026deg;C. Gas was collected the next day into a 2 L sampling bag using a flow-through system similar as described for \u003cem\u003ein situ\u003c/em\u003e CO\u003csub\u003e2\u003c/sub\u003e sampling. If the CO\u003csub\u003e2\u003c/sub\u003e concentration inside the bottle was high enough for \u003csup\u003e14\u003c/sup\u003eC analysis (\u0026gt;\u0026thinsp;2000 ppm), we pumped the air from the bottle through a 2 L air bag, which was pre-filled with CO\u003csub\u003e2\u003c/sub\u003e-free air, to maintain the pressure inside the flow-through system. The sampling was completed when the air from the bottle and 2 L air bag was thoroughly mixed within the system (i.e., CO\u003csub\u003e2\u003c/sub\u003e concentration remained constant). δ\u0026sup1;\u0026sup3;C of root respiration was sampled in the same way as for \u003cem\u003ein situ\u003c/em\u003e soil respiration. Soil incubations followed the same procedure and were conducted for each depth layer, resulting in one heterotrophic ∆\u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e value per treatment and depth. Depending on SOC content and layer, 10\u0026ndash;140 g dry-equivalent soil was incubated at 22\u0026deg;C and field moisture. Incubation duration ranged from one to seven days, depending on respiration rates. Basal respiration was calculated over the full incubation period and weighted by dry mass per depth.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIsotopic analysis of bulk soil samples\u003c/h3\u003e\n\u003cp\u003eRadiocarbon (\u0026sup1;⁴C) was analysed in soil (all depths) and root (5\u0026ndash;10 cm depth) samples excavated in 2019. Inorganic C was removed from all samples through fumigation with 37% HCl (Komada et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Walthert et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Samples were acidified at 60\u0026deg;C for 72 hours and subsequently neutralized using NaOH pellets under the same conditions. Prior to use, all glassware was pre-combusted at 550\u0026deg;C for 5 hours to eliminate potential contaminants. \u003csup\u003e14\u003c/sup\u003eC measurements of SOC in bulk soil were conducted using a MIni radioCArbon DAting System (MICADAS, ETH Zurich, Switzerland; Synal et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) equipped with a gas ion source and coupled to an Elemental Analyzer (EA vario MICRO cube, Elementar, Germany; Ruff et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) at the Laboratory of Ion Beam Physics, ETH Zurich. Measurement uncertainties ranged from 6 to 8\u0026permil;.\u003c/p\u003e\n\u003ch3\u003eIsotopic analysis of CO samples\u003c/h3\u003e\n\u003cp\u003eThe \u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e content of all gas samples was measured using an isotope-ratio mass spectrometer (IRMS Gas-Bench II coupled with a Delta-V Advanced IRMS, Thermo GmbH, Germany) at the Swiss Federal Research Institute of Forest, Snow and Landscape WSL. For \u003csup\u003e14\u003c/sup\u003eC analysis, gas samples were graphitized using an Air Loading Facility (ALF; Gautschi, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) coupled to an Automated Graphitization Equipment (AGE3, ETH Zurich, Switzerland; Wacker, Němec, et al., 2010) with an integrated zeolite trap to adsorb CO\u003csub\u003e2\u003c/sub\u003e from the sampling bag. The \u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e content of all gas samples were measured using a MICADAS (ETH Zurich, Switzerland) or a Low Energy AMS (LEA, ETH Zurich \u0026amp; IonPlus AG, Switzerland; Ramsperger et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Measurement uncertainties were \u0026lt;\u0026thinsp;2\u0026permil;. For data evaluation, the standard Oxalic Acid II (Mann, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) and blank material from the \u003csup\u003e14\u003c/sup\u003eC-free phthalic anhydride (PhA) were measured alongside the samples and evaluated with the BATS software (Wacker, Christl, et al., 2010). Graphitization and \u003csup\u003e14\u003c/sup\u003eC measurements were performed at the Laboratory of Ion Beam Physics, ETH Zurich, Switzerland. In samples from 10\u0026ndash;20 cm soil depth, ẟ\u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e values were less depleted than ẟ\u003csup\u003e13\u003c/sup\u003eC values of bulk SOC, suggesting a potential contribution of carbonate weathering to the soil-emitted CO\u003csub\u003e2\u003c/sub\u003e. To estimate the potential contribution of this process, we applied a two-endmember mass balance model, assuming a ẟ\u003csup\u003e13\u003c/sup\u003eC of 0\u0026permil; for carbonate and ẟ\u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e = ẟ\u003csup\u003e13\u003c/sup\u003eC-SOC. The estimated contribution of carbonate weathering to soil respiration and its isotopic signatures was minimal (\u0026lt;\u0026thinsp;1\u0026ndash;2%), consistent with findings from other forest sites (e.g., Schindlbacher et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Also, their contribution would not affect the share of autotrophic respiration and therefore would not affect our result interpretation.