Satellite images are useful for studying remote or expansive areas that are otherwise difficult to reach and for detecting land surface changes over time. Remote sensing methods using satellite imagery are applied to a wide range of studies including urban expansion ( Wang et al., 2014 ), agricultural land use change ( Dong et al., 2015 ), and glacial retreats ( Wei et al., 2014 ). Many data sets are free, easily accessible, and have adequate resolution for its purpose. Thus, remote sensing provides an ideal tool for quantifying white and red snows at large spatial and temporal extents. Here we describe the use of remote sensing to quantify white and red snows at large spatial and temporal extents. Metagenomic comparisons of white and red snows were also performed to investigate whether these snows differed in their microbial ecology.

Permitting for this work was from the Russian Federation (Ministry of Education and Research #71; June 3, 2013). Red snows were sampled on Nansen Island (“Nansen”) and Greely Island (“Greely_1” and “Greely_2”) of Franz Josef Land. Red snow samples were examined with microscopy to confirm the presence of C. nivalis based on morphology ( Muller et al., 1998 ). Three red snow samples of ∼15 L were collected, melted, and passed through a 0.22 µm sterivex filter. Greely_1 and Greely_2 represent two different sterivex filters that were both extracted from the same homogenized sample. Total DNA was extracted in the field using the Soil DNA Isolation kit with a custom vacuum manifold (cat# 26560; Norgen BioTek Corp.,Thorold, Ontario, Canada). From the total DNA, a NexteraXT library kit was used to prepare DNA libraries for sequencing on the Illumina MiSeq. The Nansen, Greely_1, and Greely_2 libraries had 135,749 reads, 86,932 reads and 47,507 reads, respectively (see Table S2 for MG-RAST IDs to obtain unfiltered data). Each metagenome was passed through the following quality control pipeline. PrinSeq was used to quality filter reads below 100 bp in length and below an average quality score of 25, and to remove duplicates and sequence tags ( Schmieder & Edwards, 2011b ). Reads assigned as human were removed using DeconSeq ( Schmieder & Edwards, 2011a ). Post quality control, the Nansen library contained 121,455 reads, Greely_1 contained 69,918 reads, and Greely_2 contained 40,344 reads. Seven publicly accessible white snow metagenomes from Svalbard glaciers (a.k.a., ‘white snow’ throughout manuscript) sampled April through June were downloaded from MG-RAST (see Table S2 for MG-RAST IDs), and reads were quality filtered using the same pipeline as the Franz Josef Land red snow libraries ( Maccario, Vogel & Larose, 2014 ). Metagenomes were analyzed using KEGG and M5NR databases within MG-RAST version 3.3 ( Meyer et al., 2008 ). The red snow and white snow libraries were compared to the KEGG database to assign reads to KEGG pathways ( e -value <1 × 10 −5 ; >60% identity; >15 aa minimum alignment length). Estimations of taxonomic composition of communities were based on translated comparisons to the non-redundant protein database M5NR ( e -value <1 × 10 −5 ; >60% identity; >15 aa minimum alignment length). The dataset was normalized to ensure similar numbers of reads were used for each sample, and then raw read counts were log transformed. Statistical differences between red snow and white snow in the numbers of reads assigned to KEGG pathway groups were identified by ANOVA. Multivariate statistics were performed in R using the vegan ( Dixon, 2003 ), clustsig and the stats packages. The adonis function was used to compare metagenome compositions; vegdist was used to generate distance matrices; simprof was used to cluster metagenomes based on similarity; and prcomp was used to perform Principal Component Analysis.

To estimate the algal biomass for each location, the surface area belonging to each reflectance band ratio category was multiplied by the mean algal biomass of that category. Although the extent of the area of interest is the same for all three images, they have varying amounts of surface area where red snow can exist due to shifts in snow and ice coverage. Therefore, in addition to the total algal biomass, the total area of snow coverage and the percentage of the total area of snow that was covered with different abundances of red algae were calculated. A pixel was categorized as snow if its normalized difference snow index (NDSI) was greater than 0.4 and, to mask out water, if its near-infrared reflectance value was greater than 0.11 ( Sibandze et al., 2014 ). The number of pixels that meet these conditions was multiplied by the area of the pixel to get the total area of ground covered by snow/ice. To calculate the percentage of the total area of snow that is covered with algae, the total area with each algal abundance level was divided by the total area of snow coverage.

(A) A time series comparison of the percentage of total snow or ice that is covered with algae at selected alpine and polar regions throughout the world, according to data derived from satellite images. (B) A time series comparison of the total area of snow and sea ice, total algal biomass, and percentage of total snow that is covered with algae within the map extent near Nansen Island, Franz Josef Land, for years 1986, 2002, 2006 and 2015. The colored time series shows spatial distribution maps of algal densities of the Nansen Island area in Franz Josef Land.

ArcGIS version 10.2 was used to calculate the reflectance band ratios. Previous research indicates areas with reflectance band ratios >1.02 are bright red when observed in the field ( Takeuchi et al., 2006 ). For this analysis, areas with reflectance band ratios greater than 1.0 were considered to have a significant amount of red snow because such values have been shown to have an algal cell volume of 100 ml m −2 ( Takeuchi et al., 2006 ). Using the positive linear correlation between algal cell volume biomass and reflectance band ratio, it was assumed that the higher the reflectance band ratio, the higher the algal cell volume biomass. With this in mind, the reflectance band ratios were divided into five categories for optimal visualization of various levels of concentrations of red snow ( Table S1 and Fig. 1B ).

