18S rRNA gene
18S rRNA gene on World Data Ocean: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on 18s rrna gene in some way — the news, the analysis, the deep dives, and the occasional surprise find. A Global Hub for Ocean Intelligence 4 World Data Ocean is a centralized digital platform where researchers, scientists, and ocean enthusiasts converge to explore, analyze, and… New stories are added to this page as we find them, so check back if you want to keep up with what is happening around 18s rrna gene, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything World Data Ocean is covering right now.
Beneath the surface: DNA metabarcoding reveals shifting biofouling patterns on marine artificial structures across season, depth, and substrate
Recent research illuminates the complex dynamics of marine biofouling, revealing how seasonal shifts, depth, and substrate type significantly influence community composition on artificial structures. Utilizing DNA metabarcoding across two seasonal periods and three depths, our study identified over 10,000 genetic variants, demonstrating that season explains the largest variation in biofouling biomass and diversity. These findings underscore the need for adaptive antifouling strategies considering interacting environmental factors—a perspective further explored in our related article, "Machine learning predictions for microbial eukaryotic plankton."
Machine learning predictions for microbial eukaryotic plankton: implications from unevenly structured data
Machine learning offers a promising avenue for predicting eukaryotic microbial plankton diversity from environmental data; however, model generalizability remains a critical challenge. This study utilized XGBoost to model 18S rRNA gene Shannon Diversity Index (SDI) across the Mediterranean Sea, revealing significant limitations in transferability due to unevenly structured data. Performance declined substantially when tested against independent datasets, highlighting the need for spatially explicit evaluation and standardized protocols. Understanding these constraints is essential for robust ocean intelligence.