Lorentzian Quantum Gravity and the Graviton Spectral Function.

We present the first direct and nonperturbative computation of the graviton spectral function in quantum gravity. This is achieved with the help of a novel Lorentzian renormalization group approach, combined with a spectral representation of correlation functions. We find a positive graviton spectral function, showing a massless one-graviton peak and a multigraviton continuum with an asymptotically safe scaling for large spectral values. We also study the impact of a cosmological constant. Further steps to investigate scattering processes and unitarity in asymptotically safe quantum gravity are indicated.

Taxon-rich transcriptomics supports higher-level phylogeny and major evolutionary trends in Foraminifera.

Foraminifera, classified in the supergroup Rhizaria, are a common and highly diverse group of mainly marine protists. Despite their evolutionary and ecological importance, only limited genomic data (one partial genome and nine transcriptomic datasets) have been published for this group. Foraminiferal molecular phylogeny is largely based on 18S rRNA gene sequence analysis. However, due to highly variable evolutionary rates of substitution in ribosomal genes plus the existence of intragenomic variation at this locus, the relationships between and within foraminiferal classes remain uncertain. We analyze transcriptomic data from 28 species, adding 19 new species to the previously published dataset, including members of the strongly under-represented class Monothalamea. A phylogenomic reconstruction of Rhizaria, rooted with alveolates and stramenopiles, based on 199 genes and 68 species supports the monophyly of Foraminifera and their sister relationship to Polycystinea. The phylogenomic tree of Foraminifera is very similar to the 18S rRNA tree, with the paraphyletic single-chambered monothalamids giving rise to the multi-chambered Tubothalamea and Globothalamea. Within the Monothalamea, our analyses confirm the monophyly of the giant, deep-sea xenophyophores that branch within clade C and indicate the basal position of monothalamous clades D and E. The multi-chambered Globothalamea are monophyletic and comprise the paraphyletic Textulariida and monophyletic Rotaliida. Our phylogenomic analyses support major evolutionary trends of Foraminifera revealed by ribosomal phylogenies and reinforce their current higher-level classification.

Biotic signals associated with benthic impacts of salmon farms from eDNA metabarcoding of sediments.

Environmental DNA (eDNA) metabarcoding can rapidly characterize the composition and diversity of benthic communities, thus it has high potential utility for routine assessments of benthic impacts of marine finfish farming. In this study, 126 sediment grab samples from 42 stations were collected at six salmon farms in British Columbia, Canada. Benthic community changes were assessed by both eDNA metabarcoding of metazoans and macrofaunal polychaete surveys. The latter was done by analysing 11,466 individuals using a combination of morphology-based taxonomy and DNA barcoding. Study objectives were to: (i) compare biotic signals associated with benthic impacts of salmon farming in the two data sources, and (ii) identify potential eDNA indicators to facilitate monitoring in Canada. Alpha diversity parameters were consistently reduced near fish cage edge and negatively correlated with pore-water sulphide concentration, with coefficients ranging from -0.62 to -0.48. Although Polychaeta are a common indicator group, the negative correlation with pore-water sulphide concentration was much stronger for Nematoda OTU richness (correlation coefficient: -0.86) than for Polychaeta (correlation coefficient: -0.38). Presence/absence of Capitella generally agreed well between the two methods despite that they differed in the volume of sediments sampled and the molecular marker used. Multiple approaches were used to identify OTUs related to organic enrichment statuses. We demonstrate that eDNA metabarcoding generates biotic signals that could be leveraged for environmental assessment of benthic impacts of fish farms in multiple ways: both alpha diversity and Nematoda OTU richness could be used to assess the spatial extent of impact, and OTUs related to organic enrichment could be used to develop local biotic indices.

Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes.

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 programmes to assess EQ around salmon aquaculture cages, we therefore recommend bacterial DNA metabarcodes in combination with SML to classify EQ categories based on molecular signatures.

Benthic monitoring of oil and gas offshore platforms in the North Sea using environmental DNA metabarcoding.

