Assessing the response of an urban stream ecosystem to salinization under different flow regimes.

Urban streams are exposed to a variety of anthropogenic stressors. Freshwater salinization is a key stressor in these ecosystems that is predicted to be further exacerbated by climate change, which causes simultaneous changes in flow parameters, potentially resulting in non-additive effects on aquatic ecosystems. However, the effects of salinization and flow velocity on urban streams are still poorly understood as multiple-stressor experiments are often conducted at pristine rather than urban sites. Therefore, we conducted a mesocosm experiment at the Boye River, a recently restored stream located in a highly urbanized area in Western Germany, and applied recurrent pulses of salinity along a gradient (NaCl, 9 h daily of +0 to +2.5 mS/cm) in combination with normal and reduced current velocities (20 cm/s vs. 10 cm/s). Using a comprehensive assessment across multiple organism groups (macroinvertebrates, eukaryotic algae, fungi, parasites) and ecosystem functions (primary production, organic-matter decomposition), we show that flow velocity reduction has a pervasive impact, causing community shifts for almost all assessed organism groups (except fungi) and inhibiting organic-matter decomposition. Salinization affected only dynamic components of community assembly by enhancing invertebrate emigration via drift and reducing fungal reproduction. We caution that the comparatively small impact of salt in our study can be due to legacy effects from past salt pollution by coal mining activities >30 years ago. Nevertheless, our results suggest that urban stream management should prioritize the continuity of a minimum discharge to maintain ecosystem integrity. Our study exemplifies a holistic approach for the assessment of multiple-stressor impacts on streams, which is needed to inform the establishment of a salinity threshold above which mitigation actions must be taken.

Effects of flow reduction and artificial light at night (ALAN) on litter decomposition and invertebrate communities in streams: A flume experiment.

River ecosystems are heavily impacted by multiple stressors, where effects can cascade downstream of point sources. However, a spatial approach to assess the effects of multiple stressors is missing. We assessed the local and downstream effects on litter decomposition, and associated invertebrate communities of two stressors: flow reduction and artificial light at night (ALAN). We used an 18-flow-through mesocosm system consisting of two tributaries, where we applied the stressors, merging in a downstream section. We assessed the changes in decomposition rate and invertebrate community structure in leaf bags. We found no effect of ALAN or its interaction with flow reduction on the litter decomposition or the invertebrate community in the tributaries. Flow reduction alone led to a 14.8 % reduction in decomposition rate in the tributaries. We recorded no effect of flow reduction on the macroinvertebrates community composition in the litter bags. We also observed no effects of the spatial arrangement of the stressors on the litter decomposition and macroinvertebrate community structure downstream. Overall, our results suggest the impact of stressors on litter decomposition and macroinvertebrate communities remained local in our experiment. Our work thus calls for further studies to identify the mechanisms and the conditions under which spatial effects dominate over local processes.

Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data.

Introduction: Microbes are increasingly (re)considered for environmental assessments because they are powerful indicators for the health of ecosystems. The complexity of microbial communities necessitates powerful novel tools to derive conclusions for environmental decision-makers, and machine learning is a promising option in that context. While amplicon sequencing is typically applied to assess microbial communities, metagenomics and total RNA sequencing (herein summarized as omics-based methods) can provide a more holistic picture of microbial biodiversity at sufficient sequencing depths. Despite this advantage, amplicon sequencing and omics-based methods have not yet been compared for taxonomy-based environmental assessments with machine learning. Methods: In this study, we applied 16S and ITS-2 sequencing, metagenomics, and total RNA sequencing to samples from a stream mesocosm experiment that investigated the impacts of two aquatic stressors, insecticide and increased fine sediment deposition, on stream biodiversity. We processed the data using similarity clustering and denoising (only applicable to amplicon sequencing) as well as multiple taxonomic levels, data types, feature selection, and machine learning algorithms and evaluated the stressor prediction performance of each generated model for a total of 1,536 evaluated combinations of taxonomic datasets and data-processing methods. Results: Sequencing and data-processing methods had a substantial impact on stressor prediction. While omics-based methods detected a higher diversity of taxa than amplicon sequencing, 16S sequencing outperformed all other sequencing methods in terms of stressor prediction based on the Matthews Correlation Coefficient. However, even the highest observed performance for 16S sequencing was still only moderate. Omics-based methods performed poorly overall, but this was likely due to insufficient sequencing depth. Data types had no impact on performance while feature selection significantly improved performance for omics-based methods but not for amplicon sequencing. Discussion: We conclude that amplicon sequencing might be a better candidate for machine-learning-based environmental stressor prediction than omics-based methods, but the latter require further research at higher sequencing depths to confirm this conclusion. More sampling could improve stressor prediction performance, and while this was not possible in the context of our study, thousands of sampling sites are monitored for routine environmental assessments, providing an ideal framework to further refine the approach for possible implementation in environmental diagnostics.

Probing the Nucleic Acid Flexibility to Disarm the Viral Counter-Defense Machinery: Design and Characterization of Potent p19 Inhibitors.

Plant viruses are highly destructive and significant contributors to several global pandemics and epidemics in plants. A viral disease outbreak in plants can cause a scarcity of food supply and is a severe concern to humanity. The siRNA (small interfering RNA)-mediated RNA-induced silencing complex (RISC) formation is a primary defense mechanism in plants against viruses, where the RISC binds and degrades viral mRNAs. As a counter-defense, many viruses encode RNA-silencing suppressor proteins (e.g., the p19 protein from the Tombusviridae family) for viral proliferation in plants. The functional form of p19 (homodimer) binds to plant siRNA with high affinities, thereby interrupting the RISC formation and thus preventing the viral mRNA silencing in plants. By altering the RISC formation, the p19 protein helps the virus invasion in the plant and ultimately stunts host growth. In this study, we designed several modified siRNA-based molecules for p19 inhibition. The viral p19 protein is known to interact predominantly through H-bonds with 2'-OH and phosphates of the plant siRNA. We utilized this information and in silico-designed flexible substituents of siRNA, where we removed the C2'-C3' bond in each nucleotide unit. We performed all-atom explicit-solvent molecular dynamics simulations (400 ns, 3 replicates each) for control/modified siRNA─p19 complexes (8 in total) followed by energetic estimations. Strikingly, in a few modified complexes, the siRNA not only retained the double-helical structural integrity but also displayed remarkably enhanced p19 binding compared to the control siRNA; hence, we consider it important to perform biological and chemical in vitro and in vivo studies on proposed flexible nucleic acids as p19 inhibitors for crop protection.

Transcriptomic sequencing data illuminate insecticide-induced physiological stress mechanisms in aquatic non-target invertebrates.

Pesticides are major agricultural stressors for freshwater species. Exposure to pesticides can disrupt the biotic integrity of freshwater ecosystems and impair associated ecosystem functions. Unfortunately, physiological mechanisms through which pesticides affect aquatic organisms are largely unknown. For example, the widely-used insecticide chlorantraniliprole is supposed to be highly selective for target pest species, i.e. Lepidoptera (butterflies), but its effect in aquatic non-target taxa is poorly studied. Using RNA-sequencing data, we quantified the insecticide effect on three aquatic invertebrate species: the caddisfly Lepidostoma basale, the mayfly Ephemera danica and the amphipod Gammarus pulex. Further, we tested how the insecticide-induced transcriptional response is modulated by biotic interaction between the two leaf-shredding species L. basale and G. pulex. While G. pulex was only weakly affected by chlorantraniliprole exposure, we detected strong transcriptional responses in L. basale and E. danica, implying that the stressor receptors are conserved between the target taxon Lepidoptera and other insect groups. We found in both insect species evidence for alterations of the developmental program. If transcriptional changes in the developmental program induce alterations in emergence phenology, pronounced effects on food web dynamics in a cross-ecosystem context are expected.

The Asymmetric Response Concept explains ecological consequences of multiple stressor exposure and release.

