Nitrogen-fixing bacteria in Cladophora: A potential nitrogen source

Session: Poster session

Murulee Byappanahalli, U.S. Geological Survey, Great Lakes Science Center, [email protected]
Meredith Nevers, U.S. Geological Survey - Great Lakes Science Center, [email protected]
Kasia Kelly, USGS-Lake Michigan Ecological Research Station, [email protected]
Satoshi Ishii, University of Minnesota, [email protected]
Aaron Aunins, U.S. Geological Survey, [email protected]

Abstract

The nitrogen-fixing bacteria are among the epiphytic communities in Cladophora, potentially benefitting the algae in nutrient-deficient waters, but their abundance and diversity remain unexplored. In this study, we determined the abundance and composition of the nitrogen-fixing bacteria in Cladophora collected from rocks, breakwall structures, or submerged dreissenid mussel beds around southern Lake Michigan (N=34), using two complementary genomic techniques: real-time PCR (qPCR) and shotgun metagenomic sequencing. Genomic DNA was extracted from processed algal pellets, and the nitrogen-fixing bacteria were quantified by qPCR by targeting the NifH gene. Mean nifH concentrations (log₁₀NifH copy numbers/g algae fresh weight ± SE) were 5.55 ±0.09, ranging from 4.31 to 6.57. Mean NifH gene concentrations in water samples (log₁₀NifH copy numbers/ml of water ± SE) nearly mirrored algal samples: 5.19 ± 0.09, ranging from 3.37 to 5.90. Shotgun sequencing of a subset of samples (N=10) revealed a wide diversity of nitrogen-fixing bacteria, representing eubacterial and archaeal domains: Rhizobiales, Rhodobacterales, Sphingomonadales, Cyanobacteria, and Methanomicrobia. In summary, an understanding of the nitrogen inputs from this biological process is critical to refine empirical models for estimating the Cladophora biomass and to develop appropriate nutrient management strategies to minimize the nuisance problem.  

1. Keyword
Cladophora

2. Keyword
nutrients

4. Additional Keyword
Nitrogen cycle

5. Additional Keyword
Nitrogen-fixing bacteria

6. Additional Keyword
biomass modeling