staff

Tristan Cordier

Associate scientist in Molecular Systematics & Environmental Genomics

  • T: +41 22 379 30 77
  • office 4078b (Sciences III)
  • Benthic monitoring of oil and gas offshore platforms in the North Sea using environmental DNA metabarcoding. Mol Ecol 2020 Oct;():. 10.1111/mec.15698.

    abstract

    Since 2010, considerable efforts have been undertaken to monitor the environmental status of European marine waters and ensuring the development of methodological standards for the evaluation of this status. However, the current routine biomonitoring implicates time-consuming and costly manual sorting and morphological identification of benthic macrofauna. Environmental DNA (eDNA) metabarcoding represents an alternative to the traditional monitoring method with very promising results. Here, we tested it further by performing eDNA metabarcoding of benthic eukaryotic communities in the vicinity of two offshore oil and gas platforms in the North Sea. Three different genetic markers (18S V1V2, 18S V9 and COI) were used to assess the environmental pressures induced by the platforms. All markers showed patterns of alpha and beta diversity consistent with morphology-based macrofauna analyses. In particular, the communities structure inferred from metabarcoding and morphological data significantly changed along distance gradients from the platforms. The impact of the operational discharges was also detected by the variation of biotic indices values, AMBI index showing the best correlation between morphological and eDNA datasets. Finally, the sediment physicochemical parameters were used to build a local de novo pressure index that served as benchmark to test the potential of a taxonomy-free approach. Our study demonstrates that metabarcoding approach outperforms morphology-based approach and can be used as a cost and time-saving alternative solution to the traditional morphology-based monitoring in order to monitor more efficiently the impact of industrial activities on marine biodiversity.

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  • Monitoring the ecological status of rivers with diatom eDNA metabarcoding: a comparison of taxonomic markers and analytical approaches for the inference of a molecular diatom index. Mol. Ecol. 2020 Sep;():. 10.1111/mec.15646.

    abstract

    Recently, several studies demonstrated the usefulness of diatom eDNA metabarcoding as an alternative to assess the ecological quality of rivers and streams. However, the choice of the taxonomic marker as well as the methodology for data analysis differ between these studies, hampering the comparison of their results and effectiveness. The aim of this study was to compare two taxonomic markers commonly used in diatom metabarcoding and three distinct analytical approaches to infer a molecular diatom index. We used the values of classical morphological diatom index as a benchmark for this comparison. We amplified and sequenced both a fragment of the rbcL gene and the V4 region of the 18S rRNA gene for 112 epilithic samples from Swiss and French rivers. We inferred index values using three analytical approaches: by computing it directly from taxonomically assigned sequences, by calibrating de novo the ecovalues of all metabarcodes, and by using a supervised machine learning algorithm to train predictive models. In general, the values of index obtained using the two "taxonomy-free" approaches, encompassing molecular assignment and machine learning, were closer correlated to the values of the morphological index than the values based on taxonomically assigned sequences. The correlations of the three analytical approaches were higher in the case of rbcL compared to the 18S marker, highlighting the importance of the reference database which is more complete for the rbcL marker. Our study confirms the effectiveness of diatom metabarcoding as an operational tool for rivers ecological quality assessment and shows that the analytical approaches by-passing the taxonomic assignments are particularly efficient when reference databases are incomplete.

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  • Benthic foraminiferal metabarcoding and morphology-based assessment around three offshore gas platforms: Congruence and complementarity. Environ Int 2020 Aug;144():106049. S0160-4120(20)32004-3. 10.1016/j.envint.2020.106049.

