Restoration practices and soil properties influence potential denitrification in agricultural floodplain wetlands

Restoration practices and soil properties influence potential denitrification in agricultural floodplain wetlands | 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 Restoration practices and soil properties influence potential denitrification in agricultural floodplain wetlands Shrijana Duwadi, Robert S. Brown, Spencer G. Womble, Justin N. Murdock This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9237776/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Riparian wetlands play a crucial role in nutrient retention and water quality maintenance in agricultural watersheds. Restoring wetland function in these systems is becoming increasingly important as negative impacts of eutrophication continue to increase in both local and downstream ecosystems. This study identified factors regulating wetland soil denitrification rates, a major nitrogen (N) removal pathway, across various wetland restoration practices (based on hydrology and plant structure) in restored agricultural bottomland hardwood forested wetlands. Soil cores from five distinct restoration practices, natural vegetation regeneration, remnant forest, tree planting areas, and constructed shallow water areas (wet and dry), were collected in 23 restored wetlands in Kentucky and Tennessee, USA. Flow-through soil core incubations were used to estimate denitrification as nitrogen gas (N 2 ) flux during a simulated 2-day flood event. All restoration practices produced N 2 at each timepoint, and the rates were greater at 48 h for all practices. Mean N 2 production was highest in natural regeneration and lowest in shallow water-wet areas throughout the 48 h incubation period. However, shallow water-wet areas exhibited the greatest percentage increase between 24 and 48 h, increasing by 48%. The predicted N 2 production was correlated with sediment oxygen demand (SOD), initial soil moisture, and extractable soil phosphorus (P). These results suggest that all restoration practices efficiently remove N over a 48 h flood period; however, the highest removal rates can depend on the vegetation type, flooding duration, and site-specific soil properties. Wetland restoration monitoring Nitrogen removal Flow-through incubation Natural regeneration Sediment oxygen demand Figures Figure 1 Figure 2 Figure 3 Introduction Wetlands provide an outsized ecosystem service relative to their land area, providing critical water filtration services, wildlife habitat, and supports biodiversity (Balmford et al., 2002 ; Ghermandi et al., 2010 ; Palay, 2021 ). Wetland restoration is therefore promoted as a strategy to mitigate nutrient pollution and improve water quality (Mitsch et al., 2001 ). Nitrate (NO₃⁻), largely derived from fertilizers, is a primary pollutant of concern in agricultural waterbodies, as excessive inputs during cropping can cause eutrophication and subsequent hypoxia(Rabalais et al., 2002 ), loss of aquatic and riparian biodiversity (Carpenter et al., 1998 ), and drinking water contamination (Mishra & Tripathi, 2023 ). Eutrophication-related damages in the United States (US) alone are estimated at $ 2.2 billion annually (Dodds et al., 2009 ). One of the most important processes for NO₃⁻ removal in wetlands is denitrification. Denitrification is the bioconversion process that transforms NO 3 − into nitrogen gas (N 2 ), effectively removing bioavailable nitrogen (N) and releasing it back into the atmosphere (Bernhard, 2010 ). This process can remove as much as 90% of the N that enters wetlands (Gilliam, 1994 ; R. Hunter et al., 2009 ). Denitrification is primarily controlled by the presence of NO 3 − , organic carbon (C), redox potential, and temperature (Arango et al., 2007 ; Beauchamp et al., 1989 ; Christensen et al., 1990 ; Mulholland et al., 2009 ), which are in turn influenced by hydrology, land use, and underlying geology (Osborne & Wiley, 1988 ; Stanley & Boulton, 1995 ). Despite their critical ecological functions, wetland loss continues to be a global problem, particularly for conversion to arable land and development. In the US, roughly 50% of natural wetlands have been lost since the 1780s. The US lost approximately 271,000 ha of wetlands between 2009 and 2019, accounting for > 50% net loss since the previous Wetlands Trend study (2004–2009) (Lang et al., 2024 ). One approach to wetland restoration is by landowner compensation through conservation easements. The US Department of Agriculture’s (USDA) Wetland Reserve Program (WRP), now the Wetland Restoration Enhancement Partnership (WREP), has spent more than $ 4.2 billion to restore and protect wetlands since it was established in the 1990 US Farm Bill (Hansen et al., 2015 ). As of 2025, WRP has restored nearly 1 million ha of wetlands (USDA NRCS, 2025 ). However, the information on the overall effectiveness of this program in retaining nutrients from the watershed is limited (Shrestha et al., 2017 ), and very few studies have evaluated restored easements within the Mississippi Alluvial Valley (Faulkner et al., 2011 ). Effective monitoring programs allow restoration goals to be assessed and guide improvements to future strategies (Block et al., 2001 ). Limited monitoring can hinder broader adoption of the most successful restoration strategies in the future as optimal restoration strategies are unknown or underutilized (Galatowitsch & Bohnen, 2021 ). Therefore, monitoring restored wetlands for their ecological functions and their relation to soil properties is essential to ensure the long-term success of restoration efforts. The co-restoration of both wetland structure and function are common goals during restoration, but often one or the other is a priority. Because many wetland components interact synergistically to create a functioning system (Sutton-Grier et al., 2010 ), successful restoration of wetland function (e.g., denitrification) may depend on soil properties, hydrology, vegetation, and their interactions. This study aimed to better understand how different wetland restoration practices and soil structure influence wetland functional recovery, and specifically N removal through soil denitrification. Previous studies have measured denitrification rates and identified their key drivers in restored floodplain soils (Mayer et al., 2022 ; Newcomer Johnson et al., 2014 ). Soil structure has been tightly linked to its denitrification function in wetlands (Kaden et al., 2021 ; Yu et al., 2012 )and soil properties (e.g., redox, C, NO 3 − , and pH) that influence denitrification in riparian wetlands have been well characterized (Meng et al., 2020 ; Xiong et al., 2015 ). Interactions between vegetation and hydrology further regulate nutrient cycling rates (R. G. Hunter & Faulkner, 2001 ; Mitsch et al., 2015 ), and denitrification can vary across small spatial scales due to these interactions (Faulkner et al., 2011 ). Establishing links between N transformations, restoration practices and measurable soil properties could yield robust indicators of ecosystem functions such as denitrification, thereby enhancing functional assessment methods post-restoration (Kaushal et al., 2023 ; Peralta et al., 2010 ). The WRP/WREP program emphasizes both vegetation and hydrology restoration. In the lower Mississippi River Valley, the primary vegetation endpoint is bottomland hardwood forests, necessitating the planting of several dozen native wetland tree species including species of cypress, oak, hickory, and many others. To restore natural hydrological conditions, wetland managers have implemented practices like blocking drainage channels across many wetlands (Armstrong et al., 2010 ; Howie et al., 2009 ; Wallage et al., 2006 ). Understanding the interactions among vegetation types, hydrology, and soil properties is crucial for optimizing nutrient retention recovery in restored wetlands and reducing downstream nutrient exports. The specific objectives of this study were to: i) evaluate variation in N 2 production among restoration practices in riparian wetlands, ii) assess variability in soil properties among the restoration practices, and iii) determine the relationship between soil properties and restoration practices, and N 2 production. By elucidating how restoration efforts and soil properties influence N 2 production, our findings can inform future restoration strategies and their effective implementation. Methods Easement selection and restoration practices Twenty-three sites were selected from USDA Natural Resources Conservation Service (NRCS) WRP/WREP wetlands easements in western Tennessee and western Kentucky. Easements were located along direct tributaries of the Mississippi River, including Mayfield Creek, Obion Creek, and Bayou de Chien in Kentucky, and the Obion, Forked Deer, and Hatchie rivers in Tennessee (Fig. 1 ). The restored wetlands ranged in age from 3–23 years since restoration. Areas selected for soil core collection within easements were representative of the dominant NRCS restoration practices. Restoration practices were identified via NRCS restoration maps, recent satellite images, and field visits during core collection. The most common restoration practices included natural regeneration areas, constructed shallow water areas, and tree planting areas. Remnant forests were sampled to approximate a successional endpoint representative of bottomland hardwood forests (Fig. S1 ). Natural regeneration areas revegetated through natural plant succession (USDA NRCS, 2003 ) and are mostly shaped by various biotic and abiotic factors, often producing climate-adapted heterogeneous plant communities that enhance patch diversity and ecosystem resilience (Prach & del Moral, 2015; Zivec et al., 2023 ). At the time of soil core collection, these areas were dominated by a mixture of grasses and woody shrubs. Shallow water areas were created to restore site hydrology, often by plugging ditches previously installed for drainage and managing water levels with control structures (The Nature Conservancy (TNC), 2020 ). These areas are dominated by emergent vegetation such as grasses and sedges that can be maintained indefinitely without succession to shrubs or forest communities under stable water conditions (Cole et al., 2025 ). Soil cores collected from shallow water areas were classified as dry or wet based on visible water presence above the soil surface during sampling (Fig. S1 ). Tree planting included areas where trees were planted to aid forest regeneration (USDA NRCS, 2016). Native, mast-producing species like bald cypress ( Taxodium distichum (L.) Rich), swamp white oak ( Quercus bicolor Willd.), river birch ( Betula nigra L.), black gum ( Nyssa sylvatica Marshall.), and many others were planted at approximately 435 seedlings per acre, with selection based on wetland soil type and flooding frequency (Tennessee Wildlife Federation, 2025 ; TNC, 2020). Remnant forests were areas with native tree species that have not been in recent agriculture, as shown by aerial images from the 1980s and 1990s. The age of remnant forests could not be determined, due to the absence of historical aerial images, however all remnant forests were observed on USDA images from the 1980’s showing there have been mature trees for at least 40 years. All easements were historical wetlands that had been converted to row crop agricultural production and were in production until just prior to restoration actions. Not all restoration practices were present at every easement. Soil core collection Paired soil core incubations facilitate correlation of denitrification rates at the soil-water interface with wetland soil physicochemical properties (Brown et al., 2025 ). Applying soil core incubations across many wetland restoration practices facilitates identification of attributes potentially regulating denitrification across a broad area of interest. Soil cores were collected from May through August between 2020 and 2022. Thirty paired cores (60 total), one for function (potential denitrification rates) and one for structure (soil physicochemical properties) were collected from each easement. Each pair of cores were collected within a 30 cm 2 area. An equal number of paired cores were collected from each restoration practice whenever feasible, with efforts made for even distribution within each restoration practice to address spatial variation. However, in the shallow water areas, collection was limited to the edge due to access limitations and safety concerns of deeper water. Following methods from Brown et al. ( 2025 ), approximately 15-cm deep soil/sediment cores were collected using acrylic tubes (7.62 cm diameter × 30 cm height) housed inside a metal coring device or manually pushed into the sediments (Fig. S2). Cores from the shallow water-wet areas were filled with water on-site to minimize sediment surface disturbance during transportation. Cores were sealed with rubber bottoms secured with pipe straps and plastic tops, then placed upright in coolers with ice to inhibit microbial activity. Upon returning to the lab at Tennessee Tech, water from shallow water cores was siphoned out carefully. Soil function cores were placed in the environmental chamber at 24 o C and acclimated overnight to simulate the average summer regional air temperature. Incubations started the following morning. Soil structure cores were transferred to a walk-in-cooler at 4 o C and processed the following day. Incubation water preparation and core incubation Laboratory-made incubation water was prepared following the methods outlined by Brown et al. ( 2025 ) and was based on the historical average water quality data reported by the United States Geological Survey (USGS) for Bayou de Chien, Kentucky (1970–2007; USGS gauge # 07024000) and the Obion River, Tennessee (1990–2005; USGS gauge # 07026040). These two rivers periodically feed the study WRP easements during floods. However, the concentration of NO 3 − -N and phosphate (PO 4 3− -P) was increased to 10 mg L − 1 and 1 mg L − 1 , respectively, to saturate nutrient uptake rates and provide consistent nutrient availability across easements. Therefore, the rates derived from incubations represent potential denitrification rates as opposed to ambient rates. The incubation took place in a dark walk-in environmental chamber maintained at 24°C to simulate the average summer regional air temperature. Incubation began at 8 am the day after collection, using a continuous flow-through system to simulate a 48 h flood. Plastic tops on the cores were replaced with acrylic lids equipped with inflow and outflow ports (i.d. 1 mm and 1.25 mm, respectively) and secured with pipe straps (Fig. S3). Lab water was delivered to individual cores at approximately 1.8 mL min − 1 through an inflow tubing connected to a Masterflex L/S peristaltic pump. Water flowed out of the cores through outflow tubings into sample containers, with outflow rates measured for each core. Outflowing water was not recycled. Water residence time in a core was approximately six hours. Dissolved gas sampling and analysis Water samples were collected from outflow tubes in triplicate 12 mL exetainers at 24 and 48 h of incubation. Vials were allowed to overflow three times before sample collection. All samples were then treated immediately with 180 µL zinc chloride (ZnCl 2 ) to inhibit microbial activities. After quick capping and agitation for uniform ZnCl 2 distribution, samples were stored underwater at 4°C and analyzed within one month. Dissolved gas concentrations in water samples were determined using a Membrane Inlet Mass Spectrometer (MIMS) (Kana et al., 1994 ). The instrument measured dissolved N 2 , O 2 , and Argon (Ar) concentrations in the water using the MIMS Faraday detector in 2020 and 2021 and MIMS Secondary Electron Multiplier (SEM) in 2022. Triplicate standards were measured after every six samples to calculate a calibration factor and correct for the drift in MIMS signal over time. Sample N 2 and O 2 concentrations were calculated using the Ar ratio method in R ( mimsy package) (Kelly, 2020 ). The output gas concentrations from MIMS were expressed as mg L − 1 of N 2 and O 2 (Kana et al., 2006 ). The areal N 2 flux for each core was calculated according to Speir et al. ( 2017 ) as follows: $$\:Areal\:Flux\:\left(mg\:{m}^{-2}\:{h}^{-1}\right)=\left(\frac{\left[\right(Core{)}_{out}-\left(Core{)}_{in}\right]*{Q\:}_{core}}{A\:\left({m}^{2}\right)}\right)$$ where, \(\:(Core{)}_{out\:}\) and \(\:(Core{)}_{in\:}\) = outflow and inflow concentrations (mg L − 1 ) of N 2 and O 2 in incubation core, \(\:{Q\:}_{core}\) = flow rate of incubation core (L h − 1 ), and A = surface area of soil in a core (m 2 ). Positive flux indicates a net gain (release) of N 2 or O 2 in the water column and negative flux indicates a net loss (removal) of N 2 or O 2 from the water column. More negative O 2 flux rates correspond to higher SOD, and a more positive N 2 flux corresponds to higher denitrification rates. Soil structure core processing and analysis Soil structure cores were processed to determine soil properties. After transferring the core to a clean aluminum sheet, detritus and vegetation were removed. Cores were then divided at 10 cm depth (0–10 cm) using a spackle knife and soil below 10 cm depth was discarded. After removing root pieces and gravel, each soil section was homogenized manually to get a uniform mixture through repeated mixing. The homogenization process involved breaking down the soil core by gloved hands as much as possible, followed by using a spackle knife to break large clumps, and through mixing using a spackle knife and hands alternately. Spreading, breaking, and mixing were done until the soil texture was consistent throughout the sample. Once the soil was homogenized, subsamples were taken to measure soil moisture, bulk density, pH, total carbon (TC), total N (TN), and extractable phosphorus (soil P). Details of soil properties analyses are described in Online Resource 1. Soil properties data for each structure core was presumed to be representative of the corresponding incubation core. Data processing Flux rates and soil properties of each core specific to each restoration practice and sampling timepoint were averaged, resulting in a singular representative value for each restoration practice within each easement. This approach prevented pseudo-replication. Before averaging, individual core-level N 2 flux rates that deviated beyond the 2.5% and 97.5% quantiles at each sampling timepoint were excluded to minimize the influence of extreme values during statistical analysis. This ensures that the analysis reflects the typical system behavior as extreme values can distort the results and lead to misleading conclusions. Although these extreme values likely represent real biogeochemical hotspots, hotspot analysis is beyond the scope of this study. Corresponding soil properties data for these excluded cores were also removed. Cores with missing data were similarly excluded from the averaging process. After extreme values removal and subsequent averaging, the resulting sampling sizes for restoration practices were: n = 7 for natural regeneration, n = 16 for remnant forest, n = 15 (at 24 h) and 14 (at 48h) for shallow water-dry, n = 18 for shallow water-wet, and n = 17 for tree planting (Table S1 ). Statistical analysis Statistical analysis was performed using RStudio version 4.4.1 (R Core Team, 2024 ). All results are shown as arithmetic means ± 95% confidence interval (CI). Significance of all tests was accepted at P < 0.05. Nitrogen gas flux rates were compared across restoration practices using Analysis of Variance (ANOVA). Residuals from each ANOVA were first tested for normality using the Shapiro–Wilk test and then for variance homogeneity using Bartlett’s test. When either assumption was violated, Kruskal–Wallis test was used. Post-hoc tests included Tukey's Honestly Significant Difference (HSD) for ANOVA and Dunn's test for the Kruskal–Wallis test. The relationships between N 2 flux rates and explanatory variables were analyzed using linear mixed effects models (R package nlme ) (Pinheiro et al., 2022 ), conducted separately at 24 h and 48 h timepoints. Restoration practice was the primary predictor, while soil properties including, SOD, soil moisture, bulk density, pH, soil TC, soil TN, and soil P were used as covariates. Soil TC and soil TN were log-transformed prior to analysis to address heteroscedasticity. Interaction components included restoration practice: soil moisture and restoration practice: logTC and were chosen utilizing ecological insights and thorough examination of the data. The varIdent variance structure available in the “nlme” package was assigned to the “restoration practice” to account for unequal variance across groups (Zuur et al., 2009 ). “Easement” (i.e., “site”) was used as a random effect. For the reliable estimation of random effects, easements with a single observation (n = 2) were excluded from the dataset. Prior to analysis, continuous predictor variables were standardized (scaled to mean = 0 and standard deviation = 1) to facilitate direct comparison of their relative effects (Schielzeth, 2010 ). Maximum likelihood (ML) was used to compare nested models during backward selection of fixed effects with selection based on log-likelihood ratio tests. Final models were then refitted using restricted maximum likelihood (REML) estimations. The conditional and marginal R 2 were computed using the r2 function in the performance package (Nakagawa & Schielzeth, 2013 ). An ANOVA with type III sums of squares was applied to the final model. Soil properties were compared across restoration practices using the same statistical approach applied to N 2 flux rates. Results Nitrogen gas (N 2 ) flux over time Nitrogen gas was produced from all restoration practices at both 24 h and 48 h, with production differing significantly among practices at both timepoints (24 h: ANOVA F (4, 68) = 9.47, p < 0.001; 48 h: F (4, 67) = 8.34, p < 0.001). Pairwise comparisons at 24 h showed that the natural regeneration area (6.92 ± 1.60 mg m − 2 h − 1 ) had the highest mean N 2 production, followed by the tree planting (5.52 ± 0.76 mg m − 2 h − 1 ), shallow water-dry (5.22 ± 1.27 mg m − 2 h − 1 ), and remnant forest (4.12 ± 0.85 mg m − 2 h − 1 ) areas. The lowest N 2 production occurred in the shallow water-wet area (2.95 ± 0.68 mg m − 2 h − 1 ) (Fig. 2 a). At 48 h, the mean N 2 production was highest in the natural regeneration area (8.35 ± 2.11 mg m − 2 h − 1 ), followed by the shallow water-dry (5.91 ± 1.16 mg m − 2 h − 1 ) and tree planting (5.70 ± 0.69 g m − 2 h − 1 ) areas. The lowest N 2 production rates were recorded in the remnant forest and shallow water-wet areas, at 4.53 ± 0.88 and 4.38 ± 0.77 mg m − 2 h − 1 , respectively (Fig. 2 b). Nitrogen gas production increased between 24 and 48 h of incubation in all restoration practices. Shallow water-wet area showed the highest percentage increase in mean N 2 production by 48%. In