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Abstract Primary production is fundamental to ecosystems, and in many extreme environments production is facilitated by microbial mats. Microbial mats are complex assemblages of photo- and heterotrophic microorganisms colonizing sediment and soil surfaces. These communities are the dominant producers of the McMurdo Dry Valleys, Antarctica, where they occupy lentic and lotic environments as well as intermittently wet soils. While the influence of microbial mats on stream nutrient dynamics and lake organic matter cycling is well documented, the influence of microbial mats on underlying soil is less well understood, particularly the effects of microbial mat nitrogen and carbon fixation. Taylor Valley soils occur across variable levels of inorganic phosphorus availability, with the Ross Sea drift containing four times that of the Taylor drifts, providing opportunities to examine how soil geochemistry influences microbial mats and the ecological functions they regulate. We found that inorganic phosphorus availability is positively correlated with microbial mat biomass, pigment concentration and nitrogen fixation potential. Additionally, our results demonstrate that dense microbial mats influence the ecological functioning of underlying soils by enriching organic carbon and total nitrogen stocks (two times higher). This work contributes to ongoing questions regarding the sources of energy fuelling soil food webs and the regional carbon balance in the McMurdo Dry Valleys. 

The reliable detection of the global 21-cm signal, a key tracer of Cosmic Dawn and the Epoch of Reionization, requires meticulous data modelling and robust statistical frameworks for model validation and comparison. In Paper I of this series, we presented the beam-factor-based chromaticity correction (BFCC) model for spectrometer data processed using BFCC to suppress instrumentally induced spectral structure. We demonstrated that the BFCC model, with complexity calibrated by Bayes factor-based model comparison (BFBMC), enables unbiased recovery of a 21-cm signal consistent with the one reported by The Experiment to Detect the Global Epoch of Reionization Signature (EDGES) from simulated data. Here, we extend the evaluation of the BFCC model to lower amplitude 21-cm signal scenarios where deriving reliable conclusions about a model’s capacity to recover unbiased 21-cm signal estimates using BFBMC is more challenging. Using realistic simulations of chromaticity-corrected EDGES-low spectrometer data, we evaluate three signal amplitude regimes – null, moderate, and high. We then conduct a Bayesian comparison between the BFCC model and three alternative models previously applied to 21-cm signal estimation from EDGES data. To mitigate biases introduced by systematics in the 21-cm signal model fit, we incorporate the Bayesian Null-Test-Evidence-Ratio (BaNTER) validation framework and implement a Bayesian inference workflow based on posterior odds of the validated models. The BaNTER-validated posterior-odds-based methodology presented here is general and transferable to other global 21-cm experiments employing Bayesian signal inference. We demonstrate that, unlike BFBMC alone, this approach consistently recovers 21-cm signal estimates that align with the true signal across all amplitude regimes, advancing robust global 21-cm signal detection methodologies. 

Abstract Antarctic soils are unique from those found nearly anywhere else on Earth yet can still harbor a broad diversity of microorganisms able to tolerate the challenging conditions typical of the continent. For these reasons, microbiologists have been drawn to Antarctica for decades. However, our understanding of which microbes thrive in Antarctic soils and how they to do so remains limited. To help resolve these knowledge gaps, we analyzed a collection of 200 archived Antarctic soils—from Livingston Island on the Antarctic Peninsula to Cape Hallett in northern Victoria Land. We analyzed the prokaryotic and fungal communities in these soils using both cultivation-independent marker gene sequencing and cultivation-dependent approaches (microbial isolation), paired with extensive soil geochemical analyses. Our cultivation-independent analyses indicate that colder, saltier, and drier soils harbor less diverse communities of bacteria and fungi, distinct from those found in soils with less challenging conditions. We also built a culture collection from a subset of these soils that encompasses more than 50 bacterial and fungal genera, including cold-tolerant organisms, such asCryobacteriumandCryomyces. By directly comparing the diversity of our cultured isolates against our cultivation-independent data, we show that many of the more abundant Antarctic taxa are not readily cultivated and highlight bacterial and fungal taxa that should be the focus of future cultivation efforts. Together, we hope that our collection of isolates, the comprehensive data compiled from the cultivation-independent analyses, and our geochemical analyses will serve as a community resource to accelerate the study of Antarctic soil microbes. 

