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Abstract

BackgroundThe pelagic environment may present a mosaic of biogeographical domains that regional oceanographic processes can influence. Here, a coastal-to-open ocean microbiome investigation was conducted on 64 water samples from the Santos Basin (SB), South Atlantic Ocean. Using metagenomics and machine learning approaches, we assessed the diversity and distribution of pelagic microbes, identified key bacterial and archaeal taxa, and inferred their ecosystem functions. ResultsUnsupervised machine learning revealed a clear spatial and vertical (light availability) distribution pattern across SB, with some indicator taxa previously observed in other marine waters. Supervised learning further revealed that environmental variables, notably phosphate, salinity, and nitrate, which are key markers of local upwelling and the La Plata River plume, are primary drivers of microbial community structure. Furthermore, we recovered 307 metagenome-assembled genomes with 45% of Archaea and 42% of Bacteria possible new taxa. In terms of functionality, the SB dataset revealed a pelagic ecosystem resembling typical marine (e.g., Atlantic Ocean) waters, with photoautotrophs and nitrogen fixers in the photic zone and different autotrophic pathways in the aphotic environment. Surprisingly, the SB dataset revealed genes for CO bio-oxidation and algal dimethylsulfoniopropionate (DMSP) degradation at all depths. Furthermore, we observed potential non- cyanobacterial diazotrophs in dark water. ConclusionsOur results revealed that the SB represents a unique ecosystem with local oceanographic processes shaping the distribution of diverse and uncharacterized microbiomes. Additionally, these findings highlight the importance of mixotrophic microbes in SB biogeochemical cycles. This massive investigation of the SB pelagic microbiome provided knowledge-based data for understanding local ecosystem health, services, and dynamics, which are essential for future sustainable ocean management.

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02 /Specification & Classification

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