# Hydrological and temperature controls on CO2 and CH4 exchange between a mid-altitude mountain peatland and the atmosphere

Alexandre Lhosmot, Marc Steinmann, Adrien Jacotot, Philippe Binet, Robin Calisti, Edward a D Mitchell, Marie-Laure Toussaint, Anne Boetsch, Fatima Laggoun-Défarge, Guillaume Bertrand

► To cite this version:

Alexandre Lhosmot, Marc Steinmann, Adrien Jacotot, Philippe Binet, Robin Calisti, et al. Hydrological and temperature controls on CO2 and CH4 exchange between a mid-altitude mountain peatland and the atmosphere. Mires and Peat, 2025, 32 (14), 10.19189/001c.143644. insu-05295444

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# Hydrological and temperature controls on CO₂ and CH₄ exchange between a mid-altitude mountain peatland and the atmosphere

Alexandre Lhosmot¹,², Marc Steinmann¹, Adrien Jacotot³,⁴, Philippe Binet¹,
Robin Calisti⁵, Edward A.D. Mitchell⁵, Marie-Laure Toussaint¹,
Anne Boetsch¹,⁶, Fatima Laggoun-Défarge⁷, Guillaume Bertrand¹

¹Chrono-Environnement, Université Marie et Louis Pasteur, UMR6249, CNRS, France

²Département de géographie, Université de Montréal, Montréal, Canada

³SARL SEMBIOSE, Saint-Denis-de-Gastines, France

⁴Sol, Agro et hydrosystèmes, Spatialisation (SAS), UMR1069, INRAE, Institut Agro, Rennes, France

⁵Laboratoire de Biodiversité du Sol, Université de Neuchâtel, Neuchâtel, Switzerland

⁶OSU Theta, Besançon, France

⁷Institut Des Sciences de La Terre d'Orléans (ISTO), UMR7327, CNRS, Université d'Orléans, Orléans, France

## SUMMARY

The aim of the present study is to understand the variability and the environmental factors controlling the fluxes of carbonaceous greenhouse gases (GHGs), methane (CH₄) and carbon dioxide (CO₂) between a temperate *Sphagnum*-dominated mid-altitude mountain peatland and the atmosphere. We conducted monthly measurements of GHG fluxes over 20 months using the chamber method. Specifically, we assessed the effects of (1) air temperature and (2) water level (WL) on GHG emissions. Open Top Chambers (OTC) were used to simulate a warming effect by passive heating of the air above the soil. To assess the effect of WL, we studied a hydrological gradient along a 35 m long transect from a near-surface "WET" area, through an "INTER" area with an intermediate WL, to a "DRY" area with a lower WL. The WET area featured a higher cover of *Sphagnum* species while the vegetation cover in the DRY area contained more vascular plants. Although all plots showed the same seasonality of GHG fluxes, considerable variability was observed among them. Raising the temperature using OTCs, which increased annual average air temperature by 0.2 °C to 0.6 °C, did not significantly affect CH₄ and CO₂ respiration (Reco) fluxes. In contrast, hydrological conditions played an important role in explaining flux variability. CH₄ fluxes were significantly higher in the WET and INTER areas (median [95 % CI] values: 17.5 [14.2, 29.0] and 20.0 [14.8, 30.4] nmol m⁻² s⁻¹) compared to the DRY area (3.4 [1.9, 9.4] nmol m⁻² s⁻¹) during all hydrological periods, i.e., humid spring, humid summer and dry summer. Reco did not vary significantly along the hydrological gradient overall, but the fluxes were lower in the WET area under humid spring (0.4 [0.3, 0.6] µmol m⁻² s⁻¹) and summer (1.7 [1.25, 2.75] µmol m⁻² s⁻¹) conditions compared to the DRY area (1.6 [1.3, 2.0] µmol m⁻² s⁻¹ in spring and 3.8 [2.7, 4.6] µmol m⁻² s⁻¹ in summer). Conversely, greater fluxes (by ~0.5 µmol m⁻² s⁻¹) were observed in the WET area during summer drought. Given that Reco emissions are expected to be higher during droughts in the DRY area, we hypothesise a possible threshold effect, such as inhibition of phenoloxidase activity and/or other enzymatic activities, which would limit organic matter decomposition. Moreover, increasing WL in the WET and INTER areas led to a drastic drop in gross primary production (GPP) corresponding to *Sphagnum* immersion.

**KEY WORDS:** chambers, greenhouse gas, hydrology, Open Top Chamber, *Sphagnum*

## INTRODUCTION

Greenhouse gas (GHG) exchanges between terrestrial surfaces and the atmosphere are spatially and temporally heterogeneous due to the large variability of ecosystems and human land uses (Luyssaert et al. 2012, Premke et al. 2016). Peatlands, which occupy only a small proportion (~3 %) of the global land area, constitute an

outstanding carbon stock of about one-third of total soil organic matter (Yu et al. 2010, Xu et al. 2018, Nichols & Peteet 2019). As a result, GHG exchanges between peatlands and the atmosphere have played a significant role in climate regulation during the Holocene, through the sequestration of atmospheric CO₂ (Yu 2011, IPCC 2013). The current and future carbon exchanges between peatlands and the atmosphere are therefore crucial for evaluating their
carbon sink or source strength (Humpenöder et al. 2020, Loisel et al. 2021, Tanneberger et al. 2021).

In peatlands, the two main GHGs (CO₂ and CH₄) are produced or consumed by biogeochemical processes which are directly and indirectly influenced by water level (WL) and soil temperature (Moore & Dalva 1993, Lafleur et al. 2005, Abdalla et al. 2016, Samson et al. 2018, Evans et al. 2021). These two factors play key roles in controlling the biotic participants (vegetation, microorganisms) involved in carbon dynamics and, as a result, have become a major focus for research (Andersen et al. 2013, Jassey et al. 2018, Antala et al. 2022). While anaerobic conditions enable peat accumulation and carbon storage by limiting the decomposition of organic matter, they also lead to production of CH₄, a powerful GHG with global warming potential 28 to 32 times that of CO₂ over a 100-year horizon (IPCC 2013, Etminan et al. 2016, Dean et al. 2018). In contrast, the respiration of aerobic microorganisms produces CO₂ and favours oxidation of CH₄ before it is released to the atmosphere (Moore & Dalva 1993, Lai 2009). Prolonged aerobic conditions caused by drainage lead to peat decomposition (i.e., peat mineralisation) and have several long-term effects on peat properties (reduced macro-porosity, elasticity and water storage capacity, increased compaction and consolidation, surface elevation loss, and overall homogenisation of soil properties and ecosystem functionality; Price et al. 2003, Gabriel et al. 2018, Gauthier et al. 2018, Howie & Hebda 2018, Liu et al. 2020). The recovery of peat properties to the pre-degradation state after rewetting is strongly influenced by the duration and intensity of prior drainage, which can lead to significant physical and chemical alterations of the peat. Severely disturbed and long-drained peatlands often exhibit increased bulk density, reduced porosity and decreased hydraulic conductivity, all of which impede rapid restoration. While rewetting can stabilise carbon losses and initiate recovery, natural conditions may take decades or longer to re-establish, depending on the severity of the degradation (Kreyling et al. 2021). Rewetting, which aims to slow down peat degradation and to restore the carbon sink function of peatlands, generally leads to a short-term (years to decades) increase in CH₄ production (Vanselow-Algan et al. 2015, Abdalla et al. 2016). However, the positive radiative effect of CH₄ emission over the short term is expected to be compensated by CO₂ sequestration over the long term (spanning several decades to centuries; Nugent et al. 2019, Günther et al. 2020, Humpenöder et al. 2020, Zak & McInnes 2022).

Climate change including an associated rise in air temperature has, together with anthropogenic disturbance (e.g., drainage for agriculture, forestry, and peat extraction), led to the desiccation of temperate and tropical peatlands. This, in turn, promotes CO₂ fluxes to the atmosphere and leads to contrasting and unclear effects on CH₄ emissions (Swindles et al. 2019, Loisel et al. 2021, Kang et al. 2022). In addition, increased temperature and lowered WL favour the colonisation of peatlands by vascular plants, resulting in positive feedback that further amplifies the decrease in WL and the increase of CO₂ emissions (Antala et al. 2022).

Moreover, the complex interactions between vegetation, microorganisms, peat properties, water chemistry and WL complicate the assessment of consequences for GHG exchange between peatlands and the atmosphere (Parish et al. 2008, Bragazza et al. 2013, Waddington et al. 2015, Arsenault et al. 2019, Antala et al. 2022). Therefore, these global changes lead to non-linear responses in the carbon cycles of peatlands, with threshold effects (Bragazza 2008, Page & Baird 2016, Jassey et al. 2018, Li et al. 2021b, Liu et al. 2022).

Peatlands are also highly diversified ecosystems and thus subject to small-scale variations in WL, vegetation, microtopography and hydrophysical peat properties (Joosten & Clarke 2002, Lindsay 2010, Juszczak et al. 2013, Arsenault et al. 2019, Ahmad et al. 2020a, Miaorun Wang et al. 2021, Briones et al. 2022, Couwenberg et al. 2022). For example, microtopography causes significant variability in water depth with respect to surface vegetation, which leads to diversified microhabitats (e.g., hummocks, lawns or hollows). Microtopography also influences organic matter recycling, leading to variations in peat growth rates and GHG emissions (Chaudhary et al. 2018, Ming Wang et al. 2021, Perryman et al. 2022).

Beyond the internal heterogeneity of an individual peatland, considerable differences exist in peatland types and locations depending on altitude, latitude or geological framework (Joosten 2016, Wheeler & Proctor 2000). All of these heterogeneities, operating across spatial and temporal scales, influence GHG exchange with the atmosphere and must therefore be quantified to better delineate the future of peatlands regarding their ecosystem services (Limpens et al. 2008). Within this general scheme, temperate peatlands located at the southern distribution limit of northern peatlands (Xu et al. 2018) may be regarded “as ‘ecosystem sentinels’ for climate change, acting as early warning indicators of climate-carbon feedbacks” (Briones et al. 2022). Indeed, they may

<figure><img src="image_2.png" /></figure>
experience temperature conditions that ecosystems farther north will not reach until later.

Temperature is a key driver for aerobic and anaerobic respiration, as well as for photosynthesis. Therefore, the increase of air temperature induced by climate change raises questions about the direct and indirect (e.g., vegetation, WL) effects of temperature on GHG exchange between peatlands and the atmosphere (Harenda et al. 2018, Bertrand et al. 2021, Li et al. 2021b, Antala et al. 2022). One approach to improving our ability to predict future fluxes is to install Open Top Chambers (OTC) which can locally induce passive warming of the air above ground (Arft et al. 1999, Li et al. 2021a). To our knowledge, few recent studies have investigated the effects of OTCs on GHG fluxes in temperate peatlands, and even fewer have been conducted in mid-altitude mountain settings (Johnson et al. 2013, Ward et al. 2013, Pearson et al. 2015, Lamentowicz et al. 2016, Oestmann et al. 2022, Salmon et al. 2022).

# METHODS

## Study site

The study site is located in the French Jura Mountains near the town of Frasne (840 m a.s.l., 46.826° N, 6.173° E; Figure 1A). The Frasne peatland is a large (> 300 ha) complex of different peatland types (e.g., fen, raised bog, wooded and transitional peatland). Its ecological value has been recognised by the RAMSAR convention (RAMSAR site), it is part of the European Natura 2000 network, and approximately 100 ha of the complex is also a regional nature reserve. The Forbonnet site is a transitional peatland within the complex that has been monitored since 2008. The site has belonged to the National Observatory of Peatlands (SNO Tourbières; Bertrand et al. 2021, Gogo et al. 2021) since 2012 and is integrated into the French Critical Zone research infrastructure (OZCAR; Gaillardet et al. 2018). The site also serves as an observatory for the Zone Atelier of Arc Jurassien, which is dedicated to studying the interplay between humans and nature (ZAAJ website). As a French priority research site, Forbonnet benefits from detailed spatio-temporal monitoring of meteorology and hydrology.

According to the Köppen-Geiger classification the climate of the site falls between classes Cfb and Dfb (Rubel et al. 2017), defining a temperate climate with contrasted seasons. Average annual air

temperature is 7 °C and monthly averages range from 0 °C (December to February) to 15 °C in July and August. Annual precipitation ranges from 1293 to 2110 mm (average ± standard deviation = 1618 ± 258 mm) and is uniformly distributed throughout the year with a mean of 135 ± 25 mm month⁻¹ (averages over the period 2009–2019; Toussaint et al. 2020a).

Ecologically, the Forbonnet peatland is a *Sphagnum*-dominated zone, where species such as *Andromeda polifolia*, *Vaccinium oxycoccos*, *Eriophorum vaginatum*, *Scheuchzeria palustris*, *Drosera* spp. and *Calluna vulgaris* are also observed. The peatland is surrounded by more mature and topographically higher peatlands covered with pine and spruce trees, that are progressively colonising the centre of the site. In addition, patches of *Phragmites* on the contours of the Forbonnet peatland indicate localised minerotrophic (i.e., nutrient-rich) conditions.

The Forbonnet peatland was equipped with an experimental research station in 2008 to study the effect of air temperature warming using OTC devices according to the ITEX protocol (Arft et al. 1999, Laggoun-Défarge et al. 2008). The chambers were installed along a hydrological gradient (Figures 1B–D) characterised by differences in WL (Toussaint et al. 2020b), microtopography and vegetation cover. The “WET” area is almost flat and homogeneous (lawn area) while the “DRY” area is characterised by more-pronounced microtopography with hummocks. The proportion of *Sphagnum* is significantly lower in the DRY than in the WET area, and reciprocally for vascular plants (Buttler et al. 2015). Along this hydrological gradient, which includes an intermediate location (referred to as INTER), measuring plots were distributed across the gradient with both “control” and “heated” OTC stations: DC (control) and DH (heated) in the DRY area, IC (control) and IH (heated) in the INTER area; and WC (control) and WH (heated) in the WET area.

## Hydrometeorological monitoring

Air temperature was measured at each plot 10 cm above the soil surface using thermocouples (Mesurex, TT20100T). The research station is also equipped with a meteorological station recording air temperature and relative humidity (T, RH, HMP155A, Vaisala, Vantaa Finland), atmospheric pressure (278, SETRA, Boxborough MA, USA), Photosynthetic Photon Flux Density (PPFD, SKP215 Skye Ltd, Llandrindod Wells, UK), wind direction, and wind speed every 30 minutes (Windsonic Gill, Lymington,
UK; see Gogo *et al.* (2021) for more details). Due to technical issues with the automatic raingauge during the study period, precipitation was measured monthly on site with a rain collector normalised for isotope characterisation (Palmex Rain Sampler 1C; see Lhosmot *et al.* 2022a). The WL was monitored every 30 minutes by two piezometers equipped with OTT Orpheus Mini pressure probes and installed in the WET and DRY areas along the research station boardwalk (± 0.1 %; Toussaint *et al.* 2020b).

**GHG flux and auxiliary data measurements**

Carbonaceous greenhouse gas (CO₂ and CH₄) fluxes between the peatland and the atmosphere were measured monthly from February 2020 to September 2021, using the non-steady state closed chamber method (Hutchinson & Livingston 1993). Fluxes were measured on six plots, constituting three pairs of control (C) and OTC (H) plots (Figure 1C). These three pairs were placed along the hydrological DRY-INTER-WET gradient (Figure 1C).

<figure><img src="image_3.png" /></figure>

Figure 1. (A) Location of the Frasne peatland (CapCarto, 2016). (B) Location of the study plot and the boardwalk research station (IGN, 2018). (C) Location of the WET, INTER and DRY study areas (Open Lab Dream, UBFC, G. Bertrand, March 2021). The beige-blue graph next to the boardwalk is a simplified microtopographic cross-section along the WET-INTER-DRY transect. It symbolises the hummocks in the DRY area in beige and the transition towards the almost flat and wetter INTER and WET areas in blue. The median water level over the two study years (2020 and 2021) was -0.10 m and -0.16 m for the DRY and WET areas, respectively, based on 30 minute interval data. (D) Open Top Chamber.

<figure><img src="image_4.png" /></figure>
To determine the GHG fluxes, we used a Los Gatos Research infrared laser spectrometer analyser (LGR “Greenhouse Gas Analyzer [CH4, CO2, H2O] Ultraportable,” 2019), connected to a transparent plexiglass chamber (29 cm diameter and 51 cm height) in which GHG concentrations were measured for 2–4 minutes. To ensure proper homogenisation, the air inside the chamber was continuously mixed using an internal pump system. Since the chamber is translucent, the measurements represent the Net Ecosystem Exchange (NEE) of CO₂. To measure the ecosystem respiration (Reco), a second measurement was conducted using the same chamber, this time with an opaque cover. After the opaque cover was installed, measurements were considered following a stabilisation period of up to 20 seconds. The gross primary production (GPP) was then calculated as follows:

$$ (NEE = \text{Reco} - \text{GPP}) \quad [1] $$

where Reco and GPP are expressed as absolute values. In this study, negative fluxes are defined as fluxes from the atmosphere to the soil, and reciprocally. Accordingly, a negative sign was assigned to GPP values. Methane measurements were similarly performed with and without the opaque cover, but no difference was observed. All flux measurements were conducted during daytime, typically between 10 a.m. and 6 p.m. Finally, the flux of GHG between the peatland and the atmosphere ($F_{CO2, CH4}$) was calculated using Equation 2 (Jacotot et al. 2019, Korkiakoski et al. 2019):

$$ F_{(CO2, CH4)} = \frac{\frac{V}{S} \times dC}{R \times T} \times P \quad [2] $$

where $F_{(CO2, CH4)}$ is the flux (µmol m⁻² s⁻¹) of CO₂ and CH₄ respectively, V the volume of the chamber (m³), S the area of soil covered by the chamber (m²), dC the difference of GHG concentration in the chamber between the start and the end of the measurement (ppm), dt the duration of the measurement (s), P the air pressure in the chamber (Pa), R the universal gas constant (8.314 J mol⁻¹ K⁻¹), and T the air temperature inside the chamber (K).

During each measurement, both air temperature and pressure were measured in the chamber with a Hobo Tidbit V2 probe (accuracy ± 0.21 °C) and a Baro Diver (Van Essen instruments, accuracy ± 49 Pa), respectively. In addition, the Photosynthetic Photon Flux Density (PPFD) was recorded every 30 seconds in the chamber with a light meter (ULM-500, WALZ).

## Evaluation of CO₂ ecosystem respiration (Reco) sensitivity to temperature: the apparent Q₁₀

The sensitivity of ecosystem respiration to temperature variation is critical in the context of global warming. This sensitivity can be quantified using the Q₁₀ parameter (Davidson et al. 2006), which is a temperature coefficient based on the Arrhenius (1889) law that relates variations of chemical reaction rates to temperature changes. More precisely, k, the reaction rate (s⁻¹), is related to the temperature and the activation energy by the following equation (Arrhenius 1889):

$$ k = A e^{\frac{-E_a}{RT}} \quad [3] $$

where A is the Arrhenius constant in s⁻¹ with ln(A) representing the y-intercept of the Arrhenius plot (ln(k) = 1/T), Eₐ is the activation energy required to initiate a chemical reaction in J mol⁻¹, R is the universal gas constant in J mol⁻¹ K⁻¹ and T is the absolute temperature (K).

