# Snow drought alters soil microbial communities and greenhouse gas fluxes in a subalpine grassland

Nicolas Bonfanti, Jerome Poulenard, Pascal Salze, Jerome Foret, Lucie Liger, Cindy Arnoldi, Tim Goodall, Robert Griffiths, Jeremy Puissant, Jean-Christophe Clement

► To cite this version:

Nicolas Bonfanti, Jerome Poulenard, Pascal Salze, Jerome Foret, Lucie Liger, et al.. Snow drought alters soil microbial communities and greenhouse gas fluxes in a subalpine grassland. Geoderma, 2026, 465, pp.117663. 10.1016/j.geoderma.2025.117663. hal-05515959

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# Snow drought alters soil microbial communities and greenhouse gas fluxes in a subalpine grassland

<figure><img src="image_2.png" /></figure>

Nicolas Bonfanti<sup>a,b,*</sup>, Jerome Poulenard<sup>b</sup>, Pascal Salze<sup>d</sup>, Jerome Foret<sup>d</sup>, Lucie Liger<sup>d,†</sup>,
Cindy Arnoldi<sup>c</sup>, Tim Goodall<sup>e,‡</sup>, Robert Griffiths<sup>f</sup>, Jeremy Puissant<sup>c,**</sup>,
Jean-Christophe Clement<sup>a</sup>

## ARTICLE INFO

Handling Editor: Dr Naoise Nunan

Keywords:

- Mountain ecosystems
- Climate change
- Soil organic matter
- Gas flux
- Microbial activity
- Mineralization rate
- N₂O

## ABSTRACT

Snow acts as an insulating layer on soils, preserving microbial function and promoting soil organic matter (SOM) mineralization over winter. Climate change is expected to increase the frequency of winter drought in temperate mountain ecosystems leading to snow-free winter, exposing soils to freezing and drying conditions that can disrupt microbial activity and key biogeochemical processes. However, the consequences of extreme snow drought event on microbial communities and associated C and N dynamics remain poorly understood, particularly from a functional and compositional perspective. This study aimed to investigate the ecological consequences of an extreme snow drought in subalpine grasslands by experimentally excluding all winter snowfall. By isolating the effects of a snow-free winter, without the confounding influences of warming or vegetation change, we were able to trace its impacts on ecosystem functioning from winter through the subsequent spring and summer. We observed a sharp spike in N₂O emissions (+700 %) and a significant drop in CO₂ fluxes (–70 %) during the snow-free winter, measured through discrete greenhouse gas flux sampling throughout the year, including winter. These changes coincided with immediate soil freezing and were linked to shifts in microbial community composition and function, assessed at three key periods—winter, spring, and peak growing season—using a combination of DNA-based community profiling, biomass quantification, and enzymatic assays. Functional markers showed widespread declines in microbial activity, including respiration, decomposition, and ammonification, along with a compositional shift toward anaerobic taxa and increased denitrification. These functional disruptions were further reflected in SOM mineralization dynamics, characterized via infrared spectroscopy and labile carbon fractions, and in reduced nitrogen cycling, measured through NH₄⁺, NO₃⁻ content, and resin bag analyses. Although an extended growing season and compensatory microbial responses partially offset winter impacts, full functional recovery was not achieved by the end of the growing season. These findings highlight how snow-free winters, though extreme, can profoundly disrupt soil functioning, leaving lasting carry-over effects that last into subsequent seasons.

## 1. Introduction

Snow cover is a key driver of ecosystem functioning in cold, polar, and mountainous regions. However, climate change is rapidly altering snowpack characteristics, increasing the frequency of extreme events

such as winter droughts that can result in snow-free winters (Beniston et al., 2018; Notarnicola, 2022). Shorter snow cover duration and reduced snow depth directly affect soil pedoclimatic conditions by diminishing the snowpack's insulating effect and exposing soils to harsher freezing conditions (Freppaz et al., 2008; Hardy et al., 2001). In

<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, F-38000 Grenoble, France</footnote>

<footnote><sup>d</sup> Univ. Grenoble Alpes, CNRS, Jardin du Lautaret, F-38000 Grenoble, France</footnote>

<footnote><sup>e</sup> UK Centre for Ecology & Hydrology (UKCEH), Wallingford, Oxfordshire OX10 10 8BB, United Kingdom</footnote>

<footnote><sup>f</sup> Bangor University, Bangor, Gwynedd LL57 2DG, United Kingdom</footnote>

<footnote>* Corresponding author at: Univ. Savoie Mont Blanc, INRAE, CARRTEL, Thonon-Les-Bains, France.</footnote>

<footnote>** Corresponding author.</footnote>

<footnote>E-mail addresses: nicolas.bonfanti.usmb@gmail.com (N. Bonfanti), jeremy.puissant@univ-grenoble-alpes.fr (J. Puissant).</footnote>
temperate mountain regions, the subalpine belt is particularly vulnerable, with projections indicating snow depth reductions of up to 80 % by 2060 (Marty et al., 2017; Rumpf et al., 2022; Steger et al., 2013). Extreme weather events—especially winter droughts caused by persistent high-pressure systems—are becoming more frequent, leading to significant reductions in snowfall and snowpack accumulation in mountain ecosystems (Beniston, 2012; Chandanpurkar et al., 2025). Two distinct types of winter droughts have been identified: “dry snow droughts,” characterized by a lack of winter precipitation (e.g., winters 2011 and 2020 in the European Alps; Colombo et al., (2023), and “warm snow droughts,” driven by unusually high winter temperatures that cause precipitation to fall as rain instead of snow (Huning and Agha-Kouchak, 2020; Zhang et al., 2025). Understanding the ecological impacts of these abrupt winter droughts is essential to assess the vulnerability and resilience of subalpine soil microbial communities, as well as the ecosystem processes they regulate.

Snow cover, by maintaining stable temperatures near 0 °C, allows heterotrophic activity to persist through winter (Buckeridge et al., 2013; Hobbie and Chapin, 1996; Lipson et al., 1999). Previous studies estimated that winter heterotrophic respiration can contribute up to 50 % of total annual carbon emissions (Schindlbacher et al., 2014; Shi et al., 2014), and winter N₂O fluxes can reach nearly 50 % of growing season emissions (Enanga et al., 2016; Schürmann et al., 2002). Additionally, snowmelt triggers a pulse of C and N mineralization as accumulated nutrients are released (Bonfanti et al., 2025b; Clément et al., 2012; Kuzyakov, 2010). Microbial communities beneath the snowpack continue to mineralize soil organic matter (SOM) and immobilize inorganic nitrogen, which becomes available to plants following snowmelt (Ibanez et al., 2021; Legay et al., 2013; Schmidt and Lipson, 2004). The winter snowpack regulates soil C and N cycling and protects below-ground life, making it critical to study the effects of winter drought on ecosystem functioning in subalpine and alpine regions (Edwards et al., 2007).

The lack of snow cover impacts aerobic conditions (Rixen et al., 2022), microbial properties and processes, including microbial biomass (Brooks et al., 1997), microbial activity (Bokhorst et al., 2013; Piton et al., 2020), denitrification (Broadbent et al., 2024; Yanai et al., 2007), and nitrification (Groffman et al., 2001b; Jusselme et al., 2016). Soil freezing can induce microbial (Tierney et al., 2001) and root (Cleavitt et al., 2008) mortality due to cell lysis, reducing microbial immobilization of nutrients (Brooks et al., 1998) and triggering a priming effect upon thaw due to increased labile substrate availability (Gavazov et al., 2017). The absence of snow alters ecosystem gas fluxes as soil freezing reduces CO₂ emissions (Maljanen et al., 2007) while significantly increasing N₂O production (Heuchan et al., 2024; Risk et al., 2013). Bokhorst et al., (2010) and Ibanez et al., (2021) reported reduced litter decomposition in frozen soils, as well as Baptist et al., (2010) reporting higher decomposition rates in ecosystems with longer snow cover periods. This reduction in SOM decomposition due to lack of snow leads to changes in SOM properties, including labile carbon accumulation (Bonfanti et al., 2025e; Yan et al., 2024). Additionally, snow removal has been linked to substantial nitrogen losses through both leaching (Fitzhugh et al., 2001; Freppaz et al., 2008) and gaseous emissions (Ruan and Robertson, 2017).

While previous studies have examined the impact of snow reduction on carbon and nitrogen dynamics, particularly CO₂ and N₂O emissions (Brin et al., 2018; Ruan and Robertson, 2017), the microbial mechanisms driving these responses remain poorly understood in mountain soils, including both change in activity and composition (Brin et al., 2019; Luo et al., 2025). This is especially true, as recently highlighted by Hua et al. (2024), who identified a critical gap in understanding how snow cover alteration affects microbial communities, particularly bacterial and fungal abundance and composition, thereby limiting insights into the mechanisms underlying both immediate and long-term ecosystem functional responses. Although snow removal experiments have shown effects on winter gas fluxes (e.g. CO₂, N₂O), it remains

unclear whether winter-induced changes in microbial community traits persist into the growing season, potentially generating carry-over effects on ecosystem functioning.

Based on these gaps, our study focused specifically on microbial trait changes as causal mechanisms, and on their potential seasonal legacy. We simulated an extreme snow drought by experimentally maintaining a snow-free winter using an automated shelter system, compared to a control plot with natural snow cover, in a subalpine grassland. Since the aim of our study was to simulate an extreme winter drought event, we conducted a single-year experiment focused specifically on microbial mechanisms. As highlighted by Jones et al. (2025), single-year studies can yield robust insights when grounded in mechanistic understanding and can significantly contribute to *meta*-analyses and model development. We hypothesized that: (1) snow-free winters alter microbial community composition and functional traits that regulate litter decomposition and greenhouse gas emissions (CO₂ and N₂O); and (2) even after snowmelt, these winter-induced microbial shifts partly persist and continue influencing carbon and nitrogen cycling into spring and summer.

## 2. Materials & methods

### 2.1. Study site & turf sampling

The sampling site was located in the French Alps (45.040492, 6.419922), near the Lautaret Pass, at an elevation of 1920 m a.s.l. (Fig. S1). The southeast-facing site rested on a bedrock of Wurmian moraines over sandstone flysch. During the summer months, extensive grazing activities involving sheep and cattle took place in the area. The plant community was dominated by *Patzkea paniculata* (EUNIS code: E4.331), and the soil type was classified as Dystric Cambisols (see Table S1). The vegetation period spanned approximately 200 days, with an average precipitation of 490 mm during this time (for more details on sites, see Bonfanti et al., (2025b)).

To characterize the thermal regime, we used growing degree days (GDD) to represent the annual accumulation of daily temperatures above 1 °C, and freezing degree days (FDD) for daily temperatures below 0 °C. Phenological periods were defined by: snow season length (SSL), the annual number of days with snow cover; freezing season length (FSL), the annual number of days with mean daily temperatures below -1 °C; and growing season length (GSL), the annual number of days without snow cover and with mean daily soil temperatures above 1 °C. At the study site, the 10 years – average GDD and FDD were 2 523 °C (± 142) and 0 °C (± 0), respectively. The average SSL was 148 (± 15) days, FSL was 0 day, and GSL was 218 (± 15) days.

In September 2022, we collected 12 soil turfs, each measuring 40 × 40 cm and 20 cm depth, randomly distributed across the study area (~1400 m²). These plots were subsequently transplanted into the nearby cryotron experimental device (see § 2.2 – Cryotron device and Figs. S1 & S2). On the same day, six soil cores (Ø4 cm) were collected from randomly selected locations within the study area, specifically from a depth of 0 to 10 cm. These core samples were intended to represent local conditions and were used to characterize initial soil properties.

### 2.2. Cryotron device & soil sampling

The “Cryotron” is an automated device developed by Lautaret Garden (CNRS/UGA) and AnaEE France research infrastructure (www.ardae.fr). This device allows to simulate *in situ* snow reduction (Figs. S1 & S2). The system consists of two 4 m × 4 m boxes: a “control” box (hereafter coded *W snow* for winter with snow) and a “treatment” box (*W free*, for snow-free winter). Above the treatment box, a mobile roof is operated by two probes—one for precipitation (RB-RW2-230 V) and one for temperature (Hygrovue 10). An acquisition unit (Campbell Scientific CR6) controls a single-phase asynchronous motor (230 V) that
extends or retracts the roof using a winch. When the probes detect precipitation during more than 15' and temperatures below 1.5 °C, the roof automatically closes; after a 1-hour interval, if precipitation stops or the temperature rises above 1.5 °C, the roof reopens. As a result, soils in the *W_free* box remain uncovered by snow throughout the winter and exposed to precipitation as rain, while soils in the *W_snow* box are naturally covered by snow and exposed to all precipitation (rain and snow).

Six turfs were transplanted in each box (Figs. S1 & S2). The Cryotron was located at a similar exposure and elevation as the study site. Water-permeable geotextile was used to surround the turfs, preventing ex-changes with the surrounding soil. To minimize edge effects, turfs were positioned over one meter from the box edges (Patel et al. (2018); Fig. S2). Each turf was equipped with temperature and moisture probes installed 5 cm below the surface to continuously monitor soil conditions. The experiment spanned one hydrological year, from September 2022 to October 2023. Three soil sampling campaigns were conducted at key intervals: at the end of winter, just before snowmelt (March 29, 2023; WI = winter), immediately post-snowmelt (April 27, 2023; SP = spring (or vernal transition according to Groffman et al. (2012) and Contosta et al. (2017)), and at the end of the growing season (after one year of experimentation: September 6, 2023; SU = summer). Soil samples from the 0–10 cm horizon were collected from the six treatment turfs and six control turfs, with each sample consisting of approximately fifteen pooled cores (Ø1 cm) per turf. This represented a total of 36 soil sam-ples: 6 turfs*3 dates*2 treatments. A subsample was used to measure gravimetric soil water content and the rest of the sample was used for the characterization of soil and microbial properties (Table S4).

## 2.3. Discrete gas flux measurements

During the experiment, we measured greenhouse gas (GHG) fluxes using a Gasmet DX-4015 Fourier Transform Infrared-Multicomponent Gas Analyzer (FTIR-MGA; Gasmet Technologies Inc., SISTEC, France) connected to closed chambers with a 1.2 L internal volume (Brummell and Siciliano, 2011). Two types of chambers were employed: a transparent chamber (89 % UV transmission) and an opaque chamber, to monitor GHG emissions from collars (Ø13 cm) inserted 10 cm into the surface soil for each turf (6 treatment and 6 control, see Fig. S2). Transparent chambers allowed the measurement of diurnal net ecosystem exchange (NEE), i.e., the balance between photosynthesis and ecosystem respiration. Opaque chambers measured the ecosystem respiration, i.e., sum of heterotrophic and autotrophic respiration.