Our capacity to predict trajectories of ecosystem degradation and recovery is limited, especially when impairments are caused by multiple stressors. Recovery may be fast or slow and either complete or partial, sometimes result in novel ecosystem states or even fail completely. Here, we introduce the Asymmetric Response Concept (ARC) that provides a basis for exploring and predicting the pace and magnitude of ecological responses to, and release from, multiple stressors. The ARC holds that three key mechanisms govern population, community and ecosystem trajectories. Stress tolerance is the main mechanism determining responses to increasing stressor intensity, whereas dispersal and biotic interactions predominantly govern responses to the release from stressors. The shifting importance of these mechanisms creates asymmetries between the ecological trajectories that follow increasing and decreasing stressor intensities. This recognition helps to understand multiple stressor impacts and to predict which measures will restore communities that are resistant to restoration.

Morphology, nuclear SNPs and mate selection reveal that COI barcoding overestimates species diversity in a Mediterranean freshwater amphipod by an order of magnitude.

DNA sequence information has revealed many morphologically cryptic species worldwide. For animals, DNA-based assessments of species diversity usually rely on the mitochondrial cytochrome c oxidase subunit I (COI) gene. However, a growing amount of evidence indicate that mitochondrial markers alone can lead to misleading species diversity estimates due to mito-nuclear discordance. Therefore, reports of putative species based solely on mitochondrial DNA should be verified by other methods, especially in cases where COI sequences are identical for different morphospecies or where divergence within the same morphospecies is high. Freshwater amphipods are particularly interesting in this context because numerous putative cryptic species have been reported. Here, we investigated the species status of the numerous mitochondrial molecular operational taxonomic units (MOTUs) found within Echinogammarus sicilianus. We used an integrative approach combining DNA barcoding with mate selection observations, detailed morphometrics and genome-wide double digest restriction site-associated DNA sequencing (ddRAD-seq). Within a relatively small sampling area, we detected twelve COI MOTUs (divergence = 1.8-20.3%), co-occurring in syntopy at two-thirds of the investigated sites. We found that pair formation was random and there was extensive nuclear gene flow among the ten MOTUs co-occurring within the same river stretch. The four most common MOTUs were also indistinguishable with respect to functional morphology. Therefore, the evidence best fits the hypothesis of a single, yet genetically diverse, species within the main river system. The only two MOTUs sampled outside the focal area were genetically distinct at the nuclear level and may represent distinct species. Our study reveals that COI-based species delimitation can significantly overestimate species diversity, highlighting the importance of integrative taxonomy for species validation, especially in hyperdiverse complexes with syntopically occurring mitochondrial MOTUs.

Three perspectives on the prediction of chemical effects in ecosystems.

The increasing production, use and emission of synthetic chemicals into the environment represents a major driver of global change. The large number of synthetic chemicals, limited knowledge on exposure patterns and effects in organisms and their interaction with other global change drivers hamper the prediction of effects in ecosystems. However, recent advances in biomolecular and computational methods are promising to improve our capacity for prediction. We delineate three idealised perspectives for the prediction of chemical effects: the suborganismal, organismal and ecological perspective, which are currently largely separated. Each of the outlined perspectives includes essential and complementary theories and tools for prediction but captures only part of the phenomenon of chemical effects. Links between the perspectives may foster predictive modelling of chemical effects in ecosystems and extrapolation between species. A major challenge for the linkage is the lack of data sets simultaneously covering different levels of biological organisation (here referred to as biological levels) as well as varying temporal and spatial scales. Synthesising the three perspectives, some central aspects and associated types of data seem particularly necessary to improve prediction. First, suborganism- and organism-level responses to chemicals need to be recorded and tested for relationships with chemical groups and organism traits. Second, metrics that are measurable at many biological levels, such as energy, need to be scrutinised for their potential to integrate across levels. Third, experimental data on the simultaneous response over multiple biological levels and spatiotemporal scales are required. These could be collected in nested and interconnected micro- and mesocosm experiments. Lastly, prioritisation of processes involved in the prediction framework needs to find a balance between simplification and capturing the essential complexity of a system. For example, in some cases, eco-evolutionary dynamics and interactions may need stronger consideration. Prediction needs to move from a static to a real-world eco-evolutionary view.

