Influence of Sampling Time on CO₂ and CH₄ Fluxes in a Temperate Peatland Under Nitrogen and Warming Treatments

Influence of Sampling Time on CO₂ and CH₄ Fluxes in a Temperate Peatland Under Nitrogen and Warming Treatments | 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 Influence of Sampling Time on CO₂ and CH₄ Fluxes in a Temperate Peatland Under Nitrogen and Warming Treatments Jun-Xiao Ma, Fan Lu, Vladimir Chakov, Victoria Kuptsova, Ying Gao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8448377/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Understanding the temporal variability of greenhouse gas (GHG) fluxes is critical for accurately assessing peatland carbon dynamics. Some recommended time periods have been used for a long time, but whether temporal variations within the periods affect comparisons between treatments remains unexamined. In this study, we investigated the effects of sampling time on CO₂ and CH₄ fluxes under control, nitrogen addition, and warming treatments in a temperate peatland ecosystem, and compared these results with random sampling. We found that gross primary productivity and CH₄ fluxes exhibited relatively stable patterns across different sampling periods. In contrast, ecosystem respiration and net ecosystem productivity were more sensitive to sampling time, particularly under N addition. With the delay in sampling time, CO₂ emissions increased significantly, causing the peatland to shift from a carbon sink to a source. This was attributed to asynchronous diurnal variations between gross primary productivity and ecosystem respiration driven by changes in vegetation structure and soil microclimate. Our findings highlight the importance of sampling design in GHG flux monitoring and recommend the use of multi-timepoint or randomized sampling strategies to minimize temporal bias and variation between treatments, improving the accuracy of peatland carbon budget assessments. Carbon exchange Ecosystem respiration Plant mediation Sphagnum Climate change Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Carbon dioxide (CO₂) and methane (CH₄) are the two most significant greenhouse gases (GHGs), and their rising atmospheric concentrations may further exacerbate global warming (IPCC 2021 ). Consequently, quantifying GHG fluxes across different ecosystems is essential for accurately simulating and assessing global environmental change. The closed static chamber method is a widely used technique for measuring gas fluxes at the soil–plant–atmosphere interface and has been applied for nearly a century (Matthias et al. 1980 ). Compared with the eddy covariance and automated chamber methods that have emerged in recent years, the static chamber method remains one of the mainstream approaches in current research due to its low cost and high feasibility to measure various components of gas fluxes at small spatial scales (Lu et al. 2023 ; Gong et al. 2024 ). It is particularly advantageous for investigating the influence of microenvironmental factors on gas fluxes (Evans et al. 2021 ; Gray et al. 2021 ). However, the static chamber method is labor-intensive, requiring manual relocation of the chambers between measurements. Consequently, measurements can only be simultaneously conducted on a limited number of plots (Pumpanen et al. 2004 ), compromising the temporal comparability of the flux data. In the review by Pavelka et al.(2018), manual chamber measurements were recommended to be conducted every four hours, with at least four measurements per day. However, in actual field experiments, measurements are often concentrated within a specific time period to save time and cover more sampling plots. The resulting flux values are then used to represent the diurnal average (Cueva et al. 2017 ; Savage and Davidson 2003 ), which makes it difficult to assess measurement accuracy. Due to the influence of temperature on plant and microbial activities (Niu et al. 2024 ), the choice of sampling time period largely determines whether the obtained flux data are representative. For soil respiration, many studies have suggested that flux measurements taken at 10:00 a.m. are more representative of the daily mean due to the moderate soil temperature at that time (Darenova et al. 2014 ; Jian et al. 2018 ). However, considering the presence of vegetation, this time point may not be suitable for estimating ecosystem respiration (ER). The optimal temperatures for plant and microbial activities differ, with high temperatures potentially enhancing soil respiration while suppressing plant physiological processes (Garen and Michaletz 2025 ). Therefore, the optimal measurement time may need to be reconsidered when assessing net ecosystem productivity (NEP) or CH₄. Peatlands are the largest terrestrial soil carbon stocks, regulating climate change as a carbon sink over millennia (Frolking and Roulet 2007 ). The persistently anaerobic, cold, and nutrient-poor conditions in northern peatlands facilitate carbon accumulation, but also render these ecosystems highly sensitive to environmental changes such as warming and nitrogen deposition (Dieleman et al. 2016 ; Dise 2009 ). These disturbances can induce significant emissions of GHGs from peatlands to the atmosphere, potentially exacerbating global warming. Although many field studies have been conducted in peatlands to simulate environmental changes and monitor GHGs fluxes, the standard procedures for using static chambers are still predominantly adapted from grassland and forest ecosystems (Jian et al. 2018 ; Pavelka et al. 2018 ). The limited understanding of flux dynamics specific to peatlands may introduce substantial uncertainties in flux estimation, potentially affecting the accuracy of ecosystem carbon sink assessments and hindering the development of effective climate-related policies. The impact of sampling time on flux measurements in temperate peatlands, particularly whether it interferes with the interpretation of relationships between different simulated environmental change treatments in the field, remains unclear. In this study, we divided the commonly used sampling time of 9:00 AM to 3:00 PM into three periods and measured CO 2 and CH 4 fluxes in three long-term simulated environmental change plots in a temperate peatland using the static chamber method. We compared the results of repeat sampling, random sampling and specific time sampling. We hypothesized that: (1) the optimal measurement time differs for each gas type due to the varying responses of microorganisms and plants to environmental changes; (2) random sampling will outperform specific time sampling for all flux types. Long-term treatments altered plot environments and plant compositions, and (3) specific time sampling cannot accurately reflect the true relationships between fluxes across treatments. MATERIALS AND METHODS Study Site Our study site Hani is a transitional mire located in the Changbai Mountains, northeastern China (42°12'50'' N, 126°31'05'' E), which has a temperate continental mountain humid monsoon climate with an average annual temperature ranging from 2.3 to 3.6°C and annual precipitation between 757 and 930 mm, most of which occurs during the summer. The peatland has an average elevation of approximately 900 m, covers an area of about 16.8 km², and the peat depth ranges from 3 to 10 m. The peatland is dominated by common plant species including the dwarf shrub Betula ovalifolia Rupr., Ledum palustre L. and Chamaedaphne calyculata L. and graminoid Carex lasiocarpa Ehrh. and Phragmites australis Trin. The ground layer is primarily covered by Sphagnum magellanicum Brid, S. fuscum (Schimp.) Klinggr., S. imbricatum Hornsch. ex Russow, and S. subsecundum Nees. Experimental Design The experiment was conducted at the long-term environmental change simulation platform established in 2007. In this study, three treatments were included: control, warming, and nitrogen addition, with four replicates per treatment (0.8 m × 0.8 m each). To compare diurnal variation in GHG fluxes among different treatments, we divided the commonly used daytime period in peatlands for measurement by the closed static-chamber method into three time intervals: T1 (9:00–11:00), T2 (11:00–13:00), and T3 (13:00–15:00). All data from 9:00 to 15:00 