ALBERT

Description: Greenhouse gas (GHG) emissions from abrupt thaw beneath thermokarst lakes were projected to at least double radiative forcing from circumpolar permafrost-soil carbon fluxes by the end of this century, primarily through the release of methane, a much stronger GHG than CO2. Thermokarst lagoons represent the first stage of a thermokarst lake transition to a marine setting with so far neglected consequences for GHG production and release. We expected that along the transition from a thermokarst lake to a thermokarst lagoon, sediment concentrations of terminal electron acceptors like sulfate increase with an associated drop in methanogenic activity, a shift towards non-competitive methylotrophic methanogenesis, and the occurrence of sulfate-driven anaerobic methane oxidation (AOM). To explore this, we targeted a variety of geochemical and microbial parameters including sediment methane and CO2 concentrations, gaseous carbon isotopic signatures, hydrochemistry, GHG production rates, ratios of CH4/CO2, and occurrence of methane-cycling microbial taxa in sediments of two thermokarst lakes and a thermokarst lagoon on the Bykovsky Peninsula located in northeastern Siberia adjacent to Tiksi Bay. We found multiple lines of evidence that AOM in sediment layers influenced by Tiksi Bay water (i.e. the lagoon) functions as effective microbial methane filter. Annually, the lagoon is decoupled from Tiksi Bay for more than six months, resulting in more saline conditions below the ice cover compared to Tiksi Bay. Despite sub-zero near-surface sediment temperatures for approximately nine months per year, we show that, at least in early spring, AOM led to near-surface sediment methane concentrations approximating only about 1% of those measured in near-surface thermokarst lake sediments. Structural equation modelling stresses pore-water chemistry and increases in anaerobic methanotrophic abundance as main controls for the drop of in-situ methane concentrations and the corresponding increase in carbon isotopic signature. Shallow sediment layers (i.e. younger carbon) corresponded with higher rates of potential methane production, especially in the non-lagoon settings but even in the lagoon, potential methane production rates in the surface sediment layers were relatively unaffected by the marine influence. We propose that this reflects the overall dominance of non-competitive methylotrophic methanogenesis independent of pore-water chemistry and sediment depth. Overall, our study suggests that thermokarst lake to lagoon transitions have the potential to offset atmospheric methane fluxes from abrupt thaw lake structures long before thermokarst lakes fully transgress onto the Arctic shelf.

Description: Three strains of methanotrophic bacteria (EbAT, EbBT and Eb1) were isolated from the River Elbe, Germany. These Gram-negative, rod-shaped or coccoid cells contain intracytoplasmic membranes perpendicular to the cell surface. Colonies and liquid cultures appeared bright-pink. The major cellular fatty acids were 12:0 and 14:0, in addition in Eb1 the FA 16:1ω5t was also dominant. Methane and methanol were utilized as sole carbon sources by EbBT and Eb1, while EbAT could not use methanol. All strains oxidize methane using the particulate methane monooxygenase. Both strains contain an additional soluble methane monooxygenase. The strains grew optimally at 15–25 °C and at pH 6 and 8. Based on 16S rRNA gene analysis recovered from the full genome, the phylogenetic position of EbAT is robustly outside any species clade with its closest relatives being Methylomonas sp. MK1 (98.24%) and Methylomonas sp. 11b (98.11%). Its closest type strain is Methylomonas methanica NCIMB11130 (97.91%). The 16S rRNA genes of EbBT are highly similar to Methylomonas methanica strains with Methylomonas methanica R-45371 as the closest relative (99.87% sequence identity). However, average nucleotide identity (ANI) and digital DNA-DNA-hybridization (dDDH) values reveal it as distinct species. The DNA G + C contents were 51.07 mol% and 51.5 mol% for EbAT and EbBT, and 50.7 mol% for Eb1, respectively. Strains EbAT and EbBT are representing two novel species within the genus Methylomonas. For strain EbAT we propose the name Methylomonas albis sp. nov (LMG 29958, JCM 32282) and for EbBT, we propose the name Methylomonas fluvii sp. nov (LMG 29959, JCM 32283). Eco-physiological descriptions for both strains are provided. Strain Eb1 (LMG 30323, JCM 32281) is a member of the species Methylovulum psychrotolerans. This genus is so far only represented by two isolates but Eb1 is the first isolate from a temperate environment; so, an emended description of the species is given.

