Comparing Light and Dark Chamber Measurements of CH4 Fluxes in Drained and Rewetted Raised Bogs of Ireland

Comparing Light and Dark Chamber Measurements of CH4 Fluxes in Drained and Rewetted Raised Bogs of Ireland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparing Light and Dark Chamber Measurements of CH4 Fluxes in Drained and Rewetted Raised Bogs of Ireland Stephen Barry, Kenneth Byrne, Kenneth Crawford, Mike Clancy, O’Doherty Clare, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6810432/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Oct, 2025 Read the published version in Wetlands Ecology and Management → Version 1 posted 7 You are reading this latest preprint version Abstract The important factors regulating methane (CH 4 ) fluxes in rewetted peatlands such as the vegetation types, water table depths (WTDs) and in-situ conditions (pH, redox, soil temperature and moisture) are widely reported, but the impact of light and dark conditions on CH 4 fluxes from multiple vegetation types are not widely reported. This field study investigated if the CH 4 fluxes from multiple vegetation communities ( Sphagnum communities, Eriophorum angustifolium , Molinia caerulea , Typha latifolia , Phragmites australis , Juncus effusus , Calluna vulgaris, Carex rostrata and open water) responded differently to light and dark conditions. Triplicate simultaneous light and dark measurements of carbon dioxide (CO 2 ) and CH 4 fluxes were measured on the same day using the chamber method from the above-mentioned vegetation communities from five peatland sites located in the Irish midlands. The field measurements showed that the CH 4 fluxes were higher in light conditions compared to dark conditions for Carex rostrata (0.05 ±0.02 in light, 0.02 ±0.01 g CH₄ m⁻² hr⁻¹ in dark) and Eriophorum angustifolium (0.02 ±0.01 in light, 0.01 ±0.00 g CH₄ m⁻² hr⁻¹ in dark) compared to other vegetation communities. The mixed-effect model results indicated that differences between light and dark measurements were strongly related to CO 2 fluxes. When the vegetation was sequestering CO 2 , CH 4 fluxes increased, alternatively, during the respiration, CH 4 fluxes decreased. Future work should examine the impact of vegetation specific phenological mechanisms that influence CH 4 fluxes in light and dark conditions using multiple years of field data. Peatlands Restoration Methane fluxes Manual closed chamber method Raised bogs Figures Figure 1 Figure 2 Figure 3 Introduction Natural or near-natural peatlands store vast amounts of carbon over millennial time scales and typically have a net radiative cooling effect (Strack et al. 2022 ). However, when they are drained and degraded they become large sources of carbon dioxide (CO 2 ). Globally, drained peatlands emit approximately 2 gigatons of CO 2 annually (Joosten et al. 2016 ) despite covering only 3% of the Earth's land surface (Leifeld & Menichetti 2018 ). Moreover, peatlands, which store 30% of the world's soil carbon, present a twofold scenario: a chance to maintain or enhance their carbon sink capacity, or a danger of further escalating carbon losses if degradation continues (Gorham 1991 ; Leifeld et al. 2019 ; Strack et al. 2022 ). Total global emissions from drained and degraded peatlands are estimated to be 57.4 Gt CO 2 -eq in 2022 (UNEP 2023 ) which accounts to 1–4.5% of global emissions. The rewetting is the deliberate action of raising the groundwater table to the peat surface in the previously drained peatland (Ojanen & Minkkinen 2020; Tanneberger et al. 2021). The drained peatland is considered rewetted if the average annual WTD is equal to or shallower than 30 cm from the peat surface (IPCC 2014). However, peatland restoration is the process of actively or passively assisting the recovery of the degraded peatland back to the state that existed before the degradation (Khan et al. 2025). The global potential for peatland restoration estimated to be 1.1 to 2.6 Gt CO 2 -eq per year (Strack et al. 2022 ). However, even with ambitious and careful restoration programmes, the benefits of peatland restoration will take time to achieve and are vulnerable to external pressures like poor water quality and climate change. For instance, Wilson et al. ( 2022 ) found that even if emissions are reduced in line with the low emission scenarios (SSP1-1.9), peatland restoration will have a warming effect until 2085. Importantly, Günther et al. ( 2020 ) concluded that the continued CO 2 emissions from degraded peatlands will result in greater radiative forcing than the CH 4 emissions, indicating the critical importance of peatland restoration. Drained peatlands have been found to have small to no CH 4 fluxes (Aitova et al. 2023 ). However, rewetted peatlands have been found to experience elevated CH 4 fluxes (Evans & Gauci 2023 ). Three processes govern the CH 4 emissions from peat: production, storage, and transport (Rey 2014 ; Roland et al. 2015 ). CH 4 production occurs under the anoxic conditions, where methanogens break down carbohydrates into CH 4 (Lai 2009 ). Peat soils, with high moisture contents create anoxic conditions due to persistently shallow water table at the peat surface (Hobbs 1986 ). Restored peatlands that were previously subjected to industrial extraction and having a depth of less than 1.5 m, exhibited higher CH₄ emissions (Aitova et al. 2023 ) possibly due to increased alkalinity, which favored methanogen activity (Evans & Gauci 2023 ). However, not all the produced CH 4 reaches the atmosphere, as methanotrophs in the aerobic zone consume some CH₄ before it escapes to the atmosphere (Hanson & Hansen 1996 ). But in waterlogged conditions, limited pore space and reduced diffusion increases ebullition (sudden CH₄ bursts) or plant-mediated transport via aerenchyma tissues (Vroom et al. 2022). These pathways bypass the aerobic oxidation, increasing CH₄ emissions. Vegetation plays a crucial role in CH₄ fluxes by facilitating direct transport to the atmosphere, reinforcing the link between plant communities and CH₄ dynamics (Ge et al. 2024). CH 4 fluxes also depend upon the vegetation type (Chanton et al. 1993 ), nutrient status (Lai et al. 2009), biological processes (Hanson & Hansen 1996 ), hydrology (Evans et al. 2021 ), climatic conditions and landuse (Aitova et al. 2023 ). If an incomplete understanding of factors governing the production or emission of CH 4 exist, it is important to determine which specific environmental and biological variables most significantly impact these processes. However, the incorporation of light levels into CH 4 flux models remains limited, representing a significant area of uncertainty. If CH 4 fluxes follow a diurnal pattern independent of commonly used predictors such as soil temperature and WTD, further investigation is warranted. Differences in CH 4 fluxes between daylight and night-time conditions, if unaccounted for in annual flux models, this can lead to inaccurate greenhouse gas (GHG) balances (Saunois et al. 2020 ). Failure to consider light conditions in chamber measurement programs may also affect emission inventories, potentially leading to over-or underestimation of emissions and contributing to discrepancies between atmospheric CH 4 concentrations and bottom-up inventories (Saunois et al. 2020 ). Observational studies focusing on this pattern have typically been conducted in single vegetation communities like Phragmites australis (Minke et al. 2014 ) or Sphagnum dominated communities (Lhosmot et al. 2023 ). Other studies have focused on a small sample of vegetation communities, including Phragmites australis , wetland sedges, grasses, and herbs (Juutinen et al. 2004 ), Eriophorum angustifolium and Eriophorum vaginatum (Dooling et al. 2018 ), and Sphagnum mosses, evergreen shrubs, sedges, and forbs (Lai et al. 2012 ). Studies examining this have used manual closed chamber measurements with the study duration lasting few days (Dooling et al. 2018 ) or over two summers (Minke et al. 2014 ) or four months (Juutinen et al. 2004 ) and using a combination of automated chambers and flux towers over several years (Lai et al. 2012 ). These studies observed differences betweeen day and night flux measurements (Bäckstrand et al. 2008 ; Lai et al. 2012 ). However to date, most studies have not examined this from a seasonal perspective given the short duration of past studies. Different mechanisms have been proposed to explain this including processes that affect CH 4 production (Doane & Rongzhong 2022 ) or transport processes (Chanton et al. 1993 ). Recently, photochemical reactions identified as potentially important sources of CH 4 production. A link between CH 4 fluxes and photochemistry specifically in agricultural settings and forestry, albiet under mostly mineral soils was reported (Doane & Rongzhong 2022 ). Photochemical reactions were found to increase the number of ketones produced in vegetation, decayed matter and peat. This was identified as the best predictor of CH 4 emission (Doane & Rongzhong 2022 ). While it is often assumed that the microbial activity and associated CH 4 emissions decline in winter, this photochemical reaction was found to continue regardless of temperature if sufficient light levels are present and resulted in the continued production of CH 4 . Chanton et al. ( 1993 ) found that as the sunlight increased, it caused air to move through the plants, opening the stomata (tiny openings on the plant's surface). This ventilation process helped release the trapped methane from inside the plant stems, leading to an increase in methane emissions. Minke et al. ( 2014 ) found a significant negative correlation between CO 2 and CH 4 fluxes, though the strength of this correlation varied greatly between different plots. All recorded parameters showed a strong correlation with Photosynthetically Active Radiation (PAR), which regulates the CO 2 -consuming process of photosynthesis. This study also observed that the CO 2 concentrations within the chamber are typically higher at night when PAR and photosynthesis were absent, while CH 4 emissions were low (Minke et al. 2014 ). In contrast, the study found that during the daytime, when PAR and photosynthesis are active, both CO 2 and CH 4 emissions increased. Although the effect of CO 2 was significant, this study further suggested that the PAR was likely the main factor driving the differences in CH 4 fluxes. This implies that the variations in CH 4 emissions are more directly related to light availability and the resulting photosynthetic activity, rather than CO 2 levels alone (Minke et al. 2014 ). Additionally, some studies have been unable to detect any dirunal pattern (Mengyu et al. 2024 ) while other studies found that light oxidised CH 4 emissions, (Lhosmot et al. 2022). This field study investigates whether there is a significant difference in CH 4 flux measurements taken under light and dark conditions using the manual closed chamber method. By comparing flux measurements across nine vegetation communities from five peatland sites, the study aims to evaluate the influence of light and dark conditions on CH 4 fluxes. Additionally, it seeks to assess the scale of this effect by comparing its influence with environmental parameters, such as WTDs and soil temperature. Therefore this research aims to enhance our understanding of CH 4 flux variations between light and dark conditions across diverse wetland vegetation communities. In doing so, it seeks to determine whether this pattern is widespread across multiple vegetation communities. Additionally, this study will assess whether the effect of light and dark conditions is significant compared to other important environmental factors. Methods Site Description The locations used in this study were former raised bogs located in Co. Offaly and Co. Kildare in the midlands of Ireland (Fig. 1). All the bogs were drained and subjected to peat extraction except the Sphagnum fuscum dominated site located in Mouds bog, Co. Kildare which was drained but not extracted. The vegetation communities at Blackriver, Ballycon and Derries were rewetted approximately 40, 20 and 10 years prior to this study respectively. The Calluna vulgaris and Molinia grasses monitored at Clonad and Mouds were former extraction sites that were revegetated. All the vegetation communities except at Mouds and Clonad (drained and revegetated) had shallow peat depths averaging less than 1.5 m (Table 1). Nine vegetation communities commonly found in degraded shallow and deep peatlands, as well as those associated with rewetted sites, were selected for monitoring (Table 1). Sphagnum communities were aggregated together for the purposes of this study. Duplicate sites (e.g. vegetation communities sampled at two separate bog locations) were included to assess intra-site variation, except for Phragmites australis and Open Water. The peat depth was shallow at most vegetation communities, due to peat removal, while pH and presence of vegetation varied depending on hydrology and peat depth. Calluna vulgaris and Molinia Caerulea were found in dry areas, Typha latifolia and Phragmites australis dominated in wet areas, while basic conditions with shallow peat had variety of Sphagnum mosses (hereafter referred to as Sphagnum dominated communities) and Eriophorum angustifolium were found over a range of peat depths with varying nutrient statuses (Table 1). Table 1 Site descriptions of selected locations used in this study. Chamber monitoring locations are located over nine different vegetation communities and land use status (rewetted, drained or drained and extracted) Site County Area (Ha) Vegetation Community Rewetted Rewetted Status Extraction pH Mean WTD (cm) Depth (cm) Ballycon Co. Kildare 281 Sphagnum palustre Y 20 years ago, 1960–2001 6.5 -13 100 Ballycon Co. Offaly 281 Eriophorum a ngustifolium Y 20 years ago, 1960–2001 6.5 -13 100 Ballycon Co. Offaly 281 Molinia caerulea Y 20 years ago, 1960–2001 NA >-50 50 Ballycon Co. Offaly 281 Typha latifolia Y 20 years ago, 1960–2001 6.5 8 100 Ballycon Co. Offaly 281 Phragmites australis Y 20 years ago, 1960–2001 6.6 10 100 Blackriver Co. Kildare 6 Eriophorum angustifolum Y 40 years ago, Ceased in 1980s 5.1 2.7 50 Blackriver Co. Kildare 6 Juncus effusus Y 40 years ago, Ceased in 1980s 5.1 -0.18 50 Blackriver Co. Kildare 6 Sphagnum papillosum Y 40 years ago, Ceased in 1980s 5.1 -1.2 50 Clonad Co. Offaly 447 Calluna vulgaris N Drained, extracted 1970–2019 4.3 -35 300 Clonad Co. Offaly 447 Molinia careulea N Drained, extracted 1970–2019 4.9 -14.6 300 Mouds Co. Kildare 411 Calluna vulgaris N Drained, extracted Ceased in 2019 4.7 -40.2 250 Mouds Co. Kildare 411 Sphagnum fuscum N Drained, not extracted Unknow when drained 4.6 -19.8 > 300 Derries Co. Offaly 371 Typha latifolia Y Rewetted 10 years ago 1960–2005 5.9 4.2 100 Derries Co. Offaly 371 Open water Y Rewetted 10 years ago 1960–2005 5.6 5 100 Derries Co. Offaly 371 Carex rostrata Y Rewetted 10 years ago 1960–2005 6.1 4.2 100 Greenhouse Gas Flux Measurement Methodology The experimental set-up for measuring greenhouse gas fluxes