were recorded as repeated sampling (Rep) results. The warming treatment was passively achieved by using square open-top chambers (OTCs) with a base area of 1.2 m × 1.2 m and a top area of 0.8 m × 0.8 m, this equipment was able to increase the air temperature by about 0.6°C during the growing season(Yi et al. 2024 ). The nitrogen addition treatment involved monthly spraying of 300 mL of NH₄NO₃ solution at a rate of 5 g N m⁻² yr⁻¹ from May to September each year, while an equal volume of distilled water was applied to the other plots as a control. PVC collars with grooves were installed within each plot, and boardwalks were constructed around the plots to minimize disturbance to GHG flux measurement caused by experiment activities. Measuring Greenhouse Gas Fluxes In the middle of the growing season (July and August) of 2021, GHGs fluxes were measured biweekly with a portable greenhouse gas analyzer (LGR-915-0011, Los Gatos Research Inc., San Jose, CA, USA). ER and CH 4 fluxes were measured using a dark chamber (25 cm in diameter and 50 cm in height) and NEP was measured by using a transparent chamber. Gross primary productivity (GPP) was the balance between ER and NEP. There was only approximately a one-minute interval between measurements using the transparent chamber (for NEP) and the dark chamber (for ER). Therefore, estimating GPP based on NEP and ER measurements taken at the same plot was both reasonable and accurate (Voigt et al. 2017 ). To ensure an airtight connection with the chamber, each PVC collar was sealed with water in its groove during measurements. 180 consecutive values of GHGs concentration were recorded at 1Hz time frequency during each 3 min measurement period. The chamber was ventilated for approximately 1 min after each measurement to ensure that the GHGs concentration returned to ambient levels before beginning the next measurement. The GHGs fluxes were calculated by: $$\:Flux=\frac{dC}{dt}·\rho\:·\frac{P}{{P}_{0}}·\frac{{T}_{0}}{T}·\frac{V}{S}$$ where Flux is gas flux (mg m − 2 s − 1 ); \(\:\frac{dC}{dt}\:\) is the slope of the gas concentration change curve within three minutes of measurement time (ppm s − 1 ); ρ represents the gas density (mg m − 3 ); P and P 0 are the atmospheric pressure of the chamber during the sampling duration and the atmospheric pressure under standard conditions (Pa), respectively. T and T 0 are the air temperature during the sampling time (K) and the air temperature under standard conditions (K), respectively. V is the volume of the chamber (m 3 ), and S is the area covered by the chamber (m 2 ). Moreover, a linear regression was applied to the 180 records in the time series of concentration to calculate \(\:\frac{dC}{dt}\) . Fluxes values were accepted if the coefficient of determination R² ≥ 0.80. To avoid overestimating gas fluxes, low flux values near zero were also accepted regardless of their R² value (Gong et al. 2024 ). In our calculations, positive gas flux values indicate uptakes, while negative values indicate emissions. Measuring Environmental Parameters During the experiment, iButton temperature loggers (1-Wire; Maxim Integrated, San Jose, CA, USA) were installed 10 cm above the Sphagnum surface to monitor air temperature in both control and warming plots. During each gas sampling campaign, soil temperature at 5 cm below the Sphagnum surface was measured using a Delta TRAK digital thermometer, soil moisture was determined using a Stevens moisture meter (TZS-1K, Zhejiang Top Yunnong Technology Co. Ltd, Zhejiang, China), with readings taken at four points within the soil respiration collar of each plot and averaged to represent the soil moisture of that plot. Water table depth was measured using a PVC observation well installed at each plot. Vegetation Cover The vegetation composition and plant cover within each collar were assessed by visually estimating the percentage cover of graminoids, shrubs, and Sphagnum mosses based on their vertical projection area. All estimations were performed by the same observer to minimize inter-observer variability. To ensure accuracy, vertical photographs of each collar were taken from a height of 20 cm above the moss surface, and vegetation cover estimates were corrected in the laboratory when necessary. Statistical Analysis Data normality was assessed using residual plots, and log transformation was applied when necessary prior to analysis. Tukey’s HSD test was conducted to evaluate differences in GPP, ER, NEP, and CH₄ fluxes among the control (CK), nitrogen addition, and warming treatments. Additionally, Tukey’s HSD test was used to assess differences in these gas fluxes across different sampling times (T1, T2, T3) within each treatment. Linear regression models were applied to examine the relationships between CO₂ fluxes and environmental factors. All statistical analyses were performed using R 3.5.3 (R Core Team 2025 ). A significance level of p < 0.1 was adopted for all tests. To simulate random time sampling, we used all available data. On each sampling date, one gas flux value was randomly selected from each plot. Each sampling result thus consisted of flux data from four sampling dates, three treatments, and four replicates. This random time sampling process was independently repeated 9,999 times to ensure an adequate sample size for subsequent analyses. Furthermore, the “ nlme ” package (José Pinheiro et al. 2025) was used to examine the effects of sampling period and environmental factors on gas fluxes among treatments, with sampling date specified as a random effect. RESULTS Environmental Factors and Vegetation Cover During the daily gas flux measurements, the air temperature under the warming treatment was on average 1.83°C higher than control (Fig. 1 a). Across all treatments, soil temperature increased steadily throughout the sampling period (Fig. 1 b). Long-term N addition and warming treatments significantly reduced the water table depth in the peatland ( p \(\:<\) 0.001, Fig. 1 c), which consequently led to significant differences in soil water content compared to the control group ( p \(\:<\) 0.001). However, no significant differences in water table depth or soil moisture were observed among sampling periods. There were significant differences in vegetation composition among the different treatments (Table 1 ). In the CK, Sphagnum mosses had the highest cover, reaching approximately 85%, while the cover of shrubs and graminoids was significantly lower than that under N addition and warming treatments (shrubs: p = 0.007; graminoids: p = 0.011). Table 1 Vegetation cover under different treatments (Mean ± SE, n = 4). Vegetation cover (%) Shrubs Graminoids Sphagna CK 51.5 ± 8.2 a 11.6 ± 3.0 a 85.8 ± 4.1 c N addition 68.4 ± 3.4 b 20.2 ± 1.8 b 10.0 ± 6.2 a Warming 64.1 ± 4.9 b 18.5 ± 4.4 b 72.3 ± 6.3 b CK: control; N addition: nitrogen addition. Different lowercase letters represent significant differences among treatments (Tukey’ s HSD test, p < 0.1). Effect of Sampling Time on Gas fluxes Sampling time had a significant effect on NEP in the control group, and on both ER and NEP under the N addition treatment (Fig. 2 ), but no significant effects were observed on GPP or CH₄ fluxes across all treatments. ER increased with the later sampling period, and significant differences were only found under N addition. Furthermore, the average ER value of all sampling time periods was similar to T3 (Fig. 2 d–f). NEP exhibited a general decreasing trend with later sampling times, particularly under the CK and N addition treatments (Fig. 2 g–i). The relationships of GPP and NEP between treatments were consistent across all sampling periods. Both N addition ( p \(\:<\) 0.001) and warming ( p \(\:<\) 0.001) significantly decreased GPP, and warming significantly decreased NEP ( p \(\:<\) 0.001). For CH₄ fluxes, warming significantly increases CH 4 emissions at T1 ( p = 0.016), while N addition had a significant effect at T2, the fluxes are higher in both warming and N addition treatment. Comparison of Different Sampling Methods Compared to specific time sampling, random time sampling more closely approximated the results of Rep (Table 2 ). There were virtually no significant differences between random time sampling and Rep results ( p > 0.1), with at least 96% of