natural regeneration, remnant forest, shallow water-dry, and tree planting areas, the production rates increased by 21%, 10%, 13%, and 3%, respectively. Factors influencing N 2 flux at 24 h At 24 h, the estimated mean N 2 production was significantly correlated with SOD (Chi-squared (χ 2 ) = 31.70, p < 0.001) and soil moisture (χ² = 17.47, p < 0.001) (Table 1 ). On average, each standard deviation increase in SOD and soil moisture was associated with a 1.05 mg m − 2 h − 1 increase and a 0.66 mg m − 2 h − 1 decrease in N 2 production, respectively. The final model explained 91.0% of the total variation in N 2 flux (conditional R 2 = 0.910). Fixed effects (restoration practice, SOD, and soil moisture) accounted for 68.2% of the variation (marginal R 2 = 0.682), while site-to-site differences among easements contributed to 22.8%, confirming the importance of accounting for spatial heterogeneity in the model structure (Table 1 ). Table 1 Fixed effects from the linear mixed-effects model examining N 2 flux rates in relation to restoration practice, sediment oxygen demand (SOD), and soil moisture at 24 h. SOD and soil moisture values were standardized prior to analysis. The intercept represents the baseline flux for the reference restoration practice (natural regeneration), while values for all other practices represent differences relative to this reference Variables Value SE t p R 2 (Conditional: 0.910, Marginal: 0.682) Intercept 5.48 0.53 10.44 < 0.001 Remnant forest -0.90 0.58 -1.55 0.128 Shallow water-dry 0.04 0.66 0.05 0.958 Shallow water-wet -2.06 0.59 -3.47 0.001 Tree-planting -0.68 0.49 -1.39 0.172 SOD -1.05 0.19 -5.63 < 0.001 Soil moisture -0.66 0.16 -4.18 < 0.001 Factors influencing N 2 flux at 48 h The estimated N 2 production at 48 h was correlated with SOD (χ 2 = 6.05, p = 0.014) and soil P (χ 2 = 8.15, p = 0.004) (Table 2 ). For every standard deviation increase in SOD and soil P, N 2 production increased by 0.49 and 0.58 mg m⁻ 2 h⁻ 1 , respectively. The final model explained 71.0% of the total variation in N 2 flux (conditional R 2 = 0.710), with fixed effects (restoration practice, SOD, and soil P) accounting for 41.5% of the variation (marginal R 2 = 0.415). The remaining 29.5% was attributed to site-to-site differences among easements, indicating substantial spatial heterogeneity in N 2 flux patterns (Table 2 ). Table 2 Fixed effects from the linear mixed-effects model examining N 2 flux rates in relation to restoration practice, sediment oxygen demand (SOD), and extractable phosphorus (soil P) at 48 h. SOD and soil P values were standardized prior to analysis. The intercept represents the baseline flux for the reference restoration practice (natural regeneration), while values for all other practices represent differences relative to this reference Variables Value SE t p R 2 (Conditional: 0.710, Marginal: 0.415) Intercept 7.31 0.86 8.50 < 0.001 Remnant forest -2.74 0.89 -3.08 0.004 Shallow water-dry -1.44 0.92 -1.58 0.122 Shallow water-wet -2.73 0.89 -3.08 0.004 Tree-planting -1.65 0.86 -1.93 0.060 SOD -0.49 0.20 -2.46 0.018 Soil P 0.58 0.20 -2.85 0.007 Soil properties among restoration practices Most soil properties varied considerably both within and among restoration practices (Table S1 ). The values of SOD ranged from − 89.14 to -21.70 mg m − 2 h − 1 at 24 h and from − 125.61 to -72.02 mg m − 2 h − 1 at 48 h. Soil moisture ranged from 0.11–1.17 g g − 1 , while soil P from 0.01–0.09 mg g − 1 . Significant differences across restoration practices were observed for SOD (24h: Kruskal-Wallis χ 2 (4) = 21.49, p < 0.001; 48 h: F (4) = 2.94, p = 0.027) and soil moisture (χ 2 (4) = 26.43, p < 0.001), whereas soil P did not differ significantly (F (4) = 2.18, p = 0.081). Natural regeneration had the highest mean SOD and remnant forest had the lowest at both 24 h and 48 h (Fig. 3 a & b). The highest mean soil moisture was observed in the shallow water-wet areas and lowest in the tree planting areas (Fig. 3 c). Although natural regeneration areas had the highest soil P, it showed the least variation across restoration practices (Fig. 3 d). Soil TC differed significantly across restoration practices (χ 2 (4) = 14.01, p = 0.007), but it did not correlate with N 2 flux rates. No significant differences were observed for bulk density (F (4) = 0.99, p = 0.418) or soil TN (χ 2 (4) = 8.62, p = 0.071). While soil pH showed an overall significant difference (F (4) = 2.68, p = 0.039), post hoc tests revealed no pairwise differences among restoration practices (Table S1 ). Discussion This study assessed whether N 2 production rates and soil properties differ among various restoration practices in riparian wetlands and to establish the connection between N 2 production and soil properties. Our results indicated that the restored riparian wetlands showed high spatial variation in N 2 production and most of the measured soil properties both within and among restoration practices as seen in previous studies (Bruland et al., 2006 ; Xiong et al., 2015 ). However, only SOD, soil moisture, and soil P had a measurable potential influence on variation in N 2 flux among restoration practices. Nitrogen gas (N 2 ) production among restoration practices At both 24 and 48 h, N 2 gas production varied among restoration practices. Surprisingly, N 2 production from the shallow water-wet areas was the lowest at both times, despite having the highest initial soil moisture. Previous studies have shown that higher soil moisture typically enhances denitrification, likely due to increased denitrification enzyme activity in wetter environments compared to drier ones (Ballantine et al., 2017 ; Burgin et al., 2010 ). However, our study found that N 2 production was highest in the natural regeneration areas at both sampling times (Fig. 2 ), even though it had intermediate soil moisture content. In natural regeneration areas, vegetation was likely more heterogeneous, as nature selects which species will regenerate naturally (Z. Zhang et al., 2021 ). Together with hydrology and soils, plants are one of the “big three” wetland parameters (Cole & Kentula, 2011 ). Organic root exudates from plants are known to fuel denitrification (Vymazal, 2013 ). On average, plants can increase denitrification rates by 55% (Alldred & Baines, 2016 ). Herbaceous vegetation has been shown to put more organic matter into shallow soils relative to woody vegetation due to both less primary production reaching the ground in woody plants, and differences in soil C decomposition rates (Eclesia et al., 2016 ; J. Wang & Hu, 2025 ). Additionally, heterogeneity in plant species in the natural regeneration areas likely produces a diverse suite of root exudates, supporting functional gene abundance and, consequently, higher total N removal and N 2 production (Brisson et al., 2020 ; C. Zhang et al., 2025 ) By 48 h, N 2 production was the second highest in shallow water-dry areas (Fig. 2 b), likely due to a NO 3 − pulse effect. These cores were collected from the dry edges of the shallow water-wet areas, an area frequently exposed to alternating drying and then re-wetting during the flooding season. At the time of core collection, no standing water was present in the shallow water-dry areas. The preceding dry phase may have promoted mineralization and nitrification, leading to NO 3 − accumulation—a "nitrate bank" (Venterink et al., 2002 ). Upon re-wetting during flow-through incubation in the lab, this stored NO 3 − likely became readily available to denitrifiers, fueling enhanced N 2 production. In short, drying followed by rewetting likely promoted coupled nitrification–denitrification in the shallow-water dry cores, where NO 3 − produced under oxic conditions is subsequently reduced via denitrification under anoxic conditions upon flow-through incubation in the lab, thereby increasing N 2 production (Marchant et al., 2016 ; Nielsen, 1992 ; Risgaard-Petersen, 2003 ). Between the 24 h and 48 h sampling intervals, we observed an increase in N 2 production, with values ranging from only 3% in the tree planting areas to 48% in the shallow water-wet areas. This temporal stability in tree planting areas suggests that denitrification there reached near saturation point early in the incubation, preventing further denitrification enhancement. For other restoration practices, the finding emphasizes the importance of longer water residence time on N 2 production in restored wetlands. Water residence time is one of the key determinants of wetland denitrification efficiency (Poe et al., 2003 ; Tomaszek et al., 1997 ). Extended residence times enhance sediment-water interactions, facilitating longer contact time between NO 3 − -rich water and denitrifying sediments (Spieles & Mitsch, 2000 ; Xue et al., 1999 ). It has also been documented that longer water residence time increases denitrifier community diversity (Kjellin et al., 2007 ), further emphasizing the importance of this parameter in wetland restoration. Relationship between soil properties and N 2 production Sediment oxygen demand (SOD) The significant correlation between SOD and N 2 production suggests that redox potential, which is a measure of the oxidation/reduction status of sediments, plays a crucial role in regulating N 2 fluxes. Redox potential is widely recognized as one of the primary controls on denitrification in wetlands (Seo & DeLaune, 2010 ), and SOD has been strongly correlated to denitrification rates in other aquatic sediments (McCarthy et al., 2007 ; Seitzinger, 1988 ). In the restored wetlands we studied, elevated SOD likely accelerated the creation of an anoxic, low redox environment that is ideal for denitrification (Cornwell et al., 1999 ). Similar patterns were reported by de Klein et al. ( 2017 ), who found that reduced O 2 availability in surface sediments enhanced potential denitrification. Therefore, SOD may serve as a reliable predictor of the N 2 production potential in restored wetlands, as reflected in the observed correlation during the 48 h incubation period. Soil moisture Initial soil moisture was correlated to N 2 production rates during the first 24 h of flooding, but this relationship was no longer present at 48 h. Interestingly, unlike previous studies that reported increased denitrification rates with higher soil moisture in riparian wetlands (Burgin et al., 2010 ; Ma et al., 2020 ; Xiong et al., 2017 ), our study found a negative relationship between soil moisture at the time of collection and in N 2 production at 24 h. This contrasting finding likely reflects our experimental setup, where all soil cores were fully flooded during laboratory incubation, creating conditions distinct from natural field moisture gradients. Thus, our study looked at soil denitrification during flooding, and not between floods. Soil cores with higher initial moisture were likely already approaching O 2 -depleted states, which may have limited further O 2 consumption and the associated stimulation of anaerobic microbial activity when flooded. Furthermore, higher initial soil moisture may have promoted prior leaching of important substrates (i.e., dissolved organic C (DOC) and bioavailable N compounds), resulting in depleted labile substrate pools even when bulk nutrient stocks remain adequate, thereby reducing their availability to denitrifiers. The absence of the correlations of N 2 production with soil TC and TN in our model supports this interpretation, as total nutrient pools may have been adequate in these restored wetlands, but the readily available substrate fractions may have been depleted in wetter soils through leaching processes. Even when adequate NO₃⁻ is available, its removal can be constrained by limited availability of labile organic substrates (Castaldelli et al., 2019 ). Such substrate limitations can lead to decreased denitrification rates(H. Wang et al., 2023 ; Xiong et al., 2017 ) and, consequently, lower N 2 production. Soil nutrients While previous studies have reported higher denitrification rates in wetland soils with higher soil TC (Attard et al., 2011 ) and soil TN (Brown et al., 2025 ; Xiong et al., 2015 ), we observed no such relationship in our study, suggesting that soil TC and TN were not limiting factors for the denitrification process. However, the previous statement suggesting differences in soil NO 3 − could be important in regulating denitrification rates, which shows TN may be composed of different N organic and inorganic components in different restoration practices. Alternatively, there could be different N mineralization rates, and/or nitrification rates across practices. Additionally, soil P had a positive correlation with N 2 production, which is consistent with elevated soil P levels enhancing denitrification and increasing NO 3 − retention (O’Neill et al., 2022 ; K. Zhang et al., 2012 ). An increase in soil P can stimulate microbial growth and metabolic activity, since P serves as an essential nutrient for microbial growth and activity and can exert both direct and indirect influences on denitrification rates (Henderson et al., 2010 ; Houlton & Bai, 2009 ). This result also shows that denitrification can be potentially co-limited by both N and P in wetland soils. Soil properties among restoration practices The SOD was highest in the natural regeneration areas and lowest in the remnant forest areas, despite remnant forest containing the highest soil TC (22.48 mg g⁻¹) among restoration practices (Table S1 ). This apparent paradox suggests that organic matter quality, rather than quantity, may determine biogeochemical processes. Higher SOD in natural regeneration areas likely resulted from enhanced microbial respiration fueled by the accumulation of fresh, labile organic matter from early successional vegetation. In contrast, remnant forests are likely dominated by already highly decomposed, recalcitrant organic matter that slows down microbial respiration. This pattern aligns with broader observations that organic matter decomposition is often inversely related to soil C stocks (Middleton, 2020 ). Fresh biomass decomposes rapidly (within days to weeks), leaving behind more resistant compounds that persist for longer periods (Zonneveld et al., 2010 ). Over time, decomposition further declines, and organic matter accumulates as they enter a recalcitrant phase (Moore et al., 2017 ). Thus, the SOD differences are likely related to the predominance of labile organic matter in natural regeneration areas compared to mature remnant forests. The observed difference in SOD can also be interpreted in the context of ecosystem maturity. The moderate soil moisture in natural regeneration (0.46 g g⁻¹) and remnant forest (0.41 g g⁻¹) areas (Table S1 ) suggests that moisture was not a limiting factor. Rather, the developmental stage of these ecosystems—active succession vs. biogeochemical equilibrium—was likely the primary driver. Natural regeneration areas, representing early successional stages, may support higher microbial activity, whereas mature remnant forests reflect a more stable state. These findings further suggest that restored wetlands may experience elevated SOD during early establishment phases before gradually transitioning to stable, low-demand conditions characteristic of mature bottomland hardwood forest ecosystems. Soil moisture was highest in the shallow-water wet areas, a pattern largely influenced by the presence of water control structures. These structures effectively hold water on-site, raising the ground water table and increasing soil moisture, even in the absence of active flooding which explains higher soil moisture of these areas. After shallow water areas, soil moisture was similarly high in both natural regeneration and remnant forest areas. However, the variability in soil moisture was highest in the natural regeneration areas. Such variability can increase niche availability and therefore increase the potential for vegetation diversity in these areas (Gaberščik et al., 2018 ). The natural regeneration areas in our restored wetlands may have formed when abundant moisture was present to regenerate and in part reflects the restoration of wetland hydroperiod. Adequate moisture increases the potential for wetland vegetation establishment through natural regeneration (Stroh et al., 2012 ), and the regeneration of many wetland species is often observed only when appropriate moisture regimes are restored (De Steven et al., 2006 ; Mitsch & Wilson, 1996 ). Newly regenerated vegetation may increase shade and create microclimate refuges that facilitate more regeneration through seed banks and favorable conditions for further plant establishment (Zivec et al., 2023 ). Another likely mechanism for the high but variable soil moisture in these areas is the recovery of various woody and herbaceous plant communities, which can influence hydrology at localized scales by intercepting runoff and regulating evapotranspiration. In remnant forest areas, shading by mature trees, the presence of abundant old vegetation and high amount of organic matter deposition most likely contributed to higher water retention(Dabrowska-Zielinska et al., 2016 ) and consequentially elevated soil moisture levels. In addition, tree roots and organic matter can improve soil porosity, often leading to higher infiltration rates and therefore increased soil moisture (Hudson, 1994 ). Despite variations in mean soil TC levels across the restoration practices, mean soil P levels were quite similar (Table S1 ). During flooding events, all restored areas may have received similar inputs from external nutrient sources, including agricultural runoff, and P-rich suspended sediment deposited by flood waters, which could explain the comparable soil P levels observed across the restoration practices. This pattern may also reflect legacy effects of past farming and fertilization, as riparian wetlands are effective at retaining P through sediment deposition (Gordon et al., 2020 ). Consequently, accumulated legacy P can continue to impact wetland nutrient dynamics for centuries, even after external watershed inputs have been eliminated (Goyette et al., 2018 ). This may explain why soil P levels in our restored wetlands remained relatively consistent across practices even after restoration. Conclusion While all restoration practices showed potential to reduce downstream N transport through denitrification, N 2 production was highest in natural regeneration areas. Therefore, natural regeneration, with its lower associated costs, could be a viable alternative to active restoration methods for N removal and water quality improvement in the Mississippi River Basin. Also, since herbaceous vegetation in these areas is eventually taken over by volunteer hardwood trees, successional resetting of the vegetation in parts of these wetlands may be a management strategy to help optimize N removal rates. Some restored wetlands may fail without disturbance (Middleton, 1999 ), and resetting could be an option to reduce dominant species, promote greater plant diversity, and enhance heterogeneity (Osland et al., 2011 ). However, widespread adoption of all restoration approaches would benefit from further investigation across more restored floodplain wetlands. We recognize that there are limitations in our study to access full potential denitrification rates based on 48 h incubation. Longer flooding durations may be necessary to optimize N 2 production and to identify the sequence in which restoration practices reach saturation, beyond which further denitrification gains are minimal. The correlation of soil properties with N 2 production was more limited than expected. In many freshwater sediments, denitrification is primarily fueled by NO 3 − produced locally within the soil rather than from the overlying water (Seitzinger, 1988 ). Subsequent research could examine the relative contributions of water-column vs. soil-derived NO 3 − across restoration practices using isotope pairing, in which the incubation water is enriched with 15 NO 3 − and the resulting 15 N 2 gas is measured (Li & Twilley, 2021 ; Nielsen, 1992 ). The fact that SOD was highest in natural regeneration and lowest in remnant forest areas stresses the need to analyze organic matter quality (labile vs. recalcitrant fractions). Future studies could also benefit from examining vegetation diversity and root associated microbial communities to clarify their influence on soil properties and wetland functions. Targeted experimental manipulation of NO 3 − (Speir et al., 2017 , 2023 ) may help pinpoint true hotspots for denitrification across these landscapes, while manipulations of DOC could reveal its limiting role, especially when using high nutrient incubation water (Nifong et al., 2019 ). Moving forward, a comprehensive analysis that considers variations in soil properties, microbial communities, topography, and the presence of localized hotspots and microsites will be essential for effectively predicting outcomes of restoration efforts. Declarations Conflict of Interest The authors declare no conflicts of interest. Declarations All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Supplementary information Online Resource 1 provides additional details on analytical methods, soil properties analysis (Table S1 ), and images of restoration practices (Fig. S1 ), core collection (Fig. S2), and flow-through soil core incubation setup ( Fig. S3). Funding This project was funded by the USDA Natural Resources Conservation Service (USDA NRCS) and The Nature Conservancy (TNC). Author Contribution SD: study design, data collection and analysis, original manuscript draft. RSB: study design, data collection and analysis, manuscript drafts review and editing. SGW: study design, data collection and analysis, manuscript drafts review and editing. JNM: secured funding, study design, data collection and analysis, manuscript drafts review and editing. All authors evaluated and approved the final version of the manuscript. Acknowledgement We extend our gratitude to Stephenie Driscoll, Trevor Crawford, Peter Blum, Morgan Michael, Andy Rosson, and Ryan Hanscom for their invaluable support in field and laboratory work. We acknowledge the Tennessee Tech Water Center for their contribution to research infrastructure. Data Availability The data produced in this study are available from the corresponding author upon reasonable request. References Alldred, M., & Baines, S. B. (2016). Effects of wetland plants on denitrification rates: a meta-analysis. Ecological Applications , 26 (3), 676–685. https://doi.org/10.1890/14-1525 Arango, C. P., Tank, J. L., Schaller, J. L., Royer, T. V., Bernot, M. J., & David, M. B. (2007). Benthic organic carbon influences denitrification in streams with high nitrate concentration. Freshwater Biology , 52 (7), 1210–1222. https://doi.org/10.1111/j.1365-2427.2007.01758.x Armstrong, A., Holden, J., Kay, P., Francis, B., Foulger, M., Gledhill, S., McDonald, A. T., & Walker, A. (2010). The impact of peatland drain-blocking on dissolved organic carbon loss and discolouration of water; results