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 index values, AMBI index showing the best correlation between morphological and eDNA data sets. 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.

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.

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.

Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap.

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 programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

Environmental DNA: What's behind the term? Clarifying the terminology and recommendations for its future use in biomonitoring.

The last decade brought a spectacular development of so-called environmental (e)DNA studies. In general, "environmental DNA" is defined as DNA isolated from environmental samples, in contrast to genomic DNA that is extracted directly from specimens. However, the variety of different sources of eDNA and the range of taxonomic groups that are targeted by eDNA studies is large, which has led to some discussion about the breadth of the eDNA concept. In particular, there is a recent trend to restrict the use of the term "eDNA" to the DNA of macro-organisms, which are not physically present in environmental samples. In this paper, we argue that such a distinction may not be ideal, because the eDNA signal can come from organisms across the whole tree of life. Consequently, we advocate that the term "eDNA" should be used in its generic sense, as originally defined, encompassing the DNA of all organisms present in environmental samples, including microbial, meiofaunal and macrobial taxa. We first suggest specifying the environmental origin of the DNA sample, such as water eDNA, sediment eDNA or soil eDNA. A second specification would then define the taxonomic group targeted through polymerase chain reaction amplification, such as fish eDNA, invertebrate eDNA and bacterial eDNA. This terminology does also not require assumptions about the specific state of the DNA sampled (intracellular or extracellular). We hope that such terminology will help better define the scope of eDNA studies, especially for environmental managers, who use them as reference in routine biomonitoring and bioassessment.

SLIM: a flexible web application for the reproducible processing of environmental DNA metabarcoding data.

BACKGROUND: 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. RESULTS: We introduce SLIM, an open-source web application that simplifies the creation and execution of metabarcoding data processing pipelines through an intuitive Graphic User Interface (GUI). The GUI interact with well-established software and their associated parameters, so that the processing steps are performed seamlessly from the raw sequencing data to an annotated OTU-to-sample matrix. Thanks to a module-centered organization, SLIM can be used for a wide range of metabarcoding cases, and can also be extended by developers for custom needs or for the integration of new software. The pipeline configuration (i.e. the modules chaining and all their parameters) is stored in a file that can be used for reproducing the same analysis. CONCLUSION: This web application has been designed to be user-friendly for non-specialists yet flexible with advanced settings and extensibility for advanced users and bioinformaticians. The source code along with full documentation is available on the GitHub repository ( https://github.com/yoann-dufresne/SLIM ) and a demonstration server is accessible through the application website ( https://trtcrd.github.io/SLIM/ ).

Responses of foraminifera communities to aquaculture-derived organic enrichment as revealed by environmental DNA metabarcoding.

Current monitoring methods to assess benthic impacts of marine finfish aquaculture are based on complex biological indices and/or geochemistry data. The former requires benthic macrofauna morpho-taxonomic characterization that is time- and cost-intensive, while the latter provides rapid assessment of the organic enrichment status of sediments but does not directly measure biotic impacts. In this study, sediment samples were collected from seven stations at six salmon farms in British Columbia, Canada, and analyzed for geochemical parameters and by eDNA metabarcoding to investigate linkages between geochemistry and foraminifera. Sediment texture across farm sites ranged from sand to silty loam, while the maximum sediment pore-water sulphide concentration at each site ranged from 1,000 to 13,000 µM. Foraminifera alpha diversity generally increased with distance from cage edge. Adonis analyses revealed that farm site explained the most variation in foraminifera community, followed by sediment type, enrichment status, and distance from cage edge. Farm-specific responses were observed in diversity analyses, taxonomic difference analyses, and correlation analyses. Results demonstrated that species diversity and composition of foraminifera characterized by eDNA metabarcoding generated signals consistent with benthic biodiversity being impacted by finfish farming activities. This substantiates the validity of eDNA metabarcoding for augmenting current approaches to benthic impact assessments by providing more cost-effective and practicable biotic measures than traditional morpho-taxonomy. To capitalize on this potential, further work is needed to design a new nomogram that combines eDNA metabarcoding data and geochemistry data to enable accurate monitoring of benthic impacts of fish farming in a time- and cost-efficient way.

