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A microbial sea of possibilities: current knowledge and prospects for an improved understanding of the fish microbiome
Open accessAbstract
The mucosal surfaces of fish represent an important barrier that supports and regulates a diverse array of microbial assemblages and contributes to the overall health and fitness of the host. For farmed species, knowledge of how these host–microbial systems adapt and respond to various stressors is pivotal for managing health, nutrition and optimizing productivity in aquaculture. While our understanding of these communities and the factors that shape them now suggest that a diverse balanced microbiota is critical for healthy functioning in fish, the mechanisms behind these interactions are still poorly understood. Much of the existing research has focused on characterizing the taxonomic diversity of these assemblages in different fish species, across body surfaces (e.g. skin, gills and gastrointestinal tract), and in response to changing nutrition, health and environmental conditions. However, the specific functional contributions of these communities (or specific members) remain elusive, especially in farmed or diseased fish. Here, we review our current understanding of the microbiota in fish, their interplay and the likely functional involvement with the host. We also seek to address and identify gaps in knowledge and explore the future prospects for improving our understanding of these communities in aquaculture.
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Metadata
- Authors
- Legrand, T. P. R. A., Wynne, J. W., Weyrich, L. S., & Oxley, A. P. A.
- Venue
- Reviews in Aquaculture
- Publisher
- Wiley
- Published
- 2019-08-13
- Volume
- 12
- Issue
- 2
- Pages
- 1101-1134
- ISSN
- 1753-5123 (ISSN-L)
- 1753-5131
- OpenAlex
- W2968718616
- Cited by
- 265
- References
- 255
- FWCI
- 6.97
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.