Enhancing georeferenced biodiversity inventories: automated information extraction from literature records reveal the gaps.

We use natural language processing (NLP) to retrieve location data for cheilostome bryozoan species (text-mined occurrences (TMO)) in an automated procedure. We compare these results with data combined from two major public databases (DB): the Ocean Biodiversity Information System (OBIS), and the Global Biodiversity Information Facility (GBIF). Using DB and TMO data separately and in combination, we present latitudinal species richness curves using standard estimators (Chao2 and the Jackknife) and range-through approaches.

Chemical signatures of soft tissues distinguish between vertebrates and invertebrates from the Carboniferous Mazon Creek Lagerstätte of Illinois.

The chemical composition of fossil soft tissues is a potentially powerful and yet underutilized tool for elucidating the affinity of problematic fossil organisms. In some cases, it has proven difficult to assign a problematic fossil even to the invertebrates or vertebrates (more generally chordates) based on often incompletely preserved morphology alone, and chemical composition may help to resolve such questions. Here, we use in situ Raman microspectroscopy to investigate the chemistry of a diverse array of invertebrate and vertebrate fossils from the Pennsylvanian Mazon Creek Lagerstätte of Illinois, and we generate a ChemoSpace through principal component analysis (PCA) of the in situ Raman spectra.

Controlling the error probabilities of model selection information criteria using bootstrapping.

The Akaike Information Criterion (AIC) and related information criteria are powerful and increasingly popular tools for comparing multiple, non-nested models without the specification of a null model. However, existing procedures for information-theoretic model selection do not provide explicit and uniform control over error rates for the choice between models, a key feature of classical hypothesis testing. We show how to extend notions of Type-I and Type-II error to more than two models without requiring a null.

Text-mined fossil biodiversity dynamics using machine learning.

Documented occurrences of fossil taxa are the empirical foundation for understanding large-scale biodiversity changes and evolutionary dynamics in deep time. The fossil record contains vast amounts of understudied taxa. Yet the compilation of huge volumes of data remains a labour-intensive impediment to a more complete understanding of Earth's biodiversity history.