Enzymatic Activities and Carbon Flux through the Microbial Compartment in the Adriatic Sea

  • R. La Ferla
  • R. Zaccone
  • G. Caruso
  • M. Azzaro


Carbon flux through the microbial community by the determination of biomass, heterotrophic bacteria, aminopeptidase and respiratory activities has been studied in two areas of the Adriatic sea with different trophic characteristics during four oceanographic surveys, carried out in June 96,97 and February 97,98. In front of the Po delta (area A), the average rates of the carbon released by aminopeptidase activity ranged from 4.9 to 9.9 μg Cl−1 h−1 and near the Ancona coast (area B) from 3.1 to 7.6μg Cl−1 h−1, whereas the microbial respiration as metabolic carbon production (CO2) ranged from 0.19 to 2.29 and from 0.24 to 1.40μg Cl−1 h−1 in the two areas, respectively.

In area A, both the microbial activities showed a seasonal trend with higher values in summer than in winter. In area B, respiration increased during years whilst aminopeptidase activity decreased; bacterioplankton abundance increased on average in the second year of the research (June 97 and February 98). However, all these differences were not statistically significant. The biovolume resulted always significantly higher during summer.

Relationships between bacterial carbon production and organic carbon content were discussed. Bacterial growth efficiency ranged from 17 to 38% and from 13 to 44% in areas A and B, respectively. The relationship between hydrolysis of peptides and bacterial carbon production indicated a good coupling of processes during June 97. Various ratios between bacterial activities were reckoned with the aim to define the role of bacteria in the biogeochemical flux of C in the Northern Adriatic Sea. The C transfer from the biotic versus the abiotic compartment seems to flow better in summer, particularly in area A.


Aminopeptidase Activity Bacterial Growth Efficiency Electron Transport System Activity Mississippi River Plume Microbial Enzymatic Activity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Italia 2001

Authors and Affiliations

  • R. La Ferla
    • 1
  • R. Zaccone
    • 1
  • G. Caruso
    • 1
  • M. Azzaro
    • 1
  1. 1.Istituto Sperimentale TalassograficoCNRMessinaItaly

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