# Prolonged warming leads to carbon depletion and increases nutrient availability in alpine soils

Nicolas Bonfanti, Jean-Christophe Clément, Tamara Münkemüller, Pierre Barré, François Baudin, Jérôme Poulenard

## ► To cite this version:

Nicolas Bonfanti, Jean-Christophe Clément, Tamara Münkemüller, Pierre Barré, François Baudin, et al. Prolonged warming leads to carbon depletion and increases nutrient availability in alpine soils. Applied Soil Ecology, 2025, 213, pp.106239. 10.1016/j.apsoil.2025.106239. hal-05105389

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# Prolonged warming leads to carbon depletion and increases nutrient availability in alpine soils

Nicolas Bonfanti<sup>a,b,*</sup>, Jean-Christophe Clément<sup>a</sup>, Tamara Münkemüller<sup>c</sup>, Pierre Barré<sup>d</sup>, François Baudin<sup>e</sup>, Jérôme Poulenard<sup>b</sup>

## ARTICLE INFO

**Keywords:**

Ecosystem functioning

Climate change

Carbon stability

Soil fertility

Alpine environments

C, N, P mineralization

## ABSTRACT

Alpine ecosystems, shaped by cold temperatures and prolonged snow cover, are warming twice as fast as lowlands, making them particularly vulnerable to climate change. This rapid warming alters ecosystem functioning by increasing soil temperatures and shifting snow regimes, leading to shorter snow cover periods and longer growing seasons. Such changes impact soil organic matter (SOM), which regulates carbon storage and soil fertility through microbial mineralization. We investigated long-term SOM changes following experimental warming in alpine grasslands. By transplanting soil plots downslope (2470 m a.s.l. to 1920 m a.s.l.), we increased mean annual temperature by 3 °C and extended the growing season by 77 days. Seven years later, we analyzed soil nutrients content, SOM characteristics (chemistry, labile pools, and thermal stability) and conducted discrete flux measurements to determine net ecosystem exchange (NEE). Soil incubations assessed microbial traits and their acclimation to warming. Our results indicate that, even after seven years, alpine soil microbial activities showed limited acclimation to warming, contributing to SOM destabilization and soil nutrients enrichment by boosting mineralization. Indeed, warmed plots acted as carbon sources, with an 18 % decrease in SOC stocks and increased NEE. Carbon losses exceeded C gains from plant productivity, primarily depleting labile pools. This may create a positive feedback loop between carbon cycling and climate warming. These findings highlight the long-term consequences of temperature increases and snow regime shifts on alpine ecosystem functioning and suggest that soil carbon losses in warming mountain environments may continue over time.

## 1. Introduction

Soil organic matter (SOM) is crucial in regulating nutrient cycling, soil fertility, water retention, and serving as a major carbon sink in terrestrial ecosystems. Soils store more carbon than the atmosphere and all plant biomass combined, with approximately 1500 Pg of carbon, making SOM essential to the global carbon cycle (IPCC, 2023; Jobbagy and Jackson, 2000). The carbon in SOM enhances ecosystem resilience by improving soil structure, nutrient availability, and plant productivity. SOM stocks result from a balance between plant inputs and microbial mineralization. The latter recycles carbon, nitrogen, and phosphorus to support plant growth and is therefore closely linked to soil fertility (Adhikari and Hartemink, 2016; Hoffland et al., 2020). Temperature is a

key driver of plant growth and mineralization rates, suggesting that climate warming could affect SOM stocks and soil fertility. However, many uncertainties remain, including whether warming will increase SOM through increased plant production or decrease SOM through increased microbial mineralization and thus whether warmed soils will become carbon sinks or sources (Ren et al., 2024).

In mountain environments, soils are effective carbon sinks due to the cold temperatures, which limit microbial decomposition and allow organic matter to accumulate (Bonfanti et al., 2025a, 2025d; Budge et al., 2011). Alpine and subalpine soils often store significantly more carbon than those at lower elevations, making them hotspots of carbon sequestration (Guggenberger and Zech, 1999). Moreover, mountain SOM appears to be more labile than lowland SOM, meaning that it needs

<footnote><sup>a</sup> Univ. Savoie Mont Blanc, INRAE, CARRTEL, Thonon-Les-Bains, France</footnote>

<footnote><sup>b</sup> Univ. Savoie Mont Blanc, CNRS, EDYTEM, Chambéry, France</footnote>

<footnote><sup>c</sup> Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Grenoble, France</footnote>

<footnote><sup>d</sup> Laboratoire de Géologie, Ecole Normale Supérieure, CNRS, PSL University, IPSL, Paris, France</footnote>

<footnote><sup>e</sup> Sorbonne Université, CNRS, ISTE, 75005 Paris, France</footnote>

<footnote>* Corresponding author at: Univ. Savoie Mont Blanc, INRAE, CARRTEL, Thonon-Les-Bains, France.
E-mail address: nicolas.bonfanti.usmb@gmail.com (N. Bonfanti).</footnote>
less activation energy to be decomposed and, therefore, it is more vulnerable to climatic change (Schmidt et al., 2011).

SOM stocks are thus highly sensitive to climate change, with the effects being especially pronounced in cold environments (EEA, 2024; Gobiet et al., 2014). On one hand, warming enhances plant productivity and increases SOM inputs (Choler et al., 2021; Rumpf et al., 2022). Conversely, rising temperatures can accelerate SOM mineralization, particularly the mineralization of labile SOM stabilized by cold temperatures. The process could result in the increased release of stored carbon as CO₂ into the atmosphere, potentially creating a positive feedback loop that amplifies global warming (Crowther et al., 2016). It also has significant implications for ecosystem functioning, including altered nutrient cycling, changes in soil fertility, and shifts in the functional composition of plant communities (Bardgett et al., 2005; Hoffland et al., 2020). Climate change have two main consequences on SOM dynamics in alpine environments: rising temperatures, which enhance organism activity and decomposition, and shifts in seasonality, such as shorter snow cover and longer growing seasons (Rumpf et al., 2022). Snow cover helps retain nutrients during cold months when plant productivity and SOM mineralization slow. Climate change is altering both temperature and seasonality (Broadbent et al., 2024; Ernakovich et al., 2014), extending growing seasons and reducing snow cover duration. While longer growing seasons may boost primary productivity and photosynthesis, they can also extend microbial activity, potentially increasing SOM decomposition (Dorrepaal et al., 2009). However, this response depends on microorganism's acclimation. On the one hand, microbial communities have been shown to acclimate their efficiency to use carbon substrates for biomass production in response to changing temperature by adjusting their physiological features (compensatory hypothesis) (Bradford et al., 2019; Manzoni et al., 2018; Sun et al., 2025). On the other hand, warming could lead to an enhanced response, where shifts in microbial community composition and functional traits favor oligotrophic strategies better specialized in the degradation of recalcitrant compounds (Bond-Lamberty et al., 2016). In this context, warming may reduce the activation energy required for decomposing less energetically efficient carbon substrates, i.e., recalcitrant carbon (Conant et al., 2011).

The acclimation of microbial sensitivity to warmer temperature is crucial to determine its consequences on ecosystem functioning and properties, especially the response of SOM mineralization and its associated impacts on soil nutrient contents, SOM stocks and SOM quality (Kpemoua et al., 2023). The acclimation lag in microbial, or plant, communities could lead to transient SOM dynamics that are challenging to predict (Bektaş et al., 2021; Crowther et al., 2016). The microbial response to warming contains some uncertainties about its temperature sensitivity and physiological acclimation (Marañón-Jiménez et al., 2018). The sensitivity of microbial respiration to temperature (Q₁₀) may decline as labile organic substrates are depleted, following the "substrate-depletion hypothesis" (Davidson and Janssens, 2006; Melillo et al., 2002). Physiological adjustments or shifts in microbial communities may contribute to this decline, "thermal adaptation hypothesis" (Bradford et al., 2008; Bárdenas-Moreno et al., 2009). Conversely, Q₁₀ could increase due to a relative increase in the proportion of recalcitrant substrates that require higher activation energy for decomposition (Knorr et al., 2005; Wagai et al., 2013).

In order to test the warming responses of microbial communities and the resulting dynamics of SOM stocks in situ, we experimentally warmed alpine grassland ecosystems by downslope transplantation and studied microbial and SOM responses after seven years. Previous transplant experiments to simulate warming in alpine ecosystems have already provided some initial insights into the potential impacts of rising temperatures on SOM stocks. Some studies indicated a trend towards SOM decline with warming, with some shorter term studies suggesting that transplantation primarily affected labile carbon pools (Conant et al., 2011; Garten Jr, 2008; Khedim et al., 2023), while others pointed to the destabilization of previously more stable SOM fractions, such as

mineral-associated organic matter, due to structural changes (Puissant et al., 2017). Khedim et al. (2023) found that 1 °C warming during the growing season alone did not significantly affect SOM properties. However, abrupt warming combined with changes in growing season length did lead to noticeable -albeit not significant-alterations. Similarly, Yu et al. (2014) found that in the short term, SOC and total nitrogen remained largely unaffected by warmed transplants, though inorganic forms of carbon and nitrogen responded more strongly.

Based on these earlier results and on warming responses of the productivity of plant communities (Bektaş et al., 2021), we hypothesize that:

i) Alpine soil microbial communities will still not be fully acclimated to increased temperature and extended growing seasons after seven-year of warming experiment, i.e., they will not have the same features such as respiration sensitivity to temperature/ moisture than soil microbial communities of subalpine control plots exposed since long to this climate.

ii) As a consequence, in the warmed plots, we expect a destabilization of SOM stocks and an improved soil fertility due to enhanced microbial mineralization, resulting in the loss of labile carbon and increases in soil nutrient contents (N, P).

iii) Finally, based on flux measurements, we explore if the expected increase in SOM mineralization rate is mainly due to temperature increase during the growing season or to the lengthening of the growing season itself.

