Back

Composition, growth, succession, and function in the Cladophora microbiome: insights from quantitative Stable Isotope Probing and NanoSIMS imaging

Fitzpatrick, R. M.; Hungate, B. A.; Power, M. E.; Schwartz, E.; Hayer, M.; Samo, T.; Weber, P.; Pett-Ridge, J.; Foley, M.; Marks, J. C.

2025-05-13 microbiology
10.1101/2025.05.12.652736 bioRxiv
Show abstract

The branching green macroalga Cladophora glomerata and its epiphytic microbiome dominate summer biomass in the Eel River, a Northern California river under Mediterranean (summer drought, winter rain) seasonality. Green Cladophora streamers proliferate in early summer, then change to yellow and then red-brown as epiphyte loads increase. We characterized successional changes in epiphytic bacteria on Cladophora, examining both community composition and growth rates, using quantitative Stable Isotope Probing (qSIP) and16S rRNA gene amplicon sequencing. The number of bacterial taxa increased with succession while growth rates peaked in the middle stage. NanoSIMS imaging confirmed high sulfur (S) concentrations in Cladophora cell walls relative to surrounding biomass, coinciding with a bloom of sulfur bacteria (bacteria that reduce or oxidize sulfur/sulfates). In general, relative abundances and growth rates were independent, indicating that either metric alone is insufficient for understanding how taxonomy and functional groups affect ecosystem processes. For instance, the relative abundance of nitrogen fixers peaked in the late summer when their relative growth rates were slowest. Such patterns may be driven by space competition limiting growth. Together, changes in abundance and relative growth rates suggest different limiting factors for different functional groups in the Cladophora microbiome at multiple successional stages.

Published in Environmental Microbiology (predicted rank #2) · training set

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.