    abstract

    Since the 1960 s, there has been a rapid expansion of drilling activities in the central and northern Adriatic Sea to meet the increasing global energy demand. The discharges of organic and inorganic pollutants, as well as the alteration of the sediment substrate, are among the main impacts associated with these activities. In the present study, we evaluate the response of benthic foraminifera to the activities of three gas platforms in the northwestern Adriatic Sea, with a special focus on the Armida A platform for which extensive geochemical data (organic matter, trace elements, polycyclic aromatic hydrocarbons, other hydrocarbons, and volatile organic compounds) are available. The response to disturbance is assessed by analyzing the foraminiferal diversity using the traditional morphology-based approach and by 18S rDNA-based metabarcoding. The two methods give congruent results, showing relatively lower foraminiferal diversity and higher dominance values at stations closer to the platforms (<50 m). The taxonomic compositions of the morphological and metabarcoding datasets are very different, the latter being dominated by monothalamous, mainly soft-walled species. However, compositional changes consistently occur at 50 m from the platform and can be related to variations in sediment grain-size variation and higher concentrations of Ni, Zn, Ba, hydrocarbons and total organic carbon. Additionally, several morphospecies and Molecular Operational Taxonomic Units (MOTUs) show strong correlations with distance from the platform and with environmental parameters extracted from BIOENV analysis. Some of these MOTUs have the potential to become new bioindicators, complementing the assemblage of hard-shelled foraminiferal species detected through microscopic analyses. The congruence and complementarity between metabarcoding and morphological approaches support the application of foraminiferal metabarcoding in routine biomonitoring surveys as a reliable, time- and cost-effective methodology to assess the environmental impacts of marine industries.

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  • Ecosystems monitoring powered by environmental genomics: a review of current strategies with an implementation roadmap. Mol. Ecol. 2020 May;():. 10.1111/mec.15472.

    abstract

    A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (A) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (B) De novo bioindicator analyses; (C) Structural community metrics including inferred ecological networks; and (D) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programs that leverage recent analytical advancements, while pointing out current limitations and future research needs.

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  • Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes. Mol. Ecol. 2020 Apr;():. 10.1111/mec.15434.

    abstract

    Increasing anthropogenic impact and global change effects on natural ecosystems has prompted the development of less expensive and more efficient bioassessments methodologies. One promising approach is the integration of DNA metabarcoding in environmental monitoring. A critical step in this process is the inference of ecological quality (EQ) status from identified molecular bioindicator signatures that mirror environmental classification based on standard macroinvertebrate surveys. The most promising approaches to infer EQ from biotic indices (BI) are supervised machine learning (SML) and the calculation of indicator values (IndVal). In this study we compared the performance of both approaches using DNA metabarcodes of bacteria and ciliates as bioindicators obtained from 152 samples collected from seven Norwegian salmon farms. Results from standard macroinvertebrate-monitoring of the same samples were used as reference to compare the accuracy of both approaches. First, SML outperformed the IndVal approach to infer EQ from eDNA metabarcodes. The Random Forest (RF) algorithm appeared to be less sensitive to noisy data (a typical feature of massive environmental sequence data sets) and uneven data coverage across EQ classes (a typical feature of environmental compliance monitoring scheme) compared to a widely used method to infer IndVals for the calculation of a BI. Second, bacteria allowed for a more accurate EQ assessment than ciliate eDNA metabarcodes. For the implementation of DNA metabarcoding into routine monitoring programs to assess ecological quality around salmon aquaculture cages, we therefore recommend bacterial DNA metabarcodes in combination with SML to classify EQ categories based on molecular signatures.

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  • Bacterial communities’ taxonomic and functional turnovers both accurately predict marine benthic ecological quality status. Environmental DNA. 2019; 00: 1– 9.

    abstract

    The acquisition of biodiversity data is of prime importance to perform an efficient ecosystem management and ensure the sustainable provisioning of marine ecosystem services. Recent efforts are enforcing environmental genomic methodologies, especially environmental DNA (eDNA) metabarcoding, as a way forward to document biodiversity in a faster and cost‐effective way. Currently implemented biomonitoring regulations, such as the Marine Strategy Framework Directive (MSFD), rely on benthic macroinvertebrate indicator taxa to compute biotic indices (BIs) values and derive an ecological quality assessment. Recent work demonstrated that targeting those macroinvertebrates remains challenging, because of various technical and biological biases. Instead, bacterial communities’ profiles depicted by eDNA metabarcoding data have been shown to mirror macroinvertebrate communities’ variation and can be used to accurately predict the BI values using supervised machine learning (SML). Other studies showed that functional profiles, as obtained by metagenomic or metatranscriptomic approaches, vary along pollution gradient in aquatic ecosystems, demonstrating their potential as powerful indicators. Here, a 16S bacterial dataset collected in the vicinity of aquaculture sites, impacted by organic enrichment of the sea bottom, was used to generate two set of features consisting in either OTU profiles (taxonomic turnover) or predicted gene content using the tax4fun tool (functional turnover) that uses taxonomic annotations to infer functional capabilities based on evolutionary models. Both datasets were leading to accurate predictive models, with comparative performance. These results indicate that both taxonomic and functional turnovers of bacterial communities encompass powerful indicators that could be leveraged by marine biomonitoring programs.

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  • Benthic foraminiferal DNA metabarcodes significantly vary along a gradient from abyssal to hadal depths and between each side of the Kuril-Kamchatka trench. Progress in Oceanography, Volume 178, November 2019, 102175

    abstract

    Foraminiferal assemblages are a ubiquitous and abundant component of the deep-sea benthos, even in the deepest ocean trenches. While their distribution seems not constrained over large geographical distance, the current knowledge of foraminifera in trench is solely based on morphological observations. In this study, we document the first DNA metabarcoding dataset from a deep-sea trench focusing specifically on benthic foraminifera. Here we show that, consistent with previous molecular studies of abyssal fauna, trench foraminifera include diverse sequences of yet unknown species captured only by their molecular traces in the sediment. The molecular assemblages of foraminifera significantly differed along a depth gradient of almost 5000 m in the Kuril-Kamchatka trench. The deepest stations at nearly 9500 m were composed of unique phylotypes that were not identified in shallower stations, which means that these assemblages are unlikely the result of a sinking effect from shallower depths. Finally, both sides of the trench harbored very different communities, which could imply that the trench constitutes a physical barrier for the dispersion of some deep-sea foraminiferal species.

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  • Multi-marker eDNA metabarcoding survey to assess the environmental impact of three offshore gas platforms in the North Adriatic Sea (Italy). Mar. Environ. Res. 2019 Apr;146():24-34. S0141-1136(18)30678-0. 10.1016/j.marenvres.2018.12.009.

    abstract

    The environmental DNA (eDNA) metabarcoding represents a new promising tool for biomonitoring and environmental impact assessment. One of the main advantages of eDNA metabarcoding, compared to the traditional morphotaxonomy-based methods, is to provide a more holistic biodiversity information that includes inconspicuous morphologically non-identifiable taxa. Here, we use eDNA metabarcoding to survey marine biodiversity in the vicinity of the three offshore gas platforms in North Adriatic Sea (Italy). We isolated eDNA from 576 water and sediment samples collected at 32 sampling sites situated along four axes at increasing distances from the gas platforms. We obtained about 46 million eDNA sequences for 5 markers from nuclear 18S V1V2, 18S V4, 18S 37F and mitochondrial 16S and COI genes that cover a wide diversity of benthic and planktonic eukaryotes. Our results showed some impact of platform activities on benthic and pelagic communities at very close distance (<50 m), while communities for intermediate (125 m, 250 m, 500 m) and reference (1000 m, 2000 m) sites did not show any particular biodiversity changes that could be related to platforms activities. The most significant community change along the distance gradient was obtained with the 18S V1V2 marker targeting benthic eukaryotes, even though other markers showed similar trends, but to a lesser extent. These results were congruent with the AMBI index inferred from the eDNA sequences assigned to benthic macrofauna. We finally explored the relation between various physicochemical parameters, including hydrocarbons, on benthic community in the case of one of the platforms. Our results showed that these communities were not significantly impacted by most of hydrocarbons, but rather by macro-elements and sediment texture.

