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1. Stronger increase of methane emissions from coastal wetlands by non‐native Spartina alterniflora than non‐native Phragmites australis

   https://doi.org/10.1002/ppp3.10578  

   Fuchs, Andrea; Davidson, Ian C; Megonigal, J Patrick; Devaney, John L; Simkanin, Christina; Noyce, Genevieve L; Lu, Meng; Cott, Grace M (January 2025, PLANTS, PEOPLE, PLANET)

   Societal Impact StatementThe invasive speciesS. alternifloraandP. australisare fast growing coastal wetland plants sequestering large amounts of carbon in the soil and protect coastlines against erosion and storm surges. In this global analysis, we found thatSpartinaandPhragmitesincrease methane but not nitrous oxide emissions, withPhragmiteshaving a lesser effect. The impact of the invasive species on emissions differed greatly among different types of native plant groups, providing valuable information to managers and policymakers during coastal wetland planning and restoration efforts. Further, our estimated net emissions per wetland plant group facilitate regional and national blue carbon estimates. SummaryGlobally,Spartina alternifloraandPhragmites australisare among the most pervasive invasive plants in coastal wetland ecosystems. Both species sequester large amounts of atmospheric carbon dioxide (CO₂) and biogenic carbon in soils but also support production and emission of methane (CH₄). In this study, we investigated the magnitude of their net greenhouse gas (GHG) release from invaded and non‐invaded habitats.We conducted a meta‐analysis of GHG fluxes associated with these two species and related soil carbon content and plant biomass in invaded coastal wetlands.Our results show that both invasive species increase CH₄fluxes compared to uninvaded coastal wetlands, but they do not significantly affect CO₂and N₂O fluxes. The magnitude of emissions fromSpartinaandPhragmitesdiffers among native habitats. GHG fluxes, soil carbon and plant biomass ofSpartina‐invaded habitats were highest compared to uninvaded mudflats and succulent forb‐dominated wetlands, while being lower compared to uninvaded mangroves (except for CH₄).This meta‐analysis highlights the important role of individual plant traits as drivers of change by invasive species on plant‐mediated carbon cycles. 

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2. CO2 and CH4 Concentrations in Headwater Wetlands Influenced by Morphology and Changing Hydro-Biogeochemical Conditions

   https://doi.org/10.1007/s10021-024-00936-7  

   López_Lloreda, Carla; Maze, James; Wardinski, Katherine; Corline, Nicholas; McLaughlin, Daniel; Jones, C Nathan; Scott, Durelle; Palmer, Margaret; Hotchkiss, Erin R (October 2024, Ecosystems)

   Abstract Headwater wetlands are important sites for carbon storage and emissions. While local- and landscape-scale factors are known to influence wetland carbon biogeochemistry, the spatial and temporal heterogeneity of these factors limits our predictive understanding of wetland carbon dynamics. To address this issue, we examined relationships between carbon dioxide (CO₂) and methane (CH₄) concentrations with wetland hydrogeomorphology, water level, and biogeochemical conditions. We sampled water chemistry and dissolved gases (CO₂and CH₄) and monitored continuous water level at 20 wetlands and co-located upland wells in the Delmarva Peninsula, Maryland, every 1–3 months for 2 years. We also obtained wetland hydrogeomorphologic metrics at maximum inundation (area, perimeter, and volume). Wetlands in our study were supersaturated with CO₂(mean = 315 μM) and CH₄(mean = 15 μM), highlighting their potential role as carbon sources to the atmosphere. Spatial and temporal variability in CO₂and CH₄concentrations was high, particularly for CH₄, and both gases were more spatially variable than temporally. We found that groundwater is a potential source of CO₂in wetlands and CO₂decreases with increased water level. In contrast, CH₄concentrations appear to be related to substrate and nutrient availability and to drying patterns over a longer temporal scale. At the landscape scale, wetlands with higher perimeter:area ratios and wetlands with higher height above the nearest drainage had higher CO₂and CH₄concentrations. Understanding the variability of CO₂and CH₄in wetlands, and how these might change with changing environmental conditions and across different wetland types, is critical to understanding the current and future role of wetlands in the global carbon cycle. 

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3. FLUXNET-CH₄: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands

   https://doi.org/10.5194/essd-13-3607-2021  

   Delwiche, Kyle B.; Knox, Sara Helen; Malhotra, Avni; Fluet-Chouinard, Etienne; McNicol, Gavin; Feron, Sarah; Ouyang, Zutao; Papale, Dario; Trotta, Carlo; Canfora, Eleonora; et al (January 2021, Earth System Science Data)

   null (Ed.)