from a national survey. Journal of Hydrology , 381 (1–2), 112–120. https://doi.org/10.1016/j.jhydrol.2009.11.031 Attard, E., Recous, S., Chabbi, A., De Berranger, C., Guillaumaud, N., Labreuche, J., Philippot, L., Schmid, B., & Le Roux, X. (2011). Soil environmental conditions rather than denitrifier abundance and diversity drive potential denitrification after changes in land uses. Global Change Biology , 17 (5), 1975–1989. https://doi.org/10.1111/j.1365-2486.2010.02340.x Ballantine, K. A., Anderson, T. R., Pierce, E. A., & Groffman, P. M. (2017). Restoration of denitrification in agricultural wetlands. Ecological Engineering , 106 , 570–577. https://doi.org/10.1016/j.ecoleng.2017.06.033 Balmford, A., Bruner, A., Cooper, P., Costanza, R., Farber, S., Green, R. E., Jenkins, M., Jefferiss, P., Jessamy, V., Madden, J., Munro, K., Myers, N., Naeem, S., Paavola, J., Rayment, M., Rosendo, S., Roughgarden, J., Trumper, K., & Turner, R. K. (2002). Economic Reasons for Conserving Wild Nature. Science , 297 (5583), 950–953. https://doi.org/10.1126/science.1073947 Beauchamp, E. G., Trevors, J. T., & Paul, J. W. (1989). Carbon Sources for Bacterial Denitrification. In Advances in Soil Science (pp. 113–142). https://doi.org/10.1007/978-1-4613-8847-0_3 Bernhard, A. (2010). The Nitrogen Cycle: Processes, Players, and Human Impact. Nature Education Knowledge , 3 (10), 25. Block, W. M., Franklin, A. B., Ward, J. P., Ganey, J. L., & White, G. C. (2001). Design and Implementation of Monitoring Studies to Evaluate the Success of Ecological Restoration on Wildlife. Restoration Ecology , 9 (3), 293–303. https://doi.org/10.1046/j.1526-100x.2001.009003293.x Brisson, J., Rodriguez, M., Martin, C. A., & Proulx, R. (2020). Plant diversity effect on water quality in wetlands: a meta-analysis based on experimental systems. Ecological Applications , 30 (4). https://doi.org/10.1002/eap.2074 Brown, R. S., Murdock, J. N., Womble, S. G., & Duwadi, S. (2025). Floodwater monitoring and soil incubations reveal complementary nutrient retention patterns in a reconnected floodplain wetland. Freshwater Science , 44 (3), 000–000. https://doi.org/10.1086/737226 Bruland, G. L., Richardson, C. J., & Whalen, S. C. (2006). Spatial variability of denitrification potential and related soil properties in created, restored, and paired natural wetlands. Wetlands , 26 , 1042–1056. https://doi.org/https://doi.org/10.1672/0277-5212(2006)26[1042:SVODPA]2.0.CO;2 Burgin, A. J., Groffman, P. M., & Lewis, D. N. (2010). Factors Regulating Denitrification in a Riparian Wetland. Soil Science Society of America Journal , 74 (5), 1826–1833. https://doi.org/10.2136/sssaj2009.0463 Carpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharpley, A. N., & Smith, V. H. (1998). Nonpoint Pollution of Surface Waters with Phosphorus and Nitrogen. Ecological Applications , 8 (3), 559. https://doi.org/10.2307/2641247 Castaldelli, G., Colombani, N., Soana, E., Vincenzi, F., Fano, E. A., & Mastrocicco, M. (2019). Reactive nitrogen losses via denitrification assessed in saturated agricultural soils. Geoderma , 337 , 91–98. https://doi.org/10.1016/j.geoderma.2018.09.018 Christensen, P. B., Nielsen, L. P., Sørensen, J., & Revsbech, N. P. (1990). Denitrification in nitrate-rich streams: Diurnal and seasonal variation related to benthic oxygen metabolism. Limnology and Oceanography , 35 (3), 640–651. https://doi.org/10.4319/lo.1990.35.3.0640 Cole, C. A., Brooks, R. P., & Brittingham, M. C. (2025). Managing Your Restored Wetland . https://extension.psu.edu/managing-your-restored-wetland Cole, C. A., & Kentula, M. E. (2011). Monitoring and Assessment—What to Measure … and Why. In B. A. LePage (Ed.), Wetlands (pp. 137–152). Springer Netherlands. https://doi.org/10.1007/978-94-007-0551-7_8 Cornwell, J. C., Kemp, W. M., & Kana, T. M. (1999). Denitrification in coastal ecosystems: methods, environmental controls, and ecosystem level controls, a review. Aquatic Ecology , 33 (1), 41–54. https://doi.org/10.1023/A:1009921414151 Dabrowska-Zielinska, K., Budzynska, M., Tomaszewska, M., Malinska, A., Gatkowska, M., Bartold, M., & Malek, I. (2016). Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data. Remote Sensing , 8 (9), 756. https://doi.org/10.3390/rs8090756 de Klein, J. J. M., Overbeek, C. C., Juncher Jørgensen, C., & Veraart, A. J. (2017). Effect of Temperature on Oxygen Profiles and Denitrification Rates in Freshwater Sediments. Wetlands , 37 (5), 975–983. https://doi.org/10.1007/s13157-017-0933-1 De Steven, D., Sharitz, R. R., Singer, J. H., & Barton, C. D. (2006). Testing a Passive Revegetation Approach for Restoring Coastal Plain Depression Wetlands. Restoration Ecology , 14 (3), 452–460. https://doi.org/10.1111/j.1526-100X.2006.00153.x Dodds, W. K., Bouska, W. W., Eitzmann, J. L., Pilger, T. J., Pitts, K. L., Riley, A. J., Schloesser, J. T., & Thornbrugh, D. J. (2009). Eutrophication of U.S. Freshwaters: Analysis of Potential Economic Damages. Environmental Science & Technology , 43 (1), 12–19. https://doi.org/10.1021/es801217q Eclesia, R. P., Jobbagy, E. G., Jackson, R. B., Rizzotto, M., & Piñeiro, G. (2016). Stabilization of new carbon inputs rather than old carbon decomposition determines soil organic carbon shifts following woody or herbaceous vegetation transitions. Plant and Soil , 409 (1–2), 99–116. https://doi.org/10.1007/s11104-016-2951-9 Faulkner, S., Barrow, W., Keeland, B., Walls, S., & Telesco, D. (2011). Effects of conservation practices on wetland ecosystem services in the Mississippi Alluvial Valley. Ecological Applications , 21 (sp1), S31–S48. https://doi.org/10.1890/10-0592.1 Gaberščik, A., Krek, J. L., & Zelnik, I. (2018). Habitat diversity along a hydrological gradient in a complex wetland results in high plant species diversity. Ecological Engineering , 118 , 84–92. https://doi.org/10.1016/j.ecoleng.2018.04.017 Galatowitsch, S., & Bohnen, J. (2021). Long-Term Recovery of a Restored Palustrine Wetland: the Role of Monitoring and Adaptive Management. Wetlands , 41 (6), 80. https://doi.org/10.1007/s13157-021-01478-7 Ghermandi, A., van den Bergh, J. C. J. M., Brander, L. M., de Groot, H. L. F., & Nunes, P. A. L. D. (2010). Values of natural and human-made wetlands: A meta-analysis. Water Resources Research , 46 (12). https://doi.org/10.1029/2010WR009071 Gilliam, J. W. (1994). Riparian Wetlands and Water Quality. Journal of Environmental Quality , 23 (5), 896–900. https://doi.org/10.2134/jeq1994.00472425002300050007x Gordon, B. A., Dorothy, O., & Lenhart, C. F. (2020). Nutrient Retention in Ecologically Functional Floodplains: A Review. Water , 12 (10), 2762. https://doi.org/10.3390/w12102762 Goyette, J.-O., Bennett, E. M., & Maranger, R. (2018). Low buffering capacity and slow recovery of anthropogenic phosphorus pollution in watersheds. Nature Geoscience , 11 (12), 921–925. https://doi.org/10.1038/s41561-018-0238-x Hansen, L., Hellerstein, D., Ribaudo, M., Williamson, J., Nulph, D., Loesch, C., & Crumpton, W. (2015). Targeting Investments To Cost Effectively Restore and Protect Wetland Ecosystems: Some Economic Insights. Report No. 183 . Economic Research Service/USDA. 56 pp. https://ers.usda.gov/sites/default/files/_laserfiche/publications/45347/51895_err183.pdf?v=79577 Henderson, S. L., Dandie, C. E., Patten, C. L., Zebarth, B. J., Burton, D. L., Trevors, J. T., & Goyer, C. (2010). Changes in Denitrifier Abundance, Denitrification Gene mRNA Levels, Nitrous Oxide Emissions, and Denitrification in Anoxic Soil Microcosms Amended with Glucose and Plant Residues. Applied and Environmental Microbiology , 76 (7), 2155–2164. https://doi.org/10.1128/AEM.02993-09 Houlton, B. Z., & Bai, E. (2009). Imprint of denitrifying bacteria on the global terrestrial biosphere. Proceedings of the National Academy of Sciences , 106 (51), 21713–21716. https://doi.org/10.1073/pnas.0912111106 Howie, S. A., Whitfield, P. H., Hebda, R. J., Munson, T. G., Dakin, R. A., & Jeglum, J. K. (2009). Water Table and Vegetation Response to Ditch Blocking: Restoration of a Raised Bog in Southwestern British Columbia. Canadian Water Resources Journal , 34 (4), 381–392. https://doi.org/10.4296/cwrj3404381 Hudson, B. D. (1994). Soil organic matter and available water capacity. Journal of Soil and Water Conservation , 49 (2), 189–194. https://doi.org/https://doi.org/10.1080/00224561.1994.12456850 Hunter, R. G., & Faulkner, S. P. (2001). Denitrification Potentials in Restored and Natural Bottomland Hardwood Wetlands. Soil Science Society of America Journal , 65 (6), 1865–1872. https://doi.org/10.2136/sssaj2001.1865 Hunter, R., Lane, R., Day, J., Lindsey, J., Day, J., & Hunter, M. (2009). Nutrient Removal and Loading Rate Analysis of Louisiana Forested Wetlands Assimilating Treated Municipal Effluent. Environmental Management , 44 (5), 865–873. https://doi.org/10.1007/s00267-009-9348-y Kaden, U. S., Fuchs, E., Geyer, S., Hein, T., Horchler, P., Rupp, H., Scholz, M., Schulz-Zunkel, C., & Weigelhofer, G. (2021). Soil Characteristics and Hydromorphological Patterns Control Denitrification at the Floodplain Scale. Frontiers in Earth Science , 9 . https://doi.org/10.3389/feart.2021.708707 Kana, T. M., Cornwell, J. C., & Zhong, L. (2006). Determination of denitrification in the Chesapeake Bay from measurements of N 2 accumulation in bottom water. Estuaries and Coasts , 29 (2), 222–231. https://doi.org/10.1007/BF02781991 Kana, T. M., Darkangelo, Christina., Hunt, M. Duane., Oldham, J. B., Bennett, G. E., & Cornwell, J. C. (1994). Membrane Inlet Mass Spectrometer for Rapid High-Precision Determination of N2, O2, and Ar in Environmental Water Samples. Analytical Chemistry , 66 (23), 4166–4170. https://doi.org/10.1021/ac00095a009 Kaushal, S. S., Fork, M. L., Hawley, R. J., Hopkins, K. G., Ríos-Touma, B., & Roy, A. H. (2023). Stream restoration milestones: monitoring scales determine successes and failures. Urban Ecosystems , 26 (4), 1131–1142. https://doi.org/10.1007/s11252-023-01370-8 Kelly, M. (2020). mimsy: Calculate MIMS Dissolved Gas Concentrations Without Getting a Headache. R package version 0.6.2 . https://CRAN.R-project.org/package=mimsy Kjellin, J., Hallin, S., & Wörman, A. (2007). Spatial variations in denitrification activity in wetland sediments explained by hydrology and denitrifying community structure. Water Research , 41 (20), 4710–4720. https://doi.org/10.1016/j.watres.2007.06.053 Lang, M. W., Ingebritsen, J. C., & Griffin, R. K. (2024). Status and Trends of Wetlands in the Conterminous United States 2009 to 2019 . U.S. Department of the Interior; Fish and Wildlife Service, Washington, D.C. 43 pp. https://www.fws.gov/sites/default/files/documents/2024-03/wetlands-status-and-trends-2009-2019-signed.pdf Li, S., & Twilley, R. R. (2021). Nitrogen Dynamics of Inundated Sediments in an Emerging Coastal Deltaic Floodplain in Mississippi River Delta Using Isotope Pairing Technique to Test Response to Nitrate Enrichment and Sediment Organic Matter. Estuaries and Coasts , 44 (7), 1899–1915. https://doi.org/10.1007/s12237-021-00913-6 Ma, L., Jiang, X., Liu, G., Yao, L., Liu, W., Pan, Y., & Zuo, Y. (2020). Environmental Factors and Microbial Diversity and Abundance Jointly Regulate Soil Nitrogen and Carbon Biogeochemical Processes in Tibetan Wetlands. Environmental Science & Technology , 54 (6), 3267–3277. https://doi.org/10.1021/acs.est.9b06716 Marchant, H. K., Holtappels, M., Lavik, G., Ahmerkamp, S., Winter, C., & Kuypers, M. M. M. (2016). Coupled nitrification–denitrification leads to extensive N loss in subtidal permeable sediments. Limnology and Oceanography , 61 (3), 1033–1048. https://doi.org/10.1002/lno.10271 Mayer, P. M., Pennino, M. J., Newcomer-Johnson, T. A., & Kaushal, S. S. (2022). Long-term assessment of floodplain reconnection as a stream restoration approach for managing nitrogen in ground and surface waters. Urban Ecosystems , 25 (3), 879–907. https://doi.org/10.1007/s11252-021-01199-z McCarthy, M. J., Gardner, W. S., Lavrentyev, P. J., Moats, K. M., Jochem, F. J., & Klarer, D. M. (2007). Effects of Hydrological Flow Regime on Sediment-water Interface and Water Column Nitrogen Dynamics in a Great Lakes Coastal Wetland (Old Woman Creek, Lake Erie). Journal of Great Lakes Research , 33 (1), 219–231. https://doi.org/10.3394/0380-1330(2007)33[219:EOHFRO]2.0.CO;2 Meng, Y., He, Z., Liu, B., Chen, L., Lin, P., & Luo, W. (2020). Soil Salinity and Moisture Control the Processes of Soil Nitrification and Denitrification in a Riparian Wetlands in an Extremely Arid Regions in Northwestern China. Water , 12 (10), 2815. https://doi.org/10.3390/w12102815 Middleton, B. A. (1999). Wetland restoration, flood pulsing, and disturbance dynamics. (1st ed.). Wiley, New York, NY. Middleton, B. A. (2020). Trends of litter decomposition and soil organic matter stocks across forested swamp environments of the southeastern US. PLOS ONE , 15 (1), e0226998. https://doi.org/10.1371/journal.pone.0226998 Mishra, R. K., & Tripathi, A. K. (2023). The Effect of Eutrophication on Drinking Water. Journal of Earth Science & Climatic Change , 14 (1), 659. Mitsch, W. J., Bernal, B., & Hernandez, M. E. (2015). Ecosystem services of wetlands. International Journal of Biodiversity Science, Ecosystem Services & Management , 11 (1), 1–4. https://doi.org/10.1080/21513732.2015.1006250 Mitsch, W. J., Day, J. W., Gilliam, J. W., Groffman, P. M., Hey, D. L., Randall, G. W., & Wang, N. (2001). Reducing nitrogen loading to the Gulf of Mexico from the Mississippi River Basin: Strategies to counter a persistent ecological problem. BioScience , 51 (5), 373–388. https://doi.org/https://doi.org/10.1641/0006-3568(2001)051[0373:RNLTTG]2.0.CO;2 Mitsch, W. J., & Wilson, R. F. (1996). Improving the Success of Wetland Creation and Restoration with Know-How, Time, and Self‐Design. Ecological Applications , 6 (1), 77–83. https://doi.org/10.2307/2269554 Moore, T. R., Trofymow, J. A., Prescott, C. E., & Titus, B. D. (2017). Can short-term litter-bag measurements predict long-term decomposition in northern forests? Plant and Soil , 416 (1–2), 419–426. https://doi.org/10.1007/s11104-017-3228-7 Mulholland, P. J., Hall, R. O., Sobota, D. J., Dodds, W. K., Findlay, S. E. G., Grimm, N. B., Hamilton, S. K., McDowell, W. H., O’Brien, J. M., Tank, J. L., Ashkenas, L. R., Cooper, L. W., Dahm, C. N., Gregory, S. V., Johnson, S. L., Meyer, J. L., Peterson, B. J., Poole, G. C., Valett, H. M., … Thomasn, S. M. (2009). Nitrate removal in stream ecosystems measured by 15N addition experiments: Denitrification. Limnology and Oceanography , 54 (3), 666–680. https://doi.org/10.4319/lo.2009.54.3.0666 Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R 2 from generalized linear mixed-effects models. Methods in Ecology and Evolution , 4 (2), 133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x Newcomer Johnson, T. A., Kaushal, S. S., Mayer, P. M., & Grese, M. M. (2014). Effects of stormwater management and stream restoration on watershed nitrogen retention. Biogeochemistry , 121 (1), 81–106. https://doi.org/10.1007/s10533-014-9999-5 Nielsen, L. P. (1992). Denitrification in sediment determined from nitrogen isotope pairing. FEMS Microbiology Letters , 86 (4), 357–362. https://doi.org/10.1111/j.1574-6968.1992.tb04828.x Nifong, R. L., Taylor, J. M., & Moore, M. T. (2019). Mulch-Derived Organic Carbon Stimulates High Denitrification Fluxes from Agricultural Ditch Sediments. Journal of Environmental Quality , 48 (2), 476–484. https://doi.org/10.2134/jeq2018.09.0341 O’Neill, R. M., Duff, A. M., Brennan, F. P., Gebremichael, A. W., Girkin, N. T., Lanigan, G. J., Krol, D. J., Wall, D. P., Renou-Wilson, F., Müller, C., Richards, K. G., & Deveautour, C. (2022). Linking long-term soil phosphorus management to microbial communities involved in nitrogen reactions. Biology and Fertility of Soils , 58 (4), 389–402. https://doi.org/10.1007/s00374-022-01627-y Osborne, L. L., & Wiley, M. J. (1988). Empirical relationships between land use/cover and stream water quality in an agricultural watershed. Journal of Environmental Management. , 26 (1), 9–27. Osland, M. J., González, E., & Richardson, C. J. (2011). Restoring diversity after cattail expansion: disturbance, resilience, and seasonality in a tropical dry wetland. Ecological Applications , 21 (3), 715–728. https://doi.org/10.1890/09-0981.1 Palay, I. (2021). Three ways wetlands can influence climate change . International Water Management Institute. https://www.iwmi.cgiar.org/2021/02/three-ways-wetlands-can-influence-climate-change/ Peralta, A. L., Matthews, J. W., & Kent, A. D. (2010). Microbial Community Structure and Denitrification in a Wetland Mitigation Bank. Applied and Environmental Microbiology , 76 (13), 4207–4215. https://doi.org/10.1128/AEM.02977-09 Pinheiro, J., Bates, D., & R Core Team. (2022). nlme: Linear and Nonlinear Mixed Effects Models . Poe, A. C., Piehler, M. F., Thompson, S. P., & Paerl, H. W. (2003). Denitrification in a constructed wetland receiving agricultural runoff. Wetlands , 23 , 817–826. https://doi.org/https://doi.org/10.1672/0277-5212 (2003)023[0817:DIACWR]2.0.CO;2 Prach, K., & del Moral, R. (2015). Passive restoration is often quite effective: response to Zahawi et al. (2014). Restoration Ecology , 23 (4), 344–346. https://doi.org/10.1111/rec.12224 R Core Team. (2024). R: A language and environment for statistical computing (4.4.1). R Foundation for Statistical Computing. Rabalais, N. N., Turner, R. E., & Wiseman, W. J. (2002). Gulf of Mexico Hypoxia, A.K.A. “The Dead Zone.” Annual Review of Ecology and Systematics , 33 (1), 235–263. https://doi.org/10.1146/annurev.ecolsys.33.010802.150513 Risgaard-Petersen, N. (2003). Coupled nitrification-denitrification in autotrophic and heterotrophic estuarine sediments: On the influence of benthic microalgae. Limnology and Oceanography , 48 (1), 93–105. https://doi.org/10.4319/lo.2003.48.1.0093 Schielzeth, H. (2010). Simple means to improve the interpretability of regression coefficients. Methods in Ecology and Evolution , 1 (2), 103–113. https://doi.org/10.1111/j.2041-210X.2010.00012.x Seitzinger, S. P. (1988). Denitrification in freshwater and coastal marine ecosystems: Ecological and geochemical significance. Limnology and Oceanography , 33 (4part2), 702–724. https://doi.org/10.4319/lo.1988.33.4part2.0702 Seo, D. C., & DeLaune, R. D. (2010). Fungal and bacterial mediated denitrification in wetlands: Influence of sediment redox condition. Water Research , 44 (8), 2441–2450. https://doi.org/10.1016/j.watres.2010.01.006 Shrestha, S., Farrelly, J., Eggleton, M., & Chen, Y. (2017). Effects of conservation wetlands on stream habitat, water quality and fish communities in agricultural watersheds of the lower Mississippi River Basin. Ecological Engineering , 107 , 99–109. https://doi.org/10.1016/j.ecoleng.2017.06.054 Speir, S. L., Tank, J. L., Taylor, J. M., & Grose, A. L. (2023). Temperature and carbon availability interact to enhance nitrous oxide production via denitrification in alluvial plain river sediments. Biogeochemistry , 165 (2), 191–203. https://doi.org/10.1007/s10533-023-01074-3 Speir, S. L., Taylor, J. M., & Scott, J. T. (2017). Seasonal Differences in Relationships between Nitrate Concentration and Denitrification Rates in Ditch Sediments Vegetated with Rice Cutgrass. Journal of Environmental Quality , 46 (6), 1500–1509. https://doi.org/10.2134/jeq2016.11.0450 Spieles, D. J., & Mitsch, W. J. (2000). The effects of season and hydrologic and chemical loading on nitrate retention in constructed wetlands: a comparison of low- and high-nutrient riverine systems. Ecological Engineering , 14 (1–2), 77–91. https://doi.org/10.1016/S0925-8574(99)00021-X Stanley, E. H., & Boulton, A. J. (1995). Hyporheic processes during flooding and drying in a Sonoran Desert stream. I. Hydrologic and chemical dynamics. Archiv Fur Hydrobiologie , 134 (1), 1–26. https://doi.org/10.1127/archiv-hydrobiol/134/1995/1 Stroh, P. A., Hughes, F. M. R., Sparks, T. H., & Mountford, J. O. (2012). The Influence of Time on the Soil Seed Bank and Vegetation across a Landscape-Scale Wetland Restoration Project. Restoration Ecology , 20 (1), 103–112. https://doi.org/10.1111/j.1526-100X.2010.00740.x Sutton-Grier, A. E., Kenney, M. A., & Richardson, C. J. (2010). Examining the relationship between ecosystem structure and function using structural equation modelling: A case study examining denitrification potential in restored wetland soils. Ecological Modelling , 221 (5), 761–768. https://doi.org/10.1016/j.ecolmodel.2009.11.015 Tennessee Wildlife Federation. (2025). Rebuilding Habitats One Tree at a Time [Video recording]. https://www.youtube.com/watch?v=zxG0nyjmZ_g&t=63s The Nature Conservancy (TNC). (2020). Restoring Western Kentucky Wetlands and Floodplains . https://www.nature.org/en-us/about-us/where-we-work/united-states/kentucky/stories-in-kentucky/western-kentucky-floodplains-wetlands/ Tomaszek, J. A., Gardner, W. S., & Johengen, T. H. (1997). Denitrification in Sediments of a Lake Erie Coastal Wetland (Old Woman Creek, Huron, Ohio, USA). Journal of Great Lakes Research , 23 (4), 403–415. https://doi.org/10.1016/S0380-1330(97)70922-5 USDA NRCS. (2003). Wetland restoration, enhancement, and management . Wetland Science Institute. https://www.scribd.com/document/50133961/USDA-Wetland-Restoration-Enhancement-and-Management USDA NRCS. (2023). Conservation Practice Standard Overview: Tree/Shrub Establishment (Code 612) . https://www.nrcs.usda.gov/sites/default/files/2023-08/612_NHCP_CPS_Tree-Shrub_Establishment_2023.pdf USDA NRCS. (2025). Easement Program Restoration Data . U.S. Department of Agriculture Natural Resources Conservation Service. https://www.farmers.gov/data/easements-restoration Venterink, H. O., Davidsson, T. E., Kiehl, K., & Leonardson, L. (2002). Impact of drying and re-wetting on N, P and K dynamics in a wetland soil. Plant and Soil , 243 (1), 119–130. https://doi.org/10.1023/A:1019993510737 Vymazal, J. (2013). Plants in constructed, restored and created wetlands. Ecological Engineering , 61 , 501–504. https://doi.org/10.1016/j.ecoleng.2013.10.035 Wallage, Z. E., Holden, J., & McDonald, A. T. (2006). Drain blocking: An effective treatment for reducing dissolved organic carbon loss and water discolouration in a drained peatland. Science of The Total Environment , 367 (2–3), 811–821. https://doi.org/10.1016/j.scitotenv.2006.02.010 Wang, H., Yan, Z., Ju, X., Song, X., Zhang, J., Li, S., & Zhu-Barker, X. (2023). Quantifying nitrous oxide production rates from nitrification and denitrification under various moisture conditions in agricultural soils: Laboratory study and literature synthesis. Frontiers in Microbiology , 13 . https://doi.org/10.3389/fmicb.2022.1110151 Wang, J., & Hu, X. (2025). Woody plant reduces soil organic carbon controlled by precipitation. Journal of Environmental Management , 377 , 124581. https://doi.org/10.1016/j.jenvman.2025.124581 Xiong, Z., Guo, L., Zhang, Q., Liu, G., & Liu, W. (2017). Edaphic Conditions Regulate Denitrification Directly and Indirectly by Altering Denitrifier Abundance in Wetlands along the Han River, China. Environmental Science & Technology , 51 (10), 5483–5491. https://doi.org/10.1021/acs.est.6b06521 Xiong, Z., Li, S., Yao, L., Liu, G., Zhang, Q., & Liu, W. (2015). Topography and land use effects on spatial variability of soil denitrification and related soil properties in riparian wetlands. Ecological Engineering , 83 , 437–443. https://doi.org/10.1016/j.ecoleng.2015.04.094 Xue, Y., Kovacic, D. A., David, M. B., Gentry, L. E., Mulvaney, R. L., & Lindau, C. W. (1999). In Situ Measurements of Denitrification in Constructed Wetlands. Journal of Environmental Quality , 28 (1), 263–269. https://doi.org/10.2134/jeq1999.00472425002800010032x Yu, H., Song, Y., Xi, B., Du, E., He, X., & Tu, X. (2012). Denitrification potential and its correlation to physico-chemical and biological characteristics of saline wetland soils in semi-arid regions. Chemosphere , 89 (11), 1339–1346. https://doi.org/10.1016/j.chemosphere.2012.05.088 Zhang, C., Cai, M., Ndungu, C. N., Ma, L., & Liu, W. (2025). Plant diversity promotes soil nitrogen retention and removal processes in wetlands. New Phytologist , 248 (2), 587–599. https://doi.org/10.1111/nph.70491 Zhang, K., Cheng, X., Dang, H., Ye, C., & Zhang, Q. (2012). Soil nitrogen and denitrification potential as affected by land use and stand age following agricultural abandonment in a headwater catchment. Soil Use and Management , 28 (3), 361–369. https://doi.org/10.1111/j.1475-2743.2012.00420.x Zhang, Z., Bortolotti, L. E., Li, Z., Armstrong, L. M., Bell, T. W., & Li, Y. (2021). Heterogeneous Changes to Wetlands in the Canadian Prairies Under Future Climate. Water Resources Research , 57 (7). https://doi.org/10.1029/2020WR028727 Zivec, P., Sheldon, F., & Capon, S. J. (2023). Natural regeneration of wetlands under climate change. Frontiers in Environmental Science , 11 . https://doi.org/10.3389/fenvs.2023.989214 Zonneveld, K. A. F., Versteegh, G. J. M., Kasten, S., Eglinton, T. I., Emeis, K.