The change in GHG production rates as a function of temperature variation serves as an indicator of organic matter decomposition sensitivity to temperature (Curiel Yuste et al. 2004, Davidson et al. 2006). As our study is not directly based on GHG production rates, but on GHG fluxes at the peatland–atmosphere interface, the calculated Eₐ and Q₁₀ values correspond to apparent values (referred to as Eₐ and Q₁₀; Davidson et al. 2006). Q₁₀ is an index based on the difference in reaction rate triggered by a temperature change of 10 °C. It can therefore be expressed as follows:

$$ Q_{10} = e^{\left(\frac{E_a}{R}\left(\frac{10}{T(T+10)}\right)\right)} \quad [4] $$

Eₐ was calculated by the following equation after using the logarithm:

$$ F_{GHG} = A e^{\frac{-E_a}{RT}} \quad [5] $$

The air temperature measured in each plot at 10 cm above the soil surface was used to calculate Q₁₀. We showed in a previous study that methanotrophy is an important process controlling methane fluxes in the Forbonnet peatland (Lhosmot et al. 2022b). Hence, measured CH₄ fluxes integrate both production and oxidation of CH₄. Consequently, apparent Q₁₀ is not a direct indicator of the CH₄ production alone, and we did not use Q₁₀ for CH₄.
Analysis of GHG fluxes

To assess the effect of warming on GHG emissions (Reco and CH₄), we considered measurement campaigns for which OTC and control plot data were available along with all hydrological conditions (WET, INTER and DRY). Given that passive warming does not appear to be a discriminating factor (see section “Reco and CH₄ fluxes for OTC and control plots”), we finally merged the OTC and control plot data and grouped them only by hydrological conditions. This allowed a larger dataset to be used to assess the influence of WL on Reco and CH₄ fluxes. Considering the occurrence of two

contrasting hydrological conditions during the summers of 2020 and 2021 and the implementation of a high frequency GHG measurement campaign during the wet spring of 2021, we split the dataset into three seasonal conditions: (1) dry/drought summer (June–September 2020), (2) wet summer (June–September 2021), and wet spring (May 2021).

PPFD, which strongly controls GPP (Haraguchi & Yamada 2011, Leroy et al. 2019), showed coherent seasonal variations but high within-day variation due to cloud cover and diurnal cycles (Figure 2). Consequently, rapid variations in both PPFD and T data limited the availability of simultaneous

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

Figure 2. Daily means of hydro-meteorological data measured at the meteorological station. (A) Air Temperature. (B) Photosynthetic Photon Flux Density (PPFD, µmol m⁻² s⁻¹). (C) Water Level (WL, m) at the WET and DRY areas (left y-axis) and average daily precipitation (mm, right y-axis). The black vertical dashed line indicates the days of chamber GHG flux measurements. The grey vertical band in May 2021 indicates an intensive campaign of chamber GHG flux measurements.

<figure><img src="image_6.png" /></figure>
measurements across the plots, thereby restricting the analysis of the relationship between GPP, air temperature and WL. To address this, the dataset was divided into two groups (DRY and WET-INTER areas) and environmental conditions favourable for photosynthetic activity (air temperature above 10 °C and PPFD between 100 and 2000 µmol m⁻² s⁻¹) were selected. This approach allowed us to identify a hydrological influence on GPP (see section “GPP response to different hydrological conditions”).

## Statistical comparison

Statistical tests were conducted to assess differences in data distribution across similar periods for WL (between WET and DRY areas), air temperature above the soil surface (between OTC and control plots), and GHG fluxes (between OTC and control plots, as well as across different hydrological conditions [WET, INTER, DRY]). Due to the lack of normality and/or homoscedasticity in the datasets and because measurements were paired, the Wilcoxon signed-rank test was employed.

# RESULTS

## Hydrometeorological conditions

The study period integrated two growing seasons (2020 and 2021, Figures 2A and 2B). The daily air temperatures in the peatland were highest in summer, reaching 23 °C in July 2020, and lowest in winter, reaching -11.9 °C in January 2021, with a mean of 7.2 °C for 2020–2021 (Figure 2A). The PPFD showed a seasonal pattern similar to that for temperature, with the highest mean daily (24 hours) values in summer (848 µmol m⁻² s⁻¹) and values close to 0 in winter (Figure 2B). The cumulative rainfall in 2020 (from 28 Jan 2020 to 14 Dec 2020) and 2021 (from 29 Jan 2021 to 14 Dec 2021) was 1328 and 1618 mm, respectively. Reflecting precipitation, WL median in the WET area was significantly lower in 2020 (median [95 % CI]; -0.13 [-0.14, -0.11] m) compared to 2021 (-0.10 [-0.11, -0.10] m; p-value <0.001; Figures 2C, 3A and 3B). The growing seasons (May to September) were particularly contrasting, with rainfall totalling 653 mm in 2020 and 1016 mm in 2021, resulting in a significant WL difference during this period (2020: -0.19 [-0.21, -0.17] m and 2021: -0.11 [-0.12, -0.09] m; p-value <0.001; data from the WET area; Figure 2C). The cumulative potential evapotranspiration over May to September, calculated from daily air temperature using the formula of Oudin et al. (2005), was slightly higher in 2020 (472 mm) than in 2021 (449 mm). Furthermore, the WL distribution over the

two years showed significantly lower values in the DRY (-0.16 [-0.17, -0.15] m) compared to the WET area (-0.10 [-0.11, -0.10] m; p-value <0.001; Figures 3A and 3B).

The effect of OTC on air T was evaluated across the different plots for 2020–2021 (Table S1 in the supplementary material). For all areas (DRY, INTER, WET), mean annual air T was 0.2 to 0.6 °C higher in OTC plots compared to their corresponding control plots. This temperature difference increased with PPFD (p-value < 0.001; Table S1, Figures 4A and 4B). For PPFD values above 1000 µmol m⁻² s⁻¹, OTCs increased air T from 0.6 to 1.5 °C. The temperature difference reached 1.7 to 2.0 °C for PPFD above 2000 µmol m⁻² s⁻¹ (Table S1). In contrast, for PPFD below 1 µmol m⁻² s⁻¹ the temperature difference dropped to 0.2 to 0.5 °C (Figure 4A).

## GHG fluxes

### Seasonal variations of Reco, NEE, GPP and CH₄ fluxes

Raw flux data with associated environmental variables are available in the supplementary material (Table S2). While all fluxes were close to zero in winter, Reco reached 5.9 µmol m⁻² s⁻¹ on 23 Jun 2020, and 5.1 µmol m⁻² s⁻¹ on 14 Sep 2021. GPP reached

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

Figure 3. Violin plot showing the distribution of water level (WL) measurements for DRY and WET areas in 2020 (A) and 2021 (B), based on data for 30-minute intervals. The distribution is based on Kernel Density Estimation. The embedded boxplot indicates the interquartile range (IQR), with the median marked by a horizontal line. Whiskers extend to 1.5 times the IQR.
-9.6 and -8.9 µmol m⁻² s⁻¹ on 20 July in 2020 and 2021, respectively. NEE ranged from 3.4 to -6.1 µmol m⁻² s⁻¹. Over the two years, the highest NEE values were recorded on 20 Jul 2020 (-4.6 µmol m⁻² s⁻¹) and 20 Jul 2021 (-6.1 µmol m⁻² s⁻¹). Finally, CH₄ fluxes were mostly between 0 and 100 nmol m⁻² s⁻¹, reaching 187 and 344 nmol m⁻² s⁻¹ on 20 Jul 2020 and 14 Sep 2021, respectively. Flux variability between plots was close to zero in winter and fluxes increased in summer, reaching maxima of 4.6, 5.8, 8.1 µmol m⁻² s⁻¹ and 326 nmol m⁻² s⁻¹ for NEE, Reco, GPP and CH₄, respectively (Figures 5A, 5B, 5C, 5D). The general trend indicates that the contrast between plots increases with increasing fluxes.

### Reco and CH₄ fluxes for OTC and control plots

The statistical comparison showed no significant differences in Reco and CH₄ fluxes between the OTC and control plots along the hydrological gradient (Figures 6A and 6B; p-value > 0.05; Wilcoxon signed-rank test).

### Reco and CH₄ fluxes along the hydrological gradient
Figures 7A and 7B show a statistical comparison of the Reco and CH₄ fluxes along the WET-INTER-DRY hydrological gradient, including OTC and control plots (Wilcoxon signed-rank test). For this comparison, only datasets from field campaigns where fluxes were available for all six plots were used.

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

Figure 4. Violin plot showing air temperature (T, °C) distribution for the six plots over the 2020–2021 period. The measurements were taken every 30 minutes at 10 cm above the soil surface. Subplots show (A) low light conditions (PPFD < 1 µmol m⁻² s⁻¹) and (B) elevated light conditions (PPFD > 1000 µmol m⁻² s⁻¹), respectively. The distribution is based on Kernel Density Estimation. The embedded boxplot indicates the interquartile range (IQR), with the median marked by a horizontal line. Whiskers extend to 1.5 times the IQR.

<figure><img src="image_9.png" /></figure>
<figure><img src="image_10.png" /><figcaption>Figure 5. GHG fluxes of the individual plots. DC: control plot in the DRY area; DH: OTC plot in the DRY area; IC: control plot in the INTER area; IH: OTC plot in the INTER area; WC: control plot in the WET area; and WH: OTC plot in the WET area.</figcaption></figure>

<figure><img src="image_11.png" /></figure>
<figure><img src="image_12.png" /><figcaption>Figure 6. Violin plot showing Reco (A) and CH₄ flux (B) distribution for each study plot. The distribution is based on Kernel Density Estimation. The embedded boxplot indicates the interquartile range (IQR), with the median marked by a horizontal line. Whiskers extend to 1.5 times the IQR. The statistical comparison (p-value) between control and OTC plots was realised using the Wilcoxon signed-rank test. The y-axis of subplot B has been shortened to improve readability and to prevent the display of two extreme values (for IH, CH₄ flux = 344 nmol m⁻² s⁻¹; and for WH, CH₄ flux = 187 nmol m⁻² s⁻¹).</figcaption></figure>

<figure><img src="image_13.png" /></figure>
<figure><img src="image_14.png" /><figcaption>Figure 7. Violin plot showing Reco (A) and CH₄ flux (B) distribution for each hydrological condition (DRY, INTER and WET areas) including OTC and control plots. The distribution is based on Kernel Density Estimation. The embedded boxplot indicates the interquartile range (IQR), with the median marked by a horizontal line. Whiskers extend to 1.5 times the IQR. The statistical comparison (p-value) between plots of different hydrological condition was realised using the Wilcoxon signed-rank test. The y-axis of subplot B has been shortened to improve readability and to prevent the display of two extreme values (for IH, CH₄ flux = 344 nmol m⁻² s⁻¹; and for WH, CH₄ flux = 187 nmol m⁻² s⁻¹).</figcaption></figure>

<figure><img src="image_15.png" /></figure>
For Reco, the median flux for the DRY (median [95% CI]; 1.7 [0.9, 2.3] µmol m⁻² s⁻¹) and INTER (1.7 [1.0, 2.4] µmol m⁻² s⁻¹) plots was slightly higher than for the WET area (1.2 [0.8, 1.7] µmol m⁻² s⁻¹; Figure 7A). Regarding specific hydrological conditions (i.e., dry/drought summer [Jun–Sep 2020], wet summer [Jun–Sep 2021] and wet spring [May 2021]), the WET–DRY gradient was consistent with the gradient observed for the entire dataset in 2021 (with wet summer and wet spring conditions). For the wet summer and wet spring periods of 2021, Reco was substantially lower for the WET (1.7 [1.25, 2.75] µmol m⁻² s⁻¹ and 0.4 [0.3, 0.6] µmol m⁻² s⁻¹) than for the DRY area (3.8 [2.7, 4.6] µmol m⁻² s⁻¹ and 1.6 [1.3, 2.0] µmol m⁻² s⁻¹, respectively). However, during the dry summer of 2020, Reco for the DRY area (2.0 [1.3, 3.8] µmol m⁻² s⁻¹) was slightly lower than for the INTER and WET areas (2.7 [1.7, 5.0] and 2.5 [1.4, 3.8] µmol m⁻² s⁻¹, respectively). In addition, Reco for the DRY area was lower in the dry (2.0 [1.3, 3.8] µmol m⁻² s⁻¹) than in the wet summer (3.8 [2.7, 4.6] µmol m⁻² s⁻¹), while average WL was -0.27 [-0.29, -0.25] m for the dry and -0.15 [-0.17, -0.15] m

for the wet summer (June–September, DRY area, Figure 2C).

The mean CH₄ fluxes calculated from all measurements were significantly higher (p-value <0.001) for the WET and INTER areas (17.5 [14.2, 29.0] and 20.0 [14.8, 30.4] nmol m⁻² s⁻¹) compared to the DRY area (3.4 [1.9, 9.4] nmol m⁻² s⁻¹, Figure 7B). In contrast, the mean CH₄ fluxes for INTER and WET areas were not significantly different (p-value = 0.92, Figure 7B). This spatial pattern was also observed at the seasonal scale.

### GPP response to different hydrological conditions

In the WET-INTER areas, GPP showed a slight negative correlation with WL, with a distinct break observed at a WL of approximately -8 cm. For WL values below -8 cm GPP exhibited strong variability, ranging from 0 to -10 µmol m⁻² s⁻¹. In contrast, for shallow WL values above -8 cm, GPP converged towards a narrow range between -2 and 0 µmol m⁻² s⁻¹ (Figure 8A). No similar trend was observed for the DRY area, where GPP did not display a clear relationship with WL (Figure 8B).