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSource partitioning of soil respiration\u003c/h2\u003e \u003cp\u003eSoil-respired CO₂ collected \u003cem\u003ein situ\u003c/em\u003e from the chamber headspace was partitioned into autotrophic, heterotrophic, and residual atmospheric sources using Bayesian mixing models. Source apportionment was performed with the MixSIAR package (R version 3.1.12; Moore \u0026amp; Semmens, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Stock et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and with a custom implementation in Python. A detailed description of the approach is presented in Minich et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e. For source partitioning in March, we used heterotrophic Δ\u0026sup1;⁴CO₂ values from incubated soil samples collected in August.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eLinear mixed-effect models (LME) were fitted to investigate the effect of treatment and soil depths on Δ\u003csup\u003e14\u003c/sup\u003eC values of heterotrophically respired CO\u003csub\u003e2\u003c/sub\u003e and SOC in bulk soil, as well as on SOC stocks, fine root biomass, and C mineralization with plots included as random effects (R package lmerTest, version 3.1.3; Kuznetsova et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to account for spatial heterogeneity. Transformations of the response variables were conducted in case the assumption of normal distribution (tested with Shapiro-Wilk normality test) or homoscedasticity (tested with Levene\u0026rsquo;s test and plots of fitted vs. residuals) was violated. Analysis of variance (ANOVA) was conducted on the fixed effects (treatment, depth) of the models to assess their significance and interaction. Following the main effects, post hoc pairwise comparisons were conducted using estimated marginal means (EMMs) (R package emmeans, version 1.10.4; Lenth, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to assess significant differences between treatment within each depth and vice versa, adjusted for multiple comparisons using Tukey\u0026rsquo;s method. Generalized least square models (GLS) were fitted to investigate the effect of treatment on long-term soil respiration rates, soil moisture, and soil temperature (R package nlme, version 3.1.166; Pinheiro et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The model included seasonal cycling terms \u0026ndash; sin(2π x month / 12) and cos(2π x month / 12) \u0026ndash; to capture intra-annual variation in the soil respiration, temperature, and moisture. An interaction term between treatment and these seasonal predictors was included to assess whether seasonal responses differed across treatments. To account for temporal autocorrelation a first-order autoregressive structure was specified within each year. The response variables were log-transformed to meet model assumptions regarding normality and homoscedasticity of residuals. ANOVA was performed on the fixed effects (treatment, seasonality terms) to assess their significance. We further fitted a GLS to investigate how soil temperature and moisture affected soil respiration under dry control and irrigated conditions. The seasonal cycling terms were excluded as the seasonal variation is already reflected in soil temperature and moisture. Interaction terms between all predictor variables (soil temperature, soil moisture, treatment) allowed us to assess whether relationships between environmental drivers and respiration rates varied between treatments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLong-term irrigation effects on soil respiration\u003c/h2\u003e \u003cp\u003eLong-term measurements revealed that irrigation significantly increased soil respiration (SR; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table S2), by ~\u0026thinsp;66% on an annual basis with an overall mean respiration rate of 111\u0026thinsp;\u0026plusmn;\u0026thinsp;64 mg CO\u003csub\u003e2\u003c/sub\u003e-C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in dry control and 185\u0026thinsp;\u0026plusmn;\u0026thinsp;111 mg CO\u003csub\u003e2\u003c/sub\u003e-C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in irrigation plots (averaged across 2012\u0026ndash;2024; Table S1). The increase in SR in the irrigation treatment was most pronounced during the irrigation period (May-October) (+\u0026thinsp;79%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Table S1, Table S2). Although not significant, SR increased by 18% in the non-irrigation period (November-April) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Table S1). While the increase of SR under irrigation relative to the dry control treatment (i.e., drought effect: SR\u003csub\u003edry control\u003c/sub\u003e / SR\u003csub\u003eirrigation\u003c/sub\u003e) exhibited seasonal variation with lower values in summer than in winter, there was no trend across the entire period between 2012 and 2024 (Figure S2). However, soil respiration in the dry control closely corresponded to precipitation during the irrigation period, reducing the apparent drought effect in years with higher precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figure S2). The relationship between the drought effect and soil moisture in the dry control treatment was well described by a Boltzmann fit, showing that SR in the dry control remained lower than in the irrigation treatment even at soil moisture levels above 35% (w-% of moist soil; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eWhile soil moisture significantly differed between treatments during the irrigation period (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Figure S1, Table S2), soil temperature was almost identical in dry control and irrigated plots (Figure S1, Table S2). In the irrigation treatment, soil temperature had a significant effect (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020, Table S3) on respiration rates which increased exponentially with increasing temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). In the dry control treatment, soil moisture was the main driving factor of respiration rates (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, Table S3), while the effect of soil temperature was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.23, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The interaction between soil temperature and soil moisture was highly significant in the dry control treatment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but less pronounced under irrigation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, Table S3). Irrigation significantly increased the effect of soil temperature on soil respiration (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010, Table S3, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLong-term irrigation effects on source contribution\u003c/h2\u003e \u003cp\u003eΔ\u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e values of \u003cem\u003ein situ\u003c/em\u003e soil respiration were consistently lower in the irrigation (August: 1\u0026permil;, March: -5\u0026permil;) compared to the dry control treatment (August: 19\u0026permil;, March: 4\u0026permil;) in both seasons (Figure S4). The difference between treatments was more pronounced in August than in March. Overall, \u003cem\u003ein situ\u003c/em\u003e Δ\u0026sup1;⁴CO₂ values were lower in March than in August but exceeded the atmospheric background levels measured in both months (March: +32\u0026permil;; August: \u0026minus;\u0026thinsp;14\u0026permil;).\u003c/p\u003e \u003cp\u003eΔ\u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e values of autotrophic respiration were slightly lower (August: -12\u0026permil;, March: -16\u0026permil;) and thus closer to atmospheric Δ\u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e in the irrigation treatment than in the dry control treatment (August: -8\u0026permil;, March: -13\u0026permil;). Although Δ\u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e values of heterotrophic respiration were slightly higher in the irrigation treatment (mean across the soil profile: 18\u0026permil;) than in the dry control treatment (mean across the soil profile: 12\u0026permil;; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figure S4), this difference was not significant (Table S4, Table S5).\u003c/p\u003e \u003cp\u003eOverall, source partitioning showed that the relative contribution of heterotrophic respiration was higher in August than in March (Fig.\u0026nbsp;3a). Heterotrophic respiration dominated total soil respiration in the dry control, with relative contributions of 86% in August and 58% in March. In the irrigated soils, autotrophic and heterotrophic respiration contributed equally to total soil respiration in August, while autotrophic respiration accounted for 64% in March (Fig.\u0026nbsp;3). While absolute heterotrophic respiration from the mineral soil was similar between treatments, respired CO₂ originating from the organic layer and autotrophic sources was higher from irrigated soils. During August, these two sources respired 10 to 13 times more CO₂ in irrigated soils compared to the dry control (Fig.\u0026nbsp;3b, Figure S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLong-term irrigation effects on C inputs, SOC distribution, and C mineralization\u003c/h2\u003e \u003cp\u003eBoth aboveground (i.e., litterfall) and belowground (i.e., fine root biomass) C inputs increased under irrigation. On average across the years 2016\u0026ndash;2023, litterfall increased by 49% from 333\u0026thinsp;\u0026plusmn;\u0026thinsp;47 g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the dry control treatment to 496\u0026thinsp;\u0026plusmn;\u0026thinsp;98 g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e under irrigation. Total fine root biomass (Scots pine\u0026thinsp;+\u0026thinsp;other species in OL\u0026thinsp;+\u0026thinsp;0\u0026ndash;20 cm soil depth) increased by 25% under irrigation, with mean values rising from 285 g m⁻\u0026sup2; in the dry control to 356 g m⁻\u0026sup2; in the irrigation treatment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;4). While fine root biomass increased significantly in the mineral soil under irrigation (+\u0026thinsp;44%; Table S4), especially at 2\u0026ndash;5 cm (+\u0026thinsp;78%) and 5\u0026ndash;10 cm depth (+\u0026thinsp;72%), it was reduced in the organic layer (-66%; Fig.\u0026nbsp;4), also considering the reduced organic layer mass compared to the dry control (\u0026minus;\u0026thinsp;36%; Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the organic layer, SOC stocks decreased by 1.0 kg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, whereas in the mineral soil, SOC stocks increased by 0.8 kg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCumulative C mineralization across the soil profile measured upon one month incubation at 20\u0026deg;C was 44% higher in the irrigation treatment (45.