-C., Huguet, C., Koch, B. P., de Lange, G. J., de Leeuw, J. W., Middelburg, J. J., Mollenhauer, G., Prahl, F. G., Rethemeyer, J., & Wakeham, S. G. (2010). Selective preservation of organic matter in marine environments; processes and impact on the sedimentary record. Biogeosciences , 7 (2), 483–511. https://doi.org/10.5194/bg-7-483-2010 Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R . Springer New York. https://doi.org/10.1007/978-0-387-87458-6 Additional Declarations No competing interests reported. Supplementary Files ESM1.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 May, 2026 Reviews received at journal 07 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 26 Mar, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9237776","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620343197,"identity":"351ce56d-46bb-478a-b259-ca45ffd9dd0a","order_by":0,"name":"Shrijana Duwadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDCCA0DMY8CQwMAMxAwMNkDM2HiAFC1pIC0NRGhhACsHgcNwQZyA70aO4Yc3BXV55u0MDz8X1Jy3W9t+GGhLjU00Li2SN3KMJecYHC6WOcyQLD3j2O3kbWcSgVqOpeU24NBicCN3gzSPwYHEGUC/SPOw3U42OwDUwthwGJ+Wzb95DOpAWpJ/8/w7l2x2/iFBLduAtjCDtKRJ87YdsDO7QcAWyTPvv1kC/QLWYs3bl5xgdgNoSwIev/AdT0u+8eYP0GH8Z5Jv83yzszc7n/7wwYcaG5xakABPAohMBKtMIKwcBNgPgEh74hSPglEwCkbBSAIAEFJl4WcQcLkAAAAASUVORK5CYII=","orcid":"","institution":"Tennessee Technological University","correspondingAuthor":true,"prefix":"","firstName":"Shrijana","middleName":"","lastName":"Duwadi","suffix":""},{"id":620343198,"identity":"bb9aae18-16b6-44f8-8b74-e115bb77eb87","order_by":1,"name":"Robert S. Brown","email":"","orcid":"","institution":"Tennessee Technological University","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"S.","lastName":"Brown","suffix":""},{"id":620343199,"identity":"3c54261a-ef2b-44d7-b556-aacb3650b67c","order_by":2,"name":"Spencer G. Womble","email":"","orcid":"","institution":"Tennessee Technological University","correspondingAuthor":false,"prefix":"","firstName":"Spencer","middleName":"G.","lastName":"Womble","suffix":""},{"id":620343200,"identity":"7c80b288-df53-4e7c-bab2-2b54fb8d4932","order_by":3,"name":"Justin N. Murdock","email":"","orcid":"","institution":"Tennessee Technological University","correspondingAuthor":false,"prefix":"","firstName":"Justin","middleName":"N.","lastName":"Murdock","suffix":""}],"badges":[],"createdAt":"2026-03-26 20:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9237776/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9237776/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106607538,"identity":"1835f42a-c37e-4697-8405-3d16787c1c43","added_by":"auto","created_at":"2026-04-10 11:32:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":744273,"visible":true,"origin":"","legend":"\u003cp\u003eMap of study sites in Kentucky and Tennessee. Wetland Restoration Enhancement Partnership (WREP) easements are represented by black pins\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9237776/v1/49cc8685e094920382e4dcf2.png"},{"id":106607539,"identity":"afdd1cbd-098b-4fd5-b757-6e58130434e9","added_by":"auto","created_at":"2026-04-10 11:32:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132738,"visible":true,"origin":"","legend":"\u003cp\u003eMean N\u003csub\u003e2\u003c/sub\u003e flux rate among the restoration practices at (a) 24 h and (b) 48 h. Restoration practices were natural regeneration, remnant forest, shallow water-dry, shallow water-wet, and tree planting. Error bars represent 95% CI. Means associated with different lowercase letters are significantly different by post hoc analysis using Tukey's HSD\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9237776/v1/faf84751d33f8a4a995fc6e4.png"},{"id":106726382,"identity":"44a1d84e-3be5-41ef-9be3-3f6f676628b6","added_by":"auto","created_at":"2026-04-12 18:35:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":196895,"visible":true,"origin":"","legend":"\u003cp\u003eMean values of (a) O\u003csub\u003e2\u003c/sub\u003e flux at 24 h, and (b) O\u003csub\u003e2\u003c/sub\u003e flux at 48 h, (c) soil moisture, and (d) soil P across restoration practices. Sediment oxygen demand (SOD) is negative O\u003csub\u003e2\u003c/sub\u003e flux. Restoration practices were natural regeneration, remnant forest, shallow water dry, shallow water-wet, and tree planting. Error bars represent 95% CI. Means associated with different lowercase letters are significantly different by post hoc analysis\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9237776/v1/6012bd58f2cddfd42db58f7c.png"},{"id":106727662,"identity":"328846d2-81fd-4ee8-8931-1bc82db4837d","added_by":"auto","created_at":"2026-04-12 18:40:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1715927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9237776/v1/45a53cde-652c-4161-87e1-98156b315296.pdf"},{"id":106607537,"identity":"bc01405d-da0f-4f2e-8f60-93b0f90d6eb2","added_by":"auto","created_at":"2026-04-10 11:32:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":644782,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9237776/v1/9c84229815211c6e3a61a09b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Restoration practices and soil properties influence potential denitrification in agricultural floodplain wetlands","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWetlands provide an outsized ecosystem service relative to their land area, providing critical water filtration services, wildlife habitat, and supports biodiversity (Balmford et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Ghermandi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Palay, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Wetland restoration is therefore promoted as a strategy to mitigate nutrient pollution and improve water quality (Mitsch et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Nitrate (NO₃⁻), largely derived from fertilizers, is a primary pollutant of concern in agricultural waterbodies, as excessive inputs during cropping can cause eutrophication and subsequent hypoxia(Rabalais et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), loss of aquatic and riparian biodiversity (Carpenter et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and drinking water contamination (Mishra \u0026amp; Tripathi, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Eutrophication-related damages in the United States (US) alone are estimated at \u003cspan\u003e$\u003c/span\u003e2.2\u0026nbsp;billion annually (Dodds et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the most important processes for NO₃⁻ removal in wetlands is denitrification. Denitrification is the bioconversion process that transforms NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e into nitrogen gas (N\u003csub\u003e2\u003c/sub\u003e), effectively removing bioavailable nitrogen (N) and releasing it back into the atmosphere (Bernhard, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This process can remove as much as 90% of the N that enters wetlands (Gilliam, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; R. Hunter et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Denitrification is primarily controlled by the presence of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, organic carbon (C), redox potential, and temperature (Arango et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Beauchamp et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Christensen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Mulholland et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), which are in turn influenced by hydrology, land use, and underlying geology (Osborne \u0026amp; Wiley, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Stanley \u0026amp; Boulton, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite their critical ecological functions, wetland loss continues to be a global problem, particularly for conversion to arable land and development. In the US, roughly 50% of natural wetlands have been lost since the 1780s. The US lost approximately 271,000 ha of wetlands between 2009 and 2019, accounting for \u0026gt;\u0026thinsp;50% net loss since the previous Wetlands Trend study (2004\u0026ndash;2009) (Lang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). One approach to wetland restoration is by landowner compensation through conservation easements. The US Department of Agriculture\u0026rsquo;s (USDA) Wetland Reserve Program (WRP), now the Wetland Restoration Enhancement Partnership (WREP), has spent more than \u003cspan\u003e$\u003c/span\u003e4.2\u0026nbsp;billion to restore and protect wetlands since it was established in the 1990 US Farm Bill (Hansen et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As of 2025, WRP has restored nearly 1\u0026nbsp;million ha of wetlands (USDA NRCS, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the information on the overall effectiveness of this program in retaining nutrients from the watershed is limited (Shrestha et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and very few studies have evaluated restored easements within the Mississippi Alluvial Valley (Faulkner et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Effective monitoring programs allow restoration goals to be assessed and guide improvements to future strategies (Block et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Limited monitoring can hinder broader adoption of the most successful restoration strategies in the future as optimal restoration strategies are unknown or underutilized (Galatowitsch \u0026amp; Bohnen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, monitoring restored wetlands for their ecological functions and their relation to soil properties is essential to ensure the long-term success of restoration efforts.\u003c/p\u003e \u003cp\u003eThe co-restoration of both wetland structure and function are common goals during restoration, but often one or the other is a priority. Because many wetland components interact synergistically to create a functioning system (Sutton-Grier et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), successful restoration of wetland function (e.g., denitrification) may depend on soil properties, hydrology, vegetation, and their interactions. This study aimed to better understand how different wetland restoration practices and soil structure influence wetland functional recovery, and specifically N removal through soil denitrification. Previous studies have measured denitrification rates and identified their key drivers in restored floodplain soils (Mayer et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Newcomer Johnson et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Soil structure has been tightly linked to its denitrification function in wetlands (Kaden et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)and soil properties (e.g., redox, C, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, and pH) that influence denitrification in riparian wetlands have been well characterized (Meng et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Interactions between vegetation and hydrology further regulate nutrient cycling rates (R. G. Hunter \u0026amp; Faulkner, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Mitsch et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and denitrification can vary across small spatial scales due to these interactions (Faulkner et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Establishing links between N transformations, restoration practices and measurable soil properties could yield robust indicators of ecosystem functions such as denitrification, thereby enhancing functional assessment methods post-restoration (Kaushal et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peralta et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe WRP/WREP program emphasizes both vegetation and hydrology restoration. In the lower Mississippi River Valley, the primary vegetation endpoint is bottomland hardwood forests, necessitating the planting of several dozen native wetland tree species including species of cypress, oak, hickory, and many others. To restore natural hydrological conditions, wetland managers have implemented practices like blocking drainage channels across many wetlands (Armstrong et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Howie et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wallage et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Understanding the interactions among vegetation types, hydrology, and soil properties is crucial for optimizing nutrient retention recovery in restored wetlands and reducing downstream nutrient exports. The specific objectives of this study were to: i) evaluate variation in N\u003csub\u003e2\u003c/sub\u003e production among restoration practices in riparian wetlands, ii) assess variability in soil properties among the restoration practices, and iii) determine the relationship between soil properties and restoration practices, and N\u003csub\u003e2\u003c/sub\u003e production. By elucidating how restoration efforts and soil properties influence N\u003csub\u003e2\u003c/sub\u003e production, our findings can inform future restoration strategies and their effective implementation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eEasement selection and restoration practices\u003c/p\u003e \u003cp\u003eTwenty-three sites were selected from USDA Natural Resources Conservation Service (NRCS) WRP/WREP wetlands easements in western Tennessee and western Kentucky. Easements were located along direct tributaries of the Mississippi River, including Mayfield Creek, Obion Creek, and Bayou de Chien in Kentucky, and the Obion, Forked Deer, and Hatchie rivers in Tennessee (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The restored wetlands ranged in age from 3\u0026ndash;23 years since restoration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAreas selected for soil core collection within easements were representative of the dominant NRCS restoration practices. Restoration practices were identified via NRCS restoration maps, recent satellite images, and field visits during core collection. The most common restoration practices included natural regeneration areas, constructed shallow water areas, and tree planting areas. Remnant forests were sampled to approximate a successional endpoint representative of bottomland hardwood forests (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNatural regeneration areas revegetated through natural plant succession (USDA NRCS, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and are mostly shaped by various biotic and abiotic factors, often producing climate-adapted heterogeneous plant communities that enhance patch diversity and ecosystem resilience (Prach \u0026amp; del Moral, 2015; Zivec et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the time of soil core collection, these areas were dominated by a mixture of grasses and woody shrubs. Shallow water areas were created to restore site hydrology, often by plugging ditches previously installed for drainage and managing water levels with control structures (The Nature Conservancy (TNC), \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These areas are dominated by emergent vegetation such as grasses and sedges that can be maintained indefinitely without succession to shrubs or forest communities under stable water conditions (Cole et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Soil cores collected from shallow water areas were classified as dry or wet based on visible water presence above the soil surface during sampling (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTree planting included areas where trees were planted to aid forest regeneration (USDA NRCS, 2016). Native, mast-producing species like bald cypress (\u003cem\u003eTaxodium distichum\u003c/em\u003e (L.) Rich), swamp white oak (\u003cem\u003eQuercus bicolor\u003c/em\u003e Willd.), river birch (\u003cem\u003eBetula nigra\u003c/em\u003e L.), black gum (\u003cem\u003eNyssa sylvatica\u003c/em\u003e Marshall.), and many others were planted at approximately 435 seedlings per acre, with selection based on wetland soil type and flooding frequency (Tennessee Wildlife Federation, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; TNC, 2020). Remnant forests were areas with native tree species that have not been in recent agriculture, as shown by aerial images from the 1980s and 1990s. The age of remnant forests could not be determined, due to the absence of historical aerial images, however all remnant forests were observed on USDA images from the 1980\u0026rsquo;s showing there have been mature trees for at least 40 years. All easements were historical wetlands that had been converted to row crop agricultural production and were in production until just prior to restoration actions. Not all restoration practices were present at every easement.\u003c/p\u003e \u003cp\u003eSoil core collection\u003c/p\u003e \u003cp\u003ePaired soil core incubations facilitate correlation of denitrification rates at the soil-water interface with wetland soil physicochemical properties (Brown et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Applying soil core incubations across many wetland restoration practices facilitates identification of attributes potentially regulating denitrification across a broad area of interest. Soil cores were collected from May through August between 2020 and 2022. Thirty paired cores (60 total), one for function (potential denitrification rates) and one for structure (soil physicochemical properties) were collected from each easement. Each pair of cores were collected within a 30 cm\u003csup\u003e2\u003c/sup\u003e area. An equal number of paired cores were collected from each restoration practice whenever feasible, with efforts made for even distribution within each restoration practice to address spatial variation. However, in the shallow water areas, collection was limited to the edge due to access limitations and safety concerns of deeper water.\u003c/p\u003e \u003cp\u003eFollowing methods from Brown et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), approximately 15-cm deep soil/sediment cores were collected using acrylic tubes (7.62 cm diameter \u0026times; 30 cm height) housed inside a metal coring device or manually pushed into the sediments (Fig. S2). Cores from the shallow water-wet areas were filled with water on-site to minimize sediment surface disturbance during transportation. Cores were sealed with rubber bottoms secured with pipe straps and plastic tops, then placed upright in coolers with ice to inhibit microbial activity. Upon returning to the lab at Tennessee Tech, water from shallow water cores was siphoned out carefully. Soil function cores were placed in the environmental chamber at 24\u003csup\u003eo\u003c/sup\u003eC and acclimated overnight to simulate the average summer regional air temperature. Incubations started the following morning. Soil structure cores were transferred to a walk-in-cooler at 4\u003csup\u003eo\u003c/sup\u003eC and processed the following day.\u003c/p\u003e \u003cp\u003eIncubation water preparation and core incubation\u003c/p\u003e \u003cp\u003eLaboratory-made incubation water was prepared following the methods outlined by Brown et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and was based on the historical average water quality data reported by the United States Geological Survey (USGS) for Bayou de Chien, Kentucky (1970\u0026ndash;2007; USGS gauge # 07024000) and the Obion River, Tennessee (1990\u0026ndash;2005; USGS gauge # 07026040). These two rivers periodically feed the study WRP easements during floods. However, the concentration of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e -N and phosphate (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e -P) was increased to 10 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, to saturate nutrient uptake rates and provide consistent nutrient availability across easements. Therefore, the rates derived from incubations represent potential denitrification rates as opposed to ambient rates.\u003c/p\u003e \u003cp\u003eThe incubation took place in a dark walk-in environmental chamber maintained at 24\u0026deg;C to simulate the average summer regional air temperature. Incubation began at 8 am the day after collection, using a continuous flow-through system to simulate a 48 h flood. Plastic tops on the cores were replaced with acrylic lids equipped with inflow and outflow ports (i.d. 1 mm and 1.25 mm, respectively) and secured with pipe straps (Fig. S3). Lab water was delivered to individual cores at approximately 1.8 mL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e through an inflow tubing connected to a Masterflex L/S peristaltic pump. Water flowed out of the cores through outflow tubings into sample containers, with outflow rates measured for each core. Outflowing water was not recycled. Water residence time in a core was approximately six hours.