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  • Comparison and validation of Oomycetes metabarcoding primers for Phytophthora high throughput sequencing. Journal of Plant Pathology. August 2019, Volume 101, Issue 3, pp 743–748

    abstract

    Oomycetes are eukaryotic plant pathogens that require health monitoring. High-throughput sequencing (HTS) methods replace progressively cultivation-based approaches in soil surveys of Oomycetes, but very little control has been done from synthetic communities. Indeed, several potential biases do exist and need to be assessed for Oomycetes communities. We created a mock community by mixing DNA from 24 Phytophthora species. We amplified two barcode regions with Oomycete-specific primers before HTS. With this aim, we used three primer sets in nested PCR amplification, targeting the ITS-1 region or the RAS gene region. The three nested PCR strategies proved to be a reliable qualitative approach, identifying approximately 95% of the species after Illumina Miseq sequencing and bioinformatic analysis. However, quantitative proportions of each species showed distortions compared to the original mixture of the mock. In addition, we compared the two ITS primer sets on soil environmental DNA sampled from temperate forests. The ‘oom18S-ITS7/18ph2f-5.8S-1R’ primer set, more specific to Phytophthora, was able to detect seven Phytophthora species, confirming what was expected for temperate forests. Using the ‘DC6-ITS7/oom18S-ITS7’ primer set that covers the broader Peronosporaceans, we detected only one Phytophthora species among the dominance of Pythium and Phytopythium species. We concluded that ‘oom18S-ITS7/18ph2f-5.8S-1R’ primer set is a reliable tool for the qualitative description of environmental Phytophthora communities.

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  • SLIM: a flexible web application for the reproducible processing of environmental DNA metabarcoding data. BMC Bioinformatics 2019 Feb;20(1):88. 10.1186/s12859-019-2663-2. 10.1186/s12859-019-2663-2.

    abstract

    High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) has become a routine tool for biodiversity survey and ecological studies. By including sample-specific tags in the primers prior PCR amplification, it is possible to multiplex hundreds of samples in a single sequencing run. The analysis of millions of sequences spread into hundreds to thousands of samples prompts for efficient, automated yet flexible analysis pipelines. Various algorithms and software have been developed to perform one or multiple processing steps, such as paired-end reads assembly, chimera filtering, Operational Taxonomic Unit (OTU) clustering and taxonomic assignment. Some of these software are now well established and widely used by scientists as part of their workflow. Wrappers that are capable to process metabarcoding data from raw sequencing data to annotated OTU-to-sample matrix were also developed to facilitate the analysis for non-specialist users. Yet, most of them require basic bioinformatic or command-line knowledge, which can limit the accessibility to such integrative toolkits. Furthermore, for flexibility reasons, these tools have adopted a step-by-step approach, which can prevent an easy automation of the workflow, and hence hamper the analysis reproducibility.

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  • Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring. Trends Microbiol. 2018 Nov;():. S0966-842X(18)30238-5. 10.1016/j.tim.2018.10.012.

    abstract

    Genomics is fast becoming a routine tool in medical diagnostics and cutting-edge biotechnologies. Yet, its use for environmental biomonitoring is still considered a futuristic ideal. Until now, environmental genomics was mainly used as a replacement of the burdensome morphological identification, to screen known morphologically distinguishable bioindicator taxa. While prokaryotic and eukaryotic microbial diversity is of key importance in ecosystem functioning, its implementation in biomonitoring programs is still largely unappreciated, mainly because of difficulties in identifying microbes and limited knowledge of their ecological functions. Here, we argue that the combination of massive environmental genomics microbial data with machine learning algorithms can be extremely powerful for biomonitoring programs and pave the way to fill important gaps in our understanding of microbial ecology.