Impacts of multiple anthropogenic stressors on the transcriptional response of Gammarus fossarum in a mesocosm field experiment.

BACKGROUND: Freshwaters are exposed to multiple anthropogenic stressors, leading to habitat degradation and biodiversity decline. In particular, agricultural stressors are known to result in decreased abundances and community shifts towards more tolerant taxa. However, the combined effects of stressors are difficult to predict as they can interact in complex ways, leading to enhanced (synergistic) or decreased (antagonistic) response patterns. Furthermore, stress responses may remain undetected if only the abundance changes in ecological experiments are considered, as organisms may have physiological protective pathways to counteract stressor effects. Therefore, we here used transcriptome-wide sequencing data to quantify single and combined effects of elevated fine sediment deposition, increased salinity and reduced flow velocity on the gene expression of the amphipod Gammarus fossarum in a mesocosm field experiment. RESULTS: Stressor exposure resulted in a strong transcriptional suppression of genes involved in metabolic and energy consuming cellular processes, indicating that G. fossarum responds to stressor exposure by directing energy to vitally essential processes. Treatments involving increased salinity induced by far the strongest transcriptional response, contrasting the observed abundance patterns where no effect was detected. Specifically, increased salinity induced the expression of detoxification enzymes and ion transporter genes, which control the membrane permeability of sodium, potassium or chloride. Stressor interactions at the physiological level were mainly antagonistic, such as the combined effect of increased fine sediment and reduced flow velocity. The compensation of the fine sediment induced effect by reduced flow velocity is in line with observations based on specimen abundance data. CONCLUSIONS: Our findings show that gene expression data provide new mechanistic insights in responses of freshwater organisms to multiple anthropogenic stressors. The assessment of stressor effects at the transcriptomic level and its integration with stressor effects at the level of specimen abundances significantly contribute to our understanding of multiple stressor effects in freshwater ecosystems.

APSCALE: advanced pipeline for simple yet comprehensive analyses of DNA metabarcoding data.

SUMMARY: DNA metabarcoding is an emerging approach to assess and monitor biodiversity worldwide and consequently the number and size of data sets increases exponentially. To date, no published DNA metabarcoding data processing pipeline exists that is (i) platform independent, (ii) easy to use [incl. graphical user interface (GUI)], (iii) fast (does scale well with dataset size) and (iv) complies with data protection regulations of e.g. environmental agencies. The presented pipeline APSCALE meets these requirements and handles the most common tasks of sequence data processing, such as paired-end merging, primer trimming, quality filtering, clustering and denoising of any popular metabarcoding marker, such as internal transcribed spacer, 16S or cytochrome c oxidase subunit I. APSCALE comes in a command line and a GUI version. The latter provides the user with additional summary statistics options and links to GUI-based downstream applications. AVAILABILITY AND IMPLEMENTATION: APSCALE is written in Python, a platform-independent language, and integrates functions of the open-source tools, VSEARCH (Rognes et al., 2016), cutadapt (Martin, 2011) and LULU (Frøslev et al., 2017). All modules support multithreading to allow fast processing of larger DNA metabarcoding datasets. Further information and troubleshooting are provided on the respective GitHub pages for the command-line version (https://github.com/DominikBuchner/apscale) and the GUI-based version (https://github.com/TillMacher/apscale_gui), including a detailed tutorial. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Individual small in-stream barriers contribute little to strong local population genetic structure five strictly aquatic macroinvertebrate taxa.

