Plants,microorganisms, and soil temperatures contribute to a decrease in methane fluxes on a drained Arctic floodplain

As surface temperatures are expected to rise in the future, ice‐rich permafrost may thaw, altering soil topography and hydrology and creating a mosaic of wet and dry soil surfaces in the Arctic. Arctic wetlands are large sources of CH₄, and investigating effects of soil hydrology on CH₄ fluxes is of great importance for predicting ecosystem feedback in response to climate change. In this study, we investigate how a decade‐long drying manipulation on an Arctic floodplain influences CH₄‐associated microorganisms, soil thermal regimes, and plant communities. Moreover, we examine how these drainage‐induced changes may then modify CH₄ fluxes in the growing and nongrowing seasons. This study shows that drainage substantially lowered the abundance of methanogens along with methanotrophic bacteria, which may have reduced CH₄ cycling. Soil temperatures of the drained areas were lower in deep, anoxic soil layers (below 30 cm), but higher in oxic topsoil layers (0–15 cm) compared to the control wet areas. This pattern of soil temperatures may have reduced the rates of methanogenesis while elevating those of CH₄ oxidation, thereby decreasing net CH₄ fluxes. The abundance of Eriophorum angustifolium, an aerenchymatous plant species, diminished significantly in the drained areas. Due to this decrease, a higher fraction of CH₄ was alternatively emitted to the atmosphere by diffusion, possibly increasing the potential for CH₄ oxidation and leading to a decrease in net CH₄ fluxes compared to a control site. Drainage lowered CH₄ fluxes by a factor of 20 during the growing season, with postdrainage changes in microbial communities, soil temperatures, and plant communities also contributing to this reduction. In contrast, we observed CH₄ emissions increased by 10% in the drained areas during the nongrowing season, although this difference was insignificant given the small magnitudes of fluxes. This study showed that long‐term drainage considerably reduced CH₄ fluxes through modified ecosystem properties.     

Plant Community Composition as a Predictor of Regional Soil Carbon Storage in Alaskan Boreal Black Spruce Ecosystems

The boreal forest is the largest terrestrial biome in North America and holds a large portion of the world’s reactive soil carbon. Therefore, understanding soil carbon accumulation on a landscape or regional scale across the boreal forest is useful for predicting future soil carbon storage. Here, we examined the relationship between floristic composition and ecosystem parameters, such as soil carbon pools, the carbon-to-nitrogen (C/N) ratio of live black spruce needles, and normalized basal area increment (NBAI) of trees in black spruce communities, the most widespread forest type in the boreal forest of Alaska. Variability in ecosystem properties among black spruce stands was as large as that which had previously been documented among all forest types in the central interior of Alaska; we found an eightfold range in NBAI and fivefold range in mineral soil carbon and nitrogen pools. Acidic black spruce communities had significantly more carbon in the organic soil horizon than did nonacidic black spruce communities, but did not differ in any other measured ecosystem parameter. We explained 48% of the variation in total soil carbon with a combination of plant community indices and abiotic and biotic factors. Plant community composition was at least as effective as any single environmental factor or stand characteristic in predicting soil C pools in Alaskan black spruce ecosystems. We conclude that among the community properties analyzed, the presence of key groups of species, overall species composition, and diversity of certain functional types, especially Sphagnum moss species, are important predictors of soil carbon sequestration in the black spruce forest type.     

Nitrogen availability increases in a tundra ecosystem during five years of experimental permafrost thaw

Perennially frozen soil in high latitude ecosystems (permafrost) currently stores 1330–1580 Pg of carbon (C). As these ecosystems warm, the thaw and decomposition of permafrost is expected to release large amounts of C to the atmosphere. Fortunately, losses from the permafrost C pool will be partially offset by increased plant productivity. The degree to which plants are able to sequester C, however, will be determined by changing nitrogen (N) availability in these thawing soil profiles. N availability currently limits plant productivity in tundra ecosystems but plant access to N is expected improve as decomposition increases in speed and extends to deeper soil horizons. To evaluate the relationship between permafrost thaw and N availability, we monitored N cycling during 5 years of experimentally induced permafrost thaw at the Carbon in Permafrost Experimental Heating Research (CiPEHR) project. Inorganic N availability increased significantly in response to deeper thaw and greater soil moisture induced by Soil warming. This treatment also prompted a 23% increase in aboveground biomass and a 49% increase in foliar N pools. The sedge Eriophorum vaginatum responded most strongly to warming: this species explained 91% of the change in aboveground biomass during the 5 year period. Air warming had little impact when applied alone, but when applied in combination with Soil warming, growing season soil inorganic N availability was significantly reduced. These results demonstrate that there is a strong positive relationship between the depth of permafrost thaw and N availability in tundra ecosystems but that this relationship can be diminished by interactions between increased thaw, warmer air temperatures, and higher levels of soil moisture. Within 5 years of permafrost thaw, plants actively incorporate newly available N into biomass but C storage in live vascular plant biomass is unlikely to be greater than losses from deep soil C pools.     

