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Oecologia

, Volume 6, Issue 2, pp 176–190 | Cite as

On the importance of marine meiofauna for benthos communities

  • S. A. Gerlach
Article

Summary

  1. 1.

    In sublittoral sediments of silty sand live about 55000–1300000 meiofauna animals, which is a minimum figure because methods are not absolutely efficient.

     
  2. 2.

    Meiofauna biomass ranges from 0.2 to 2.0 g wet weight in sublittoral silty sand, that is about 3% of macrofauna biomass.

     
  3. 3.

    A higher percentage of meiofauna biomass is recorded from brackish water regions, intertidal beaches and from the deep sea, where meiofauna and macrofauna biomass are of the same magnitude.

     
  4. 4.

    Oxygen consumption in meiofauna animals is between 200 and 2000, in small macrofauna between 200 and 500, and in larger macrofauna between 10 and 100 mm3 O2/h/g wet weight.

     
  5. 5.

    The assumption is forwarded that in general meiofauna has a metabolism five times more active than that of macrofauna.

     
  6. 6.

    Generation time in meiofauna lasts from a few days as was observed in cultures, to one or a few years, as was observed in natural populations. For a generalization the guess is forwarded that three generations per year would be an average meiofauna value.

     
  7. 7.

    Life cycle turnover rate as calculated from life cycle models of two nematodes is 2.2–3. Multiplied by three annual generations results in an average annual turnover rate of about 9 for meiobenthos, which is about five times more than that in macrobenthos.

     
  8. 8.

    If one compares meiobenthos and macrobenthos, meiobenthos importance in terms of food consumed and in terms of biomass provided for the food chain is 15% in a community like sublittoral silty sand, although meiobenthos in terms of standing stock is just 3% of macrobenthos.

     

Keywords

Biomass Beach Turnover Rate Meiofauna Silty Sand 
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 1971

Authors and Affiliations

  • S. A. Gerlach
    • 1
  1. 1.Institut für MeeresforschungBremerhavenGermany (FRG)

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