Description: Rivers are significant sources of greenhouse gases (GHGs; e.g., CH〈sub〉4〈/sub〉 and CO〈sub〉2〈/sub〉); however, our understanding of the large-scale longitudinal patterns of GHG emissions from rivers remains incomplete, representing a major challenge in upscaling. Local hotspots and moderate heterogeneities may be overlooked by conventional sampling schemes. In August 2020 and for the first time, we performed continuous (once per minute) CH〈sub〉4〈/sub〉 measurements of surface water during a 584-km-long river cruise along the German Elbe to explore heterogeneities in CH〈sub〉4〈/sub〉 concentration at different spatial scales and identify CH〈sub〉4〈/sub〉 hotspots along the river. The median concentration of dissolved CH〈sub〉4〈/sub〉 in the Elbe was 112 nmol L〈sup〉−1〈/sup〉, ranging from 40 to 1,456 nmol L〈sup〉−1〈/sup〉 The highest CH〈sub〉4〈/sub〉 concentrations were recorded at known potential hotspots, such as weirs and harbors. These hotspots were also notable in terms of atmospheric CH〈sub〉4〈/sub〉 concentrations, indicating that measurements in the atmosphere above the water are useful for hotspot detection. The median atmospheric CH〈sub〉4〈/sub〉 concentration was 2,033 ppb, ranging from 1,821 to 2,796 ppb. We observed only moderate changes and fluctuations in values along the river. Tributaries did not obviously affect CH〈sub〉4〈/sub〉 concentrations in the main river. The median CH〈sub〉4〈/sub〉 emission was 251 μmol m〈sup〉−2〈/sup〉 d〈sup〉−1〈/sup〉, resulting in a total of 28,640 mol d〈sup〉−1〈/sup〉 from the entire German Elbe. Similar numbers were obtained using a conventional sampling approach, indicating that continuous measurements are not essential for a large-scale budget. However, we observed considerable lateral heterogeneity, with significantly higher concentrations near the shore only in reaches with groins. Sedimentation and organic matter mineralization in groin fields evidently increase CH〈sub〉4〈/sub〉 concentrations in the river, leading to considerable lateral heterogeneity. Thus, river morphology and structures determine the variability of dissolved CH〈sub〉4〈/sub〉 in large rivers, resulting in smooth concentrations at the beginning of the Elbe versus a strong variability in its lower parts. In conclusion, groin construction is an additional anthropogenic modification following dam building that can significantly increase GHG emissions from rivers.

Description: Recent discussions in many scientific disciplines stress the necessity of “FAIR” data. FAIR data, however, does not necessarily include information on data trustworthiness, where trustworthiness comprises reliability, validity and provenience/provenance. This opens up the risk of misinterpreting scientific data, even though all criteria of “FAIR” are fulfilled. Especially applications such as secondary data processing, data blending, and joint interpretation or visualization efforts are affected. This paper intends to start a discussion in the scientific community about how to evaluate, describe, and implement trustworthiness in a standardized data evaluation approach and in its metadata description following the FAIR principles. It discusses exemplarily different assessment tools regarding soil moisture measurements, data processing and visualization and elaborates on which additional (metadata) information is required to increase the trustworthiness of data for secondary usage. Taking into account the perspectives of data collectors, providers and users, the authors identify three aspects of data trustworthiness that promote efficient data sharing: 1) trustworthiness of the measurement 2) trustworthiness of the data processing and 3) trustworthiness of the data integration and visualization. The paper should be seen as the basis for a community discussion on data trustworthiness for a scientifically correct secondary use of the data. We do not have the intention to replace existing procedures and do not claim completeness of reliable tools and approaches described. Our intention is to discuss several important aspects to assess data trustworthiness based on the data life cycle of soil moisture data as an example.