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1 mg CO₂\u0026ndash;C g SOC⁻\u0026sup1; month⁻\u0026sup1;) compared to the dry control treatment (65.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7 mg CO₂\u0026ndash;C g SOC ⁻\u0026sup1; month⁻\u0026sup1;; Fig.\u0026nbsp;4). This higher C mineralization was most pronounced in the organic layer and upper 0\u0026ndash;2 cm of the mineral soil (Fig.\u0026nbsp;4, Table\u0026nbsp;4.1, Table S5).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean values of SOC stocks, fine root biomass, C mineralization, Δ\u0026sup1;⁴C values of bulk SOC and heterotrophically respired CO\u003csub\u003e2\u003c/sub\u003e across soil depth profiles under dry control and irrigated conditions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil depth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOC stock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFine root biomass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC mineralization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔ\u003csup\u003e14\u003c/sup\u003eC-SOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eΔ\u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emg CO\u003csub\u003e2\u003c/sub\u003e-C g SOC\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e month\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026permil;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026permil;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.1\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.2\u0026thinsp;\u0026plusmn;\u0026thinsp;26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-13.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOe/Oa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.1\u0026thinsp;\u0026plusmn;\u0026thinsp;33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.6\u0026thinsp;\u0026plusmn;\u0026thinsp;29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.2\u0026thinsp;\u0026plusmn;\u0026thinsp;26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107.6\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.2\u0026thinsp;\u0026plusmn;\u0026thinsp;23.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.1\u0026thinsp;\u0026plusmn;\u0026thinsp;23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrrigation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.1\u0026thinsp;\u0026plusmn;\u0026thinsp;27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOe/Oa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.1\u0026thinsp;\u0026plusmn;\u0026thinsp;25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104.5\u0026thinsp;\u0026plusmn;\u0026thinsp;19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107.5\u0026thinsp;\u0026plusmn;\u0026thinsp;31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.9\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRadiocarbon contents in SOC, respired CO\u003csub\u003e2\u003c/sub\u003e, and fine roots\u003c/h2\u003e \u003cp\u003eΔ\u0026sup1;⁴C-SOC values mirrored the depth-dependent differences in SOC stocks between the two treatments. In the organic layer and 0\u0026ndash;2 cm of the mineral soil, mean Δ\u0026sup1;⁴C-SOC values were significantly lower under irrigation compared to the dry control (~\u0026thinsp;50\u0026permil; vs. ~76\u0026permil;; \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFH\u003c/sub\u003e \u0026lt; 0.0001, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/sub\u003e = 0.098; Table S5). At greater depths (2\u0026ndash;20 cm), this pattern reversed, with slightly higher Δ\u0026sup1;⁴C-SOC values in the irrigation treatment than in the dry control (~\u0026thinsp;89\u0026permil; vs. ~84\u0026permil;), although this difference was statistically not significant. Mean Δ\u0026sup1;⁴C values of fine roots were lower and closer to atmospheric \u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e levels in 2019 in the irrigation treatment (Scots pine: 37\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u0026permil;, other species: 38\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u0026permil;) than in the dry control treatment (Scots pine: 48\u0026thinsp;\u0026plusmn;\u0026thinsp;25\u0026permil;, other species: 46\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u0026permil;). Compared with the decline in atmospheric \u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e, this corresponds to ages of 9 and 11 years, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study showed that long-term irrigation in a dry pine forest led to a fundamental change in C cycling in the plant-soil continuum, increasing the in- and outputs, altering the Δ\u003csup\u003e14\u003c/sup\u003eC values of belowground C pools and fluxes, and thus changing the time that C spends in the ecosystem. Although irrigation enhanced the redistribution of SOC stocks and Δ\u003csup\u003e14\u003c/sup\u003eC values from the organic layer to the mineral soil, effects on total SOC stocks remained negligible after 22 treatment years.