Marine biomonitoring: predicting biotic indices from eDNA metabarcoding
Citation
MGnify (2019). Marine biomonitoring: predicting biotic indices from eDNA metabarcoding. Sampling event dataset https://doi.org/10.15468/jw6vob accessed via GBIF.org on 2024-12-12.Description
We investigated the possibility of using supervised machine learning (SML) algorithms to build predictive models from eDNA metabarcoding data targeting groups that are not commonly used for benthic monitoring. We tested our approach on benthic foraminifera, a group of unicellular eukaryotes known to be sensitive to organic enrichment associated with marine aquacultureSampling Description
Sampling
We investigated the possibility of using supervised machine learning (SML) algorithms to build predictive models from eDNA metabarcoding data targeting groups that are not commonly used for benthic monitoring. We tested our approach on benthic foraminifera, a group of unicellular eukaryotes known to be sensitive to organic enrichment associated with marine aquacultureMethod steps
- Pipeline used: https://www.ebi.ac.uk/metagenomics/pipelines/4.1
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originatorUniversity of Geneva
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University of Geneva
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University of Geneva