Remote sensing methods were used to estimate abundances of red snow at eleven locations around the world (see Supplemental Information 1 ). Landsat satellite images were acquired from the USGS Earth Explorer site ( http://earthexplorer.usgs.gov/ ) and image analysis methods were adapted from Takeuchi et al. (2006) as described in the Supplemental Information 1 . Red to green reflectance band ratios with wavelengths 630–690 nanometers and 520–600 nanometers, respectively, were used to detect red snow in the satellite images. The spectral reflectance of red snow shows that it has higher reflectance in the red band than in the green band, while the spectral reflectance of white snow and ice has higher reflectance in the green band than the red band ( Takeuchi et al., 2006 ). Therefore, red to green reflectance band ratios that are less than 1.0 are more likely to signify white snow or ice while band ratios that are greater than 1.0 are more likely to signify red snow or ice.

Results and Discussion

Detection of red snow in a global sample of satellite images Satellite images with spectral reflectance data were used to approximate snow and ice cover, and red algae abundance (Takeuchi, 2009; Takeuchi et al., 2006) over several years in Franz Josef Land, as well as eleven other regions of United States, Canada, Greenland, Norway, Austria, India, and New Zealand (Fig. S1). Red snow was detected at all eleven locations in all the years (Fig. 1A). The total area of snow and ice were lowest in the most recent year (2013, 2014 or 2015, depending on the location; Fig. S2; Greenland was the exception to this trend). At least 50% of the total snow/ice area was covered with red algae for the most recent year analyzed (Fig. S2; exception New Zealand and Franz Josef Land). In seven of the locations, over 80% of the total snow and ice fields were covered in red algae in the most recent year analyzed (Fig. S2). A walking transect from sea level to the glacier on Nansen Island in Franz Josef Land was performed in August 2013 (to be described in a separate manuscript). Therefore, this region was targeted for more detailed analysis. Around and on Nansen, the total red snow algal biomass increased by 124% from 1986 to 2002 and by 15% from 2002 to 2006, then decreased by 63% from 2006 to 2015 (Fig. 1B). These changes in algal cover co-occurred with a total decline in the snow and ice cover (Fig. 1B). Visual inspection of the snow and ice on Nansen Island in August of 2013 confirmed the presence of red colored snow and microscopy of red snow samples showed C. nivalis cells. Taken together, these results show that even as total snow and ice cover declines, red snow cover is still highly prevalent or increasing both in Franz Josef Land and other alpine/polar regions.

Microbes present in white snow and red snow For metagenomic sequencing, red snow samples were taken from Nansen and Greeley Islands, respectively. Seven white snow metagenomes from Svalbard glaciers were also downloaded and analyzed for comparison (see ‘Methods’ & Table S2 for MG-RAST ID numbers). The genus-level taxonomic compositions of white snow and red snow were significantly different (ADONIS; F = 4.567; p = 0.007). When samples were clustered according to their taxonomical similarities, one red snow sample taken at Greely Island grouped with a Svalbard glacier sample; otherwise the red snow and white snow samples clustered separately (Fig. S3). This indicates minimal overlap in microbial composition at the genus level. Community DNA sequences were further compared using multivariate analyses with the top 10 most variable taxa (Fig. S4). The first two principal components explained 70% of the between-sample variation in microbial community members. The first principal component described red snow as having higher abundances of species from the bacterial genera Pseudoalteromonas, Alteromonas, Vibrio, and Pedobacter, whereas white snow had higher abundances of species from the eukaryotic genera Aspergillus and Neurospora, as well as the bacterial genera Nostoc, Bacillus and Spirosoma. Red snow had greater overall abundances of Bacteria and viruses (Fig. 2A) and a lower abundance of Eukaryotes (Fig. 2A). The bacterial communities associated with red snow have also been observed in an alpine region (Thomas & Duval, 1995) and are probably supported by photosynthate from the C. nivalis. Evidence also suggests that bacterial cells may physically attach to the outer mucilaginous coating of C. nivalis in red snow, forming an Arctic holobiont (Bordenstein & Theis, 2015; Remias, Lutz-Meindl & Lutz, 2005; Thomas & Duval, 1995). Figure 2: Abundances of microbes in red snow and white snow samples. (A) Abundances of viruses, Bacteria and Eukaryotes in samples from red snow and snow communities. The y-axis shows abundances after normalizing and standardizing raw read counts to ensure cross-sample comparisons are valid. (B) Bar plots showing abundances of two Eukaryotic phyla found in red snow and snow communities. Chlorophyta is the phylum that contains the genus Chlamydomonas. The metagenomes were also used to verify the presence of Chlamydomonas in snow samples (Fig. S5). Of the sequence reads assigned to Eukaryotes, the proportion of reads assigned to the Chlamydomonas-containing phylum Chlorophyta was higher in red snow than white snow (Fig. 2B). Conversely, the proportion of reads assigned to the fungal phylum Ascomycota was higher in white snow (Fig. 2B).