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLong-term increases and shifting sources of soil respiration under irrigation\u003c/h2\u003e \u003cp\u003eAnnual soil respiration was increased by 66% in the irrigation treatment, indicating that irrigation removed the moisture limitation of respiratory processes. This is also reflected in the tight positive relationship between soil respiration and soil temperature in the irrigation treatment, while soil moisture was the main regulating factor in dry control conditions (Table S3). However, soil respiration in the irrigation treatment was not only enhanced during the irrigation period \u0026ndash; when soil moisture differences were most pronounced \u0026ndash; but also during non-irrigated periods in winter, despite similar moisture levels in both treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This suggests that in addition to immediate plant and microbial responses to soil moisture, ecosystem adaptation must have contributed to the increased respiration rates in the irrigation treatment. In the following, we will discuss three ecosystem adaptations which likely affected responses of soil respiration to irrigation: (i) increased belowground carbon allocation by plants, (ii) increased root growth due to elevated plant water demand, and (iii) increased decomposition in the organic layer.\u003c/p\u003e \u003cp\u003eOur analysis of Δ\u0026sup1;⁴C values of \u003cem\u003ein situ\u003c/em\u003e soil respiration and its sources revealed that the observed increase in total soil respiration in the irrigation treatment is primarily driven by increased autotrophic respiration as well as increased heterotrophic respiration in the organic layer (Fig.\u0026nbsp;3). We attribute higher autotrophic contributions in the irrigation treatment mainly to significant increases in fine root biomass (Fig.\u0026nbsp;4), enhancing autotrophic respiration (e.g., Apostolakis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003ee \u0026amp; Buchmann, 2005; Zhang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, both faster and significantly enhanced allocation of newly assimilated C from plants to the rhizosphere (Gao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hartmann et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Joseph et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) likely contributed to higher autotrophic respiration under irrigation. In a \u0026sup1;\u0026sup3;C pulse-labelling study conducted in this forest, Gao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e observed that irrigation resulted in a three- to four-fold increase in the amount of C assimilates transferred to and respired from the soil, relative to the smaller increase observed in total soil respiration \u0026ndash; suggesting that irrigation disproportionately enhanced autotrophic processes. This aligns with findings of reduced allocation and metabolization of recent C assimilates under dry conditions in grasslands and young beech forests, which reduced autotrophic respiration more strongly than total soil respiration (Fuchslueger et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hagedorn et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe magnitude of the increase in soil respiration in the irrigation treatment compared to dry control conditions varied with the amount of summer precipitation but generally remained relatively stable between 2012 and 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figure S2). This suggests that most structural changes in the ecosystem which were responsible for the increase in soil respiration must have occurred during the initial decade of the experiment (2003\u0026ndash;2012), prior to the beginning of continuous soil respiration measurements. Bose et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e reported that trees initially responded to increased water availability in irrigated plots with increased aboveground growth, evidenced by wider tree rings and expanded crown areas. While the enhancement in aboveground growth remained constant (Bose et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and was even followed by a slight regression to pre-irrigation growth rates of individual trees (Vitali et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), it likely induced a greater water demand (Zweifel et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This may have triggered a shift toward belowground biomass allocation (Poorter \u0026amp; Nagel, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), which is supported by the observed continuous increase in Scots pine fine root biomass from the start of the experiment in 2003 through 2016 (Brunner et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and in 2019 measured here (Fig.\u0026nbsp;4). The irrigation effect on fine root biomass became not significant before 2012. One reason for the retarded belowground response could be the slow fine root growth, evidenced by the Δ\u003csup\u003e14\u003c/sup\u003eC-derived fine root ages of several years (Fig.\u0026nbsp;4). Taken together, we propose that both the higher contribution of autotrophic respiration as well as consistently higher total soil respiration rates in the irrigation treatment reflect a long-term adjustment in belowground biomass driven by sustained water availability and demand.\u003c/p\u003e \u003cp\u003eIn addition to enhanced autotrophic respiration, the increase in soil respiration in the irrigation treatment was also driven by accelerated heterotrophic respiration in the organic layer, which contributed to as much as 60% to total heterotrophic respiration in irrigated soils and showed a 10-fold higher rate than dry soils in August (Fig.