involved the installation of collars (60 x 60cm) made of stainless steel. These were inserted 12 cm into the peat profile to create a gas-tight seal and remained in place throughout the study. Three steel collars were installed per vegetation community at each monitoring location. The monitoring frequency at each study site was twice per month during the growing season (April to September) and once per month over the non-growing season (October to March). During each site visit, CH 4 fluxes was measured three times from each collar: (i) using a clear chamber, (ii) clear chamber covered by a semi-transparent net and (iii) clear chamber covered by a non-transparent tarpaulin (Wilson et al. 2016 ) using a LICOR 7810 CH 4 and CO 2 gas analyser. The chamber measurements were taken in succession with a small break period (2–3 min) to allow the sensor to re-calibrate to atmospheric conditions and to allow the chamber headspace to equilibrate with the atmospheric conditions. Temperature probes were installed at 5 cm depth (Wilson et al. 2022 ) to continuously monitor soil temperature. Boardwalks were constructed to minimize soil compaction and disturbance during measurements, particularly to reduce ebullition events driven by trampling during CH 4 measurements. Chambers were equipped with a small 5V fan for air mixing and PAR sensors are placed in the chamber during measurements. The closure time of the chambers was 90 seconds with 30 seconds mixing time and 60 seconds (1 min) monitoring time. Concentrations were recorded every 2 seconds and then averaged over a 6-second period. This process was repeated to collect a total of 10 measurements during the entire closure period. This approach was adopted to minimise temperature, pressure and changes of light within the headspace of the chamber. If PAR measurements changed significantly over the measurement period, the measurement was retaken. Fluxes were only accepted if an R 2 value of > 0.90 was detected. The flux of CH 4 was estimated simultaneously using Eq. 1: Equation 1: \(\:{F}_{c}=\frac{10V{P}_{0}(1-\frac{{W}_{0}}{1000})}{RS({T}_{0}+273.15)}\frac{\partial\:C{\prime\:}}{\partial\:t}\) (LICOR, 2024) Where V is the chamber Volume (m 3 ), P is the Air Pressure (Hpa), W 0 is the initial H 2 O concentration (mmol/mol), R is the ideal gas constant (8.3144), T 0 is air temperature taken from an air temperature probe (Kelvin), ∂C' is the change in CH 4 concentration and ∂t is the time elapsed (seconds). CO 2 fluxes (Net Ecosystem Exchange) were measured simultaneously. Methane flux measurements were carried out from July 2023 to May 2024. Given that site set up was ongoing during this time, the number of flux measurements per site varied. These measurements were compiled into a single data base and organised according to vegetation community and whether the flux was taken in light or dark conditions. Water table levels were obtained via manual dip measurements, internal chamber PAR values using SQ-520: Full-Spectrum Smart Quantum Sensor, pH using a YSI pH probe and soil temperature using a Hobo soil temperature probe. Analysis of differences between light and dark CH chamber measurements To assess differences in CH₄ flux between light and dark conditions, Mann-Whitney U tests and a linear mixed-effect model (Lindstrom & Bates 1988 ) were employed. Given the non-normal distribution of the data, the Mann-Whitney U test compared rankings between groups, while the mixed-effect model accounted for fixed effects (light/dark) and random effects (vegetation communities) to analyse CH₄ flux variability. Preliminary tests, including the Shapiro-Wilk test for normality and Levene’s test for heteroscedasticity, were conducted, with QQ plots and residuals examined using PYTHON 3.1.1 (Figures S1 -S9; Supplementary material). The Mann-Whitney U test provided p-values to assess significance, and Cohen’s d test quantified effect size. As CH 4 fluxes are influenced by multiple factors such as temperature, water table, CO₂ flux, and pH, the mixed-effect model was essential for capturing these interactions. Cross-validation was performed by partitioning the dataset into multiple subsets to evaluate predictive performance. Model outputs included intercepts (baseline CH₄ flux) and coefficients indicating effect magnitude and direction, where a positive coefficient for "Dark to Light" suggested increased CH₄ flux under light conditions. Model accuracy was assessed using Mean Square Error (MSE), with lower values indicating better performance (Lohse et al. 2023 ). Results This study found that the vegetation communities such as the Carex rostrata and Eriophorum angustifolium exhibited higher CH 4 fluxes in light conditions compared to dark conditions, while the other vegetation communities did not show any observable differences between light and dark CH 4 fluxes (Fig. 2). However, on average, the Carex rostrata exhibited the largest difference (0.03 g CH 4 m − 2 hr − 1 , p-value < 0.05) followed by Eriophorum angustifolium (0.01 g CH 4 m − 2 hr − 1 , p-value < 0.05). But the Juncus effusus exhibited the opposite trend i.e., the CH 4 fluxes were higher in dark conditions compared to the light conditions, albiet with a non-significant effect. The box plots of each individual vegetation highlight the differences between the light and dark measurements (Fig. 3). The Carex rostrata and Eriophorum angustifolium had notable differences in CH 4 flux values (median and upper quartile) (Fig. 3) while other species such as Calluna Vulgaris, Juncus effusus, Molinia Caerulea and Sphagnum communities showed minor to no differences in CH 4 fluxes between light and dark conditions (Fig. 3). Table 2 shows the results of Shapiro-Wilk Test and Levene’s test. The CH 4 flux data was found to be non-normally distributed across all nine vegetation communities (e.g. p-values were < 0.05) and this was supported by the QQ plots (Figures S1 -S9; Supplementary material) and histograms and Kernal Density Estimates (KDEs) (Figure S10; Supplementary material). Outliers retained in the dataset given that the ebullition is an important process within bogs that results in large and abrupt CH 4 fluxes (Bieniada & Strack 2021). Heteroscedasticity was detected in Carex rostrata and Eriophorum angustifolium (e.g. p-values were < 0.05) meaning that the parametric tests on these vegetation communities may be biased and invalid. However, given the non-normality, the Mann-Whitney U statistic was used to determine if there was a significant difference between light and dark CH 4 flux measurements (Table 3). The results showed a significant difference in light and dark measurements for Carex rostrata and Eriophorum angustifolium compared to other vegetation communities (Table 4). Table 2 Sample size of chamber measurements included in the study at each vegetation community and provides an assessment of normality and heteroscedasticity No. of Chamber Measurements Shapiro-Wilk Test Levene's Test Vegetation Communities Dark Light W-statistic p-value W-statistic p-value Carex rostrata 35 25 0.71 0.00 6.31 0.01 Eriophorum angustifolium 53 36 0.63 0.00 6.81 0.01 Calluna vulgaris 37 19 0.62 0.00 0.41 0.52 Juncus effusus 29 24 0.46 0.00 0.99 0.32 Molinia Caerulea 46 30 0.85 0.00 1.28 0.26 Open Water 22 17 0.30 0.00 0.13 0.72 Phragmites australis 10 13 0.82 0.00 3.61 0.07 Sphagnum Communities 83 40 0.69 0.00 0.97 0.33 Typha latifolia 12 15 0.77 0.00 0.12 0.74 Table 3 Assesses if there is a significant difference between vegetation communities. The t-statistic assumes normality while the Mann-Whitney U statistic does not Vegetation Communities Mann-Whitney U statistic p-value Carex rostrata 603.00 0.01 Eriophorum angustifolium 1217.00 0.03 Calluna vulgaris 332.00 0.74 Juncus effusus 324.00 0.67 Molinia Caerulea 765.00 0.43 Open Water 147.00 0.26 Phragmites australis 75.00 0.56 Sphagnum Communities 1674.50 0.94 Typha latifolia 109.00 0.36 The mixed-effect model provided additional information as compared to the non-parametric tests. It also showed that Carex Rostrata and Eriophorum angustifolium had significant differences in CH 4 fluxes. Table 4 shows that when switched from dark to light conditions the fluxes increased by 0.0201 g CH 4 m − 2 hr − 1 for Carex Rostrata and 0.0153 g CH 4 m − 2 hr − 1 for Eriophorum angustifolium . An opposite trend was observed for Juncus effusus and Typha latifolia where fluxes decreased in light conditions (-0.0135 & -0.0104 g CH 4 m − 2 hr − 1 respectively). The presence of outliers best explained these differences. The overlapping confidence intervals also suggests a less clearly defined role between light and dark conditions. Moreover, the influence of pH and WTD were found to have an equally important effect on CH 4 fluxes. pH was found to influence CH 4 fluxes from Juncus Effusus and open water with higher pH values (more alkaline conditions) associated with higher fluxes (Figures S11, S12 and S13; Supplementary material). Typha latifolia also showed that higher pH values influenced CH 4 fluxes (-0.0102 g CH 4 m − 2 hr −−1 ), in addition to the switch between dark to light conditions (-0.01043 g CH 4 m − 2 hr − 1 ) and water table level (+ 0.0141 g CH 4 m − 2 hr − 1 ). CO 2 fluxes were also found to strongly influence CH 4 fluxes in Carex Rostrata , Eriophorum angustifolium , Open Water, Sphagnum Communities and Typha Latifolia (Table 4). In nearly all vegetation communities, this was found to be a negative effect, implying that while these vegetation communities sequester CO 2 , CH₄ emissions increase simultaneously. Conversely, during respiration, CH₄ emissions decrease. In the Sphagnum communities and Open water, the opposite trend was observed. Table 4 Presents the intercept, coefficients, and cross-validation mean squared error (MSE) for various vegetation communities, considering factors such as dark to light transitions, water table (WT), pH, soil temperature, and photosynthetically active Vegetation Communities Intercept Dark to Light WT pH Soil Temp PAR CO2 CV MSE Carex Rostrata -0.15086 0.02010 -0.00260 0.03392 -0.00372 0.00000 -0.02134 0.00164 Eriophorum angustifolium -0.03823 0.01536 0.00202 0.00568 0.00197 -0.00002 -0.02650 0.00081 Calluna vulgaris 0.00003 -0.00005 0.00000 0.00002 0.00000 0.00000 -0.00012 0.00000 Juncus effusus -0.14121 -0.01357 0.00073 0.02787 0.00109 0.00001 -0.00786 0.00061 Molinia Caerulea 0.00077 0.00001 0.00001 -0.00009 0.00005 0.00000 -0.00015 0.00000 Open Water 0.05148 -0.00335 0.00008 -0.00947 0.00011 0.00000 0.07981 0.00009 Phragmites australis -0.00667 -0.00025 0.00006 0.00036 0.00055 -0.00000 -0.00049 0.00001 Sphagnum communities -0.00087 0.00029 0.00000 0.00002 0.00015 -0.00000 0.01203 0.00000 Typha latifolia 0.08231 -0.01043 0.01411 -0.01023 -0.00637 0.00006 -0.04793 0.00355 Discussion The mixed-effect model found that the Carex rostrata and Eriophorum angustifolium had the largest differences between light and dark conditions. The effect of light and dark conditions was significant and found to have a more profound impact than the water table, soil temperature and PAR on CH 4 fluxes (Table 4). In contrast, the Juncus effusus was found to have higher fluxes in the dark conditions compared to the light conditions. However, research to date on CH₄ fluxes under varying light conditions has yielded mixed results. For instance, some studies found higher CH 4 fluxes in light conditions compared to the dark conditions (Minke et al. 2014 ), while other studies found no differences between CH 4 fluxes in light and dark conditions (Mengyu et al. 2024 ) while some studies showed higher CH 4 fluxes in dark conditions compared to light conditions (Dooling et al. 2018 ; Lhosmot et al. 2022). All three scenarios were replicated in this study albeit for different vegetation communities. This indicates a high degree of heterogeneity in CH 4 fluxes among peatland species in response to varying light and dark conditions. To understand why this might be the case, it is important to distinguish whether the observed differences arise from CH₄ production or CH 4 transport. This distinction helps to determine whether the vegetation is actively producing CH₄ or primarily facilitating its movement to the atmosphere. This distinction is not only important for accurately estimating CH 4 fluxes, but also for informing management decisions in restored bog and fens, such as water table regulation and vegetation control. Previous research has reported both scenarios, where differences between light and dark measurements were due to the changes in the CH 4 production rate (Dooling et al. 2018 ) or changes in the rate of CH 4 transport (Minke et al. 2014 ). Processes that account for increases in the CH 4 fluxes are due to increasing rates of CH 4 production via root exudates (Dooling et al. 2018 ) or via photochemical reactions linked with the production of keytones (Doane & Rongzhong 2022 ). Studies found that either atmospheric turbulence (Lai et al. 2012 ) or stomatal conductivity which was affected by higher CO 2 concentrations (Minke et al. 2014 ) were the main drivers of CH 4 fluxes between light and dark conditions. While turbulence was not measured here, given that the light and dark measurements were taken concurrently, it’s unlikely that the atmospheric turbulence can account for differences in this study. Vegetation species such as Carex rostrata, Eriophorum angustifolium, Juncus effusus, Phragmites australis and Typha latifolia , all possess aerenchyma tissues allowing transport of CH 4 from peat-surface to atmosphere. However, not all species containing aerenchyma exhibited differences between light and dark measurements such as Phragmites australis and Typha latifolia . Phragmites australis was found to have low CH 4 fluxes in general which was surprising given it was an aerenchyma species. Alternatively, Typha latifolia exhibited the highest CH 4 fluxes in both light and dark conditions. This provides evidence that production may not be responsible for observed differences between light and dark conditions. If light levels were important in determining the production of CH 4 , a pronounced effect should be observable in all vegetation communities with high CH 4 fluxes. However, this was not the case. No difference was detected for Typha latifolia suggesting that the light levels were not influencing CH 4 production. This contrasts with the findings of other studies which found the CH 4 production to be the main reason for differences between light and dark CH 4 fluxes (Dooling et al. 2018 ; Doane & Rongzhong 2022 ). Minke et al. ( 2014 ) found that because of the dense network of interlinked rhizomes, the CH 4 emissions may be rerouted to rhizomes which may be located outside of the closed chamber due to the pressure gradients created by the manual closed chamber method. Therefore, both CH 4 fluxes and the differences observed between light and dark measurements may be obscured by enhanced transport between the stands. This further supports the importance of understanding the role of transport mechanisms of gases within vegetation