Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes-Reference-Cited by-同舟云学术

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Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes

Published:2020-05-15 Issue:13 Volume:30 Page:2988-3006
ISSN:0962-1083
Container-title:Molecular Ecology
language:en
Short-container-title:Mol Ecol

Author:

Frühe Larissa¹,Cordier Tristan²^ORCID,Dully Verena¹,Breiner Hans‐Werner¹,Lentendu Guillaume¹^ORCID,Pawlowski Jan²³⁴,Martins Catarina⁵,Wilding Thomas A.⁶,Stoeck Thorsten¹^ORCID

Affiliation:

1. Ecology Group Technische Universität Kaiserslautern Kaiserslautern Germany

2. Department of Genetics and Evolution University of Geneva Geneva Switzerland

3. ID‐Gene Ecodiagnostics Ltd Geneva Switzerland

4. Institute of Oceanology Polish Academy of Sciences Sopot Poland

5. MOWI Bergen Norway

6. Scottish Marine Institute Scottish Association for Marine Science Oban Scotland

Funder

Deutsche Forschungsgemeinschaft

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Wiley

Subject

Genetics,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mec.15434

Reference103 articles.

1. Characterization of the anaerobic microbial community in oil-polluted subtidal sediments: aromatic biodegradation potential after thePrestigeoil spill

2. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

3. Taxonomy-free molecular diatom index for high-throughput eDNA biomonitoring

4. Molecular phylogenetic and chemical analyses of the microbial mats in deep-sea cold seep sediments at the northeastern Japan Sea

5. Environmental Status Assessment Using DNA Metabarcoding: Towards a Genetics Based Marine Biotic Index (gAMBI)

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