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Monitoring biodiversity is essential to understand the impacts of human activities and for effective management of ecosystems. Thereby, biodiversity can be assessed through direct collection of targeted organisms, through indirect evidence of their presence (e.g. signs, environmental DNA, camera trap, etc.), or through extrapolations from species distribution and species richness models. Differences in approaches used in biodiversity assessment, however, may come with individual challenges and hinder cross-study comparability. In the context of rapidly developing techniques, we compared three different approaches in order to better understand assessments of aquatic macroinvertebrate diversity. Specifically, we compared the community composition and species richness of three orders of aquatic macroinvertebrates (mayflies, stoneflies, and caddisflies, hereafter EPT) obtained via eDNA metabarcoding and via traditional in situ kicknet sampling to catchment-level based predictions of a species richness model. We used kicknet data from 24 sites in Switzerland and compared taxonomic lists to those obtained using eDNA amplified with two different primer sets. Richness detected by these methods was compared to the independent predictions made by a statistical species richness model, that is, a generalized linear model using landscape-level features to estimate EPT diversity. Despite the ability of eDNA to consistently detect some EPT species found by traditional sampling, we found important discrepancies in community composition between the kicknet and eDNA approaches, particularly at a local scale. We found the EPT-specific primer set fwhF2/EPTDr2n, detected a greater number of targeted EPT species compared to the more general primer set mlCOIintF/HCO2198. Moreover, we found that the species richness measured by eDNA from either primer set was poorly correlated to the richness measured by kicknet sampling (Pearson correlation = 0.27) and that the richness estimated by eDNA and kicknet were poorly correlated with the prediction of the species richness model (Pearson correlation = 0.30 and 0.44, respectively). The weak relationships between the traditional kicknet sampling and eDNA with this model indicates inherent limitations in upscaling species richness estimates, and possibly a limited ability of the model to meet real world expectations. It is also possible that the number of replicates was not sufficient to detect ambiguous correlations. Future challenges include improving the accuracy and sensitivity of each approach individually, yet also acknowledging their respective limitations, in order to best meet stakeholder demands and address the biodiversity crisis we are facing., Metabarcoding and Metagenomics, 6, ISSN:2534-9708

This book represents a synthesis of knowledge and best practice in the field of DNA-based biomonitoring at the time of writing. It has been written with end-users of molecular tools in mind, as well as those who are new to the field in research settings and are looking to gain an overall grounding in the subject area. For each of the main types of sample (water, soil / sediment, bulk invertebrates and diatoms), and for each stage of the field and laboratory processes, we outline key considerations, decisions that need to be made, factors that might influence those decisions, and trade-offs inherent in the choices made. We hope that this will help users, practitioners, and those commissioning DNA-based monitoring programmes to navigate this large field and critically evaluate the strengths and weaknesses of different analysis workflows based on context, project aims and available resources. DNA-based methods for species detection and identification have revolutionised our ability to assess biodiversity in terrestrial, freshwater and marine ecosystems. Starting from the seminal study that used eDNA to detect invasive american bullfrogs in France (Ficetola et al. 2008), research conducted over the last decade has demonstrated the power of these approaches for surveying a wide range of species and groups. Early applications included the use of eDNA to monitor Asian Carp in the USA (Jerde et al. 2013). Following heavy scrutiny, the method was eventually adopted, and is still employed today by the United States Geological Survey (USGS). A flurry of research followed, with tests designed for many threatened and invasive species including New zealand mudsnails (Goldberg et al. 2013), american crayfish (Geerts et al. 2018), gammarids (Blackman et al. 2017), and great crested newts (Biggs et al. 2015). The great crested newt eDNA test has been employed for regulatory monitoring in the UK since 2014. During the same time period, there was a proliferation of research studies that used high-throughput sequencing approaches to describe whole communities of organisms from mixed species and environmental samples, using an approach termed DNA metabarcoding (Taberlet et al. 2012c). As the field developed fast and the approaches were applied to a wide range of research and monitoring objectives, a high level of methodological variation was introduced at all stages of the workflow (Seymour 2019). Thus, while a significant