No abstract

Our world is in the midst of unprecedented change—climate shifts and sustained, widespread habitat degradation have led to dramatic declines in biodiversity rivaling historical extinction events. At the same time, new approaches to publishing and integrating previously disconnected data resources promise to help provide the evidence needed for more efficient and effective conservation and management. Stakeholders have invested considerable resources to contribute to online databases of species occurrences. However, estimates suggest that only 10% of biocollections are available in digital form. The biocollections community must therefore continue to promote digitization efforts, which in part requires demonstrating compelling applications of the data. Our overarching goal is therefore to determine trends in use of mobilized species occurrence data since 2010, as online systems have grown and now provide over one billion records. To do this, we characterized 501 papers that use openly accessible biodiversity databases. Our standardized tagging protocol was based on key topics of interest, including: database(s) used, taxa addressed, general uses of data, other data types linked to species occurrence data, and data quality issues addressed. We found that the most common uses of online biodiversity databases have been to estimate species distribution and richness, to outline data compilation and publication, and to assist in developing species checklists or describing new species. Only 69% of papers in our dataset addressed one or more aspects of data quality, which is low considering common errors and biases known to exist in opportunistic datasets. Globally, we find that biodiversity databases are still in the initial stages of data compilation. Novel and integrative applications are restricted to certain taxonomic groups and regions with higher numbers of quality records. Continued data digitization, publication, enhancement, and quality control efforts are necessary to make biodiversity science more efficient and relevant in our fast-changing environment.

Massive strides have been made in technologies for collecting genome-scale data. However, tools for efficiently and flexibly assembling raw outputs into downstream analytical workflows are still nascent. aTRAM 1.0 was designed to assemble any locus from genome sequencing data but was neither optimized for efficiency nor able to serve as a single toolkit for all assembly needs. We have completely re-implemented aTRAM and redesigned its structure for faster read retrieval while adding a number of key features to improve flexibility and functionality. The software can now (1) assemble single- or paired-end data, (2) utilize both read directions in the database, (3) use an additional de novo assembly module, and (4) leverage new built-in pipelines to automate common workflows in phylogenomics. Owing to reimplementation of databasing strategies, we demonstrate that aTRAM 2.0 is much faster across all applications compared to the previous version.