The Arctic Ocean is changing rapidly due to climate change and the response of microbial communities, which are at the very base of the ecosystem’s food web and biogeochemical cycling, remains uncertain. Changes in the Arctic Ocean water composition are particularly worrisome because its microbial communities have been shown to be exceptionally sensitive to local conditions and under-ice phytoplankton blooms, with a significant increase in overall annual primary production in the Arctic Ocean.

There are fundamental gaps in current research, hindering our understanding of the microbial response to climate change in the Arctic Ocean. Namely, the majority of studies (1) are regionally limited; (2) do not present a multi-year (> 2 years) standardized sampling at the same geographical location; (3) look into only a few environmental variables; (4) look almost exclusively into diversity analysis of the microbial community; and (5) still have not applied an emerging plethora of data science tools (most notably, machine learning approaches) to extract more complex, non-linear and non-redundant patterns from microbial communities composition, encompassing several types of data simultaneously.

For this exploratory project, we will focus on analyzing long-term datasets which are publicly available. We will start by characterizing the multi-year and spatial distribution of the microbial communities from the Arctic Ocean. This characterization will work as a baseline to enquire what novel insights alternative machine learning strategies are able to provide.

By combining our multidisciplinary expertise in the interpretation and comparison of the baseline characterization and machine learning approaches, we will be able to answer the following questions: 1) to what extent can machine learning tools, tailored for understanding the response of microbiomes from the Arctic Ocean to climate change, improve our understanding of complex interactions between multiple environmental and biological variables and 2) how can machine learning tools increase our ability to explain the microbial response to climate change in the Arctic Ocean?

This project brings an innovative way of understanding the microbiome responses to the Arctic warming.