FloraTraiter: Automated parsing of traits from descriptive biodiversity literature

Premise: Plant trait data are essential for quantifying biodiversity and function across Earth, but these data are challenging to acquire for large studies. Diverse strategies are needed, including the liberation of heritage data locked within specialist literature such as floras and taxonomic monographs. Here we report FloraTraiter, a novel approach using rule-based natural language processing (NLP) to parse computable trait data from biodiversity literature.

Methods: FloraTraiter was implemented through collaborative work between programmers and botanical experts and customized for both online floras and scanned literature. We report a strategy spanning optical character recognition, recognition of taxa, iterative building of traits, and establishing linkages among all of these, as well as curational tools and code for turning these results into standard morphological matrices.

Results: Over 95% of treatment content was successfully parsed for traits with <1% error. Data for more than 700 taxa are reported, including a demonstration of common downstream uses.

Conclusions: We identify strategies, applications, tips, and challenges that we hope will facilitate future similar efforts to produce large open-source trait data sets for broad community reuse. Largely automated tools like FloraTraiter will be an important addition to the toolkit for assembling trait data at scale.