Flux measurements were obtained by tracking gas concentration changes within the chamber over a 10-minute period. The FTIR-MGA collected one spectral sample every 100 ms, with onboard software recording gas concentrations averaged over 20-second intervals (Bonfanti et al., 2025c). Measurements alternated between transparent and opaque chambers and between treatment and control turfs. Mea-surements were conducted exclusively on sunny, cloudless days to maintain consistency with the transparent chambers, typically once or twice per month (18 measurement days over the study year). CO₂ and N₂O, fluxes were calculated using linear regression of the change in gas concentration over time, with the slope of the curve representing gas accumulation or consumption (Davidson et al., 2002). Regressions with p > 0.05 or R² < 0.6 were considered non-significant and interpreted as zero net flux.

During the snow-covered season, as the control turfs were under snow, chambers remained installed to the collars throughout the winter. Tubes connected the Gasmet to the chambers crossed the snow, allowing measurements after purging with ambient air. These chambers were positioned immediately after the first substantial snowfall. Consequently, although snow was covering the ground and the gas chambers, there was no snow accumulation inside the gas chambers. Three turfs were equipped with transparent chambers and three with opaque chambers. In the treatment box, both transparent and opaque chamber

measurements were conducted on each turf.

## 2.4. Litter bags and resin bags

Leaf litter decomposition rates were measured using litter bags (Bernard et al., 2019). Freshly dried Patzkea paniculata leaves (1.000 g) of the study site were inserted in 5 × 5 cm nylon bags with a mesh of 0.5 mm (Ibanez et al., 2021). Two bags were placed on the soil surface in each turf immediately after transplantation. After one year, bags were retrieved, and the remaining litter was weighed. Mass loss was then used to calculate the decomposition rate.

In-situ availability of NH₄⁺ and NO₃⁻ was assessed using ion exchange resin bags (Amberlite IRN150, VWR International S.A.S., Fontenay-sous-Bois, France) (Legay et al., 2014). In each turf, resin bags were inserted into the soil (5–10 cm depth at a 45° angle) during the winter period (November to March) and the snowmelt season (March–April). The resin bags, made of 5 × 5 cm nylon and containing 5.0 g of mixed anion-cation exchange resin, were then retrieved. Nitrate and ammonium were extracted from the resins with 1 M K₂SO₄ and quantified using a colorimetric method on a sequential analyzer (Gallery Water +, Ther-moFisher Inc.). This approach allowed us to assess in situ nitrification and ammonification fluxes.

## 2.5. Soil organic matter characterization

Soil organic matter functional groups were analyzed on dry soil using DRIFT spectroscopy with a Nicolet iS10 spectrometer (Thermo Fisher Scientific). Spectra were recorded across a range of 400 cm⁻¹ to 4000 cm⁻¹ with a resolution of 0.4 cm⁻¹. These spectra were corrected for atmospheric interferences (H₂O and CO₂) and adjusted using a reference region (4000–3950 cm⁻¹), setting the minimum absorbance value to zero. Spectrum integration enabled calculation of relative indices, including the aliphatic index (CH₂, CH₃) between 3000 and 2750 cm⁻¹, the carbonyl and carboxyl index (C=O) between 1750 and 1670 cm⁻¹, and the aromatic index (C=C) between 1618 and 1576 cm⁻¹. These regions were selected for their relevance to carbon functional groups and their low sensitivity to soil mineralogy (Bonfanti et al., 2025a; Soucémarianadin et al., 2019).

Water-extractable organic carbon (WEOC) was quantified by extracting the fresh soil with distilled water (shaking at 250 rpm for 20 min) and then filtering the extract through a 0.45 µm GF/F glass fiber filter. Non-purgeable organic carbon (NPOC) was measured in a sub-sample using a TOC analyzer (Shimadzu TOC-V), calibrated within a range of 0–20 mg C.L⁻¹. Absorbance at 254 nm was measured on a separate subsample to calculate the specific ultraviolet absorbance (SUVA) as the ratio of absorbance at 254 nm to NPOC (Weishaar et al., 2003). The SUVA is associated with aromaticity and serves as an indicator of WEOC quality, where higher values suggest greater microbial utilization of organic carbon (Kalbitz et al., 2003).

The chemical nature of the WEOC was analyzed using three-dimensional UV-visible spectrofluorescence (Varian Cary-Eclipse). The samples were placed in a high-precision quartz suprasil cell (Hellma Analytics) and excited with wavelengths ranging from 230 to 350 nm in 10 nm increments. Fluorescence was measured between 250 and 600 nm for each excitation wavelength. The emission spectra were simulated using a linear combination of log-normal functions (Siano and Metzler, 1969) via a constrained least squares approach to determine the center and width of user-defined bands. This method allowed for the detection of protein-like fluorophores, whose emissions are often masked by Rayleigh or Raman scattering. Using four excitation/emission pairs, we quantified four molecular groups: tyrosine-like (230/310), tryptophan-like (270/350), fulvic-like (340/420), and humic-like (340/480) compounds. The fluorescence ratio (Fluo index) was calculated as the sum of the protein-like (tyrosine + tryptophan) compounds divided by the sum of the humic-like (humic + fulvic) compounds (Bonfanti et al., 2025b).

To estimate the proportion of labile carbon, we used the potassium
permanganate extraction method described by Culman et al. (2021). The carbon oxidized by permanganate (POxC) corresponds to the active carbon fraction, which is characterized by a short mean residence time.

## 2.6. Soil nutrients

Soil NO₃⁻ and NH₄⁺ contents were extracted from fresh soil using a 0.5 M K₂SO₄ solution and quantified via colorimetric methods using a sequential analyzer (Gallery Water +, ThermoFisher Inc.). Bioavailable phosphorus was assessed using the Olsen method, which involves extracting soil with 0.5 M NaHCO₃ (Fardeau et al., 1988; Le Noë et al., 2016). Total organic carbon and total nitrogen contents were determined using a CHN elemental analyzer (Flash EA 1112, Thermo Electron Corporation, ThermoFisher Inc.).

## 2.7. Exoenzyme activities

We measured eight extracellular soil enzymes that catalyze key C-, N- and P-cycle processes. These included hydrolytic exoenzymes, β-glucosidase (GLU), chitinase (CHIN), phosphatase (PHO), cellulase (CEL), hemicellulase (HEM), leucine amino peptidase (LEU), and α-glucosidase (ALP). We used fluorogenic methods with 4-MUB (4-methylumbelliferone) and 7-AMC (7-amino-4-methyl coumarin), as described in Puissant et al. (2019). Enzymatic reactions were incubated in the dark for 3 h at 15 °C, with fluorometric measurements taken every 30 min using a Varioskan Flash spectrophotometer set to 330 nm and 342 nm for excitation, and 450 nm and 440 nm for emission for the 4-MUB and 7-AMC substrates, respectively. Each sample was run in triplicate (sample + buffer + substrate), and a quenched standard (sample + buffer + 4-MUB or 7-AMC) was included. Control samples, containing only the 4-MUB- or 7-AMC-linked substrate and the buffer solution, were used to monitor fluorescence evolution in the absence of enzyme degradation over the course of the assay. Stoichiometric enzyme ratios were calculated using the direct ratio method, as recommended by Puissant, (2025), such as C:N = β-glucosidase/leucine aminopeptidase and C:P = β-glucosidase/phosphatase. We normalized enzyme activity per microbial biomass content to calculated specific enzyme activity (activity per g of biomass), which reflects resource allocation, either toward the degradation of recalcitrant substrates or microbial growth (Raiesi and Beheshti, 2014).

## 2.8. Microbial biomass

Soil microbial biomass was quantified as microbial carbon (C<sub>MB</sub>) and microbial nitrogen (N<sub>MB</sub>) using the chloroform fumigation-extraction method (Brookes et al., 1985; Vance et al., 1987) on subsamples of 10 g of freshly sieved (2 mm) soil. A conversion factor of 0.45 was applied to calculate microbial carbon and microbial nitrogen.

## 2.9. Microbial composition

DNA extraction and sequence processing were performed as Knight et al., (2024). Briefly, total DNA were extracted from 200 mg of soil stored at -20 degrees until extraction using the DNeasy PowerSoil Pro Kit, following the manufacturer's instructions (Qiagen, UK). Prokaryotic and fungal communities were assessed by sequencing the V4-5 region of 16S rRNA genes using the primers 515 forward GTGY-CAGCMGCCGCGGTAA and 806 reverse GGACTACNVGGGTWTCTAAT (Walters et al., 2015), and the primers fITS7 (GTGARTCATC-GAATCTTTG) and ITS4 (TCCTCCGCTTATTGATATGC) coding the ITS2 region (Ihrmark et al., 2012), respectively. We followed the PCR protocols of the Earth Microbiome Project (Thompson et al., 2017), and sequencing was performed using a two-step Nextera approach on the Illumina MiSeq platform with V3 chemistry (Illumina). We used the DADA2 pipeline (Callahan et al., 2016) in R to trim, quality-filter denoise and dereplicate the sequences, for generation of ASV tables

and to assign taxonomies. The UNITE dynamic database 25.07.2023 (Abarenkov et al., 2024) and SILVA SSU r138.1 (Quast et al., 2013) were used for fungal and bacterial taxonomic assignment, respectively, with a minimum bootstrapping value of 80. Samples were rarefied to 10,500 reads for bacteria and to 10,000 reads for fungi.

To identify microbial species that were more prevalent in one treatment group than in others, we performed an Indicator Species Analysis (De Cáceres et al., 2010). After taxonomic assignment, we analyzed their functional roles in carbon and nitrogen cycling by using Funguild database for fungi (Nguyen et al., 2016) and Bactotraits database for bacteria (Cébron et al., 2021), or by using current findings on functional role of specific taxon (e.g., Cannon and Kirk, (2007); Quaedvlieg et al., (2014); Tedersoo et al., (2014); Pölme et al., (2020).

## 2.10. Statistical analysis

For each variable within each treatment (6 turf replicates), we removed extreme outliers for each variable within each treatment using Grubbs' test at a significance level of 0.01. We tested the effects of treatment (with winter snow cover = 'W_snow' and without snow = 'W_free'), sampling date (winter = 'WI', spring/post snowmelt = 'SP' and summer/growing season = 'SU'), and their interactions on response variables: SOM properties, nutrient contents, discrete flux measurements (CO₂ & N₂O), microbial enzymatic activity, microbial biomass (C and N), and microbial alpha diversity indices. We used linear mixed-effect models for each variable, followed by pairwise comparisons using Tukey's HSD post-hoc test, with *p* < 0.05 considered statistically significant, after adjustment for multiple comparisons. Turf ID was included as a random effect. Log or square-root transformations were used to respect the conditions of residuals normality and homoscedasticity. Statistical analyses were conducted in R version 3.5 (R Core Team, 2023), using lmerTest package (Kuznetsova et al., 2017).

Non-metric multidimensional scaling (NMDS) was used to visualize OTU beta diversity for fungi and bacteria across all samples (with or without snow across the three sampling seasons). Statistical differences between groups (season × treatment) were assessed using analysis of similarities (ANOSIM) and considered significant at *p* < 0.05. To summarize and analyze variable covariance, we performed principal component analysis (PCA), incorporating soil properties (SOM and nutrients) and microbial properties (enzymatic activities and microbial biomass stoichiometry). PCA were run using FactoMineR package (Husson et al., 2023). The effects of season and snow-free winter on variable covariance were tested using a permutational analysis of variance approach (PERMANOVA) with the 'vegan' package (Oksanen et al., 2022). To illustrate the effect size of the snow-free winter, we measured the Euclidean distance on the first two dimensions of the PCA between the barycenters of the two treatments.

# 3. Results

## 3.1. Pedoclimatic regime

Reference turfs (W_snow) followed a typical seasonal cycle, with stable conditions during the snow season (~0.5 °C, 0.4 V:V). A continuous winter snow cover lasted 152 days, while the growing season extended for 213 days. The soil accumulated 2430 °C of growing degree day (GDD, i.e., the sum of mean daily temperature above 1 °C) of and remained unfrozen throughout the study year (Fig. 1). Turfs with snow-free winter (W_free) also exhibited a strong effect of season but experienced prolonged and intense soil freezing during winter (only one freeze/thaw cycle), with 87 days of continuous freezing and a cumulative FDD of -108 °C (freezing degree day, i.e., the sum of mean daily temperature below 0 °C). The growing season was longer than in W_snow, lasting approximately 256 days and accumulating 2777 °C GDD.

The winter drought treatment resulted in the exclusion of 241 mm
<figure><img src="image_3.png" /></figure>

<table><thead><tr><th>Treatment</th><th>GDD (°C)</th><th>FDD (°C)</th><th>SSL (day)</th><th>FSL (day)</th><th>GSL (day)</th></tr></thead><tbody><tr><td>Snow-free winter (W_free)</td><td>2777 (± 152)</td><td>-108 (± 18.5)</td><td>0 (± 0)</td><td>87 (± 12)</td><td>256 (± 5)</td></tr><tr><td>Control (W_snow)</td><td>2430 (± 174)</td><td>0 (± 0)</td><td>152 (± 1.5)</td><td>0 (± 0)</td><td>213 (± 2)</td></tr></tbody></table>

**Fig. 1.** Pedoclimate monitoring throughout the study year. Soil moisture is not represented when the soil is frozen because the measurement was not representative of actual conditions. Vertical black lines mark soil sampling dates: winter under snow (WI), post-snowmelt (spring, SP), and end of the growing season (summer, SU). The table displays the thermal regime as characterized by GDD (annual sum of mean daily temperatures > 1°C), FDD (annual sum of mean daily temperatures < – 1°C), SSL (snow cover duration), FSL (days with mean temperature < – 1°C), and GSL (growing season length). Horizontal red lines represent the limits of 1 °C and –1 °C used to calculate GSL, FSL, FDD and GDD.

water equivalent of snowfall between November 1st, 2022, and April 15th, 2023—equivalent to approximately 50 % of the precipitation typically received during the growing season. Nearly all winter precipitation fell as snow, with only 30 mm of rainfall recorded during this period, indicating that our experiment simulated a “dry snow drought”. Soil moisture was and remained significantly lower than in W_snow for 1.5 months after thawing, indicating that the experiment induced a lasting alteration in the annual soil water regime (Fig. 1).

## 3.2. CO₂ & N₂O emissions

Snow-free winter led to significantly higher N₂O emissions during both winter and summer (Fig. 2A, p < 0.01). In control turfs, N₂O fluxes remained stable year-round, averaging ~38.4 µg N₂O·N·m⁻²·day⁻¹, with a peak during the snowmelt period (77 µg N₂O·N·m⁻²·day⁻¹). In contrast, N₂O fluxes in turfs with snow-free winter were highly variable, particularly in winter, reaching up to 1.5 mg N₂O·N·m⁻²·day⁻¹. Elevated emissions persisted into the following summer, suggesting prolonged effects of altered soil conditions on N₂O dynamics (Fig. 2A & S3).

Both diurnal NEE (flux under transparent chambers) and ecosystem respiration (flux under opaque chambers) were lower during the winter period for the snow-free winter turfs (Fig. 2B & S3). However, during spring, carbon fluxes were higher in the snow-free winter turfs compared to controls. During the growing season, NEE of snow-free winter turfs was closer to zero than in control turfs (Fig. 2B).

## 3.3. Annual litter decomposition rate

Litterbags provided an annual integrative measure of leaf litter decomposition rates and showed significant lower values in turfs exposed to snow-free winter compared to reference turfs (p < 0.01), with rates of 0.41 ± 0.11 µg litter·day⁻¹ and 0.68 ± 0.08 µg litter·day⁻¹, respectively (Table 1).

## 3.4. SOM properties

SOM properties exhibited a strong seasonal effect, with significant differences across sampling dates for TOC, TN, DRIFT index, POxC, SUVA, and Fluo<sub>index</sub> (p < 0.05, Table 2, Fig. 3A). During winter, control turfs contained a high proportion of labile organic matter (POxC, aliphatic index, Fluo index), which declined throughout the year. Snow-free winter had no effect on TOC, TN, or SOM chemistry but decreased POxC content, SUVA, and Fluo<sub>index</sub> during the winter and the spring (Table 2, Fig. 3A).

## 3.5. Soil nutrient dynamics

Both soil NO₃⁻ and NH₄⁺ showed strong seasonal effect in both treatments. Soil NO₃⁻ decreased over the year, while NH₄⁺ increased (p < 0.01, Table 3, Fig. 3B). Snow-free winter reduced soil nitrate content in winter (11.7 µg N·g<sub>soil</sub>⁻¹ ± 2.1) compared to control turfs (16.1 µg N·g<sub>soil</sub>⁻¹
A

<figure><img src="image_4.png" /></figure>

<table><caption>Diurnal N<sub>2</sub>O flux (µgN-N<sub>2</sub>O/m<sup>2</sup>/day)</caption><thead><tr><th>Treatment</th><th>Winter (WI)<br>N=5</th><th>Spring (SP)<br>N=3</th><th>Summer (SU)<br>N=5</th><th>Year<br>N=14</th></tr></thead><tbody><tr><td>W_free</td><td>404 (± 208)</td><td>17 (± 54)</td><td>468 (± 442)</td><td>357 (± 393)</td></tr><tr><td>W_snow</td><td>73 (± 239)</td><td>40 (± 102)</td><td>24 (± 72)</td><td>50 (± 178)</td></tr></tbody><tfoot><tr><td></td><td colspan="2">Treat *** - Date ***<br>Treat x Date ***</td><td colspan="2">Treat *** - Date *<br>Treat x Date **</td></tr></tfoot></table>

B

<figure><img src="image_5.png" /></figure>

<table><caption>Diurnal NEE (gC-CO<sub>2</sub>/m<sup>2</sup>/day)</caption><thead><tr><th>Treatment</th><th>Winter (WI)<br>N=5</th><th>Spring (SP)<br>N=3</th><th>Summer (SU)<br>N=5</th><th>Year<br>N=14</th></tr></thead><tbody><tr><td>W_free</td><td>0.38 (± 0.10)</td><td>1.53 (± 0.38)</td><td>-1.08 (± 1.41)</td><td>-0.05 (± 1.43)</td></tr><tr><td>W_snow</td><td>1.32 (± 1.18)</td><td>0.98 (± 0.17)</td><td>-1.97 (± 0.44)</td><td>0.08 (± 0.89)</td></tr></tbody><tfoot><tr><td></td><td colspan="2">Treat * - Date NS<br>Treat x Date NS</td><td colspan="2">Treat ** - Date *<br>Treat x Date NS</td></tr></tfoot></table>

**Fig. 2.** GHG emissions throughout the study year. A. Mean (±SD) diurnal soil N₂O emissions (dotted lines; soil T° records are presented in the background with continuous lines) measured via discrete measurements. B. Diurnal NEE flux measured via discrete measurements. Comparison of the NEE for winter snow-free turfs relative to control turfs on given days. Colors represent measurement periods (refer to Fig. 1 for period definitions). The red curve indicates the linear mixed model outcomes with turf number as a random effect. Tables display the mean flux (±SD) for each season and treatment. Statistical results show linear mixed model outcomes with turf as a random effect (*: p < 0.05; **: p < 0.01; ***: p < 0.001).

<table><caption>**Table 1**
Leaf litter decomposition rate integrating over the study year using litter bags. (*: p < 0.05; **: p < 0.01; ***: p < 0.001).</caption><caption>Leaf litter decomposition rate over a year (litterbag) – µg litter.day<sup>-1</sup></caption><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>W_free</td><td>0.414 (±0.11)</td><td>Treat ***</td></tr><tr><td>W_snow</td><td>0.676 (±0.084)</td><td></td></tr></tbody></table>

± 0.8), and increased soil ammonium content during the growing season, but not significantly in winter (Table 3). Resin bag surveys, conducted in winter and spring only, revealed higher NO₃⁻ and NH₄⁺ fluxes in spring compared to winter for both treatments (Table 3). Snow-free winter had no effect on NO₃⁻ flux but tended to decrease NH₄⁺ flux (Table 3). Available P showed seasonal patterns, peaking in winter and decreasing over the year. Snow-free winter reduced this seasonal effect on available P by decreasing the P<sub>Olsen</sub> peak during winter (Table 3, Fig. 3B).

### 3.6. Soil microbial biomass

Microbial biomass peaked in winter for both treatments. Microbial biomass C declined after winter, while microbial biomass N sharply

increased during snowmelt, leading to a pronounced drop in the microbial C:N ratio from 20 to 6 (Table 4, Fig. 3D). Snow-free winter turfs showed a tendency to reduce microbial biomass (both C and N) and to increase the microbial C:N ratio during winter and spring, though this effect disappeared in summer. Snow-free winter reduced the seasonal variability of microbial C while amplifying the seasonal effect of microbial N and the microbial C:N ratio (Table 4, Fig. 3D).

### 3.7. Soil microbial enzymatic activity

Potential enzymatic activities were influenced by both season and treatment (Table 4, Fig. 3C). Enzymes involved in carbon degradation (ALP, CEL, CHIN, GLU, and HEM) decreased from winter to spring but increased during the growing season. Snow-free winter significantly reduced enzymatic activity, particularly at the end of winter for all analyzed enzymes (p < 0.05). This effect temporarily disappeared during snowmelt but re-emerged during the growing season for ALP, CEL, CHIN, and HEM. Protease activity (LEU) increased throughout the year, and snow-free winter tended to limit this activity, especially during winter (Table 4, Fig. 3C). Enzymatic activity involved in the phosphorus cycle (mono ester phosphatase, PHO) declined during the growing season, with snow-free winter showing a non-significant reduction (Table 4, Fig. 3C). Enzymatic activity ratios (C:N and N:P) varied seasonally but remained unaffected by snow-free treatment (Table 4). The seasonal pattern of specific enzyme activity showed an increase
<caption>**Table 2**</caption>

Soil organic matter properties throughout the study year and the effects of snow-free winter. TOC and TN represent total organic carbon and nitrogen contents respectively. Aliphatic, Carbonyl, and Aromatic are DRIFT indices assessing SOM chemistry. POxC (permanganate oxidizable carbon) serves as a proxy for labile and available carbon. WEOC is the water-extractable organic carbon characterized by its absorbance at 254 nm (SUVA, an indicator of aromaticity, where higher values suggest increased microbial utilization of organic carbon) and by its fluorescence index (the ratio of protein-like to humic-like molecules). Significant differences among treatments and sampling dates were assessed using a linear mixed-effects model, with significance levels: ***p < 0.001, **p < 0.01, *p < 0.05. Letters indicate significant difference after post-hoc Tukey HSD test.

<table><thead><tr><th></th><th>Transplantation</th><th>Treatment</th><th>End-winter<br>WI</th><th>Post-snowmelt<br>SP (spring)</th><th>End-summer<br>SU</th><th>Date and<br>treatment effect</th></tr></thead><tbody><tr><td>SOM characterization</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>TOC</td><td>60.4</td><td>W_free</td><td>67.6 (±3.3) ab</td><td>68.2 (±5.5) ab</td><td>64.8 (±7) ab</td><td>Treat NS – Date **</td></tr><tr><td>mg C.g<sub>soil</sub><sup>-1</sup></td><td>(±0.7)</td><td>W_snow</td><td>78.9 (±16) b</td><td>63.2 (±9.8) a</td><td>59.7 (±4) a</td><td>Treat x Date **</td></tr><tr><td>TN</td><td>5.0</td><td>W_free</td><td>5.3 (±0.2) ab</td><td>5.3 (±0.1) ab</td><td>5.1 (±0.6) a</td><td>Treat NS – Date **</td></tr><tr><td>mg N.g<sub>soil</sub><sup>-1</sup></td><td>(±0.5)</td><td>W_snow</td><td>6.3 (±1.2) b</td><td>5.1 (±0.7) a</td><td>4.8 (±0.2) a</td><td>Treat x Date **</td></tr><tr><td>C:N</td><td>12.03<br>(±0.15)</td><td>W_free</td><td>12.8 (±0.2) a</td><td>13.1 (±0.6) a</td><td>12.6 (±0.6) a</td><td>Treat ** – Date NS</td></tr><tr><td></td><td></td><td>W_snow</td><td>12.5 (±0.4) a</td><td>12.3 (±0.7) a</td><td>12.4 (±0.4) a</td><td>Treat x Date NS</td></tr><tr><td>Aliphatic</td><td>0.418<br>(±0.013)</td><td>W_free</td><td>0.421 (±0.011) b</td><td>0.42 (±0.007) b</td><td>0.397 (±0.004) a</td><td>Treat NS – Date ***</td></tr><tr><td></td><td></td><td>W_snow</td><td>0.427 (±0.012) b</td><td>0.42 (±0.007) b</td><td>0.399 (±0.007) a</td><td>Treat x Date NS</td></tr><tr><td>Carbonyl</td><td>0.368<br>(±0.006)</td><td>W_free</td><td>0.367 (±0.003) b</td><td>0.367 (±0.004) b</td><td>0.376 (±0.002) a</td><td>Treat * – Date ***</td></tr><tr><td></td><td></td><td>W_snow</td><td>0.363 (±0.005) b</td><td>0.366 (±0.004) b</td><td>0.376 (±0.003) a</td><td>Treat x Date NS</td></tr><tr><td>Aromatic</td><td>0.213<br>(±0.009)</td><td>W_free</td><td>0.212 (±0.007) b</td><td>0.213 (±0.004) b</td><td>0.226 (±0.004) a</td><td>Treat NS – Date ***</td></tr><tr><td></td><td></td><td>W_snow</td><td>0.21 (±0.008) b</td><td>0.214 (±0.005) b</td><td>0.225 (±0.005) a</td><td>Treat x Date NS</td></tr><tr><td>POxC</td><td>NA</td><td>W_free</td><td>3.11 (±0.65) ac</td><td>2.64 (±0.48) a</td><td>3.08 (±0.61) ac</td><td>Treat *** – Date ***</td></tr><tr><td>mg POxC.g<sub>soil</sub><sup>-1</sup></td><td></td><td>W_snow</td><td>4.52 (±0.56) b</td><td>3.8 (±0.19) bc</td><td>2.94 (±0.44) a</td><td>Treat x Date ***</td></tr><tr><td>WEOC</td><td>43.64</td><td>W_free</td><td>39.6 (±3.6) a</td><td>37.76 (±2.62) a</td><td>37.71 (±5.7) a</td><td>Treat NS – Date NS</td></tr><tr><td>mg WEOC.g<sub>soil</sub><sup>-1</sup></td><td>(±9.07)</td><td>W_snow</td><td>38.33 (±13.49) a</td><td>31.09 (±5.2) a</td><td>40.79 (±5.93) a</td><td>Treat x Date NS</td></tr><tr><td>SUVA<sub>254</sub></td><td>87.17<br>(±8.84)</td><td>W_free</td><td>60.63 (±5.15) b</td><td>36.37 (±3.72) b</td><td>99.17 (±15.95) a</td><td>Treat *** – Date ***</td></tr><tr><td></td><td></td><td>W_snow</td><td>96.41 (±33.21) a</td><td>51.28 (±6.06) b</td><td>109.7 (±35.69) a</td><td>Treat x Date NS</td></tr><tr><td>Fluo<sub>index</sub></td><td>1.35<br>(±0.03)</td><td>W_free</td><td>1.31 (±0.07) c</td><td>1.36 (±0.05) c</td><td>1.67 (±0.08) b</td><td>Treat *** – Date ***</td></tr><tr><td></td><td></td><td>W_snow</td><td>1.73 (±0.11) ab</td><td>1.86 (±0.14) a</td><td>1.8 (±0.15) ab</td><td>Treat x Date ***</td></tr></tbody></table>

from winter to summer, indicating more growth relative to enzyme activity in winter. While snow-free winter did not affect this trend during winter and spring, it led to a decrease in the following summer (Table S3).

## 3.8. Soil microbial composition

Metabarcoding analysis gave 401 (± 79) and 394 (± 89) ASV per sample for fungi and bacteria, respectively. Fungal alpha diversity remained unaffected by snow-free winter but showed a seasonal decline during the snowmelt period (Table S2). In contrast, bacterial alpha diversity was stable across seasons but tended to increase slightly with snow-free winter. However, the effects of season and snow-free winter were not strongly pronounced (Table S2). Fungal beta diversity was influenced by snow-free winter, with significant differences in winter and spring (Test<sub>anosim</sub> < 0.05). *Chytridiomycota*, *Mortierellomycota* and *Mucoromycota* were more abundant during the winter (Fig. 4A). However, this difference was no longer significant in summer (Fig. 4A). Bacterial beta diversity was less affected by season and treatment, though a non-significant seasonal pattern was observed. *Verrucomicrobiota* were more abundant in summer, while *Actinobacteriota*, *Gemmatimonadota*, and *Bacteroidota* dominated in winter (Fig. 4B). Spring bacterial communities appeared to be a mix of winter and summer taxa. Indicator species analysis identified several denitrifiers and anaerobic bacteria that thrived with the snow-free winter and soil freezing, both in winter and spring: *Roseimicrobium* (anammox), *Gemmataceae* (generally anammox), *Opitutus* (some denitrifiers), *Sphingomonas* (denitrifiers), *Paenibacillus* (denitrifiers), *Anaerosporomusa* (anaerobe) (Fig. 4B). Snow-free winter also favored bacterial communities adapted to

fluctuating aerobic and anaerobic conditions and tolerant to high temperature variations (Fig. S4).

## 3.9. The nexus between gas emissions, SOM characteristics and microbial properties

To summarize the results, a principal component analysis (PCA) was performed across all variables (Fig. 5, see also Fig. S6). The first two axes explained a significant proportion of the covariance between variables (51 %). Axis 1 captured the seasonal dynamics of soil and microbial properties, transitioning from winter to summer (negative direction). Winter was characterized by highly labile organic matter (aliphatic, POxC, TOC), elevated $NO_3^-$ level, high microbial biomass C content, positive NEE. In contrast, summer was associated with more complex organic matter molecules (oxygenated and aromatic compounds), higher $NH_4^+$ content, higher ecosystem respiration, and changes in fungal composition (FUNG1). Axis 2 represented the treatment effect, which was strongest in winter ($p_{PERMANOVA}$ < 0.05) and decreased over time. Snow-free winter primarily reduced enzymatic activity, as well as Fluo<sub>index</sub>, SUVA, and microbial biomass N content, while it increased $N_2O$ emissions. It also changed fungal composition (FUNG2) and bacterial functional traits inferred from Bactotraits database, including their capacity to switch between aerobic and anaerobic conditions, their capacity to support high deltas of temperature, and bacterial cell size (Fig. 4B & S4).

## 4. Discussion

Our results provide new insights into subalpine grassland
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Seasonal and snow-free winter effects on SOM properties (A), soil nutrients (B), enzymatic activity involved in C, N and P mineralization (C) and microbial biomass (D). Statistical results show linear mixed model outcomes with turf as a random effect (*: p < 0.05; **: p < 0.01; ***: p < 0.001). P-value indicate differences based on pairwise Tukey HSD test.</figcaption></figure>

functioning, showing how winter drought can significantly alter soil microbial community composition, SOM recycling and greenhouse gas fluxes. We observed an increase in N₂O emissions during winter, which persisted into spring and summer, along with a shift toward anaerobic and cold-adapted microbial taxa. The experiment also disrupted the seasonal cycling of SOM, with reduced decomposition under snow-free conditions. Interestingly, the snow-free microbial community did not fully revert to its original state in spring and summer. This incomplete recovery of microbial and biogeochemical functions suggests a carry-over effect of a snow-free winter, which could have long-term consequences for ecosystem stability if such winter events were to become more frequent under future climate scenarios.

## 4.1. Shifts in soil microbial community composition and functional traits

Consistent with our first hypothesis (H1), microbial community

composition and functional traits shifted in response to the treatment. Beyond its direct impact on soil fertility, frost induced by snow drought also acts as a strong abiotic selection, selecting for organisms that appear to be adapted to extreme conditions. This is supported by our findings on bacterial traits (inferred from Bactotraits database), which indicate that bacteria with finer cell structures and a greater tolerance to temperature fluctuations were more resilient in soils with snow-free winter (Fig. S4). First, organisms with finer cell structures are likely less vulnerable to physical disruption caused by freezing-induced soil expansion and may remain better insulated within micropores, reducing their exposure to frost damage (Kreyling et al., 2012). Second, the ability to withstand wide temperature fluctuations is likely advantageous in environments experiencing repeated freeze-thaw cycles. These selective pressures may lead to microbial community restructuring, as suggested by Hua et al., (2024), who observed an increase in fungal abundance in frozen soils due to a lack of snow coverage.
<caption>**Table 3**</caption>

Nutrient contents throughout the study year and the effects of snow-free winter. Nitrate and ammonium concentrations were measured in soil samples for each sampling period and in resin bags spanning the winter and snowmelt seasons. Bioavailable phosphorus was assessed using the Olsen method. Significant differences among treatments and sampling dates were assessed using a linear mixed-effects model, with significance levels: ***p < 0.001, **p < 0.01, *p < 0.05. Letters indicate significant difference after post-hoc Tukey HSD test.

<table><thead><tr><th></th><th>Treatment</th><th>End-winter WI</th><th>Post-snowmelt SP (spring)</th><th>End-summer SU</th><th>Date and treatment effect</th></tr></thead><tbody><tr><td colspan="6">Nutrients in soil – (N &amp; P)</td></tr><tr><td>NO<sub>3</sub><sup>-</sup></td><td>W_free</td><td>11.73<br>(±2.08) c</td><td>8.64<br>(±1.35) c</td><td>3.66<br>(±1.13) a</td><td>Treat *** –<br>Date ***</td></tr><tr><td>µg N.g<sub>soil</sub><sup>-1</sup></td><td>W_snow</td><td>16.1<br>(±0.76) b</td><td>9.9<br>(±3.84) c</td><td>3.32<br>(±1.34) a</td><td>Treat x Date<br>**</td></tr><tr><td>NH<sub>4</sub><sup>+</sup></td><td>W_free</td><td>2.54<br>(±1.19) ab</td><td>3.64<br>(±1.41) a</td><td>7.49<br>(±1.8) c</td><td>Treat *** –<br>Date ***</td></tr><tr><td>µg N.g<sub>soil</sub><sup>-1</sup></td><td>W_snow</td><td>1.14<br>(±0.51) b</td><td>2.34<br>(±0.84) ab</td><td>4.35<br>(±0.81) a</td><td>Treat x Date<br>NS</td></tr><tr><td>P<sub>available</sub></td><td>W_free</td><td>8.88<br>(±0.58) c</td><td>7.67<br>(±1.08) ac</td><td>5.61<br>(±0.69) a</td><td>Treat *** –<br>Date ***</td></tr><tr><td>µg P.g<sub>soil</sub><sup>-1</sup></td><td>W_snow</td><td>14.27<br>(±3.61) b</td><td>7.78<br>(±0.74) ac</td><td>5.74<br>(±0.78) a</td><td>Treat x Date<br>**</td></tr><tr><td colspan="6">Nutrients in resin – (N)</td></tr><tr><td>NO<sub>3</sub><sup>-</sup> flux</td><td>W_free</td><td>0.13<br>(±0.07) a</td><td>0.69<br>(±0.57) bc</td><td>NA</td><td>Treat NS –<br>Date **</td></tr><tr><td>µg N.<br>week<sup>-1</sup></td><td>W_snow</td><td>0.18<br>(±0.05) ab</td><td>0.77<br>(±0.55) c</td><td>NA</td><td>Treat x Date<br>NS</td></tr><tr><td>NH<sub>4</sub><sup>+</sup> flux</td><td>W_free</td><td>0.13<br>(±0.07) a</td><td>1.2 (±0.6) b</td><td>NA</td><td>Treat * –<br>Date ***</td></tr><tr><td>µg N.<br>week<sup>-1</sup></td><td>W_snow</td><td>1.11<br>(±0.93) ab</td><td>3.04<br>(±1.72) c</td><td>NA</td><td>Treat x Date<br>NS</td></tr></tbody></table>

Similarly, it might be expected to select more spore formers, such as bacilli, in frozen soils. In our study, the observed microbial response is likely due to differences in freezing tolerance and moisture sensitivity among microbial taxa. Ibanez et al., (2021) suggested that copiotrophic microbes, which are more susceptible to freeze–thaw cycles than oligotrophs (Fierer, 2017), were preferentially suppressed under harsh abiotic conditions in the absence of snow. This suppression likely led to a release and an accumulation of labile substrates in the soil, which may have consequences for ecosystem processes and fertility in the subsequent growing season.

Since temperature is a key factor regulating microbial activity, the cold soil temperatures induced by snow-free winter could explain the observed decrease in enzymatic activity consistent with previous studies. For example, Puissant (2015) showed that reduced winter snow cover or winter snow-free episodes led to reduced enzyme activities. We observed a marked increase in the C:N ratio of microbial biomass during winter, consistent with previous findings (Gavazov et al., 2017). This shift likely reflects a transition toward fungal dominance, as fungal biomass typically exhibits higher C:N ratios than bacterial biomass. Such a pattern is consistent with the tendency of fungi to prevail under cold or resource-limited conditions, whereas increased substrate availability generally favours bacterial communities. Another explanation is that soil microorganisms experience carbon limitation during the cold season, leading to adjustments in nitrogen mineralization—likely through the release of organic nitrogen reserves in response to carbon scarcity. These results underscore the preferential incorporation of carbon into

<caption>**Table 4**</caption>

Microbial properties throughout the study year and the effects of snow-free winter. Upper table shows the stoichiometry of microbial biomass (C, N and C:N). Lower table presents microbial enzymatic activities involved in carbon (ALP, CEL, CHIN, GLU, HEM), nitrogen (LEU) and phosphorus (PHO) cycling and stoichiometry of these activities (C:N and C:P). Significant differences among treatments and sampling dates were assessed using a linear mixed-effects model, with significance levels: ***p < 0.001, **p < 0.01, *p < 0.05. Letters indicate significant difference after post-hoc Tukey HSD test.

<table><thead><tr><th></th><th>Treatment</th><th>End-winter WI</th><th>Post-snowmelt SP (spring)</th><th>End-summer SU</th><th>Date and treatment effect</th></tr></thead><tbody><tr><td colspan="6">Microbial biomass</td></tr><tr><td>C<sub>MB</sub></td><td>W_free</td><td>3.97<br>(±0.56) c</td><td>2.03<br>(±0.33) a</td><td>1.99<br>(±0.28) a</td><td>Treat *** –<br>Date ***</td></tr><tr><td>mg C.<br>g<sub>soil</sub><sup>-1</sup></td><td>W_snow</td><td>5.24<br>(±0.78) b</td><td>2.48<br>(±0.39) a</td><td>1.98<br>(±0.51) a</td><td>Treat x Date<br>**</td></tr><tr><td>N<sub>MB</sub></td><td>W_free</td><td>0.205<br>(±0.048) c</td><td>0.319<br>(±0.057) b</td><td>0.36<br>(±0.055)</td><td>Treat *** –<br>Date ***</td></tr><tr><td>mg N.<br>g<sub>soil</sub><sup>-1</sup></td><td>W_snow</td><td>0.304<br>(±0.079) bc</td><td>0.439<br>(±0.067) a</td><td>0.352<br>(±0.085) ab</td><td>Treat x Date<br>*</td></tr><tr><td>C<sub>MB</sub><br>N<sub>MB</sub></td><td>W_free</td><td>19.9 (±3.6) b</td><td>6.4 (±0.2) a</td><td>5.5 (±0.1) a</td><td>Treat NS –<br>Date ***</td></tr><tr><td>W_snow</td><td>W_free</td><td>17.7 (±2.7) b</td><td>5.6 (±0.2) a</td><td>5.6 (±0.3) a</td><td>Treat x Date<br>NS</td></tr><tr><td colspan="6">Enzymatic activity</td></tr><tr><td>ALP</td><td>W_free</td><td>1.46<br>(±0.54) c</td><td>1.26<br>(±0.22) c</td><td>1.29<br>(±0.39) c</td><td>Treat *** –<br>Date **</td></tr><tr><td>W_snow</td><td>W_free</td><td>2.26<br>(±0.53) b</td><td>1.35<br>(±0.33) ac</td><td>2.2 (±1.07) ab</td><td>Treat x Date<br>NS</td></tr><tr><td>CEL</td><td>W_free</td><td>3.32<br>(±0.94) c</td><td>6.6 (±1.76) ab</td><td>5.61<br>(±1.69) bc</td><td>Treat *** –<br>Date **</td></tr><tr><td>W_snow</td><td>W_free</td><td>6.73<br>(±2.15) ab</td><td>6.32<br>(±2.05) ab</td><td>8.31<br>(±2.89) a</td><td>Treat x Date<br>**</td></tr><tr><td>CHIN</td><td>W_free</td><td>10.81<br>(±3.48) c</td><td>5.06<br>(±1.02) b</td><td>9.63 (±2.8) c</td><td>Treat *** –<br>Date ***</td></tr><tr><td>W_snow</td><td>W_free</td><td>14.84<br>(±2.61) a</td><td>7.83<br>(±1.59) bc</td><td>14.66<br>(±1.86) a</td><td>Treat x Date<br>NS</td></tr><tr><td>GLU</td><td>W_free</td><td>13.31<br>(±3.23) c</td><td>14.21<br>(±2.38) c</td><td>15.22<br>(±3.1) bc</td><td>Treat *** –<br>Date **</td></tr><tr><td>W_snow</td><td>W_free</td><td>24.5<br>(±4.95) a</td><td>16.18<br>(±3.7) bc</td><td>20.07<br>(±3.6) ab</td><td>Treat x Date<br>***</td></tr><tr><td>HEM</td><td>W_free</td><td>4.63<br>(±1.03) bc</td><td>4.84<br>(±1.09) bc</td><td>4.29<br>(±0.79) c</td><td>Treat *** –<br>Date NS</td></tr><tr><td>W_snow</td><td>W_free</td><td>7.12<br>(±1.26) a</td><td>5.57<br>(±1.03) ac</td><td>6.05<br>(±1.52) ab</td><td>Treat x Date<br>NS</td></tr><tr><td>LEU</td><td>W_free</td><td>31.29<br>(±11.25) b</td><td>53.87<br>(±8.94) bc</td><td>63.4<br>(±22.18) ac</td><td>Treat *** –<br>Date ***</td></tr><tr><td>W_snow</td><td>W_free</td><td>52.09<br>(±13.34) c</td><td>66.75<br>(±14.34) ac</td><td>84.72<br>(±30.31) a</td><td>Treat x Date<br>NS</td></tr><tr><td>PHO</td><td>W_free</td><td>2.98<br>(±1.39) ab</td><td>3.73<br>(±0.85) bc</td><td>1.94<br>(±0.92) a</td><td>Treat *** –<br>Date ***</td></tr><tr><td>W_snow</td><td>W_free</td><td>4.16<br>(±0.66) bc</td><td>4.65<br>(±1.12) c</td><td>2.84<br>(±0.49) ab</td><td>Treat x Date<br>NS</td></tr><tr><td>C:N</td><td>W_free</td><td>0.86<br>(±0.02) bc</td><td>0.84<br>(±0.03) ac</td><td>0.83<br>(±0.03) a</td><td>Treat NS –<br>Date ***</td></tr><tr><td>W_snow</td><td>W_free</td><td>0.89<br>(±0.02) b</td><td>0.83<br>(±0.03) a</td><td>0.83<br>(±0.02) a</td><td>Treat x Date<br>NS</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 4 (continued)**</caption><thead><tr><th></th><th>Treatment</th><th>End-winter<br>WI</th><th>Post-<br>snowmelt<br>SP (spring)</th><th>End-<br>summer<br>SU</th><th>Date and<br>treatment<br>effect</th></tr></thead><tbody><tr><td rowspan="3">C:P</td><td>W_free</td><td>1.28<br>(±0.09) bc</td><td>1.23<br>(±0.04) c</td><td>1.44<br>(±0.17) a</td><td>Treat NS –<br>Date ***</td></tr><tr><td>W_snow</td><td>1.29<br>(±0.02) bc</td><td>1.2 (±0.05)<br>c</td><td>1.35<br>(±0.03) ab</td><td>Treat x Date<br>NS</td></tr><tr><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>