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  • A Comparison of Different Ciliate Metabarcode Genes as Bioindicators for Environmental Impact Assessments of Salmon Aquaculture. J. Eukaryot. Microbiol. 2018 Jul;():. 10.1111/jeu.12670.

    abstract

    Ciliates are powerful indicators for monitoring the impact of aquaculture and other industrial activities in the marine environment. Here we tested the efficiency of four different genetic markers (V4 and V9 regions of the SSU rRNA gene, D1 and D2 regions of the LSU rRNA gene, obtained from environmental (e)DNA and environmental (e)RNA) of benthic ciliate communities for environmental monitoring. We obtained these genetic metabarcodes from sediment samples collected along a transect extending from below salmon cages towards the open sea. These data were compared to benchmark data from traditional macrofauna surveys of the same samples. In beta-diversity analyses of ciliate community structures, the V4 and V9 markers had a higher resolution power for sampling sites with different degrees of organic enrichment compared to the D1 and D2 markers. The eDNA and eRNA V4 markers had a higher discriminatory power than the V9 markers. However, results obtained with the eDNA V9 marker corroborated better with the traditional macrofauna monitoring. This allows for a more direct comparison of ciliate metabarcoding with the traditional monitoring. We conclude that the ciliate eDNA V9 marker is the best choice for implementation in routine monitoring programs in marine aquaculture. This article is protected by copyright. All rights reserved.

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  • Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring. Mol Ecol Resour 2018 Jul;():. 10.1111/1755-0998.12926.

    abstract

    Biodiversity monitoring is the standard for environmental impact assessment of anthropogenic activities. Several recent studies showed that high-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) could overcome many limitations of the traditional morphotaxonomy-based bioassessment. Recently, we demonstrated that supervised machine learning (SML) can be used to predict accurate biotic indices values from eDNA metabarcoding data, regardless of the taxonomic affiliation of the sequences. However, it is unknown to which extent the accuracy of such models depends on taxonomic resolution of molecular markers or how SML compares with metabarcoding approaches targeting well-established bioindicator species. In this study, we address these issues by training predictive models upon five different ribosomal bacterial and eukaryotic markers and measuring their performance to assess the environmental impact of marine aquaculture on independent datasets. Our results show that all tested markers are yielding accurate predictive models, and that they all outperform the assessment relying solely on taxonomically assigned sequences. Remarkably, we did not find any significant difference in the performance of the models built using universal eukaryotic or prokaryotic markers. Using any molecular marker with a taxonomic range broad enough to comprise different potential bioindicator taxa, SML approach can overcome the limits of taxonomy-based eDNA bioassessment. This article is protected by copyright. All rights reserved.

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  • BBI: an R package for the computation of Benthic Biotic Indices from composition data. Metabarcoding and Metagenomics 2: e25649

    abstract

    The monitoring of impacts of anthropic activities in marine environments, such as aquaculture, oil-drilling platforms or deep-sea mining, relies on Benthic Biotic Indices (BBI). Several indices have been formalised to reduce the multivariate composition data into a single continuous value that is ascribed to a discrete ecological quality status. Such composition data is traditionally obtained from macrofaunal inventories, which is time-consuming and expertise-demanding. Important efforts are ongoing towards using High-Throughput Sequencing of environmental DNA (eDNA metabarcoding) to replace or complement morpho-taxonomic surveys for routine biomonitoring. The computation of BBI from such composition data is usually being undertaken by practitioners with excel spreadsheets or through custom script. Hence, the updating of reference morpho-taxonomic tables and cross studies comparison could be hampered. Here we introduce the R package BBI for the computation of BBI from composition data, either obtained from traditional morpho-taxonomic inventories or from metabarcoding data. Its aim is to provide an open-source, transparent and centralised method to compute BBI for routine biomonitoring.