the random time sampling showing no significant difference from Rep across all treatments for both CO₂ and CH₄ fluxes. In contrast, specific time sampling deviated more from Rep. Correlation Analysis A significant negative correlation between GPP and soil water content was observed only in the CK ( p < 0.001, R 2 = 0.21), but this relationship disappeared under N addition and warming treatments (Fig. 3 a). ER showed a significant negative correlation with soil temperature (Fig. 3 b), indicating that higher soil temperatures were associated with higher respiration rate. Table 2 Differences between repeated sampling and specific time sampling, and the probability of no significant differences ( p > 0.1) between repeated sampling and 9999 simulated random samplings. Specific time sampling Simulated random sampling T1 T2 T3 p > 0.1 p p p CK GPP 0.237 (90%) 0.288 0.880 100% ER 0.449 0.940 0.416 100% NEP 0.013 0.196 0.278 99% CH 4 0.558 0.546 0.958 100% N addition GPP 0.402 0.584 0.161 98% ER 0.029 0.072 0.818 99% NEP 0.001 0.152 0.060 96% CH 4 0.492 0.218 0.432 99% Warming GPP 0.891 0.668 0.757 98% ER 0.340 0.537 0.165 98% NEP 0.356 0.916 0.307 98% CH 4 0.438 0.174 0.674 99% CK: control; N addition: nitrogen addition. Specific time sampling was conducted at T1 (09:00–11:00), T2 (11:00–13:00), and T3 (13:00–15:00). DISCUSSION Effects of Sampling Time on Environmental Factors Unlike the generally synchronous relationship between air and soil temperatures reported in other ecosystems (Parkin and Kaspar 2003 ), we found in this peatland that air temperature peaked around 11:00 a.m., whereas soil temperature continued to rise through the late morning and early afternoon (11:00–15:00). This pattern reveals a temporal asynchrony between air and soil temperatures in the peatland ecosystem (Fig. 1 ). This phenomenon may be attributed to the prevalent waterlogged conditions or high groundwater levels in peatlands, where soil temperature is strongly influenced by moisture status, limiting the rapid transmission of air temperature changes into the soil (O'Donnell et al. 2009 ). Our results showed that the N addition treatment had the lowest water table depth (Table 1 ), meaning that the water surface was closest to the soil surface, resulting in the highest soil water content. The elevated soil moisture absorbs substantial heat during evaporation, which makes it less sensitive to air temperature change (Al-Kayssi et al. 1990 ). Additionally, differences in plant community composition among treatments likely contributed to the varied responses of soil temperature to environmental changes (Hedwall et al. 2017 ; Weltzin et al. 2003 ). Long-term N addition suppressed the growth of Sphagnum mosses but promoted the growth of shrubs and Graminoids. The leaves of these vascular plants provided shading to the soil surface, reducing direct solar radiation and thereby helping to lower soil temperatures (Laine et al. 2021 ). Overall, while sampling time primarily affected air and soil temperatures, it had minimal influence on water table depth and soil water content. In contrast, changes in plant community composition and water levels induced by warming and N addition treatments modulated the soil temperature response to sampling time to some extent. Effects of Environmental Factors on GHG Fluxes In the CK treatment, GPP exhibited a significant negative correlation with soil water content, indicating that higher soil moisture was associated with lower GPP (Fig. 3 ). However, this relationship was not significant under N addition or warming treatments. The soil water content in the CK group was approximately 10%, while it increased to around 40% and 20% under N addition and warming treatments, respectively. The higher soil moisture levels in these treatments may have reduced the sensitivity of GPP to soil moisture variation (Laine et al. 2021 ). Moreover, the photosynthetic rate of Sphagnum mosses is species-specific and exhibits an optimal moisture response, with a sharp decline when soil moisture falls below the optimal range (Hájek et al. 2009 ; Jassey and Signarbieux 2019 ). In this study, the Sphagnum cover in the CK group reached 85%, making it particularly sensitive to changes in soil moisture. This may explain the significant negative correlation between GPP and soil moisture observed in the CK treatment. On the other hand, soil water content varied little across different sampling time periods and was not significantly affected by sampling time. Consequently, no significant effect of sampling time on GPP was observed. We found a significant negative correlation between soil temperature and ER across all treatments (Fig. 3 ). Ecosystem respiration was enhanced by rising soil temperature, consistent with results from previous research (Hopple et al. 2020 ; Nyberg and Hovenden 2020 ; Voigt et al. 2017 ). This pattern may be attributed to multiple interacting mechanisms. First, elevated soil temperatures typically enhance microbial activity, accelerating the decomposition of soil organic matter and thereby releasing more CO₂ (Le Geay et al. 2024 ; Xu et al. 2023 ). Second, higher soil temperatures can stimulate root metabolism and respiration (Li et al. 2020 ), Warmer conditions also facilitate the translocation of photosynthates to the rhizosphere (Zeh et al. 2019 ), increasing the availability of labile carbon substrates for microbial use and stimulating microbial respiration, which may promote the collapse of surface peat layers. Last, increased temperatures may lead to the evaporation of surface water in peatlands, thereby improving soil aeration and promoting the proliferation and metabolic activity of aerobic microorganisms, further enhancing CO₂ emissions (Boonman et al. 2022 ; Wilmoth et al. 2021 ). In this study, soil temperature was the environmental factor most strongly influenced by sampling time, which explained why we found its significant impact on ER, especially under N addition treatments (Fig. 2 ). Selection of Sampling Method and Time In current study, NEP showed a more pronounced sensitivity to sampling time, particularly under N addition treatments, where both ER and NEP exhibited significant diurnal variation (Fig. 2 ). In contrast, GPP and CH₄ fluxes vary somewhat between sampling time periods, they were generally less affected by sampling time. During the T2 and T3 periods (11:00–15:00), CO₂ emissions increased substantially, shifting the peatland from a carbon sink to a carbon source. This discrepancy is mainly due to the different diurnal variation mechanisms of GPP and ER, as NEP represents the net balance between GPP and ER, it is highly sensitive to asynchronous fluctuations between them, meaning that even non-significant changes in GPP or ER individually can result in amplified variations in NEP. The diurnal variation of GPP was relatively stable, approaching light saturation at noon, and the photosynthetic rate tended to be stable (Dang et al. 1991 ; Korrensalo et al. 2017 ). Photoinhibition may even occur in the Sphagnum -dominated community, further weakening the response of GPP to sampling time (Haraguchi and Yamada 2011 ). CH₄ fluxes were generally less sensitive to sampling time across treatments, with the exception of the N addition treatment, where greater variability may be attributed to changes in plant functional types and rhizosphere emission dynamics (Ge et al. 2023 ; Lu et al. 2025 ). Under N addition, the enhanced sensitivity of ER and NEP to sampling time may be driven by three mechanisms. First, it is caused by changes in vegetation structure, with a decrease in Sphagnum cover and an increase in the cover of shrubs and graminoids. These plants have well-developed root systems and high metabolic activity, and their root respiration and rhizosphere microorganisms are more sensitive to temperature changes (Mueller and Megonigal 2024 ; Zeh et al. 2019 ). Second, N addition significantly increased soil water content, which buffered temperature fluctuations to a certain extent, but the high-water content limited aeration and inhibited aerobic microorganisms (Drenovsky et al. 2004 ), thereby changing the response pattern of ER to temperature. Despite the overall decline in ER temperature sensitivity, enhanced daytime rhizospheric activity may still lead to increased ER in the afternoon (Luan et al. 2011 ; Yao et al. 2022 ). This, coupled with a limited increase in GPP, ultimately causes a sharp decline in NEP. Third, N addition increases the complexity of CO₂ flux sources, thereby amplifying the temporal variability of ER and NEP. ER and NEP during the T1 period (9:00–11:00) were significantly higher than during T2 and T3, further indicating a decline in net carbon uptake capacity in the afternoon. A similar trend was observed in the CK treatment, with NEP in periods T2 and T3 being significantly lower than that in T1, indicating that the carbon sink function of peatlands weakened as daytime temperature increased. In summary, GPP and CH₄ fluxes are relatively stable to the sampling time, while ER and NEP are more susceptible to changes in temperature and vegetation structure, and are more sensitive to the sampling period. Since NEP is calculated as the difference between GPP and ER, its temporal variability is easily amplified, potentially introducing significant uncertainty into carbon flux assessments. Therefore, we recommend that greenhouse gas flux studies adopt Rep or random time sampling strategies throughout the day to minimize time-related bias and improve the accuracy of peatland carbon budget evaluations. CONCLUSIONS This study demonstrates that while GPP and CH₄ fluxes remain relatively stable across different sampling times, ER and NEP are significantly influenced by diurnal temperature variations and vegetation structural changes, particularly under long-term N addition treatment. as the balance between GPP and ER, NEP is sensitive to temporal fluctuations, which leads to amplified variability, potentially compromising the accuracy of carbon flux evaluations. Long-term environmental change led to a shift in plant community composition, with reduced Sphagnum moss cover and increased abundance of shrubs and graminoids. These functional groups have more active root systems and associated rhizosphere microbial communities, making ecosystem respiration more responsive to temperature fluctuations, especially during midday. This shift in vegetation structure enhanced ER and limited the compensatory increase in GPP, resulting in a marked decline in NEP during afternoon periods. We conclude that accurate assessment of peatland carbon fluxes under global change scenarios requires not only careful consideration of temporal sampling strategies but also an integrated understanding of vegetation dynamics and their role in regulating GHG emissions. Declarations Funding This work was Supported by the National Nature Science Foundation of China (Nos. U23A2003, 42371050 and 41871046) and Jilin Provincial Science and Technology Development Project (No. 20230203002SF). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jun-Xiao Ma, Fan Lu, Ying Gao and Xuefei Gao. The first draft of the manuscript was written by Jun-Xiao Ma, Zucheng Wang and Zhao-Jun Bu. Vladimir Chakov and Victoria Kuptsova revised the 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 corresponding author on reasonable request. References Al-Kayssi AW, Al-Karaghouli AA, Hasson AM, Beker SA (1990) Influence of soil moisture content on soil temperature and heat storage under greenhouse conditions. 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Plant Soil 502:573–588. https://doi.org/10.1007/s11104-024-06569-y Zeh L, Limpens J, Erhagen B, Bragazza L, Kalbitz K (2019) Plant functional types and temperature control carbon input via roots in peatland soils. Plant Soil 438:19–38. https://doi.org/10.1007/s11104-019-03958-6 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 09 Jan, 2026 Reviewers invited by journal 06 Jan, 2026 Editor invited by journal 05 Jan, 2026 Editor assigned by journal 30 Dec, 2025 First submitted to journal 28 Dec, 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. We do this by developing innovative software and high quality services for the global research community. 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(13:00-15:00).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8448377/v1/78081739e7e1ea56f12da825.jpg"},{"id":99776259,"identity":"a3e22d24-cd05-4ed6-9f73-27f472d59599","added_by":"auto","created_at":"2026-01-08 09:54:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1678017,"visible":true,"origin":"","legend":"\u003cp\u003eGross primary productivity (GPP, a-c), ecosystem respiration (ER, d-f), net ecosystem productivity (NEP, g-i), and CH\u003csub\u003e4\u003c/sub\u003e fluxes (j-i) under different treatments, measured using specific period sampling and repeated sampling (Mean ± SE, \u003cem\u003en\u003c/em\u003e = 4). Specific period sampling was conducted during T1 (09:00–11:00), T2 (11:00–13:00), and T3 (13:00–15:00), while repeat sampling (Rep) involved three measurements between 09:00 and 15:00. Different lowercase letters and asterisks indicate significant differences among sampling times (Tukey’s HSD test, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.1).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8448377/v1/67373b64290227da9897af1d.jpg"},{"id":99776282,"identity":"60af800b-d83e-45cd-9caf-6101889efd4d","added_by":"auto","created_at":"2026-01-08 09:55:01","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1290843,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations of gross primary productivity (GPP) with soil water content (a) and ecosystem respiration (ER) with soil temperature (b) under different treatments.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8448377/v1/7bf58ee48bce8275716a414f.jpg"},{"id":99805588,"identity":"ce8bf1d1-7107-4759-abe3-d458a1472b4f","added_by":"auto","created_at":"2026-01-08 14:16:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5158206,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8448377/v1/2428ab7d-3c6b-4ba1-b996-8068b5e812fa.pdf"}],"financialInterests":"","formattedTitle":"Influence of Sampling Time on CO₂ and CH₄ Fluxes in a Temperate Peatland Under Nitrogen and Warming Treatments","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCarbon dioxide (CO₂) and methane (CH₄) are the two most significant greenhouse gases (GHGs), and their rising atmospheric concentrations may further exacerbate global warming (IPCC \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, quantifying GHG fluxes across different ecosystems is essential for accurately simulating and assessing global environmental change. The closed static chamber method is a widely used technique for measuring gas fluxes at the soil\u0026ndash;plant\u0026ndash;atmosphere interface and has been applied for nearly a century (Matthias et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). Compared with the eddy covariance and automated chamber methods that have emerged in recent years, the static chamber method remains one of the mainstream approaches in current research due to its low cost and high feasibility to measure various components of gas fluxes at small spatial scales (Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gong et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is particularly advantageous for investigating the influence of microenvironmental factors on gas fluxes (Evans et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gray et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the static chamber method is labor-intensive, requiring manual relocation of the chambers between measurements. Consequently, measurements can only be simultaneously conducted on a limited number of plots (Pumpanen et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), compromising the temporal comparability of the flux data. In the review by Pavelka et al.(2018), manual chamber measurements were recommended to be conducted every four hours, with at least four measurements per day. However, in actual field experiments, measurements are often concentrated within a specific time period to save time and cover more sampling plots. The resulting flux values are then used to represent the diurnal average (Cueva et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Savage and Davidson \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), which makes it difficult to assess measurement accuracy.\u003c/p\u003e \u003cp\u003eDue to the influence of temperature on plant and microbial activities (Niu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the choice of sampling time period largely determines whether the obtained flux data are representative. For soil respiration, many studies have suggested that flux measurements taken at 10:00 a.m. are more representative of the daily mean due to the moderate soil temperature at that time (Darenova et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jian et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, considering the presence of vegetation, this time point may not be suitable for estimating ecosystem respiration (ER). The optimal temperatures for plant and microbial activities differ, with high temperatures potentially enhancing soil respiration while suppressing plant physiological processes (Garen and Michaletz \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, the optimal measurement time may need to be reconsidered when assessing net ecosystem productivity (NEP) or CH₄.\u003c/p\u003e \u003cp\u003ePeatlands are the largest terrestrial soil carbon stocks, regulating climate change as a carbon sink over millennia (Frolking and Roulet \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The persistently anaerobic, cold, and nutrient-poor conditions in northern peatlands facilitate carbon accumulation, but also render these ecosystems highly sensitive to environmental changes such as warming and nitrogen deposition (Dieleman et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dise \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These disturbances can induce significant emissions of GHGs from peatlands to the atmosphere, potentially exacerbating global warming. Although many field studies have been conducted in peatlands to simulate environmental changes and monitor GHGs fluxes, the standard procedures for using static chambers are still predominantly adapted from grassland and forest ecosystems (Jian et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pavelka et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The limited understanding of flux dynamics specific to peatlands may introduce substantial uncertainties in flux estimation, potentially affecting the accuracy of ecosystem carbon sink assessments and hindering the development of effective climate-related policies.\u003c/p\u003e \u003cp\u003eThe impact of sampling time on flux measurements in temperate peatlands, particularly whether it interferes with the interpretation of relationships between different simulated environmental change treatments in the field, remains unclear. In this study, we divided the commonly used sampling time of 9:00 AM to 3:00 PM into three periods and measured CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e fluxes in three long-term simulated environmental change plots in a temperate peatland using the static chamber method. We compared the results of repeat sampling, random sampling and specific time sampling. We hypothesized that: (1) the optimal measurement time differs for each gas type due to the varying responses of microorganisms and plants to environmental changes; (2) random sampling will outperform specific time sampling for all flux types. Long-term treatments altered plot environments and plant compositions, and (3) specific time sampling cannot accurately reflect the true relationships between fluxes across treatments.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Site\u003c/h2\u003e \u003cp\u003eOur study site Hani is a transitional mire located in the Changbai Mountains, northeastern China (42\u0026deg;12'50'' N, 126\u0026deg;31'05'' E), which has a temperate continental mountain humid monsoon climate with an average annual temperature ranging from 2.3 to 3.6\u0026deg;C and annual precipitation between 757 and 930 mm, most of which occurs during the summer. The peatland has an average elevation of approximately 900 m, covers an area of about 16.8 km\u0026sup2;, and the peat depth ranges from 3 to 10 m. The peatland is dominated by common plant species including the dwarf shrub \u003cem\u003eBetula ovalifolia\u003c/em\u003e Rupr., \u003cem\u003eLedum palustre\u003c/em\u003e L. and \u003cem\u003eChamaedaphne calyculata\u003c/em\u003e L. and graminoid \u003cem\u003eCarex lasiocarpa\u003c/em\u003e Ehrh. and \u003cem\u003ePhragmites australis\u003c/em\u003e Trin. The ground layer is primarily covered by \u003cem\u003eSphagnum magellanicum\u003c/em\u003e Brid, \u003cem\u003eS. fuscum\u003c/em\u003e (Schimp.) Klinggr., \u003cem\u003eS. imbricatum\u003c/em\u003e Hornsch. ex Russow, and \u003cem\u003eS. subsecundum\u003c/em\u003e Nees.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental Design\u003c/h3\u003e\n\u003cp\u003eThe experiment was conducted at the long-term environmental change simulation platform established in 2007. In this study, three treatments were included: control, warming, and nitrogen addition, with four replicates per treatment (0.8 m \u0026times; 0.8 m each). To compare diurnal variation in GHG fluxes among different treatments, we divided the commonly used daytime period in peatlands for measurement by the closed static-chamber method into three time intervals: T1 (9:00\u0026ndash;11:00), T2 (11:00\u0026ndash;13:00), and T3 (13:00\u0026ndash;15:00). All data from 9:00 to 15:00 were recorded as repeated sampling (Rep) results. The warming treatment was passively achieved by using square open-top chambers (OTCs) with a base area of 1.2 m \u0026times; 1.2 m and a top area of 0.8 m \u0026times; 0.8 m, this equipment was able to increase the air temperature by about 0.6\u0026deg;C during the growing season(Yi et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The nitrogen addition treatment involved monthly spraying of 300 mL of NH₄NO₃ solution at a rate of 5 g N m⁻\u0026sup2; yr⁻\u0026sup1; from May to September each year, while an equal volume of distilled water was applied to the other plots as a control. PVC collars with grooves were installed within each plot, and boardwalks were constructed around the plots to minimize disturbance to GHG flux measurement caused by experiment activities.\u003c/p\u003e\n\u003ch3\u003eMeasuring Greenhouse Gas Fluxes\u003c/h3\u003e\n\u003cp\u003eIn the middle of the growing season (July and August) of 2021, GHGs fluxes were measured biweekly with a portable greenhouse gas analyzer (LGR-915-0011, Los Gatos Research Inc., San Jose, CA, USA). ER and CH\u003csub\u003e4\u003c/sub\u003e fluxes were measured using a dark chamber (25 cm in diameter and 50 cm in height) and NEP was measured by using a transparent chamber. Gross primary productivity (GPP) was the balance between ER and NEP. There was only approximately a one-minute interval between measurements using the transparent chamber (for NEP) and the dark chamber (for ER). Therefore, estimating GPP based on NEP and ER measurements taken at the same plot was both reasonable and accurate (Voigt et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To ensure an airtight connection with the chamber, each PVC collar was sealed with water in its groove during measurements. 180 consecutive values of GHGs concentration were recorded at 1Hz time frequency during each 3 min measurement period. The chamber was ventilated for approximately 1 min after each measurement to ensure that the GHGs concentration returned to ambient levels before beginning the next measurement. The GHGs fluxes were calculated by:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Flux=\\frac{dC}{dt}\u0026middot;\\rho\\:\u0026middot;\\frac{P}{{P}_{0}}\u0026middot;\\frac{{T}_{0}}{T}\u0026middot;\\frac{V}{S}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eFlux\u003c/em\u003e is gas flux (mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{dC}{dt}\\:\\)\u003c/span\u003e\u003c/span\u003eis the slope of the gas concentration change curve within three minutes of measurement time (ppm s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); \u003cem\u003eρ\u003c/em\u003e represents the gas density (mg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e); \u003cem\u003eP\u003c/em\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e are the atmospheric pressure of the chamber during the sampling duration and the atmospheric pressure under standard conditions (Pa), respectively. \u003cem\u003eT\u003c/em\u003e and \u003cem\u003eT\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e are the air temperature during the sampling time (K) and the air temperature under standard conditions (K), respectively. V is the volume of the chamber (m\u003csup\u003e3\u003c/sup\u003e), and S is the area covered by the chamber (m\u003csup\u003e2\u003c/sup\u003e). Moreover, a linear regression was applied to the 180 records in the time series of concentration to calculate \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{dC}{dt}\\)\u003c/span\u003e\u003c/span\u003e. Fluxes values were accepted if the coefficient of determination R\u0026sup2; \u0026ge; 0.80. To avoid overestimating gas fluxes, low flux values near zero were also accepted regardless of their R\u0026sup2; value (Gong et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our calculations, positive gas flux values indicate uptakes, while negative values indicate emissions.