Machine learning analyses of bacterial oligonucleotide frequencies to assess the benthic impact of aquaculture
Aquaculture is a rapidly expanding industry and is now one of the primary sources of all consumed seafood. Intensive aquaculture production is associated with organic enrichment, which occurs as organic material settles onto the seafloor, creating anoxic conditions which disrupt ecological processes...
Published in: | Aquaculture Environment Interactions |
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ftdoajarticles:oai:doaj.org/article:8e5290aa20c1406f84564d321af0e286 2023-05-15T17:22:35+02:00 Machine learning analyses of bacterial oligonucleotide frequencies to assess the benthic impact of aquaculture Armstrong, EG Verhoeven, JTP 2020-04-01T00:00:00Z https://doi.org/10.3354/aei00353 https://doaj.org/article/8e5290aa20c1406f84564d321af0e286 EN eng Inter-Research https://www.int-res.com/abstracts/aei/v12/p131-137/ https://doaj.org/toc/1869-215X https://doaj.org/toc/1869-7534 1869-215X 1869-7534 doi:10.3354/aei00353 https://doaj.org/article/8e5290aa20c1406f84564d321af0e286 Aquaculture Environment Interactions, Vol 12, Pp 131-137 (2020) Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5 article 2020 ftdoajarticles https://doi.org/10.3354/aei00353 2022-12-31T09:03:19Z Aquaculture is a rapidly expanding industry and is now one of the primary sources of all consumed seafood. Intensive aquaculture production is associated with organic enrichment, which occurs as organic material settles onto the seafloor, creating anoxic conditions which disrupt ecological processes. Bacteria are sensitive bioindicators of organic enrichment, and supervised classifiers using features derived from 16s rRNA gene sequences have shown potential to become useful in aquaculture environmental monitoring. Current taxonomy-based approaches, however, are time intensive and built upon emergent features which cannot easily be condensed into a monitoring pipeline. Here, we used a taxonomy-free approach to examine 16s rRNA gene sequences derived from flocculent matter underneath and in proximity to hard-bottom salmon aquaculture sites in Newfoundland, Canada. Tetranucleotide frequencies (k = 4) were tabulated from sample sequences and included as features in a machine learning pipeline using the random forest algorithm to predict 4 levels of benthic disturbance; resulting classifications were compared to those obtained using a published taxonomy-based approach. Our results show that k-mer count features can effectively be used to create highly accurate predictions of benthic disturbance and can resolve intermediate changes in seafloor condition. In addition, we present a robust assessment of model performance which accounts for the effect of randomness in model creation. This work outlines a flexible framework for environmental assessments at aquaculture sites that is highly reproducible and free of taxonomy-assignment bias. Article in Journal/Newspaper Newfoundland Directory of Open Access Journals: DOAJ Articles Canada Aquaculture Environment Interactions 12 131 137 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5 |
spellingShingle |
Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5 Armstrong, EG Verhoeven, JTP Machine learning analyses of bacterial oligonucleotide frequencies to assess the benthic impact of aquaculture |
topic_facet |
Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5 |
description |
Aquaculture is a rapidly expanding industry and is now one of the primary sources of all consumed seafood. Intensive aquaculture production is associated with organic enrichment, which occurs as organic material settles onto the seafloor, creating anoxic conditions which disrupt ecological processes. Bacteria are sensitive bioindicators of organic enrichment, and supervised classifiers using features derived from 16s rRNA gene sequences have shown potential to become useful in aquaculture environmental monitoring. Current taxonomy-based approaches, however, are time intensive and built upon emergent features which cannot easily be condensed into a monitoring pipeline. Here, we used a taxonomy-free approach to examine 16s rRNA gene sequences derived from flocculent matter underneath and in proximity to hard-bottom salmon aquaculture sites in Newfoundland, Canada. Tetranucleotide frequencies (k = 4) were tabulated from sample sequences and included as features in a machine learning pipeline using the random forest algorithm to predict 4 levels of benthic disturbance; resulting classifications were compared to those obtained using a published taxonomy-based approach. Our results show that k-mer count features can effectively be used to create highly accurate predictions of benthic disturbance and can resolve intermediate changes in seafloor condition. In addition, we present a robust assessment of model performance which accounts for the effect of randomness in model creation. This work outlines a flexible framework for environmental assessments at aquaculture sites that is highly reproducible and free of taxonomy-assignment bias. |
format |
Article in Journal/Newspaper |
author |
Armstrong, EG Verhoeven, JTP |
author_facet |
Armstrong, EG Verhoeven, JTP |
author_sort |
Armstrong, EG |
title |
Machine learning analyses of bacterial oligonucleotide frequencies to assess the benthic impact of aquaculture |
title_short |
Machine learning analyses of bacterial oligonucleotide frequencies to assess the benthic impact of aquaculture |
title_full |
Machine learning analyses of bacterial oligonucleotide frequencies to assess the benthic impact of aquaculture |
title_fullStr |
Machine learning analyses of bacterial oligonucleotide frequencies to assess the benthic impact of aquaculture |
title_full_unstemmed |
Machine learning analyses of bacterial oligonucleotide frequencies to assess the benthic impact of aquaculture |
title_sort |
machine learning analyses of bacterial oligonucleotide frequencies to assess the benthic impact of aquaculture |
publisher |
Inter-Research |
publishDate |
2020 |
url |
https://doi.org/10.3354/aei00353 https://doaj.org/article/8e5290aa20c1406f84564d321af0e286 |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_source |
Aquaculture Environment Interactions, Vol 12, Pp 131-137 (2020) |
op_relation |
https://www.int-res.com/abstracts/aei/v12/p131-137/ https://doaj.org/toc/1869-215X https://doaj.org/toc/1869-7534 1869-215X 1869-7534 doi:10.3354/aei00353 https://doaj.org/article/8e5290aa20c1406f84564d321af0e286 |
op_doi |
https://doi.org/10.3354/aei00353 |
container_title |
Aquaculture Environment Interactions |
container_volume |
12 |
container_start_page |
131 |
op_container_end_page |
137 |
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1766109341488775168 |