communities. Given the observed association between CH 4 and CO 2 fluxes, our study results are consistent with Minke et al. ( 2014 ), where field measurements showed that the CH 4 fluxes increased when the CO 2 concentrations decreased and vice versa. Chanton and Whitney (1994) found this pattern to be related to decreased stomatal conductance at high CO 2 concentrations. One important difference between our study and that of Minke et al. ( 2014 ), was that PAR and relative humidity were found to strongly control CH 4 fluxes in Minke et al. ( 2014 ) while the PAR was not observed to control CH 4 fluxes in our study. Of note, both Minke et al. ( 2014 ) and this study agreed that little differences were observed in CH 4 fluxes from Phragmites australis in light or dark conditions. The results of this study support Chanton and Whitney (1994) findings that the stomatal conductivity could be controlling CH 4 fluxes. This aligns with Noyce et al. ( 2014 ) who have found that the removal of above-ground biomass in the case of Carex rostrata reduced CH 4 fluxes by 40–70%, indicating that the transport may be the main factor controlling CH 4 fluxes. Therefore, considering light levels could improve the precision of CH 4 flux estimation. As our results show evidence of differences in light and dark measurements for two vegetation communities, this may result in underestimation or over estimation of CH 4 fluxes. The implications of this study mean that the inclusion of light and dark measurements in future chamber measurement programs is advisable and subsequent modelling approaches should seek to incorporate light and dark response curves and CO 2 fluxes in addition to water table and soil temperature (Wilson et al. 2016 ). Conclusion This study utilized statistical techniques (Mann Whitney U test, mixed-effects model) to determine if there were any potential differences in CH 4 fluxes from different wetland vegetation types ( Sphagnum communities, Eriophorum angustifolium , Molinia caerulea , Typha latifolia , Phragmites australis , Juncus effusus , Calluna vulgaris , and Carex rostrata ) subjected to light and dark conditions over a one-year period. The mixed-effect model showed that only two wetland vegetation species ( Carex rostrata and Eriophorum angustifolium ) exhibited different CH 4 fluxes in light and dark conditions and this effect was greater than the impact exerted by measured in-situ environmental variables such as pH, redox and soil temperature. However, it is important to note that the limitations of this study include the presence of outliers, the abnormal distribution of the vegetation communities and heteroscedasticity, meaning that some statistical analyses need to be interpreted with caution. However, all statistical tests show the same result-a difference between light and dark measurements for two vegetation species ( Eriophorum angustifolium and Carex rostrata ). As the study was conducted over a single year, it does not account for interannual variability, so future research should seek to conduct multiple year measurements. While this study identifies differences in CH₄ fluxes between light and dark conditions, it does not elucidate the underlying mechanisms driving these differences. Without direct measurements of plant specific phenological characteristics, soil microbial activity or pore water CH 4 concentrations to explain the observed patterns, the underlying drivers remain somewhat speculative. To identify the driving mechanism/s impacting the plant-specific CH 4 fluxes, future field monitoring programs can measure plant biomass, amount of permeable root surface (taking proxy measurements of root length), presence and diversity of methanogens and methanotrophs within the plant shoots along with the measurements of in-situ environmental parameters (pore-water CH 4 concentrations, WTDs, pH and soil temperature). Declarations Acknowledgements We appreciate the critical reviews provided by the journal referees and the journal editor/co-editor. Funding This work was supported by the Department of Environment, Climate and Communications under the Peatland Climate Action Scheme. Amey Tilak was funded by the Irish Environmental Protection Agency (EPA) and the Department of Agriculture, Food and Marine respectively (grant number for CH 4 project: 2021-CE-1060). The EPA Research Program 2021-2030 is a Government of Ireland initiative funded by the Department of Communications, Climate Action, and Environment. This research program is administered by the Environment Protection Agency (EPA), which has the statutory function of coordinating and promoting environmental research. M Clancy was funded by Science Foundation Ireland (grant number SFI 20/SPP/3705). KA Byrne acknowledges funding from Science Foundation Ireland (grant number SFI 20/SPP/3705) and the University of Limerick. Author information and affiliations School of Architecture, Building and Environment, Technological University Dublin, Bolton Street, Dublin 1, Ireland Stephen Barry Bord na Mona, Leabeg, Boora Ave, Co. Offaly, Ireland Kenneth Crawford, Clare O’Doherty, G Heagney, Harry Kelly, Mark McCorry, Hannah Mealy, Brian Mollahan Department of Biological Sciences & Bernal Institute, Faculty of Science and Engineering, University of Limerick, Limerick, Ireland Mike Clancy, Amey S. Tilak and Kenneth A. Byrne School of Natural Sciences, Botany Discipline, Trinity College, Dublin, Ireland Matthew Saunders Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Harry Kelly, Clare O’Doherty, Hannah Mealy, Brian Mollahan, Amey Tilak, Mike Clancy, Ken Byrne, Matt Saunders and Stephen Barry, ecology surveys were carried out by Mark McCorry. The first draft of the manuscript was written by Stephen Barry and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data availability The datasets generated during and/or analysed during the current study are available from the first and corresponding authors on reasonable request. Corresponding author Stephen Barry [email protected] Ethics declaration Competing interests The authors have no relevant financial or non-financial interests to disclose. References Aitova E, Morely T, Wilson D, Renou-Wilson F (2023) A review of greenhouse gas emissions and removals from Irish peatlands. Mires and Peat 1-17. http://mires-and-peat.net/pages/volumes/map29/map2904.php Bäckstrand K, Crill P, Mastepanov M, Christensen T, Bastviken D (2008) Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden. J Geophys Res 113, G03026. https://doi.org/10.1029/2008JG000703 Bord na Móna (BnM) (2024) Peatlands Climate Action Scheme . Retrieved from https://www.bnmpcas.ie/: https://www.bnmpcas.ie/#:~:text=Learn%20More%20%3E-,Climate%20Action%20Benefits,rich%20vegetation%20in%20suitable%20conditions. Accessed on May 22 nd 2024. Breiman L (2001) Random Forests. Machine Learning , 45: 5-32. https://doi.org/10.1023/A:1010933404324 Chandra N, Patra P, Fujita R, Höglund-Isaksson L, Umezawa T, Goto D, Morimoto S, Vaugh B.H, Röckmann T (2024) Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990-2020. Communications Earth & Environment 5-147. https://doi.org/10.1038/s43247-024-01286-x Chanton J, Whiting G, Happell J, Ghislan G (1993) Contrasting rates and diurnal patterns of methane emission from emergent aquatic macrophytes. Aquatic Botany , 46(2): 111-128. https://doi.org/10.1016/0304-3770(93)90040-4 Chapin C, Bridgham S, Pastor J (2004) pH and nutrient effects on above-ground net primary production in a Minnesota, USA bog and fen. Wetlands 24: 186-201. https://doi.org/10.1672/0277-5212(2004)024[0186:PANEOA]2.0.CO;2 Doane T, Rongzhong Y (2022) Photochemical Soil Methane Emission. ACS Earth and Space Chemistry 6 (7): 1742-1749. https://doi.org/10.1021/acsearthspacechem.2c00055 Dooling G, Chapman P, Baird A, Shepherd M, Kohler T (2018) Daytime-only measurements underestimate CH₄ emissions from a restored bog. Écoscience 259-270. https://doi.org/10.1080/11956860.2018.1449442 Dunfield P, Knowles R, Dumont R, Moore T (1993) Methane production and consumption in temperate and subarctic peat soils: Response to temperature and pH. SoiI Biol, Biochem 321-326. https://doi.org/10.1016/0038-0717(93)90130-4 European Environment Agency (2024) European Climate Risk Assessment. Copenhagen: European Environment Agency. Evans C, Gauci V (2023) Wetlands and Methane - Technical Paper. UK: Wetlands International. Evans CD, Peacock M, Baird AJ, Artz RRE, Burden A, Callaghan N, Chapman PJ, Cooper HM, Coyle M, Craig E, Cumming A, Dixon S, Gauci V, Grayson RP, Helfter C, Heppell CM, Holden J, Jones DL, Kaduk J, Levy P, Matthews R, McNamara NP, Misselbrook T, Oakley S, Page SE, Rayment M, Ridley LM, Stanley KM, Williamson JL, Worrall F, Morrison R (2021) Overriding water table control on managed peatland greenhouse gas emissions. Nature 593: 548-552. https://doi.org/10.1038/s41586-021-03523-1. Evans C, Artz R, Moxley J, Smyth M-A, Taylor E, Archer N, Burden A, Williamson J, Donnelly D, Thomson A, Buys G, Malcolm H, Wilson D, Renou-Wilson F, Potts J (2017) Implementation of an emission inventory for UK peatlands. Report to the Department for Business, Energy and Industrial Strategy, Centre for Ecology and Hydrology, Bangor.88pp. Accessed Dec 05 2024. https://uk-air.defra.gov.uk/reports/cat07/1904111135_UK_peatland_GHG_emissions.pdf Goode K, Ries D, McClernon K (2024) Characterizing climate pathways using feature importance on echo state networks. Statistical Analysis and Data Mining , 17. https://doi.org/10.1002/sam.11706 Gorham E (1991) Northern peatlands: role in the carbon cycle and probably responses to climate warming. Ecological Applications 182-185. https://doi.org/10.2307/1941811 Günther A, Barthelmes A, Huth V, Joosten H, Jurasinski G, Koebsch F, Couwenberg J (2020) Prompt rewetting of drained peatlands reduces climate warming despite methane emissions. Nat Commun 11 : 1644. https://doi.org/10.1038/s41467-020-15499-z Hanson R, Hansen T (1996) Methanotrophic bacteria. Microbial Review 60(2): 439-471. https://doi.org/10.1128/mr.60.2.439-471.1996 Hobbs N (1986) Mire morphology and the properties and behaviour of some British and foreign peats. Quarterly Journal of Engineering Geology , 19(1):7-80. https://doi.org/10.1144/GSL.QJEG.1986.019.01.02 IPCC (2013) Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press: T.F. Stocker, D. Qin, G.-K. Plattner, et al. (Eds.)). Joosten, H., Sirin, A., Couwenberg, J., Laine, J., & Smith, P. (2016). The role of peatlands in climate regulation. In A. Bonn, T. Allott, M. Evans, H. Joosten, & R. Stoneman (Eds.), Peatland Restoration and Ecosystem Services: Science, Policy and Practice (pp. 63-76). Cambridge University Press. https://doi.org/10.1017/CBO9781139177788.005 Juutinen S, Alm J, Larmola T, Saarnio S, Martikainen P, Silvola J (2004) Stand-specific diurnal dynamics of CH 4 fluxes in boreal lakes: Patterns and controls. Journal of Geophysical Research 1-11. https://doi.org/10.1029/2004JD004782 Lai D (2009) Methane Dynamics in Northern Peatlands: A Review. Pedosphere, 19(4): 409-421. https://doi.org/10.1016/S1002-0160(09)00003-4 Lai D, Roulet N, Humpreys E, Moore, T, Dalva M (2012) The effect of atmospheric turbulence and chamber deployment period on autochamber CO 2 and CH 4 flux measurements in anombrotrophic peatland. Biogeosciences , 3305-3322. https://doi.org/10.5194/bg-9-3305-2012. Le Mer, J, Roger P (2001) Production, oxidation, emission and consumption of methane by soils: A review. European Journal of Soil Biology 37(1): 25-50. https://doi.org/10.1016/S1164-5563(01)01067-6 Leifeld J, Menichetti, L (2018) The underappreciated potential of peatlands in global climate change mitigation strategies. Nature Communications 9. https://doi.org/10.1038/s41467-018-03406-6 Leifeld J, Wüst-Galley C, Page, S (2019) Intact and managed peatland soils as a source and sink of GHGs from 1850 to 2100. Nature Climate Change 9:945-947. https://doi.org/10.1038/s41558-019-0615-5 Lhosmot A, Jacotot A, Steinmann M, Binet P, Toussaint M, Gogo S, Gilbert D, Coffinet S, Laggoun-Deffarge F, Bertrand G (2023) Biotic and Abiotic Control Over Diurnal CH 4 Fluxes in a Temperate Transitional Poor Fen Ecosystem. Ecosystems 26: 951-968. https://doi.org/10.1007/s10021-022-00809-x LI-COR (2024) LI-7810 CH 4 /CO 2 /H 2 O Trace Gas Analyzer. Retrieved https://www.licor.com/ on May 18 th 2024. https://www.licor.com/env/products/soil-flux/LI-7810 Lindstrom M, Bates D (1988) Newton-Raphson and EM algorithms for linear mixed-effects models for repeated-measures data. Journal of the American Statistical Association 1014-1022. https://doi.org/10.1080/01621459.1988.10478693. Lohse K, Kozlowski A, Strube M (2023) Model Specification in Mixed-Effects Models: A Focus on Random Effects. Communications in Kinesiology 1(5) https://doi.org/10.51224/cik.2023.52 Mengyu G, Korrensalo A, Putkinen A, Laiho R., Kohl L, Pihlatie M, Lohila A, Makiranta, P, Siljanen H, Tuittila, E-T, Koskinen M (2024) CH 4 transport in wetland plants under controlled environmental conditions-untangling the impacts of phenology. Research Square, 1-33. https://doi.org/10.1101/2023.10.08.561392 Minke M, Augustin J, Hagemann U, Joosten H (2014) Similar methane fluxes measured by transparent and opaque chambers point at belowground connectivity of Phragmites australisbeyond the chamber footprint. Aquatic Botany , 63-71. https://doi.org/10.1016/j.aquabot.2013.11.006 Noyce G, Varner, R, Bubier J, Frolking S (2014) Effect of Carex rostrata on seasonal and inter annual variability in peatland methane emissions. J. Geophys. Res. Biogeosci, 24-34. https://doi.org/10.1002/2013JG002474 RAMSAR (2018). 