## 2. Materials & methods

### 2.1. Study sites

The study sites are located in the French Alps, near the Galibier and Lautaret passes. Both alpine and subalpine sites share similar characteristics, including southwest-facing exposure (Fig. 1), geological substrate (glacial deposits and Quaternary moraines on flysch), soil type (Dystric Cambisols, Table S1), topography (uniform slope of ~15°), and human activities (extensive grazing with cows and sheep). The alpine site (45.0542°, 6.400°), at 2470 m a.s.l., is dominated by plant species such as Carex sempervirens, Festuca nigrescens, Poa alpina, Potentilla aurea, Trifolium alpinum, and Geum montanum. The plant communities at the subalpine site (45.0400°, 6.419°), at 1920 m a.s.l., are dominated by Patzkea paniculata, Festuca nigrescens, Centaurea uniflora, Helianthemum nummularium, and Meum athamanticum. Continuous measurements of soil temperature, humidity (5 cm deep, with TMS-4 probe - TOMST), and NDVI (S2-412-SS probe, Apogee Instrument) are recorded since 2016. Soil temperature measurements allow to calculate the growing degree days (GDD), i.e., cumulative yearly sum of mean daily degrees above a daily mean temperature of 1 °C, and to calculate the growing season length (GSL), i.e., the number of days in a year with mean daily soil temperature above 1 °C. Snow cover season (SCS) represents the number of days during the year with mean daily soil temperature below 1 °C, i.e., when the soil is snow-covered and/or when soil biological activity is reduced. Soil characterization and description were conducted at both sites through soil pit examinations close to the experiment.

### 2.2. Transplantation

We simulated a ~3 °C soil warming and ~77 days lengthening of the growing season by transplanting plots from the alpine site to the subalpine site in 2016 (see also Bektaş et al. (2021)). In total, we transplanted 10 plots of 4 m² from the alpine to the subalpine site (AWarm). Each plot was divided into four 1 m² sub-plots for transport and was then reassembled in the original configuration at the subalpine site. Each transplanted plot consisted of its vegetation and at least 20 cm of the intact below-ground part of the ecosystem, including the root system, soil and soil biota (A-horizon). To additionally simulate cooling effects
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Study sites, reciprocal transplant and pedoclimatic regime in both sites. Panel B shows pedoclimatic conditions with blue and green curves for the alpine and subalpine sites, respectively, and moisture data (black dotted curve). GDD = Growing degree day, GSL = Growing season length, SCS = snow cover season.</figcaption></figure>

on subalpine communities, we transplanted 10 plots from the subalpine to the alpine site with the same technique as described above (SCool). By using reciprocal transplantation, which included both warming and cooling treatments, we were able to test whether cooling and shortened growing seasons triggered opposing SOM and microbial responses compared to warming (Fig. 1).

To account for transplantation stress, 10 plots of 1 m² were transplanted horizontally at both sites, serving as control plots (coded SC and AC for Subalpine and Alpine Control). Water-permeable root barriers were installed around the plots to prevent root ingrowth from surrounding vegetation (Suseela et al., 2012), and electric fences were set up to exclude cattle and sheep.

## 2.3. Soil sampling and measurements in the transplant experiment

Seven years after the transplantations, we carried out a sampling campaign on the 40 plots in September 2023. Composite soil samples (200 g per plot) were collected by pooling smaller soil cores sampled with a soil core sampler (1 cm diameter, 0–10 cm depth), ensuring that samples were taken at least 20 cm from the plot edges to avoid edge

effects. Soil samples were immediately sieved at 2 mm and stored at –20 °C for subsequent analyses.

Soil pH<sub>H2O</sub> was determined following international standards ISO 10390:2005. The concentrations of NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, and PO<sub>4</sub><sup>3-</sup> in the soil were assessed from 0.5 M K<sub>2</sub>SO<sub>4</sub> soil extracts, and analyzed with a sequential analyzer (Gallery +, ThermoFisher). Bioavailable phosphorus was quantified using the Olsen method, which involved soil extraction with 0.5 M NaHCO<sub>3</sub>, as outlined by Fardeau et al. (1988) and Le Noë et al. (2016). Total soil organic carbon and nitrogen contents were measured with a CHN elemental analyzer (Flash EA 1112, Thermo Electron Corporation, Thermo Fisher). The chemical composition of SOM was estimated with the DRIFT method (Nicolet iS10 spectrometer, Thermo Fisher Scientific), with three indices: aliphatic, carbonyl, and aromatic, derived by integrating spectral regions between 3000 and 2750 cm<sup>-1</sup>, 1750–1670 cm<sup>-1</sup>, and 1618–1576 cm<sup>-1</sup>, respectively (Bonfanti et al. 2025b). The aliphaticity index reflects the relative abundance of CH (CH<sub>2</sub>, CH<sub>3</sub>) bonds in the SOM, the aromaticity index characterizes the relative content of aromatic rings, and the carbonyl index quantifies the relative presence of CO (carbonyl and carboxyl) bonds in SOM (Table S3).
The proportion of labile carbon was obtained after potassium permanganate oxidation (Culman et al., 2021). The carbon oxidized by permanganate (POxC) was considered to a labile carbon fraction, characterized by a short mean residence time (Table S3). Additionally, we quantified dissolved organic carbon (DOC, <0.45 µm) in a soil water extraction by using a TOC analyzer (Shimadzu TOC-V; Bonfanti et al. (2025b). We characterized water-extractable organic carbon (WEOC) by measuring absorbance at 254 nm. The absorbance, normalized to DOC content, was used to calculate SUVA, an index of SOM aromaticity (Weishaar et al., 2003). For a detailed molecular composition of WEOC, we performed 3D UV-visible fluorescence analysis (Varian Cary-Eclipse). Using four emission/excitation pairs, we quantified four molecular groups: tyrosine-like (230/310), tryptophan-like (270/350), fulvic-like (340/420), and humic-like (340/480) compounds (Bonfanti et al., 2025b). A fluorescence ratio (Ratio<sub>fluo</sub>) was calculated as the protein-like (tyrosine + tryptophan) to humic-like (humic + fulvic) compounds ratio (Table S3).

SOM thermal stability was assessed using the Rock-Eval® 6 method (Baudin, 2023) (Vinci Technologies, France). Briefly, soil sample was placed in an oven where we analyzed carbon gaseous effluents (CO, CO₂, HC) during two phases. The first phase is pyrolysis, consisting of a 3-min isotherm at 200 °C, followed by a temperature ramp from 200 °C to 650 °C at a heating rate of 30 °C per minute in a nitrogen (N₂) atmosphere. The second phase is oxidation, beginning with a 1-min isotherm at 300 °C, followed by a temperature ramp from 300 °C to 850 °C at a rate of 20 °C per minute, with a 5-min isotherm at 850 °C in a laboratory air atmosphere. During this analysis, CO and CO₂ emissions are quantified using an infrared detector, while volatile hydrocarbon effluents (HC) are measured with a flame ionization detector.

Several indices, derived from the ratios of carbon forms (CO₂, CO or HC) released during the pyrolysis and oxidation phases, serve as indicators of SOM biogeochemical stability, as recommended by (Barré et al., 2016; Sebag et al., 2016). Specifically, we used the Hydrogen Index (HI), Oxygen Index (OI), PC/TOC ratio and T50_CO₂_pyr indices. The HI and OI indices act as proxies for SOM stoichiometry, reflecting the H/C and O/C ratios, respectively. HI reflects the relative abundance of hydrogen in the SOM. Hydrogen-rich molecules may be considered as chemically labile molecules, i.e., they need less activation energy to be decomposed. The PC/TOC ratio represents the proportion of pyrolyzable carbon to total organic carbon. This index can be used as a proxy of carbon lability. T50_CO₂_pyr index corresponds to the temperature (°C) at which 50 % of CO₂ is released during the pyrolysis phase. The higher these temperatures, the more biogeochemically stable the SOC, as it requires more activation energy (Table S3).

## 2.4. In situ flux measurements and flux calculation

In 2023, we conducted CO₂ flux measurements in the field using a Gasmet DX-4015 FTIR-Multicomponent Gas Analyzer (Gasmet Technologies Inc.), connected to closed chambers with an internal volume of 1300 cm³ (Bonfanti et al., 2025c; Brummell and Siciliano, 2011). We employed two types of chambers: one transparent chamber (89 % UV transmission) and one opaque chamber (0 % UV transmission) to assess diurnal net ecosystem exchange (NEE, with positive values indicating carbon source and negative ones, indicating carbon sink) and respiration of ecosystems (Reco) respectively. These chambers were used to monitor CO₂ exchanges from collars (11 cm in diameter) inserted 10 cm into the surface soil (including vegetation) across all plots (10 AC, 10 AWarm, 10 SC), except for SCool for logistical reasons of sampling effort. While conceptually valuable, analyzing NEE on the SCool plots required excessive sampling effort and was not central to the primary objective of assessing the effects of global warming. Additionally, including these plots would have complicated data collection, as it would have been impossible to conduct all measurements on the same day, increasing exposure to meteorological variability and potentially undermining the reliability of cross-site comparisons. Flux measurements were recorded

by tracking gas concentration changes inside the chamber over a 10-min period. The FTIR-MGA collected one spectral sample every 100 ms, with the onboard software averaging gas concentrations over 20-s intervals. Measurements were conducted exclusively during sunny, cloudless conditions to ensure consistency with the transparent chambers, typically occurring once or twice per month (Table S2). All flux measurements for AC, AWarm, and SC plots were completed on the same day or one day apart (if the access road to the alpine site was accessible, i.e., snow-cleared). Fluxes were calculated using linear regression between change in gas concentration and time, where the slope of the curve indicated gas accumulation or consumption (Davidson et al., 2002). Regressions with $p > 0.05$ or $R^2 < 0.6$ were considered non-significant and interpreted as zero net flux.

Discrete NEE measurements enable the assessment of the relative contributions of extended GSL and temperature increase to changes in SOM stocks. By comparing the mean daily NEE of AC and AWarm during the alpine growing season, we isolated the effect of temperature increase alone (Fig. 2.B). Additionally, the annual cumulative NEE allowed to evaluate the combined impact of temperature rise and GSL extension, thereby estimating the specific contribution of GSL changes (Fig. 2.B).