\u003c/p\u003e \u003cp\u003eDissolved gas sampling and analysis\u003c/p\u003e \u003cp\u003eWater samples were collected from outflow tubes in triplicate 12 mL exetainers at 24 and 48 h of incubation. Vials were allowed to overflow three times before sample collection. All samples were then treated immediately with 180 \u0026micro;L zinc chloride (ZnCl\u003csub\u003e2\u003c/sub\u003e) to inhibit microbial activities. After quick capping and agitation for uniform ZnCl\u003csub\u003e2\u003c/sub\u003e distribution, samples were stored underwater at 4\u0026deg;C and analyzed within one month.\u003c/p\u003e \u003cp\u003eDissolved gas concentrations in water samples were determined using a Membrane Inlet Mass Spectrometer (MIMS) (Kana et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). The instrument measured dissolved N\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e, and Argon (Ar) concentrations in the water using the MIMS Faraday detector in 2020 and 2021 and MIMS Secondary Electron Multiplier (SEM) in 2022. Triplicate standards were measured after every six samples to calculate a calibration factor and correct for the drift in MIMS signal over time. Sample N\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e concentrations were calculated using the Ar ratio method in R (\u003cem\u003emimsy\u003c/em\u003e package) (Kelly, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe output gas concentrations from MIMS were expressed as mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of N\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e (Kana et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The areal N\u003csub\u003e2\u003c/sub\u003e flux for each core was calculated according to Speir et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Areal\\:Flux\\:\\left(mg\\:{m}^{-2}\\:{h}^{-1}\\right)=\\left(\\frac{\\left[\\right(Core{)}_{out}-\\left(Core{)}_{in}\\right]*{Q\\:}_{core}}{A\\:\\left({m}^{2}\\right)}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere,\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:(Core{)}_{out\\:}\\)\u003c/span\u003e \u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(Core{)}_{in\\:}\\)\u003c/span\u003e\u003c/span\u003e= outflow and inflow concentrations (mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of N\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e in incubation core,\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Q\\:}_{core}\\)\u003c/span\u003e \u003c/span\u003e = flow rate of incubation core (L h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and\u003c/p\u003e \u003cp\u003eA\u0026thinsp;=\u0026thinsp;surface area of soil in a core (m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003ePositive flux indicates a net gain (release) of N\u003csub\u003e2\u003c/sub\u003e or O\u003csub\u003e2\u003c/sub\u003e in the water column and negative flux indicates a net loss (removal) of N\u003csub\u003e2\u003c/sub\u003e or O\u003csub\u003e2\u003c/sub\u003e from the water column. More negative O\u003csub\u003e2\u003c/sub\u003e flux rates correspond to higher SOD, and a more positive N\u003csub\u003e2\u003c/sub\u003e flux corresponds to higher denitrification rates.\u003c/p\u003e \u003cp\u003eSoil structure core processing and analysis\u003c/p\u003e \u003cp\u003eSoil structure cores were processed to determine soil properties. After transferring the core to a clean aluminum sheet, detritus and vegetation were removed. Cores were then divided at 10 cm depth (0\u0026ndash;10 cm) using a spackle knife and soil below 10 cm depth was discarded. After removing root pieces and gravel, each soil section was homogenized manually to get a uniform mixture through repeated mixing. The homogenization process involved breaking down the soil core by gloved hands as much as possible, followed by using a spackle knife to break large clumps, and through mixing using a spackle knife and hands alternately. Spreading, breaking, and mixing were done until the soil texture was consistent throughout the sample. Once the soil was homogenized, subsamples were taken to measure soil moisture, bulk density, pH, total carbon (TC), total N (TN), and extractable phosphorus (soil P). Details of soil properties analyses are described in Online Resource 1. Soil properties data for each structure core was presumed to be representative of the corresponding incubation core.\u003c/p\u003e \u003cp\u003eData processing\u003c/p\u003e \u003cp\u003eFlux rates and soil properties of each core specific to each restoration practice and sampling timepoint were averaged, resulting in a singular representative value for each restoration practice within each easement. This approach prevented pseudo-replication. Before averaging, individual core-level N\u003csub\u003e2\u003c/sub\u003e flux rates that deviated beyond the 2.5% and 97.5% quantiles at each sampling timepoint were excluded to minimize the influence of extreme values during statistical analysis. This ensures that the analysis reflects the typical system behavior as extreme values can distort the results and lead to misleading conclusions. Although these extreme values likely represent real biogeochemical hotspots, hotspot analysis is beyond the scope of this study. Corresponding soil properties data for these excluded cores were also removed. Cores with missing data were similarly excluded from the averaging process. After extreme values removal and subsequent averaging, the resulting sampling sizes for restoration practices were: n\u0026thinsp;=\u0026thinsp;7 for natural regeneration, n\u0026thinsp;=\u0026thinsp;16 for remnant forest, n\u0026thinsp;=\u0026thinsp;15 (at 24 h) and 14 (at 48h) for shallow water-dry, n\u0026thinsp;=\u0026thinsp;18 for shallow water-wet, and n\u0026thinsp;=\u0026thinsp;17 for tree planting (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using RStudio version 4.4.1 (R Core Team, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). All results are shown as arithmetic means\u0026thinsp;\u0026plusmn;\u0026thinsp;95% confidence interval (CI). Significance of all tests was accepted at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Nitrogen gas flux rates were compared across restoration practices using Analysis of Variance (ANOVA). Residuals from each ANOVA were first tested for normality using the Shapiro\u0026ndash;Wilk test and then for variance homogeneity using Bartlett\u0026rsquo;s test. When either assumption was violated, Kruskal\u0026ndash;Wallis test was used. Post-hoc tests included Tukey's Honestly Significant Difference (HSD) for ANOVA and Dunn's test for the Kruskal\u0026ndash;Wallis test.\u003c/p\u003e \u003cp\u003eThe relationships between N\u003csub\u003e2\u003c/sub\u003e flux rates and explanatory variables were analyzed using linear mixed effects models (R package \u003cem\u003enlme\u003c/em\u003e) (Pinheiro et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), conducted separately at 24 h and 48 h timepoints. Restoration practice was the primary predictor, while soil properties including, SOD, soil moisture, bulk density, pH, soil TC, soil TN, and soil P were used as covariates. Soil TC and soil TN were log-transformed prior to analysis to address heteroscedasticity. Interaction components included restoration practice: soil moisture and restoration practice: logTC and were chosen utilizing ecological insights and thorough examination of the data. The varIdent variance structure available in the \u0026ldquo;nlme\u0026rdquo; package was assigned to the \u0026ldquo;restoration practice\u0026rdquo; to account for unequal variance across groups (Zuur et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). \u0026ldquo;Easement\u0026rdquo; (i.e., \u0026ldquo;site\u0026rdquo;) was used as a random effect. For the reliable estimation of random effects, easements with a single observation (n\u0026thinsp;=\u0026thinsp;2) were excluded from the dataset. Prior to analysis, continuous predictor variables were standardized (scaled to mean\u0026thinsp;=\u0026thinsp;0 and standard deviation\u0026thinsp;=\u0026thinsp;1) to facilitate direct comparison of their relative effects (Schielzeth, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Maximum likelihood (ML) was used to compare nested models during backward selection of fixed effects with selection based on log-likelihood ratio tests. Final models were then refitted using restricted maximum likelihood (REML) estimations. The conditional and marginal R\u003csup\u003e2\u003c/sup\u003e were computed using the \u003cem\u003er2\u003c/em\u003e function in the \u003cem\u003eperformance\u003c/em\u003e package (Nakagawa \u0026amp; Schielzeth, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). An ANOVA with type III sums of squares was applied to the final model.\u003c/p\u003e \u003cp\u003eSoil properties were compared across restoration practices using the same statistical approach applied to N\u003csub\u003e2\u003c/sub\u003e flux rates.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eNitrogen gas (N\u003csub\u003e2\u003c/sub\u003e) flux over time\u003c/p\u003e \u003cp\u003eNitrogen gas was produced from all restoration practices at both 24 h and 48 h, with production differing significantly among practices at both timepoints (24 h: ANOVA F \u003csub\u003e(4, 68)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;9.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 48 h: F \u003csub\u003e(4, 67)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Pairwise comparisons at 24 h showed that the natural regeneration area (6.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) had the highest mean N\u003csub\u003e2\u003c/sub\u003e production, followed by the tree planting (5.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), shallow water-dry (5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and remnant forest (4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) areas. The lowest N\u003csub\u003e2\u003c/sub\u003e production occurred in the shallow water-wet area (2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eAt 48 h, the mean N\u003csub\u003e2\u003c/sub\u003e production was highest in the natural regeneration area (8.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), followed by the shallow water-dry (5.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and tree planting (5.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69 g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) areas. The lowest N\u003csub\u003e2\u003c/sub\u003e production rates were recorded in the remnant forest and shallow water-wet areas, at 4.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88 and 4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eNitrogen gas production increased between 24 and 48 h of incubation in all restoration practices. Shallow water-wet area showed the highest percentage increase in mean N\u003csub\u003e2\u003c/sub\u003e production by 48%. In natural regeneration, remnant forest, shallow water-dry, and tree planting areas, the production rates increased by 21%, 10%, 13%, and 3%, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFactors influencing N\u003csub\u003e2\u003c/sub\u003e flux at 24 h\u003c/p\u003e \u003cp\u003eAt 24 h, the estimated mean N\u003csub\u003e2\u003c/sub\u003e production was significantly correlated with SOD (Chi-squared (χ\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;31.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and soil moisture (χ\u0026sup2; = 17.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On average, each standard deviation increase in SOD and soil moisture was associated with a 1.05 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e increase and a 0.66 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e decrease in N\u003csub\u003e2\u003c/sub\u003e production, respectively. The final model explained 91.0% of the total variation in N\u003csub\u003e2\u003c/sub\u003e flux (conditional R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.910). Fixed effects (restoration practice, SOD, and soil moisture) accounted for 68.2% of the variation (marginal R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.682), while site-to-site differences among easements contributed to 22.8%, confirming the importance of accounting for spatial heterogeneity in the model structure (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eFixed effects from the linear mixed-effects model examining N\u003csub\u003e2\u003c/sub\u003e flux rates in relation to restoration practice, sediment oxygen demand (SOD), and soil moisture at 24 h. SOD and soil moisture values were standardized prior to analysis. The intercept represents the baseline flux for the reference restoration practice (natural regeneration), while values for all other practices represent differences relative to this reference\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e(Conditional: 0.910, Marginal: 0.682)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShallow water-dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShallow water-wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree-planting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil moisture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eFactors influencing N\u003csub\u003e2\u003c/sub\u003e flux at 48 h\u003c/p\u003e \u003cp\u003eThe estimated N\u003csub\u003e2\u003c/sub\u003e production at 48 h was correlated with SOD (χ\u003csup\u003e2\u003c/sup\u003e \u003csub\u003e=\u003c/sub\u003e 6.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and soil P (χ\u003csup\u003e2\u003c/sup\u003e \u003csub\u003e=\u003c/sub\u003e 8.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For every standard deviation increase in SOD and soil P, N\u003csub\u003e2\u003c/sub\u003e production increased by 0.49 and 0.58 mg m⁻\u003csup\u003e2\u003c/sup\u003e h⁻\u003csup\u003e1\u003c/sup\u003e, respectively. The final model explained 71.0% of the total variation in N\u003csub\u003e2\u003c/sub\u003e flux (conditional R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.710), with fixed effects (restoration practice, SOD, and soil P) accounting for 41.5% of the variation (marginal R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.415). The remaining 29.5% was attributed to site-to-site differences among easements, indicating substantial spatial heterogeneity in N\u003csub\u003e2\u003c/sub\u003e flux patterns (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eFixed effects from the linear mixed-effects model examining N\u003csub\u003e2\u003c/sub\u003e flux rates in relation to restoration practice, sediment oxygen demand (SOD), and extractable phosphorus (soil P) at 48 h. SOD and soil P values were standardized prior to analysis. The intercept represents the baseline flux for the reference restoration practice (natural regeneration), while values for all other practices represent differences relative to this reference\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e(Conditional: 0.710, Marginal: 0.415)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShallow water-dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShallow water-wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree-planting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\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\u003eSoil properties among restoration practices\u003c/p\u003e \u003cp\u003eMost soil properties varied considerably both within and among restoration practices (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The values of SOD ranged from \u0026minus;\u0026thinsp;89.14 to -21.70 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 24 h and from \u0026minus;\u0026thinsp;125.61 to -72.02 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 48 h. Soil moisture ranged from 0.11\u0026ndash;1.17 g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while soil P from 0.01\u0026ndash;0.09 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Significant differences across restoration practices were observed for SOD (24h: Kruskal-Wallis χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e(4)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;21.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 48 h: F\u003csub\u003e(4)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) and soil moisture (χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e(4)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;26.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas soil P did not differ significantly (F\u003csub\u003e(4)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.081). Natural regeneration had the highest mean SOD and remnant forest had the lowest at both 24 h and 48 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea \u0026amp; b). The highest mean soil moisture was observed in the shallow water-wet areas and lowest in the tree planting areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Although natural regeneration areas had the highest soil P, it showed the least variation across restoration practices (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eSoil TC differed significantly across restoration practices (χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e(4)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;14.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), but it did not correlate with N\u003csub\u003e2\u003c/sub\u003e flux rates. No significant differences were observed for bulk density (F\u003csub\u003e(4)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.418) or soil TN (χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e(4)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.071). While soil pH showed an overall significant difference (F\u003csub\u003e(4)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039), post hoc tests revealed no pairwise differences among restoration practices (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study assessed whether N\u003csub\u003e2\u003c/sub\u003e production rates and soil properties differ among various restoration practices in riparian wetlands and to establish the connection between N\u003csub\u003e2\u003c/sub\u003e production and soil properties. Our results indicated that the restored riparian wetlands showed high spatial variation in N\u003csub\u003e2\u003c/sub\u003e production and most of the measured soil properties both within and among restoration practices as seen in previous studies (Bruland et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, only SOD, soil moisture, and soil P had a measurable potential influence on variation in N\u003csub\u003e2\u003c/sub\u003e flux among restoration practices.\u003c/p\u003e \u003cp\u003eNitrogen gas (N\u003csub\u003e2\u003c/sub\u003e) production among restoration practices\u003c/p\u003e \u003cp\u003eAt both 24 and 48 h, N\u003csub\u003e2\u003c/sub\u003e gas production varied among restoration practices. Surprisingly, N\u003csub\u003e2\u003c/sub\u003e production from the shallow water-wet areas was the lowest at both times, despite having the highest initial soil moisture. Previous studies have shown that higher soil moisture typically enhances denitrification, likely due to increased denitrification enzyme activity in wetter environments compared to drier ones (Ballantine et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Burgin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, our study found that N\u003csub\u003e2\u003c/sub\u003e production was highest in the natural regeneration areas at both sampling times (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), even though it had intermediate soil moisture content. In natural regeneration areas, vegetation was likely more heterogeneous, as nature selects which species will regenerate naturally (Z. Zhang et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Together with hydrology and soils, plants are one of the \u0026ldquo;big three\u0026rdquo; wetland parameters (Cole \u0026amp; Kentula, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Organic root exudates from plants are known to fuel denitrification (Vymazal, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). On average, plants can increase denitrification rates by 55% (Alldred \u0026amp; Baines, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Herbaceous vegetation has been shown to put more organic matter into shallow soils relative to woody vegetation due to both less primary production reaching the ground in woody plants, and differences in soil C decomposition rates (Eclesia et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; J. Wang \u0026amp; Hu, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, heterogeneity in plant species in the natural regeneration areas likely produces a diverse suite of root exudates, supporting functional gene abundance and, consequently, higher total N removal and N\u003csub\u003e2\u003c/sub\u003e production (Brisson et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; C. Zhang et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eBy 48 h, N\u003csub\u003e2\u003c/sub\u003e production was the second highest in shallow water-dry areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), likely due to a NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e pulse effect. These cores were collected from the dry edges of the shallow water-wet areas, an area frequently exposed to alternating drying and then re-wetting during the flooding season. At the time of core collection, no standing water was present in the shallow water-dry areas. The preceding dry phase may have promoted mineralization and nitrification, leading to NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e accumulation\u0026mdash;a \"nitrate bank\" (Venterink et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Upon re-wetting during flow-through incubation in the lab, this stored NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e likely became readily available to denitrifiers, fueling enhanced N\u003csub\u003e2\u003c/sub\u003e production. In short, drying followed by rewetting likely promoted coupled nitrification\u0026ndash;denitrification in the shallow-water dry cores, where NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e produced under oxic conditions is subsequently reduced via denitrification under anoxic conditions upon flow-through incubation in the lab, thereby increasing N\u003csub\u003e2\u003c/sub\u003e production (Marchant et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nielsen, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Risgaard-Petersen, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBetween the 24 h and 48 h sampling intervals, we observed an increase in N\u003csub\u003e2\u003c/sub\u003e production, with values ranging from only 3% in the tree planting areas to 48% in the shallow water-wet areas. This temporal stability in tree planting areas suggests that denitrification there reached near saturation point early in the incubation, preventing further denitrification enhancement.