Tristan Cordier

Postdoctoral fellow in Molecular Systematics & Environmental Genomics

  • T: +41 22 379 30 77
  • office 4078b (Sciences III)
  • Predicting the ecological quality status of marine environments from eDNA metabarcoding data using supervised machine learning. Environ. Sci. Technol. 2017 Jun;():. 10.1021/acs.est.7b01518.


    Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which is time-consuming and taxonomic-expertise demanding. High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) represents a promising alternative for benthic monitoring. However, an important fraction of eDNA sequences remains unassigned or belong to taxa of unknown ecology, which prevent their use for assessing the ecological quality status. Here, we show that supervised machine learning (SML) can be used to build robust predictive models for benthic monitoring, regardless of the taxonomic assignment of eDNA sequences. We tested three SML approaches to assess the environmental impact of marine aquaculture using benthic foraminifera eDNA, a group of unicellular eukaryotes known to be good bioindicators, as features to infer macro-invertebrates based biotic indices. We found similar ecological status as obtained from macro-invertebrates inventories. We argue that SML approaches could overcome and even bypass the cost and time-demanding morpho-taxonomic approaches in future biomonitoring.

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  • Benthic monitoring of salmon farms in Norway using foraminiferal metabarcoding AEI 8:371-386 (2016) - doi:10.3354/aei00182


    The rapid growth of the salmon industry necessitates the development of fast and accurate tools to assess its environmental impact. Macrobenthic monitoring is commonly used to measure the impact of organic enrichment associated with salmon farm activities. However, classical benthic monitoring can hardly answer the rapidly growing demand because the morphological identification of macro-invertebrates is time-consuming, expensive and requires taxonomic expertise. Environmental DNA (eDNA) metabarcoding of meiofauna-sized organisms, such as Foraminifera, was proposed to overcome the drawbacks of macrofauna-based benthic monitoring. Here, we tested the application of foraminiferal metabarcoding to benthic monitoring of salmon farms in Norway. We analysed 140 samples of eDNA and environmental RNA (eRNA) extracted from surface sediment samples collected at 4 salmon farming sites in Norway. We sequenced the variable region 37f of the 18S rRNA gene specific to Foraminifera. We compared our data to the results of macrofaunal surveys of the same sites and tested the congruence between various diversity indices inferred from metabarcoding and morphological data. The results of our study confirm the usefulness of Foraminifera as bioindicators of organic enrichment associated with salmon farming. The foraminiferal diversity increased with the distance to fish cages, and metabarcoding provides an assessment of the ecological quality comparable to the morphological analyses. The foraminiferal metabarcoding approach appears to be a promising alternative to classical benthic monitoring, providing a solution to the morpho-taxonomic bottleneck of macrofaunal surveys.

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