## 2.5. Year-to-date flux calculation

Discrete flux measurements were employed to determine CO₂ ex-changes between the ecosystem and the atmosphere throughout the day, specifically to calculate the daily net ecosystem exchange (NEE). The flux measured in the transparent chamber ($T_{flux}$) was attributed to the duration of daylight, while the flux from the opaque chamber ($O_{flux}$) was associated with the nighttime duration. The relative lengths of day and night were derived from solar radiation data collected at a nearby weather station at the Lautaret Pass (https://www.romma.fr/station_24.php?id=237), <2 km from both sites at an elevation of 2100 m. For C3 plants, it has been shown that photosynthesis typically begins at light intensities around 50–100 W/m² (Björkman, 1981; Connor et al., 2011; Lambers et al., 2008). Thus, a threshold of 50 W/m², averaged over 10 min was used to distinguish between day and night. Values above this threshold indicated daylight, corresponding to the $T_{flux}$, whereas values below indicated nighttime, corresponding to the $O_{flux}$. The relative durations of day ($day_i$) and night ($night_i$) were calculated for each day. To extrapolate these measurements to an annual scale, daily NEE was computed using the $T_{flux}$ and $O_{flux}$ values from the nearest measurement day within the same timeframe, as outlined in Eq. (1).

$$NEE_{annual} = \sum_{i=1}^{n=365} day_i * T_{flux_j} + night_i * O_{flux_j} \quad (1)$$

With: NEE<sub>annual</sub> (gCO₂-C/m²) = net ecosystem exchange, i.e., annual balance between photosynthesis and respiration; $day_i$ = relative daylight period duration for day i, i.e., with $PAR \ge 50 W/m^2$; $T_{flux}$ (gCO₂-C/m²/day) = carbon flux measurement with transparent chamber during day j, nearest measurement day of i in the same period; $night_i$ = relative night period duration for day i, i.e., with $PAR < 50 W/m^2$; $O_{flux}$ (gCO₂-C/m²/day) = carbon flux measurement with opaque chamber during day j, nearest measurement day of i in the same period.

This method for calculating annual NEE involves several approximations and biases. However, it was valuable for comparative purposes, both between sites and across seasons. The high frequency of measurements helped mitigating some of the associated uncertainties. In order to avoid bias due to extrapolation, statistical comparisons of daily NEE were made on the raw values (transparent chamber) without extrapolation, with the treatment as a fixed effect and the sampling date and plot as random effects. Moreover, to strengthen these measurements, we compared them with CO₂ flux measured by Eddy covariance tower (http://meta.icos-cp.eu/resources/stations/ES_FR-CLt) located close to subalpine site, and showing a good correlation with our measurements (Fig. S7).
<figure><img src="image_4.png" /><figcaption>Fig. 2. Conceptual framework of the research questions. (A) Quantifying warming effects and acclimation/adjustment lags. Based on reduced dimensional space (through PCA method), we test if microbial features, SOM properties and soil nutrients of the AW were acclimated to the new climatic conditions by comparing it with the properties of SC. We expect the strongest differences between the AC and SC, and that the AW is somewhere on the shortest trajectory between. The distance between AC and AW represents the warming effect and the distance between AW and SC the acclimation lag. Theta angle represents over- or undercompensating warming responses. (B) Discrete gas measurements allow distinguish between the effects of temperature changes and seasonality changes on NEE.</figcaption></figure>

## 2.6. Assessment of microbial respiration response to warming via soil incubation and modelling

We conducted soil incubations to evaluate the sensitivity of heterotrophic respiration to temperature changes. For each of the 40 sampled soils (10 AC, 10 AWarm, 10 SCool, 10 SC), we incubated 15 g of dry soil in 150 mL flasks at 30, 60 and 90 % of the water holding capacity (i.e., 120 flasks) and at 0 °C, 5 °C, 10 °C, 15 °C and 20 °C (with an additional incubation at 10 °C to assess potential effects substrate availability, Fig. S8). Before each incubation period, we weighted flasks to ensure moisture stability, and we added water if necessary. Following a 24-hour acclimation period at the respective incubation temperature, we measured heterotrophic respiration using gas chromatography (Agilent µGC 490 with a Poraplot Q column). To minimize the initial CO₂ concentration in the headspace, we vented the flasks with ambient air before measuring the initial CO₂ levels with the gas chromatography (Zhou et al., 2014). We incubated the soil for varying durations—18 h at 0 °C, 6 h at 5 °C and 10 °C, 3 h at 15 °C, and 2 h at 20 °C—to achieve maximal CO₂ concentrations within the µGC's detection range (2000–4000 ppm). We measured CO₂ content at the end of each incubation period, and flux calculations were performed according to Robertson et al. (1999). More detailed information on the incubation protocol are available in Bonfanti et al. (2025b).

The response of heterotrophic respiration to temperature and moisture was modeled using Mielnick-Dugas model (Eq. (2)) (Mielnick and Dugas, 2000; Suseela et al., 2012). Model fitting was performed individually for each soil sample to manage data dependencies. Model fitting enabled the calculation of: i) the basal respiration (“a”

parameter), i.e., the respiration rate at low temperatures; ii) the Q₁₀, i.e., the temperature sensitivity of heterotrophic respiration which represents the factor by which respiration rates increase with a 10 °C rise in temperature (Hamdi et al., 2013); and iii) the moisture sensitivity of heterotrophic respiration (“m” parameter). Models were fitted by using non-linear regression (‘minpack.lm’ R package; Grothendieck, 2022, Elzhov et al., 2023).

$$R_h = (a^*e^{b^*T})^*[2.12*(\theta_v - 0.06)*(0.6 - \theta_v)^m] \quad (2)$$

With: Rh = heterotrophic respiration (µgC-CO₂/gPOxC/day); T = incubation temperature (°C); θv = incubation moisture (V/V); 0.06 and 0.6 min and max moisture measured in sites; “a”, “b”, “m” = model parameters; “a” characterized the basal respiration, “b” characterized temperature sensitivity and “m” characterized moisture sensitivity.

## 2.7. Statistical analysis

We compared the four treatments (AC, AWarm, SCool, SC) for all measured variables (SOM indices, soil nutrients, CO₂ fluxes, soil incubations). Before this comparison, we removed extreme outliers for each variable within each treatment using Grubbs' test at a significance level of 0.01. More specifically, we compared: (i) AC-AWarm to assess the warming effect (i.e. the same plant and soil communities at the beginning of the experiment and all differences due to the climatic change); (ii) SC-SCool to assess the cooling effect; (iii) SC-AWarm to evaluate acclimation to warming including potential acclimation lags (i. e. assuming that the subalpine control plots are fully acclimated and that all differences from this show an acclimation lag); and (iv) AC-SCool to
evaluate acclimation to cooling. We did so based on one-way ANOVAs for each variable across the four treatments, followed by pairwise post-hoc comparisons using Tukey's HSD post-hoc test, with *p* < 0.05 considered statistically significant after adjustment for the multiple comparisons. Statistical analyses were conducted in R version 3.5 (R Core Team, 2023) using the ade4 packages (Dray et al., 2007).

We used principal component analyses (PCA) to summarize the variation of soil properties within and between treatments (AC, AWarm, SCool, SC), allowing visualization of soil functional adjustment, following a framework developed by Bektaş et al. (2021). Separate PCAs were run for SOM properties and soil nutrients. We selected significative PCA axes with Horn's parallel analysis in 'PARAN' R package (Dinno, 2024). For both groups of soil characteristics, the two first axes were selected. Following Bektaş et al. (2021), we hypothesized that the properties of AWarm soils would be in-between those of their origin (AC) and their hypothetical final state (SC). We then calculated the percentage of adjustment remaining relative to a full adjustment under subalpine conditions, as well as the deviation from the shortest adjustment trajectory, represented as the angle (θ) between the shortest acclimation trajectory and the warming effect (Fig. 2A; see Fig. 1D in Bektaş et al., 2021). Here, we used the term 'adjustment' rather than 'acclimation' (as in Bektaş et al., 2021), since we apply the method to ecosystem properties rather than to living organism. We referred to an 'adjustment lag' when significant differences were observed between SC and AWarm (lag in warming adjustment) or between AC and SCool (lag in cooling adjustment), and 'complete adjustment' when no significant difference existed. A divergent warming or cooling effect was indicated if the observed trajectory deviated from the shortest adjustment path.

# 3. Results

## 3.1. Site specific pedoclimatic regime

The pedoclimatic conditions at both sites remained consistent from the start of the transplant experiment in 2016 to the data sampling of this study in 2023. Over the first three years (2017–2019), Khedim et al. (2023) reported GDD of 1490 °C (±121) at the alpine site and 2654 °C (±34) at the subalpine site. Our data from the subsequent four years showed slightly lower GDD values of 1459 °C (±161) and 2523 °C (±142) for the alpine and subalpine sites, respectively (Fig. 2). However, within the experimental timeframe, annual conditions can be considered stable (Fig. S1).

Similarly, the duration of snow cover (SCS) remained relatively constant throughout the experiment. Bektaş et al. (2021) reported growing season lengths (GSL) of 152 (±0) and 211 (±12) days for the alpine and subalpine sites during the first three years. Over the following four years, these periods were 142 (±12) and 218 (±15) days, respectively (Fig. 2). This consistency suggested that no extreme weather events or unusual years occurred. Seasonal conditions at both sites were typical of these ecosystems, with stable temperature and moisture levels during winter and fluctuations in summer (Fig. 2). These observations confirmed that the pedoclimatic dynamics remained stable across the experimental period.