\u003c/p\u003e\n\u003ch3\u003eMeasuring Environmental Parameters\u003c/h3\u003e\n\u003cp\u003eDuring the experiment, iButton temperature loggers (1-Wire; Maxim Integrated, San Jose, CA, USA) were installed 10 cm above the \u003cem\u003eSphagnum\u003c/em\u003e surface to monitor air temperature in both control and warming plots. During each gas sampling campaign, soil temperature at 5 cm below the \u003cem\u003eSphagnum\u003c/em\u003e surface was measured using a Delta TRAK digital thermometer, soil moisture was determined using a Stevens moisture meter (TZS-1K, Zhejiang Top Yunnong Technology Co. Ltd, Zhejiang, China), with readings taken at four points within the soil respiration collar of each plot and averaged to represent the soil moisture of that plot. Water table depth was measured using a PVC observation well installed at each plot.\u003c/p\u003e\n\u003ch3\u003eVegetation Cover\u003c/h3\u003e\n\u003cp\u003eThe vegetation composition and plant cover within each collar were assessed by visually estimating the percentage cover of graminoids, shrubs, and \u003cem\u003eSphagnum\u003c/em\u003e mosses based on their vertical projection area. All estimations were performed by the same observer to minimize inter-observer variability. To ensure accuracy, vertical photographs of each collar were taken from a height of 20 cm above the moss surface, and vegetation cover estimates were corrected in the laboratory when necessary.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData normality was assessed using residual plots, and log transformation was applied when necessary prior to analysis. Tukey\u0026rsquo;s HSD test was conducted to evaluate differences in GPP, ER, NEP, and CH₄ fluxes among the control (CK), nitrogen addition, and warming treatments. Additionally, Tukey\u0026rsquo;s HSD test was used to assess differences in these gas fluxes across different sampling times (T1, T2, T3) within each treatment. Linear regression models were applied to examine the relationships between CO₂ fluxes and environmental factors. All statistical analyses were performed using \u003cem\u003eR\u003c/em\u003e 3.5.3 (R Core Team \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A significance level of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 was adopted for all tests.\u003c/p\u003e \u003cp\u003eTo simulate random time sampling, we used all available data. On each sampling date, one gas flux value was randomly selected from each plot. Each sampling result thus consisted of flux data from four sampling dates, three treatments, and four replicates. This random time sampling process was independently repeated 9,999 times to ensure an adequate sample size for subsequent analyses. Furthermore, the \u0026ldquo;\u003cem\u003enlme\u003c/em\u003e\u0026rdquo; package (Jos\u0026eacute; Pinheiro et al. 2025) was used to examine the effects of sampling period and environmental factors on gas fluxes among treatments, with sampling date specified as a random effect.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental Factors and Vegetation Cover\u003c/h2\u003e \u003cp\u003eDuring the daily gas flux measurements, the air temperature under the warming treatment was on average 1.83\u0026deg;C higher than control (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Across all treatments, soil temperature increased steadily throughout the sampling period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Long-term N addition and warming treatments significantly reduced the water table depth in the peatland (\u003cem\u003ep\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e 0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), which consequently led to significant differences in soil water content compared to the control group (\u003cem\u003ep\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e 0.001). However, no significant differences in water table depth or soil moisture were observed among sampling periods.\u003c/p\u003e \u003cp\u003eThere were significant differences in vegetation composition among the different treatments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the CK, \u003cem\u003eSphagnum\u003c/em\u003e mosses had the highest cover, reaching approximately 85%, while the cover of shrubs and graminoids was significantly lower than that under N addition and warming treatments (shrubs: \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.007; graminoids: \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.011).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVegetation cover under different treatments (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eVegetation cover (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShrubs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGraminoids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSphagna\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN addition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3 \u003csup\u003eb\u003c/sup\u003e\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\u003eCK: control; N addition: nitrogen addition. Different lowercase letters represent significant differences among treatments (Tukey\u0026rsquo; s HSD test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEffect of Sampling Time on Gas fluxes\u003c/h2\u003e \u003cp\u003eSampling time had a significant effect on NEP in the control group, and on both ER and NEP under the N addition treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), but no significant effects were observed on GPP or CH₄ fluxes across all treatments. ER increased with the later sampling period, and significant differences were only found under N addition. Furthermore, the average ER value of all sampling time periods was similar to T3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed\u0026ndash;f). NEP exhibited a general decreasing trend with later sampling times, particularly under the CK and N addition treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg\u0026ndash;i).\u003c/p\u003e \u003cp\u003eThe relationships of GPP and NEP between treatments were consistent across all sampling periods. Both N addition (\u003cem\u003ep\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e 0.001) and warming (\u003cem\u003ep\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e 0.001) significantly decreased GPP, and warming significantly decreased NEP (\u003cem\u003ep\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e 0.001). For CH₄ fluxes, warming significantly increases CH\u003csub\u003e4\u003c/sub\u003e emissions at T1 (\u003cem\u003ep\u003c/em\u003e = 0.016), while N addition had a significant effect at T2, the fluxes are higher in both warming and N addition treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Different Sampling Methods\u003c/h2\u003e \u003cp\u003eCompared to specific time sampling, random time sampling more closely approximated the results of Rep (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There were virtually no significant differences between random time sampling and Rep results (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1), with at least 96% of the random time sampling showing no significant difference from Rep across all treatments for both CO₂ and CH₄ fluxes. In contrast, specific time sampling deviated more from Rep.