<figure><img src="image_16.png" /></figure>

Figure 8. Correlation plot between GPP (µmol m⁻² s⁻¹) and WL (m) (A) in the WET and INTER areas and (B) in the DRY area. Flux data are shown only for air temperatures above 10 °C and PPFD values between 100 and 2000 µmol m⁻² s⁻¹. The blue dashed line in subplot A represents the WL threshold of -0.08 m discussed in the text. The colours of the plot symbols reflect the PPFD conditions when fluxes were measured, specified by the gradient bar to the right.
## Apparent temperature sensitivity of ecosystem respiration

We sorted the GHG fluxes by plot using all available data and then calculated the apparent $Q_{10}$ and the apparent $E_a$ for Reco (Table 1). $E_a$ ranged from 48 to 88.6 KJ mol$^{-1}$ (average of 65 KJ mol$^{-1}$). It was slightly lower in the DRY area (~48 KJ mol$^{-1}$) compared to the INTER and WET areas (60.3 to 88.6 KJ mol$^{-1}$). No discernible difference was observed between OTC and control plots. Consequently, the $Q_{10}$ (0–10 °C) was around 2.1 in the DRY area and achieved higher values of 2.6–4 in the INTER and WET areas (Table 1).

## DISCUSSION

Northern peatlands have captured significant amounts (545–1055 Gt) of carbon from the atmosphere over the past millennia (Gorham 1991, Nichols & Peteet 2019). However, both climate and land use changes currently affect the complex functioning of these ecosystems, which in turn influences the rate of carbon exchange with the atmosphere (Loisel et al. 2021). Understanding the processes involved in the control of these exchanges remains a major challenge, particularly for modelling fluxes at continental scale and for their integration into global climate models (Frolking et al. 2009). In the present study, the flux ranges observed at Forbonnet are comparable to those measured in other temperate peatlands. For instance, Reco ranges from 0 to 5.9 µmol m$^{-2}$ s$^{-1}$, which is within the range reported by Samson et al. (2018) for northern Poland (0 to 4.9 µmol m$^{-2}$ s$^{-1}$) and by Lafleur et al. (2005) for southern Canada (<1 to 4.5 µmol m$^{-2}$ s$^{-1}$). However, the values are lower than those observed by Juszczak et al. (2013) in western Poland (up to 15 µmol m$^{-2}$ s$^{-1}$).

Regarding GPP, the fluxes remained below -10 µmol m$^{-2}$ s$^{-1}$ in our study, which is consistent with results from other *Sphagnum*-dominated temperate peatlands (Johnson et al. 2013, Leroy et al. 2019), although higher values have also been documented (e.g., up to 20 µmol m$^{-2}$ s$^{-1}$; Ueyama et al. 2020). Similarly, our CH$_4$ fluxes, predominantly ranging from 0 to 100 nmol m$^{-2}$ s$^{-1}$, agree with other studies conducted in temperate peatlands (Leroy et al. 2019, Ueyama et al. 2020).

However, the data reported in the present study indicate that both Reco and CH$_4$ fluxes exhibit large variability over a distance of only 35 m (Figure 5). Identification of the drivers behind this variability represents a substantial challenge for our comprehension of GHG emissions from peatlands. The aim of the following sections is, therefore, to examine CO$_2$ respiration and CH$_4$ fluxes with respect to the two primary forcing parameters, which are air temperature and hydrological conditions.

## Passive warming does not directly affect GHG emissions

The comparison of Reco and CH$_4$ fluxes between the OTC and control plots across the different hydrological conditions (DRY, INTER and WET) did not show significant differences (Figure 6). This outcome differs from the findings of Oestmann et al. (2022) and Samson et al. (2018), who both observed increased GHG emissions in OTC plots on temperate peatland, although the patterns were complex. Oestmann et al. (2022) observed increased GHG emissions, dominated by CH$_4$ fluxes, in OTCs on a near-natural bog in north-western Germany. Samson et al. (2018) observed higher Reco in OTC plots under WET and DRY conditions (due to water level manipulation) in a poor fen in northern Poland. Over the course of a two-year experiment, these authors

<table><caption>Table 1. Apparent $E_a$ and $Q_{10}$ of the Reco fluxes for the different plots.</caption><thead><tr><th>Hydrological gradient</th><th>Temperature control</th><th>Plot</th><th>E<sub>a</sub> (KJ mol<sup>-1</sup>)</th><th>Q<sub>10</sub> (0–10 °C)</th><th>Q<sub>10</sub> (10–20 °C)</th></tr></thead><tbody><tr><td rowspan="2">DRY</td><td>Control</td><td>DC</td><td>48.0</td><td>2.1</td><td>2.0</td></tr><tr><td>OTC</td><td>DH</td><td>48.3</td><td>2.1</td><td>2.0</td></tr><tr><td rowspan="2">INTER</td><td>Control</td><td>IC</td><td>88.6</td><td>4.0</td><td>3.6</td></tr><tr><td>OTC</td><td>IH</td><td>60.3</td><td>2.6</td><td>2.4</td></tr><tr><td rowspan="2">WET</td><td>Control</td><td>WC</td><td>66.1</td><td>2.8</td><td>2.6</td></tr><tr><td>OTC</td><td>WH</td><td>79.1</td><td>3.4</td><td>3.1</td></tr></tbody></table>
measured the highest Reco in plots under both DRY and OTC conditions, demonstrating that the combined effect of drying and warming is stronger than either factor alone. However, in a cold-temperate peatland in the northern USA, no contrasts in Reco and CH₄ fluxes were observed between OTC and control plots (Johnson *et al.* 2013). Similarly, Pearson *et al.* (2015) found no significant differences in Reco and CH₄ fluxes between OTC and control plots across three boreal peatlands in Finland, under a variety of hydrological conditions (pristine, short-term and long-term drainage).

The non-uniform response of GHG fluxes to OTC treatment might be explained by several factors. First, the OTC effect on soil temperature is not homogeneous. Johnson *et al.* (2013) found no difference in the seasonal average peat temperature between OTC and control plots. Regarding daily patterns, they observed that the difference in peat temperature at 5 cm depth was highly variable, depending on radiation and wind conditions. We observed that OTC increased air temperature above the soil most noticeably under high PPFD conditions (Table S1) but, using the OTC and control plots of the Forbonnet station, Delarue *et al.* (2011) found no significant temperature differences in peat below 7 cm depth. In contrast to an expected increase of soil temperature (on average 0.3 to 0.5 °C) in OTC plots, as observed by Oestmann *et al.* (2022), some studies have even showed that the surface peat temperature in OTC plots is lower due to enhanced latent heat energy loss (Dabros *et al.* 2010, Górecki *et al.* 2021). As the temperature of the peat surface is a key driver for CH₄ and Reco fluxes (Ueyama *et al.* 2020, Li *et al.* 2021b), the non-significant or possible cooling effect of OTC on peat temperature could partially explain the absence of coherent trends.

