Peter Grobe

Insects are the most speciose group of animals, but the phylogenetic relationships of many major lineages remain unresolved. We inferred the phylogeny of insects from 1478 protein-coding genes. Phylogenomic analyses of nucleotide and amino acid sequences, with site-specific nucleotide or domain-specific amino acid substitution models, produced statistically robust and congruent results resolving previously controversial phylogenetic relations hips. We dated the origin of insects to the Early Ordovician [~479 million years ago (Ma)], of insect flight to the Early Devonian (~406 Ma), of major extant lineages to the Mississippian (~345 Ma), and the major diversification of holometabolous insects to the Early Cretaceous. Our phylogenomic study provides a comprehensive reliable scaffold for future comparative analyses of evolutionary innovations among insects.

BACKGROUND: Invertebrate nervous systems are highly disparate between different taxa. This is reflected in the terminology used to describe them, which is very rich and often confusing. Even very general terms such as 'brain', 'nerve', and 'eye' have been used in various ways in the different animal groups, but no consensus on the exact meaning exists. This impedes our understanding of the architecture of the invertebrate nervous system in general and of evolutionary transformations of nervous system characters between different taxa. RESULTS: We provide a glossary of invertebrate neuroanatomical terms with a precise and consistent terminology, taxon-independent and free of homology assumptions. This terminology is intended to form a basis for new morphological descriptions. A total of 47 terms are defined. Each entry consists of a definition, discouraged terms, and a background/comment section. CONCLUSIONS: The use of our revised neuroanatomical terminology in any new descriptions of the anatomy of invertebrate nervous systems will improve the comparability of this organ system and its substructures between the various taxa, and finally even lead to better and more robust homology hypotheses.

Biodiversity research brings together the many facets of biological environmental research. Its data management is characterized by integration and is particularly challenging due to the large volume and tremendous heterogeneity of the data. At the same time, it is particularly important: A lot of the data is not reproducible. Once it is gone, potential knowledge that could have been gained from it is irrevocably lost. In this paper, we describe challenges to biodiversity data management along the data life cycle and sketch the solution that is currently being developed within the GFBio project, a collaborative effort of nineteen German research institutions ranging from museums and archives to biodiversity researchers and computer scientists.

Biodiversity loss is mainly driven by human activity. While concern grows over the fate of hot spots of biodiversity, contemporary species losses still prevail in industrialized nations. Therefore, strategies were formulated to halt or reverse the loss, driven by evidence for its value for ecosystem services. Maintenance of the latter through conservation depends on correctly identified species. To this aim, the German Federal Ministry of Education and Research is funding the GBOL project, a consortium of natural history collections, botanic gardens, and universities working on a barcode reference database for the country's fauna and flora. Several noticeable findings could be useful for future campaigns: (i) validating taxon lists to serve as a taxonomic backbone is time-consuming, but without alternative; (ii) offering financial incentives to taxonomic experts, often citizen scientists, is indispensable; (iii) completion of the libraries for widespread species enables analyses of environmental samples, but the process may not hold pace with technological advancements; (iv) discoveries of new species are among the best stories for the media; (v) a commitment to common data standards and repositories is needed, as well as transboundary cooperation between nations; (vi) after validation, all data should be published online via the BOLD to make them searchable for external users and to allow cross-checking with data from other countries.

Abstract In recent years, large‐scale DNA barcoding campaigns have generated an enormous amount of COI barcodes, which are usually stored in NCBI 's GenBank and the official Barcode of Life database ( BOLD ). BOLD data are generally associated with more detailed and better curated meta‐data, because a great proportion is based on expert‐verified and vouchered material, accessible in public collections. In the course of the initiative German Barcode of Life data were generated for the reference library of 2,846 species of Coleoptera from 13,516 individuals. Confronted with the high effort associated with the identification, verification and data validation, a bioinformatic pipeline, “Tax CI ” was developed that (1) identifies taxonomic inconsistencies in a given tree topology (optionally including a reference dataset), (2) discriminates between different cases of incongruence in order to identify contamination or misidentified specimens, (3) graphically marks those cases in the tree, which finally can be checked again and, if needed, corrected or removed from the dataset. For this, “Tax CI ” may use DNA ‐based species delimitations from other approaches (e.g. mPTP ) or may perform implemented threshold‐based clustering. The data‐processing pipeline was tested on a newly generated set of barcodes, using the available BOLD records as a reference. A data revision based on the first run of the Tax CI tool resulted in the second Tax CI analysis in a taxonomic match ratio very similar to the one recorded from the reference set (92% vs. 94%). The revised dataset improved by nearly 20% through this procedure compared to the original, uncorrected one. Overall, the new processing pipeline for DNA barcode data allows for the rapid and easy identification of inconsistencies in large datasets, which can be dealt with before submitting them to public data repositories like BOLD or GenBank. Ultimately, this will increase the quality of submitted data and the speed of data submission, while primarily avoiding the deterioration of the accuracy of the data repositories due to ambiguously identified or contaminated specimens.