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis\u003c/h2\u003e \u003cp\u003eA significant negative correlation between GPP and soil water content was observed only in the CK (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.21), but this relationship disappeared under N addition and warming treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). ER showed a significant negative correlation with soil temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), indicating that higher soil temperatures were associated with higher respiration rate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \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\u003eDifferences between repeated sampling and specific time sampling, and the probability of no significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1) between repeated sampling and 9999 simulated random samplings.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eSpecific time sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSimulated random sampling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eT1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eT2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eCK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.237 (90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eN addition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eWarming\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99%\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\u003eCK: control; N addition: nitrogen addition. Specific time sampling was conducted at T1 (09:00\u0026ndash;11:00), T2 (11:00\u0026ndash;13:00), and T3 (13:00\u0026ndash;15:00).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEffects of Sampling Time on Environmental Factors\u003c/h2\u003e \u003cp\u003eUnlike the generally synchronous relationship between air and soil temperatures reported in other ecosystems (Parkin and Kaspar \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), we found in this peatland that air temperature peaked around 11:00 a.m., whereas soil temperature continued to rise through the late morning and early afternoon (11:00\u0026ndash;15:00). This pattern reveals a temporal asynchrony between air and soil temperatures in the peatland ecosystem (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This phenomenon may be attributed to the prevalent waterlogged conditions or high groundwater levels in peatlands, where soil temperature is strongly influenced by moisture status, limiting the rapid transmission of air temperature changes into the soil (O'Donnell et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Our results showed that the N addition treatment had the lowest water table depth (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), meaning that the water surface was closest to the soil surface, resulting in the highest soil water content. The elevated soil moisture absorbs substantial heat during evaporation, which makes it less sensitive to air temperature change (Al-Kayssi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Additionally, differences in plant community composition among treatments likely contributed to the varied responses of soil temperature to environmental changes (Hedwall et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Weltzin et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Long-term N addition suppressed the growth of \u003cem\u003eSphagnum\u003c/em\u003e mosses but promoted the growth of shrubs and Graminoids. The leaves of these vascular plants provided shading to the soil surface, reducing direct solar radiation and thereby helping to lower soil temperatures (Laine et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Overall, while sampling time primarily affected air and soil temperatures, it had minimal influence on water table depth and soil water content. In contrast, changes in plant community composition and water levels induced by warming and N addition treatments modulated the soil temperature response to sampling time to some extent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEffects of Environmental Factors on GHG Fluxes\u003c/h2\u003e \u003cp\u003eIn the CK treatment, GPP exhibited a significant negative correlation with soil water content, indicating that higher soil moisture was associated with lower GPP (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, this relationship was not significant under N addition or warming treatments. The soil water content in the CK group was approximately 10%, while it increased to around 40% and 20% under N addition and warming treatments, respectively. The higher soil moisture levels in these treatments may have reduced the sensitivity of GPP to soil moisture variation (Laine et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the photosynthetic rate of \u003cem\u003eSphagnum\u003c/em\u003e mosses is species-specific and exhibits an optimal moisture response, with a sharp decline when soil moisture falls below the optimal range (H\u0026aacute;jek et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jassey and Signarbieux \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this study, the \u003cem\u003eSphagnum\u003c/em\u003e cover in the CK group reached 85%, making it particularly sensitive to changes in soil moisture. This may explain the significant negative correlation between GPP and soil moisture observed in the CK treatment. On the other hand, soil water content varied little across different sampling time periods and was not significantly affected by sampling time. Consequently, no significant effect of sampling time on GPP was observed.\u003c/p\u003e \u003cp\u003eWe found a significant negative correlation between soil temperature and ER across all treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Ecosystem respiration was enhanced by rising soil temperature, consistent with results from previous research (Hopple et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nyberg and Hovenden \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Voigt et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This pattern may be attributed to multiple interacting mechanisms. First, elevated soil temperatures typically enhance microbial activity, accelerating the decomposition of soil organic matter and thereby releasing more CO₂ (Le Geay et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Second, higher soil temperatures can stimulate root metabolism and respiration (Li et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Warmer conditions also facilitate the translocation of photosynthates to the rhizosphere (Zeh et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), increasing the availability of labile carbon substrates for microbial use and stimulating microbial respiration, which may promote the collapse of surface peat layers. Last, increased temperatures may lead to the evaporation of surface water in peatlands, thereby improving soil aeration and promoting the proliferation and metabolic activity of aerobic microorganisms, further enhancing CO₂ emissions (Boonman et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wilmoth et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, soil temperature was the environmental factor most strongly influenced by sampling time, which explained why we found its significant impact on ER, especially under N addition treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSelection of Sampling Method and Time\u003c/h2\u003e \u003cp\u003eIn current study, NEP showed a more pronounced sensitivity to sampling time, particularly under N addition treatments, where both ER and NEP exhibited significant diurnal variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, GPP and CH₄ fluxes vary somewhat between sampling time periods, they were generally less affected by sampling time. During the T2 and T3 periods (11:00\u0026ndash;15:00), CO₂ emissions increased substantially, shifting the peatland from a carbon sink to a carbon source. This discrepancy is mainly due to the different diurnal variation mechanisms of GPP and ER, as NEP represents the net balance between GPP and ER, it is highly sensitive to asynchronous fluctuations between them, meaning that even non-significant changes in GPP or ER individually can result in amplified variations in NEP. The diurnal variation of GPP was relatively stable, approaching light saturation at noon, and the photosynthetic rate tended to be stable (Dang et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Korrensalo et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Photoinhibition may even occur in the \u003cem\u003eSphagnum\u003c/em\u003e-dominated community, further weakening the response of GPP to sampling time (Haraguchi and Yamada \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). CH₄ fluxes were generally less sensitive to sampling time across treatments, with the exception of the N addition treatment, where greater variability may be attributed to changes in plant functional types and rhizosphere emission dynamics (Ge et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnder N addition, the enhanced sensitivity of ER and NEP to sampling time may be driven by three mechanisms. First, it is caused by changes in vegetation structure, with a decrease in \u003cem\u003eSphagnum\u003c/em\u003e cover and an increase in the cover of shrubs and graminoids. These plants have well-developed root systems and high metabolic activity, and their root respiration and rhizosphere microorganisms are more sensitive to temperature changes (Mueller and Megonigal \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zeh et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Second, N addition significantly increased soil water content, which buffered temperature fluctuations to a certain extent, but the high-water content limited aeration and inhibited aerobic microorganisms (Drenovsky et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), thereby changing the response pattern of ER to temperature. Despite the overall decline in ER temperature sensitivity, enhanced daytime rhizospheric activity may still lead to increased ER in the afternoon (Luan et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yao et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This, coupled with a limited increase in GPP, ultimately causes a sharp decline in NEP. Third, N addition increases the complexity of CO₂ flux sources, thereby amplifying the temporal variability of ER and NEP. ER and NEP during the T1 period (9:00\u0026ndash;11:00) were significantly higher than during T2 and T3, further indicating a decline in net carbon uptake capacity in the afternoon. A similar trend was observed in the CK treatment, with NEP in periods T2 and T3 being significantly lower than that in T1, indicating that the carbon sink function of peatlands weakened as daytime temperature increased.\u003c/p\u003e \u003cp\u003eIn summary, GPP and CH₄ fluxes are relatively stable to the sampling time, while ER and NEP are more susceptible to changes in temperature and vegetation structure, and are more sensitive to the sampling period. Since NEP is calculated as the difference between GPP and ER, its temporal variability is easily amplified, potentially introducing significant uncertainty into carbon flux assessments. Therefore, we recommend that greenhouse gas flux studies adopt Rep or random time sampling strategies throughout the day to minimize time-related bias and improve the accuracy of peatland carbon budget evaluations.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study demonstrates that while GPP and CH₄ fluxes remain relatively stable across different sampling times, ER and NEP are significantly influenced by diurnal temperature variations and vegetation structural changes, particularly under long-term N addition treatment. as the balance between GPP and ER, NEP is sensitive to temporal fluctuations, which leads to amplified variability, potentially compromising the accuracy of carbon flux evaluations. Long-term environmental change led to a shift in plant community composition, with reduced \u003cem\u003eSphagnum\u003c/em\u003e moss cover and increased abundance of shrubs and graminoids. These functional groups have more active root systems and associated rhizosphere microbial communities, making ecosystem respiration more responsive to temperature fluctuations, especially during midday. This shift in vegetation structure enhanced ER and limited the compensatory increase in GPP, resulting in a marked decline in NEP during afternoon periods. We conclude that accurate assessment of peatland carbon fluxes under global change scenarios requires not only careful consideration of temporal sampling strategies but also an integrated understanding of vegetation dynamics and their role in regulating GHG emissions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was Supported by the National Nature Science Foundation of China (Nos. U23A2003, 42371050 and 41871046) and Jilin Provincial Science and Technology Development Project (No. 20230203002SF).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jun-Xiao Ma, Fan Lu, Ying Gao and Xuefei Gao. The first draft of the manuscript was written by Jun-Xiao Ma, Zucheng Wang and\u0026nbsp;Zhao-Jun Bu. Vladimir Chakov and Victoria Kuptsova revised the previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl-Kayssi AW, Al-Karaghouli AA, Hasson AM, Beker SA (1990) Influence of soil moisture content on soil temperature and heat storage under greenhouse conditions. 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Plant Soil 438:19\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-019-03958-6\u003c/span\u003e\u003cspan address=\"10.1007/s11104-019-03958-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Carbon exchange, Ecosystem respiration, Plant mediation, Sphagnum, Climate change","lastPublishedDoi":"10.21203/rs.3.rs-8448377/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8448377/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the temporal variability of greenhouse gas (GHG) fluxes is critical for accurately assessing peatland carbon dynamics. Some recommended time periods have been used for a long time, but whether temporal variations within the periods affect comparisons between treatments remains unexamined. In this study, we investigated the effects of sampling time on CO₂ and CH₄ fluxes under control, nitrogen addition, and warming treatments in a temperate peatland ecosystem, and compared these results with random sampling. We found that gross primary productivity and CH₄ fluxes exhibited relatively stable patterns across different sampling periods. In contrast, ecosystem respiration and net ecosystem productivity were more sensitive to sampling time, particularly under N addition. With the delay in sampling time, CO₂ emissions increased significantly, causing the peatland to shift from a carbon sink to a source. This was attributed to asynchronous diurnal variations between gross primary productivity and ecosystem respiration driven by changes in vegetation structure and soil microclimate. Our findings highlight the importance of sampling design in GHG flux monitoring and recommend the use of multi-timepoint or randomized sampling strategies to minimize temporal bias and variation between treatments, improving the accuracy of peatland carbon budget assessments.\u003c/p\u003e","manuscriptTitle":"Influence of Sampling Time on CO₂ and CH₄ Fluxes in a Temperate Peatland Under Nitrogen and Warming Treatments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 09:54:30","doi":"10.21203/rs.3.rs-8448377/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-01-09T18:56:04+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T01:18:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Wetlands","date":"2026-01-05T16:50:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-31T03:08:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wetlands","date":"2025-12-29T03:22:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5e2a72b6-1611-483e-b721-b31c0591f09e","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T19:18:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-08 09:54:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8448377","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8448377","identity":"rs-8448377","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}