In situ observed data are commonly used as species occurrence response variables in species distribution models. However, the use of remotely observed data from high‐resolution multispectral remote‐sensing images as a source of presence/absence data for species distribution models remains under‐developed. Here, we describe an ensemble species distribution model of black microbial mats "Nostoc" using presence/absence points derived from the unmixing of 4‐m resolution WorldView‐2 and WorldView‐3 images in the Lake Fryxell basin region of Taylor Valley, Antarctica. Environmental and topographical characteristics such as soil moisture, snow, elevation, slope, and aspect were used as predictor variables in our models. We demonstrate that we can build and run ensemble species distribution models using both dependent and independent variables derived from remote‐sensing data to generate spatially explicit habitat suitability maps. Snow and soil moisture were found to be the most important variables accounting for about 80% of the variation in the distribution of black mats throughout the Fryxell basin. This study highlights the potential contribution of high‐resolution remote‐sensing to species distribution modeling and informs new studies incorporating remotely derived species occurrences in species distribution models, especially in remote areas where access to in situ data is often limited. 

Abstract Drylands are unique among terrestrial ecosystems in that they have a significant proportion of primary production facilitated by non‐vascular plants such as colonial cyanobacteria, moss, and lichens, i.e., biocrusts, which occur on and in the surface soil. Biocrusts inhabit all continents, including Antarctica, an increasingly dynamic continent on the precipice of change. Here, we describe in‐situ field surveying and sampling, remote sensing, and modeling approaches to assess the habitat suitability of biocrusts in the Lake Fryxell basin of Taylor Valley, Antarctica, which is the main site of the McMurdo Dry Valleys Long‐Term Ecological Research Program. Soils suitable for the development of biocrusts are typically wetter, less alkaline, and less saline compared to unvegetated soils. Using random forest models, we show that gravimetric water content, electrical conductivity, and snow frequency are the top predictors of biocrust presence and biomass. Areas most suitable for the growth of dense biocrusts are soils associated with seasonal snow patches. Using geospatial data to extrapolate our habitat suitability model to the whole basin predicts that biocrusts are present in 2.7 × 10⁵m²and contain 11–72 Mg of aboveground carbon, based on the 90% probability of occurrence. Our study illustrates the synergistic effect of combining field and remote sensing data for understanding the distribution and biomass of biocrusts, a foundational community in the carbon balance of this region. Extreme weather events and changing climate conditions in this region, especially those influencing snow accumulation and persistence, could have significant effects on the future distribution and abundance of biocrusts and therefore soil organic carbon storage in the McMurdo Dry Valleys. 

This data package offers comprehensive insights into Antarctic soil microbial diversity and composition. From 2003 to 2023, a total of 186 samples were collected from diverse locations spanning the Antarctic Peninsula to East Antarctica, representing a wide range of environmental gradients and climatic conditions. Soils were stored at -20°C to preserve their integrity for downstream analyses. This data package integrates cultivation-independent sequencing of prokaryotic and fungal communities alongside a robust cultivation-dependent culture collection to enable direct comparisons across microbial diversity assessment methods. Accompanying geochemical, physicochemical, and environmental parameters provide critical context for biogeographical analyses, offering a valuable resource for studying microbial adaptations and community dynamics in extreme Antarctic environments. 

ABSTRACT Accurately accounting for spectral structure in spectrometer data induced by instrumental chromaticity on scales relevant for detection of the 21-cm signal is among the most significant challenges in global 21-cm signal analysis. In the publicly available Experiment to Detect the Global Epoch of Reionization Signature low-band data set, this complicating structure is suppressed using beam-factor-based chromaticity correction (BFCC), which works by dividing the data by a sky-map-weighted model of the spectral structure of the instrument beam. Several analyses of these data have employed models that start with the assumption that this correction is complete. However, while BFCC mitigates the impact of instrumental chromaticity on the data, given realistic assumptions regarding the spectral structure of the foregrounds, the correction is only partial. This complicates the interpretation of fits to the data with intrinsic sky models (models that assume no instrumental contribution to the spectral structure of the data). In this paper, we derive a BFCC data model from an analytical treatment of BFCC and demonstrate using simulated observations that, in contrast to using an intrinsic sky model for the data, the BFCC data model enables unbiased recovery of a simulated global 21-cm signal from beam-factor chromaticity-corrected data in the limit that the data are corrected with an error-free beam-factor model.