\u003c/p\u003e \u003cp\u003eFor other restoration practices, the finding emphasizes the importance of longer water residence time on N\u003csub\u003e2\u003c/sub\u003e production in restored wetlands. Water residence time is one of the key determinants of wetland denitrification efficiency (Poe et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Tomaszek et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Extended residence times enhance sediment-water interactions, facilitating longer contact time between NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-rich water and denitrifying sediments (Spieles \u0026amp; Mitsch, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Xue et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). It has also been documented that longer water residence time increases denitrifier community diversity (Kjellin et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), further emphasizing the importance of this parameter in wetland restoration.\u003c/p\u003e \u003cp\u003eRelationship between soil properties and N\u003csub\u003e2\u003c/sub\u003e production\u003c/p\u003e\n\u003ch3\u003eSediment oxygen demand (SOD)\u003c/h3\u003e\n\u003cp\u003eThe significant correlation between SOD and N\u003csub\u003e2\u003c/sub\u003e production suggests that redox potential, which is a measure of the oxidation/reduction status of sediments, plays a crucial role in regulating N\u003csub\u003e2\u003c/sub\u003e fluxes. Redox potential is widely recognized as one of the primary controls on denitrification in wetlands (Seo \u0026amp; DeLaune, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and SOD has been strongly correlated to denitrification rates in other aquatic sediments (McCarthy et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Seitzinger, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). In the restored wetlands we studied, elevated SOD likely accelerated the creation of an anoxic, low redox environment that is ideal for denitrification (Cornwell et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Similar patterns were reported by de Klein et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who found that reduced O\u003csub\u003e2\u003c/sub\u003e availability in surface sediments enhanced potential denitrification. Therefore, SOD may serve as a reliable predictor of the N\u003csub\u003e2\u003c/sub\u003e production potential in restored wetlands, as reflected in the observed correlation during the 48 h incubation period.\u003c/p\u003e\n\u003ch3\u003eSoil moisture\u003c/h3\u003e\n\u003cp\u003eInitial soil moisture was correlated to N\u003csub\u003e2\u003c/sub\u003e production rates during the first 24 h of flooding, but this relationship was no longer present at 48 h. Interestingly, unlike previous studies that reported increased denitrification rates with higher soil moisture in riparian wetlands (Burgin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), our study found a negative relationship between soil moisture at the time of collection and in N\u003csub\u003e2\u003c/sub\u003e production at 24 h. This contrasting finding likely reflects our experimental setup, where all soil cores were fully flooded during laboratory incubation, creating conditions distinct from natural field moisture gradients. Thus, our study looked at soil denitrification during flooding, and not between floods. Soil cores with higher initial moisture were likely already approaching O\u003csub\u003e2\u003c/sub\u003e-depleted states, which may have limited further O\u003csub\u003e2\u003c/sub\u003e consumption and the associated stimulation of anaerobic microbial activity when flooded.\u003c/p\u003e \u003cp\u003eFurthermore, higher initial soil moisture may have promoted prior leaching of important substrates (i.e., dissolved organic C (DOC) and bioavailable N compounds), resulting in depleted labile substrate pools even when bulk nutrient stocks remain adequate, thereby reducing their availability to denitrifiers. The absence of the correlations of N\u003csub\u003e2\u003c/sub\u003e production with soil TC and TN in our model supports this interpretation, as total nutrient pools may have been adequate in these restored wetlands, but the readily available substrate fractions may have been depleted in wetter soils through leaching processes. Even when adequate NO₃⁻ is available, its removal can be constrained by limited availability of labile organic substrates (Castaldelli et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such substrate limitations can lead to decreased denitrification rates(H. Wang et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and, consequently, lower N\u003csub\u003e2\u003c/sub\u003e production.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSoil nutrients\u003c/h2\u003e \u003cp\u003eWhile previous studies have reported higher denitrification rates in wetland soils with higher soil TC (Attard et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and soil TN (Brown et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), we observed no such relationship in our study, suggesting that soil TC and TN were not limiting factors for the denitrification process. However, the previous statement suggesting differences in soil NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e could be important in regulating denitrification rates, which shows TN may be composed of different N organic and inorganic components in different restoration practices. Alternatively, there could be different N mineralization rates, and/or nitrification rates across practices. Additionally, soil P had a positive correlation with N\u003csub\u003e2\u003c/sub\u003e production, which is consistent with elevated soil P levels enhancing denitrification and increasing NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e retention (O\u0026rsquo;Neill et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; K. Zhang et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). An increase in soil P can stimulate microbial growth and metabolic activity, since P serves as an essential nutrient for microbial growth and activity and can exert both direct and indirect influences on denitrification rates (Henderson et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Houlton \u0026amp; Bai, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This result also shows that denitrification can be potentially co-limited by both N and P in wetland soils.\u003c/p\u003e \u003cp\u003eSoil properties among restoration practices\u003c/p\u003e \u003cp\u003eThe SOD was highest in the natural regeneration areas and lowest in the remnant forest areas, despite remnant forest containing the highest soil TC (22.48 mg g⁻\u0026sup1;) among restoration practices (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This apparent paradox suggests that organic matter quality, rather than quantity, may determine biogeochemical processes. Higher SOD in natural regeneration areas likely resulted from enhanced microbial respiration fueled by the accumulation of fresh, labile organic matter from early successional vegetation. In contrast, remnant forests are likely dominated by already highly decomposed, recalcitrant organic matter that slows down microbial respiration. This pattern aligns with broader observations that organic matter decomposition is often inversely related to soil C stocks (Middleton, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Fresh biomass decomposes rapidly (within days to weeks), leaving behind more resistant compounds that persist for longer periods (Zonneveld et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Over time, decomposition further declines, and organic matter accumulates as they enter a recalcitrant phase (Moore et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thus, the SOD differences are likely related to the predominance of labile organic matter in natural regeneration areas compared to mature remnant forests.\u003c/p\u003e \u003cp\u003eThe observed difference in SOD can also be interpreted in the context of ecosystem maturity. The moderate soil moisture in natural regeneration (0.46 g g⁻\u0026sup1;) and remnant forest (0.41 g g⁻\u0026sup1;) areas (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) suggests that moisture was not a limiting factor. Rather, the developmental stage of these ecosystems\u0026mdash;active succession vs. biogeochemical equilibrium\u0026mdash;was likely the primary driver. Natural regeneration areas, representing early successional stages, may support higher microbial activity, whereas mature remnant forests reflect a more stable state. These findings further suggest that restored wetlands may experience elevated SOD during early establishment phases before gradually transitioning to stable, low-demand conditions characteristic of mature bottomland hardwood forest ecosystems.\u003c/p\u003e \u003cp\u003eSoil moisture was highest in the shallow-water wet areas, a pattern largely influenced by the presence of water control structures. These structures effectively hold water on-site, raising the ground water table and increasing soil moisture, even in the absence of active flooding which explains higher soil moisture of these areas. After shallow water areas, soil moisture was similarly high in both natural regeneration and remnant forest areas. However, the variability in soil moisture was highest in the natural regeneration areas. Such variability can increase niche availability and therefore increase the potential for vegetation diversity in these areas (Gaberščik et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The natural regeneration areas in our restored wetlands may have formed when abundant moisture was present to regenerate and in part reflects the restoration of wetland hydroperiod. Adequate moisture increases the potential for wetland vegetation establishment through natural regeneration (Stroh et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and the regeneration of many wetland species is often observed only when appropriate moisture regimes are restored (De Steven et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mitsch \u0026amp; Wilson, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Newly regenerated vegetation may increase shade and create microclimate refuges that facilitate more regeneration through seed banks and favorable conditions for further plant establishment (Zivec et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another likely mechanism for the high but variable soil moisture in these areas is the recovery of various woody and herbaceous plant communities, which can influence hydrology at localized scales by intercepting runoff and regulating evapotranspiration. In remnant forest areas, shading by mature trees, the presence of abundant old vegetation and high amount of organic matter deposition most likely contributed to higher water retention(Dabrowska-Zielinska et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and consequentially elevated soil moisture levels. In addition, tree roots and organic matter can improve soil porosity, often leading to higher infiltration rates and therefore increased soil moisture (Hudson, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite variations in mean soil TC levels across the restoration practices, mean soil P levels were quite similar (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). During flooding events, all restored areas may have received similar inputs from external nutrient sources, including agricultural runoff, and P-rich suspended sediment deposited by flood waters, which could explain the comparable soil P levels observed across the restoration practices. This pattern may also reflect legacy effects of past farming and fertilization, as riparian wetlands are effective at retaining P through sediment deposition (Gordon et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, accumulated legacy P can continue to impact wetland nutrient dynamics for centuries, even after external watershed inputs have been eliminated (Goyette et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This may explain why soil P levels in our restored wetlands remained relatively consistent across practices even after restoration.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhile all restoration practices showed potential to reduce downstream N transport through denitrification, N\u003csub\u003e2\u003c/sub\u003e production was highest in natural regeneration areas. Therefore, natural regeneration, with its lower associated costs, could be a viable alternative to active restoration methods for N removal and water quality improvement in the Mississippi River Basin. Also, since herbaceous vegetation in these areas is eventually taken over by volunteer hardwood trees, successional resetting of the vegetation in parts of these wetlands may be a management strategy to help optimize N removal rates. Some restored wetlands may fail without disturbance (Middleton, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), and resetting could be an option to reduce dominant species, promote greater plant diversity, and enhance heterogeneity (Osland et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, widespread adoption of all restoration approaches would benefit from further investigation across more restored floodplain wetlands.\u003c/p\u003e \u003cp\u003eWe recognize that there are limitations in our study to access full potential denitrification rates based on 48 h incubation. Longer flooding durations may be necessary to optimize N\u003csub\u003e2\u003c/sub\u003e production and to identify the sequence in which restoration practices reach saturation, beyond which further denitrification gains are minimal. The correlation of soil properties with N\u003csub\u003e2\u003c/sub\u003e production was more limited than expected. In many freshwater sediments, denitrification is primarily fueled by NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e produced locally within the soil rather than from the overlying water (Seitzinger, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Subsequent research could examine the relative contributions of water-column vs. soil-derived NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e across restoration practices using isotope pairing, in which the incubation water is enriched with \u003csup\u003e15\u003c/sup\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and the resulting \u003csup\u003e15\u003c/sup\u003eN\u003csub\u003e2\u003c/sub\u003e gas is measured (Li \u0026amp; Twilley, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nielsen, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe fact that SOD was highest in natural regeneration and lowest in remnant forest areas stresses the need to analyze organic matter quality (labile vs. recalcitrant fractions). Future studies could also benefit from examining vegetation diversity and root associated microbial communities to clarify their influence on soil properties and wetland functions. Targeted experimental manipulation of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (Speir et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) may help pinpoint true hotspots for denitrification across these landscapes, while manipulations of DOC could reveal its limiting role, especially when using high nutrient incubation water (Nifong et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moving forward, a comprehensive analysis that considers variations in soil properties, microbial communities, topography, and the presence of localized hotspots and microsites will be essential for effectively predicting outcomes of restoration efforts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e \u003cdiv class=\"Heading\"\u003eDeclarations\u003c/div\u003e \u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on \u0026ldquo;Ethical responsibilities of Authors\u0026rdquo; as found in the Instructions for Authors.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eSupplementary information\u003c/h2\u003e \u003cp\u003eOnline Resource 1 provides additional details on analytical methods, soil properties analysis (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and images of restoration practices (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), core collection (Fig. S2), and flow-through soil core incubation setup ( Fig. S3).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis project was funded by the USDA Natural Resources Conservation Service (USDA NRCS) and The Nature Conservancy (TNC).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSD: study design, data collection and analysis, original manuscript draft. RSB: study design, data collection and analysis, manuscript drafts review and editing. SGW: study design, data collection and analysis, manuscript drafts review and editing. JNM: secured funding, study design, data collection and analysis, manuscript drafts review and editing. All authors evaluated and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe extend our gratitude to Stephenie Driscoll, Trevor Crawford, Peter Blum, Morgan Michael, Andy Rosson, and Ryan Hanscom for their invaluable support in field and laboratory work. We acknowledge the Tennessee Tech Water Center for their contribution to research infrastructure.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data produced in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlldred, M., \u0026amp; Baines, S. B. (2016). Effects of wetland plants on denitrification rates: a meta-analysis. \u003cem\u003eEcological Applications\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(3), 676\u0026ndash;685. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/14-1525\u003c/span\u003e\u003cspan address=\"10.1890/14-1525\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArango, C. P., Tank, J. L., Schaller, J. L., Royer, T. V., Bernot, M. J., \u0026amp; David, M. B. (2007). Benthic organic carbon influences denitrification in streams with high nitrate concentration. \u003cem\u003eFreshwater Biology\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(7), 1210\u0026ndash;1222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2427.2007.01758.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2427.2007.01758.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmstrong, A., Holden, J., Kay, P., Francis, B., Foulger, M., Gledhill, S., McDonald, A. T., \u0026amp; Walker, A. (2010). The impact of peatland drain-blocking on dissolved organic carbon loss and discolouration of water; results from a national survey. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e381\u003c/em\u003e(1\u0026ndash;2), 112\u0026ndash;120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2009.11.031\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2009.11.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAttard, E., Recous, S., Chabbi, A., De Berranger, C., Guillaumaud, N., Labreuche, J., Philippot, L., Schmid, B., \u0026amp; Le Roux, X. (2011). Soil environmental conditions rather than denitrifier abundance and diversity drive potential denitrification after changes in land uses. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(5), 1975\u0026ndash;1989. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2486.2010.02340.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2486.2010.02340.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBallantine, K. A., Anderson, T. R., Pierce, E. A., \u0026amp; Groffman, P. M. (2017). Restoration of denitrification in agricultural wetlands. \u003cem\u003eEcological Engineering\u003c/em\u003e, \u003cem\u003e106\u003c/em\u003e, 570\u0026ndash;577. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoleng.2017.06.033\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoleng.2017.06.033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalmford, A., Bruner, A., Cooper, P., Costanza, R., Farber, S., Green, R. E., Jenkins, M., Jefferiss, P., Jessamy, V., Madden, J., Munro, K., Myers, N., Naeem, S., Paavola, J., Rayment, M., Rosendo, S., Roughgarden, J., Trumper, K., \u0026amp; Turner, R. K. (2002). Economic Reasons for Conserving Wild Nature. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e297\u003c/em\u003e(5583), 950\u0026ndash;953. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1073947\u003c/span\u003e\u003cspan address=\"10.1126/science.1073947\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeauchamp, E. G., Trevors, J. T., \u0026amp; Paul, J. W. (1989). Carbon Sources for Bacterial Denitrification. In \u003cem\u003eAdvances in Soil Science\u003c/em\u003e (pp. 113\u0026ndash;142). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-4613-8847-0_3\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4613-8847-0_3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernhard, A. (2010). The Nitrogen Cycle: Processes, Players, and Human Impact. \u003cem\u003eNature Education Knowledge\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(10), 25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlock, W. M., Franklin, A. B., Ward, J. P., Ganey, J. L., \u0026amp; White, G. C. (2001). Design and Implementation of Monitoring Studies to Evaluate the Success of Ecological Restoration on Wildlife. \u003cem\u003eRestoration Ecology\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), 293\u0026ndash;303. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1526-100x.2001.009003293.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1526-100x.2001.009003293.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrisson, J., Rodriguez, M., Martin, C. A., \u0026amp; Proulx, R. (2020). Plant diversity effect on water quality in wetlands: a meta-analysis based on experimental systems. \u003cem\u003eEcological Applications\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/eap.2074\u003c/span\u003e\u003cspan address=\"10.1002/eap.2074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown, R. S., Murdock, J. N., Womble, S. G., \u0026amp; Duwadi, S. (2025). Floodwater monitoring and soil incubations reveal complementary nutrient retention patterns in a reconnected floodplain wetland. \u003cem\u003eFreshwater Science\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(3), 000\u0026ndash;000. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/737226\u003c/span\u003e\u003cspan address=\"10.1086/737226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruland, G. L., Richardson, C. J., \u0026amp; Whalen, S. C. (2006). Spatial variability of denitrification potential and related soil properties in created, restored, and paired natural wetlands. \u003cem\u003eWetlands\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e, 1042\u0026ndash;1056. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1672/0277-5212(2006)26[1042:SVODPA]2.0.CO;2\u003c/span\u003e\u003cspan address=\"10.1672/0277-5212(2006)26[1042:SVODPA]2.0.CO;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgin, A. J., Groffman, P. M., \u0026amp; Lewis, D. N. (2010). Factors Regulating Denitrification in a Riparian Wetland. \u003cem\u003eSoil Science Society of America Journal\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(5), 1826\u0026ndash;1833. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2136/sssaj2009.0463\u003c/span\u003e\u003cspan address=\"10.2136/sssaj2009.0463\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharpley, A. N., \u0026amp; Smith, V. H. (1998). Nonpoint Pollution of Surface Waters with Phosphorus and Nitrogen. \u003cem\u003eEcological Applications\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(3), 559. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2641247\u003c/span\u003e\u003cspan address=\"10.2307/2641247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastaldelli, G., Colombani, N., Soana, E., Vincenzi, F., Fano, E. A., \u0026amp; Mastrocicco, M. (2019). Reactive nitrogen losses via denitrification assessed in saturated agricultural soils. \u003cem\u003eGeoderma\u003c/em\u003e, \u003cem\u003e337\u003c/em\u003e, 91\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geoderma.2018.09.018\u003c/span\u003e\u003cspan address=\"10.1016/j.geoderma.2018.09.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristensen, P. B., Nielsen, L. P., S\u0026oslash;rensen, J., \u0026amp; Revsbech, N. P. (1990). Denitrification in nitrate-rich streams: Diurnal and seasonal variation related to benthic oxygen metabolism. \u003cem\u003eLimnology and Oceanography\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(3), 640\u0026ndash;651. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4319/lo.1990.35.3.0640\u003c/span\u003e\u003cspan address=\"10.4319/lo.1990.35.3.0640\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCole, C. A., Brooks, R. P., \u0026amp; Brittingham, M. C. (2025). \u003cem\u003eManaging Your Restored Wetland\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://extension.psu.edu/managing-your-restored-wetland\u003c/span\u003e\u003cspan address=\"https://extension.psu.edu/managing-your-restored-wetland\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCole, C. A., \u0026amp; Kentula, M. E. (2011). Monitoring and Assessment\u0026mdash;What to Measure \u0026hellip; and Why. In B. A. LePage (Ed.), \u003cem\u003eWetlands\u003c/em\u003e (pp. 137\u0026ndash;152). Springer Netherlands. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-94-007-0551-7_8\u003c/span\u003e\u003cspan address=\"10.1007/978-94-007-0551-7_8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCornwell, J. C., Kemp, W. M., \u0026amp; Kana, T. M. (1999). Denitrification in coastal ecosystems: methods, environmental controls, and ecosystem level controls, a review. \u003cem\u003eAquatic Ecology\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), 41\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/A:1009921414151\u003c/span\u003e\u003cspan address=\"10.1023/A:1009921414151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDabrowska-Zielinska, K., Budzynska, M., Tomaszewska, M., Malinska, A., Gatkowska, M., Bartold, M., \u0026amp; Malek, I. (2016). Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(9), 756. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs8090756\u003c/span\u003e\u003cspan address=\"10.3390/rs8090756\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Klein, J. J. M., Overbeek, C. C., Juncher J\u0026oslash;rgensen, C., \u0026amp; Veraart, A. J. (2017). Effect of Temperature on Oxygen Profiles and Denitrification Rates in Freshwater Sediments. \u003cem\u003eWetlands\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(5), 975\u0026ndash;983. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13157-017-0933-1\u003c/span\u003e\u003cspan address=\"10.1007/s13157-017-0933-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Steven, D., Sharitz, R. R., Singer, J. H., \u0026amp; Barton, C. D. (2006). Testing a Passive Revegetation Approach for Restoring Coastal Plain Depression Wetlands. \u003cem\u003eRestoration Ecology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3), 452\u0026ndash;460. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1526-100X.2006.00153.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1526-100X.2006.00153.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDodds, W. K., Bouska, W. W., Eitzmann, J. L., Pilger, T. J., Pitts, K. L., Riley, A. J., Schloesser, J. T., \u0026amp; Thornbrugh, D. J. (2009). Eutrophication of U.S. Freshwaters: Analysis of Potential Economic Damages. \u003cem\u003eEnvironmental Science \u0026amp; Technology\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(1), 12\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/es801217q\u003c/span\u003e\u003cspan address=\"10.1021/es801217q\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEclesia, R. P., Jobbagy, E. G., Jackson, R. B., Rizzotto, M., \u0026amp; Pi\u0026ntilde;eiro, G. (2016). Stabilization of new carbon inputs rather than old carbon decomposition determines soil organic carbon shifts following woody or herbaceous vegetation transitions. \u003cem\u003ePlant and Soil\u003c/em\u003e, \u003cem\u003e409\u003c/em\u003e(1\u0026ndash;2), 99\u0026ndash;116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-016-2951-9\u003c/span\u003e\u003cspan address=\"10.1007/s11104-016-2951-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaulkner, S., Barrow, W., Keeland, B., Walls, S., \u0026amp; Telesco, D. (2011). Effects of conservation practices on wetland ecosystem services in the Mississippi Alluvial Valley. \u003cem\u003eEcological Applications\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(sp1), S31\u0026ndash;S48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/10-0592.1\u003c/span\u003e\u003cspan address=\"10.1890/10-0592.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaberščik, A., Krek, J. L., \u0026amp; Zelnik, I. (2018). Habitat diversity along a hydrological gradient in a complex wetland results in high plant species diversity. \u003cem\u003eEcological Engineering\u003c/em\u003e, \u003cem\u003e118\u003c/em\u003e, 84\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoleng.2018.04.017\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoleng.2018.04.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalatowitsch, S., \u0026amp; Bohnen, J. (2021). Long-Term Recovery of a Restored Palustrine Wetland: the Role of Monitoring and Adaptive Management. \u003cem\u003eWetlands\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(6), 80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13157-021-01478-7\u003c/span\u003e\u003cspan address=\"10.1007/s13157-021-01478-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhermandi, A., van den Bergh, J. C. J. M., Brander, L. M., de Groot, H. L. F., \u0026amp; Nunes, P. A. L. D. (2010). Values of natural and human-made wetlands: A meta-analysis. \u003cem\u003eWater Resources Research\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2010WR009071\u003c/span\u003e\u003cspan address=\"10.1029/2010WR009071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilliam, J. W. (1994). Riparian Wetlands and Water Quality. \u003cem\u003eJournal of Environmental Quality\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(5), 896\u0026ndash;900. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/jeq1994.00472425002300050007x\u003c/span\u003e\u003cspan address=\"10.2134/jeq1994.00472425002300050007x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGordon, B. A., Dorothy, O., \u0026amp; Lenhart, C. F. (2020). Nutrient Retention in Ecologically Functional Floodplains: A Review. \u003cem\u003eWater\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(10), 2762. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w12102762\u003c/span\u003e\u003cspan address=\"10.3390/w12102762\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoyette, J.-O., Bennett, E. M., \u0026amp; Maranger, R. (2018). Low buffering capacity and slow recovery of anthropogenic phosphorus pollution in watersheds. \u003cem\u003eNature Geoscience\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(12), 921\u0026ndash;925. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41561-018-0238-x\u003c/span\u003e\u003cspan address=\"10.1038/s41561-018-0238-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansen, L., Hellerstein, D., Ribaudo, M., Williamson, J., Nulph, D., Loesch, C., \u0026amp; Crumpton, W. (2015). \u003cem\u003eTargeting Investments To Cost Effectively Restore and Protect Wetland Ecosystems: Some Economic Insights. Report No. 183\u003c/em\u003e. Economic Research Service/USDA. 56 pp. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ers.usda.gov/sites/default/files/_laserfiche/publications/45347/51895_err183.pdf?v=79577\u003c/span\u003e\u003cspan address=\"https://ers.usda.gov/sites/default/files/_laserfiche/publications/45347/51895_err183.pdf?v=79577\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenderson, S. L., Dandie, C. E., Patten, C. L., Zebarth, B. J., Burton, D. L., Trevors, J. T., \u0026amp; Goyer, C. (2010). Changes in Denitrifier Abundance, Denitrification Gene mRNA Levels, Nitrous Oxide Emissions, and Denitrification in Anoxic Soil Microcosms Amended with Glucose and Plant Residues. \u003cem\u003eApplied and Environmental Microbiology\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(7), 2155\u0026ndash;2164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/AEM.02993-09\u003c/span\u003e\u003cspan address=\"10.1128/AEM.02993-09\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoulton, B. Z., \u0026amp; Bai, E. (2009). Imprint of denitrifying bacteria on the global terrestrial biosphere. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e106\u003c/em\u003e(51), 21713\u0026ndash;21716. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0912111106\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0912111106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowie, S. A., Whitfield, P. H., Hebda, R. J., Munson, T. G., Dakin, R. A., \u0026amp; Jeglum, J. K. (2009). Water Table and Vegetation Response to Ditch Blocking: Restoration of a Raised Bog in Southwestern British Columbia. \u003cem\u003eCanadian Water Resources Journal\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(4), 381\u0026ndash;392. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4296/cwrj3404381\u003c/span\u003e\u003cspan address=\"10.4296/cwrj3404381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHudson, B. D. (1994). Soil organic matter and available water capacity. \u003cem\u003eJournal of Soil and Water Conservation\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(2), 189\u0026ndash;194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1080/00224561.1994.12456850\u003c/span\u003e\u003cspan address=\"10.1080/00224561.1994.12456850\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunter, R. G., \u0026amp; Faulkner, S. P. (2001). Denitrification Potentials in Restored and Natural Bottomland Hardwood Wetlands. \u003cem\u003eSoil Science Society of America Journal\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(6), 1865\u0026ndash;1872. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2136/sssaj2001.1865\u003c/span\u003e\u003cspan address=\"10.2136/sssaj2001.1865\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunter, R., Lane, R., Day, J., Lindsey, J., Day, J., \u0026amp; Hunter, M. (2009). Nutrient Removal and Loading Rate Analysis of Louisiana Forested Wetlands Assimilating Treated Municipal Effluent. \u003cem\u003eEnvironmental Management\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(5), 865\u0026ndash;873. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00267-009-9348-y\u003c/span\u003e\u003cspan address=\"10.1007/s00267-009-9348-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaden, U. S., Fuchs, E., Geyer, S., Hein, T., Horchler, P., Rupp, H., Scholz, M., Schulz-Zunkel, C., \u0026amp; Weigelhofer, G. (2021). Soil Characteristics and Hydromorphological Patterns Control Denitrification at the Floodplain Scale. \u003cem\u003eFrontiers in Earth Science\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/feart.2021.708707\u003c/span\u003e\u003cspan address=\"10.3389/feart.2021.708707\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKana, T. M., Cornwell, J. C., \u0026amp; Zhong, L. (2006). Determination of denitrification in the Chesapeake Bay from measurements of N\u003csub\u003e2\u003c/sub\u003e accumulation in bottom water. \u003cem\u003eEstuaries and Coasts\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(2), 222\u0026ndash;231. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02781991\u003c/span\u003e\u003cspan address=\"10.1007/BF02781991\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKana, T. M., Darkangelo, Christina., Hunt, M. Duane., Oldham, J. B., Bennett, G. E., \u0026amp; Cornwell, J. C. (1994). Membrane Inlet Mass Spectrometer for Rapid High-Precision Determination of N2, O2, and Ar in Environmental Water Samples. \u003cem\u003eAnalytical Chemistry\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e(23), 4166\u0026ndash;4170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/ac00095a009\u003c/span\u003e\u003cspan address=\"10.1021/ac00095a009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaushal, S. S., Fork, M. L., Hawley, R. J., Hopkins, K. G., R\u0026iacute;os-Touma, B., \u0026amp; Roy, A. H. (2023). Stream restoration milestones: monitoring scales determine successes and failures. \u003cem\u003eUrban Ecosystems\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(4), 1131\u0026ndash;1142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11252-023-01370-8\u003c/span\u003e\u003cspan address=\"10.1007/s11252-023-01370-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly, M. (2020). \u003cem\u003emimsy: Calculate MIMS Dissolved Gas Concentrations Without Getting a Headache. R package version 0.6.2\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=mimsy\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=mimsy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKjellin, J., Hallin, S., \u0026amp; W\u0026ouml;rman, A. (2007). Spatial variations in denitrification activity in wetland sediments explained by hydrology and denitrifying community structure. \u003cem\u003eWater Research\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(20), 4710\u0026ndash;4720. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.watres.2007.06.053\u003c/span\u003e\u003cspan address=\"10.1016/j.watres.2007.06.053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLang, M. W., Ingebritsen, J. C., \u0026amp; Griffin, R. K. (2024). \u003cem\u003eStatus and Trends of Wetlands in the Conterminous United States 2009 to 2019\u003c/em\u003e. U.S. Department of the Interior; Fish and Wildlife Service, Washington, D.C. 43 pp. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fws.gov/sites/default/files/documents/2024-03/wetlands-status-and-trends-2009-2019-signed.pdf\u003c/span\u003e\u003cspan address=\"https://www.fws.gov/sites/default/files/documents/2024-03/wetlands-status-and-trends-2009-2019-signed.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, S., \u0026amp; Twilley, R. R. (2021). Nitrogen Dynamics of Inundated Sediments in an Emerging Coastal Deltaic Floodplain in Mississippi River Delta Using Isotope Pairing Technique to Test Response to Nitrate Enrichment and Sediment Organic Matter. \u003cem\u003eEstuaries and Coasts\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(7), 1899\u0026ndash;1915. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12237-021-00913-6\u003c/span\u003e\u003cspan address=\"10.1007/s12237-021-00913-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa, L., Jiang, X., Liu, G., Yao, L., Liu, W., Pan, Y., \u0026amp; Zuo, Y. (2020). Environmental Factors and Microbial Diversity and Abundance Jointly Regulate Soil Nitrogen and Carbon Biogeochemical Processes in Tibetan Wetlands. \u003cem\u003eEnvironmental Science \u0026amp; Technology\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(6), 3267\u0026ndash;3277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.est.9b06716\u003c/span\u003e\u003cspan address=\"10.1021/acs.est.9b06716\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarchant, H. K., Holtappels, M., Lavik, G., Ahmerkamp, S., Winter, C., \u0026amp; Kuypers, M. M. M. (2016). Coupled nitrification\u0026ndash;denitrification leads to extensive N loss in subtidal permeable sediments. \u003cem\u003eLimnology and Oceanography\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(3), 1033\u0026ndash;1048. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/lno.10271\u003c/span\u003e\u003cspan address=\"10.1002/lno.10271\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayer, P. M., Pennino, M. J., Newcomer-Johnson, T. A., \u0026amp; Kaushal, S. S. (2022). Long-term assessment of floodplain reconnection as a stream restoration approach for managing nitrogen in ground and surface waters. \u003cem\u003eUrban Ecosystems\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(3), 879\u0026ndash;907. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11252-021-01199-z\u003c/span\u003e\u003cspan address=\"10.1007/s11252-021-01199-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCarthy, M. J., Gardner, W. S., Lavrentyev, P. J., Moats, K. M., Jochem, F. J., \u0026amp; Klarer, D. M. (2007). Effects of Hydrological Flow Regime on Sediment-water Interface and Water Column Nitrogen Dynamics in a Great Lakes Coastal Wetland (Old Woman Creek, Lake Erie). \u003cem\u003eJournal of Great Lakes Research\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), 219\u0026ndash;231. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3394/0380-1330(2007)33[219:EOHFRO]2.0.CO;2\u003c/span\u003e\u003cspan address=\"10.3394/0380-1330(2007)33[219:EOHFRO]2.0.CO;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng, Y., He, Z., Liu, B., Chen, L., Lin, P., \u0026amp; Luo, W. (2020). Soil Salinity and Moisture Control the Processes of Soil Nitrification and Denitrification in a Riparian Wetlands in an Extremely Arid Regions in Northwestern China. \u003cem\u003eWater\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(10), 2815. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w12102815\u003c/span\u003e\u003cspan address=\"10.3390/w12102815\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiddleton, B. A. (1999). \u003cem\u003eWetland restoration, flood pulsing, and disturbance dynamics.\u003c/em\u003e (1st ed.). Wiley, New York, NY.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiddleton, B. A. (2020). Trends of litter decomposition and soil organic matter stocks across forested swamp environments of the southeastern US. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), e0226998. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0226998\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0226998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra, R. K., \u0026amp; Tripathi, A. K. (2023). The Effect of Eutrophication on Drinking Water. \u003cem\u003eJournal of Earth Science \u0026amp; Climatic Change\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 659.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitsch, W. J., Bernal, B., \u0026amp; Hernandez, M. E. (2015). Ecosystem services of wetlands. \u003cem\u003eInternational Journal of Biodiversity Science, Ecosystem Services \u0026amp; Management\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 1\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/21513732.2015.1006250\u003c/span\u003e\u003cspan address=\"10.1080/21513732.2015.1006250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitsch, W. J., Day, J. W., Gilliam, J. W., Groffman, P. M., Hey, D. L., Randall, G. W., \u0026amp; Wang, N. (2001). Reducing nitrogen loading to the Gulf of Mexico from the Mississippi River Basin: Strategies to counter a persistent ecological problem. \u003cem\u003eBioScience\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(5), 373\u0026ndash;388. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1641/0006-3568(2001)051[0373:RNLTTG]2.0.CO;2\u003c/span\u003e\u003cspan address=\"10.1641/0006-3568(2001)051[0373:RNLTTG]2.0.CO;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitsch, W. J., \u0026amp; Wilson, R. F. (1996). Improving the Success of Wetland Creation and Restoration with Know-How, Time, and Self‐Design. \u003cem\u003eEcological Applications\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 77\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2269554\u003c/span\u003e\u003cspan address=\"10.2307/2269554\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore, T. R., Trofymow, J. A., Prescott, C. E., \u0026amp; Titus, B. D. (2017). Can short-term litter-bag measurements predict long-term decomposition in northern forests? \u003cem\u003ePlant and Soil\u003c/em\u003e, \u003cem\u003e416\u003c/em\u003e(1\u0026ndash;2), 419\u0026ndash;426. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-017-3228-7\u003c/span\u003e\u003cspan address=\"10.1007/s11104-017-3228-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulholland, P. J., Hall, R. O., Sobota, D. J., Dodds, W. K., Findlay, S. E. G., Grimm, N. B., Hamilton, S. K., McDowell, W. H., O\u0026rsquo;Brien, J. M., Tank, J. L., Ashkenas, L. R., Cooper, L. W., Dahm, C. N., Gregory, S. V., Johnson, S. L., Meyer, J. L., Peterson, B. J., Poole, G. C., Valett, H. M., \u0026hellip; Thomasn, S. M. (2009). Nitrate removal in stream ecosystems measured by 15N addition experiments: Denitrification. \u003cem\u003eLimnology and Oceanography\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(3), 666\u0026ndash;680. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4319/lo.2009.54.3.0666\u003c/span\u003e\u003cspan address=\"10.4319/lo.2009.54.3.0666\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakagawa, S., \u0026amp; Schielzeth, H. (2013). A general and simple method for obtaining \u003cem\u003eR\u003c/em\u003e \u003csup\u003e2\u003c/sup\u003e from generalized linear mixed-effects models. \u003cem\u003eMethods in Ecology and Evolution\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2), 133\u0026ndash;142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.2041-210x.2012.00261.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2041-210x.2012.00261.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewcomer Johnson, T. A., Kaushal, S. S., Mayer, P. M., \u0026amp; Grese, M. M. (2014). Effects of stormwater management and stream restoration on watershed nitrogen retention. \u003cem\u003eBiogeochemistry\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e(1), 81\u0026ndash;106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10533-014-9999-5\u003c/span\u003e\u003cspan address=\"10.1007/s10533-014-9999-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNielsen, L. P. (1992). Denitrification in sediment determined from nitrogen isotope pairing. \u003cem\u003eFEMS Microbiology Letters\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e(4), 357\u0026ndash;362. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1574-6968.1992.tb04828.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1574-6968.1992.tb04828.