Also working at Forbonnet, Binet *et al.* (2017) showed that the OTC effect changed the fungal root symbiosis and enzyme activities of *A. polifolia* that are involved in carbon cycling and the associated Reco, albeit to varying degrees depending on the hydrological gradient. Hence, the temporal variability of sunlight and wind (Figure 2B), as well as the complexity of the OTC effect on peat temperature and microbial activity, could explain the lack of observable OTC effects on Reco and CH₄ fluxes. Given the superposition of various and complex factors controlling carbon fluxes in peatlands, the OTC effect might be overridden by other drivers. In our study the variability amongst plots of vegetation, microbial communities and peat properties introduces potential for a significant OTC effect on GHG fluxes to be obscured owing to the small number of plots, consistently with observations

elsewhere (Juszczak *et al.* 2013, Ward *et al.* 2013). For instance, Ward *et al.* (2013) observed that CH₄ emissions were more strongly controlled by vegetation composition than by warming in a temperate blanket bog in northern England.

## Hydrological contrasts affect GHG emissions depending on the season

### CO₂ ecosystem respiration (Reco)

The higher Reco measured in the DRY area aligns with expectations. The drier conditions, coupled with a higher proportion of vascular plants and their associated litter, favour aerobic decomposition of organic matter, thereby increasing Reco (Limpens *et al.* 2008, Leroy *et al.* 2017, Li *et al.* 2021b). The evaluation of the apparent sensitivity of Reco ($Q_{10}$, $E_a$) to temperature variations highlighted consistent differences between the WET-INTER ($Q_{10}$ between 2.6 and 4) and DRY areas ($Q_{10}$ = 2.1; Table 1). The difference in $Q_{10}$ values is even more significant than the absolute value of $Q_{10}$ itself. The temperature range over which $Q_{10}$ is calculated (i.e., $Q_{10}$ for temperature variation between 0 and 10 °C) and the specific temperature utilised (i.e., air or different soil depths) can change the absolute value of $Q_{10}$ (Samson *et al.* 2018, Helbig *et al.* 2019). The variability of $Q_{10}$ observed at Forbonnet may reflect variations between the WET-INTER and DRY areas in terms of the lability of organic matter, which is a determining factor triggering CO₂ production from organic carbon decomposition (Conant *et al.* 2008). This hypothesis is consistent with observations from Leroy *et al.* (2017), who conducted a mesocosm experiment with peat cores from a temperate peatland. These authors observed higher sensitivity of Reco to temperature in plots dominated by *Sphagnum* ($Q_{10}$ = 3.75, $E_a$ = 92.2 KJ mol⁻¹) compared to plots with vascular plants (*Molinia*, $Q_{10}$ = 2.58, $E_a$ = 65.7 KJ mol⁻¹) that were suspected to release exudates of labile organic compounds. Consistent with our findings, Samson *et al.* (2018), found higher $Q_{10}$ for wetter (from 2.9 to 6.1) than for drier areas (2 to 4) in a temperate peatland. However, this is the opposite of what is generally observed in peatlands, i.e., an increase in $Q_{10}$ under drier conditions (Liu *et al.* 2024). This contrast suggests that, in the studies of Samson *et al.* (2018) and ourselves, higher temperatures were generally associated with lower water level and thus drier conditions. According to Samson *et al.* (2018), the higher $Q_{10}$ in the wettest area indicated “a high reactivity of respiration rate when the organic matter got exposed to aerobic conditions”.

Interestingly, the higher Reco observed in the DRY area did not persist during the dry summer
period of 2020. Although the difference was not significant, it may indicate a threshold at which Reco slows down. While it is commonly accepted that Reco increases with WL drawdown, the underlying processes are complex and non-linear. For instance, Jassey et al. (2018) showed, in a peatland in northern Poland, that the increase in Reco only became significant at WL below -25 cm. In contrast, Liu et al. (2022) found in a mountainous peatland in China that Reco increased with WL drawdown until a value of -30 cm was reached, beyond which the flux no longer changed with WL depth. Other studies concluded that the heterotrophic respiration of microorganisms and the related activity of phenol-oxidase enzymes may be drastically reduced during intense droughts (Criquet et al. 2000, Kang et al. 2022). This mechanism may explain why Reco was lower in the DRY area during 2020, when WL dropped below -30 cm (Figure 2C).

### CH₄ fluxes

Higher CH₄ fluxes for the WET and INTER areas strongly suggest a hydrological control over these fluxes. The reduced thickness of the aerobic peat layer due to the near-surface WL in these environments might limit oxidation and simultaneously favour CH₄ production by the thicker anaerobic compartment. Consistently, a positive relationship between CH₄ emissions and WL has been observed in numerous studies (Moore & Knowles 1989, Turetsky et al. 2014, Abdalla et al. 2016). In addition, our results align with those of Johnson et al. (2013), who observed lower CH₄ emissions in the driest hydrological environment (hummocks) of a cold-temperate peatland.

The hydrological WET-INTER-DRY gradient promoted non-gradual significant differences in CH₄ fluxes. Indeed, there was a marked change at the DRY-INTER transition, suggesting rapid lateral shifts in the biogeochemical behaviour of the peatland at the metre scale. Additional factors beyond WL may have contributed to this rapid spatial change of CH₄ fluxes. One of these could be the WL-controlled spatial variation of vegetation between the DRY and WET-INTER areas. This botanical contrast, characterised by denser vascular plant coverage in the DRY area, could have led to changes in plant litter and microbial communities, both of which play key roles in organic matter recycling (Trinder et al. 2008, Jassey et al. 2011a,b, Andersen et al. 2013). For instance, Strakova et al. (2011) showed that vegetation type and its associated litter influence microbial activity more strongly than decreasing WL. While the flat terrain in our experimental transect suggested gradual WL changes,

it is likely that the pronounced microtopography in the DRY area accentuated the WL difference compared to the INTER and WET areas.

### Effect of water level on GPP

While the variability of GPP reflected the seasonality of PPFD intensity and of plant phenological stages (Figures 2A and 2B), our data also show faster fluctuations superimposed on this seasonality (Figure 5C). The seasonal dynamics of plant phenology and differences in vegetation cover between plots, as well as hydrological and passive warming effects, provide a potential explanation of this short-term and inter-plot variability in GPP (Tuittila et al. 2004, Korrensalo et al. 2017, Walker et al. 2017, Järveoja et al. 2018, Peichl et al. 2018). GPP responds rapidly to changes in PPFD (e.g., diurnal cycle, fluctuations in cloud cover; Chojnicki et al. 2010) because the photosynthetic rate increases with PPFD until it reaches a saturation point which depends on plant species (Haraguchi & Yamada 2011, Leroy et al. 2019). In our experiment, the variability of PPFD complicated the analysis of the forcing factors influencing GPP, which is an essential component of the net ecosystem carbon balance (Chapin et al. 2006). A more extensive dataset, including many GPP values across a wide range of PPFD fluxes under different conditions and seasons (e.g., by using different light filters on the chambers) would be necessary to disentangle the forcing effects on GPP. Nevertheless, our results show a clear influence of hydrological conditions on GPP (Figures 8A and 8B). The abrupt drop in GPP in the WET and INTER areas at WL above -8 cm could be attributed to the immersion of *Sphagnum* mosses, which significantly limited GPP due to the lower diffusion of CO₂ in water compared to air (Williams & Flanagan 1996). Field observations indicated that *Sphagnum* mosses in the WET and INTER areas are indeed partially immersed when WL exceeded -8 cm. This result aligns with Tuittila et al. (2004), who observed the photosynthetic response of *Sphagnum* in a restored peatland. They reported a unimodal response curve with a WL optimum of -12 cm and a significant decrease below -22 cm and above 1 cm.