Water flow in river networks is frequently regulated by man-made in-stream barriers. These obstacles can hinder dispersal of aquatic organisms and isolate populations leading to the loss of genetic diversity. Although millions of small in-stream barriers exist worldwide, their impact on dispersal of macroinvertebrates remains unclear. Therefore, we, therefore, assessed the effects of such barriers on the population structure and effective dispersal of five macroinvertebrate species with strictly aquatic life cycles: the amphipod crustacean Gammarus fossarum (clade 11), three snail species of the Ancylus fluviatilis species complex and the flatworm Dugesia gonocephala. We studied populations at nine weirs and eight culverts (3 pipes, 5 tunnels), built 33-109 years ago, mainly in the heavily fragmented catchment of the river Ruhr (Sauerland, Germany). To assess fragmentation and barrier effects, we generated genome-wide SNP data using ddRAD sequencing and evaluated clustering, differentiation between populations up- and downstream of each barrier and effective migration rates among sites and across barriers. Additionally, we applied population genomic simulations to assess expected differentiation patterns under different gene flow scenarios. Our data show that populations of all species are highly isolated at regional and local scales within few kilometers. While the regional population structure likely results from historical processes, the strong local differentiation suggests that contemporary dispersal barriers exist. However, we identified significant barrier effects only for pipes (for A. fluviatilis II and III) and few larger weirs (>1.3 m; for D. gonocephala). Therefore, our data suggest that most small in-stream barriers can probably be overcome by all studied taxa frequently enough to prevent fragmentation. However, it remains to be tested if the strong local differentiation is a result of a cumulative effect of small barriers, or if larger in-stream barriers, land use, chemical pollution, urbanization, or a combination of these factors impede gene flow.

Strategies for sample labelling and library preparation in DNA metabarcoding studies.

Metabarcoding of DNA extracted from environmental or bulk specimen samples is increasingly used to profile biota in basic and applied biodiversity research because of its targeted nature that allows sequencing of genetic markers from many samples in parallel. To achieve this, PCR amplification is carried out with primers designed to target a taxonomically informative marker within a taxonomic group, and sample-specific nucleotide identifiers are added to the amplicons prior to sequencing. The latter enables assignment of the sequences back to the samples they originated from. Nucleotide identifiers can be added during the metabarcoding PCR and during "library preparation", that is, when amplicons are prepared for sequencing. Different strategies to achieve this labelling exist. All have advantages, challenges and limitations, some of which can lead to misleading results, and in the worst case compromise the fidelity of the metabarcoding data. Given the range of questions addressed using metabarcoding, ensuring that data generation is robust and fit for the chosen purpose is critically important for practitioners seeking to employ metabarcoding for biodiversity assessments. Here, we present an overview of the three main workflows for sample-specific labelling and library preparation in metabarcoding studies on Illumina sequencing platforms; one-step PCR, two-step PCR, and tagged PCR. Further, we distill the key considerations for researchers seeking to select an appropriate metabarcoding strategy for their specific study. Ultimately, by gaining insights into the consequences of different metabarcoding workflows, we hope to further consolidate the power of metabarcoding as a tool to assess biodiversity across a range of applications.

TaxonTableTools: A comprehensive, platform-independent graphical user interface software to explore and visualise DNA metabarcoding data.

DNA metabarcoding is increasingly used as a tool to assess biodiversity in research and environmental management. Powerful analysis software exists to process raw data. However, the translation of sequence read data into biological information and downstream analyses may be difficult for end users with limited expertise in bioinformatics. Thus, there is a growing need for easy-to-use, graphical user interface (GUI) software to analyse and visualise DNA metabarcoding data. Here, we present TaxonTableTools (TTT), a new platform-independent GUI that aims to fill this gap by providing simple, reproducible analysis and visualisation workflows. At its base, TTT uses a "TaXon table", which is a data format that can be generated easily within TTT from two input files: a read table and a taxonomy table obtained using various published metabarcoding pipelines. TTT analysis and visualisation modules include Venn diagrams to compare taxon overlap among replicates, samples, or analysis methods. TTT analyses and visualises basic statistics, such as read proportion per taxon, as well as more sophisticated visualisations, such as interactive Krona charts for taxonomic data exploration. Various ecological analyses can be produced directly, including alpha or beta diversity estimates, and rarefaction analysis ordination plots. Metabarcoding data can be converted into formats required for traditional, taxonomy-based analyses performed by regulatory bioassessment programs. In addition, TTT is able to produce html-based interactive graphics that can be analysed in any web browser. The software comes with a manual and tutorial, is free and publicly available through GitHub (https://github.com/TillMacher/TaxonTableTools) or the Python package index (https://pypi.org/project/taxontabletools/).

