Utilization of multiple organisms in a proposed early-warning biomonitoring system for real-time detection of contaminants: preliminary results and modeling

Maradona, Aryo; Marshall, Gillianne; Mehrvar, Mehrab; Pushchak, Ronald; Laursen, Andrew E.; McCarthy, Lynda H.; Bostan, Vadim; Gilbride, Kimberley

doi:10.1016/j.jhazmat.2012.03.064

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…Given the ability to multiplex hundreds of samples and to perform analyses rapidly, cost-effective biomonitoring solutions could be proposed in matters of days instead of months . Hence, an eDNA-based early warning system adapted for the pro-active management of marine industrial activities could be envisioned, as it has been already proposed for others ecosystems and type of bioindicators. , …”

Section: Discussionmentioning

confidence: 99%

Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning

Cordier

Esling

Lejzerowicz

et al. 2017

Environ. Sci. Technol.

162

126

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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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Section: Discussionmentioning

confidence: 99%

Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning

Cordier

Esling

Lejzerowicz

et al. 2017

Environ. Sci. Technol.

162

126

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

“…More recently, the newly proposed BEWS rely on different organisms that exhibit various levels of sensitivity and reaction times to the contaminants (Maradona et al, 2012;Bae and Park, 2014). Multispecies monitors provide more reliable results and can minimize the risks of false positives or false negatives (Maradona et al, 2012;Bae and Park, 2014). As underlined in the recent review by Bae and Park (2014), the worldwide use of BEWS for surveillance of natural aquatic ecosystems, drinking water, and wastewater effluents has many advantages but also poses the difficulty of analysing and interpreting large amounts of data.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity to selected contaminants in a biological early warning system using <i>Anodonta woodiana</i> (Mollusca)

Giari¹,

Vincenzi²,

Fano³

et al. 2017

WSA

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“…Changes in locomotory behaviour are environmentally relevant, as they can provide information about the general physiological condition of organisms. They are also associated with reproduction, foraging, prédation and escape, all of which can change the community structure in aquatic ecosystems (Baillieul & Scheunders 1998;Charoy & Janssen 1999;Jeon et al 2008 responses are also readily visible and faster than mortality studies, and can be detectable in much less than 24 h. Automated biomonitoring may be capable of detecting small concentrations of contaminants, synergistic effects between contaminants, and contaminants which may not be tested for in a battery of chemical tests (Maradona et al 2012). They also provide a large cost advantage, as test organisms are readily available and systems ideally run with minimal supervision.…”

Section: Introductionmentioning

confidence: 99%

Automated image analysis of Euglena gracilis Klebs (Euglenophyta) for measuring sublethal effects of three model contaminants

Netto

Bostan

McCarthy

et al. 2012

Water Science and Technology

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The short-term impacts of atrazine (herbicide), tributyltin (organometal) and copper on the behaviour of Euglena gracilis Klebs (Euglenophyta) were assessed. First, the ECOTOX automated image analysis system was used, which measured swimming velocity, cell shape, percentage of cells swimming upwards, and randomness of swimming. Next, visual observation by microscopy was used to measure percentage of cell motility and cell shape. Behavioural changes can be used as an indicator of stress in less than 24 h, potentially making them suitable for inclusion in early-warning systems for water quality. Findings indicate that E. gracilis is a very sensitive organism to copper, showing inhibition of motility with visual observation at 0.8 μmol/L within 1 h. The image analysis system was in general less sensitive than visual observation for detecting behavioural changes after incubation in copper. In contrast, after exposure to organic contaminants atrazine and tributyltin, the ECOTOX system detected small changes in the number of cells swimming upwards (antigravitactic behaviour) at higher concentrations.

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Utilization of multiple organisms in a proposed early-warning biomonitoring system for real-time detection of contaminants: preliminary results and modeling

Cited by 11 publications

References 24 publications

Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning

Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning

Sensitivity to selected contaminants in a biological early warning system using <i>Anodonta woodiana</i> (Mollusca)

Automated image analysis of Euglena gracilis Klebs (Euglenophyta) for measuring sublethal effects of three model contaminants

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