Unveiling ophiuroid biodiversity across North Atlantic habitats via an integrative perspective.

The depths of the North Atlantic Ocean host a species-rich fauna providing heterogeneous habitats from thermal vent fields to cold-water coral reefs. With the increasing threat of destruction of deep-sea habitats due to human impacts, such as demersal fishing and the beginning of deep-sea mining, an analysis of the diversity and distribution of species is crucial for conservation efforts. Brittle stars occur in high biomasses, contributing to the biodiversity of the seafloor. Specimens were collected during several scientific expeditions to gain a more detailed insight into the brittle star diversity in the North Atlantic Ocean. An integrative approach to identify the species with DNA barcoding (mtCOI) in combination with morphological studies revealed 24 species. Most species have been previously identified in the North Atlantic, but sequences for 13 species are newly added to public repositories. Additionally, the MALDI-TOF-MS proteomic analysis was successfully applied for 197 specimens with known COI barcodes. Results are congruent with other molecular species delimitations demonstrating the functionality of proteomics for the identification of brittle stars. This dataset significantly expands our understanding of the taxonomic and genetic diversity of brittle stars and contributes to publicly available data. It emphasizes the importance of considering habitat heterogeneity for large scale patterns of biodiversity.

A universal tool for marine metazoan species identification: towards best practices in proteomic fingerprinting.

Proteomic fingerprinting using MALDI-TOF mass spectrometry is a well-established tool for identifying microorganisms and has shown promising results for identification of animal species, particularly disease vectors and marine organisms. And thus can be a vital tool for biodiversity assessments in ecological studies. However, few studies have tested species identification across different orders and classes. In this study, we collected data from 1246 specimens and 198 species to test species identification in a diverse dataset. We also evaluated different specimen preparation and data processing approaches for machine learning and developed a workflow to optimize classification using random forest. Our results showed high success rates of over 90%, but we also found that the size of the reference library affects classification error. Additionally, we demonstrated the ability of the method to differentiate marine cryptic-species complexes and to distinguish sexes within species.