ALBERT

Description: Sequence-based specimen identification, known as DNA barcoding, is a common method complementing traditional morphology-based taxonomic assignments. The fundamental resource in DNA barcoding is the availability of a taxonomically reliable sequence database to use as a reference for sequence comparisons. Here, we provide a reference library including 579 sequences of the mitochondrial cytochrome c oxidase subunit I for 113 North Sea mollusc species. We tested the efficacy of this library by simulating a sequence-based specimen identification scenario using Best Match, Best Close Match (BCM) and All Species Barcode (ASB) criteria with three different threshold values. Each identification result was compared with our prior morphology-based taxonomic assignments. Our simulation resulted in 87.7% congruent identifications (93.8% when excluding singletons). The highest number of congruent identifications was obtained with BCM and ASB and a 0.05 threshold. We also compared identifications with genetic clustering (Barcode Index Numbers, BINs) computed by the Barcode of Life Datasystem (BOLD). About 68% of our morphological identifications were congruent with BINs created by BOLD. Forty-nine sequences were clustered in 16 discordant BINs, and these were divided in two classes: sequences from different species clustered in a single BIN and conspecific sequences divided in more BINs. Whereas former incongruences were probably caused by BOLD entries in need of a taxonomic update, the latter incongruences regarded taxa requiring further investigations. These include species with amphi-Atlantic distribution, whose genetic structure should be evaluated over their entire range to produce a reliable sequence-based identification system.

Description: Background: During the last years, the effectiveness of DNA barcoding for animal species identification has been proven in many studies, analyzing both vertebrate and invertebrate taxa. In terms of marine organisms, however, most barcoding studies typically focus on economically relevant species, for example, fish, as well asonthedocumentationof hotspots of species diversity, for example, tropical coral reefs or regions of the almost unexplored deep sea regions. In contrast to this, species diversity of “well-known” habitats is nearly neglected. As part of our running project we started to build up a comprehensive DNA barcode library for the metazoan taxa of the North Sea, one of the most extensively studied ecosystems of the world. The North Sea is characterized by a highamountof anthropogenic pressure such as intensive fishing and ship traffic as well as offshore installations. Environmental parameters (e.g., depth, sediment characteristics, temperature, and salinity) of this semi-enclosed shelf sea follow a distinct pattern: high seasonal fluctuations can be observed in southern areas, but low fluctuations are given in the northern regions. This heterogeneity is also displayed in macrobenthic community structures, with a lower number of species in the shallow southern parts (i.e., the German Bight) and more species in the central and northern North Sea. In addition to this, species with a typical Mediterranean-Lusitanean distribution are also known to occur in parts of the North Sea where oceanic influences prevail. Results: Our barcode library includes a broad variety of taxa, including typical taxa of marine barcoding studies, for example, fish or decapod crustaceans. Our on-growing library also includes groups that are often ignored as cnidarians, parasitic crustaceans, echinoderms, mollusks, pantopods, polychaets, and others. In total, our library includes more than 4200 DNA barcodes of more than 600 species at the moment. By using the Barcode of Life Data Systems (BOLD), unique BINs were identified for more than 90% of the analyzed species. Significance: Our data represent a first step towards the establishment of a comprehensive DNA barcode library of the Metazoa of the North Sea. Despite the fact that various taxa are still missing or are currently underrepresented, our results clearly underline the usefulness of DNA barcodes to discriminate the vast majority of the analyzed species. It should be also kept in mind that the benefits of DNA barcoding are not restricted to taxonomic or systematic research only. The rise of modern high-throughput sequencing technologies will change biomonitoring applications and surveys significantly in the coming years. Following this, reference datasets such as ours will become essential for a correct identification of specimens sequenced as part of a metabarcoding study. This is especially true for the North Sea, a marine region that has been massively affected by cargo ship traffic, the exploitation of oil and gas resources, offshore wind parks, and in particular extensive long-term fisheries.

Description: During the last years DNA barcoding has become a popular method of choice for molecular specimen identification. Here we present a comprehensive DNA barcode library of various crustacean taxa found in the North Sea, one of the most extensively studied marine regions of the world. Our data set includes 1,332 barcodes covering 205 species, including taxa of the Amphipoda, Copepoda, Decapoda, Isopoda, Thecostraca, and others. This dataset represents the most extensive DNA barcode library of the Crustacea in terms of species number to date. By using the Barcode of Life Data Systems (BOLD), unique BINs were identified for 198 (96.6%) of the analyzed species. Six species were characterized by two BINs (2.9%), and three BINs were found for the amphipod species Gammarus salinus Spooner, 1947 (0.4%). Intraspecific distances with values higher than 2.2% were revealed for 13 species (6.3%). Exceptionally high distances of up to 14.87% between two distinct but monophyletic clusters were found for the parasitic copepod Caligus elongatus Nordmann, 1832, supporting the results of previous studies that indicated the existence of an overlooked sea louse species. In contrast to these high distances, haplotype-sharing was observed for two decapod spider crab species, Macropodia parva Van Noort & Adema, 1985 and Macropodia rostrata (Linnaeus, 1761), underlining the need for a taxonomic revision of both species. Summarizing the results, our study confirms the application of DNA barcodes as highly effective identification system for the analyzed marine crustaceans of the North Sea and represents an important milestone for modern biodiversity assessment studies using barcode sequences

Description: Accurate and reliable biodiversity estimates of marine zooplankton are a prerequisite to understand how changes in diversity can affect whole ecosystems. Species identification in the deep sea is significantly impeded by high numbers of new species and decreasing numbers of taxonomic experts, hampering any assessment of biodiversity. We used in parallel morphological, genetic, and proteomic characteristics of specimens of calanoid copepods from the abyssal South Atlantic to test if proteomic fingerprinting can accelerate estimating biodiversity. We cross-validated the respective molecular discrimination methods with morphological identifications to establish COI and proteomic reference libraries, as they are a pre-requisite to assign taxonomic information to the identified molecular species clusters. Due to the high number of new species only 37% of the individuals could be assigned to species or genus level morphologically. COI sequencing was successful for 70% of the specimens analysed, while proteomic fingerprinting was successful for all specimens examined. Predicted species richness based on morphological and molecular methods was 42 morphospecies, 56 molecular operational taxonomic units (MOTUs) and 79 proteomic operational taxonomic units (POTUs), respectively. Species diversity was predicted based on proteomic profiles using hierarchical cluster analysis followed by application of the variance ratio criterion for identification of species clusters. It was comparable to species diversity calculated based on COI sequence distances. Less than 7% of specimens were misidentified by proteomic profiles when compared with COI derived MOTUs, indicating that unsupervised machine learning using solely proteomic data could be used for quickly assessing species diversity.