Preliminary evidence for the microbial loop in Antarctic sea ice using microcosm simulations
Sea ice microalgae actively contribute to the pool of dissolved organic matter (DOM) available for bacterial metabolism, but this link has historically relied on bulk correlations between chlorophyll a (a surrogate for algal biomass) and bacterial abundance. We incubated microbes from both the botto...
Published in: | Antarctic Science |
---|---|
Main Authors: | , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Cambridge Univ Press
2012
|
Subjects: | |
Online Access: | https://doi.org/10.1017/S0954102012000491 http://ecite.utas.edu.au/82711 |
Summary: | Sea ice microalgae actively contribute to the pool of dissolved organic matter (DOM) available for bacterial metabolism, but this link has historically relied on bulk correlations between chlorophyll a (a surrogate for algal biomass) and bacterial abundance. We incubated microbes from both the bottom (congelation layer) and surface brine region of Antarctic fast ice for nine days. Algal-derived DOM was manipulated by varying the duration of irradiance, restricting photosynthesis with 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU) or incubating in the dark. The bacterial response to changes in DOM availability was examined by performing cell counts, quantifying bacterial metabolic activity and examining community composition with denaturing gradient gel electrophoresis. The percentage of metabolically active bacteria was relatively low in the surface brine microcosm (1020% of the bacterial community), the treatment with DCMU indirectly restricted bacterial growth and there was some evidence for changes in community structure. Metabolic activity was higher (3569%) in the bottom ice microcosm, and while there was no variation in community structure, bacterial growth was restricted in the treatment with DCMU compared to the light/dark treatment. These results are considered preliminary, but provide a useful illustration of sea ice microbial dynamics beyond the use of snapshot biomass correlations. |
---|