## 3.2. Acclimation of microbial respiration sensitivity

To assess the microbial acclimation after seven years under warmer conditions, the parameters of heterotrophic respiration models provided insights into their physiological characteristics. Mielnick-Dugas models showed a good fit with respiration measurements from the incubation experiments (Table 1, see also Fig. S6). These results revealed a significant decrease in basal respiration ("a" parameter) with warming and similar but non-significant trend with cooling (Table 1). Furthermore, warming reduced the temperature sensitivity of heterotrophic respiration compared to cooling that tended to increase $Q_{10}$ values (Table 1, *p* < 0.05). Moisture sensitivity of heterotrophic respiration was impacted

<caption>**Table 1**</caption>

Mielnick-Dugas model for heterotrophic respiration rate. $Q_{10}$ corresponds to temperature sensitivity, i.e., multiplication rate of heterotrophic respiration for 10 °C increase; "m" parameter corresponds to moisture sensitivity of heterotrophic respiration; $R_{adj}^2$ is an indicator of the respiration model adjustment and $R^2$ represents the variance explained by the difference between treatments. Letters indicate significant difference with *P*value < 0.05 of Tukey post-hoc HSD test.

<table><thead><tr><th>Treatment</th><th>Basal respiration ("a")</th><th>$Q_{10}$ exp("b" * 10)</th><th>Moisture sensitivity ("m")</th><th>$R_{adj}^2$</th></tr></thead><tbody><tr><td>AC</td><td>475.8 (159) a</td><td>1.74 (0.09) a</td><td>1.15 (0.21) a</td><td>0.53</td></tr><tr><td>AWarm</td><td>181.3 (62.9) b</td><td>1.61 (0.05) b</td><td>0.88 (0.22) b</td><td>0.50</td></tr><tr><td>SCool</td><td>187.5 (58.8) b</td><td>1.63 (0.05) b</td><td>0.36 (0.19) d</td><td>0.67</td></tr><tr><td>SC</td><td>211 (80.7) b</td><td>1.47 (0.12) c</td><td>0.71 (0.25) c</td><td>0.65</td></tr><tr><td>Fstatistic</td><td>124.8</td><td>18.0</td><td>67.9</td><td></td></tr><tr><td>$R^2$</td><td>0.91</td><td>0.60</td><td>0.85</td><td></td></tr><tr><td>Pvalue</td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td><td></td></tr></tbody></table>

by transplantation, with a significant decrease of "m" parameter for both warming and cooling (Table 1, *p* < 0.05). PCA of respiration model parameters showed that microbial features in AWarm were not fully acclimated to the warmer subalpine conditions, with a lag of 30 % (Fig. 3.A). Similarly, SCool were not acclimated to cooler alpine conditions with a lag of 106 %. These acclimation lags could have consequences on ecosystem functioning, especially on SOM mineralization.

## 3.3. Warming and cooling effects on soil organic matter and nutrients contents

AC contained more SOC than SC (*p* < 0.05), and more particularly labile SOC, with higher POxC, WEOC, HI and Aliphatic index (*p* < 0.05, Table 2). Seven years after transplantation, we found significant negative warming effects on several measured variables describing SOC stocks. The SOC stocks in alpine warmed plots (AWarm, 6.3 kg/m² ± 1.1) were nearly 18 % lower than in alpine control plots (AC, 7.6 kg/m² ± 1.6) (*p* < 0.05) (Table 2, see also Fig. S2). More specifically, we found that carbon lost with warming was labile. POxC stocks were 65 % lower in AWarm (160 g/m² ± 136) than in AC plots (456 g/m² ± 167; *p* < 0.01 Table 2). WEOC stocks were 40 % lower in AWarm (5.1 g/m² ± 1.4) than AC (8.8 g/m² ± 2.0, Table 2). Mirroring this effect, SCool showed a tendency to increase POxC stocks (Table 2). WEOC contained more complex organic molecules (humic-like) in AWarm, as indicated by a Ratiofluo of 1.57 (±0.2) compared to 1.77 (±0.13) in AC (*p* < 0.05, Table 2). Cooling showed a non-significant opposite trend. The C/N ratio was higher in AC than in SC. There was neither a significant warming nor a significant cooling effect, but warming tended to decrease and cooling tended to increase it (Table 2).

For the indicators of SOM chemistry (DRIFT), warming reduced the relative proportion of aliphatic chains while it increased the relative proportion of more complex molecules such as oxygenated groups and aromatic rings (*p* < 0.01, Table 2). Cooling tended to have the opposite effect, although not significantly. For indicators of SOM stoichiometry, warming caused a reduction in the proportion of hydrogen (HI) and an increase in the proportion of oxygen (OI, *p* < 0.01, Table 2). Cooling showed a non-significant opposite trend. The majority of thermal indices indicated higher thermal stability in AWarm than AC (Fig. S3). Cooling showed an inverse trend, though not always statistically significant. Lastly, the PC/TOC ratio, as an indicator of labile, easily mineralizable carbon, showed a significant decline under warming conditions and a non-significant positive trend under cooling (*p* < 0.01, Table 2).

The PCA of SOM properties clearly separated the four treatments and two axes were sufficient for this separation (Fig. 3.B). The first axis
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Principal Component Analysis (PCA) of microbial features (A), SOM properties (B) and soil nutrients (C) across the four treatments after seven years of transplantation. Each point represents an individual plot (N = 40), while larger points indicate the barycenter of each treatment group.</figcaption></figure>

represented 50.8 % of variation, was mainly linked to Rock-Eval® indices and SOC stocks and completely separated the two control treatments, with AC characterized by high SOC and C/N and SC characterized by high OI and T50_CO2_PYR. The second axis represented 16.9 % of variation, was mainly linked to POxC, Aliphatic and WEOC and further helped separating warmed and cooled plots, with SCool tending towards higher POxC values. Based on the PCA and our conceptual framework (Fig. 1.A), we could show that the acclimation lag after cooling (71.3 %) was more important than the acclimation lag after warming (59.8 %). Both climate change treatments resulted in considerable deviations from the shortest trajectory towards acclimation, with considerable deviation angles for both cooling (36.2°) and warming (35.4°). The deviation indicated an increase in POxC and aliphatic

compounds for SCool, and a decrease in these variables for AWarm.

Soil inorganic nitrogen contents were comparable in the two control treatments with approximately 2.2–2.3 mg NO₃-N/kg and 4.1–4.4 mg NH₄-N/kg (Table 3, see also Fig. S4). In contrast, warming caused a significant overshoot of soil inorganic nitrogen contents that was observable seven years after the start of the experiment (p < 0.01), with 5.5 mg NO₃-N/kg and 15.9 mg NH₄-N/kg. Cooling showed a non-significant opposite trend. Bioavailable phosphorus was significantly higher in AC than in SC, however warming and cooling had no effects (Table 3). The two first axes of the PCA of SOM nutrients explained together 84 % of variation. However, while control plots of both sites were relatively close together, transplanted plots, and especially AWarm plots, were distant from the closest trajectory between controls (Fig. 3).
<caption>**Table 2**</caption>

SOM properties for each treatment seven years after transplantation, mean (sd), N = 10. SOC = stocks of soil organic carbon; POxC and WEOC = SOC extraction with KMnO₄ and water; SUVA₂₅₄ = WEOC absorbance at 254 nm; Ratiofluo = Proteinlike/Humiclike; HI, OI, PC/TOC = Rock-Eval® indices; Aliphatic, Carbonyl, Aromatic = DRIFT indices. Letters indicate significative difference with Pvalue < 0.05 of Tukey post-hoc HSD test.

<table><thead><tr><th>Treatment</th><th>SOC<br>kgC/<br>m<sup>2</sup></th><th>C/N</th><th>POxC<br>g/m<sup>2</sup></th><th>WEOC<br>g/m<sup>2</sup></th><th>SUVA<sub>254</sub></th><th>Ratiofluo</th></tr></thead><tbody><tr><td>AC</td><td>7.6<br>(1.6) a</td><td>13.5<br>(0.6) a</td><td>456<br>(167) a</td><td>8.8<br>(2.0) a</td><td>0.04<br>(0.02) a</td><td>1.77<br>(0.13) a</td></tr><tr><td>AWarm</td><td>6.3<br>(1.1) b</td><td>13.0<br>(0.8)<br>ab</td><td>160<br>(136) b</td><td>5.13<br>(1.4) b</td><td>0.05<br>(0.01) a</td><td>1.57 (0.2)<br>b</td></tr><tr><td>SCool</td><td>6.6<br>(0.7)<br>ab</td><td>13.0<br>(0.3)<br>ab</td><td>560<br>(181) a</td><td>8.72<br>(2.98) a</td><td>0.09<br>(0.03) b</td><td>1.6 (0.11)<br>ab</td></tr><tr><td>SC</td><td>SC 6.1<br>(0.4) b</td><td>12.8<br>(0.5) b</td><td>364<br>(218)<br>ab</td><td>4.33<br>(0.61) b</td><td>0.09<br>(0.02) b</td><td>1.44<br>(0.15) b</td></tr><tr><td>Fstatistic</td><td>3.9</td><td>2.8</td><td>9.1</td><td>13.2</td><td>15.4</td><td>8.1</td></tr><tr><td>R<sup>2</sup></td><td>0.25</td><td>0.19</td><td>0.43</td><td>0.52</td><td>0.56</td><td>0.42</td></tr><tr><td>P<sub>value</sub></td><td>0.016</td><td>0.055</td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td></tr><tr><th>Treatment</th><th>HI</th><th>OI</th><th>PC/<br>TOC</th><th>Aliphatic</th><th>Carbonyl</th><th>Aromatic</th></tr><tr><td>AC</td><td>378<br>(23) a</td><td>187<br>(5) a</td><td>0.37<br>(0.02)<br>a</td><td>0.40<br>(0.01) a</td><td>0.38<br>(0.01) a</td><td>0.22<br>(0.01) a</td></tr><tr><td>AWarm</td><td>355<br>(23) b</td><td>200<br>(13) b</td><td>0.35<br>(0.02)<br>b</td><td>0.38<br>(0.01) b</td><td>0.39<br>(0.01) b</td><td>0.22<br>(0.01) b</td></tr><tr><td>SCool</td><td>320<br>(10) c</td><td>204<br>(4) bc</td><td>0.33<br>(0.01)<br>c</td><td>0.40<br>(0.01) ac</td><td>0.38<br>(0.01) a</td><td>0.22<br>(0.01) ab</td></tr><tr><td>SC</td><td>305<br>(7) c</td><td>211<br>(6) c</td><td>0.32<br>(0.01)<br>c</td><td>0.39<br>(0.01) bc</td><td>0.38<br>(0.01) a</td><td>0.23<br>(0.01) b</td></tr><tr><td>Fstatistic</td><td>36.6</td><td>15.3</td><td>33.1</td><td>17.5</td><td>10.1</td><td>14.2</td></tr><tr><td>R<sup>2</sup></td><td>0.75</td><td>0.57</td><td>0.73</td><td>0.59</td><td>0.46</td><td>0.55</td></tr><tr><td>P<sub>value</sub></td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td></tr></tbody></table>

C). Accordingly, acclimation lags and deviation angles were large for cooling and warming (141.5 % and 336.6 %, 156.4° and 105.1 for cooling and warming respectively). This deviation from the shortest trajectory was mainly driven by soil nitrogen overshoots under warming and by soil phosphorus overshoots under cooling.