Selector Screening for Enantioseparation of dl‐α‐Methyl Phenylglycine Amide by Liquid–Liquid Extraction

Enantioseparation through liquid extraction technology is an emerging field, e.g., enantioseparations of amino acids (and derivatives thereof), amino alcohols, amines, and carboxylic acids have been reported. Often, when a new selector is developed, the versatility of substrate scope is investigated. From an industrial point of view, the problem is typically approached the other way around, and for a target racemate, a selector needs to be found in order to accomplish the desired enantioseparation. This study presents such a screening approach for the separation of the enantiomers of dl ‐α‐methyl phenylglycine amide (dl ‐α‐MPGA), a model amide racemate with high industrial relevance. Chiral selectors that were reported for other classes of racemates were investigated, i.e., several macrocyclic selectors and Pd‐BINAP complexes. It appeared very challenging to obtain both high extraction yields and good enantioselectivity for most selectors, but Pd‐BINAP‐based selectors performed well, with enantioselectivities up to 7.4 with an extraction yield of the desired enantiomer of 95.8%. These high enantioselectivities were obtained using dichloromethane as solvent. Using less volatile chlorobenzene or 1‐chloropentane, reasonable selectivities of up to 1.7 were measured, making these the best alternative solvents for dichloromethane. Chirality 27:123–130, 2015. © 2014 Wiley Periodicals, Inc.     

The effect of nutrient deposition on bacterial communities in Arctic tundra soil

The microbial communities of high‐latitude ecosystems are expected to experience rapid changes over the next century due to climate warming and increased deposition of reactive nitrogen, changes that will likely affect microbial community structure and function. In moist acidic tundra (MAT) soils on the North Slope of the Brooks Range, Alaska, substantial losses of C and N were previously observed after long‐term nutrient additions. To analyse the role of microbial communities in these losses, we utilized 16S rRNA gene tag pyrosequencing coupled with community‐level physiological profiling to describe changes in MAT bacterial communities after short‐ and long‐term nutrient fertilization in four sets of paired control and fertilized MAT soil samples. Bacterial diversity was lower in long‐term fertilized plots. The Acidobacteria were one of the most abundant phyla in all soils and distinct differences were noted in the distributions of Acidobacteria subgroups between mineral and organic soil layers that were also affected by fertilization. In addition, Alpha‐ and Gammaproteobacteria were more abundant in long‐term fertilized samples compared with control soils. The dramatic increase in sequences within the Gammaproteobacteria identified as Dyella spp. (order Xanthomonadales) in the long‐term fertilized samples was confirmed by quantitative PCR (qPCR) in several samples. Long‐term fertilization was also correlated with shifts in the utilization of specific substrates by microbes present in the soils. The combined data indicate that long‐term fertilization resulted in a significant change in microbial community structure and function linked to changes in carbon and nitrogen availability and shifts in above‐ground plant communities.     

Nonlinear CO2 flux response to 7 years of experimentally induced permafrost thaw

Rapid Arctic warming is expected to increase global greenhouse gas concentrations as permafrost thaw exposes immense stores of frozen carbon (C) to microbial decomposition. Permafrost thaw also stimulates plant growth, which could offset C loss. Using data from 7 years of experimental Air and Soil warming in moist acidic tundra, we show that Soil warming had a much stronger effect on CO₂ flux than Air warming. Soil warming caused rapid permafrost thaw and increased ecosystem respiration (R_eco), gross primary productivity (GPP), and net summer CO₂ storage (NEE). Over 7 years R_eco, GPP, and NEE also increased in Control (i.e., ambient plots), but this change could be explained by slow thaw in Control areas. In the initial stages of thaw, R_eco, GPP, and NEE increased linearly with thaw across all treatments, despite different rates of thaw. As thaw in Soil warming continued to increase linearly, ground surface subsidence created saturated microsites and suppressed R_eco, GPP, and NEE. However R_eco and GPP remained high in areas with large Eriophorum vaginatum biomass. In general NEE increased with thaw, but was more strongly correlated with plant biomass than thaw, indicating that higher R_eco in deeply thawed areas during summer months was balanced by GPP. Summer CO₂ flux across treatments fit a single quadratic relationship that captured the functional response of CO₂ flux to thaw, water table depth, and plant biomass. These results demonstrate the importance of indirect thaw effects on CO₂ flux: plant growth and water table dynamics. Nonsummer R_eco models estimated that the area was an annual CO₂ source during all years of observation. Nonsummer CO₂ loss in warmer, more deeply thawed soils exceeded the increases in summer GPP, and thawed tundra was a net annual CO₂ source.     