75% of Earth's land areas are degraded; wetlands have been hit hardest, with 87% lost globally in the last 300 years . Retrieved https://www.ramsar.org/news/75-earths-land-areas-are-degraded-wetlands-have-been-hit-hardest-87-lost-globally-last-300 Renou-Wilson F, Moser G, Fallon D, Farrell C, Müller C, Wilson D (2019) Rewetting degraded peatlands for climate and biodiversity benefits: Results from two raised bogs. Ecological Engineering 547-560. https://doi.org/10.1016/j.ecoleng.2018.02.014 Rey A (2014) Mind the gap: non-biological processes contributing to soil CO 2 efflux. Global Change Biology 1752-1761. https://doi.org/10.1111/gcb.12821 Roland M, Vicca S, Bahn M, Ladreiter-Knauss T, Schmitt M, Janssens IA (2015) Importance of nondiffusive transport for soil CO 2 efflux in a temperate mountain grassland. J. Geophys. Res. Biogeosci . , 502-512. https://doi.org/10.1002/2014JG002788 Saunois M, Stavert, AR, Poulter B, Bousquet P, Canadell JG, Jackson RB, Raymond PA, Dlugokencky EJ, Houweling S, Patra PK, Ciais P, Arora VK, Bastviken D, Bergamaschi P, Blake DR, Brailsford G, Bruhwiler L, Carlson KM, Carrol M, Castaldi S, Chandra N, Crevoisier C, Crill PM, Covey K, Curry CL, Etiope G, Frankenberg C, Gedney N, Hegglin MI, Höglund-Isaksson L, Hugelius G, Ishizawa M, Ito A, Janssens-Maenhout G, Jensen KM, Joos F, Kleinen T, Krummel PB, Langenfelds RL, Laruelle GG, Liu L, Machida T, Maksyutov S, McDonald KC, McNorton J, Miller PA, Melton JR, Morino I, Müller J, Murguia-Flores F, Naik V, Niwa Y, Noce S, O'Doherty S, Parker RJ, Peng C, Peng S, Peters GP, Prigent C, Prinn R, Ramonet M, Regnier P, Riley WJ, Rosentreter JA, Segers A, Simpson IJ, Shi H, Smith SJ, Steele LP, Thornton BF, Tian H, Tohjima Y, Tubiello FN, Tsuruta A, Viovy N, Voulgarakis A, Weber TS, van Weele M, van der Werf GR, Weiss RF, Worthy D, Wunch D, Yin Y, Yoshida Y, Zhang W, Zhang Z, Zhao Y, Zheng B, Zhu Q, Zhu Q, Zhuang Q (2020) The Global Methane Budget 2000-2017, Earth Syst. Sci. Data, 12, 1561-1623, https://doi.org/10.5194/essd-12-1561-2020 Serrano-Silva N, Sarria-Guzmán Y, Dendooven L, Luna-Guido M (2014) Methanogenesis and Methanotrophy in Soil: A Review. Pedosphere 24(3): 291-307. https://doi.org/10.1016/S1002-0160(14)60016-3 Simpon G (2018) Modelling Palaeoecological Time Series Using Generalised Additive Models. Frontiers in Ecology and Evolution 6:149. https://doi.org/10.3389/fevo.2018.00149 Strack M, Davidson S, Hirano T, Dunn C (2022) The Potential of Peatlands as Nature‑Based Climate Solutions. Current Climate Change Reports 71-82. https://doi.org/10.1007/s40641-022-00183-9 Swenson M, Regan S, Bremmers D, Lawless J, Saunders M, Gill L (2019) Carbon balance of a restored and cutover raised bog: implications for restoration and comparison to global trends. Biogeosciences 16:713-731. https://doi.org/10.5194/bg-16-713-2019 Tanneberger F, Appulo L, Ewert S, Lakner S, O'Brolcháin N, Peters J, Wichtmann W (2020) The Power of Nature-Based Solutions: How Peatlands Can Help Us to Achieve Key EU Sustainability Objectives. Advanced Sustainable Systems, Vol 5. https://doi.org/10.1002/adsu.202000146 UNEP (2023) Emissions Gap Report 2023. UNEP. https://www.unep.org/resources/emissions-gap-report-2023 Whalen C (2005) Biogeochemistry of methane exchange between natural wetlands and the atmosphere. Environmental Engineering Science, 22(1): 73-94. https://doi.org/10.1089/ees.2005.22.73 Wilson D, Farrell C, Fallon D, Moser G. Müller C, Renou-Wilson F (2016) Multiyear greenhouse gas balances at a rewetted temperate peatland. Global Change Biology, 4080-4095. https://doi.org/10.1111/gcb.13325 Wilson D, Mackin F, Tuovinen J-P, Moser G, Farrell C, Renou-Wilson F (2022) Carbon and climate implications of rewetting a raised bog in Ireland. Global Change Biology, 6349-6365. https://doi.org/10.1111/gcb.16359 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial1.docx Cite Share Download PDF Status: Published Journal Publication published 13 Oct, 2025 Read the published version in Wetlands Ecology and Management → Version 1 posted Editorial decision: Revision requested 01 Jul, 2025 Reviews received at journal 01 Jul, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers invited by journal 10 Jun, 2025 Editor assigned by journal 05 Jun, 2025 Submission checks completed at journal 04 Jun, 2025 First submitted to journal 03 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6810432","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469422499,"identity":"6183ec1c-c8f0-4204-85a5-ebfa96141d76","order_by":0,"name":"Stephen 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1","display":"","copyAsset":false,"role":"figure","size":64547,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of peatlands in this study, where nine vegetation communities were monitored for\u003cbr\u003e\nCO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e fluxes. The latitude and longitude of Ballycon bog is 53.282412, -7.194488, Blackriver bog is 53.261918, -6.977151, Clonad is 53.272977, -7.280195, Mouds bog is 53.251345, -6.793346 and finally Derries is 53.254687, -7.756066\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6810432/v1/1c7ada61c8f426c8c1aea3dd.png"},{"id":84463178,"identity":"26b7f527-af7e-4eca-a6a3-89c70a53ab07","added_by":"auto","created_at":"2025-06-12 09:16:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":198777,"visible":true,"origin":"","legend":"\u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e flux measurements collected using manual closed chamber measurement technique between August 2023 and May 2024\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6810432/v1/b5fb89698ebc0cfe2302aba0.png"},{"id":84463180,"identity":"e10a7fcb-c497-4f4b-a769-fb1124f1a9e0","added_by":"auto","created_at":"2025-06-12 09:16:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67799,"visible":true,"origin":"","legend":"\u003cp\u003eHighlights that the vegetation communities like\u003cem\u003e Carex rostrata\u003c/em\u003e and \u003cem\u003eEriophorum angustifolium \u003c/em\u003ehad notable differences in median and upper quartile values while other species like \u003cem\u003eCalluna Vulgaris, Juncus effusus, Molinia Caerulea \u003c/em\u003eand\u003cem\u003eSphagnum \u003c/em\u003ecommunities showed only minor or no differences between light and dark conditions\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6810432/v1/fa5a947980e9dca34735b8df.png"},{"id":93956131,"identity":"609de585-6125-4e24-8ad3-a0117061921a","added_by":"auto","created_at":"2025-10-20 16:11:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1275816,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6810432/v1/16e868fe-6448-4309-8591-08aeff1b539a.pdf"},{"id":84463185,"identity":"0b6ba212-f18e-4f6f-9c1c-670259d8bc0d","added_by":"auto","created_at":"2025-06-12 09:16:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1517286,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6810432/v1/cb4c7a855891ca3a9c1f106d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eComparing Light and Dark Chamber Measurements of CH4 Fluxes in Drained and Rewetted Raised Bogs of Ireland\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNatural or near-natural peatlands store vast amounts of carbon over millennial time scales and typically have a net radiative cooling effect (Strack et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, when they are drained and degraded they become large sources of carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e). Globally, drained peatlands emit approximately 2 gigatons of CO\u003csub\u003e2\u003c/sub\u003e annually (Joosten et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) despite covering only 3% of the Earth's land surface (Leifeld \u0026amp; Menichetti \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, peatlands, which store 30% of the world's soil carbon, present a twofold scenario: a chance to maintain or enhance their carbon sink capacity, or a danger of further escalating carbon losses if degradation continues (Gorham \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Leifeld et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Strack et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Total global emissions from drained and degraded peatlands are estimated to be 57.4 Gt CO\u003csub\u003e2\u003c/sub\u003e-eq in 2022 (UNEP \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) which accounts to 1\u0026ndash;4.5% of global emissions. The rewetting is the deliberate action of raising the groundwater table to the peat surface in the previously drained peatland (Ojanen \u0026amp; Minkkinen 2020; Tanneberger et al. 2021). The drained peatland is considered rewetted if the average annual WTD is equal to or shallower than 30 cm from the peat surface (IPCC 2014). However, peatland restoration is the process of actively or passively assisting the recovery of the degraded peatland back to the state that existed before the degradation (Khan et al. 2025). The global potential for peatland restoration estimated to be 1.1 to 2.6 Gt CO\u003csub\u003e2\u003c/sub\u003e-eq per year (Strack et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, even with ambitious and careful restoration programmes, the benefits of peatland restoration will take time to achieve and are vulnerable to external pressures like poor water quality and climate change. For instance, Wilson et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that even if emissions are reduced in line with the low emission scenarios (SSP1-1.9), peatland restoration will have a warming effect until 2085. Importantly, G\u0026uuml;nther et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) concluded that the continued CO\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e emissions from degraded peatlands will result in greater radiative forcing than the CH\u003csub\u003e4\u003c/sub\u003e emissions, indicating the critical importance of peatland restoration.\u003c/p\u003e \u003cp\u003eDrained peatlands have been found to have small to no CH\u003csub\u003e4\u003c/sub\u003e fluxes (Aitova et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, rewetted peatlands have been found to experience elevated CH\u003csub\u003e4\u003c/sub\u003e fluxes (Evans \u0026amp; Gauci \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Three processes govern the CH\u003csub\u003e4\u003c/sub\u003e emissions from peat: production, storage, and transport (Rey \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Roland et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). CH\u003csub\u003e4\u003c/sub\u003e production occurs under the anoxic conditions, where methanogens break down carbohydrates into CH\u003csub\u003e4\u003c/sub\u003e (Lai \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Peat soils, with high moisture contents create anoxic conditions due to persistently shallow water table at the peat surface (Hobbs \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Restored peatlands that were previously subjected to industrial extraction and having a depth of less than 1.5 m, exhibited higher CH₄ emissions (Aitova et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) possibly due to increased alkalinity, which favored methanogen activity (Evans \u0026amp; Gauci \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, not all the produced CH\u003csub\u003e4\u003c/sub\u003e reaches the atmosphere, as methanotrophs in the aerobic zone consume some CH₄ before it escapes to the atmosphere (Hanson \u0026amp; Hansen \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). But in waterlogged conditions, limited pore space and reduced diffusion increases ebullition (sudden CH₄ bursts) or plant-mediated transport via aerenchyma tissues (Vroom et al. 2022). These pathways bypass the aerobic oxidation, increasing CH₄ emissions. Vegetation plays a crucial role in CH₄ fluxes by facilitating direct transport to the atmosphere, reinforcing the link between plant communities and CH₄ dynamics (Ge et al. 2024). CH\u003csub\u003e4\u003c/sub\u003e fluxes also depend upon the vegetation type (Chanton et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), nutrient status (Lai et al. 2009), biological processes (Hanson \u0026amp; Hansen \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), hydrology (Evans et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), climatic conditions and landuse (Aitova et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). If an incomplete understanding of factors governing the production or emission of CH\u003csub\u003e4\u003c/sub\u003e exist, it is important to determine which specific environmental and biological variables most significantly impact these processes.\u003c/p\u003e \u003cp\u003eHowever, the incorporation of light levels into CH\u003csub\u003e4\u003c/sub\u003e flux models remains limited, representing a significant area of uncertainty. If CH\u003csub\u003e4\u003c/sub\u003e fluxes follow a diurnal pattern independent of commonly used predictors such as soil temperature and WTD, further investigation is warranted. Differences in CH\u003csub\u003e4\u003c/sub\u003e fluxes between daylight and night-time conditions, if unaccounted for in annual flux models, this can lead to inaccurate greenhouse gas (GHG) balances (Saunois et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Failure to consider light conditions in chamber measurement programs may also affect emission inventories, potentially leading to over-or underestimation of emissions and contributing to discrepancies between atmospheric CH\u003csub\u003e4\u003c/sub\u003e concentrations and bottom-up inventories (Saunois et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Observational studies focusing on this pattern have typically been conducted in single vegetation communities like \u003cem\u003ePhragmites australis\u003c/em\u003e (Minke et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) or \u003cem\u003eSphagnum\u003c/em\u003e dominated communities (Lhosmot et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Other studies have focused on a small sample of vegetation communities, including \u003cem\u003ePhragmites australis\u003c/em\u003e, wetland sedges, grasses, and herbs (Juutinen et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), \u003cem\u003eEriophorum angustifolium\u003c/em\u003e and \u003cem\u003eEriophorum vaginatum\u003c/em\u003e (Dooling et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and \u003cem\u003eSphagnum\u003c/em\u003e mosses, evergreen shrubs, sedges, and forbs (Lai et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Studies examining this have used manual closed chamber measurements with the study duration lasting few days (Dooling et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) or over two summers (Minke et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) or four months (Juutinen et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and using a combination of automated chambers and flux towers over several years (Lai et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These studies observed differences betweeen day and night flux measurements (B\u0026auml;ckstrand et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lai et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However to date, most studies have not examined this from a seasonal perspective given the short duration of past studies.