Ovalopodium rosalinum sp. nov., Planopodium haveli gen. nov, sp. nov., Planopodium desertum comb. nov. and new insights into phylogeny of the deeply branching members of the order Himatismenida (Amoebozoa).

The order Himatismenida (Amoebozoa, Discosea) comprises naked amoebae with an organic coat that is located on the dorsal surface of the cell. The phylogenetic relationships among deeply branching genera of the Himatismenida are unclear, as data on the species diversity of the himatismenid genera is largely restricted to the derived genus Cochliopodium. Here, we describe two new amoeba species that branch at the base of the order Himatismenida, evidenced by SSU rRNA gene and multigene analyses. Among them, a freshwater species Planopodium haveli gen. nov., sp. nov. has a dorsal cell coat consisting of flat, oval scales. This species forms a clade at the base of the Himatismenida, and the previously described Ovalopodium desertum, its closest relative, is transferred into the new genus as Planopodium desertum comb. nov. Although the two species are barely distinguishable by their sequence data, they are clearly distinct in morphology. Using this data, we can report the first evidence of a dorsal cell coat consisting of scales outside of the genus Cochliopodium. The other species has a marine origin and branches deeply, close to the root of the phylogenetic tree of Himatismenida. Based on the morphology of this amoeba, it should be described as Ovalopodium rosalinum sp. nov., a new species of the genus Ovalopodium. Analyses of the phylogenetic relationships and the ultrastructure of the deeply branching himatismenids, together with several of the newly obtained gene sequences of Parvamoeba and Cochliopodium, suggest that some elements of the dorsal cell coat of Ovalopodium may be ancestral for Himatismenida and have been partly retained in various more derived species of this clade, in particular, Cochliopodium gallicum. Although actin and Cox1 gene data do not resolve the higher-level relationships in Himatismenida, they correspond to the grouping of species within most genera.

A Comparison of Different Ciliate Metabarcode Genes as Bioindicators for Environmental Impact Assessments of Salmon Aquaculture.

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 toward 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.

Revisions to the Classification, Nomenclature, and Diversity of Eukaryotes.

This revision of the classification of eukaryotes follows that of Adl et al., 2012 [J. Euk. Microbiol. 59(5)] and retains an emphasis on protists. Changes since have improved the resolution of many nodes in phylogenetic analyses. For some clades even families are being clearly resolved. As we had predicted, environmental sampling in the intervening years has massively increased the genetic information at hand. Consequently, we have discovered novel clades, exciting new genera and uncovered a massive species level diversity beyond the morphological species descriptions. Several clades known from environmental samples only have now found their home. Sampling soils, deeper marine waters and the deep sea will continue to fill us with surprises. The main changes in this revision are the confirmation that eukaryotes form at least two domains, the loss of monophyly in the Excavata, robust support for the Haptista and Cryptista. We provide suggested primer sets for DNA sequences from environmental samples that are effective for each clade. We have provided a guide to trophic functional guilds in an appendix, to facilitate the interpretation of environmental samples, and a standardized taxonomic guide for East Asian users.

Assessing SSU rDNA Barcodes in Foraminifera: A Case Study using Bolivina quadrata.

The Small Subunit Ribosomal RNA gene (SSU rDNA) is a widely used tool to reconstruct phylogenetic relationships among foraminiferal species. Recently, the highly variable regions of this gene have been proposed as DNA barcodes to identify foraminiferal species. However, the resolution of these barcodes has not been well established, yet. In this study, we evaluate four SSU rDNA hypervariable regions (37/f, 41/f, 43/e, and 45/e) as DNA barcodes to distinguish among species of the genus Bolivina, with particular emphasis on Bolivina quadrata for which ten new sequences (KY468817-KY468826) were obtained during this study. Our analyses show that a single SSU rDNA hypervariable sequence is insufficient to resolve all Bolivina species and that some regions (37/f and 41/f) are more useful than others (43/e and 45/e) to distinguish among closely related species. In addition, polymorphism analyses reveal a high degree of variability. In the context of barcoding studies, these results emphasize the need to assess the range of intraspecific variability of DNA barcodes prior to their application to identify foraminiferal species in environmental samples; our results also highlight the possibility that a longer SSU rDNA region might be required to distinguish among species belonging to the same taxonomic group (i.e. genus).

