In the biodiverse cold seep landscapes of the South China Sea (SCS), defining species-specific habitats intersect to form distinct ecological zones, each harboring unique macrofaunal assemblages. These ecological zones are hypothesized to be pivotal in shaping the metabolic pathways and community structures of the local microorganisms. Yet, the depth of insight into the prokaryotic communities and their functional attributes within these zones remains scant. Our work explored these ecological zones, from the surface layers of sediment down to the deep layers. By applying a suite of sophisticated methods, including 16S rRNA and metagenomic sequencing, detailed chemical and isotopic diagnostics, we have revealed the microbial community and metabolisms within different ecological zones of cold seeps in the South China Sea (SCS). Machine learning algorithms were instrumental in dissecting the complex interplay of abiotic forces and biotic adaptation, with a keen focus on untangling the metabolic intricacies fostered by prevalent geochemical gradients. Our research revealed a tight connection between the composition and metabolism of microbial communities and the unique characteristics of their associated ecological zones. Many microbes have exhibited species-level genomic differentiation, allowing them to not only thrive but also perform specialized metabolisms tailored to various ecological zones. This study not only expands our comprehension of microbial eco-dynamics but also accentuates the profound influence of ecological zones as architects of microbial diversity and metabolic innovation in marine sedimentary environments.
 

Ecological zones, Deep-sea sediments, Machine learning, Cold seeps, Prokaryotic community, Metagenomes