microbial biomass (Brooks et al., 2005). Winter drought further amplified this C:N increase, indicating a more severe carbon limitation under snow-free conditions. This may be attributed to reduced substrate availability, driven by both soil freezing and limited water input. These interpretations are supported by the observed decline in SUVA, C:N of enzyme activity and CO₂ fluxes—reliable proxies for microbial activity during the non-growing season (Hua et al., 2024). Additionally, low temperatures may suppress N₂O reductase activity, leading to increased N₂O production by denitrification relative to N₂ (Holtan-Hartwig et al.,

2002). Finally, the observed winter increase in microbial biomass is consistent with previous findings, which highlight its role in inorganic nitrogen immobilization during snowmelt (Brooks et al., 1998). The snow drought decreased the microbial biomass during the winter suggesting a decrease of its buffer role during the spring which could lead to nutrient losses.

## 4.2. Consequences on ecosystem properties: N₂O spikes, net ecosystem exchange, SOM and nutrients dynamics

As expected under hypothesis H1, the snow drought-induced shifts in soil microbial functioning impacted key ecosystem processes, including greenhouse gas emissions, litter decomposition, soil organic matter cycling, and nutrient dynamics. These functional changes were primarily driven by the promotion of anaerobic processes and the concurrent inhibition of aerobic pathways, such as litter decomposition.

### 4.2.1. N₂O dynamics

A consequence of soil freezing is the development of anaerobic

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** NMDS of fungal (A) and bacterial (B) communities throughout the study period. Capital letters indicate significant differences based on pairwise ANOSIM tests. Boxplots represent coordinates on Axis 1 and Axis 2. Note that only five ellipses are represented in bacteria NMDS due to limited number of replicates during spring (only 3 replicates). Note that axes 1 and 2 of fungi NMDS were used in PCA analysis (Fig. 5), and labelled as FUNG1 and FUNG2. Tables display indicative taxa for each sampling and treatment.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Principal Component Analysis (PCA) of CO₂ & N₂O emissions with soil and microbial properties throughout the study period. Boxplots represent coordinates on Axis 1 and Axis 2. Axis 1 captures the seasonal effect, while Axis 2 reflects the treatment effect, which is most pronounced in winter and gradually diminishes over the year. (Temp<sub>season</sub> = average daily temperature of the season; N<sub>MB</sub>, C<sub>MB</sub>=N and C of microbial biomass; Bacterial traits derived from Bactotraits database: DeltaT20<sub>bac</sub>, DeltaT30<sub>bac</sub> = bacterial tolerance of delta temperature; Fine cell<sub>bac</sub> = abundance of bacteria with cell < 0.65 µm; An-Aerobic<sub>bac</sub> = abundance of tolerant bacteria to fluctuations in aerobic conditions; Heterotrophic<sub>bac</sub> = abundance of heterotrophic bacteria; FUNG1, FUNG2 = beta fungal diversity, i.e., axis 1 and 2 of fungal NMDS, Fig. 4).</figcaption></figure>

conditions due to reduced gas diffusion in the soil. These anaerobic
conditions could act as an abiotic selection, favoring anaerobic micro-
organisms such as methanogens, anammox bacteria, and denitrifiers.
This was supported by our results, suggesting that the absence of winter
snow cover favors anaerobic organisms in winter and spring (Fig. 4B).
Further indication comes from the physiological traits of bacteria in the
Bactotraits database, which highlight the capacity of certain taxa to
transition between aerobic and anaerobic conditions (Fig. S4). In
particular, the conditions generated by snow-free winter were highly
conducive to denitrification, characterized by limited O₂ diffusion

(frost), elevated nitrate concentrations (electron acceptor for denitrification), and an abundance of labile carbon from microbial cell lysis (as suggested by the decline in microbial C biomass and the fluorescence index). These findings are consistent with the review by Risk et al., (2013), which identified denitrification as the primary mechanism driving N₂O emissions following freeze-thaw cycles (see also Ostrom et al., 2010). While N₂O fluxes in control turfs remained stable and consistent with values reported in the literature (0.07 ± 0.2 mg N₂O-N m⁻² day⁻¹ in our study; Enanga et al., (2016): 0.04–0.1 mg N₂O-N m⁻² day⁻¹), fluxes in snow-free turfs deviated substantially from this range.
Both observed reduction in nitrate availability (even though the nitri-
fication rate appeared to be constant based on the results obtained with
the resin bags) and a significant increase in N₂O emissions are likely a
consequence of increased denitrification (Fig. 2A). Snow-free winter led
to lower nitrate content (Table 3), despite similar nitrification rates in
both treatments (with or without snow), as indicated by resin bag data
(Table 3). Therefore, the decline in soil nitrate concentrations can only
be attributed to enhanced denitrification. However, the increase in N₂O
fluxes was not constant, as observed in previous studies (Ruan and
Robertson, 2017). One possible explanation is that the thawing phase
releases N₂O that accumulated in soil pores when soil was frozen and
promoted denitrification. Furthermore, it may be a one-off event or a
phenomenon that occurs irregularly, as is the case, for example, with
methane emissions during the freezing of Arctic landscapes (Tagesson
et al., 2012).

4.2.2. *Net ecosystem exchange, decomposition rate and SOM dynamics*

We identified classical seasonal shifts in soil organic matter proper-
ties (TOC, TN, DRIFT, POxC, SUVA, and fluorescence of WEOC) which is
consistent with patterns described by Siles et al., (2017). During winter,
labile SOM content (e.g., POxC and aliphatic compounds) was higher,
likely due to the input of root exudates and litter material (Bonfanti
et al., 2025d). As the growing season progressed, these labile SOM pools
declined, driven by reduced inputs and increased microbial minerali-
zation associated with rising temperatures, as evidenced by enhanced
ecosystem respiration. In control turfs, winter CO₂ fluxes remained
stable and aligned with values reported in the literature (1.32 ± 1.18 g
CO₂-C m⁻² day⁻¹; Maljanen et al., (2007): 1.2 g CO₂-C m⁻² day⁻¹). In
contrast, snow drought strongly reduced SOM mineralization, as indi-
cated by lower CO₂ fluxes, decreased litter decomposition rates, and
reduced enzymatic activity. These effects were associated with changes
in SOM properties (e.g., POxC, SUVA), likely linked to induced soil
freezing (Fig. 1), which can cause physical disruption of soil aggregates
and microbial cell lysis (Robroek et al., 2013; Wang et al., 2012). As
shown by Cécillon et al., (2010) and Ruan & Robertson, (2017), snow-
free conditions reduce the abundance of soil macroaggregates by late
winter. This structural disruption may release labile organic matter, a
phenomenon supported by our observed increases in POxC (Liu et al.,
2018).

4.2.3. *Nutrient's dynamics*

Our results also contrasted with previous studies (Broadbent et al., 2024; Maljanen et al., 2007), showing a decrease in soil NH₄⁺ and an increase in NO₃⁻ throughout the year. In our study, the accumulation of soil NO₃⁻ over winter may result from reduced plant uptake and minimal soil-water movement, which limits NO₃⁻ leaching, as suggested by Foster et al., (1989). Its subsequent decline could be attributed to plant uptake and snowmelt leaching (Edwards et al., 2007). Meanwhile, the increase in NH₄⁺ may reflect enhanced mineralization rates during the growing season, driven by higher temperatures that stimulate microbial activity. Some studies have highlighted that soil freezing can contribute to the production of inorganic nitrogen through physical degradation of SOM, rather than microbial decomposition (Freppaz et al., 2008; Patel et al., 2018). These consequences may explain the observed trend of increasing NH₄⁺ content during the winter in soils without snow cover. Another well-known consequence of soil freezing is cell lysis, which affects both microbial organisms and fine plant roots (Groffman et al., 2001a). SUVA values suggested that this process also occurred in subalpine soils without snow cover.

4.3. Incomplete recovery and carry-over effects following snow-free
winter conditions

Although microbial resilience mitigated the consequences of snow
drought over time, its carry-over effects remained evident in soil mi-
crobial communities, ecosystem functioning, and associated gas fluxes

in the following spring and summer (H2).

4.3.1. Absence of snowmelt in spring

In our study, the snow drought event resulted in a complete absence
of snowmelt events. This phase is critical for subalpine ecosystems as it
usually induces a high input of nutrients to the soil (Bonfanti et al.,
2025b, d). For instance, Edwards et al., (2007) *meta*-analysis reported
that NO₃⁻ concentrations in snowpack range from 1 to 13.5 µg.L⁻¹. The
absence of snowmelt eliminated these nutrient inputs, which could
trigger a priming effect on SOM mineralization at the onset of the
growing season, when plant nutrient demand is at its peak (Bonfanti
et al., 2025d). This suggests that the initial phase of the growing season
in turfs with snow-free winter may be characterized by nutrient limi-
tation. However, the absence of percolating meltwater also reduced
lixiviation of SOM and nutrients. This is supported by the higher water-
extractable organic carbon (WEOC) content observed in soils without
winter snow cover during spring, consistent with findings by Patel et al.,
(2018) and Hua et al., (2024). This retention of organic matter and
nutrients may have mitigated the severity of nutrient limitation caused
by the lack of snowmelt. Another significant consequence of snowmelt
absence is water limitation, as we observed a prolonged delay in soil
moisture recovery under snow-free winter soils which could have con-
sequences on ecosystem functioning (Fig. 1).

4.3.2. *Lasting effects by the end of summer*

While these effects persisted, the system also showed signs of resi-
ience, with the effect size of the snow-free winter treatment decreasing
over time (Fig. 5). Specifically, we detected no significant differences
between treatments in SOM properties (TOC, TN, C:N, DRIFT indices,
POxC, WEOC, SUVA, and Fluo<sub>index</sub>), soil nitrate and available phos-
phorus contents, or microbial biomass (C, N, C:N) in following summer
(Tables 2–4). Two main processes could explain this partial ecosystem
recovery: (i) an extended growing season that offsets the winter lag
caused by lower temperatures and (ii) compensatory processes (boosts)
that mitigate the initial disruptions.

The lengthening of the growing season could compensate for the
delay and reduction in microbial activity caused by winter soil freezing,
providing more time for the development of microbial resilience. In
particular, an extended growing season allows for a prolonged period of
high heterotrophic respiration, facilitating the SOC mineralization that
accumulated during winter. Under snow cover, SOC would typically be
mineralized throughout the winter; however, without snow cover, the
delayed mineralization occurs primarily during the growing season. This
seasonal shift in SOC dynamics is accompanied by an increase in pro-
cesses contributing to the recovery of ecosystem functions. For instance,
we observed higher summer NEE and ecosystem respiration in turfs with
snow-free winter. This suggests that during summer, turfs without
winter snow cover experienced greater carbon losses than turfs under
control conditions, whereas the opposite trend was observed in winter,
tending to re-adjust ecosystem properties (Fig. 2.B). These findings align
with the *meta*-analysis by Hua et al., (2024), which suggests that altered
soil freezing patterns can cause organism's damage and mortality,
providing additional labile substrates (necromass) that enhance soil
respiration (Hua et al., 2024; Reinmann and Templer, 2018). The
observed summer over-mineralization in winter snow-free turfs is
further supported by elevated NH₄⁺ concentrations, also indicating a
stimulation of ammonification processes. However, our experiment
suggested that microbial resilience appeared to drive nitrogen cycling
recovery beyond its initial state.

The seasonal pattern of enzymatic activity related to carbon and
phosphorus cycling (β-glucosidase [GLU] and phosphatase [PHO]) in
snow-covered soils during winter aligns with patterns reported by Siles
et al. (2017). We observed an increase in specific enzyme activity during
summer, suggesting a microbial metabolic shift from growth to resource
acquisition—likely driven by intensified competition with plants for
diminishing substrates. In contrast, snow drought led to reduced specific
enzymatic activity in the following summer. This suggests that the winter slowdown in microbial activity resulted in the accumulation of labile SOM, reducing the need for enzymatic investment by microbial communities during the growing season, which may explain the observed patterns and lead to a compensatory process.