Revealing environmentally driven population dynamics of an Arctic diatom using a novel microsatellite PoolSeq barcoding approach.

Ecological stability under environmental change is determined by both interspecific and intraspecific processes. Particularly for planktonic microorganisms, it is challenging to follow intraspecific dynamics over space and time. We propose a new method, microsatellite PoolSeq barcoding (MPB), for tracing allele frequency changes in protist populations. We successfully applied this method to experimental community incubations and field samples of the diatom Thalassiosira hyalina from the Arctic, a rapidly changing ecosystem. Validation of the method found compelling accuracy in comparison with established genotyping approaches within different diversity contexts. In experimental and environmental samples, we show that MPB can detect meaningful patterns of population dynamics, resolving allelic stability and shifts within a key diatom species in response to experimental treatments as well as different bloom phases and years. Through our novel MPB approach, we produced a large dataset of populations at different time-points and locations with comparably little effort. Results like this can add insights into the roles of selection and plasticity in natural protist populations under stable experimental but also variable field conditions. Especially for organisms where genotype sampling remains challenging, MPB holds great potential to efficiently resolve eco-evolutionary dynamics and to assess the mechanisms and limits of resilience to environmental stressors.

Deep learning and computer vision will transform entomology.

Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.

Social-ecological interactions in the Draa River Basin, southern Morocco: Towards nature conservation and human well-being using the IPBES framework.

Water is essential to human societies and a prerequisite for flourishing nature, especially in arid regions. Yet, climate change and socio-economic developments are expected to exacerbate current and future stresses on water resources, demanding innovative approaches to balance water needs for society and nature conservation. In this study, we use the IPBES conceptual framework to combine ecological and socio-economic insights and analyse the connections between people and nature in the water scarce Draa River Basin, southern Morocco. We study the diversity of desert benthic macroinvertebrates as one component of nature using DNA barcoding and their potential to serve as bioindicators of human impact by relating species occurrences to environmental parameters. Furthermore, based on 87 interviews with farmers and key institutional stakeholders, we investigate how farmers perceive water related changes and how water is managed in the basin. Regarding benthic macroinvertebrates, 41 families were identified, 475 DNA barcodes generated and assigned to 118 putative species (Barcode Index Numbers) of which 60 were first records. This indicates a lack of reference sequences for known, but also a potentially high number of undescribed species. Environmental parameters, which are partly influenced by human activities, such as aquatic stages, salinity and intermittency, were the most important variables explaining invertebrate richness and community composition in generalized linear models. We further describe farmers' perceptions of decreasing water quality and quantity. Farmers generally believe that they are able to cope with water related changes, although perceptions are regionally differentiated with farmers downstream being less optimistic. With growing concerns, water policies currently focus on increasing water supply and less on reducing water demands. Based on these findings, the usefulness of the IPBES framework for understanding social-ecological system dynamics is reflected, and recommendations for future freshwater management and research are derived.

Connecting high-throughput biodiversity inventories: Opportunities for a site-based genomic framework for global integration and synthesis.