### 3.4. Warming and cooling effects on NEE

NEE throughout the year followed the typical seasonal dynamics of mountain ecosystems in both AC and SC. Winter was characterized by positive NEE, followed by a transition to carbon sequestration (negative NEE) during the first half of the growing season, and finally to positive NEE during senescence (Fig. 4.A). Note that AWarm plots contributed rarely to carbon sequestration (Fig. 4.A). Annual NEE was observed to act as source carbon under all treatments (Fig. 4C), which aligns with data from a close-by Eddy covariance tower, confirming that the area acted as a carbon source during the observation year. However, warming strongly accelerated carbon release, with AWarm loosing on average 55 gCO₂-C/m² more than AC. Net carbon loss was significantly higher in AWarm (180 gCO₂-C/m² ± 70) than in SC (110 gCO₂-C/m² ± 40) and

<caption>**Table 3**</caption>

Soil nutrients for each treatment seven years after transplantation, mean (sd), N = 10. N<sub>inorg</sub> = NO<sub>3</sub><sup>-</sup> + NH<sub>4</sub><sup>+</sup>, N<sub>org</sub> = TN - N<sub>inorg</sub>, P<sub>avail</sub> = P<sub>Olsen</sub>. Letters indicate significative difference with P<sub>value</sub> < 0.05 of Tukey post-hoc HSD test.

<table><thead><tr><th>Treatment</th><th>N<sub>org</sub><br>mg/kg</th><th>N<sub>inorg</sub><br>mg/kg</th><th>NO<sub>3</sub><sup>-</sup><br>mg/kg</th><th>NH<sub>4</sub><sup>+</sup><br>mg/kg</th><th>PO<sub>4</sub><sup>3-</sup><br>mg/kg</th><th>P<sub>avail</sub><br>mg/kg</th></tr></thead><tbody><tr><td>AC</td><td>68.6<br>(11.7) a</td><td>6.2<br>(4.4) a</td><td>2.2<br>(0.8) a</td><td>4.1<br>(3.7) a</td><td>0.23<br>(0.48) a</td><td>15.9<br>(1.8) a</td></tr><tr><td>AWarm</td><td>41.6<br>(8.5) b</td><td>21.4<br>(4.1) b</td><td>5.5<br>(1.2) b</td><td>15.9<br>(4.4) b</td><td>0.69<br>(0.69) a</td><td>16.1<br>(2.2) a</td></tr><tr><td>SCool</td><td>52.8<br>(6.4) b</td><td>4.1<br>(1.6) a</td><td>2.1<br>(0.3) a</td><td>2.1<br>(1.4) a</td><td>0.66<br>(0.31) a</td><td>10.3<br>(1.3) b</td></tr><tr><td>SC</td><td>47.5<br>(7.5) b</td><td>6.7<br>(2.7) a</td><td>2.3<br>(0.5) a</td><td>4.4<br>(2.2) a</td><td>0.3<br>(0.11) a</td><td>8.8<br>(1.2) b</td></tr><tr><td>Fstatistic</td><td>16.2</td><td>50.3</td><td>42.9</td><td>35.1</td><td>2.8</td><td>50.7</td></tr><tr><td>R<sup>2</sup></td><td>0.6</td><td>0.83</td><td>0.78</td><td>0.77</td><td>0.2</td><td>0.82</td></tr><tr><td>P<sub>value</sub></td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td><td>0.052</td><td>&lt;0.01</td></tr></tbody></table>

AC plots (55 gCO₂-C/m² ± 40, Fig. 4.A), even 7 years after the transplantation. Mean daily NEE during the snow cover season was similar across SC, AC, and AWarm plots, with daily fluxes ranging from 0.5 to 1 gCO₂-C/m²/day (Fig. 4.A). The comparison of mean diurnal NEE during the alpine growing season provides insight into the isolated impact of temperature changes, excluding the potential effects of the lengthening of the growing season. Interestingly, while the mean diurnal NEE of AWarm plots was slightly higher than that of AC plots during the alpine growing season, the difference was not statistically significant (Fig. 4.B, see also Fig. S5). Thus, this daily effect alone due to increase in mean temperature was insufficient to account for the cumulative annual difference, as a substantial portion of the cumulative NEE in AWarm plots was released outside the snow cover season and alpine growing season. This carbon release from AWarm occurred during the extension of the GS (i.e., when the alpine site was still in SCS while the subalpine/ AWarm was already in GS) (Fig. 4.C).

### 4. Discussion

Our results indicate a lag in temperature sensitivity of microbial respiration after seven years under warmed conditions. This lag had consequences on soil organic matter mineralization rates, leading to labile carbon depletion and increase in soil nutrient contents. The increase of mineralization rate was caused both by the extended growing season length and increased temperature.

#### 4.1. Microbial acclimation response

Soil incubation experiments were conducted to assess the acclimation of specific microbial traits. Results indicated slight shifts in microbial traits under warming conditions, with opposing trends observed under cooling. We found that the temperature sensitivity of heterotrophic respiration in alpine soils decreased with warming, particularly at lower temperatures, and increased with cooling. These findings aligned with several long-term warming studies that showed a decline in Q₁₀ over time (Luo et al., 2001; Allison et al., 2010; Billings and Ballantyne IV, 2013). However, laboratory incubations have shown mixed results, with some studies finding an increase in Q₁₀ over incubation time (Wang et al., 2013) or no change (Maranón-Jiménez et al., 2018). These results suggested either a depletion of labile substrates following an initial acceleration in mineralization rates (Davidson and Janssens, 2006; Melillo et al., 2002), and/or physiological acclimation within microbial communities (Bradford et al., 2008; Bárcenas-Moreno et al., 2009). The decrease of basal respiration with warming supported the second hypothesis, suggesting thermal acclimations in microbial communities. By
A

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Net ecosystem exchange (NEE) during 2023 for AC, AWarm and SC. Panel A: measured NEE values (black points, mean ± SD, n = 10) and extrapolated NEE values between measurement days based on daylight duration (green line, mean ± SD, see Fig. S6 for more details on NEE extrapolation). The grey line indicates cumulative NEE from the start of measurements (October 15, 2022, mean ± SD). Panel B: mean daily NEE during the snow cover season (SCS) and the growing season (GS). It shows also mean daily NEE of AWarm during alpine growing season, allowing to test the effect of temperature on NEE, this comparison captures the effects of temperature increase between AC and AWarm plots. Panel C: cumulative NEE for each season (SCS and GS) and cumulative NEE of AWarm during alpine GS. Letters indicate significative difference according Tukey HSD post-hoc test.</figcaption></figure>

B

<figure><img src="image_7.png" /></figure>

C

<figure><img src="image_8.png" /></figure>

comparing microbial acclimation with plant acclimation, we can better understand the causes of the adjustment lag in SOM properties. As our incubation results suggested on microbial communities, Bektaş et al. (2021) observed an acclimation lag in the plant community's annual productivity and phenological adaptation in the same transplant experiment. Consequently, the combined lack of acclimation in both microbial and plant communities, along with shifts in pedoclimatic properties, resulted in altered ecosystem properties. During the adjustment lag, some key functions are affected, such as soil fertility, SOM stocks, and related processes. This suggestion is supported by the trend of decreasing C/N ratio of SOM with warming and increase with cooling. These results suggest that warming reduces the contribution of plant-derived carbon while enhancing microbial carbon contribution.

On the other hand, the adjustment lag may be attributed to the functional limits of microbial and plant species. For instance, the carbon capture capacity of alpine plant communities may have reached its upper limit. Alpine species such as *Nardus stricta* and *Carex sempervirens* are unlikely to achieve the size and biomass volume of subalpine species like *Patzkea paniculata*, resulting in different maximum quantities and fluxes of carbon fixation between these communities. Thus, even though warming may enhance the productivity of alpine communities, their biomass input may have already reached a threshold. For microbial

communities, however, while warming may not alter their functional traits, the high abundance of available substrate coupled with reduced temperature limitations might allow them to be more active (in AWarm).

## 4.2. Warming leads to labile carbon losses and nutrient overshoot

Our findings indicate a 18 % decrease in alpine SOC stocks after 7 years of 3 °C warming. These results align with Link et al. (2003) who observed a 32 % reduction in carbon content in the 0–5 cm soil horizon five years after a transplanting experiment simulating a 5 °C warming. Similarly, Marañón-Jiménez et al. (2018) reported an 8 % decrease in SOC 7 years after 1.8 °C warming and Puissant et al. (2017) reported 14 % of SOC stocks decrease five years after 4 °C-warming. These results raised question on consequences on ecosystem functioning and ecosystem services. Optimizing ecosystem functions of SOM stocks are often viewed as a dilemma between soil carbon storage and fertility (Adhikari and Hartemink, 2016; Janzen, 2015), raising questions about whether to prioritize increasing SOM stocks or maximizing mineralization for optimal soil fertility. Our results indicate that climate change may significantly influence the balance between these two ecosystem functions, suggesting a positive feedback loop, similar to that observed
in tundra ecosystems where temperature increases lead to SOM mineralization boost and thus carbon releases (De Deyn et al., 2008; Wookey et al., 2009).