level of consensus on scientific best-practice now exists in many areas, this may not be readily discerned from the now-extensive body of research literature. As environmental practitioners and policy makers are now increasingly starting to integrate DNAbased methods into routine monitoring applications including protected species licensing1, statutory monitoring2 (Hänfling et al. 2016) and environmental impact assessment3, various national and international efforts have been undertaken to standardise methods and integrate them into monitoring frameworks (Pilliod et al. 2019, Loeza-Quintana et al. 2020, Minamoto et al. 2021, Pawlowski et al. 2020a4). In Europe, the EU COST Action DNAqua-Net (Leese et al. 2018) has been working towards incorporating molecular monitoring tools for Biological Quality Elements (BQEs, e.g., fish, macroinvertebrates and phytoplankton- benthos) into the Water Framework Directive (WFD, 2000/60/EC)5 and the Marine Strategy Framework Directive (MSFD, 2008/56/EC)6. Thus, emphasis now shifts from fundamental research to robust and efficient application of DNAbased methods for operational use at large scales. This requires that scientific robustness is balanced with consideration of the practical realities faced by environmental managers. Moreover, there is increased need for strong quality assurance in a setting where non-expert field samplers and commercial laboratories are involved with the generation of data that non-specialist decision-makers then rely on to inform potentially costly action (or non-action). This places increased emphasis on robustness, replicability, traceability and ease-of-use, which may not always be the central focus of studies carried out in the academic research environment. This document aims to summarise the scientific consensus relating to every step of the field and laboratory workflows involved in the most common types of samples and analyses. We do not go into great detail regarding bioinformatics (computational processing of sequence data) and data analysis since these are extensive topics in their own right. We uniquely set the field and lab steps in the context of the practical and logistical constraints faced by environmental managers in terms of cost, logistics, safety, ease-of-use, and quality assurance, highlighting key decisions to be made and the inherent trade-offs associated with the various options. We hope that this will support non-experts, and those new to the field, to navigate the key considerations associated with planning or evaluating monitoring programmes using DNA-based monitoring methods. Additionally, it will aid decision-makers in writing and evaluating tenders and proposals, ensuring that the methods used for a given project are fit-for-purpose and that results are correctly interpreted. Alongside the many areas of emerging consensus, there remain some areas where further research is still required to balance scientific best-practice with the constraints and priorities of end-users. We hope that by shining a light on the importance of these issues, the research community will be encouraged to address them. More generally, we hope to inspire researchers in this now highly-applied scientific field to consider end-user constraints when designing and implementing research projects. This will help to accelerate uptake by users and maximise the impact of research. DNA-based bioassessment methods continue to evolve, and there are several emerging technologies that show exciting promise to move beyond even what is possible today. Examples include in-field sequencing using the MinION device from Oxford Nanopore Technologies (Pomerantz et al. 2018, Davidov et al. 2020, Hatfield et al. 2020), PCR-free metagenomic approaches (Bista et al. 2018, Giebner et al. 2020) and CRISPR for rapid detection of species, which is particularly relevant for invasive an non-native species monitoring (Williams et al. 2019, 2020). We recognise the potential of these methods, but do not consider them in detail here, since they are not yet far enough developed for routine application. EU COST Action DNAqua-Net (CA 15219 a COST (European Cooperation in Science and Technology). DNAqua-Net, European Union Horizon 2020 Published Refereed Current 14.a Mature Multi-organisational International Genetic differentiation Species distributions Method

In a recent paper, “Environmental DNA: What's behind the term? Clarifying the terminology and recommendations for its future use in biomonitoring”, Pawlowski et al. argue that the term eDNA should be used to refer to the pool of DNA isolated from environmental samples, as opposed to only extra-organismal DNA from macro-organisms. We agree with this view. However, we are concerned that their proposed two-level terminology specifying sampling environment and targeted taxa is overly simplistic and might hinder rather than improve clear communication about environmental DNA and its use in biomonitoring. Not only is this terminology based on categories that are often difficult to assign and uninformative, but it ignores what is in our opinion the most important distinction within eDNA: the type of DNA (organismal or extra-organismal) from which ecological interpretations are derived.

The genomic revolution has fundamentally changed how we survey biodiversity on earth. High‐throughput sequencing (“HTS”) platforms now enable the rapid sequencing of DNA from diverse kinds of environmental samples (termed “environmental DNA” or “eDNA”). Coupling HTS with our ability to associate sequences from eDNA with a taxonomic name is called “eDNA metabarcoding” and offers a powerful molecular tool capable of noninvasively surveying species richness from many ecosystems. Here, we review the use of eDNA metabarcoding for surveying animal and plant richness, and the challenges in using eDNA approaches to estimate relative abundance. We highlight eDNA applications in freshwater, marine and terrestrial environments, and in this broad context, we distill what is known about the ability of different eDNA sample types to approximate richness in space and across time. We provide guiding questions for study design and discuss the eDNA metabarcoding workflow with a focus on primers and library preparation methods. We additionally discuss important criteria for consideration of bioinformatic filtering of data sets, with recommendations for increasing transparency. Finally, looking to the future, we discuss emerging applications of eDNA metabarcoding in ecology, conservation, invasion biology, biomonitoring, and how eDNA metabarcoding can empower citizen science and biodiversity education. ISSN:0962-1083 ISSN:1365-294X

Species richness is a metric of biodiversity that represents the number of species present in a community. Traditional fisheries assessments that rely on capture of organisms often underestimate true species richness. Environmental DNA (eDNA) metabarcoding is an alternative tool that infers species richness by collecting and sequencing DNA present in the ecosystem. Our objective was to determine how spatial distribution of samples and “bioinformatic stringency” affected eDNA-metabarcoding estimates of species richness compared with capture-based estimates in a 2.2 ha reservoir. When bioinformatic criteria required species to be detected only in a single sample, eDNA metabarcoding detected all species captured with traditional methods plus an additional 11 noncaptured species. However, when we required species to be detected with multiple markers and in multiple samples, eDNA metabarcoding detected only seven of the captured species. Our analysis of the spatial patterns of species detection indicated that eDNA was distributed relatively homogeneously throughout the reservoir, except near the inflowing stream. We suggest that interpretation of eDNA metabarcoding data must consider the potential effects of water body type, spatial resolution, and bioinformatic stringency., La richesse spécifique est une mesure de la biodiversité qui représente le nombre d’espèces présentes dans une communauté. Les évaluations traditionnelles des ressources halieutiques qui reposent sur la capture d’organismes sous-estiment souvent la richesse spécifique réelle. Les méta-codes à barres d’ADN environnemental) (ADNe) constituent un autre outil qui permet d’inférer la richesse spécifique en recueillant et en séquençant l’ADN présent dans l’écosystème. Notre objectif consistait à déterminer comment la répartition spatiale des échantillons et la « rigueur bioinformatique » influent sur les estimations de la richesse spécifique reposant sur les méta-codes à barres d’ADNe par rapport aux estimations reposant sur la capture, dans un réservoir de 2,2 ha. Quand les critères bioinformatiques exigeaient la détection d’une espèce dans un seul échantillon, la méthode des méta-codes à barres d’ADNe a détecté toutes les espèces capturées par les méthodes traditionnelles en plus de 11 autres espèces non capturées. Toutefois, quand il fallait que les espèces soient détectées sur la base de plus d’un marqueur et dans plus d’un échantillon, les méta-codes à barres d’ADNe n’ont détecté que sept des espèces capturées. Notre analyse de la répartition spatiale de la détection d’espèces indique que l’ADNe était réparti de manière assez uniforme dans tout le réservoir, sauf près de l’embouchure du cours d’eau qui l’alimente. Nous proposons que l’interprétation des données obtenues par la méthode des méta-codes à barres d’ADNe doit tenir compte des effets potentiels du type de plan d’eau, de la résolution spatiale et de la rigueur bioinformatique. [Traduit par la Rédaction], Canadian Journal of Fisheries and Aquatic Sciences, 74 (9), ISSN:0706-652X, ISSN:1205-7533