the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Vertebrates impact on bacterial community structure of coastal Arctic snowpacks in the spring
Abstract. Snow covers up to 35 % of the Earth's surface seasonally and forms a microbial habitat despite harsh and variable conditions. While atmospheric deposition is a well-known source of microbial input, the role of vertebrates in shaping snow microbiomes remains underexplored. In Arctic ecosystems, seabirds and terrestrial mammals contribute not only nutrients but also microbial communities. Here, we explore the role of vertebrates in shaping snow microbial biodiversity of Arctic terrestrial snowpacks. The study was conducted on the northern coast of Hornsund Fjord on Spitsbergen. Fourty snow samples were collected in four transects, two established along the gradient from the centre of a seabird (Alle alle) colony towards non-impacted areas and two transects along the coast. We identified a total of 8,521 bacterial OTUs using short-read sequencing of the 16S rRNA gene. Samples clustered into four snow groups based on community composition, but not linked to spatial factors such as distance to colonies. Bird and terrestrial mammal faecal indicators like Catellicoccus or Streptococcus were detected in 17 out of the 40 samples and drove the formation of two distinct clusters. These findings suggest that coastal Arctic snow microbiomes are strongly shaped by biological activity, with wildlife acting as key microbial vectors.
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Status: open (until 02 Dec 2025)
- RC1: 'Comment on egusphere-2025-5033', Anonymous Referee #1, 18 Nov 2025 reply
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The manuscript of Sulowicz et al describes a dataset of 40 bacterial community samples collected from Arctic surface snow in 4 transects. The authors clustered the bacterial community data and concluded that some communities were driven by the influence of bird colonies while one transect was influenced by mammal activity.
While the authors describe a unique dataset of scientific value, the methods for data analysis are outdated and the interpretation and conclusions are overstated. With a thorough re-analysis of the dataset with state-of-the-art methods and (taxonomy) databases and interpretation of the results as they are, the manuscript should be publishable. I suggest the following major revisions to get there:
Minor comments: