Disentangling temporal associations in marine microbial networks.

ABSTRACT:

Background

Microbial interactions are fundamental for Earth's ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. Omics-based censuses are helpful in predicting microbial interactions through the statistical inference of single (static) association networks. Yet, microbial interactions are dynamic and we have limited knowledge of how they change over time. Here, we investigate the dynamics of microbial associations in a 10-year marine time series in the Mediterranean Sea using an approach inferring a time-resolved (temporal) network from a single static network.

Results

A single static network including microbial eukaryotes and bacteria was built using metabarcoding data derived from 120 monthly samples. F

SUBMITTER: Deutschmann IM 

PROVIDER: S-EPMC10120119 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature