Matthias Labrenz

This paper develops a random forest model to predict antifouling paint presence in sediment using 16S rRNA sequencing data, achieving high accuracy in presence detection but not concentration.

This paper applies topic modeling to estuarine microbiome data, showing it performs comparably to PCA and PCoA and identifies seasonal microbial sub-communities.

This study uses comparative genomics to identify virulence-related genes in Vibrio vulnificus by analyzing clinical and environmental isolates, revealing convergent evolution and potential diagnostic markers.

The paper detects a semi-annual biotic cycle in temperate aquatic communities using machine learning on DNA metabarcoding data, linking it to insolation patterns and photoautotrophs.

This study investigates how microbial communities adapt after coalescence by mixing freshwater and seawater samples in controlled experiments, finding that environmental filtering has a stronger impact than mixing ratios.

This study examines Vibrio spp. abundance and ecology in Baltic Sea seagrass meadows, finding higher concentrations in water above unvegetated areas and on seagrass roots.

This study investigates the effects of excess phosphate in a mesocosm experiment, showing that nitrogen addition reduces phosphate concentrations more effectively than carbon addition.

The paper examines microbial processes in the Baltic Sea during post-spring-bloom phosphate excess, showing that alkaline phosphatase activity and shifts in bacterial communities affect nutrient cycling and organic matter export.