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

Giant viral signatures on the Greenland ice sheet

BACKGROUND

Dark pigmented snow and glacier ice algae on glaciers and ice sheets contribute to accelerating melt. The biological controls on these algae, particularly the role of viruses, remain poorly understood. Giant viruses, classified under the nucleocytoplasmic large DNA viruses (NCLDV) supergroup (phylum Nucleocytoviricota), are diverse and globally distributed. NCLDVs are known to infect eukaryotic cells in marine and freshwater environments, providing a biological control on the algal population in these ecosystems. However, there is very limited information on the diversity and ecosystem function of NCLDVs in terrestrial icy habitats.

RESULTS

In this study, we investigate for the first time giant viruses and their host connections on ice and snow habitats, such as cryoconite, dark ice, ice core, red and green snow, and genomic assemblies of five cultivated Chlorophyta snow algae. Giant virus marker genes were present in almost all samples; the highest abundances were recovered from red snow and the snow algae genomic assemblies, followed by green snow and dark ice. The variety of active algae and protists in these GrIS habitats containing NCLDV marker genes suggests that infection can occur on a range of eukaryotic hosts. Metagenomic data from red and green snow contained evidence of giant virus metagenome-assembled genomes from the orders Imitervirales, Asfuvirales, and Algavirales.

CONCLUSION

Our study highlights NCLDV family signatures in snow and ice samples from the Greenland ice sheet. Giant virus metagenome-assembled genomes (GVMAGs) were found in red snow samples, and related NCLDV marker genes were identified for the first time in snow algal culture genomic assemblies; implying a relationship between the NCLDVs and snow algae. Metatranscriptomic viral genes also aligned with metagenomic sequences, suggesting that NCLDVs are an active component of the microbial community and are potential "top-down" controls of the eukaryotic algal and protistan members. This study reveals the unprecedented presence of a diverse community of NCLDVs in a variety of glacial habitats dominated by algae.

Patchy and Pink: Dynamics of a Chlainomonas sp. (Chlamydomonadales, chlorophyta) algal bloom on Bagley Lake, North Cascades, WA

Snow algal blooms frequently occur throughout alpine and polar environments during spring and summer months; however, our understanding of bloom dynamics is limited. We tracked a recurrent bloom of Chlainomonas sp. on Upper Bagley Lake in the North Cascade Mountains, USA, to assess the spatiotemporal dynamics in bloom color intensity, community photophysiology, and community composition over eight weeks. We found that the algae biomass had a dynamic patchy distribution over space and time, which was decoupled from changes in community composition and life-cycle progress averaged across the bloom. The proportional representation of Chlainomonas sp. remained consistent throughout the study while the overall community composition shows a progression through the bloom. We found that community photophysiology, measured by the maximum quantum yield of PSII (Fv/Fm), decreased on average throughout the bloom. These findings suggest that the Chlainomonas sp. community on Bagley Lake is not simply an algal bloom with rapid increase in biomass followed by a population crash, as is often seen in aquatic systems, though there is a physiological trajectory and sensitivity to environmental stress. These results contribute to our understanding of the biology of Chlainomonas sp. and its response to environmental stress, specifically an extreme warming event.

The snow alga Chloromonas kaweckae sp. nov. (Volvocales, Chlorophyta) causes green surface blooms in the high tatras (Slovakia) and tolerates high irradiance

Seasonally slowly melting mountain snowfields are populated by extremophilic microalgae. In alpine habitats, high-light sensitive, green phytoflagellates are usually observed in subsurface layers deeper in the snowpack under dim conditions, while robust orange to reddish cyst stages can be seen exposed on the surface. In this study, uncommon surface green snow was investigated in the High Tatra Mountains (Slovakia). The monospecific community found in the green surface bloom consisted of vegetative Chloromonas cells (Volvocales, Chlorophyta). Molecular data demonstrated that the field sample and the strain isolated and established from the bloom were conspecific, and they represent a new species, Chloromonas kaweckae sp. nov., which is described based on the morphology of the vegetative cells and asexual reproduction and on molecular analyses of the strain. Cells of C. kaweckae accumulated approximately 50% polyunsaturated fatty acids, which is advantageous at low temperatures. In addition, this new species performed active photosynthesis at temperatures close to the freezing point showed a light compensation point of 126 ± 22 μmol photons · m-2  · s-1 and some signs of photoinhibition at irradiances greater than 600 μmol photons · m-2  · s-1 . These data indicate that the photosynthetic apparatus of C. kaweckae could be regarded as adapted to relatively high light intensities, otherwise unusual for most flagellate stages of snow algae.

Remote sensing for mapping algal blooms in freshwater lakes: a review

A large number of freshwater lakes around the world show recurring harmful algal blooms, particularly cyanobacterial blooms, that affect public health and ecosystem integrity. Prediction, early detection, and monitoring of algal blooms are inevitable for the mitigation and management of their negative impacts on the environment and human beings. Remote sensing provides an effective tool for detecting and spatiotemporal monitoring of these events. Various remote sensing platforms, such as ground-based, spaceborne, airborne, and UAV-based, have been used for mounting sensors for data acquisition and real-time monitoring of algal blooms in a cost-effective manner. This paper presents an updated review of various remote sensing platforms, data types, and algorithms for detecting and monitoring algal blooms in freshwater lakes. Recent studies on remote sensing using sophisticated sensors mounted on UAV platforms have revolutionized the detection and monitoring of water quality. Image processing algorithms based on Artificial Intelligence (AI) have been improved recently and predicting algal blooms based on such methods will have a key role in mitigating the negative impacts of eutrophication in the future.