Abrupt permafrost thaw drives spatially heterogeneous soil moisture and carbon dioxide fluxes in upland tundra

Permafrost thaw causes the seasonally thawed active layer to deepen, causing the Arctic to shift toward carbon release as soil organic matter becomes susceptible to decomposition. Ground subsidence initiated by ice loss can cause these soils to collapse abruptly, rapidly shifting soil moisture as microtopography changes and also accelerating carbon and nutrient mobilization. The uncertainty of soil moisture trajectories during thaw makes it difficult to predict the role of abrupt thaw in suppressing or exacerbating carbon losses. In this study, we investigated the role of shifting soil moisture conditions on carbon dioxide fluxes during a 13-year permafrost warming experiment that exhibited abrupt thaw. Warming deepened the active layer differentially across treatments, leading to variable rates of subsidence and formation of thermokarst depressions. In turn, differential subsidence caused a gradient of moisture conditions, with some plots becoming consistently inundated with water within thermokarst depressions and others exhibiting generally dry, but more variable soil moisture conditions outside of thermokarst depressions. Experimentally induced permafrost thaw initially drove increasing rates of growing season gross primary productivity (GPP), ecosystem respiration (R_eco), and net ecosystem exchange (NEE) (higher carbon uptake), but the formation of thermokarst depressions began to reverse this trend with a high level of spatial heterogeneity. Plots that subsided at the slowest rate stayed relatively dry and supported higher CO₂ fluxes throughout the 13-year experiment, while plots that subsided very rapidly into the center of a thermokarst feature became consistently wet and experienced a rapid decline in growing season GPP, R_eco, and NEE (lower carbon uptake or carbon release). These findings indicate that Earth system models, which do not simulate subsidence and often predict drier active layer conditions, likely overestimate net growing season carbon uptake in abruptly thawing landscapes.     

Degradation of benzene compounds by yeasts in acidic soils

Attemps were made to demonstrate the role of yeasts in the degradation of benzene compounds under natural soil conditions. Yeasts were isolated from acidic sandy soil supplied with benzene compounds. For this purpose the slant culture method was used. Growth on the benzene compounds took place on solid growth media at 10°C. Several yeast species were isolated: Leucosporidium scottii, Rhodotorula aurantiaca, Rhodotorula mucilaginosa, Trichosporon dulcitum, Trichosporon moniliiforme and Schizoblastosporion starkeyi-henricii. Cryptococcus humicolus and Cryptococcus laurentii were isolated from liquid enrichment cultures. All these strains assimilated several benzene compounds in pure culture.Cresol removal from contaminated soil was speeded up by inoculation with Rhodotorula aurantiaca G36. It was demonstrated that this yeast utilized this compound in competition with the soil microflora.     

Model parameterization to represent processes at unresolved scales and changing properties of evolving systems

Modeling has become an indispensable tool for scientific research. However, models generate great uncertainty when they are used to predict or forecast ecosystem responses to global change. This uncertainty is partly due to parameterization, which is an essential procedure for model specification via defining parameter values for a model. The classic doctrine of parameterization is that a parameter is constant. However, it is commonly known from modeling practice that a model that is well calibrated for its parameters at one site may not simulate well at another site unless its parameters are tuned again. This common practice implies that parameter values have to vary with sites. Indeed, parameter values that are estimated using a statistically rigorous approach, that is, data assimilation, vary with time, space, and treatments in global change experiments. This paper illustrates that varying parameters is to account for both processes at unresolved scales and changing properties of evolving systems. A model, no matter how complex it is, could not represent all the processes of one system at resolved scales. Interactions of processes at unresolved scales with those at resolved scales should be reflected in model parameters. Meanwhile, it is pervasively observed that properties of ecosystems change over time, space, and environmental conditions. Parameters, which represent properties of a system under study, should change as well. Tuning has been practiced for many decades to change parameter values. Yet this activity, unfortunately, did not contribute to our knowledge on model parameterization at all. Data assimilation makes it possible to rigorously estimate parameter values and, consequently, offers an approach to understand which, how, how much, and why parameters vary. To fully understand those issues, extensive research is required. Nonetheless, it is clear that changes in parameter values lead to different model predictions even if the model structure is the same.