First evidence of microplastics in Antarctic snow

In recent years, airborne microplastics have been identified in a range of remote environments. However, data throughout the Southern Hemisphere, in particular Antarctica, are largely absent to date. We collected snow samples from 19 sites across the Ross Island region of Antarctica. Suspected microplastic particles were isolated and their composition confirmed using micro-Fourier transform infrared spectroscopy (μFTIR).We identified microplastics in all Antarctic snow samples at an average concentration of 29 particles L−1, with fibres the most common morphotype and polyethylene terephthalate (PET) the most common polymer. To investigate sources, backward air mass trajectories were run from the time of sampling. These indicate potential long-range transportation of up to 6000 kilometers, assuming a residence time of 6.5 days. Local sources were also identified as potential inputs into the environment, as the polymers identified were consistent with those used in clothing and equipment from nearby research stations. This study adds to the growing body of literature regarding microplastics as a ubiquitous airborne pollutant, and establishes their presence in Antarctica.

Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry

Single-pass interferometric synthetic aperture radar (InSAR) enables the possibility for sea ice topographic retrieval despite the inherent dynamics of sea ice. InSAR digital elevation models (DEMs) are measuring the radar scattering center height. The height bias induced by the penetration of electromagnetic waves into snow and ice leads to inaccuracies of the InSAR DEM, especially for thick and deformed sea ice with snow cover. In this study, an elevation difference between the satellite-measured InSAR DEM and the airborne-measured optical DEM is observed from a coordinated campaign over the western Weddell Sea in Antarctica. The objective is to correct the penetration bias and generate a precise sea ice topographic map from the single-pass InSAR data. With the potential of retrieving sea ice geophysical information by the polarimetric-interferometry (Pol-InSAR) technique, a two-layer-plus-volume model is proposed to represent the sea ice vertical structure and its scattering mechanisms. Furthermore, a simplified version of the model is derived, to allow its inversion with limited a priori knowledge, which is then applied to a topographic retrieval scheme. The experiments are performed across four polarizations: HH, VV, Pauli 1 (HH + VV), and Pauli 2 (HH − VV). The model-retrieved performance is validated with the optically derived DEM of the sea ice topography, showing an excellent performance with root-mean-square error as low as 0.26 m in Pauli-1 (HH + VV) polarization.

Host-associated phages disperse across the extraterrestrial analogue Antarctica

Extreme Antarctic conditions provide one of the closest analogues of extraterrestrial environments. Since air and snow samples especially from polar regions yield DNA amounts in the lower picogram range, binning of prokaryotic genomes is challenging and renders studying the dispersal of biological entities across these environments difficult. Here, we hypothesized that dispersal of host-associated bacteriophages (adsorbed, replicating or prophages) across the Antarctic continent can be tracked via their genetic signatures and benefits our understanding of virus and host dispersal across long distances. Phage genome fragments (PGFs) reconstructed from surface snow metagenomes of three Antarctic stations were assigned to four host genomes, mainly Betaproteobacteria including Ralstonia spp. Betaproteobacteria of this genus have been found in Antarctic snow as well as on space-related equipment. We reconstructed the complete genome of a temperate phage with near-complete alignment to a prophage in the reference genome of Ralstonia pickettii 12D. PGFs from different stations were related to each other at the genus level and matched similar hosts. Metagenomic read mapping and nucleotide polymorphism analysis revealed a wide dispersal of highly identical PGFs, 13 of which appeared in seawater from the Western Antarctic Peninsula with up to 5538 km to the snow sampling stations. Our results suggest that host-associated phages, especially of Ralstonia sp. disperse over long distances despite harsh conditions of the Antarctic continent. Due to the additional identification of 14 phages associated with two R. pickettii draft genomes isolated from space equipment, we conclude implications for the spread of biological contaminants in extraterrestrial settings.

Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling

In many settings, data acquisition generates outliers that can obscure inference. Therefore, practitioners often either identify and remove outliers or accommodate outliers using robust models. However, identifying and removing outliers is often an ad hoc process that affects inference, and robust methods are often too simple for some applications. In our motivating application, scientists drill snow cores and measure snow density to infer densification rates that aid in estimating snow water accumulation rates and glacier mass balances. Advanced measurement techniques can measure density at high resolution over depth but are sensitive to core imperfections, making them prone to outliers. Outlier accommodation is challenging in this setting because the distribution of outliers evolves over depth and the data demonstrate natural heteroscedasticity. To address these challenges, we present a two-component mixture model using a physically motivated snow density model and an outlier model, both of which evolve over depth. The physical component of the mixture model has a mean function with normally distributed depth-dependent heteroscedastic errors. The outlier component is specified using a semiparametric prior density process constructed through a normalized process convolution of log-normal random variables. We demonstrate that this model outperforms alternatives and can be used for various inferential tasks.

