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dc.contributor.authorLanzen, Anders
dc.contributor.authorMendibil, Inaki
dc.contributor.authorBorja, Angel
dc.contributor.authorAlonso-Saez, Laura
dc.date.accessioned2021-07-02T08:12:32Z-
dc.date.available2021-07-02T08:12:32Z-
dc.identifierISI:000541269200001
dc.identifier.issn0962-1083
dc.identifier.urihttp://dspace.azti.es/handle/24689/1110-
dc.description.abstractRoutine monitoring of benthic biodiversity is critical for managing and understanding the anthropogenic impacts on marine, transitional and freshwater ecosystems. However, traditional reliance on morphological identification generally makes it cost-prohibitive to increase the scale of monitoring programmes. Metabarcoding of environmental DNA has clear potential to overcome many of the problems associated with traditional monitoring, with prokaryotes and other microorganisms showing particular promise as bioindicators. However, due to the limited knowledge regarding the ecological roles and responses of environmental microorganisms to different types of pressure, the use of de novo approaches is necessary. Here, we use two such approaches for the prediction of multiple impacts present in estuaries and coastal areas of the Bay of Biscay based on microbial communities. The first (Random Forests) is a machine learning method while the second (Threshold Indicator Taxa Analysis and quantile regression splines) is based on de novo identification of bioindicators. Our results show that both methods overlap considerably in the indicator taxa identified, but less for sequence variants. Both methods also perform well in spite of the complexity of the studied ecosystem, providing predictive models with strong correlation to reference values and fair to good agreement with ecological status groups. The ability to predict several specific types of pressure is especially appealing. The cross-validated models and biotic indices developed can be directly applied to predict the environmental status of estuaries in the same geographical region, although more work is needed to evaluate and improve them for use in new regions or habitats.
dc.language.isoEnglish
dc.publisherWILEY
dc.subjectbiomonitoring
dc.subjectmachine learning
dc.subjectmetabarcoding
dc.subjectmicrobial
dc.subjectmultiple impact
dc.subjectmultiple pressure
dc.subjectECOLOGICAL STATUS
dc.subjectMARINE ENVIRONMENTS
dc.subjectBIOTIC INDEX
dc.subjectOIL-SPILLS
dc.subjectBACTERIAL
dc.subjectBIODIVERSITY
dc.subjectDIVERSITY
dc.subjectQUALITY
dc.subjectINTEGRITY
dc.subjectSEDIMENTS
dc.titleA microbial mandala for environmental monitoring: Predicting multiple impacts on estuarine prokaryote communities of the Bay of Biscay
dc.typeArticle; Early Access
dc.identifier.journalMOLECULAR ECOLOGY
dc.contributor.funderIkerbasque, Basque Foundation for Science
dc.contributor.funderBasque Government (Eusko Jaurlaritza)
dc.contributor.funderMinisterio de Ciencia e Innovacion, Gobierno de EspanaSpanish Government [RYC-201211404]
dc.identifier.e-issn1365-294X
dc.identifier.doi10.1111/mec.15489
Appears in Publication types:Artículos científicos



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