These findings support the observation that GPP decreases significantly in peatlands in response to extreme hydrological conditions (Ratcliffe et al. 2019). While low WL can also inhibit photosynthetic activity, this was not observed in our study (Williams & Flanagan 1996). Ahmad et al. (2020b), however, provided evidence that rewetting improves the hydrological buffering capacity, reducing the water level fluctuations in response to precipitation events. In pristine and restored peatlands, an upper elastic peat
layer is often present, albeit not always and with
spatial variability (Morris et al. 2011, Baird et al.
2016). This layer enables a hydrological self-regulating
mechanism known as “bog breathing”, which
stabilises WL relative to vegetation (Howie & Hebda
2018, Morton & Heinemeyer 2019). However, in
northern temperate latitudes, climate change
exacerbates hydrological extremes such as droughts
and heavy precipitation events. This intensification
may increase WL variability, pushing extremes beyond
the buffering capacity of bog breathing (Spinoni et al.
2018, Bertrand et al. 2021). These contrasting forces
on WL variability raise significant uncertainties
regarding the future of GPP values in peatlands.

Concluding perspectives

This study investigated the spatio-temporal
variability of GHG emissions in the peatland of
Forbonnet at Frasne, a long-term observatory of a
mid-altitude mountain peatland. This site allows the
study of GHG fluxes and associated
hydrometeorological and ecological conditions at
various timescales. Our study documents the added
value of such an observatory, especially when
combined with additional point measurements to
highlight the spatial variability of GHG fluxes. Our
analysis of monthly chamber flux data over 20
months revealed several key findings: (1) the Open
Top Chamber (OTC), which warms the air above the
soil surface, had no measurable effect on CO2 and
CH4 fluxes. Instead, we found (2) that CO2 and CH4
fluxes evolved along a 35 m transect with a natural
hydrological gradient from WET area with a near-
surface water level to DRY area with a lower water
level. However, replicating this kind of study with a
larger number of plots and at higher temporal
resolution, especially during periods with strongly
contrasting air and soil temperatures between OTC
and control plots, could potentially clarify the effects
of OTC on GHG fluxes.

Our results confirm the well-established hydric
control on CO2 and CH4 emissions (Reco and CH4
fluxes). However, we found that this control is non-
linear, raising questions about the future effects of
climate change. Projections predict increasingly
extreme weather conditions for the study area, with
wetter and warmer winters on the one hand and drier
and hotter summers on the other (Bertrand et al.
2021), which will have implications for GHG
emissions from peatlands. However, in the driest area
during summer drought conditions, our data did not
confirm the expected higher CO2 ecosystem
respiration. We attribute this observation to a
threshold effect during droughts that limits the
aerobic decomposition of organic matter.

The contrasting responses of Reco to hydrological
conditions highlight the complexity of mechanisms
and feedback loops involved in organic matter
recycling under aerobic conditions and the associated
CO2 emissions (Gilbert et al. 1998, Andersen et al.
2013, Binet et al. 2017, Jassey et al. 2018). As a result,
a more comprehensive dataset is needed to further
explore the above mechanisms, especially during
droughts combined (or not) with heatwaves, which
are becoming more frequent and more severe in
temperate latitudes (Samaniego et al. 2018, Spinoni
et al. 2018, IPCC 2022). The non-linear response of
Reco to WL variations raises questions about its
effect on NEE, provided that GPP is also limited
under more extreme conditions (Tuittila et al. 2004).

CH4 emissions from peatlands are known to
increase with wetter conditions, a behaviour
observed in the present study at the scale of a few
metres. Nonetheless, the non-progressive transition
between low- and high-emission areas suggests that
WL is not the sole controlling factor. Other factors
such as peat materials and/or microbial communities
(methanogens and methanotrophs) may also play a
role in modulating CH4 emissions. Studies including
the characterisation of both microbial communities
and GHG exchanges with the atmosphere should aid
in improving our understanding of the processes that
govern these fluxes.

ACKNOWLEDGEMENTS

The authors warmly thank the managers of the
Regional Natural Reserve of Frasne-Bouverans for
allowing access to the site. This study is part of the
CRITICAL PEAT project funded by the region of
Bourgogne Franche-Comté (Accueil de Nouvelle
Equipe de Recherche Agreement n2019-Y09069),
the French National Network of Peatland
Observatories "SNO Tourbières" (https://www.sno-
tourbieres.cnrs.fr/) and the French Network of
Critical Zone Observatories "OZCAR"
(https://wwwozcar-ri.org/fr/ozcar-observatoires-de-
la-zone-critique-applicationse t-recherche/). The
SNO Tourbières observing system was created
thanks to incentive funding from the French Ministry
of Research, which allowed existing local
observatories to be merged into a national
observatory network. The continuity of field
monitoring was made possible by continuous CNRS-
INSU funding since 2008. A. Lhosmot benefited
from a Ph.D. fellowship from the French Ministry of
Research. The authors thank the two anonymous
reviewers for their pertinent comments, which helped
to substantially improve the manuscript.
## AUTHOR CONTRIBUTIONS

AL wrote the original draft. AL, GB, AJ and PB participated in conception of the study, designed the methodology and analysed the data. AL, GB, PB, MLT, RC and AB participated to the GHG field measurements. MLT acquired soil-meteorological data. Supervision and funding management of the study: GB. All authors contributed to writing, and approved the definitive version of the manuscript.

## DATA AVAILABILITY

Chamber GHG fluxes measured in this study are available in supplementary data files. Meteorological and water level data are available on the Zenodo repository https://zenodo.org/record/3763342 and https://zenodo.org/record/3763766. Complementary data on the Forbonnet hydrology and eddy covariance measurements are available at https://onlinelibrary.wiley.com/doi/10.1002/eco.2315, https://onlinelibrary.wiley.com/doi/10.1002/hyp.14781 and https://link.springer.com/10.1007/s10021-022-00809-x.

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Submitted 05 Jul 2024, final revision 05 Feb 2025

Editor: Gerald Jurasinski