\u0026nbsp;3). The pronounced sensitivity of the organic layer to both drought and irrigation likely reflects its high reactivity to changes in soil moisture, its disconnection from the underlying mineral soil (Keith et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), its high content of labile SOM, and its role as a hotspot of biological activity (Schindlbacher et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Additionally, Hartmann et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e observed a shift from oligotrophic to copiotrophic microbial life strategies under irrigation in our experiment. While oligotrophic life strategies are dominant in soils of low C availability, copiotrophic organisms dominate in soils with higher net C mineralization rate (Fierer et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This shift might have enhanced microbial C use efficiency and microbial turnover rates (Fierer et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), thereby contributing to the observed increases in C mineralization (Fig.\u0026nbsp;4) and respiration from the organic layer (Fig.\u0026nbsp;3). The increase in both autotrophic and heterotrophic respiration from the organic layer under irrigation was more pronounced in August than in March (Fig.\u0026nbsp;3). In March, similar soil temperature and moisture conditions between treatments likely led to more comparable respiration rates and source contributions.\u003c/p\u003e \u003cp\u003eThe small effect sizes of heterotrophic respiration in the mineral soil could be explained by long-term decreases of the irrigation effect on soil water contents, due to the greater water uptake by the larger trees (Herzog et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shakas et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This effect is so pronounced that in spring, before the onset of the irrigation period, soil water potentials in irrigated treatment were even lower than in the control plots (Shakas et al., 2024). In contrast, heterotrophic respiration from the minerals soil remained largely unaffected by irrigation (Fig.\u0026nbsp;3). This is in line with previous studies, observing that reductions in heterotrophic respiration under drought are primarily related to decreased decomposition activity in the organic layer (Borken et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Cisneros-Dozal et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schindlbacher et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAutotrophic contributions to total soil respiration were generally higher in March than in August which is unexpected as autotrophic respiration in forest ecosystems typically peaks during the summer months (Borken et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Schindlbacher et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Since autotrophic respiration is more strongly influenced by plant phenology than by soil temperature (Atarashi-Andoh et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) it seems likely that measurements in March coincided with an already active phenological phase. Additionally, air temperatures exceeded soil temperatures by up to 9.5\u0026deg;C during \u003cem\u003ein situ\u003c/em\u003e CO\u003csub\u003e2\u003c/sub\u003e sampling. While low soil temperatures of 9\u0026deg;C likely suppressed heterotrophic microbial activity, the evergreen pine trees might have been metabolically active in the warmer air (Ferrari et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), resulting in a relatively greater proportion of autotrophic respiration during the measurement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eIrrigation-induced C losses in the organic layer and gains in the mineral soil\u003c/h2\u003e \u003cp\u003eIn addition to the shifted soil CO\u003csub\u003e2\u003c/sub\u003e fluxes, long-term irrigation led to a SOC redistribution in the soil profile, with decreasing SOC stocks in the organic layer but gains in the mineral soil, with minimal net effect on total SOC stocks (Fig.\u0026nbsp;4; Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The altered SOC depth distribution by irrigation is paralleled by lower Δ\u0026sup1;⁴C values in the organic layer and higher values in the mineral soil. Multiple mechanisms may contribute to this redistribution at higher soil moisture conditions, including an accelerated decomposition, an increased rhizodeposition (Gao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and an enhanced translocation and incorporation of litter materials into the mineral soils (Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Δ\u003csup\u003e14\u003c/sup\u003eC-SOC depth pattern followed historical atmospheric Δ\u0026sup1;⁴CO₂ trends, and differences in the pattern between treatments thereby allow to infer on the importance of redistribution mechanisms (Fig.\u0026nbsp;4). Δ\u003csup\u003e14\u003c/sup\u003eC values were closest to recent atmospheric levels in the organic layer, peaked in 0\u0026ndash;5 cm depth (reflecting the bomb-spike with decadal old C), followed by a decline with further depth, indicating an increasing presence of older, pre-bomb C in deeper mineral layers. Δ\u0026sup1;⁴C-SOC values closer to atmospheric levels in the organic layer and 0\u0026ndash;2 cm depth of irrigated soils indicate a faster SOC cycling and a higher input of younger litter- and root-derived inputs compared to the dry control soil. As SOC stocks in the organic layer decreased under irrigation (Fig.