Remote Sensing Phenology of Antarctic Green and Red Snow Algae Using WorldView Satellites

Snow algae are an important group of terrestrial photosynthetic organisms in Antarctica, where they mostly grow in low lying coastal snow fields. Reliable observations of Antarctic snow algae are difficult owing to the transient nature of their blooms and the logistics involved to travel and work there. Previous studies have used Sentinel 2 satellite imagery to detect and monitor snow algal blooms remotely, but were limited by the coarse spatial resolution and difficulties detecting red blooms. Here, for the first time, we use high-resolution WorldView multispectral satellite imagery to study Antarctic snow algal blooms in detail, tracking the growth of red and green blooms throughout the summer. Our remote sensing approach was developed alongside two Antarctic field seasons, where field spectroscopy was used to build a detection model capable of estimating cell density. Global Positioning System (GPS) tagging of blooms and in situ life cycle analysis was used to validate and verify our model output. WorldView imagery was then used successfully to identify red and green snow algae on Anchorage Island (Ryder Bay, 67°S), estimating peak coverage to be 9.48 × 104 and 6.26 × 104 m2, respectively. Combined, this was greater than terrestrial vegetation area coverage for the island, measured using a normalized difference vegetation index. Green snow algae had greater cell density and average layer thickness than red blooms (6.0 × 104 vs. 4.3 × 104 cells ml−1) and so for Anchorage Island we estimated that green algae dry biomass was over three times that of red algae (567 vs. 180 kg, respectively). Because the high spatial resolution of the WorldView imagery and its ability to detect red blooms, calculated snow algal area was 17.5 times greater than estimated with Sentinel 2 imagery. This highlights a scaling problem of using coarse resolution imagery and suggests snow algal contribution to net primary productivity on Antarctica may be far greater than previously recognized.

Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry

Single-pass interferometric synthetic aperture radar (InSAR) enables the possibility for sea ice topographic retrieval despite the inherent dynamics of sea ice. InSAR digital elevation models (DEM) are measuring the radar scattering centre height. The height bias induced by the penetration of electromagnetic waves into snow and ice leads to inaccuracies of the InSAR DEM, especially for multi-year sea ice with snow 5 cover. In this study, an elevation difference between the satellite-measured InSAR DEM and the airborne-measured optical DEM is observed from a coordinated campaign over the western Weddell Sea in Antarctica. The objective is to correct the penetration bias and generate a precise sea ice topographic map from the single-pass InSAR data. With the potential of retrieving sea ice geophysical information by the polarimetric-interferometry (Pol-InSAR) technique, a two-layer plus volume model is proposed to represent the sea ice vertical structure and its scattering mechanisms. Furthermore, a simplified version of the model is derived, to allow its inversion with limited a priori knowledge, which is then applied to a topographic retrieval scheme. The model-retrieved performance is validated with the optical DEM of the sea ice topography, showing an excellent performance with root-mean-square error as low as 0.22 m. The experiments are performed across four polarizations: HH, VV, Pauli-1 (HH+VV), and Pauli-2 (HH-VV), indicating the polarization-independent volume scattering property of the sea ice in the investigated co-polarized data.

Revealing the Characteristics of the Antarctic Snow Alga Chlorominima collina gen. et sp. nov. Through Taxonomy, Physiology, and Transcriptomics

Snow algae play crucial roles in cold ecosystems, however, many aspects related to their biology, adaptations and especially their diversity are not well known. To improve the identification of snow algae from colored snow, in the present study we used a polyphasic approach to describe a new Antarctic genus, Chlorominima with the species type Chlorominima collina. This new taxon was isolated of colored snow collected from the Collins Glacier (King George Island) in the Maritime Antarctic region. Microscopy revealed biflagellated ellipsoidal cells with a rounded posterior end, a C-shaped parietal chloroplast without a pyrenoid, eyespot, and discrete papillae. Several of these characteristics are typical of the genus Chloromonas, but the new isolate differs from the described species of this genus by the unusual small size of the cells, the presence of several vacuoles, the position of the nucleus and the shape of the chloroplast. Molecular analyzes confirm that the isolated alga does not belong to Chloromonas and therefore forms an independent lineage, which is closely related to other unidentified Antarctic and Arctic strains, forming a polar subclade in the Stephanosphaerinia phylogroup within the Chlamydomonadales. Secondary structure comparisons of the ITS2 rDNA marker support the idea that new strain is a distinct taxon within of Caudivolvoxa. Physiological experiments revealed psychrophilic characteristics, which are typical of true snow algae. This status was confirmed by the partial transcriptome obtained at 2°C, in which various cold-responsive and cryoprotective genes were identified. This study explores the systematics, cold acclimatization strategies and their implications for the Antarctic snow flora.