\u003c/p\u003e \u003cp\u003eDifferent mechanisms have been proposed to explain this including processes that affect CH\u003csub\u003e4\u003c/sub\u003e production (Doane \u0026amp; Rongzhong \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) or transport processes (Chanton et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Recently, photochemical reactions identified as potentially important sources of CH\u003csub\u003e4\u003c/sub\u003e production. A link between CH\u003csub\u003e4\u003c/sub\u003e fluxes and photochemistry specifically in agricultural settings and forestry, albiet under mostly mineral soils was reported (Doane \u0026amp; Rongzhong \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Photochemical reactions were found to increase the number of ketones produced in vegetation, decayed matter and peat. This was identified as the best predictor of CH\u003csub\u003e4\u003c/sub\u003e emission (Doane \u0026amp; Rongzhong \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While it is often assumed that the microbial activity and associated CH\u003csub\u003e4\u003c/sub\u003e emissions decline in winter, this photochemical reaction was found to continue regardless of temperature if sufficient light levels are present and resulted in the continued production of CH\u003csub\u003e4\u003c/sub\u003e. Chanton et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) found that as the sunlight increased, it caused air to move through the plants, opening the stomata (tiny openings on the plant's surface). This ventilation process helped release the trapped methane from inside the plant stems, leading to an increase in methane emissions. Minke et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found a significant negative correlation between CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e fluxes, though the strength of this correlation varied greatly between different plots. All recorded parameters showed a strong correlation with Photosynthetically Active Radiation (PAR), which regulates the CO\u003csub\u003e2\u003c/sub\u003e-consuming process of photosynthesis. This study also observed that the CO\u003csub\u003e2\u003c/sub\u003e concentrations within the chamber are typically higher at night when PAR and photosynthesis were absent, while CH\u003csub\u003e4\u003c/sub\u003e emissions were low (Minke et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast, the study found that during the daytime, when PAR and photosynthesis are active, both CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e emissions increased. Although the effect of CO\u003csub\u003e2\u003c/sub\u003e was significant, this study further suggested that the PAR was likely the main factor driving the differences in CH\u003csub\u003e4\u003c/sub\u003e fluxes. This implies that the variations in CH\u003csub\u003e4\u003c/sub\u003e emissions are more directly related to light availability and the resulting photosynthetic activity, rather than CO\u003csub\u003e2\u003c/sub\u003e levels alone (Minke et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, some studies have been unable to detect any dirunal pattern (Mengyu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) while other studies found that light oxidised CH\u003csub\u003e4\u003c/sub\u003e emissions, (Lhosmot et al. 2022). This field study investigates whether there is a significant difference in CH\u003csub\u003e4\u003c/sub\u003e flux measurements taken under light and dark conditions using the manual closed chamber method. By comparing flux measurements across nine vegetation communities from five peatland sites, the study aims to evaluate the influence of light and dark conditions on CH\u003csub\u003e4\u003c/sub\u003e fluxes. Additionally, it seeks to assess the scale of this effect by comparing its influence with environmental parameters, such as WTDs and soil temperature. Therefore this research aims to enhance our understanding of CH\u003csub\u003e4\u003c/sub\u003e flux variations between light and dark conditions across diverse wetland vegetation communities. In doing so, it seeks to determine whether this pattern is widespread across multiple vegetation communities. Additionally, this study will assess whether the effect of light and dark conditions is significant compared to other important environmental factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eSite Description\u003c/p\u003e\n\u003cp\u003eThe locations used in this study were former raised bogs located in Co. Offaly and Co. Kildare in the midlands of Ireland (Fig. 1). All the bogs were drained and subjected to peat extraction except the \u003cem\u003eSphagnum fuscum\u003c/em\u003e dominated site located in Mouds bog, Co. Kildare which was drained but not extracted. The vegetation communities at Blackriver, Ballycon and Derries were rewetted approximately 40, 20 and 10 years prior to this study respectively. The \u003cem\u003eCalluna vulgaris\u003c/em\u003e and \u003cem\u003eMolinia\u003c/em\u003e grasses monitored at Clonad and Mouds were former extraction sites that were revegetated. All the vegetation communities except at Mouds and Clonad (drained and revegetated) had shallow peat depths averaging less than 1.5 m (Table 1).\u003c/p\u003e\n\u003cp\u003eNine vegetation communities commonly found in degraded shallow and deep peatlands, as well as those associated with rewetted sites, were selected for monitoring (Table 1). \u003cem\u003eSphagnum\u003c/em\u003e communities were aggregated together for the purposes of this study. Duplicate sites (e.g. vegetation communities sampled at two separate bog locations) were included to assess intra-site variation, except for \u003cem\u003ePhragmites australis\u003c/em\u003e and Open Water. The peat depth was shallow at most vegetation communities, due to peat removal, while pH and presence of vegetation varied depending on hydrology and peat depth. \u003cem\u003eCalluna vulgaris\u003c/em\u003e and \u003cem\u003eMolinia Caerulea\u003c/em\u003e were found in dry areas, \u003cem\u003eTypha latifolia\u003c/em\u003e and \u003cem\u003ePhragmites australis\u003c/em\u003e dominated in wet areas, while basic conditions with shallow peat had variety of \u003cem\u003eSphagnum\u003c/em\u003e mosses (hereafter referred to as \u003cem\u003eSphagnum\u003c/em\u003e dominated communities) and \u003cem\u003eEriophorum angustifolium\u003c/em\u003e were found over a range of peat depths with varying nutrient statuses (Table 1).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSite descriptions of selected locations used in this study. Chamber monitoring locations are located over nine different vegetation communities and land use status (rewetted, drained or drained and extracted)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSite\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eArea (Ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVegetation Community\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRewetted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRewetted Status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExtraction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMean WTD \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(cm)\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDepth\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(cm)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBallycon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Kildare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSphagnum palustre\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 years ago,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1960\u0026ndash;2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBallycon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Offaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEriophorum\u003c/em\u003e a\u003cem\u003engustifolium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 years ago,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1960\u0026ndash;2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBallycon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Offaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMolinia caerulea\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 years ago,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1960\u0026ndash;2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBallycon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Offaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTypha latifolia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 years ago,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1960\u0026ndash;2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBallycon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Offaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePhragmites australis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 years ago,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1960\u0026ndash;2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlackriver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Kildare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEriophorum angustifolum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 years ago,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCeased in 1980s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlackriver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Kildare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eJuncus effusus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 years ago,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCeased in 1980s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlackriver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Kildare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSphagnum papillosum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 years ago,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCeased in 1980s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClonad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Offaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCalluna vulgaris\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrained, extracted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1970\u0026ndash;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClonad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Offaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMolinia careulea\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrained, extracted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1970\u0026ndash;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMouds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Kildare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCalluna vulgaris\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrained, extracted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCeased in 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-40.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMouds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Kildare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSphagnum fuscum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrained, not extracted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknow when drained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDerries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Offaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTypha latifolia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRewetted 10 years ago\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1960\u0026ndash;2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDerries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Offaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOpen water\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRewetted 10 years ago\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1960\u0026ndash;2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDerries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo. Offaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCarex rostrata\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRewetted 10 years ago\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1960\u0026ndash;2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eGreenhouse Gas Flux Measurement Methodology\u003c/h2\u003e\n \u003cp\u003eThe experimental set-up for measuring greenhouse gas fluxes involved the installation of collars\u003c/p\u003e\n \u003cp\u003e(60 x 60cm) made of stainless steel. These were inserted 12 cm into the peat profile to create a gas-tight seal and remained in place throughout the study. Three steel collars were installed per vegetation community at each monitoring location. The monitoring frequency at each study site was twice per month during the growing season (April to September) and once per month over the non-growing season (October to March). During each site visit, CH\u003csub\u003e4\u003c/sub\u003e fluxes was measured three times from each collar: (i) using a clear chamber, (ii) clear chamber covered by a semi-transparent net and (iii) clear chamber covered by a non-transparent tarpaulin (Wilson et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) using a LICOR 7810 CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e gas analyser. The chamber measurements were taken in succession with a small break period\u003c/p\u003e\n \u003cp\u003e(2\u0026ndash;3 min) to allow the sensor to re-calibrate to atmospheric conditions and to allow the chamber headspace to equilibrate with the atmospheric conditions. Temperature probes were installed at 5 cm depth (Wilson et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) to continuously monitor soil temperature. Boardwalks were constructed to minimize soil compaction and disturbance during measurements, particularly to reduce ebullition events driven by trampling during CH\u003csub\u003e4\u003c/sub\u003e measurements. Chambers were equipped with a small 5V fan for air mixing and PAR sensors are placed in the chamber during measurements. The closure time of the chambers was 90 seconds with 30 seconds mixing time and 60 seconds (1 min) monitoring time. Concentrations were recorded every 2 seconds and then averaged over a 6-second period. This process was repeated to collect a total of 10 measurements during the entire closure period. This approach was adopted to minimise temperature, pressure and changes of light within the headspace of the chamber. If PAR measurements changed significantly over the measurement period, the measurement was retaken. Fluxes were only accepted if an R\u003csup\u003e2\u003c/sup\u003e value of \u0026gt;\u0026thinsp;0.90 was detected. The flux of CH\u003csub\u003e4\u003c/sub\u003e was estimated simultaneously using Eq. 1:\u003c/p\u003e\n \u003cp\u003eEquation 1: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{c}=\\frac{10V{P}_{0}(1-\\frac{{W}_{0}}{1000})}{RS({T}_{0}+273.15)}\\frac{\\partial\\:C{\\prime\\:}}{\\partial\\:t}\\)\u003c/span\u003e\u003c/span\u003e (LICOR, 2024)\u003c/p\u003e\n \u003cp\u003eWhere V is the chamber Volume (m\u003csup\u003e3\u003c/sup\u003e), P is the Air Pressure (Hpa), W\u003csub\u003e0\u003c/sub\u003e is the initial H\u003csub\u003e2\u003c/sub\u003eO concentration (mmol/mol), R is the ideal gas constant (8.3144), T\u003csub\u003e0\u003c/sub\u003e is air temperature taken from an air temperature probe (Kelvin), \u0026part;C\u0026apos; is the change in CH\u003csub\u003e4\u003c/sub\u003e concentration and \u0026part;t is the time elapsed (seconds). CO\u003csub\u003e2\u003c/sub\u003e fluxes (Net Ecosystem Exchange) were measured simultaneously. Methane flux measurements were carried out from July 2023 to May 2024. Given that site set up was ongoing during this time, the number of flux measurements per site varied. These measurements were compiled into a single data base and organised according to vegetation community and whether the flux was taken in light or dark conditions. Water table levels were obtained via manual dip measurements, internal chamber PAR values using SQ-520: Full-Spectrum Smart Quantum Sensor, pH using a YSI pH probe and soil temperature using a Hobo soil temperature probe.