Evolutionary Origins of Rhizarian Parasites.

The SAR group (Stramenopila, Alveolata, Rhizaria) is one of the largest clades in the tree of eukaryotes and includes a great number of parasitic lineages. Rhizarian parasites are obligate and have devastating effects on commercially important plants and animals but despite this fact, our knowledge of their biology and evolution is limited. Here, we present rhizarian transcriptomes from all major parasitic lineages in order to elucidate their evolutionary relationships using a phylogenomic approach. Our results suggest that Ascetosporea, parasites of marine invertebrates, are sister to the novel clade Apofilosa. The phytomyxean plant parasites branch sister to the vampyrellid algal ectoparasites in the novel clade Phytorhiza. They also show that Ascetosporea + Apofilosa + Retaria + Filosa + Phytorhiza form a monophyletic clade, although the branching pattern within this clade is difficult to resolve and appears to be model-dependent. Our study does not support the monophyly of the rhizarian parasitic lineages (Endomyxa), suggesting independent origins for rhizarian animal and plant parasites.

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

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.

Accurate multiplexing and filtering for high-throughput amplicon-sequencing.

Tagging amplicons with tag sequences appended to PCR primers allow the multiplexing of numerous samples for high-throughput sequencing (HTS). This approach is routinely used in HTS-based diversity analyses, especially in microbial ecology and biomedical diagnostics. However, amplicon library preparation is subject to pervasive sample sequence cross-contaminations as a result of tag switching events referred to as mistagging. Here, we sequenced seven amplicon libraries prepared using various multiplexing designs in order to measure the magnitude of this phenomenon and its impact on diversity analyses. Up to 28.2% of the unique sequences correspond to undetectable (critical) mistags in single- or saturated double-tagging libraries. We show the advantage of multiplexing samples following Latin Square Designs in order to optimize the detection of mistags and maximize the information on their distribution across samples. We use this information in designs incorporating PCR replicates to filter the critical mistags and to recover the exact composition of mock community samples. Being parameter-free and data-driven, our approach can provide more accurate and reproducible HTS data sets, improving the reliability of their interpretations.

Reducing long-branch effects in multi-protein data uncovers a close relationship between Alveolata and Rhizaria.

Rhizaria is a major eukaryotic group of tremendous diversity, including amoebae with spectacular skeletons or tests (Radiolaria and Foraminifera), plasmodial parasites (Plasmodiophorida) and secondary endosymbionts (Chlorarachniophyta). Current phylogeny places Rhizaria in an unresolved trichotomy with Stramenopila and Alveolata (supergroup "SAR"). We assembled a 147-protein data set with extensive rhizarian coverage (M147), including the first transcriptomic data for a euglyphid amoeba. Phylogenetic pre-screening of individual proteins indicated potential problems with radically misplaced sequences due either to contamination of rhizarian sequences amplified from wild collected material and/or extremely long branches (xLBs). Therefore, two data subsets were extracted containing either all proteins consistently recovering rhizarian monophyly (M34) or excluding all proteins with ⩾3 xLBs (defined as ⩾2× the average terminal branch length for the tree). Phylogenetic analyses of M147 give conflicting results depending on the outgroup and method of analysis but strongly support an exclusive Rhizaria+Alveolata (R+A) clade with both data subsets (M34 and M37) regardless of phylogenetic method used. Support for an R+A clade is most consistent when a close outgroup is used and decreases with more distant outgroups, suggesting that support for alternative SAR topologies may reflect a long-branch attraction artifact. A survey of xLB distribution among taxa and protein functional category indicates that small "informational" proteins in particular have highly variable evolutionary rates with no consistent pattern among taxa.