Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine...

Area of Science:

• Marine microbiology
• Geochemistry
• Machine learning applications

Background:

• Oxygen is a key driver of microbial diversity in deep-sea sediments.
• Previous studies often lack the resolution to analyze oxygen gradients and transition zones.
• Understanding microbial responses to oxygen changes is crucial for deep-sea ecosystem research.

Purpose of the Study:

• To investigate how oxygen concentration influences microbial taxonomic variability in Arctic deep-sea sediments.
• To determine if higher microbial taxa can predict oxygen levels.
• To analyze the distribution of individual Operational Taxonomic Units (OTUs) along oxygen gradients.

Main Methods:

• Analysis of 184 samples from 11 Arctic Mid-Ocean Ridge sediment cores.
• Application of machine learning and differential abundance analysis.
• Consideration of the compositional nature of relative abundance data.

Main Results:

• Abundant microbial classes can predict oxygen concentration in sediments.
• Individual OTUs, even from common classes, showed limited differential abundance across oxic to low-oxic conditions.
• Microbial communities shift from homogeneous to heterogeneous as oxygen levels decrease below 25 μM.

Conclusions:

• Microbial community structure is significantly influenced by oxygen availability in deep-sea sediments.
• While higher taxa correlate with oxygen, individual OTU responses are complex and potentially driven by other factors.
• The study provides a framework for analyzing microbiome data across multiple sediment cores, highlighting oxygen's role in shaping deep-sea microbial ecosystems.