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. Our combined DB and TMO species richness curves quantitatively document a bimodal global latitudinal diversity gradient for extant cheilostomes for the first time, with peaks in the temperate zones. A total of 79% of the georeferenced species we retrieved from TMO (N = 1,408) and DB (N = 4,549) are non-overlapping. Despite clear indications that global location data compiled for cheilostomes should be improved with concerted effort, our study supports the view that many marine latitudinal species richness patterns deviate from the canonical latitudinal diversity gradient (LDG). Moreover, combining online biodiversity databases with automated information retrieval from the published literature is a promising avenue for expanding taxon-location datasets.

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. Even so, many occurrence records of species and genera in these taxa can be uncovered in the palaeontological literature. Here, we extract observations of fossils and their inferred ages from unstructured text in books and scientific articles using machine-learning approaches. We use Bryozoa, a group of marine invertebrates with a rich fossil record, as a case study. Building on recent advances in computational linguistics, we develop a pipeline to recognize taxonomic names and geologic time intervals in published literature and use supervised learning to machine-read whether the species in question occurred in a given age interval. Intermediate machine error rates appear comparable to human error rates in a simple trial, and resulting genus richness curves capture the main features of published fossil diversity studies of bryozoans. We believe our automated pipeline, that greatly reduced the time required to compile our dataset, can help others compile similar data for other taxa.

Breakdown of brain-body allometry and the encephalization of birds and mammals.

The allometric relationship between brain and body size among vertebrates is often considered a manifestation of evolutionary constraints. However, birds and mammals have undergone remarkable encephalization, in which brain size has increased without corresponding changes in body size. Here, we explore the hypothesis that a reduction of phenotypic integration between brain and body size has facilitated encephalization in birds and mammals. Using a large dataset comprising 20,213 specimens across 4,587 species of jawed vertebrates, we show that the among-species (evolutionary) brain-body allometries are remarkably constant, both across vertebrate classes and across taxonomic levels. Birds and mammals, however, are exceptional in that their within-species (static) allometries are shallower and more variable than in other vertebrates. These patterns are consistent with the idea that birds and mammals have reduced allometric constraints that are otherwise ubiquitous across jawed vertebrates. Further exploration of ontogenetic allometries in selected taxa of birds, fishes and mammals reveals that birds and mammals have extended the period of fetal brain growth compared to fishes. Based on these findings, we propose that avian and mammalian encephalization has been contingent on increased variability in brain growth patterns.