Document of bibliographic reference 310688

BibliographicReference record

Type

Bibliographic resource

Type of document

Journal article

BibLvlCode

AS

Title

Text-mined fossil biodiversity dynamics using machine learning

Abstract

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.

WebOfScience code

https://www.webofscience.com/wos/woscc/full-record/WOS:000465657800007

Bibliographic citation

Kopperud, B.T.; Lidgard, S.; Liow, L.H. (2019). Text-mined fossil biodiversity dynamics using machine learning. Proc. - Royal Soc., Biol. Sci. 286(1901): 20190022. https://dx.doi.org/10.1098/rspb.2019.0022

Is peer reviewed

true

Access rights

open access

Is accessible for free

true