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  • The future of biotic indices in the ecogenomic era: Integrating (e)DNA metabarcoding in biological assessment of aquatic ecosystems. Sci. Total Environ. 2018 May;637-638():1295-1310. S0048-9697(18)31632-2. 10.1016/j.scitotenv.2018.05.002.

    abstract

    The bioassessment of aquatic ecosystems is currently based on various biotic indices that use the occurrence and/or abundance of selected taxonomic groups to define ecological status. These conventional indices have some limitations, often related to difficulties in morphological identification of bioindicator taxa. Recent development of DNA barcoding and metabarcoding could potentially alleviate some of these limitations, by using DNA sequences instead of morphology to identify organisms and to characterize a given ecosystem. In this paper, we review the structure of conventional biotic indices, and we present the results of pilot metabarcoding studies using environmental DNA to infer biotic indices. We discuss the main advantages and pitfalls of metabarcoding approaches to assess parameters such as richness, abundance, taxonomic composition and species ecological values, to be used for calculation of biotic indices. We present some future developments to fully exploit the potential of metabarcoding data and improve the accuracy and precision of their analysis. We also propose some recommendations for the future integration of DNA metabarcoding to routine biomonitoring programs.

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  • Development and implementation of eco-genomic tools for aquatic ecosystem biomonitoring: the SYNAQUA French-Swiss program. Environ Sci Pollut Res Int 2018 May;():. 10.1007/s11356-018-2172-2. 10.1007/s11356-018-2172-2.

    abstract

    The effectiveness of environmental protection measures is based on the early identification and diagnosis of anthropogenic pressures. Similarly, restoration actions require precise monitoring of changes in the ecological quality of ecosystems, in order to highlight their effectiveness. Monitoring the ecological quality relies on bioindicators, which are organisms revealing the pressures exerted on the environment through the composition of their communities. Their implementation, based on the morphological identification of species, is expensive because it requires time and experts in taxonomy. Recent genomic tools should provide access to reliable and high-throughput environmental monitoring by directly inferring the composition of bioindicators' communities from their DNA (metabarcoding). The French-Swiss program SYNAQUA (INTERREG France-Switzerland 2017-2019) proposes to use and validate the tools of environmental genomic for biomonitoring and aims ultimately at their implementation in the regulatory bio-surveillance. SYNAQUA will test the metabarcoding approach focusing on two bioindicators, diatoms, and aquatic oligochaetes, which are used in freshwater biomonitoring in France and Switzerland. To go towards the renewal of current biomonitoring practices, SYNAQUA will (1) bring together different actors: scientists, environmental managers, consulting firms, and biotechnological companies, (2) apply this approach on a large scale to demonstrate its relevance, (3) propose robust and reliable tools, and (4) raise public awareness and train the various actors likely to use these new tools. Biomonitoring approaches based on such environmental genomic tools should address the European need for reliable, higher-throughput monitoring to improve the protection of aquatic environments under multiple pressures, guide their restoration, and follow their evolution.

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  • Environmental DNA metabarcoding of benthic bacterial communities indicates the benthic footprint of salmon aquaculture. Mar. Pollut. Bull. 2018 Feb;127():139-149. S0025-326X(17)31022-6. 10.1016/j.marpolbul.2017.11.065.

    abstract

    We evaluated benthic bacterial communities as bioindicators in environmental impact assessments of salmon aquaculture, a rapidly growing sector of seafood industry. Sediment samples (n=72) were collected from below salmon cages towards distant reference sites. Bacterial community profiles inferred from DNA metabarcodes were compared to reference data from standard macrofauna biomonitoring surveys of the same samples. Deltaproteobacteria were predominant in immediate vicinity of the salmon cages. Along the transect, significant shifts in bacterial community structures were observed with Gammaproteobacteria dominating the less-impacted sites. Alpha- and beta-diversity measures of bacterial communities correlated significantly with macrofauna diversity metrics and with five ecological status indices. Benthic bacterial communities mirror the reaction of macrofauna bioindicators to environmental disturbances caused by salmon farming. The implementation of bacterial eDNA metabarcoding in future Strategic Framework Directives is an alternative cost-effective high-throughput biomonitoring solution, providing a basis for management strategies in a matter of days rather than months.

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  • 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.

    abstract

    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

    abstract

    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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