Abstract: Species distribution models (SDMs) are increasingly used in ecological and biogeographic studies by Antarctic biologists, including for conservation and management purposes. During the modelling process, model calibration is a critical step to ensure model reliability and robustness, especially in the case of SDMs, for which the number of selected environmental descriptors and their collinearity is a recurring issue. Boosted regression trees (BRT) was previously considered as one of the best modelling approach to correct for this type of bias. In the present study, we test the performance of BRT in modelling the distribution of Southern Ocean species using different numbers of environmental descriptors, either collinear or not. Models are generated for six sea star species with contrasting ecological niches and wide distribution ranges over the entire Southern Ocean. For the six studied species, overall modelling performance is not affected by the number of environmental descriptors used to generate models, BRT using the most informative descriptors and minimizing model overfitting. However, removing collinear descriptors also helps reduce model overfitting. Our results confirm that BRTs may perform well and are relevant to deal with complex and redundant environmental information for Antarctic biodiversity distribution studies. Selecting a limited number of non-collinear descriptors before modelling may generate simpler models and facilitate their interpretation. The modelled distributions do not differ noticeably between the different species despite contrasting species ecological niches. This unexpected result stresses important limitations in using SDMs for broad scale spatial studies, based on limited, spatially aggregated data, and low-resolution descriptors.

Abstract: Geochemical investigations using FTIR, ICP-AES and C-N analyses were carried out on a single-family semi-subterranean Inuit house in order to assess the contribution of Inuit domestic activities in the formation of cryosolic soils in a periglacial archaeological context (Kuuvik Bay, Nunavik, Canada). The geochemical elements data were analyzed using box plots and enrichment factors. The information was also summarized and visualized through principal component analysis (PCA). These complementary statistical approaches provided evidence of a moderate but identifiable and significant imprint of Inuit domestic activities on soil geochemistry. The box plots indicate moderate Ca, Mn, Cu and P enrichments and more noticeable Mg, Fe and S enrichments. These enrichment factors and the ICP-AES data show that samples from the peripheral wall and the tunnel entrance of the house and its vicinity are significantly enriched. While FTIR analysis highlighted the main soil element contents (e.g., organic and mineral components), ICP-AES analysis was used to further document variations in the background elements throughout the site. However, the obtained results do not allow us to undertake a spatial analysis of Inuit activities or to describe them in detail. In addition, the data highlight the difficulty of documenting the natural variability of element contents in cryosolic soils, especially in archaeological contexts. Cryo-pedological processes in the active layer as well as house cleaning and nutrient inputs resulting from Inuit occupations may have attenuated chemical signatures of previous Inuit domestic activities.

Abstract: Characterizing the spatial distribution and variation of species communities and validating these characteristics with data from the field are key elements for an ecosystem-based approach to management. However, models of species distributions that yield community structure are usually not linked to models of community dynamics, constraining understanding and management of the ecosystem, particularly in data-poor regions. Here we use a qualitative network model to predict changes in Antarctic benthic community structure between major marine habitats characterized largely by seafloor depth and slope, and use multivariate mixture models of species distributions to validate the community dynamics. We then assess how future increases in primary production associated with anticipated loss of sea-ice may affect the ecosystem. Our study shows how both spatial and structural features of ecosystems in data-poor regions can be analyzed and possible futures assessed, with direct relevance for ecosystem-based management.