\u0026nbsp;4), the acceleration of decomposition must have been stronger than the enhancement of litter inputs. A possible underlying mechanism is that irrigation enhanced the activity of the soil fauna, which is particularly drought-sensitive and transforms the organic layer (Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the mineral soil below 2 cm depth, Δ\u0026sup1;⁴C-SOC values were higher in the irrigation treatment as compared to the control treatment, indicating higher inputs of decadal old, bomb-derived C.\u003c/p\u003e \u003cp\u003eThis pattern strongly indicates that increased SOC stocks in the mineral soil of the irrigation treatment were not caused by increased belowground plant inputs (in which case one would expect lower Δ\u0026sup1;⁴C-SOC values). Instead, this pattern suggests increased downward translocation of bomb-derived C from the organic layer and upper mineral soil (0\u0026ndash;2 cm) to deeper soil layers. One main vector appears to be faunal-mediated transfer and incorporation of litter and organic layer into the mineral soil (Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Higher water availability was found to increase the abundance, composition and diversity of micro-, meso-, and macro-fauna (e.g. Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kudureti et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which promotes both litter decomposition and C translocation to the mineral soil (G. Angst et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Enhanced DOC leaching under irrigation might additionally increase C transfer from the surface layers to the mineral soil. However, this contribution is likely minor as indicated by low DOC fluxes of 13 to 26 g DOC m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e observed in Swiss forests with shallow organic layers and comparable mean annual precipitation (Graf Pannatier et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Lastly, the interpretation that increased vertical translocation of organic material was the dominant mechanism behind the irrigation-induced shifts in SOC stocks is further supported by shifts in stable isotope values. In the mineral soil under irrigation, \u003csup\u003e13\u003c/sup\u003eC and \u003csup\u003e15\u003c/sup\u003eN values resembled the less processed SOC from the organic layer more closely as compared to the dry control soils (Guidi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTranslocation of C from surface soil layers, together with increased C inputs to the mineral phase through increased litter and rhizodeposition likely promote SOC sequestration and stabilization in deeper soil layers of the mineral soil in the irrigation treatment. Although Δ\u0026sup1;⁴C-SOC values indicate that SOC is younger under irrigation, increased C inputs likely increase the persistence of SOC through several processes. The translocation of C into deeper soil layers likely increases the potential of this translocated C to become stabilized through mineral protection as the sorption capacity for organo-mineral associations increases with soil depth (Ahrens et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, the shift from oligotrophic to copiotrophic microbial life strategies under irrigation (Hartmann et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and subsequent enhanced turnover of microbial biomass (Gao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) might increase the formation of microbial products that can be stabilized through organo-mineral associations (Cotrufo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, litter ingestion of soil fauna, especially earthworms, promote the occlusion of SOM in soil aggregates, which leads to the physical protection of POM and MAOM from decomposition (e.g., G. Angst et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Š. Angst et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bossuyt et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study shows that long-term changes in soil moisture regimes enhanced both C inputs as well as outputs and reshaped C cycling within the plant-soil system, primarily driven by structural changes in the forest ecosystem. We found that irrigation increased soil respiration and accelerated C cycling within the plant system and organic layer, shortening the time that C spent in the system from C assimilation through photosynthesis until it was respired back to the atmosphere. This is primarily driven by increased autotrophic respiration, resulting from higher fine root biomass and belowground C allocation under irrigation. Additionally, \u003csup\u003e14\u003c/sup\u003eC data revealed an accelerated SOC turnover in the organic layer and uppermost mineral soil. However, while a larger proportion of C is quickly released back to the atmosphere without entering the mineral soil, vertical changes of SOC stock and Δ\u003csup\u003e14\u003c/sup\u003eC values document a SOC redistribution under irrigation. Possibly, this results from the combined effects of greater rhizosphere inputs and translocation from upper soil layers through soil faunal activity. The enhanced translocation of C to the mineral soil resulted in a net SOC gain in the soil from 0\u0026ndash;20 cm depth, which potentially promotes C stabilization. Taken together, our findings indicate that current drought conditions limit both the magnitude and pace of C cycling within the plant-soil continuum, and potentially hinder long-term C sequestration in mineral soils.