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAnalysis of differences between light and dark CH chamber measurements\u003c/h3\u003e\n\u003cp\u003eTo assess differences in CH₄ flux between light and dark conditions, Mann-Whitney U tests and a linear mixed-effect model (Lindstrom \u0026amp; Bates \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e) were employed. Given the non-normal distribution of the data, the Mann-Whitney U test compared rankings between groups, while the mixed-effect model accounted for fixed effects (light/dark) and random effects (vegetation communities) to analyse CH₄ flux variability. Preliminary tests, including the Shapiro-Wilk test for normality and Levene\u0026rsquo;s test for heteroscedasticity, were conducted, with QQ plots and residuals examined using PYTHON 3.1.1 (Figures \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e-S9; Supplementary material). The Mann-Whitney U test provided p-values to assess significance, and Cohen\u0026rsquo;s d test quantified effect size. As CH\u003csub\u003e4\u003c/sub\u003e fluxes are influenced by multiple factors such as temperature, water table, CO₂ flux, and pH, the mixed-effect model was essential for capturing these interactions. Cross-validation was performed by partitioning the dataset into multiple subsets to evaluate predictive performance. Model outputs included intercepts (baseline CH₄ flux) and coefficients indicating effect magnitude and direction, where a positive coefficient for \u0026quot;Dark to Light\u0026quot; suggested increased CH₄ flux under light conditions. Model accuracy was assessed using Mean Square Error (MSE), with lower values indicating better performance (Lohse et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study found that the vegetation communities such as the \u003cem\u003eCarex rostrata\u003c/em\u003e and \u003cem\u003eEriophorum angustifolium\u003c/em\u003e exhibited higher CH\u003csub\u003e4\u003c/sub\u003e fluxes in light conditions compared to dark conditions, while the other vegetation communities did not show any observable differences between light and dark CH\u003csub\u003e4\u003c/sub\u003e fluxes (Fig.\u0026nbsp;2). However, on average, the \u003cem\u003eCarex rostrata\u003c/em\u003e exhibited the largest difference (0.03 g CH\u003csub\u003e4\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) followed by \u003cem\u003eEriophorum angustifolium\u003c/em\u003e (0.01 g CH\u003csub\u003e4\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). But the \u003cem\u003eJuncus effusus exhibited the\u003c/em\u003e opposite trend i.e., the CH\u003csub\u003e4\u003c/sub\u003e fluxes were higher in dark conditions compared to the light conditions, albiet with a non-significant effect. The box plots of each individual vegetation highlight the differences between the light and dark measurements (Fig.\u0026nbsp;3). The \u003cem\u003eCarex rostrata\u003c/em\u003e and \u003cem\u003eEriophorum angustifolium\u003c/em\u003e had notable differences in CH\u003csub\u003e4\u003c/sub\u003e flux values (median and upper quartile) (Fig.\u0026nbsp;3) while other species such as \u003cem\u003eCalluna Vulgaris, Juncus effusus, Molinia Caerulea\u003c/em\u003e and \u003cem\u003eSphagnum\u003c/em\u003e communities showed minor to no differences in CH\u003csub\u003e4\u003c/sub\u003e fluxes between light and dark conditions (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;2 shows the results of Shapiro-Wilk Test and Levene\u0026rsquo;s test. The CH\u003csub\u003e4\u003c/sub\u003e flux data was found to be non-normally distributed across all nine vegetation communities (e.g. p-values were \u0026lt;\u0026thinsp;0.05) and this was supported by the QQ plots (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S9; Supplementary material) and histograms and Kernal Density Estimates (KDEs) (Figure S10; Supplementary material). Outliers retained in the dataset given that the ebullition is an important process within bogs that results in large and abrupt CH\u003csub\u003e4\u003c/sub\u003e fluxes (Bieniada \u0026amp; Strack 2021). Heteroscedasticity was detected in \u003cem\u003eCarex rostrata\u003c/em\u003e and \u003cem\u003eEriophorum angustifolium\u003c/em\u003e (e.g. p-values were \u0026lt;\u0026thinsp;0.05) meaning that the parametric tests on these vegetation communities may be biased and invalid. However, given the non-normality, the Mann-Whitney U statistic was used to determine if there was a significant difference between light and dark CH\u003csub\u003e4\u003c/sub\u003e flux measurements (Table\u0026nbsp;3). The results showed a significant difference in light and dark measurements for \u003cem\u003eCarex rostrata\u003c/em\u003e and \u003cem\u003eEriophorum angustifolium\u003c/em\u003e compared to other vegetation communities (Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample size of chamber measurements included in the study at each vegetation community and provides an assessment of normality and heteroscedasticity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo. of Chamber Measurements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eShapiro-Wilk Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLevene's Test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVegetation Communities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eW-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCarex rostrata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEriophorum angustifolium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCalluna vulgaris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJuncus effusus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMolinia Caerulea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOpen Water\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhragmites australis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSphagnum Communities\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTypha latifolia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssesses if there is a significant difference between vegetation communities. The t-statistic assumes normality while the Mann-Whitney U statistic does not\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation Communities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann-Whitney U statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCarex rostrata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e603.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEriophorum angustifolium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1217.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCalluna vulgaris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e332.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJuncus effusus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e324.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMolinia Caerulea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e765.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOpen Water\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhragmites australis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSphagnum Communities\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1674.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTypha latifolia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mixed-effect model provided additional information as compared to the non-parametric tests. It also showed that \u003cem\u003eCarex Rostrata\u003c/em\u003e and \u003cem\u003eEriophorum angustifolium\u003c/em\u003e had significant differences in CH\u003csub\u003e4\u003c/sub\u003e fluxes. Table\u0026nbsp;4 shows that when switched from dark to light conditions the fluxes increased by 0.0201 g CH\u003csub\u003e4\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for \u003cem\u003eCarex Rostrata\u003c/em\u003e and 0.0153 g CH\u003csub\u003e4\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for \u003cem\u003eEriophorum angustifolium\u003c/em\u003e. An opposite trend was observed for \u003cem\u003eJuncus effusus and Typha latifolia\u003c/em\u003e where fluxes decreased in light conditions (-0.0135 \u0026amp; -0.0104 g CH\u003csub\u003e4\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively). The presence of outliers best explained these differences. The overlapping confidence intervals also suggests a less clearly defined role between light and dark conditions. Moreover, the influence of pH and WTD were found to have an equally important effect on CH\u003csub\u003e4\u003c/sub\u003e fluxes. pH was found to influence CH\u003csub\u003e4\u003c/sub\u003e fluxes from \u003cem\u003eJuncus Effusus\u003c/em\u003e and open water with higher pH values (more alkaline conditions) associated with higher fluxes (Figures S11, S12 and S13; Supplementary material). \u003cem\u003eTypha latifolia\u003c/em\u003e also showed that higher pH values influenced CH\u003csub\u003e4\u003c/sub\u003e fluxes (-0.0102 g CH\u003csub\u003e4\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e hr\u003csup\u003e\u0026minus;\u0026minus;1\u003c/sup\u003e), in addition to the switch between dark to light conditions (-0.01043 g CH\u003csub\u003e4\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and water table level (+\u0026thinsp;0.0141 g CH\u003csub\u003e4\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). CO\u003csub\u003e2\u003c/sub\u003e fluxes were also found to strongly influence CH\u003csub\u003e4\u003c/sub\u003e fluxes in \u003cem\u003eCarex Rostrata\u003c/em\u003e, \u003cem\u003eEriophorum angustifolium\u003c/em\u003e, Open Water, \u003cem\u003eSphagnum\u003c/em\u003e Communities and \u003cem\u003eTypha Latifolia\u003c/em\u003e (Table\u0026nbsp;4). In nearly all vegetation communities, this was found to be a negative effect, implying that while these vegetation communities sequester CO\u003csub\u003e2\u003c/sub\u003e, CH₄ emissions increase simultaneously. Conversely, during respiration, CH₄ emissions decrease. In the \u003cem\u003eSphagnum\u003c/em\u003e communities and Open water, the opposite trend was observed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePresents the intercept, coefficients, and cross-validation mean squared error (MSE) for various vegetation communities, considering factors such as dark to light transitions, water table (WT), pH, soil temperature, and photosynthetically active\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation Communities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDark to Light\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSoil Temp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCO2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCV MSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCarex Rostrata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.15086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.00260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.00372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.02134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEriophorum angustifolium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.02650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCalluna vulgaris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.00005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.00012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJuncus effusus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.14121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.01357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.00786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMolinia Caerulea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.00009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.00015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOpen Water\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.00335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.00947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhragmites australis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.00667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.00025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.00049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSphagnum communities\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.00087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTypha latifolia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.01043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.01023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.00637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.04793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe mixed-effect model found that the \u003cem\u003eCarex rostrata\u003c/em\u003e and \u003cem\u003eEriophorum angustifolium\u003c/em\u003e had the largest differences between light and dark conditions. The effect of light and dark conditions was significant and found to have a more profound impact than the water table, soil temperature and PAR on CH\u003csub\u003e4\u003c/sub\u003e fluxes (Table\u0026nbsp;4). In contrast, the \u003cem\u003eJuncus effusus\u003c/em\u003e was found to have higher fluxes in the dark conditions compared to the light conditions. However, research to date on CH₄ fluxes under varying light conditions has yielded mixed results. For instance, some studies found higher CH\u003csub\u003e4\u003c/sub\u003e fluxes in light conditions compared to the dark conditions (Minke et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), while other studies found no differences between CH\u003csub\u003e4\u003c/sub\u003e fluxes in light and dark conditions (Mengyu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) while some studies showed higher CH\u003csub\u003e4\u003c/sub\u003e fluxes in dark conditions compared to light conditions (Dooling et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lhosmot et al. 2022). All three scenarios were replicated in this study albeit for different vegetation communities. This indicates a high degree of heterogeneity in CH\u003csub\u003e4\u003c/sub\u003e fluxes among peatland species in response to varying light and dark conditions.