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100Effective identification of species using short DNA fragments (DNA barcoding and DNA 101 metabarcoding) requires reliable sequence reference libraries of known taxa. Both 102 taxonomically comprehensive coverage and content quality are important for sufficient 103 accuracy. For aquatic ecosystems in Europe, reliable barcode reference libraries are 104 particularly important if molecular identification tools are to be implemented in biomonitoring 105 and reports in the context of the EU Water Framework Directive (WFD) and the Marine 106Strategy Framework Directive (MSFD). We analysed gaps in the two most important 107 reference databases, Barcode of Life Data Systems (BOLD) and NCBI GenBank, with a 108 focus on the taxa most frequently used in WFD and MSFD. Our analyses show that 109 coverage varies strongly among taxonomic groups, and among geographic regions. In 110 general, groups that were actively targeted in barcode projects (e.g. fish, true bugs, 111 caddisflies and vascular plants) are well represented in the barcode libraries, while others 112 have fewer records (e.g. marine molluscs, ascidians, and freshwater diatoms). We also 113 found that species monitored in several countries often are represented by barcodes in 114 reference libraries, while species monitored in a single country frequently lack sequence 115 records. A large proportion of species (up to 50%) in several taxonomic groups are only 116represented by private data in BOLD. Our results have implications for the future strategy to 117 fill existing gaps in barcode libraries, especially if DNA metabarcoding is to be used in the 118 monitoring of European aquatic biota under the WFD and MSFD. For example, missing 119 species relevant to monitoring in multiple countries should be prioritized. We also discuss 120 why a strategy for quality control and quality assurance of barcode reference libraries is 121 needed and recommend future steps to ensure full utilization of metabarcoding in aquatic 122 biomonitoring. 123 124

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1. Changes in insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground-dwelling insects. However, currently sampling involves manually detecting and identifying specimens. Here, we test whether a convolutional neural network (CNN) can classify habitus images of ground beetles to species level, and estimate how correct classification relates to body size, number of species inside genera, and species identity.2. We created an image database of 65,841 museum specimens comprising 361 carabid beetle species from the British Isles and fine-tuned the parameters of a pretrained CNN from a training dataset. By summing up class confidence values within genus, tribe, and subfamily and setting a confidence threshold, we trade-off between classification accuracy, precision, and recall and taxonomic resolution.3. The CNN classified 51.9% of 19,164 test images correctly to species level and 74.9% to genus level. Average classification recall on species level was 50.7%.Applying a threshold of 0.5 increased the average classification recall to 74.6% at the expense of taxonomic resolution. Higher top value from the output layer and larger sized species were more often classified correctly, as were images of species in genera with few species. 4. Fine-tuning enabled us to classify images with a high mean recall for the whole test dataset to species or higher taxonomic levels, however, with high variability.This indicates that some species are more difficult to identify because of properties such as their body size or the number of related species.5. Together, species-level image classification of arthropods from museum collections and ecological monitoring can substantially increase the amount of occurrence data that can feasibly be collected. These tools thus provide new opportunities in understanding and predicting ecological responses to environmental change. Jens-Christian Svenninghttps://orcid.

A molecular phylogeny and a review of family-group classification are presented for 137 species (ca. 125 genera) of the insect family Cicadidae, the true cicadas, plus two species of hairy cicadas (Tettigarctidae) and two outgroup species from Cercopidae. Five genes, two of them mitochondrial, comprise the 4992 base-pair molecular dataset. Maximum-likelihood and Bayesian phylogenetic results are shown, including analyses to address potential base composition bias. Tettigarcta is confirmed as the sister-clade of the Cicadidae and support is found for three subfamilies identified in an earlier morphological cladistic analysis. A set of paraphyletic deep-level clades formed by African genera are together named as Tettigomyiinae n. stat. Taxonomic reassignments of genera and tribes are made where morphological examination confirms incorrect placements suggested by the molecular tree, and 11 new tribes are defined (Arenopsaltriini n. tribe, Durangonini n. tribe, Katoini n. tribe, Lacetasini n. tribe, Macrotristriini n. tribe, Malagasiini n. tribe, Nelcyndanini n. tribe, Pagiphorini n. tribe, Pictilini n. tribe, Psaltodini n. tribe, and Selymbriini n. tribe). Tribe Tacuini n. syn. is synonymized with Cryptotympanini, and Tryellina n. syn. is synonymized with an expanded Tribe Lamotialnini. Tribe Hyantiini n. syn. is synonymized with Fidicinini. Tribe Sinosenini is transferred to Cicadinae from Cicadettinae, Cicadatrini is moved to Cicadettinae from Cicadinae, and Ydiellini and Tettigomyiini are transferred to Tettigomyiinae n. stat from Cicadettinae. While the subfamily Cicadinae, historically defined by the presence of timbal covers, is weakly supported in the molecular tree, high taxonomic rank is not supported for several earlier clades based on unique morphology associated with sound production.