   Abstract. Methane (CH4) emissions from natural landscapes constituteroughly half of global CH4 contributions to the atmosphere, yet largeuncertainties remain in the absolute magnitude and the seasonality ofemission quantities and drivers. Eddy covariance (EC) measurements ofCH4 flux are ideal for constraining ecosystem-scale CH4emissions due to quasi-continuous and high-temporal-resolution CH4flux measurements, coincident carbon dioxide, water, and energy fluxmeasurements, lack of ecosystem disturbance, and increased availability ofdatasets over the last decade. Here, we (1) describe the newly publisheddataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset ofCH4 EC measurements (available athttps://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4includes half-hourly and daily gap-filled and non-gap-filled aggregatedCH4 fluxes and meteorological data from 79 sites globally: 42freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drainedecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverageglobally because the majority of sites in FLUXNET-CH4 Version 1.0 arefreshwater wetlands which are a substantial source of total atmosphericCH4 emissions; and (3) we provide the first global estimates of theseasonal variability and seasonality predictors of freshwater wetlandCH4 fluxes. Our representativeness analysis suggests that thefreshwater wetland sites in the dataset cover global wetland bioclimaticattributes (encompassing energy, moisture, and vegetation-relatedparameters) in arctic, boreal, and temperate regions but only sparselycover humid tropical regions. Seasonality metrics of wetland CH4emissions vary considerably across latitudinal bands. In freshwater wetlands(except those between 20∘ S to 20∘ N) the spring onsetof elevated CH4 emissions starts 3 d earlier, and the CH4emission season lasts 4 d longer, for each degree Celsius increase in meanannual air temperature. On average, the spring onset of increasing CH4emissions lags behind soil warming by 1 month, with very few sites experiencingincreased CH4 emissions prior to the onset of soil warming. Incontrast, roughly half of these sites experience the spring onset of risingCH4 emissions prior to the spring increase in gross primaryproductivity (GPP). The timing of peak summer CH4 emissions does notcorrelate with the timing for either peak summer temperature or peak GPP.Our results provide seasonality parameters for CH4 modeling andhighlight seasonality metrics that cannot be predicted by temperature or GPP(i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resourcefor diagnosing and understanding the role of terrestrial ecosystems andclimate drivers in the global CH4 cycle, and future additions of sitesin tropical ecosystems and site years of data collection will provide addedvalue to this database. All seasonality parameters are available athttps://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021).Additionally, raw FLUXNET-CH4 data used to extract seasonality parameterscan be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a completelist of the 79 individual site data DOIs is provided in Table 2 of this paper. 

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4. Improved global wetland carbon isotopic signatures support post-2006 microbial methane emission increase

   https://doi.org/10.1038/s43247-022-00488-5  

   Oh, Youmi; Zhuang, Qianlai; Welp, Lisa R.; Liu, Licheng; Lan, Xin; Basu, Sourish; Dlugokencky, Edward J.; Bruhwiler, Lori; Miller, John B.; Michel, Sylvia E.; et al (December 2022, Communications Earth & Environment)

   Abstract Atmospheric concentrations of methane, a powerful greenhouse gas, have strongly increased since 2007. Measurements of stable carbon isotopes of methane can constrain emissions if the isotopic compositions are known; however, isotopic compositions of methane emissions from wetlands are poorly constrained despite their importance. Here, we use a process-based biogeochemistry model to calculate the stable carbon isotopic composition of global wetland methane emissions. We estimate a mean global signature of −61.3 ± 0.7‰ and find that tropical wetland emissions are enriched by ~11‰ relative to boreal wetlands. Our model shows improved resolution of regional, latitudinal and global variations in isotopic composition of wetland emissions. Atmospheric simulation scenarios with the improved wetland isotopic composition suggest that increases in atmospheric methane since 2007 are attributable to rising microbial emissions. Our findings substantially reduce uncertainty in the stable carbon isotopic composition of methane emissions from wetlands and improve understanding of the global methane budget. 

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5. WetCH ₄ : a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022

   https://doi.org/10.5194/essd-17-2507-2025  

   Ying, Qing; Poulter, Benjamin; Watts, Jennifer D; Arndt, Kyle A; Virkkala, Anna-Maria; Bruhwiler, Lori; Oh, Youmi; Rogers, Brendan M; Natali, Susan M; Sullivan, Hilary; et al (January 2025, Earth System Science Data)

   Abstract. Wetlands are the largest natural source of methane (CH4) emissions globally. Northern wetlands (>45° N), accounting for 42 % of global wetland area, are increasingly vulnerable to carbon loss, especially as CH4 emissions may accelerate under intensified high-latitude warming. However, the magnitude and spatial patterns of high-latitude CH4 emissions remain relatively uncertain. Here, we present estimates of daily CH4 fluxes obtained using a new machine learning-based wetland CH4 upscaling framework (WetCH4) that combines the most complete database of eddy-covariance (EC) observations available to date with satellite remote-sensing-informed observations of environmental conditions at 10 km resolution. The most important predictor variables included near-surface soil temperatures (top 40 cm), vegetation spectral reflectance, and soil moisture. Our results, modeled from 138 site years across 26 sites, had relatively strong predictive skill, with a mean R2 of 0.51 and 0.70 and a mean absolute error (MAE) of 30 and 27 nmol m−2 s−1 for daily and monthly fluxes, respectively. Based on the model results, we estimated an annual average of 22.8±2.4 Tg CH4 yr−1 for the northern wetland region (2016–2022), and total budgets ranged from 15.7 to 51.6 Tg CH4 yr−1, depending on wetland map extents. Although 88 % of the estimated CH4 budget occurred during the May–October period, a considerable amount (2.6±0.3 Tg CH4) occurred during winter. Regionally, the Western Siberian wetlands accounted for a majority (51 %) of the interannual variation in domain CH4 emissions. Overall, our results provide valuable new high-spatiotemporal-resolution information on the wetland emissions in the high-latitude carbon cycle. However, many key uncertainties remain, including those driven by wetland extent maps and soil moisture products and the incomplete spatial and temporal representativeness in the existing CH4 flux database; e.g., only 23 % of the sites operate outside of summer months, and flux towers do not exist or are greatly limited in many wetland regions. These uncertainties will need to be addressed by the science community to remove the bottlenecks currently limiting progress in CH4 detection and monitoring. The dataset can be found at https://doi.org/10.5281/zenodo.10802153 (Ying et al., 2024). 

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