Past and future uses of text mining in ecology & evolution

Abstract

Ecology and evolutionary biology, like other scientific fields, are experiencing an exponentialgrowth of academic manuscripts. As domain knowledge accumulates, scientists will neednew computational approaches for identifying relevant literature to read and include informal literature reviews and meta-analyses. Importantly, these approaches can alsofacilitate automated, large-scale data synthesis tasks and build structured databases fromthe information in the texts of primary journal articles, textbooks, grey literature, andwebsites. The increasing availability of digital text, computational resources, andmachine-learning based language models have led to a revolution in text analysis andNatural Language Processing (NLP) in recent years. NLP has been widely adopted acrossthe biomedical sciences, but is rarely used in ecology and evolutionary biology. Applyingcomputational tools from text mining and NLP will increase the efficiency of data synthesis,improve the reproducibility of literature reviews, formalize analyses of research biases andknowledge gaps, and promote data-driven discovery of patterns across ecology andevolutionary biology. Here we present recent use cases from ecology and evolution, anddiscuss future applications, limitations, and ethical issues.