Abstract: The Kerguelen Islands, located in the southern part of the Indian Ocean, are very isolated geographically. The microbial diversity and communities present on the island, especially associated to geothermal springs, have never been analyzed with high-throughput sequencing methods. In this article, we performed the first metagenomics analysis of microorganisms present in Kerguelen hot springs. From four hot springs, we assembled metagenomes and recovered 42 metagenome-assembled genomes, mostly associated with new putative taxa based on phylogenomic analyses and overall genome relatedness indices. The 42 MAGs were studied in detail and showed putative affiliations to 13 new genomic species and 6 new genera of Bacteria or Archaea according to GTDB. Functional potential of MAGs suggests the presence of thermophiles and hyperthermophiles, as well as heterotrophs and primary producers possibly involved in the sulfur cycle, notably in the oxidation of sulfur compounds. This paper focused on only four of the dozens of hot springs in the Kerguelen Islands and should be considered as a preliminary study of the microorganisms inhabiting the hot springs of these isolated islands. These results show that more efforts should be made towards characterization of Kerguelen Islands ecosystems, as they represent a reservoir of unknown microbial lineages.

Abstract: Animal-borne tagging (bio-logging) generates large and complex datasets. In particular, accelerometer tags, which provide information on behaviour and energy expenditure of wild animals, produce high-resolution multi-dimensional data, and can be challenging to analyse. We tested the performance of commonly used artificial intelligence tools on datasets of increasing volume and dimensionality. By collecting bio-logging data across several sampling seasons, datasets are inherently characterized by inter-individual variability. Such information should be considered when predicting behaviour. We integrated both unsupervised and supervised machine learning approaches to predict behaviours in two penguin species. The classified behaviours obtained from the unsupervised approach Expectation Maximisation were used to train the supervised approach Random Forest. We assessed agreement between the approaches, the performance of Random Forest on unknown data and the implications for the calculation of energy expenditure. Consideration of behavioural variability resulted in high agreement (> 80%) in behavioural classifications and minimal differences in energy expenditure estimates. However, some outliers with < 70% of agreement, highlighted how behaviours characterized by signal similarity are confused. We advise the broad bio-logging community, approaching these large datasets, to be cautious when upscaling predictions, as this might lead to less accurate estimates of behaviour and energy expenditure.

Abstract: Climate change affects the Arctic more than any other region, resulting in evolving weather, vanishing sea ice and altered biochemical cycling, which may increase biotic exposure to chemical pollution. We tested thermoregulatory impacts of these changes on the most abundant Arctic seabird, the little auk (Alle alle). This small diving species uses sea ice-habitats for foraging on zooplankton and resting. We equipped eight little auks with 3D accelerometers to monitor behavior, and ingested temperature recorders to measure body temperature (Tb). We also recorded weather conditions, and collected blood to assess mercury (Hg) contamination. There were nonlinear relationships between time engaged in different behaviors and Tb. Tb increased on sea ice, following declines while foraging in polar waters, but changed little when birds were resting on water. Tb also increased when birds were flying, and decreased at the colony after being elevated during flight. Weather conditions, but not Hg contamination, also affected Tb. However, given our small sample size, further research regarding thermoregulatory effects of Hg is warranted. Results suggest that little auk Tb varies with behavior and weather conditions, and that loss of sea ice due to global warming may cause thermoregulatory and energic challenges during foraging trips at sea.

Abstract: The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. RAATD consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. These datasets and accompanying syntheses provide a greater understanding of fundamental ecosystem processes in the Southern Ocean, support modelling of predator distributions under future climate scenarios and create inputs that can be incorporated into decision making processes by management authorities. In this data paper, we present the compiled tracking data from research groups that have worked in the Antarctic since the 1990s. The data are publicly available through biodiversity.aq and the Ocean Biogeographic Information System. The archive includes tracking data from over 70 contributors across 12 national Antarctic programs, and includes data from 17 predator species, 4060 individual animals, and over 2.9 million observed locations.

Abstract: Scientific Assessment of Ozone Depletion. Chapt. 7