Patel et al., (2018) attributed the microbial resilience to spring turnover and succession of microbial community (Schmidt et al., 2007). However, some properties did not fully recover by the end of the growing season, particularly soil ammonium content, enzymatic activity related to carbon cycling (α- & β-glucosidase, cellulase, chitinase, hemicellulose), and N₂O emissions. This suggests only partial plant and microbial resilience to snow-free winter, with microbial selection driven by winter abiotic condition potentially leading to changes in microbial community structure in the following growing season. The higher soil NH₄⁺ content could result from reduced root uptake due to fine root damage, as proposed by Groffman et al., (2001b). This aligns also with Heuchan et al., (2024), who found that soil freezing induced by snow removal reduces plant nitrogen uptake. However, it is also plausible that microbial processes, rather than root activity, were responsible for the increase in soil nutrient availability in the subsequent growing season, although our study cannot disentangle their relative role. Specifically, the summer microbial community may inherit traits from the winter assemblage, with organisms favored during winter remaining over-represented, and those disadvantaged remaining under-represented. For instance, aerobic microorganisms such as nitrifiers were negatively affected by the snow-free winter (Fig. 4B, S4 & S5), which may have reduced nitrification activity and led to ammonium buildup in the soil. This hypothesis is further supported by the prolonged effect on N₂O emissions observed in spring and summer and consistent with previous studies (Maljanen et al., 2007), where an overrepresentation of denitrifiers could explain sustained N₂O emissions.

A limitation of our study is the absence of direct measurements of plant community dynamics, root turnover, and plant nitrogen uptake. As a result, we cannot fully separate the contributions of microbial activity and plant-mediated processes to the observed changes in soil ammonium and N₂O emissions. Likely, both played a role: reduced root uptake due to snow-induced soil freezing may have contributed to nutrient accumulation, while microbial selection under winter conditions influenced N cycling. Future work that integrates plant and root measurements alongside microbial analyses will be crucial to clarify these coupled plant-soil responses to snow-free winters.

### 4.3.3. *Beyond a single event: toward a broader understanding of snow drought effects*

As extreme snow-free winter episodes become more frequent with climate change, subalpine grasslands may experience lasting shifts in soil functioning, with implications for greenhouse gas emissions and the sustainability of pastoral systems. This study contributes to understanding these risks by experimentally simulating a single, intense snow drought and assessing its multi-faceted impacts from winter through the following summer. It specifically targets immediate winter responses and seasonal carry-over effects on soil microbial functioning and ecosystem processes, while also presenting several limitations: i) The study focused on a single extreme event, limiting insights into cumulative or recurrent snow drought impacts (but see Jones et al., (2025). ii) Discrete measurements, rather than continuous monitoring, restrict the resolution of short-term flux dynamics. iii) The three-season timeframe is insufficient to detect threshold responses or long-term ecosystem shifts. iv) More gradual winter changes, such as increased frequency of freeze-thaw cycles or milder forms of snow drought, were not addressed.

Although long-term snow manipulation studies exist, examining the effects of snow removal or altered snowpack over multiple years and tracking fluxes or microbial responses, few have adopted truly integrative frameworks that combine microbial functioning, nutrient dynamics, and greenhouse gas emissions. These links remain underexplored,

particularly using emerging omics-based approaches. Such studies could focus on which responses are susceptible to last a single-year, and which ones are likely to last over multiple years and could have consequences on ecosystem services. For instance, we could expect that a lower respiration rate results in accumulation of SOM and thus a potential negative climate feedback, while the increase in nutrient availability like NH₄⁺ could lead to an increase in ecosystem fertility. The resilience of certain microbial functional traits could buffer some processes in the following season, while cascading effects from SOM accumulation or nutrient shifts may influence ecosystem functioning and trajectory over multiple years.

Importantly, a rigorous assessment of GHG balance over time, both during the experimental year and in subsequent years, would benefit from combining CO₂ and N₂O fluxes: given that N₂O has a 100-year GWP roughly ~300× that of CO₂ (Saikawa et al., 2014) and an atmospheric lifetime of ~114 years. Computing a CO₂-equivalent budget over different time horizons would reveal whether snow-drought-induced winter emissions make the system a net GHG source or sink (IPCC, 2023). Such work could help disentangle “short-term” effects (responses within a single season) from lasting ecosystem shifts, guiding both ecosystem management and climate mitigation strategies in snow-sensitive regions.

To better understand how snow-free conditions affect soil ecosystem functioning, future studies should compare the impacts of warm versus dry snow droughts, integrate continuous field monitoring with targeted laboratory experiments, and extend observations over longer timescales. Building on studies like ours that specifically target the absence of snow cover, such approaches are essential to disentangle the mechanisms by which different types of snow drought—whether driven by warming or drying—alter microbial functioning, nutrient cycling, and ecosystem processes. The lack of snow protection during snow drought episodes may fundamentally alter microbial activity, nutrient cycling, and ecosystem trajectories, with implications for both short-term responses and long-term soil resilience.

## 5. Conclusion

Our study reveals that an extreme winter drought—simulated by the complete removal of snow cover—significantly disrupts subalpine grassland soil processes, primarily by altering microbial community composition and reducing microbial activity. These changes have cascading effects on SOM cycling and greenhouse gas fluxes, particularly increasing N₂O emissions. While some microbial functions recovered in the growing season, the persistence of winter-selected microbial taxa suggests a lasting carry-over effect on ecosystem functioning. These findings highlight the critical role of snow cover in maintaining soil fertility and biogeochemical stability in subalpine systems and show that rare but intensifying extreme events can have substantial seasonal impacts beyond the winter period.

### CRediT authorship contribution statement

**Nicolas Bonfanti:** Writing – review & editing, Writing – original draft, Visualization, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Jerome Poulenard:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization. **Pascal Salze:** Validation, Resources, Project administration, Methodology, Funding acquisition, Conceptualization. **Jerome Foret:** Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing. **Lucie Liger:** Resources, Project administration, Methodology, Data curation, Conceptualization. **Cindy Arnoldi:** Resources, Methodology, Investigation, Formal analysis. **Tim Goodall:** Writing – review & editing, Validation, Resources, Investigation, Formal analysis, Data curation. **Robert
**Griffiths:** Writing – review & editing, Validation, Resources, Investigation, Formal analysis, Data curation. **Jeremy Puissant:** Writing – review & editing, Validation, Supervision, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation. **Jean-Christophe Clement:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors 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 received fundings from Savoie Mont Blanc University under the CryoSoil project. This project also received financial support from the TransAlp project coordinated by Tamara Münkemüller (ANR-20-CE02-0021, Agence Nationale de la Recherche), from INEE (CNRS) and from AnaEE (Analysis and Expérimentation on Ecosystems; ANR-11-INBS-0001AnaEE-Services, Investissements d'Avenir frame). We acknowledge the support of the Lautaret Garden-UAR 3370 (Univ. Grenoble Alpes, CNRS, 38000 Grenoble, France), a member of the Zone Atelier Alpes and eLTER network, as well as AnaEE-France and OZCAR network. We acknowledge also the support of Jean Trap and L2PC platform INRAE ECO&SOL, Montpellier for microbial biomass analysis. We thank Florette Ecochard, Léa Dorel, Noémie Niquille, Vanessa Gal-land, Arthur Grand and Aurélien Grange for their help for soil sampling.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.geoderma.2025.117663.

Data availability

Data will be made available on request.

References

Abarenkov, K., Nilsson, R.H., Larsson, K.-H., Taylor, A.F.S., May, T.W., Frøslev, T.G., Pawlowska, J., Lindahl, B., Pöldmaa, K., Truong, C., Vu, D., Hosoya, T., Niskanen, T., Piirmann, T., Ivanov, F., Zirk, A., Peterson, M., Cheeke, T.E., Ishigami, Y., Jansson, A.T., Jeppesen, T.S., Kristiansson, E., Mikryukov, V., Miller, J.T., Oono, R., Ossandon, F.J., Paupério, J., Saar, I., Schigel, D., Suija, A., Tedersoo, L., Köljalg, U., 2024. The UNITE database for molecular identification and taxonomic communication of fungi and other eukaryotes: sequences, taxa and classifications reconsidered. Nucleic Acids Res. 52, D791–D797. https://doi.org/10.1093/nar/gkad1039.

Baptist, F., Yoccoz, N.G., Choler, P., 2010. Direct and indirect control by snow cover over decomposition in alpine tundra along a snowmelt gradient. Plant Soil 328, 397–410. https://doi.org/10.1007/s11104-009-0119-6.

Beniston, M., 2012. Is snow in the Alps receding or disappearing? WIREs Clim. Change 3, 349–358. https://doi.org/10.1002/wcc.179.

Beniston, M., Farinotti, D., Stoffel, M., Andreassen, L.M., Coppola, E., Eckert, N., Fantini, A., Giacona, F., Hauck, C., Huss, M., Huwald, H., Lehning, M., López-Moreno, J.-I., Magnusson, J., Marty, C., Morán-Tejeda, E., Morin, S., Naaim, M., Provenzale, A., Rabatel, A., Six, D., Stötter, J., Strasser, U., Terzago, S., Vincent, C., 2018. The European mountain cryosphere: a review of its current state, trends, and future challenges. Cryosphere 12, 759–794. https://doi.org/10.5194/tc-12-759-2018.

Bernard, L., Foulquier, A., Gallet, C., Lavorel, S., Clément, J.-C., 2019. Effects of snow pack reduction and drought on litter decomposition in subalpine grassland communities. Plant Soil 435, 225–238. https://doi.org/10.1007/s11104-018-3891-3.

Bokhorst, S., Bjerke, J.W., Melillo, J., Callaghan, T.V., Phoenix, G.K., 2010. Impacts of extreme winter warming events on litter decomposition in a sub-Arctic heathland. Soil Biol. Biochem. 42, 611–617. https://doi.org/10.1016/j.soilbio.2009.12.011.

Bokhorst, S., Metcalfe, D.B., Wardle, D.A., 2013. Reduction in snow depth negatively affects decomposers but impact on decomposition rates is substrate dependent. Soil Biol. Biochem. 62, 157–164. https://doi.org/10.1016/j.soilbio.2013.03.016.

Bonfanti, N., Choler, P., Khedim, N., Clément, J.-C., Barré, P., Goury, R., Baudin, F., Cécillon, L., Saillard, A., Thuiller, W., Poulenard, J., 2025a. Drivers of soil organic carbon stocks and stability along elevation gradients. Geoderma 461, 117452. https://doi.org/10.1016/j.geoderma.2025.117452.

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., Clément, J.-C., Münkemüller, T., Barré, P., Baudin, F., Poulenard, J., 2025c. Prolonged warming leads to carbon depletion and increases nutrient availability in alpine soils. Appl. Soil Ecol. 213, 106239. https://doi.org/10.1016/j.apsoil.2025.106239.

Bonfanti, N., Poulenard, J., Barre, P., Baudin, F., Voisin, D., Clement, J.-C., 2025d. Seasonality alters soil organic matter properties in alpine and subalpine grasslands. Ecosystems 28, 29. https://doi.org/10.1007/s10021-025-00971-y.

Bonfanti, N., Poulenard, J., Clément, J.-C., Barré, P., Baudin, F., Turtureanu, P.D., Puças, M., Saillard, A., Raguet, P., Hurdu, B.-I., Choler, P., 2025e. 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.

Brin, L.D., Goyer, C., Zebarth, B.J., Burton, D.L., Chantigny, M.H., 2019. Linking changes in snow cover with microbial nitrogen cycling functional gene abundance and expression in agricultural soil. FEMS Microbiol. Ecol. 95, fiz073. https://doi.org/10.1093/femsec/fiz073.

Brin, L.D., Goyer, C., Zebarth, B.J., Burton, D.L., Chantigny, M.H., 2018. Changes in snow cover alter nitrogen cycling and gaseous emissions in agricultural soils. Agric. Ecosyst. Environ. 258, 91–103. https://doi.org/10.1016/j.agee.2018.01.033.

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.

Brookes, P.C., Kragt, J.F., Powlson, D.S., Jenkinson, D.S., 1985. Chloroform fumigation and the release of soil nitrogen: the effects of fumigation time and temperature. Soil Biol. Biochem. 17, 831–835. https://doi.org/10.1016/0038-0717(85)90143-9.

Brooks, P., Williams, M., Schmidt, S., 1998. Inorganic nitrogen and microbial biomass dynamics before and during spring snowmelt. Biogeochemistry 43, 1–15. https://doi.org/10.1023/A:1005947511910.

Brooks, P.D., McKnight, D., Elder, K., 2005. Carbon limitation of soil respiration under winter snowpacks: potential feedbacks between growing season and winter carbon fluxes. Glob. Chang. Biol. 11, 231–238. https://doi.org/10.1111/j.1365-2486.2004.00877.x.

Brooks, P.D., Schmidt, S.K., Williams, M.W., 1997. Winter production of CO₂ and N₂O from alpine tundra: environmental controls and relationship to inter-system C and N fluxes. Oecologia 110, 403–413. https://doi.org/10.1007/PL000008814.

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.

Buckeridge, K.M., Banerjee, S., Siciliano, S.D., Grogan, P., 2013. The seasonal pattern of soil microbial community structure in mesic low arctic tundra. Soil Biol. Biochem. 65, 338–347. https://doi.org/10.1016/j.soilbio.2013.06.012.

Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869.

Cannon, P.F., Kirk, P.M., 2007. Fungal families of the world. CABI.

Cébron, A., Zeghal, E., Ussegpolio-Polatera, P., Meyer, A., Bauda, P., Lemmel, F., Leyval, C., Maunoury-Danger, F., 2021. BactoTraits – a functional trait database to evaluate how natural and man-induced changes influence the assembly of bacterial communities. Ecol. Ind. 130, 108047. https://doi.org/10.1016/j.ecolind.2021.08047.

Cécillon, L., de Mello, N.A., De Danieli, S., Brun, J.-J., 2010. Soil macroaggregate dynamics in a mountain spatial climate gradient. Biogeochemistry 97, 31–43. https://doi.org/10.1007/s10533-009-9341-9.

Chandanpurkar, H.A., Famiglietti, J.S., Gopalan, K., Wiese, D.N., Wada, Y., Kakinuma, K., Reager, J.T., Zhang, F., 2025. Unprecedented continental drying, shrinking freshwater availability, and increasing land contributions to sea level rise. Sci. Adv. 11, eadx0298. https://doi.org/10.1126/sciadv.adx0298.

Clevatt, N.L., Fahey, J.T., Groffman, P.M., Hardy, J.P., Henry, K.S., Driscoll, C.T., 2008. Effects of soil freezing on fine roots in a northern hardwood forest. Can. J. For. Res. 38, 82–91. https://doi.org/10.1139/X07-133.

Clément, J.C., Robson, T.M., Guillemin, R., Barre, P., Lochet, J., Aubert, S., Lavorel, S., Saccone, P., Lochet, J., Aubert, S., Lavorel, S., Aubert, S., Robson, T.M., Guillemin, R., 2012. The effects of snow-N deposition and snowmelt dynamics on soil-N cycling in marginal terraced grasslands in the French Alps. Biogeochemistry.

Colombo, N., Guyennon, N., Valt, M., Salerno, F., Godone, D., Cianfarra, P., Freppaz, M., Maugeri, M., Manara, V., Acquaotta, F., Petrangeli, A.B., Romano, E., 2023. Unprecedented snow-drought conditions in the Italian Alps during the early 2020s. Environ. Res. Lett. 18, 074014. https://doi.org/10.1088/1748-9326/acdb88.

Contosta, A.R., Adolph, A., Burchsted, D., Burakowski, E., Green, M., Guerra, D., Albert, M., Dibb, J., Martin, M., McDowell, W.H., Routhier, M., Wake, C., Whitaker, R., Wollheim, W., 2017. A longer vernal window: the role of winter coldness and snowpack in driving spring transitions and lags. Glob. Chang. Biol. 23, 1610–1625. https://doi.org/10.1111/gcb.13517.
Culman, S.W., Hurisso, T.T., Wade, J., 2021. Permanganate Oxidizable Carbon, in: Soil Health Series. John Wiley & Sons, Ltd, pp. 152–175. doi:10.1002/9780891189831. ch9.

Davidson, E.A., Savage, K., Verchot, L.V., Navarro, R., 2002. Minimizing artifacts and biases in chamber-based measurements of soil respiration. Agricultural and Forest Meteorology, FLUXNET 2000 Synthesis 113, 21–37. doi:10.1016/S0168-1923(02) 00100-0.

De Cáceres, M., Legendre, P., Moretti, M., 2010. Improving indicator species analysis by combining groups of sites. Oikos 119, 1674–1684. https://doi.org/10.1111/j.1600- 0706.2010.18334.x.

Edwards, A.C., Scalenghe, R., Freppaz, M., 2007. Changes in the seasonal snow cover of alpine regions and its effect on soil processes: a review. Quat. Int. 10.

Enanga, E.M., Creed, I.F., Fairweather, T., Casson, N.J., 2016. Snow-covered soils produce N₂O that is lost from forested catchments. J. Geophys. Res. Biogeo. 121, 2356–2368. https://doi.org/10.1002/2016JG003411.

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

Fierer, N., 2017. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590. https://doi.org/10.1038/ smicro.2017.87.

Fitzhugh, R.D., Driscoll, C.T., Groffman, P.M., Tierney, G.L., Fahey, T.J., Hardy, J.P., 2001. Effects of soil freezing disturbance on soil solution nitrogen, phosphorus, and carbon chemistry in a northern hardwood ecosystem. Biogeochemistry 56, 215–238. https://doi.org/10.1023/A:1013076609950.

Foster, N.W., Hazlett, P.W., Nicolson, J.A., Morrison, I.K., 1989. Ion leaching from a sugar maple forest in response to acidic deposition and nitrification. Water Air Soil Pollut. 48, 251–261. https://doi.org/10.1007/BF00282382.

Freppaz, M., Celi, L., Marchelli, M., Zanini, E., 2008. Snow removal and its influence on temperature and N dynamics in alpine soils (Vallée d'Aoste, northwest Italy). Z. Pflanzenernähr. Bodenk. 171, 672–680. https://doi.org/10.1002/ jpln.200700278.

Gavazov, K., Ingrisch, J., Hasibeder, R., Mills, R.T.E., Buttler, A., Gleixner, G., Pumpanen, J., Bahn, M., 2017. Winter ecology of a subalpine grassland: effects of snow removal on soil respiration, microbial structure and function. Sci. Total Environ. 590–591, 316–324. https://doi.org/10.1016/j.scitotenv.2017.03.010.

Groffman, P.M., Driscoll, C.T., Fahey, T.J., Hardy, J.P., Fitzhugh, R.D., Tierney, G.L., 2001a. Colder soils in a warmer world: a snow manipulation study in a northern hardwood forest ecosystem 16.

Groffman, P.M., Driscoll, C.T., Hardy, J.P., Fitzhugh, R.D., 2001b. Effects of mild winter freezing on soil nitrogen and carbon dynamics in a northern hardwood forest 24.

Groffman, P.M., Rustad, L.E., Templer, P.H., Campbell, J.L., Christenson, L.M., Lany, N. K., Socci, A.M., Vadeboncoeur, M.A., Schaberg, P.G., Wilson, G.F., Driscoll, C.T., Fahey, T.J., Fisk, M.C., Goodale, C.L., Green, M.B., Hamburg, S.P., Johnson, C.E., Mitchell, M.J., Morse, J.L., Pardo, L.H., Rodenhouse, N.L., 2012. Long-term integrated studies show complex and surprising effects of climate change in the northern hardwood forest. Bioscience 62, 1056–1066. https://doi.org/10.1525/ bio.2012.62.12.7.

Hardy, J.P., Groffman, P.M., Fitzhugh, R.D., Henry, K.S., Welman, A.T., Demers, J.D., Fahey, T.J., Driscoll, C.T., Tierney, G.L., Nolan, S., 2001. Snow depth manipulation and its influence on soil frost and water dynamics in a northern hardwood forest. Biogeochemistry 56, 151–174.

Heuchan, S.M., Wagner-Riddle, C., Henry, H.A.L., 2024. Snow removal and cover crops influence soil nitrogen transfer to the subsequent corn crop. Plant Soil 496, 473–483. https://doi.org/10.1007/s11014-023-06376-x.

Hobbie, S.E., Chapin, F.S., 1996. Winter regulation of tundra litter carbon and nitrogen dynamics. Biogeochemistry 35, 327–338. https://doi.org/10.1007/BF02179958. Hottan-Hartwig, L., Dörsch, P., Bakken, L.R., 2002. Low temperature control of soil denitrifying communities: kinetics of N₂O production and reduction. Soil Biol. Biochem. 34, 1797–1806. https://doi.org/10.1016/S0038-0717(02)00169-4.

Hua, J., Sun, Q., Marschner, P., 2024. Immediate and prolonged effects of snow coverage alteration on soil carbon dynamics and microbial activity: a meta-analysis. Geoderma 449, 117029. https://doi.org/10.1016/j.geoderma.2024.117029.

Huning, L.S., AghaKouchak, A., 2020. Global snow drought hot spots and characteristics. Proc. Natl. Acad. Sci. U.S.A. 117, 19753–19759. https://doi.org/10.1073/ pnas.1915921117.

Husson, F., Josse, J., Le, S., Mazet, J., 2023. FactoMineR: Multivariate Exploratory Data Analysis and Data Mining.

Ibanez, S., Brun, C., Millery, A., Piton, G., Bernard, L., Avrillier, J.-N., Gallet, C., Foulquier, A., Clément, J.-C., 2021. Litter and soil characteristics mediate the buffering effect of snow cover on litter decomposition. Plant and Soil 460, 511–525. https://doi.org/10.1007/s11014-020-04803-x.

Ihrmark, K., Bödeker, I.T.M., Cruz-Martinez, K., Friberg, H., Kubartova, A., Schenck, J., Strid, Y., Stenlid, J., Brandström-Durling, M., Clemmensen, K.E., Lindahl, B.D., 2012. New primers to amplify the fungal ITS2 region – evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677. https://doi. org/10.1111/j.1574-6941.2012.01437.x.

IPCC, 2023. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.

Jones, D.L., Cooleed, E.C., Chadwick, D.R., 2025. Is one year enough? A commentary on field experiment duration in agricultural research. Agric. Syst. 228, 104393. https:// doi.org/10.1016/j.agsy.2025.104393.

Jusselme, M.-D., Saccone, P., Zinger, L., Faure, M., Le Roux, X., Guillaumaud, N., Bernard, L., Clement, J.-C., Poly, F., 2016. Variations in snow depth modify N- related soil microbial abundances and functioning during winter in subalpine grassland. Soil Biol. Biochem. 92, 27–37. https://doi.org/10.1016/j. soilbio.2015.09.013.

Kalbitz, K., Schmerwitz, J., Schwesig, D., Matzner, E., 2003. Biodegradation of soil- derived dissolved organic matter as related to its properties. Geoderma 113, 273–291. https://doi.org/10.1016/S0016-7061(02)00365-8.

Knight, C.G., Nicolitch, O., Griffiths, R.I., Goodall, T., Jones, B., Weser, C., Langridge, H., Davison, J., Dellavalle, A., Eisenhauer, N., Gongalsky, K.B., Hector, A., Jardine, E., Kardol, P., Maestre, F.T., Schädler, M., Semchenko, M., Stevens, C., Tsiafouli, M.A., Vilhelmsen, O., Wanek, W., de Vries, F.T., 2024. Soil microbiomes show consistent and predictable responses to extreme events. Nature 636, 690–696. https://doi.org/ 10.1038/s41586-024-08185-3.

Kreyling, J., Persöh, D., Werner, S., Benzenberg, M., Wöllecke, J., 2012. Short-term impacts of soil freeze-thaw cycles on roots and root-associated fungi of Holcus lanatus and Calluna vulgaris. Plant Soil 353, 19–31. https://doi.org/10.1007/ s11014-011-0970-0.

Kuznetsova, A., Brockhoff, P.B., Christensen, R.H.B., 2017. ImerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26. https://doi.org/10.18637/jss. v082.i13.

Kuzyakov, Y., 2010. Priming effects: interactions between living and dead organic matter. Soil Biol. Biochem. 42, 1363–1371. https://doi.org/10.1016/j. soilbio.2010.04.003.

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

Legay, N., Baxendale, C., Grigulis, K., Krainer, U., Kastl, E., Schloter, M., Bardgett, R.D., Arnoldi, C., Bahn, M., Dumont, M., Poly, F., Pommier, T., Clément, J.C., Lavorel, S., 2014. Contribution of above- and below-ground plant traits to the structure and function of grassland soil microbial communities. Ann. Bot. 114, 1011–1021. https://doi.org/10.1093/aob/mcu169.

Legay, N., Grasein, F., Robson, T.M., Personeni, E., Bataillé, M.-P., Lavorel, S., Clément, J.-C., 2013. Comparison of inorganic nitrogen uptake dynamics following snowmelt and at peak biomass in subalpine grasslands. Biogeosciences 10, 7631–7645. https://doi.org/10.5194/bg-10-7631-2013.

Lipson, D.A., Schmidt, S.K., Monson, R.K., 1999. Links between microbial population dynamics and nitrogen availability in an alpine ecosystem. Ecology 80, 1623–1631. https://doi.org/10.1890/0012-9658(1999)080%255B1623:LBMPDA%255D2.0.CO; 2.

Liu, W., Allison, S.D., Li, P., Wang, J., Chen, D., Wang, Z., Yang, S., Diao, L., Wang, B., Liu, L., 2018. The effects of increased snow depth on plant and microbial biomass and community composition along a precipitation gradient in temperature steppes. Soil Biol. Biochem. 124, 134–141. https://doi.org/10.1016/j.soilbio.2018.06.004.

Luo, J., Peng, Y., Jia, Z., Wu, Y., Gao, Y., Jalaid, N., Zhang, X., Ge, H., Qing, B., Chen, H., Zhan, Y., Li, P., Liu, L., 2025. Moisture–microbial interaction amplifies N₂O emission hot moments under deepened snow in grasslands. Glob. Change Biol. 31, e70254. https://doi.org/10.1111/gcb.70254.

Maljanen, M., Kohonen, A.-R., Virkajäärvi, P., Martikainen, P.J., 2007. Fluxes and production of N₂O, CO₂ and CH₄ in boreal agricultural soil during winter as affected by snow cover. Tellus B: Chem. Phys. Meteorol. 59, 853–859. https://doi.org/ 10.1111/j.1600-0889.2007.00304.x.

Marty, C., Schlögl, S., Bavay, M., Lehning, M., 2017. How much can we save? Impact of different emission scenarios on future snow cover in the Alps. Cryosphere 11, 517–529. https://doi.org/10.5194/tc-11-517-2017.

Nguyen, N.H., Song, Z., Bates, S.T., Branco, S., Tedersoo, L., Menke, J., Schilling, J.S., Kennedy, P.G., 2016. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248. https://doi.org/ 10.1016/j.funeco.2015.06.006.

Notarnicola, C., 2022. Overall negative trends for snow cover extent and duration in global mountain regions over 1982–2020. Sci. Rep. 12, 13731. https://doi.org/ 10.1038/s41598-022-16743-w.

Oksanen, J., Simpson, G.L., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R., O'Hara, R.B., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H., Barbour, M., Bedward, M., Bolker, B., Borcard, D., Carvalho, G., Chirico, M., Caceres, M.D., Durand, S., Evangelista, H.B.A., FitzJohn, R., Friendly, M., Furneaux, B., Hannigan, G., Hill, M.O., Lahti, L., McGlinn, D., Ouellette, M.-H., Cunha, E.R., Smith, T., Stier, A., Braak, C.J.F.T., Weedon, J., 2022. vegan: Community Ecology Package.

Ostrom, N.E., Sutka, R., Ostrom, P.H., Grandy, A.S., Huizinga, K.M., Gandhi, H., von Fischer, J.C., Robertson, G.P., 2010. Isotopologue data reveal bacterial denitrification as the primary source of N₂O during a high flux event following cultivation of a native temperate grassland. Soil Biol. Biochem. 42, 499–506. https:// doi.org/10.1016/j.soilbio.2009.12.003.

Patel, K.F., Tatariw, C., MacRae, J.D., Ohno, T., Nelson, S.J., Fernandez, I.J., 2018. Soil carbon and nitrogen responses to snow removal and concrete frost in a northern coniferous forest. Can. J. Soil Sci. 98, 436–447. https://doi.org/10.1139/cjss-2017- 0132.

Piton, G., Foulquier, A., Martínez-García, L.B., Legay, N., Hedlund, K., Martins da Silva, P., Nascimento, E., Reis, F., Sousa, J.P., De Deyn, G.B., Clement, J.C., 2020. Disentangling drivers of soil microbial potential enzyme activity across rain regimes: an approach based on the functional trait framework. Soil Biol. Biochem. 148, 107881. https://doi.org/10.1016/j.soilbio.2020.107881.

Pölme, S., Abarenkov, K., Henrik Nilsson, R., Lindahl, B.D., Clemmensen, K.E., Kauserud, H., Nguyen, N., Kjøller, R., Bates, S.T., Baldrian, P., Frøslev, T.G., Adojaan, K., Vizzini, A., Suija, A., Pfister, D., Baral, H.-O., Järvi, H., Madrid, H., Nordén, J., Liu, J.-K., Pawlowska, J., Pöldmaa, K., Pärtel, K., Runnel, K., Hansen, K., Larsson, K.-H., Hyde, K.D., Sandoval-Denis, M., Smith, M.E., Toomey-Heller, M., Wijayawardene, N.N., Menolli, N., Reynolds, N.K., Drenkhan, R., Maharachchikumbura, S.S.N., Gibertoni, T.B., Læssøe, T., Davis, W., Tokarev, Y., Corrales, A., Soares, A.M., Agan, A., Machado, A.R., Argüelles-Moyao, A., Detheridge, A., de Meiras-Ottoni, A., Verbeken, A., Dutta, A.K., Cui, B.-K., Pradeep, C.K., Marín, C., Stanton, D., Gohar, D., Wanasinghe, D.N., Otsing, E.,
Aslani, F., Griffith, G.W., Lumbsch, T.H., Grossart, H.-P., Masigol, H., Timling, I., Hiiesalu, I., Oja, J., Kupagme, J.Y., Geml, J., Alvarez-Manjarrez, J., Ilves, K., Loit, K., Adamson, K., Nara, K., Küngas, K., Rojas-Jimenez, K., Bitenieks, K., Irinyi, L., Nagy, L.G., Soonvald, L., Zhou, L.-W., Wagner, L., Aime, M.C., Öpik, M., Mujica, M.I., Metsoja, M., Ryberg, M., Vasar, M., Murata, M., Nelsen, M.P., Cleary, M., Samarakoon, M.C., Doilom, M., Bahram, M., Hagh-Doust, N., Dulya, O., Johnston, P., Kohout, P., Chen, Q., Tian, Q., Nandi, R., Amiri, R., Pereira, R.H., dos Santos Chikowski, R., Mendes-Alvarenga, R.L., Garibay-Orijel, R., Gielen, R., Phookamsak, R., Jayawardena, R.S., Rahimlou, S., Karunarathna, S.C., Tibpromma, S., Brown, S.P., Sepp, S.-K., Mundra, S., Luo, Z.-H., Bose, T., Vahter, T., Netherway, T., Yang, T., May, T., Varga, T., Li, W., Coimbra, V.R.M., de Oliveira, V. R.T., de Lima, V.X., Mikryukov, V.S., Lu, Y., Matsuda, Y., Miyamoto, Y., Köljalg, U., Tedersoo, L., 2020. Fungal Traits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16. https://doi.org/10.1007/s13225-020-00466-2.

Puissant, 2015. Effets des changements climatiques sur la dynamique de décomposition microbienne du carbone organique du sol en prairie subalpine calcaire.

Puissant, J., 2025. Does the C:n:P 1:1:1 ratio hold? Examining log-transformation bias in enzyme stoichiometry. Glob. Chang. Biol. 31, e70228. https://doi.org/10.1111/gcb.70228.

Puissant, J., Briony, J., Goodall, T., Mang, D., Blaud, A., Gweon, H.S., Malik, A., Jones, D. L., Clark, I.M., Hirsch, P.R., Griffiths, R., 2019. The pH optimum of soil exoenzymes adapt to long term changes in soil pH. Soil Biol. Biochem. 138, 107601. https://doi.org/10.1016/j.soilbio.2019.107601.

Quaedvlieg, W., Binder, M., Groenewald, J.Z., Summerell, B.A., Carnegie, A.J., Burgess, T.I., Crous, P.W., 2014. Introducing the Consolidated Species Concept to resolve species in the Teratosphaeriaceae [WWW Document]. doi:10.3767/003158514X681981.

Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., Glöckner, F.O., 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219.

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

Raiesi, F., Beheshti, A., 2014. Soil specific enzyme activity shows more clearly soil responses to paddy rice cultivation than absolute enzyme activity in primary forests of northwest Iran. Appl. Soil Ecol. 75, 63–70. https://doi.org/10.1016/j.apsoil.2013.10.012.

Reinmann, A.B., Templer, P.H., 2018. Increased soil respiration in response to experimentally reduced snow cover and increased soil freezing in a temperate deciduous forest. Biogeochemistry 140, 359–371. https://doi.org/10.1007/s10533-018-0497-z.

Risk, N., Snider, D., Wagner-Riddle, C., 2013. Mechanisms leading to enhanced soil nitrous oxide fluxes induced by freeze–thaw cycles. Can. J. Soil Sci. 93, 401–414. https://doi.org/10.4141/cjss2012-071.

Rixen, C., Høye, T.T., Macek, P., Aerts, R., Alatalo, J.M., Anderson, J.T., Arnold, P.A., Barric, I.C., Bjerke, J.W., Björkman, M.P., Blok, D., Blume-Werry, G., Boike, J., Bokhorst, S., Carbognani, M., Christiansen, C.T., Convey, P., Cooper, E.J., Cornelissen, J.H.C., Coulson, S.J., Dorrepaal, E., Elberling, B., Elmendorf, S.C., Elphinstone, C., Forte, T.G.W., Frei, E.R., Geange, S.R., Gehrmann, F., Gibson, C., Grogan, P., Halbritter, A.H., Harte, J., Henry, G.H.R., Inouye, D.W., Irwin, R.E., Jespersen, G., Jónsdóttir, I.S., Jung, J.Y., Klinges, D.H., Kudo, G., Lämsä, J., Lee, H., Lembrechts, J.J., Lett, S., Lynn, J.S., Mann, H.M.R., Mastepanov, M., Morse, J., Myers-Smith, I.H., Olofsson, J., Paavola, R., Petraglia, A., Phoenix, G.K., Semenchuk, I.P., Siewert, M.B., Slatyer, R., Spasojevic, M.J., Suding, K., Sullivan, P., Thompson, K.L., Väisänen, M., Vandvik, V., Venn, S., Walz, J., Way, R., Welker, J.M., Wipf, S., Zong, S., 2022. Winters are changing: snow effects on Arctic and alpine tundra ecosystems. Arct. Sci. 8, 572–608. https://doi.org/10.1139/as-2020-0058.

Robroek, B.J.M., Heijboer, A., Jassey, V.E.J., Hefting, M.M., Rouwenhorst, T.G., Buttler, A., Bragazza, L., 2013. Snow cover manipulation effects on microbial community structure and soil chemistry in mountain bog. Plant and Soil 369, 151–164. https://doi.org/10.1007/s11104-012-1547-2.

Ruan, L., Robertson, G.P., 2017. Reduced snow cover increases wintertime nitrous oxide (N2O) emissions from an agricultural soil in the upper U.S. Midwest. Ecosystems 20, 917–927. https://doi.org/10.1007/s10021-016-0077-9.

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.

Saikawa, E., Prinn, R.G., Dlugokencky, E., Ishijima, K., Dutton, G.S., Hall, B.D., Langenfelds, R., Tohjima, Y., Machida, T., Manizza, M., Rigby, M., O’Doherty, S., Patra, P.K., Harth, C.M., Weiss, R.F., Krummel, P.B., Van Der Schoot, M., Fraser, P.J., Steele, L.P., Aoki, S., Nakazawa, T., Elkins, J.W., 2014. Global and regional emissions estimates for N₂O. Atmos. Chem. Phys. 14, 4617–4641. https://doi.org/10.5194/acp-14-4617-2014.

Schindlbacher, A., Jandl, R., Schindlbacher, S., 2014. Natural variations in snow cover do not affect the annual soil efflux from a mid-elevation temperate forest. Glob. Chang. Biol. 20, 622–632. https://doi.org/10.1111/gcb.12367.

Schmidt, S.K., Costello, E.K., Nemergut, D.R., Cleveland, C.C., Reed, S.C., Weintraub, M. N., Meyer, A.F., Martin, A.M., 2007. Biogeochemical consequences of rapid

microbial turnover and seasonal succession in soil. Ecology 88, 1379–1385. https://doi.org/10.1890/06-0164.

Schmidt, S.K., Lipson, D.A., 2004. Microbial growth under the snow: implications for nutrient and allochemical availability in temperate soils. Plant Soil 259, 1–7. https://doi.org/10.1023/B:PLSO000020933.32473.7e.

Schürmann, A., Mohn, J., Bachofen, R., 2002. N₂O emissions from snow-covered soils in the Swiss Alps. Tellus B: Chem. Phys. Meteorol. 54, 134. https://doi.org/10.3402/tellusb.v54i2.16654.

Shi, C., Sun, G., Zhang, H., Xiao, B., Ze, B., Zhang, N., Wu, N., 2014. Effects of warming on chlorophyll degradation and carbohydrate accumulation of alpine herbaceous species during plant senescence on the Tibetan Plateau. PLoS One 9, e107874. https://doi.org/10.1371/journal.pone.0107874.

Siano, D.B., Metzler, D.E., 1969. Band shapes of the electronic spectra of complex molecules. J. Chem. Phys. 51, 1856–1861. https://doi.org/10.1063/1.1672270.

Siles, J.A., Cajthaml, T., Filipová, A., Minerbi, S., Margesin, R., 2017. Altitudinal, seasonal and interannual shifts in microbial communities and chemical composition of soil organic matter in Alpine forest soils. Soil Biol. Biochem. 112, 1–13. https://doi.org/10.1016/j.soilbio.2017.04.014.

Soucémarianadin, L., Cécillon, L., Chenu, C., Baudin, F., Nicolas, M., Girardin, C., Delahaie, A., Barré, P., 2019. Heterogeneity of the chemical composition and thermal stability of particulate organic matter in French forest soils. Geoderma 342, 65–74. https://doi.org/10.1016/j.geoderma.2019.02.008.

Steger, C., Kotlarski, S., Jonas, T., Schär, C., 2013. Alpine snow cover in a changing climate: a regional climate model perspective. Clim. Dyn. 41, 735–754. https://doi.org/10.1007/s00382-012-1545-3.

Tagesson, T., Mölder, M., Mastepanov, M., Sigsgaard, C., Tamstorf, M.P., Lund, M., Falk, J.M., Lindroth, A., Christensen, T.R., Ström, L., 2012. Land-atmosphere exchange of methane from soil thawing to soil freezing in a high-Arctic wet tundra ecosystem. Glob. Chang. Biol. 18, 1928–1940. https://doi.org/10.1111/j.1365-2486.2012.02647.x.

Tedersoo, L., Bahram, M., Pölme, S., Köljalg, U., Yorou, N.S., Wijesundera, R., Ruiz, L.V., Vasco-Palacios, A.M., Thu, P.Q., Suija, A., Smith, M.E., Sharp, C., Saluveer, E., Saitta, A., Rosas, M., Riit, T., Ratkowsky, D., Pritsch, K., Pöldmaa, K., Piepenbring, M., Phosri, C., Peterson, M., Parts, K., Pärtel, K., Otsing, E., Nouhra, E., Njouonkou, A.L., Nilsson, R.H., Morgado, L.N., Mayor, J., May, T.W., Majuakim, L., Lodge, D.J., Lee, S.S., Larsson, K.-H., Kohout, P., Hosaka, K., Hiiesalu, I., Henkel, T. W., Harend, H., Guo, L., Greslebin, A., Grelet, G., Geml, J., Gates, G., Dunstan, W., Dunk, C., Drenkhan, R., Dearnaley, J., De Kesel, A., Dang, T., Chen, X., Buegger, F., Brearley, F.Q., Bonito, G., Anslan, S., Abell, S., Abarenkov, K., 2014. Global diversity and geography of soil fungi. Science 346, 1256688. https://doi.org/10.1126/science.1256688.

Thompson, L.R., Sanders, J.G., McDonald, D., Amir, A., Ladau, J., Locey, K.J., Prill, R.J., Tripathi, A., Gibbons, S.M., Ackermann, G., Navas-Molina, J.A., Janssen, S., Kopylova, E., Vázquez-Baeza, Y., González, A., Morton, J.T., Mirarab, S., Zech Xu, Z., Jiang, L., Haroon, M.F., Kanbar, J., Zhu, Q., Jin Song, S., Kosciolek, T., Bokulich, N. A., Lefler, J., Brislawn, C.J., Humphrey, G., Owens, S.M., Hampton-Marcell, J., Berg-Lyons, D., McKenzie, V., Fierer, N., Fuhrman, J.A., Clauset, A., Stevens, R.L., Shade, A., Pollard, K.S., Goodwin, K.D., Jansson, J.K., Gilbert, J.A., Knight, R., 2017. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463. https://doi.org/10.1038/nature24621.

Tierney, G.L., Fahey, T.J., Groffman, P.M., Hardy, J.P., Fitzhugh, R.D., Driscoll, C.T., 2001. Soil freezing alters fine root dynamics in a northern hardwood forest. Biogeochemistry 56, 175–190.

Vance, E.D., Brookes, P.C., Jenkinson, D.S., 1987. Microbial biomass measurements in forest soils: the use of the chloroform fumigation-incubation method in strongly acid soils. Soil Biol. Biochem. 19, 697–702. https://doi.org/10.1016/0038-0717(87)90051-4.

Walters, W., Hyde, E.R., Berg-Lyons, D., Ackermann, G., Humphrey, G., Parada, A., Gilbert, J.A., Jansson, J.K., Caporaso, J.G., Fuhrman, J.A., Apprill, A., Knight, R., 2015. Improved Bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems 1. https://doi.org/10.1128/msystems.00009-15.

Wang, E., Cruse, R.M., Chen, X., Daigh, A., 2012. Effects of moisture condition and freeze/thaw cycles on surface soil aggregate size distribution and stability. Can. J. Soil Sci. 92, 529–536. https://doi.org/10.4141/cjss2010-044.

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.

Yan, J., Yu, X., Sheng, L., Zou, Y., 2024. Influence of snowpack on soil organic carbon decomposition in a northern peatland. Catena 243, 108201. https://doi.org/10.1016/j.catena.2024.108201.

Yanai, Y., Toyota, K., Okazaki, M., 2007. Response of denitrifying communities to successive soil freeze–thaw cycles. Biol. Fertil. Soils 44, 113–119. https://doi.org/10.1007/s00374-007-0185-y.

Zhang, W., Liu, L., Wu, H., Zhang, T., Chen, Y., Wang, L., 2025. Snow droughts amplify compound climate extremes over the Tibetan Plateau. Commun. Earth Environ. 6, 571. https://doi.org/10.1038/s43247-025-02551-3.