Text-mined fossil biodiversity dynamics using machine learning.
Proc Biol Sci
; 286(1901): 20190022, 2019 04 24.
Article
in En
| MEDLINE
| ID: mdl-31014224
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Bryozoa
/
Biodiversity
/
Data Mining
/
Machine Learning
/
Fossils
Limits:
Animals
Language:
En
Journal:
Proc Biol Sci
Journal subject:
BIOLOGIA
Year:
2019
Document type:
Article
Affiliation country:
Noruega