The changing carbon balance of tundra ecosystems: results from a vertically-resolved peatland biosphere model

An estimated 1700 Pg of carbon is frozen in the Arctic permafrost and the fate of this carbon is unclear because of the complex interaction of biophysical, ecological and biogeochemical processes that govern the Arctic carbon budget. Two key processes determining the region’s long-term carbon budget are: (i) carbon uptake through increased plant growth, and (ii) carbon release through increased heterotrophic respiration due to warmer soils. Previous predictions for how these two opposing carbon fluxes may change in the future have varied greatly, indicating that improved understanding of these processes and their feedbacks is critical for advancing our predictive ability for the fate of Arctic peatlands. In this study, we implement and analyze a vertically-resolved model of peatland soil carbon into a cohort-based terrestrial biosphere model to improve our understanding of how on-going changes in climate are altering the Arctic carbon budget. A key feature of the formulation is that accumulation of peat within the soil column modifies its texture, hydraulic conductivity, and thermal conductivity, which, in turn influences resulting rates of heterotrophic respiration within the soil column. Analysis of the model at three eddy covariance tower sites in the Alaskan tundra shows that the vertically-resolved soil column formulation accurately captures the zero-curtain phenomenon, in which the temperature of soil layers remain at or near 0 °C during fall freezeback due to the release of latent heat, is critical to capturing observed patterns of wintertime respiration. We find that significant declines in net ecosystem productivity (NEP) occur starting in 2013 and that these declines are driven by increased heterotrophic respiration arising from increased precipitation and warming. Sensitivity analyses indicate that the cumulative NEP over the decade responds strongly to the estimated soil carbon stock and more weakly to vegetation abundance at the beginning of the simulation.

Metagenomic analysis reveals the shared and distinct features of the soil resistome across tundra, temperate prairie, and tropical ecosystems

Background Soil is an important reservoir of antibiotic resistance genes (ARGs), but their potential risk in different ecosystems as well as response to anthropogenic land use change is unknown. We used a metagenomic approach and datasets with well-characterized metadata to investigate ARG types and amounts in soil DNA of three native ecosystems: Alaskan tundra, US Midwestern prairie, and Amazon rainforest, as well as the effect of conversion of the latter two to agriculture and pasture, respectively. Results High diversity (242 ARG subtypes) and abundance (0.184–0.242 ARG copies per 16S rRNA gene copy) were observed irrespective of ecosystem, with multidrug resistance and efflux pump the dominant class and mechanism. Ten regulatory genes were identified and they accounted for 13–35% of resistome abundances in soils, among them arlR, cpxR, ompR, vanR, and vanS were dominant and observed in all studied soils. We identified 55 non-regulatory ARGs shared by all 26 soil metagenomes of the three ecosystems, which accounted for more than 81% of non-regulatory resistome abundance. Proteobacteria, Firmicutes, and Actinobacteria were primary ARG hosts, 7 of 10 most abundant ARGs were found in all of them. No significant differences in both ARG diversity and abundance were observed between native prairie soil and adjacent long-term cultivated agriculture soil. We chose 12 clinically important ARGs to evaluate at the sequence level and found them to be distinct from those in human pathogens, and when assembled they were even more dissimilar. Significant correlation was found between bacterial community structure and resistome profile, suggesting that variance in resistome profile was mainly driven by the bacterial community composition. Conclusions Our results identify candidate background ARGs (shared in all 26 soils), classify ARG hosts, quantify resistance classes, and provide quantitative and sequence information suggestive of very low risk but also revealing resistance gene variants that might emerge in the future.

Exploring environmental factors driving wildfire occurrences in Alaskan tundra

<p>Tundra fires are common across the pan-Arctic region, particularly in Alaska. Fires lead to significant impacts on terrestrial carbon balance and ecosystem functioning in the tundra. They can even affect the forage availability of herbivorous wildlife and living resources of local human communities. Also, interactions between fire and climate change can enhance the fire impacts on the Arctic ecosystems. However, the drivers and mechanisms of wildland fire occurrences in Alaskan tundra are still poorly understood. Research on modeling contemporary fire probability in the tundra is also lacking. This study focuses on exploring the critical environmental factors controlling wildfire occurrences in Alaskan tundra and modeled the fire ignition probability, accounting for ignition source, fuel types, fire weather conditions, and topography. The fractional cover maps of fuel type components developed Chapter 2 serve as input data for fuel type distribution. The probability of cloud-to-ground (CG) lightning and fire weather conditions are simulated using WRF. Topographic features are also calculated from the Digital Elevation Model (DEM) data. Additionally, fire ignition locations are extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product for Alaskan tundra from 2001 to 2019. Empirical modeling methods, including RF and logistic regression, are then utilized to model the relationships between environmental factors and wildfire occurrences in the tundra and to evaluate the roles of these factors. Our results suggested that CG lightning is the primary driver controlling fire ignitions in the tundra, while warmer and drier weather conditions also support fires. We also projected future potential of wildland fires in this tundra region with Coupled Model Intercomparison Projects Phase 6 (CMIP6) data. The results of this study highlight the important role of CG lightning in driving tundra fires and that incorporating CG lightning modeling is necessary and essential for fire monitoring and management efforts in the High Northern Latitudes (HNL).</p>

Arctic Snow Isotope Hydrology: A Comparative Snow-Water Vapor Study

The Arctic’s winter water cycle is rapidly changing, with implications for snow moisture sources and transport processes. Stable isotope values (δ18O, δ2H, d-excess) of the Arctic snowpack have potential to provide proxy records of these processes, yet it is unclear how well the isotope values of individual snowfall events are preserved within snow profiles. Here, we present water isotope data from multiple taiga and tundra snow profiles sampled in Arctic Alaska and Finland, respectively, during winter 2018–2019. We compare the snowpack isotope stratigraphy with meteoric water isotopes (vapor and precipitation) during snowfall days, and combine our measurements with satellite observations and reanalysis data. Our analyses indicate that synoptic-scale atmospheric circulation and regional sea ice coverage are key drivers of the source, amount, and isotopic composition of Arctic snowpacks. We find that the western Arctic tundra snowpack profiles in Alaska preserved the isotope values for the most recent storm; however, post depositional processes modified the remaining isotope profiles. The overall seasonal evolution in the vapor isotope values were better preserved in taiga snow isotope profiles in the eastern Arctic, where there is significantly less wind-driven redistribution than in the open Alaskan tundra. We demonstrate the potential of the seasonal snowpack to provide a useful proxy for Arctic winter-time moisture sources and propose future analyses.