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis study is based on data from the long-term experimental research platform Pfynwald, part of the Swiss Forest Lab and the eLTER research infrastructures. We are especially grateful to the Pfynwald core team for their support and for providing access to the research infrastructure. We also thank Petra d\u0026rsquo;Orico and Fluppi Sutter for sharing aerial images and site maps of the experimental area, and MeteoSwiss for supplying essential climate data. We gratefully acknowledge Michael Guggenb\u0026uuml;hl, Stefan Tobler, Clara Juliette Gund, Jan Ziegler, Logan James Banner, Thomas Laemmel, and Mathias Mayer for their valuable assistance during fieldwork. Our thanks also go to Daniel Christen, Daniel Wasner, Roger K\u0026ouml;chli, and Marco Walser for their dedicated help with laboratory work. We further appreciate the expertise of Andr\u0026eacute; Albrecht and Urs Ramsperger in sample preparation and \u0026sup1;⁴C measurements, as well as the support of Alessandro Schlumpf and Ursula Graf with \u0026sup1;\u0026sup3;C analyses. This research was funded by the Radiocarbon Inventories of Switzerland project (Grant No. 193770) of the Swiss National Science Foundation. We also gratefully acknowledge the Marie Curie funding program for supporting the PostDoc position of Claudia Guidi, whose data contributed to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhrens B, Guggenberger G, Rethemeyer J, John S, Marschner B, Heinze S, Angst G, Mueller CW, K\u0026ouml;gel-Knabner I, Leuschner C, Hertel D, Bachmann J, Reichstein M, Schrumpf M (2020) Combination of energy limitation and sorption capacity explains 14C depth gradients. 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New Phytol 227(4):1081\u0026ndash;1096. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.16582\u003c/span\u003e\u003cspan address=\"10.1111/nph.16582\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Swiss Federal Institute for Forest, Snow and Landscape Research","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Carbon cycling, drought, irrigation, radiocarbon, soil respiration, heterotrophic respiration, autotrophic respiration, source contribution","lastPublishedDoi":"10.21203/rs.3.rs-8649982/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8649982/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change is intensifying the frequency and severity of droughts, with profound implications for carbon (C) cycling in forest ecosystems. While progress has been made in understanding how drought alters plant and microbial ecophysiology, it remains unclear how these changes affect the overall magnitude and pace of C cycling within the plant-soil continuum.\u003c/p\u003e\n\u003cp\u003eIn this study, we examined the effects of 22 years of experimental irrigation in a naturally drought-prone Scots pine forest. We integrated long-term measurements of C inputs (i.e., litterfall) and outputs (i.e., soil respiration) with radiocarbon (¹⁴C) analysis of soil organic carbon, fine roots, and CO₂ from \u003cem\u003ein situ\u003c/em\u003e soil respiration and its autotrophic and heterotrophic components.\u003c/p\u003e\n\u003cp\u003eOur study demonstrates that long-term shifts in water availability enhance both C inputs and outputs, reshaping C cycling within the plant-soil system. Radiocarbon (\u003csup\u003e14\u003c/sup\u003eC) analysis revealed that irrigation accelerated C cycling within plants and the organic layer, reducing the time that assimilated C remained in the ecosystem before being respired back to the atmosphere. The observed increase in soil respiration under irrigation was largely driven by enhanced autotrophic activity, associated with greater fine root biomass. Concurrently, the decomposition of labile, young organic matter intensified under irrigation, potentially contributing to a net C loss from the organic layer. Despite this increased respiration under irrigation, ¹⁴C contents in bulk SOC indicated greater inputs of young C to the mineral soil and enhanced downward translocation of C from the organic layer to the mineral phase, possibly through rhizodeposition and soil faunal activity. The enhanced input to the mineral soil under irrigation results in net C gains and may promote C stabilization through organo-mineral interactions and aggregate formation. Our findings also indicate that drought conditions limit both the magnitude and rate of C cycling within the plant-soil continuum and potentially reduce long-term C sequestration in mineral soils.\u003c/p\u003e","manuscriptTitle":"Increased water availability accelerates C cycling in a dry forest ecosystem","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 10:26:24","doi":"10.21203/rs.3.rs-8649982/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"25886265-13a7-46a5-a7d9-195609ca52a4","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-21T10:26:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 10:26:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8649982","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8649982","identity":"rs-8649982","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}