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A comparison of methods for the quantification of bacterial sulfate reduction in coastal marine sediments: III. Estimation from chemical and bacteriological field data

J⊘rgensen, Bo Barker·1978·Geomicrobiology Journal

Abstract

Rates of bacterial sulfate reduction are calculated from the accumulation of reduced sulfur compounds in coastal sediments. The method is found to underestimate the in situ metabolism 10‐fold because it neglects diffusional losses of produced sulfide. In relation to this result, the quantitative connection between the pyrite, HCl‐extractable iron, and organic carbon contents of the sediments and the intensity of sulfate reduction are disussed. A comparison is made between colony counts of sulfate‐reducing bacteria and the rate of sulfate reduction in coastal sediments. The number of bacteria is roughly proportional to their measured rate of metabolism both when different sediment types and different depths are compared. The colony counts, however, seem to underestimate the true numbers of sulfate reducers by 1000‐fold or more.

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Metadata

Authors
J⊘rgensen, B. B.
Venue
Geomicrobiology Journal
Published
1978-01
Volume
1
Issue
1
Pages
49-64
ISSN
  • 0149-0451 (ISSN-L)
  • 1521-0529
OpenAlex
W1541796496
Cited by
210
References
0
FWCI
6.68
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A comparison of methods for the quantification of bacterial sulfate reduction in coastal marine sediments: III. Estimation from chemical and bacteriological field data. https://app.cochat.ai/papers/10.1080/01490457809377723
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J⊘rgensen, B. B. (1978). A comparison of methods for the quantification of bacterial sulfate reduction in coastal marine sediments: III. Estimation from chemical and bacteriological field data. Geomicrobiology Journal. https://doi.org/10.1080/01490457809377723

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.