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNifong, R. L., Taylor, J. M., \u0026amp; Moore, M. T. (2019). Mulch-Derived Organic Carbon Stimulates High Denitrification Fluxes from Agricultural Ditch Sediments. \u003cem\u003eJournal of Environmental Quality\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(2), 476\u0026ndash;484. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/jeq2018.09.0341\u003c/span\u003e\u003cspan address=\"10.2134/jeq2018.09.0341\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Neill, R. M., Duff, A. M., Brennan, F. P., Gebremichael, A. W., Girkin, N. T., Lanigan, G. J., Krol, D. J., Wall, D. P., Renou-Wilson, F., M\u0026uuml;ller, C., Richards, K. G., \u0026amp; Deveautour, C. (2022). Linking long-term soil phosphorus management to microbial communities involved in nitrogen reactions. \u003cem\u003eBiology and Fertility of Soils\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(4), 389\u0026ndash;402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00374-022-01627-y\u003c/span\u003e\u003cspan address=\"10.1007/s00374-022-01627-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborne, L. L., \u0026amp; Wiley, M. J. (1988). Empirical relationships between land use/cover and stream water quality in an agricultural watershed. \u003cem\u003eJournal of Environmental Management.\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(1), 9\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsland, M. J., Gonz\u0026aacute;lez, E., \u0026amp; Richardson, C. J. (2011). Restoring diversity after cattail expansion: disturbance, resilience, and seasonality in a tropical dry wetland. \u003cem\u003eEcological Applications\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(3), 715\u0026ndash;728. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/09-0981.1\u003c/span\u003e\u003cspan address=\"10.1890/09-0981.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalay, I. (2021). \u003cem\u003eThree ways wetlands can influence climate change\u003c/em\u003e. International Water Management Institute. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iwmi.cgiar.org/2021/02/three-ways-wetlands-can-influence-climate-change/\u003c/span\u003e\u003cspan address=\"https://www.iwmi.cgiar.org/2021/02/three-ways-wetlands-can-influence-climate-change/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeralta, A. L., Matthews, J. W., \u0026amp; Kent, A. D. (2010). Microbial Community Structure and Denitrification in a Wetland Mitigation Bank. \u003cem\u003eApplied and Environmental Microbiology\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(13), 4207\u0026ndash;4215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/AEM.02977-09\u003c/span\u003e\u003cspan address=\"10.1128/AEM.02977-09\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinheiro, J., Bates, D., \u0026amp; R Core Team. (2022). \u003cem\u003enlme: Linear and Nonlinear Mixed Effects Models\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoe, A. C., Piehler, M. F., Thompson, S. P., \u0026amp; Paerl, H. W. (2003). Denitrification in a constructed wetland receiving agricultural runoff. \u003cem\u003eWetlands\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e, 817\u0026ndash;826. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1672/0277-5212\u003c/span\u003e\u003cspan address=\"10.1672/0277-5212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(2003)023[0817:DIACWR]2.0.CO;2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrach, K., \u0026amp; del Moral, R. (2015). Passive restoration is often quite effective: response to Zahawi et al. (2014). \u003cem\u003eRestoration Ecology\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(4), 344\u0026ndash;346. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/rec.12224\u003c/span\u003e\u003cspan address=\"10.1111/rec.12224\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. (2024). \u003cem\u003eR: A language and environment for statistical computing (4.4.1). R Foundation for Statistical Computing.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabalais, N. N., Turner, R. E., \u0026amp; Wiseman, W. J. (2002). Gulf of Mexico Hypoxia, A.K.A. \u0026ldquo;The Dead Zone.\u0026rdquo; \u003cem\u003eAnnual Review of Ecology and Systematics\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), 235\u0026ndash;263. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.ecolsys.33.010802.150513\u003c/span\u003e\u003cspan address=\"10.1146/annurev.ecolsys.33.010802.150513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRisgaard-Petersen, N. (2003). Coupled nitrification-denitrification in autotrophic and heterotrophic estuarine sediments: On the influence of benthic microalgae. \u003cem\u003eLimnology and Oceanography\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(1), 93\u0026ndash;105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4319/lo.2003.48.1.0093\u003c/span\u003e\u003cspan address=\"10.4319/lo.2003.48.1.0093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchielzeth, H. (2010). Simple means to improve the interpretability of regression coefficients. \u003cem\u003eMethods in Ecology and Evolution\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(2), 103\u0026ndash;113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.2041-210X.2010.00012.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2041-210X.2010.00012.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeitzinger, S. P. (1988). Denitrification in freshwater and coastal marine ecosystems: Ecological and geochemical significance. \u003cem\u003eLimnology and Oceanography\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(4part2), 702\u0026ndash;724. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4319/lo.1988.33.4part2.0702\u003c/span\u003e\u003cspan address=\"10.4319/lo.1988.33.4part2.0702\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeo, D. C., \u0026amp; DeLaune, R. D. (2010). Fungal and bacterial mediated denitrification in wetlands: Influence of sediment redox condition. \u003cem\u003eWater Research\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(8), 2441\u0026ndash;2450. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.watres.2010.01.006\u003c/span\u003e\u003cspan address=\"10.1016/j.watres.2010.01.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrestha, S., Farrelly, J., Eggleton, M., \u0026amp; Chen, Y. (2017). Effects of conservation wetlands on stream habitat, water quality and fish communities in agricultural watersheds of the lower Mississippi River Basin. \u003cem\u003eEcological Engineering\u003c/em\u003e, \u003cem\u003e107\u003c/em\u003e, 99\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoleng.2017.06.054\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoleng.2017.06.054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpeir, S. L., Tank, J. L., Taylor, J. M., \u0026amp; Grose, A. L. (2023). Temperature and carbon availability interact to enhance nitrous oxide production via denitrification in alluvial plain river sediments. \u003cem\u003eBiogeochemistry\u003c/em\u003e, \u003cem\u003e165\u003c/em\u003e(2), 191\u0026ndash;203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10533-023-01074-3\u003c/span\u003e\u003cspan address=\"10.1007/s10533-023-01074-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpeir, S. L., Taylor, J. M., \u0026amp; Scott, J. T. (2017). Seasonal Differences in Relationships between Nitrate Concentration and Denitrification Rates in Ditch Sediments Vegetated with Rice Cutgrass. \u003cem\u003eJournal of Environmental Quality\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(6), 1500\u0026ndash;1509. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/jeq2016.11.0450\u003c/span\u003e\u003cspan address=\"10.2134/jeq2016.11.0450\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpieles, D. J., \u0026amp; Mitsch, W. J. (2000). The effects of season and hydrologic and chemical loading on nitrate retention in constructed wetlands: a comparison of low- and high-nutrient riverine systems. \u003cem\u003eEcological Engineering\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1\u0026ndash;2), 77\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0925-8574(99)00021-X\u003c/span\u003e\u003cspan address=\"10.1016/S0925-8574(99)00021-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStanley, E. H., \u0026amp; Boulton, A. J. (1995). Hyporheic processes during flooding and drying in a Sonoran Desert stream. I. Hydrologic and chemical dynamics. \u003cem\u003eArchiv Fur Hydrobiologie\u003c/em\u003e, \u003cem\u003e134\u003c/em\u003e(1), 1\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1127/archiv-hydrobiol/134/1995/1\u003c/span\u003e\u003cspan address=\"10.1127/archiv-hydrobiol/134/1995/1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStroh, P. A., Hughes, F. M. R., Sparks, T. H., \u0026amp; Mountford, J. O. (2012). The Influence of Time on the Soil Seed Bank and Vegetation across a Landscape-Scale Wetland Restoration Project. \u003cem\u003eRestoration Ecology\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 103\u0026ndash;112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1526-100X.2010.00740.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1526-100X.2010.00740.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSutton-Grier, A. E., Kenney, M. A., \u0026amp; Richardson, C. J. (2010). Examining the relationship between ecosystem structure and function using structural equation modelling: A case study examining denitrification potential in restored wetland soils. \u003cem\u003eEcological Modelling\u003c/em\u003e, \u003cem\u003e221\u003c/em\u003e(5), 761\u0026ndash;768. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolmodel.2009.11.015\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolmodel.2009.11.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTennessee Wildlife Federation. (2025). \u003cem\u003eRebuilding Habitats One Tree at a Time\u003c/em\u003e [Video recording]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.youtube.com/watch?v=zxG0nyjmZ_g\u0026amp;t=63s\u003c/span\u003e\u003cspan address=\"https://www.youtube.com/watch?v=zxG0nyjmZ_g\u0026amp;t=63s\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe Nature Conservancy (TNC). (2020). \u003cem\u003eRestoring Western Kentucky Wetlands and Floodplains\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nature.org/en-us/about-us/where-we-work/united-states/kentucky/stories-in-kentucky/western-kentucky-floodplains-wetlands/\u003c/span\u003e\u003cspan address=\"https://www.nature.org/en-us/about-us/where-we-work/united-states/kentucky/stories-in-kentucky/western-kentucky-floodplains-wetlands/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomaszek, J. A., Gardner, W. S., \u0026amp; Johengen, T. H. (1997). Denitrification in Sediments of a Lake Erie Coastal Wetland (Old Woman Creek, Huron, Ohio, USA). \u003cem\u003eJournal of Great Lakes Research\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(4), 403\u0026ndash;415. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0380-1330(97)70922-5\u003c/span\u003e\u003cspan address=\"10.1016/S0380-1330(97)70922-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSDA NRCS. (2003). \u003cem\u003eWetland restoration, enhancement, and management\u003c/em\u003e. Wetland Science Institute. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.scribd.com/document/50133961/USDA-Wetland-Restoration-Enhancement-and-Management\u003c/span\u003e\u003cspan address=\"https://www.scribd.com/document/50133961/USDA-Wetland-Restoration-Enhancement-and-Management\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSDA NRCS. (2023). \u003cem\u003eConservation Practice Standard Overview: Tree/Shrub Establishment (Code 612)\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nrcs.usda.gov/sites/default/files/2023-08/612_NHCP_CPS_Tree-Shrub_Establishment_2023.pdf\u003c/span\u003e\u003cspan address=\"https://www.nrcs.usda.gov/sites/default/files/2023-08/612_NHCP_CPS_Tree-Shrub_Establishment_2023.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSDA NRCS. (2025). \u003cem\u003eEasement Program Restoration Data\u003c/em\u003e. U.S. Department of Agriculture Natural Resources Conservation Service. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.farmers.gov/data/easements-restoration\u003c/span\u003e\u003cspan address=\"https://www.farmers.gov/data/easements-restoration\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenterink, H. O., Davidsson, T. E., Kiehl, K., \u0026amp; Leonardson, L. (2002). Impact of drying and re-wetting on N, P and K dynamics in a wetland soil. \u003cem\u003ePlant and Soil\u003c/em\u003e, \u003cem\u003e243\u003c/em\u003e(1), 119\u0026ndash;130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/A:1019993510737\u003c/span\u003e\u003cspan address=\"10.1023/A:1019993510737\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVymazal, J. (2013). Plants in constructed, restored and created wetlands. \u003cem\u003eEcological Engineering\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e, 501\u0026ndash;504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoleng.2013.10.035\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoleng.2013.10.035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWallage, Z. E., Holden, J., \u0026amp; McDonald, A. T. (2006). Drain blocking: An effective treatment for reducing dissolved organic carbon loss and water discolouration in a drained peatland. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e367\u003c/em\u003e(2\u0026ndash;3), 811\u0026ndash;821. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2006.02.010\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2006.02.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, H., Yan, Z., Ju, X., Song, X., Zhang, J., Li, S., \u0026amp; Zhu-Barker, X. (2023). Quantifying nitrous oxide production rates from nitrification and denitrification under various moisture conditions in agricultural soils: Laboratory study and literature synthesis. \u003cem\u003eFrontiers in Microbiology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2022.1110151\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2022.1110151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J., \u0026amp; Hu, X. (2025). Woody plant reduces soil organic carbon controlled by precipitation. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, \u003cem\u003e377\u003c/em\u003e, 124581. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jenvman.2025.124581\u003c/span\u003e\u003cspan address=\"10.1016/j.jenvman.2025.124581\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong, Z., Guo, L., Zhang, Q., Liu, G., \u0026amp; Liu, W. (2017). Edaphic Conditions Regulate Denitrification Directly and Indirectly by Altering Denitrifier Abundance in Wetlands along the Han River, China. \u003cem\u003eEnvironmental Science \u0026amp; Technology\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(10), 5483\u0026ndash;5491. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.est.6b06521\u003c/span\u003e\u003cspan address=\"10.1021/acs.est.6b06521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong, Z., Li, S., Yao, L., Liu, G., Zhang, Q., \u0026amp; Liu, W. (2015). Topography and land use effects on spatial variability of soil denitrification and related soil properties in riparian wetlands. \u003cem\u003eEcological Engineering\u003c/em\u003e, \u003cem\u003e83\u003c/em\u003e, 437\u0026ndash;443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoleng.2015.04.094\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoleng.2015.04.094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue, Y., Kovacic, D. A., David, M. B., Gentry, L. E., Mulvaney, R. L., \u0026amp; Lindau, C. W. (1999). In Situ Measurements of Denitrification in Constructed Wetlands. \u003cem\u003eJournal of Environmental Quality\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 263\u0026ndash;269. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/jeq1999.00472425002800010032x\u003c/span\u003e\u003cspan address=\"10.2134/jeq1999.00472425002800010032x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, H., Song, Y., Xi, B., Du, E., He, X., \u0026amp; Tu, X. (2012). Denitrification potential and its correlation to physico-chemical and biological characteristics of saline wetland soils in semi-arid regions. \u003cem\u003eChemosphere\u003c/em\u003e, \u003cem\u003e89\u003c/em\u003e(11), 1339\u0026ndash;1346. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chemosphere.2012.05.088\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2012.05.088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, C., Cai, M., Ndungu, C. N., Ma, L., \u0026amp; Liu, W. (2025). Plant diversity promotes soil nitrogen retention and removal processes in wetlands. \u003cem\u003eNew Phytologist\u003c/em\u003e, \u003cem\u003e248\u003c/em\u003e(2), 587\u0026ndash;599. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.70491\u003c/span\u003e\u003cspan address=\"10.1111/nph.70491\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, K., Cheng, X., Dang, H., Ye, C., \u0026amp; Zhang, Q. (2012). Soil nitrogen and denitrification potential as affected by land use and stand age following agricultural abandonment in a headwater catchment. \u003cem\u003eSoil Use and Management\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(3), 361\u0026ndash;369. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1475-2743.2012.00420.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1475-2743.2012.00420.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Z., Bortolotti, L. E., Li, Z., Armstrong, L. M., Bell, T. W., \u0026amp; Li, Y. (2021). Heterogeneous Changes to Wetlands in the Canadian Prairies Under Future Climate. \u003cem\u003eWater Resources Research\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2020WR028727\u003c/span\u003e\u003cspan address=\"10.1029/2020WR028727\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZivec, P., Sheldon, F., \u0026amp; Capon, S. J. (2023). Natural regeneration of wetlands under climate change. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fenvs.2023.989214\u003c/span\u003e\u003cspan address=\"10.3389/fenvs.2023.989214\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZonneveld, K. A. F., Versteegh, G. J. M., Kasten, S., Eglinton, T. I., Emeis, K.-C., Huguet, C., Koch, B. P., de Lange, G. J., de Leeuw, J. W., Middelburg, J. J., Mollenhauer, G., Prahl, F. G., Rethemeyer, J., \u0026amp; Wakeham, S. G. (2010). Selective preservation of organic matter in marine environments; processes and impact on the sedimentary record. \u003cem\u003eBiogeosciences\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 483\u0026ndash;511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/bg-7-483-2010\u003c/span\u003e\u003cspan address=\"10.5194/bg-7-483-2010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A., \u0026amp; Smith, G. M. (2009). \u003cem\u003eMixed effects models and extensions in ecology with R\u003c/em\u003e. Springer New York. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-0-387-87458-6\u003c/span\u003e\u003cspan address=\"10.1007/978-0-387-87458-6\" 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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Wetland restoration monitoring, Nitrogen removal, Flow-through incubation, Natural regeneration, Sediment oxygen demand","lastPublishedDoi":"10.21203/rs.3.rs-9237776/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9237776/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRiparian wetlands play a crucial role in nutrient retention and water quality maintenance in agricultural watersheds. Restoring wetland function in these systems is becoming increasingly important as negative impacts of eutrophication continue to increase in both local and downstream ecosystems. This study identified factors regulating wetland soil denitrification rates, a major nitrogen (N) removal pathway, across various wetland restoration practices (based on hydrology and plant structure) in restored agricultural bottomland hardwood forested wetlands. Soil cores from five distinct restoration practices, natural vegetation regeneration, remnant forest, tree planting areas, and constructed shallow water areas (wet and dry), were collected in 23 restored wetlands in Kentucky and Tennessee, USA. Flow-through soil core incubations were used to estimate denitrification as nitrogen gas (N\u003csub\u003e2\u003c/sub\u003e) flux during a simulated 2-day flood event. All restoration practices produced N\u003csub\u003e2\u003c/sub\u003e at each timepoint, and the rates were greater at 48 h for all practices. Mean N\u003csub\u003e2\u003c/sub\u003e production was highest in natural regeneration and lowest in shallow water-wet areas throughout the 48 h incubation period. However, shallow water-wet areas exhibited the greatest percentage increase between 24 and 48 h, increasing by 48%. The predicted N\u003csub\u003e2\u003c/sub\u003e production was correlated with sediment oxygen demand (SOD), initial soil moisture, and extractable soil phosphorus (P). These results suggest that all restoration practices efficiently remove N over a 48 h flood period; however, the highest removal rates can depend on the vegetation type, flooding duration, and site-specific soil properties.\u003c/p\u003e","manuscriptTitle":"Restoration practices and soil properties influence potential denitrification in agricultural floodplain wetlands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 11:32:05","doi":"10.21203/rs.3.rs-9237776/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T12:59:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T22:04:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221368069188467405558007280054967511622","date":"2026-04-30T10:57:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T16:25:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78740625237228500110264853990469499963","date":"2026-04-24T14:34:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18364535175551395344781621579684763750","date":"2026-04-11T15:12:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10402070049117983798434218538995992510","date":"2026-04-08T15:25:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T13:27:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T03:48:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T03:48:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2026-03-26T19:58:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3a46929c-6668-477b-8fb5-4cb309b67633","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T12:59:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T22:04:09+00:00","index":49,"fulltext":""},{"type":"reviewerAgreed","content":"221368069188467405558007280054967511622","date":"2026-04-30T10:57:40+00:00","index":47,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T13:09:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 11:32:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9237776","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9237776","identity":"rs-9237776","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}