\u003c/p\u003e \u003cp\u003eTo understand why this might be the case, it is important to distinguish whether the observed differences arise from CH₄ production or CH\u003csub\u003e4\u003c/sub\u003e transport. This distinction helps to determine whether the vegetation is actively producing CH₄ or primarily facilitating its movement to the atmosphere. This distinction is not only important for accurately estimating CH\u003csub\u003e4\u003c/sub\u003e fluxes, but also for informing management decisions in restored bog and fens, such as water table regulation and vegetation control. Previous research has reported both scenarios, where differences between light and dark measurements were due to the changes in the CH\u003csub\u003e4\u003c/sub\u003e production rate (Dooling et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) or changes in the rate of CH\u003csub\u003e4\u003c/sub\u003e transport (Minke et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Processes that account for increases in the CH\u003csub\u003e4\u003c/sub\u003e fluxes are due to increasing rates of CH\u003csub\u003e4\u003c/sub\u003e production via root exudates (Dooling et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) or via photochemical reactions linked with the production of keytones (Doane \u0026amp; Rongzhong \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies found that either atmospheric turbulence (Lai et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) or stomatal conductivity which was affected by higher CO\u003csub\u003e2\u003c/sub\u003e concentrations (Minke et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) were the main drivers of CH\u003csub\u003e4\u003c/sub\u003e fluxes between light and dark conditions. While turbulence was not measured here, given that the light and dark measurements were taken concurrently, it\u0026rsquo;s unlikely that the atmospheric turbulence can account for differences in this study.\u003c/p\u003e \u003cp\u003eVegetation species such as \u003cem\u003eCarex rostrata, Eriophorum angustifolium, Juncus effusus, Phragmites australis\u003c/em\u003e and \u003cem\u003eTypha latifolia\u003c/em\u003e, all possess aerenchyma tissues allowing transport of CH\u003csub\u003e4\u003c/sub\u003e from peat-surface to atmosphere. However, not all species containing \u003cem\u003eaerenchyma\u003c/em\u003e exhibited differences between light and dark measurements such as \u003cem\u003ePhragmites australis\u003c/em\u003e and \u003cem\u003eTypha latifolia\u003c/em\u003e. \u003cem\u003ePhragmites australis\u003c/em\u003e was found to have low CH\u003csub\u003e4\u003c/sub\u003e fluxes in general which was surprising given it was an aerenchyma species. Alternatively, \u003cem\u003eTypha latifolia\u003c/em\u003e exhibited the highest CH\u003csub\u003e4\u003c/sub\u003e fluxes in both light and dark conditions. This provides evidence that production may not be responsible for observed differences between light and dark conditions. If light levels were important in determining the production of CH\u003csub\u003e4\u003c/sub\u003e, a pronounced effect should be observable in all vegetation communities with high CH\u003csub\u003e4\u003c/sub\u003e fluxes. However, this was not the case. No difference was detected for \u003cem\u003eTypha latifolia\u003c/em\u003e suggesting that the light levels were not influencing CH\u003csub\u003e4\u003c/sub\u003e production. This contrasts with the findings of other studies which found the CH\u003csub\u003e4\u003c/sub\u003e production to be the main reason for differences between light and dark CH\u003csub\u003e4\u003c/sub\u003e fluxes (Dooling et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Doane \u0026amp; Rongzhong \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMinke et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that because of the dense network of interlinked rhizomes, the CH\u003csub\u003e4\u003c/sub\u003e emissions may be rerouted to rhizomes which may be located outside of the closed chamber due to the pressure gradients created by the manual closed chamber method. Therefore, both CH\u003csub\u003e4\u003c/sub\u003e fluxes and the differences observed between light and dark measurements may be obscured by enhanced transport between the stands. This further supports the importance of understanding the role of transport mechanisms of gases within vegetation communities. Given the observed association between CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e fluxes, our study results are consistent with Minke et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), where field measurements showed that the CH\u003csub\u003e4\u003c/sub\u003e fluxes increased when the CO\u003csub\u003e2\u003c/sub\u003e concentrations decreased and vice versa. Chanton and Whitney (1994) found this pattern to be related to decreased stomatal conductance at high CO\u003csub\u003e2\u003c/sub\u003e concentrations. One important difference between our study and that of Minke et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), was that PAR and relative humidity were found to strongly control CH\u003csub\u003e4\u003c/sub\u003e fluxes in Minke et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) while the PAR was not observed to control CH\u003csub\u003e4\u003c/sub\u003e fluxes in our study. Of note, both Minke et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and this study agreed that little differences were observed in CH\u003csub\u003e4\u003c/sub\u003e fluxes from \u003cem\u003ePhragmites australis\u003c/em\u003e in light or dark conditions. The results of this study support Chanton and Whitney (1994) findings that the stomatal conductivity could be controlling CH\u003csub\u003e4\u003c/sub\u003e fluxes. This aligns with Noyce et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) who have found that the removal of above-ground biomass in the case of \u003cem\u003eCarex rostrata\u003c/em\u003e reduced CH\u003csub\u003e4\u003c/sub\u003e fluxes by 40\u0026ndash;70%, indicating that the transport may be the main factor controlling CH\u003csub\u003e4\u003c/sub\u003e fluxes. Therefore, considering light levels could improve the precision of CH\u003csub\u003e4\u003c/sub\u003e flux estimation. As our results show evidence of differences in light and dark measurements for two vegetation communities, this may result in underestimation or over estimation of CH\u003csub\u003e4\u003c/sub\u003e fluxes. The implications of this study mean that the inclusion of light and dark measurements in future chamber measurement programs is advisable and subsequent modelling approaches should seek to incorporate light and dark response curves and CO\u003csub\u003e2\u003c/sub\u003e fluxes in addition to water table and soil temperature (Wilson et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study utilized statistical techniques (Mann Whitney U test, mixed-effects model) to determine if there were any potential differences in CH\u003csub\u003e4\u003c/sub\u003e fluxes from different wetland vegetation types (\u003cem\u003eSphagnum\u003c/em\u003e communities, \u003cem\u003eEriophorum angustifolium\u003c/em\u003e, \u003cem\u003eMolinia caerulea\u003c/em\u003e, \u003cem\u003eTypha latifolia\u003c/em\u003e, \u003cem\u003ePhragmites australis\u003c/em\u003e, \u003cem\u003eJuncus effusus\u003c/em\u003e, \u003cem\u003eCalluna vulgaris\u003c/em\u003e, and \u003cem\u003eCarex rostrata\u003c/em\u003e) subjected to light and dark conditions over a one-year period. The mixed-effect model showed that only two wetland vegetation species (\u003cem\u003eCarex rostrata\u003c/em\u003e and \u003cem\u003eEriophorum angustifolium\u003c/em\u003e) exhibited different CH\u003csub\u003e4\u003c/sub\u003e fluxes in light and dark conditions and this effect was greater than the impact exerted by measured in-situ environmental variables such as pH, redox and soil temperature. However, it is important to note that the limitations of this study include the presence of outliers, the abnormal distribution of the vegetation communities and heteroscedasticity, meaning that some statistical analyses need to be interpreted with caution. However, all statistical tests show the same result-a difference between light and dark measurements for two vegetation species (\u003cem\u003eEriophorum angustifolium\u003c/em\u003e and \u003cem\u003eCarex rostrata\u003c/em\u003e). As the study was conducted over a single year, it does not account for interannual variability, so future research should seek to conduct multiple year measurements. While this study identifies differences in CH₄ fluxes between light and dark conditions, it does not elucidate the underlying mechanisms driving these differences. Without direct measurements of plant specific phenological characteristics, soil microbial activity or pore water CH\u003csub\u003e4\u003c/sub\u003e concentrations to explain the observed patterns, the underlying drivers remain somewhat speculative. To identify the driving mechanism/s impacting the plant-specific CH\u003csub\u003e4\u003c/sub\u003e fluxes, future field monitoring programs can measure plant biomass, amount of permeable root surface (taking proxy measurements of root length), presence and diversity of methanogens and methanotrophs within the plant shoots along with the measurements of in-situ environmental parameters (pore-water CH\u003csub\u003e4\u003c/sub\u003e concentrations, WTDs, pH and soil temperature).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003eWe appreciate the critical reviews provided by the journal referees and the journal editor/co-editor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; This work was supported by the Department of Environment, Climate and Communications under the Peatland Climate Action Scheme. Amey Tilak was funded by the Irish Environmental Protection Agency (EPA) and the Department of Agriculture, Food and Marine respectively (grant number for CH\u003csub\u003e4\u003c/sub\u003e project: 2021-CE-1060). The EPA Research Program 2021-2030 is a Government of Ireland initiative funded by the Department of Communications, Climate Action, and Environment. This research program is administered by the Environment Protection Agency (EPA), which has the statutory function of coordinating and promoting environmental research. M Clancy was funded by Science Foundation Ireland (grant number SFI 20/SPP/3705). KA Byrne acknowledges funding from Science Foundation Ireland (grant number SFI 20/SPP/3705) and the University of Limerick.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information and affiliations\u003c/strong\u003e\u003cbr\u003eSchool of Architecture, Building and Environment, Technological University Dublin, Bolton Street, \u0026nbsp;\u003cbr\u003eDublin 1, Ireland\u003cbr\u003eStephen Barry\u0026nbsp;\u003cbr\u003e\u003cbr\u003eBord na Mona, Leabeg, Boora Ave, Co. Offaly, Ireland\u003cbr\u003eKenneth Crawford, Clare O\u0026rsquo;Doherty, G Heagney, Harry Kelly, Mark McCorry, Hannah Mealy, Brian Mollahan\u003cbr\u003e\u003cbr\u003eDepartment of Biological Sciences \u0026amp; Bernal Institute, Faculty of Science and Engineering, \u0026nbsp;\u003cbr\u003eUniversity of Limerick, Limerick, Ireland\u003cbr\u003eMike Clancy, Amey S. Tilak and Kenneth A. Byrne\u003cbr\u003e\u003cbr\u003eSchool of Natural Sciences, Botany Discipline, Trinity College, Dublin, Ireland\u003cbr\u003eMatthew Saunders\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Harry Kelly, Clare O\u0026rsquo;Doherty, Hannah Mealy, Brian Mollahan, Amey Tilak, Mike Clancy, Ken Byrne, Matt Saunders and Stephen Barry, ecology surveys were carried out by Mark McCorry. The first draft of the manuscript was written by Stephen Barry and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003edatasets generated during and/or analysed during the current study are available from the first and corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStephen Barry [email protected]\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003cbr\u003eCompeting interests \u0026nbsp;\u003cbr\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAitova E, Morely T, Wilson D, Renou-Wilson F (2023) A review of greenhouse gas emissions and removals from Irish peatlands. Mires and Peat 1-17. http://mires-and-peat.net/pages/volumes/map29/map2904.php\u003c/li\u003e\n \u003cli\u003eB\u0026auml;ckstrand K, Crill P, Mastepanov M, Christensen T, Bastviken D (2008) Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden. \u003cem\u003eJ Geophys Res\u003c/em\u003e 113, G03026. https://doi.org/10.1029/2008JG000703\u003c/li\u003e\n \u003cli\u003eBord na M\u0026oacute;na (BnM) (2024) \u003cem\u003ePeatlands Climate Action Scheme\u003c/em\u003e. Retrieved from https://www.bnmpcas.ie/: https://www.bnmpcas.ie/#:~:text=Learn%20More%20%3E-,Climate%20Action%20Benefits,rich%20vegetation%20in%20suitable%20conditions. Accessed on May 22\u003csup\u003end\u003c/sup\u003e 2024.\u003c/li\u003e\n \u003cli\u003eBreiman L (2001) Random Forests. \u003cem\u003eMachine Learning\u003c/em\u003e, 45: 5-32. https://doi.org/10.1023/A:1010933404324\u003c/li\u003e\n \u003cli\u003eChandra N, Patra P, Fujita R, H\u0026ouml;glund-Isaksson L, Umezawa T, Goto D, Morimoto S, Vaugh B.H, R\u0026ouml;ckmann T (2024) Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990-2020. \u003cem\u003eCommunications Earth \u0026amp; Environment\u003c/em\u003e 5-147. https://doi.org/10.1038/s43247-024-01286-x\u003c/li\u003e\n \u003cli\u003eChanton J, Whiting G, Happell J, Ghislan G (1993) Contrasting rates and diurnal patterns of methane emission from emergent aquatic macrophytes. \u003cem\u003eAquatic Botany\u003c/em\u003e, 46(2): 111-128. https://doi.org/10.1016/0304-3770(93)90040-4\u003c/li\u003e\n \u003cli\u003eChapin C, Bridgham S, Pastor J (2004) pH and nutrient effects on above-ground net primary production in a Minnesota, USA bog and fen. \u003cem\u003eWetlands\u003c/em\u003e 24: 186-201. https://doi.org/10.1672/0277-5212(2004)024[0186:PANEOA]2.0.CO;2\u003c/li\u003e\n \u003cli\u003eDoane T, Rongzhong Y (2022) Photochemical Soil Methane Emission. \u003cem\u003eACS Earth and Space Chemistry\u003c/em\u003e 6 (7): 1742-1749. https://doi.org/10.1021/acsearthspacechem.2c00055\u003c/li\u003e\n \u003cli\u003eDooling G, Chapman P, Baird A, Shepherd M, Kohler T (2018) Daytime-only measurements underestimate CH₄ emissions from a restored bog. \u003cem\u003e\u0026Eacute;coscience\u003c/em\u003e 259-270. https://doi.org/10.1080/11956860.2018.1449442\u003c/li\u003e\n \u003cli\u003eDunfield P, Knowles R, Dumont R, Moore T (1993) Methane production and consumption in temperate and subarctic peat soils: Response to temperature and pH.\u0026nbsp;\u003cem\u003eSoiI Biol, Biochem\u003c/em\u003e\u003cbr\u003e321-326. https://doi.org/10.1016/0038-0717(93)90130-4\u003c/li\u003e\n \u003cli\u003eEuropean Environment Agency (2024) \u003cem\u003eEuropean Climate Risk Assessment.\u003c/em\u003e Copenhagen: European Environment Agency.\u003c/li\u003e\n \u003cli\u003eEvans C, Gauci V (2023) \u003cem\u003eWetlands and Methane - Technical Paper.\u003c/em\u003e UK: Wetlands International.\u003c/li\u003e\n \u003cli\u003eEvans CD, Peacock M, Baird AJ, Artz RRE, Burden A, Callaghan N, Chapman PJ, Cooper HM, Coyle M, Craig E, Cumming A, Dixon S, Gauci V, Grayson RP, Helfter C, Heppell CM, Holden J, Jones DL, Kaduk J, Levy P, Matthews R, McNamara NP, Misselbrook T, Oakley S, Page SE, Rayment M, Ridley LM, Stanley KM, Williamson JL, Worrall F, Morrison R (2021) Overriding water table control on managed peatland greenhouse gas emissions. Nature 593: 548-552. https://doi.org/10.1038/s41586-021-03523-1.