Our results allowed to further characterize the nature of the lost carbon. Different characterization methods (POxC, WEOC, DRIFT, Rock-Eval®) suggest that lost carbon can be classified as chemically labile, and thermolabile carbon. We suggest that warming reduced the climatic stabilization, i.e., the limitation of mineralization by low temperatures, enabling the mineralization of labile SOM, which requires low activation energy. Consistently, cooling and the extended SCS enhanced climatic stabilization, leading to labile carbon accumulation. This labile SOM offers a high return on investment (ROI) for microbial activity. Due to its low oxidation state, it releases substantial energy upon oxidation compared to the activation energy required for decomposition. This makes it more cost-effective for microbial activity, enabling microbial processes to persist even under lower temperatures (Gunina and Kuzyakov, 2022). Labile SOM depletion may have some consequences on ecosystem functioning and organism's activity, including reduction in the availability of organic substrates for decomposers, potentially disrupting soil food webs in alpine ecosystems and leading to a homogenization similar to those found at lower elevations (Bradford et al., 2016; Lehmann and Kleber, 2015).

Stimulation of the mineralization activity leads not only to the loss of labile carbon but also to an increase in soil nutrient contents. Our observations are consistent with previous in vitro and *in natura* studies (Bonfanti et al., 2025b; Dawes et al., 2017). The soil nutrient analysis revealed similar compositions for both control sites and suggested an over-fertility induced by warming. As the warming response deviated markedly from the adjustment trajectory, it could have consequences on ecosystem functioning, including nitrogen losses and increase in plant productivity. The soil over-fertility may result from abrupt shifts in climatic regimes rather than gradual warming or cooling. Previous studies highlighted that the warming-induced effects on soil inorganic nitrogen tend to diminish seven years after warming, suggesting that these overshoots might be transient (Dawes et al., 2017). *Marañón-Jiménez et al.* (2018) showed that seven years after continuous warming ranging from +0.5 °C to +15.9 °C, the levels of soil inorganic nitrogen (NO₃⁻ and NH₄⁺) remained elevated under low to moderate warming (+0.5 °C, +1.8 °C, and +3.4 °C). However, at higher warming levels (+8.7 °C and +15.9 °C), soil nitrogen levels were comparable to the control, suggesting that the short-term effects of warming had already adjusted seven years after an above 3.4 °C warming and that no abrupt threshold effect occurred.

### 4.3. Linearity of the adjustment response

Using a space-for-time comparison, if alpine sites stabilize under subalpine climatic conditions, they are likely to experience a loss of labile carbon (Leifeld et al., 2009). It is noteworthy how prolonged the adjustment process might be, even under the relatively abrupt experimental climate change. The fact that AWarm plots still contain more labile SOC than SC plots suggests that carbon losses are expected to persist in the coming years as the system continues along its adjustment trajectory. However, this approach does not clarify the specific nature of SOM stock responses to global warming: lagged adjustments, linear trends, or response curves that flatten but remain significant even after 3 or 7 years of warming.

By comparing our results with those of Khedim et al. (2023) from the same experiment (identical warming intensity and seasonal changes), we can evaluate whether the observed responses are merely delayed or if the effects of warming accumulate over time. Khedim et al. (2023) reported a non-significant trend of an 11 % reduction in SOC stocks and indications of increasing stability indices three years after transplantation. Seven years post-transplantation in the same experiment, these differences became more pronounced, with a marked decrease in active carbon and aliphatic compounds, alongside an increase in

biogeochemical stability. The rate of SOC stock depletion and stability changes appears to slow over time, as the decrease was 11 % over the first three years and 18 % over seven years, suggesting that the effects of warming had a “flush effect” at the beginning, accumulate over time and may plateau as the system approaches a new equilibrium.

Otherwise, our reciprocal experiment, i.e., warming and cooling, provides insights into the reversibility of warming effects (Scheffer et al., 2001). Although some symmetry is observed between warming and cooling effects, the adjustment lag is considerably greater for cooling than for warming. This implies that recovering from warming-induced consequences may require significantly more time than the warming duration itself. Labile SOC, which is depleted under warming, accumulates over extended timescales, suggesting that restoring similar conditions after warming would demand considerable time for ecosystem recovery.

### 4.4. Respective contribution of increase in temperature and seasonality changes

As well as giving us an indication of the temporal dynamics of the response to global warming, the measurement of fluxes allows us to distinguish the role of temperature and changes in phenological periods. The annual cumulative NEE differences measured through discrete flux measurements aligns with the observed decrease in SOC stocks. In 2023, cumulative NEE was approximately +100 gC/m² in the AWarm plots, while the mean carbon stock in these plots decreased by -1300 gC/m² after seven years of transplantation, i.e., appr. -185 gC/m²/year. Although NEE estimates may slightly underestimate carbon losses, both methods converge on the same order of magnitude. Our findings indicate also that while both higher temperature and longer GSL contributed to the observed changes, the second appeared to be the dominant driver of SOC depletion. To disentangle the effect of temperature from the effect of changes in phenological periods, Khedim et al. (2023) conducted an open-top chambers experiment parallel to transplantations, simulating only temperature increases while maintaining the seasonal structure constant. The results showed that the effects of warming on SOM properties were less pronounced in the OTC experiment compared to the transplantation experiment. These differences reinforce the idea that seasonal changes had a more substantial impact on SOM properties than temperature increase alone.

## 5. Conclusion

Seven years after transplantation and related 3 °C warming, our study showed a significant decrease of the alpine SOM stocks by 18 %, confirming the trends already observed after three years, while offering deeper insight into the respective contributions of temperature changes and changes in seasonality. Carbon loss was strongest in the first years after experimental warming but continued after and, from our measurements and models, is expected to continue further. The depletion of SOC stocks primarily affected labile forms, previously stabilized by lower temperatures. We found that carbon loss was caused by an increase in annual net ecosystem exchange (NEE), suggesting that increase in SOC mineralization outpaced productivity and organic carbon input. The alpine SOM mineralization boost due to experimental warming was paralleled by elevated soil nutrient (N, P) levels. Importantly, we found that both higher daily temperatures as well as longer growing season length contributed to carbon loss. Additionally, in order to disentangle further the microbial drivers of carbon loss, we experimentally explored the acclimation of alpine soil microbial respiration to a gradient of soil temperature and moisture. These soil incubations suggested that microbial communities did not fully acclimatize to warmer pedoclimatic conditions, leading to SOM destabilization.
## CRediT authorship contribution statement

Nicolas Bonfanti: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Jean-Christophe Clément: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. Tamara Münkemüller: Writing – review & editing, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. Pierre Barré: Writing – review & editing, Validation, Resources, Methodology, Investigation. François Baudin: Writing – review & editing, Validation, Resources, Methodology, Investigation. Jérôme Poulenard: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Nicolas BONFANTI reports financial support was provided by French National Research Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

This study was funded by the French Agence Nationale de la Recherche through the TransAlp project (ANR-20-CE02-0021), and was conducted with the help of the Lautaret Garden-UMS 3370 (Univ. Grenoble Alpes, CNRS, SAJF, 38000 Grenoble, France), a member of the Zone Atelier Alpes and eLTER network and of AnaEE-France (ANR-11-INBS-0001AnaEE-Services, Investissements d'Avenir frame). This research was conducted within the Long-Term Socio-Ecological Research (LTSER) platform Lautaret-Oisans, a site of the European Research Infrastructure eLTER. We thank Florence Savignac for their support with the Rock-Eval® thermal analysis of soil samples and Cindy Arnoldi for CHN and Gallery+ analysis.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.apsoil.2025.106239.

## Data availability

Data will be made available on request.

## References

Adhikari, K., Hartemink, A.E., 2016. Linking soils to ecosystem services — a global review. Geoderma 262, 101–111. https://doi.org/10.1016/j.geoderma.2015.08.009.

Allison, S.D., Wallenstein, M.D., Bradford, M.A., 2010. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340. https://doi.org/10.1038/ngeo846.

Bárcenas-Moreno, G., Gómez-Brandón, M., Rousk, J., Băăth, E., 2009. Adaptation of soil microbial communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Glob. Chang. Biol. 15, 2950–2957. https://doi.org/10.1111/j.1365-2486.2009.01882.x.

Bardgett, R.D., Bowman, W.D., Kaufmann, R., Schmidt, S.K., 2005. A temporal approach to linking aboveground and belowground ecology. Trends Ecol. Evol. 20, 634–641. https://doi.org/10.1016/j.tree.2005.08.005.

Barré, P., Plante, A., Cécillon, L., Lutfalla, S., Baudin, F., Bernard, S., Christensen, B., Eglin, T., Fernandez, J., Houot, S., Kätterer, T., Guillou, C., Macdonald, A., Oort, F., Chenu, C., 2016. The energetic and chemical signatures of persistent soil organic matter. Biogeochemistry 130, 1–12. https://doi.org/10.1007/s10533-016-0246-0.

Baudin, F., 2023. The Rock-Eval Method: Principles and Application, Willey. ed. John Wiley & Sons.

Bektas, B., Thuiller, W., Saillard, A., Choler, P., Renaud, J., Colace, M., Della Vedova, R., Münkemüller, T., 2021. Lags in phenological acclimation of mountain grasslands after recent warming. J. Ecol. 109, 3396–3410. https://doi.org/10.1111/1365-2745.13727.

Billings, S.A., Ballantyne IV, F., 2013. How interactions between microbial resource demands, soil organic matter stoichiometry, and substrate reactivity determine the direction and magnitude of soil respiratory responses to warming. Glob. Chang. Biol. 19, 90–102. https://doi.org/10.1111/gcb.12029.

Björkman, O., 1981. Responses to different quantum flux densities. In: Lange, O.L., Nobel, P.S., Osmond, C.B., Ziegler, H. (Eds.), Physiological Plant Ecology I: Responses to the Physical Environment. Springer, Berlin, Heidelberg, pp. 57–107. https://doi.org/10.1007/978-3-642-68090-8_4.

