Mining for Species, Locations, Habitats, and Ecosystems from Scientific Papers in Invasion Biology: A Large-Scale Exploratory Study with Large Language Models

TL;DR

This study explores the use of large language models to extract ecological entities like species, locations, habitats, and ecosystems from invasion biology literature, highlighting their potential and current limitations for ecological knowledge extraction.

Contribution

It demonstrates the application of general-purpose LLMs for ecological entity extraction from scientific texts without domain-specific fine-tuning, revealing both capabilities and challenges.

Findings

LLMs can identify key ecological entities with reasonable accuracy.

Challenges remain in handling complex ecological terminology and linguistic nuances.

The study provides a foundation for developing automated ecological knowledge extraction tools.

Abstract

This paper presents an exploratory study that harnesses the capabilities of large language models (LLMs) to mine key ecological entities from invasion biology literature. Specifically, we focus on extracting species names, their locations, associated habitats, and ecosystems, information that is critical for understanding species spread, predicting future invasions, and informing conservation efforts. Traditional text mining approaches often struggle with the complexity of ecological terminology and the subtle linguistic patterns found in these texts. By applying general-purpose LLMs without domain-specific fine-tuning, we uncover both the promise and limitations of using these models for ecological entity extraction. In doing so, this study lays the groundwork for more advanced, automated knowledge extraction tools that can aid researchers and practitioners in understanding and managing biological invasions.