\u003c/li\u003e\n \u003cli\u003eEvans C, Artz R, Moxley J, Smyth M-A, Taylor E, Archer N, Burden A, Williamson J, Donnelly D, Thomson A, Buys G, Malcolm H, Wilson D, Renou-Wilson F, Potts J (2017) Implementation of an emission inventory for UK peatlands. Report to the Department for Business, Energy and Industrial Strategy, Centre for Ecology and Hydrology, Bangor.88pp. Accessed Dec 05 2024. https://uk-air.defra.gov.uk/reports/cat07/1904111135_UK_peatland_GHG_emissions.pdf\u003c/li\u003e\n \u003cli\u003eGoode K, Ries D, McClernon K (2024) Characterizing climate pathways using feature importance on echo state networks. \u003cem\u003eStatistical Analysis and Data Mining\u003c/em\u003e, 17. https://doi.org/10.1002/sam.11706\u003c/li\u003e\n \u003cli\u003eGorham E (1991) Northern peatlands: role in the carbon cycle and probably responses to climate warming. \u003cem\u003eEcological Applications\u003c/em\u003e 182-185. https://doi.org/10.2307/1941811\u003c/li\u003e\n \u003cli\u003eG\u0026uuml;nther A, Barthelmes A, Huth V, Joosten H, Jurasinski G, Koebsch F, Couwenberg J (2020) Prompt\u003cbr\u003erewetting of drained peatlands reduces climate warming despite methane emissions.\u003cbr\u003eNat Commun 11 : 1644. https://doi.org/10.1038/s41467-020-15499-z\u003c/li\u003e\n \u003cli\u003eHanson R, Hansen T (1996) Methanotrophic bacteria. \u003cem\u003eMicrobial Review\u003c/em\u003e 60(2): 439-471. https://doi.org/10.1128/mr.60.2.439-471.1996\u003c/li\u003e\n \u003cli\u003eHobbs N (1986) Mire morphology and the properties and behaviour of some British and foreign\u003cbr\u003epeats. \u003cem\u003eQuarterly Journal of Engineering Geology\u003c/em\u003e, 19(1):7-80. https://doi.org/10.1144/GSL.QJEG.1986.019.01.02\u003c/li\u003e\n \u003cli\u003eIPCC (2013) \u003cem\u003eClimate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.\u003c/em\u003e Cambridge University Press: T.F. Stocker, D. Qin, G.-K. Plattner, et al. (Eds.)).\u003c/li\u003e\n \u003cli\u003eJoosten, H., Sirin, A., Couwenberg, J., Laine, J., \u0026amp; Smith, P. (2016). The role of peatlands in climate regulation. In A. Bonn, T. Allott, M. Evans, H. Joosten, \u0026amp; R. Stoneman (Eds.), Peatland Restoration and Ecosystem Services: Science, Policy and Practice (pp. 63-76). Cambridge University Press. https://doi.org/10.1017/CBO9781139177788.005\u003c/li\u003e\n \u003cli\u003eJuutinen S, Alm J, Larmola T, Saarnio S, Martikainen P, Silvola J (2004) Stand-specific diurnal dynamics of CH\u003csub\u003e4\u003c/sub\u003e fluxes in boreal lakes: Patterns and controls. \u003cem\u003eJournal of Geophysical Research\u003c/em\u003e 1-11. https://doi.org/10.1029/2004JD004782\u003c/li\u003e\n \u003cli\u003eLai D (2009) Methane Dynamics in Northern Peatlands: A Review. Pedosphere, 19(4): 409-421. https://doi.org/10.1016/S1002-0160(09)00003-4\u003c/li\u003e\n \u003cli\u003eLai D, Roulet N, Humpreys E, Moore, T, Dalva M (2012) The effect of atmospheric turbulence and chamber deployment period on autochamber CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e flux measurements in anombrotrophic peatland. \u003cem\u003eBiogeosciences\u003c/em\u003e, 3305-3322. https://doi.org/10.5194/bg-9-3305-2012.\u003c/li\u003e\n \u003cli\u003eLe Mer, J, Roger P (2001) Production, oxidation, emission and consumption of methane by soils:\u003cbr\u003eA review. European Journal\u003cem\u003e\u0026nbsp;\u003c/em\u003eof Soil Biology 37(1): 25-50. https://doi.org/10.1016/S1164-5563(01)01067-6\u003c/li\u003e\n \u003cli\u003eLeifeld J, Menichetti, L (2018) The underappreciated potential of peatlands in global climate change mitigation strategies. Nature Communications 9.\u003cbr\u003ehttps://doi.org/10.1038/s41467-018-03406-6\u003c/li\u003e\n \u003cli\u003eLeifeld J, W\u0026uuml;st-Galley C, Page, S (2019) Intact and managed peatland soils as a source and sink of GHGs from 1850 to 2100. Nature Climate Change 9:945-947. https://doi.org/10.1038/s41558-019-0615-5\u003c/li\u003e\n \u003cli\u003eLhosmot A, Jacotot A, Steinmann M, Binet P, Toussaint M, Gogo S, Gilbert D, Coffinet S, Laggoun-Deffarge F, Bertrand G (2023) Biotic and Abiotic Control Over Diurnal CH\u003csub\u003e4\u003c/sub\u003e Fluxes in a Temperate Transitional Poor Fen Ecosystem. Ecosystems 26: 951-968. https://doi.org/10.1007/s10021-022-00809-x\u003c/li\u003e\n \u003cli\u003eLI-COR (2024) LI-7810 CH\u003csub\u003e4\u003c/sub\u003e/CO\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003eO Trace Gas Analyzer. Retrieved https://www.licor.com/\u003cbr\u003eon May 18\u003csup\u003eth\u003c/sup\u003e 2024. https://www.licor.com/env/products/soil-flux/LI-7810\u003c/li\u003e\n \u003cli\u003eLindstrom M, Bates D (1988) Newton-Raphson and EM algorithms for linear mixed-effects models for repeated-measures data. Journal of the American Statistical Association 1014-1022. https://doi.org/10.1080/01621459.1988.10478693.\u003c/li\u003e\n \u003cli\u003eLohse K, Kozlowski A, Strube M (2023) Model Specification in Mixed-Effects Models: A Focus on Random Effects. Communications in Kinesiology 1(5) https://doi.org/10.51224/cik.2023.52\u003c/li\u003e\n \u003cli\u003eMengyu G, Korrensalo A, Putkinen A, Laiho R., Kohl L, Pihlatie M, Lohila A, Makiranta, P, Siljanen H, Tuittila, E-T, Koskinen M (2024) CH\u003csub\u003e4\u003c/sub\u003e transport in wetland plants under controlled environmental conditions-untangling the impacts of phenology. Research Square, 1-33. https://doi.org/10.1101/2023.10.08.561392\u003c/li\u003e\n \u003cli\u003eMinke M, Augustin J, Hagemann U, Joosten H (2014) Similar methane fluxes measured by transparent and opaque chambers point at belowground connectivity of Phragmites australisbeyond the chamber footprint. \u003cem\u003eAquatic Botany\u003c/em\u003e, 63-71. https://doi.org/10.1016/j.aquabot.2013.11.006\u003c/li\u003e\n \u003cli\u003eNoyce G, Varner, R, Bubier J, Frolking S (2014) Effect of Carex rostrata on seasonal and inter annual variability in peatland methane emissions. J. Geophys. Res. Biogeosci, 24-34. https://doi.org/10.1002/2013JG002474\u003c/li\u003e\n \u003cli\u003eRAMSAR (2018). \u003cem\u003e75% of Earth\u0026apos;s land areas are degraded; wetlands have been hit hardest, with 87% lost globally in the last 300 years\u003c/em\u003e. Retrieved https://www.ramsar.org/news/75-earths-land-areas-are-degraded-wetlands-have-been-hit-hardest-87-lost-globally-last-300\u003c/li\u003e\n \u003cli\u003eRenou-Wilson F, Moser G, Fallon D, Farrell C, M\u0026uuml;ller C, Wilson D (2019) Rewetting degraded peatlands for climate and biodiversity benefits: Results from two raised bogs. Ecological Engineering 547-560. https://doi.org/10.1016/j.ecoleng.2018.02.014\u003c/li\u003e\n \u003cli\u003eRey A (2014) Mind the gap: non-biological processes contributing to soil CO\u003csub\u003e2\u003c/sub\u003e efflux. Global Change Biology 1752-1761. https://doi.org/10.1111/gcb.12821\u003c/li\u003e\n \u003cli\u003eRoland M, Vicca S, Bahn M, Ladreiter-Knauss T, Schmitt M, Janssens IA (2015) Importance of nondiffusive transport for soil CO\u003csub\u003e2\u003c/sub\u003e efflux in a temperate mountain grassland. J. Geophys.\u003cbr\u003eRes. Biogeosci\u003cem\u003e.\u003c/em\u003e, 502-512. https://doi.org/10.1002/2014JG002788\u003c/li\u003e\n \u003cli\u003eSaunois M, Stavert, AR, Poulter B, Bousquet P, Canadell JG, Jackson RB, Raymond PA,\u003cbr\u003eDlugokencky EJ, Houweling S, Patra PK, Ciais P, Arora VK, Bastviken D, Bergamaschi P, Blake DR, Brailsford G, Bruhwiler L, Carlson KM, Carrol M, Castaldi S, Chandra N, Crevoisier C, Crill PM, Covey K, Curry CL, Etiope G, Frankenberg C, Gedney N, Hegglin\u003cbr\u003eMI, H\u0026ouml;glund-Isaksson L, Hugelius G, Ishizawa M, Ito A, Janssens-Maenhout G, Jensen KM, Joos F, Kleinen T, Krummel PB, Langenfelds RL, Laruelle GG, Liu L, Machida T, Maksyutov S, McDonald KC, McNorton J, Miller PA, Melton JR, Morino I, M\u0026uuml;ller J, Murguia-Flores F, Naik V, Niwa Y, Noce S, O\u0026apos;Doherty S, Parker RJ, Peng C, Peng S, Peters GP, Prigent C,\u003cbr\u003ePrinn R, Ramonet M, Regnier P, Riley WJ, Rosentreter JA, Segers A, Simpson IJ, Shi H, Smith SJ, Steele LP, Thornton BF, Tian H, Tohjima Y, Tubiello FN, Tsuruta A, Viovy N, Voulgarakis A, Weber TS, van Weele M, van der Werf GR, Weiss RF, Worthy D, Wunch D, Yin Y, Yoshida Y, Zhang W, Zhang Z, Zhao Y, Zheng B, Zhu Q, Zhu Q, Zhuang Q (2020)\u003cbr\u003eThe Global Methane Budget 2000-2017, Earth Syst. Sci. Data, 12, 1561-1623, https://doi.org/10.5194/essd-12-1561-2020\u003c/li\u003e\n \u003cli\u003eSerrano-Silva N, Sarria-Guzm\u0026aacute;n Y, Dendooven L, Luna-Guido M (2014) Methanogenesis and Methanotrophy in Soil: A Review. Pedosphere 24(3): 291-307. https://doi.org/10.1016/S1002-0160(14)60016-3\u003c/li\u003e\n \u003cli\u003eSimpon G (2018) Modelling Palaeoecological Time Series Using Generalised Additive Models. Frontiers in Ecology and Evolution 6:149. https://doi.org/10.3389/fevo.2018.00149\u003c/li\u003e\n \u003cli\u003eStrack M, Davidson S, Hirano T, Dunn C (2022) The Potential of Peatlands as Nature‑Based Climate Solutions. Current Climate Change Reports 71-82. https://doi.org/10.1007/s40641-022-00183-9\u003c/li\u003e\n \u003cli\u003eSwenson M, Regan S, Bremmers D, Lawless J, Saunders M, Gill L (2019) Carbon balance of a restored and cutover raised bog: implications for restoration and comparison to global trends. Biogeosciences 16:713-731. https://doi.org/10.5194/bg-16-713-2019\u003c/li\u003e\n \u003cli\u003eTanneberger F, Appulo L, Ewert S, Lakner S, O\u0026apos;Brolch\u0026aacute;in N, Peters J, Wichtmann W (2020)\u003cbr\u003eThe Power of Nature-Based Solutions: How Peatlands Can Help Us to Achieve Key EU Sustainability Objectives. Advanced Sustainable Systems, Vol 5. https://doi.org/10.1002/adsu.202000146\u003c/li\u003e\n \u003cli\u003eUNEP (2023) \u003cem\u003eEmissions Gap Report 2023.\u003c/em\u003e UNEP. https://www.unep.org/resources/emissions-gap-report-2023\u003c/li\u003e\n \u003cli\u003eWhalen C (2005) Biogeochemistry of methane exchange between natural wetlands and the atmosphere. Environmental Engineering Science, 22(1): 73-94. https://doi.org/10.1089/ees.2005.22.73\u003c/li\u003e\n \u003cli\u003eWilson D, Farrell C, Fallon D, Moser G. M\u0026uuml;ller C, Renou-Wilson F (2016) Multiyear greenhouse gas balances at a rewetted temperate peatland. Global Change Biology, 4080-4095. https://doi.org/10.1111/gcb.13325\u003c/li\u003e\n \u003cli\u003eWilson D, Mackin F, Tuovinen J-P, Moser G, Farrell C, Renou-Wilson F (2022) Carbon and climate implications of rewetting a raised bog in Ireland. Global Change Biology, 6349-6365. https://doi.org/10.1111/gcb.16359\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wetlands-ecology-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wetl","sideBox":"Learn more about [Wetlands Ecology and Management](https://www.springer.com/journal/11273)","snPcode":"11273","submissionUrl":"https://submission.nature.com/new-submission/11273/3","title":"Wetlands Ecology and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Peatlands, Restoration, Methane fluxes, Manual closed chamber method, Raised bogs ","lastPublishedDoi":"10.21203/rs.3.rs-6810432/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6810432/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe important factors regulating methane (CH\u003csub\u003e4\u003c/sub\u003e) fluxes in rewetted peatlands such as the vegetation types, water table depths (WTDs) and in-situ conditions (pH, redox, soil temperature and moisture) are widely reported, but the impact of light and dark conditions on CH\u003csub\u003e4\u003c/sub\u003e fluxes from multiple vegetation types are not widely reported. This field study investigated if the CH\u003csub\u003e4\u003c/sub\u003e fluxes from multiple vegetation communities (\u003cem\u003eSphagnum\u003c/em\u003e communities, \u003cem\u003eEriophorum angustifolium\u003c/em\u003e, \u003cem\u003eMolinia caerulea\u003c/em\u003e, \u003cem\u003eTypha latifolia\u003c/em\u003e, \u003cem\u003ePhragmites australis\u003c/em\u003e, \u003cem\u003eJuncus effusus\u003c/em\u003e, \u003cem\u003eCalluna vulgaris, Carex rostrata\u003c/em\u003e and open water) responded differently to light and dark conditions. Triplicate simultaneous light and dark measurements of carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) and CH\u003csub\u003e4\u003c/sub\u003e fluxes were measured on the same day using the chamber method from the above-mentioned vegetation communities from five peatland sites located in the Irish midlands. The field measurements showed that the CH\u003csub\u003e4\u003c/sub\u003e fluxes were higher in light conditions compared to dark conditions for \u003cem\u003eCarex rostrata\u003c/em\u003e (0.05 ±0.02 in light, 0.02 ±0.01 g CH₄ m⁻² hr⁻¹ in dark) and \u003cem\u003eEriophorum angustifolium\u003c/em\u003e (0.02 ±0.01 in light, 0.01 ±0.00 g CH₄ m⁻² hr⁻¹ in dark) compared to other vegetation communities. The mixed-effect model results indicated that differences between light and dark measurements were strongly related to CO\u003csub\u003e2\u003c/sub\u003e fluxes. When the vegetation was sequestering CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e fluxes increased, alternatively, during the respiration, CH\u003csub\u003e4\u003c/sub\u003e fluxes decreased. Future work should examine the impact of vegetation specific phenological mechanisms that influence CH\u003csub\u003e4\u003c/sub\u003e fluxes in light and dark conditions using multiple years of field data.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Comparing Light and Dark Chamber Measurements of CH4 Fluxes in Drained and Rewetted Raised Bogs of Ireland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 09:16:08","doi":"10.21203/rs.3.rs-6810432/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-01T12:42:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T08:49:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101233296376543898165078018657152587658","date":"2025-06-10T19:47:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T10:18:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-05T21:28:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-04T11:36:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wetlands Ecology and Management","date":"2025-06-03T10:46:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wetlands-ecology-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wetl","sideBox":"Learn more about [Wetlands Ecology and Management](https://www.springer.com/journal/11273)","snPcode":"11273","submissionUrl":"https://submission.nature.com/new-submission/11273/3","title":"Wetlands Ecology and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c2db7612-454f-4fe0-a4cd-420b626e5a65","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:06:07+00:00","versionOfRecord":{"articleIdentity":"rs-6810432","link":"https://doi.org/10.1007/s11273-025-10089-6","journal":{"identity":"wetlands-ecology-and-management","isVorOnly":false,"title":"Wetlands Ecology and Management"},"publishedOn":"2025-10-13 15:57:02","publishedOnDateReadable":"October 13th, 2025"},"versionCreatedAt":"2025-06-12 09:16:08","video":"","vorDoi":"10.1007/s11273-025-10089-6","vorDoiUrl":"https://doi.org/10.1007/s11273-025-10089-6","workflowStages":[]},"version":"v1","identity":"rs-6810432","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6810432","identity":"rs-6810432","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}