ARPHA Conference Abstracts :
Conference Abstract
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Corresponding author: Maria João Feio (mjf@ci.uc.pt)
Received: 02 Mar 2021 | Published: 04 Mar 2021
© 2021 Maria Feio
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Feio MJ (2021) The use of machine learning predictive models to assess rivers quality with molecular data. ARPHA Conference Abstracts 4: e65380. https://doi.org/10.3897/aca.4.e65380
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Many tests have been made so far to assess the biological quality of rivers with molecular data. Most often HTS-related eDNA metabarcoding sequences clustered into Operational Taxonomic Units (OTUs) are assigned to taxa, using reference barcode databases. From there, the existing biotic indices, developed for morphological data are calculated. However, this approach has several drawbacks that may justify their lower performances compared to traditional ones, or not extracting the maximum potential from the molecular data. The first is the incompleteness of reference databases (despite their continuous evolution) - avoiding the conversion of molecular into taxonomic may overcome this issue. Yet, another likely source of bias in the assessments is at the basis of existing classification systems: a possible poor correspondence between the biological reference conditions developed based on species morphology and on molecular data. In other words, molecular-based assemblages from different rivers may not group similarly or respond to the same environmental variables as taxa. Correcting this would require rebuilding the whole systems and establishing new typological-based molecular reference values, which are then used to calculate Ecological Quality Ratios (EQR) and determine the Ecological Quality Status (EQS) of river sites. One alternative to the grouping step inherent to the typological approach, and that may be viewed as artificial (nature is a continuum), is the prediction of site-specific reference conditions based on abiotic characteristics of sites. Thus, we tested a combination of machine-learning modelling techniques to build a taxonomic-free site-specific index to assess rivers based on diatom assemblages, from 81 sites located in Portugal (
rivers bioassment
metabarcoding
eDNA
predictive modelling
quality classsification
Maria João Feio
1st DNAQUA International Conference (March 9-11, 2021)
University of Coimbra, Department of Life Sciences, Marine and Environmental Sciences Centre (MARE)