Bond-Lamberty, B., Bolton, H., Fansler, S., Heredia-Langner, A., Liu, C., McCue, L.A., Smith, J., Bailey, V., 2016. Soil respiration and bacterial structure and function after 17 years of a reciprocal soil transplant experiment. PloS One 11, e0150599. https://doi.org/10.1371/journal.pone.0150599.

Bonfanti, N., Choler, P., Khedim, N., Clément, J.-C., Barre, P., Goury, R., Baudin, F., Cécillon, L., Saillard, A., The Orchamp, C., Thuiller, W., Poulenard, J., 2025a. Drivers of soil organic carbon stocks and stability along elevation gradients. Under Reviews. Geoderma.

Bonfanti, N., Clement, J.-C., Millery-Vigues, A., Münkemüller, T., Perrette, Y., Poulenard, J., 2025b. Seasonal mineralisation of organic matter in alpine soils and responses to global warming: an in vitro approach. Eur. J. Soil Sci. 76, e70050. https://doi.org/10.1111/ejss.70050.

Bonfanti, N., Poulenard, J., Barre, P., Baudin, F., Voisin, D., Clement, J.-C., 2025c. Seasonality alters soil organic matter properties in alpine and subalpine grasslands. Ecosystems 28, 29. https://doi.org/10.1007/s10201-025-00971-y.

Bonfanti, N., Poulenard, J., Clément, J.-C., Barré, P., Baudin, F., Turtureanu, P.D., Puşcaş, M., Saillard, A., Raguet, P., Hurdu, B.-I., Choler, P., 2025d. Influence of snow cover and microclimate on soil organic carbon stability in European mountain grasslands. Catena 250, 108744. https://doi.org/10.1016/j.catena.2025.108744.

Bradford, M.A., Davies, C.A., Frey, S.D., Maddox, T.R., Melillo, J.M., Mohan, J.E., Reynolds, J.F., Treseder, K.K., Wallenstein, M.D., 2008. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol. Lett. 11, 1316–1327. https://doi.org/10.1111/j.1461-0248.2008.01251.x.

Bradford, M.A., Wieder, W.R., Bonan, G.B., Fierer, N., Raymond, P.A., Crowther, T.W., 2016. Managing uncertainty in soil carbon feedbacks to climate change. Nature Clim Change 6, 751–758. https://doi.org/10.1038/nclimate3071.

Bradford, M.A., McCulley, R.L., Crowther, T.W., Oldfield, E.E., Wood, S.A., Fierer, N., 2019. Cross-biome patterns in soil microbial respiration predictable from evolutionary theory on thermal adaptation. Nat Ecol Evol 3, 223–231. https://doi.org/10.1038/s41559-018-0771-4.

Broadbent, A.A.D., Newbold, L.K., Pritchard, W.J., Michas, A., Goodall, T., Cordero, I., Giunta, A., Snell, H.S.K., Pepper, V.V.L.H., Grant, H.K., Soto, D.X., Kaufmann, R., Schloter, M., Griffiths, R.I., Bahn, M., Bardgett, R.D., 2024. Climate change disrupts the seasonal coupling of plant and soil microbial nutrient cycling in an alpine ecosystem. Glob. Chang. Biol. 30, e17245. https://doi.org/10.1111/gcb.17245.

Brummell, M.E., Siciliano, S.D., 2011. Chapter five - measurement of carbon dioxide, methane, nitrous oxide, and water potential in soil ecosystems. In: Klotz, M.G., Stein, L.Y. (Eds.), Methods in Enzymology, Research on Nitrification and Related Processes, Part B. Academic Press, pp. 115–137. https://doi.org/10.1016/B978-0-12-386489-5.00005-1.

Budge, K., Leifeld, J., Hiltbrunner, E., Fuhrer, J., 2011. Alpine grassland soils contain large proportion of labile carbon but indicate long turnover times. Biogeosciences 8, 1911–1923. https://doi.org/10.5194/bg-8-1911-2011.

Choler, P., Bayle, A., Carlson, B.Z., Randin, C., Filippa, G., Cremonese, E., 2021. The tempo of greening in the European Alps: Spatial variations on a common theme. Glob. Chang. Biol. 27, 5614–5628. https://doi.org/10.1111/gcb.15820.

Conant, R.T., Ryan, M.G., Ågren, G.I., Birge, H.E., Davidson, E.A., Eliasson, P.E., Evans, S.E., Frey, S.D., Giardina, C.P., Hopkins, F.M., Hyvönen, R., Kirschbaum, M.U., F., Lavallee, J.M., Leifeld, J., Parton, W.J., Megan Steinweg, J., Wallenstein, M.D., Martin Wetterstedt, JÅ, Bradford, M.A., 2011. Temperature and soil organic matter decomposition rates – synthesis of current knowledge and a way forward. Glob. Chang. Biol. 17, 3392–3404. https://doi.org/10.1111/j.1365-2486.2011.02496.x.

Connor, D.J., Loomis, R.S., Cassman, K.G., 2011. Crop ecology: Productivity and Management in Agricultural Systems. Cambridge University Press.

Crowther, T.W., Todd-Brown, K.E.O., Rowe, C.W., Wieder, W.R., Carey, J.C., Machmuller, M.B., Snoek, B.L., Fang, S., Zhou, G., Allison, S.D., Blair, J.M., Bridgham, S.D., Burton, A.J., Carrillo, Y., Reich, P.B., Clark, J.S., Classen, A.T., Dijkstra, F.A., Elberling, B., Emmett, B.A., Estiarte, M., Frey, S.D., Guo, J., Harte, J., Jiang, L., Johnson, B.R., Kröel-Dulay, G., Larsen, K.S., Laudon, H., Lavallee, J.M., Luo, Y., Lupascu, M., Ma, L.N., Marhan, S., Michelsen, A., Mohan, J., Niu, S., Pendall, E., Peñuelas, J., Pfeifer-Meister, L., Poll, C., Reinsch, S., Reynolds, L.L., Schmidt, I.K., Sistla, S., Sokol, N.W., Templer, P.H., Treseder, K.K., Welker, J.M., Bradford, M.A., 2016. Quantifying global soil carbon losses in response to warming. Nature 540, 104–108. https://doi.org/10.1038/nature20150.

Culman, S.W., Hurisso, T.T., Wade, J., 2021. Permanganate oxidizable carbon. In: Soil Health Series. John Wiley & Sons, Ltd, pp. 152–175. https://doi.org/10.1002/978091189831.ch9.

Davidson, E.A., Janssens, I.A., 2006. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173. https://doi.org/10.1038/nature04514.

Davidson, E.A., Savage, K., Verchot, L.V., Navarro, R., 2002. Minimizing artifacts and biases in chamber-based measurements of soil respiration. Agric. For. Meteorol. 113, 21–37. https://doi.org/10.1016/S0168-1923(02)00100-4. FLUXNET 2000 synthesis.
Dawes, M.A., Schleppi, P., Hättenschwiler, S., Rixen, C., Hagedorn, F., 2017. Soil warming opens the nitrogen cycle at the alpine treeline. Glob. Chang. Biol. 23, 421-434. https://doi.org/10.1111/gcb.13365

De Deyn, G.B., Cornelissen, J.H.C., Bardgett, R.D., 2008. Plant functional traits and soil carbon sequestration in contrasting biomes. Ecol. Lett. 11, 516-531. https://doi.org/10.1111/j.1461-0248.2008.01164.x

Dinno, A., 2024. Paran: Horn's Test of Principal Components/Factors. https://cran.r-project.org/web/packages/paran/index.html

Dorrepaal, E., Toet, S., van Logtestijn, R.S.P., Swart, E., van de Weg, M.J., Callaghan, T. V., Aerts, R., 2009. Carbon respiration from subsurface peat accelerated by climate warming in the subarctic. Nature 460, 616-619. https://doi.org/10.1038/nature08216

Dray, S., Dufour, A.B., Chessel, D., 2007. The ade4 Package - II: Two-table and K-table Methods [WWW Document]. URL http://pbil.univ-lyon1.fr/ADE-4/article_rnew s2007.php (accessed 12.6.21).

EEA, E.E.A, 2024. European Climate Risk Assessment (Publication No. 01/2024).

Elzhov, T., Mullen, K., Spiess, A.-N., Bolker, B., 2023. Minpack.Lm: r Interface to the Levenberg-Marquardt Nonlinear Least-squares Algorithm Found in MINPACK, Plus Support for Bounds.

Ernakovich, J.G., Hopping, K.A., Berdanier, A.B., Simpson, R.T., Kachergis, E.J., Steltzer, H., Wallenstein, M.D., 2014. Predicted responses of arctic and alpine ecosystems to altered seasonality on climate change. Glob. Chang. Biol. 20, 3256-3269. https://doi.org/10.1111/gcb.12568

Fardeau, J.-C., Morel, C., Boniface, R., 1988. Pourquoi choisir la méthode Olsen pour estimer le phosphore "assimilable" des sols ? Agronomie 8, 577-584.

Garten Jr., C.T., 2008. Changes in carbon following forest soil transplants along an altitudinal gradient. Commun. Soil Sci. Plant Anal. 39, 2883-2893. https://doi.org/10.1080/00103620802432808

Gobiet, A., Kotlarski, S., Beniston, M., Heinrich, G., Rajczak, J., Stoffel, M., 2014. 21st century climate change in the European Alps—a review. Sci. Total Environ. 493, 1138-1151. https://doi.org/10.1016/j.scitotenv.2013.07.050

Grothedieck, G., 2022. nls2: Non-linear Regression with Brute Force.