High-throughput sequencing (HTS) is increasingly being used for the characterization and monitoring of biodiversity. If applied in a structured way, across broad geographical scales, it offers the potential for a much deeper understanding of global biodiversity through the integration of massive quantities of molecular inventory data generated independently at local, regional and global scales. The universality, reliability and efficiency of HTS data can potentially facilitate the seamless linking of data among species assemblages from different sites, at different hierarchical levels of diversity, for any taxonomic group and regardless of prior taxonomic knowledge. However, collective international efforts are required to optimally exploit the potential of site-based HTS data for global integration and synthesis, efforts that at present are limited to the microbial domain. To contribute to the development of an analogous strategy for the nonmicrobial terrestrial domain, an international symposium entitled "Next Generation Biodiversity Monitoring" was held in November 2019 in Nicosia (Cyprus). The symposium brought together evolutionary geneticists, ecologists and biodiversity scientists involved in diverse regional and global initiatives using HTS as a core tool for biodiversity assessment. In this review, we summarize the consensus that emerged from the 3-day symposium. We converged on the opinion that an effective terrestrial Genomic Observatories network for global biodiversity integration and synthesis should be spatially led and strategically united under the umbrella of the metabarcoding approach. Subsequently, we outline an HTS-based strategy to collectively build an integrative framework for site-based biodiversity data generation.

Standardized high-throughput biomonitoring using DNA metabarcoding: Strategies for the adoption of automated liquid handlers.

Reliable and comprehensive monitoring data are required to trace and counteract biodiversity loss. High-throughput metabarcoding using DNA extracted from community samples (bulk) or from water or sediment (environmental DNA) has revolutionized biomonitoring, given the capability to assess biodiversity across the tree of life rapidly with feasible effort and at a modest price. DNA metabarcoding can be upscaled to process hundreds of samples in parallel. However, while automated high-throughput analysis workflows are well-established in the medical sector, manual sample processing still predominates in biomonitoring laboratory workflows limiting the upscaling and standardization for routine monitoring applications. Here we present an automated, scalable, and reproducible metabarcoding workflow to extract DNA from bulk samples, perform PCR and library preparation on a liquid handler. Key features are the independent sample replication throughout the workflow and the use of many negative controls for quality assurance and quality control. We generated two datasets: i) a validation dataset consisting of 42 individual arthropod specimens of different species, and ii) a routine monitoring dataset consisting of 60 stream macroinvertebrate bulk samples. As a marker, we used the mitochondrial COI gene. Our results show that the developed single-deck workflow is free of laboratory-derived contamination and produces highly consistent results. Minor deviations between replicates are mostly due to stochastic differences for low abundant OTUs. Thus, we successfully demonstrated that robotic liquid handling can be used reliably from DNA extraction to final library preparation on a single deck, thereby substantially increasing throughput, reducing costs, and increasing data robustness for biodiversity assessments and monitoring.

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.

Morphological vs. DNA metabarcoding approaches for the evaluation of stream ecological status with benthic invertebrates: Testing different combinations of markers and strategies of data filtering.

Macroinvertebrate assemblages are the most common bioindicators used for stream biomonitoring, yet the standard approach exhibits several time-consuming steps, including the sorting and identification of organisms based on morphological criteria. In this study, we examined if DNA metabarcoding could be used as an efficient molecular-based alternative to the morphology-based monitoring of streams using macroinvertebrates. We compared results achieved with the standard morphological identification of organisms sampled in 18 sites located on 15 French wadeable streams to results obtained with the DNA metabarcoding identification of sorted bulk material of the same macroinvertebrate samples, using read numbers (expressed as relative frequencies) as a proxy for abundances. In particular, we evaluated how combining and filtering metabarcoding data obtained from three different markers (COI: BF1-BR2, 18S: Euka02 and 16S: Inse01) could improve the efficiency of bioassessment. In total, 140 taxa were identified based on morphological criteria, and 127 were identified based on DNA metabarcoding using the three markers, with an overlap of 99 taxa. The threshold values used for sequence filtering based on the "best identity" criterion and the number of reads had an effect on the assessment efficiency of data obtained with each marker. Compared to single marker results, combining data from different markers allowed us to improve the match between biotic index values obtained with the bulk DNA versus morphology-based approaches. Both approaches assigned the same ecological quality class to a majority (86%) of the site sampling events, highlighting both the efficiency of metabarcoding as a biomonitoring tool but also the need for further research to improve this efficiency.