Guggenberger, G., Zech, W., 1999. Soil organic matter composition under primary forest, pasture, and secondary forest succession, Región Huetar Norte, Costa Rica. For. Ecol. Manage. 124, 93-104. https://doi.org/10.1016/S0378-1127(99)00055-9

Gunina, A., Kuzyakov, Y., 2022. From energy to (soil organic) matter. Glob. Chang. Biol. 28, 2169-2182. https://doi.org/10.1111/gcb.16071

Hamdi, S., Moyano, F., Sall, S., Bernoux, M., Chevallier, T., 2013. Synthesis analysis of the temperature sensitivity of soil respiration from laboratory studies in relation to incubation methods and soil conditions. Soil Biol. Biochem. 58, 115-126. https://doi.org/10.1016/j.soilbio.2012.11.012

Hoffland, E., Kuyper, T.W., Comans, R.N.J., Creamer, R.E., 2020. Eco-functionality of organic matter in soils. Plant and Soil 455, 1-22. https://doi.org/10.1007/s11104- 020-04651-9

Global carbon and other biogeochemical cycles and feedbacks. In: IPCC, Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., et al. (Eds.), 2023. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp. 673-816. https://doi.org/10.1017/9781009157896.007. Ed. Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021; 673-816. IPCC; Canadell, J.G., Monteiro, P.M.S., Costa, M.H., da Cunha, L.C., Cox, P.M., Eliseev, A.V., Henson, S., Ishii, M., Jaccard, S., et al.

Janzen, H.H., 2015. Beyond carbon sequestration: soil as conduit of solar energy. Eur. J. Soil Sci. 66, 19-32. https://doi.org/10.1111/ejs.12194

Jobbagy, E.G., Jackson, R.B., 2000. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Appl. 10.

Khedim, N., Poulenard, J., Cecillon, L., Baudin, F., Barre, P., Saillard, A., Bektaş, B., Grigulis, K., Lavorel, S., Münkemüller, T., Choler, P., 2023. Soil organic matter changes under experimental pedoclimatic modifications in mountain grasslands of the French Alps. Geoderma 429, 116238. https://doi.org/10.1016/j. geoderma.2022.116238

Knorr, W., Prentice, I.C., House, J.I., Holland, E.A., 2005. Long-term sensitivity of soil carbon turnover to warming. Nature 433, 298-301. https://doi.org/10.1038/ nature03226

Kpemoua, T.P.I., Leclerc, S., Barré, P., Houot, S., Pouteau, V., Plessis, C., Chenu, C., 2023. Are carbon-storing soils more sensitive to climate change? A laboratory evaluation for agricultural temperate soils. Soil Biol. Biochem. 183, 109043. https://doi.org/10.1016/j.soilbio.2023.109043

Lambers, H., Chapin III, F.S., Pons, T.L., 2008. Plant Physiological Ecology. Springer Science & Business Media.

Le Noë, J., Bosc, S., Mercier, B., Azughi, A., Billen, G., Garnier, J., 2016. Accumulation ou déstockage du phosphore: des systèmes agricoles au territoire, p. 18.

Lehmann, J., Kleber, M., 2015. The contentious nature of soil organic matter. Nature 528, 60-68. https://doi.org/10.1038/nature16069

Leifeld, J., Zimmermann, M., Fuhrer, J., Conen, F., 2009. Storage and turnover of carbon in grassland soils along an elevation gradient in the Swiss Alps. Glob. Chang. Biol. 15, 668-679. https://doi.org/10.1111/j.1365-2486.2008.01782.x

Link, S.O., Smith, J.L., Halvorson, J.J., Bolton Jr., H., 2003. A reciprocal transplant experiment within a climatic gradient in a semiarid shrub-steppe ecosystem: effects on bunchgrass growth and reproduction, soil carbon, and soil nitrogen. Glob. Chang. Biol. 9, 1097-1105. https://doi.org/10.1046/j.1365-2486.2003.00647.x

Luo, Y., Wan, S., Hui, D., Wallace, L.L., 2001. Acclimation of soil respiration to warming in a tall grass prairie. Nature 413, 622-625. https://doi.org/10.1038/35098065.

Manzoni, S., Capek, P., Porada, P., Thurner, M., Winterdahl, M., Beer, C., Brüchert, V., Frouz, J., Herrmann, A.M., Lindahl, B.D., Lyon, S.W., Santrücková, H., Vico, G., Way, D., 2018. Reviews and syntheses: carbon use efficiency from organisms to ecosystems - definitions, theories, and empirical evidence. Biogeosciences 15, 5929-5949. https://doi.org/10.5194/bg-15-5929-2018.

Marañón-Jiménez, S., Soong, J.L., Leblans, N.I.W., Sigurdsson, B.D., Peñuelas, J., Richter, A., Asensio, D., Fransen, E., Janssens, I.A., 2018. Geothermally warmed soils reveal persistent increases in the respiratory costs of soil microbes contributing to substantial C losses. Biogeochemistry 138, 245-260. https://doi.org/10.1007/s10533-018-0443-0.

Melillo, J.M., Steudler, P.A., Aber, J.D., Newkirk, K., Lux, H., Bowles, F.P., Catricala, C., Magill, A., Ahrens, T., Morisseau, S., 2002. Soil warming and carbon-cycle feedbacks to the climate system. Science. https://doi.org/10.1126/science.1074153.

Mielnick, P.C., Dugas, W.A., 2000. Soil CO2 flux in a tallgrass prairie. Soil Biol. Biochem. 32, 221-228. https://doi.org/10.1016/S0038-0717(99)00150-9.

Puissant, J., Mills, R.T.E., Robroek, B.J.M., Gavazov, K., Perrette, Y., De Daniëli, S., Spiegelberger, T., Buttler, A., Brun, J.-J., Cécillon, L., 2017. Climate change effects on the stability and chemistry of soil organic carbon pools in a subalpine grassland. Biogeochemistry 132, 123-139. https://doi.org/10.1007/s10533-016-0291-8

R Core Team, 2023. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/.

Ren, S., Wang, T., Guenet, B., Liu, D., Cao, Y., Ding, J., Smith, P., Piao, S., 2024. Projected soil carbon loss with warming in constrained earth system models. Nat. Commun. 15, 102. https://doi.org/10.1038/s41467-023-04443-2.

Robertson, G.P., Coleman, D., Bledsoe, C.S., Sollins, P., 1999. In: LTER (Ed.), Standard Soil Methods for Long-term Ecological Research.

Rumpf, S.B., Gravey, M., Brönnimann, O., Luoto, M., Cianfrani, C., Mariethoz, G., Guisan, A., 2022. From white to green: snow cover loss and increased vegetation productivity in the European Alps. Science 376, 1119-1122. https://doi.org/10.1126/science.abn6697.

Scheffer, M., Carpenter, S., Foley, J.A., Folke, C., Walker, B., 2001. Catastrophic shifts in ecosystems. Nature 413, 591-596. https://doi.org/10.1038/35098000.

Schmidt, M.W.I., Torn, M.S., Abiven, S., Dittmar, T., Guggenberger, G., Janssens, I.A., Kleber, M., Kögel-Knabner, I., Lehmann, J., Manning, D.A.C., Nannipieri, P., Rasse, D.P., Weiner, S., Trumbore, S.E., 2011. Persistence of soil organic matter as an ecosystem property. Nature 478, 49-56. https://doi.org/10.1038/nature10386.

Sebag, D., Verrecchia, E.P., Cécillon, L., Adatte, T., Albrecht, X., Aubrecht, M., Bureau, F., Cailleau, G., Copard, Y., Decaens, T., Disnar, J.-R., Hetényi, M., Nyilas, T., Trombino, L., 2016. Dynamics of soil organic matter based on new Rock-Eval indices. Geoderma 284, 185-203. https://doi.org/10.1016/j. geoderma.2016.08.025.

Sun, X., Zhang, C., Zhou, Z., Kuzyakov, Y., Luo, Y., Wang, X., Qin, X., Wang, B., Li, Y., Xu, M., Cai, A., 2025. Contrasting exogenous and endogenous soil microbial carbon use efficiencies under global changes. Glob. Ecol. Biogeogr. 34, e70027. https://doi.org/10.1111/geb.70027.

Suseela, V., Conant, R.T., Wallenstein, M.D., Dukes, J.S., 2012. Effects of soil moisture on the temperature sensitivity of heterotrophic respiration vary seasonally in an old-field climate change experiment. Glob. Chang. Biol. 18, 336-348. https://doi.org/10.1111/j.1365-2486.2011.02516.x.

Wagai, R., Kishimoto-Mo, A.W., Yonemura, S., Shirato, Y., Hiradate, S., Yagasaki, Y., 2013. Linking temperature sensitivity of soil organic matter decomposition to its molecular structure, accessibility, and microbial physiology. Glob. Chang. Biol. 19, 1114-1125. https://doi.org/10.1111/gcb.12112.

Wang, G., Zhou, Y., Xu, X., Ruan, H., Wang, J., 2013. Temperature sensitivity of soil organic carbon mineralization along an elevation gradient in the Wuyi Mountains, China. PLoS One 8, e53914. https://doi.org/10.1371/journal.pone.0053914.

Weishaar, J.L., Aiken, G.R., Bergamaschi, B.A., Fram, M.S., Fujii, R., Mopper, K., 2003. Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon. Environ. Sci. Technol. 37, 4702-4708. https://doi.org/10.1021/es030360x.

Wookey, P.A., Aerts, R., Bardgett, R.D., Baptist, F., Bråthen, K.A., Cornelissen, J.H.C., Gough, L., Hartley, I.P., Hopkins, D.W., Lavorel, S., Shaver, G.R., 2009. Ecosystem feedbacks and cascade processes: understanding their role in the responses of Arctic and alpine ecosystems to environmental change. Glob. Chang. Biol. 15, 1153-1172. https://doi.org/10.1111/j.1365-2486.2008.01801.x.

Yu, C.-Q., Shen, Z.-X., Zhang, X.-Z., Sun, W., Fu, G., 2014. Response of soil C and N, dissolved organic C and N, and inorganic N to short-term experimental warming in an alpine meadow on the Tibetan plateau. Scientific World Journal 2014, 152576. https://doi.org/10.1155/2014/152576.

Zhou, W., Hui, D., Shen, W., 2014. Effects of soil moisture on the temperature sensitivity of soil heterotrophic respiration: a laboratory incubation study. PloS One 9, e92531. https://doi.org/10.1371/journal.pone.0092531.