Mining for Species, Locations, Habitats, and Ecosystems from Scientific Papers in Invasion Biology: A Large-Scale Exploratory Study with Large Language Models (2025)

Abstract: This study explores the use of large language models (LLMs), specifically GPT-4o, to extract key ecological entities—species, locations, habitats, and ecosystems—from invasion biology literature. This information is critical for understanding species spread, predicting future invasions, and informing conservation efforts. Without domain-specific fine-tuning, we assess the potential and limitations of GPT-4o, out-of-the-box, for this task, highlighting the role of LLMs in advancing automated knowledge extraction for ecological research and management.

## introduction human population growth and expansion drive the intentional and unintentional movement of species beyond their historic ranges, leading to significant ecological impacts @xcite . invasion biology seeks to understand these impacts across ecological scales to conserve native species and maintain functional ecosystems that provide essential services @xcite . however, alien species introductions occur at an accelerating pace globally, making it increasingly difficult for researchers to systematically track and categorize species, their locations, and relationships. this paper explores the potential of recent nlp technologies, specifically information extraction (ie) approaches based on large language models (llms) @xcite , as tools for predicting future invasions and their consequences. the extraction and categorization of information from scientific publications is a well-known nlp task @xcite @xcite @xcite @xcite @xcite @xcite . while named entity recognition (ner) and relation extraction (re) have been extensively applied in the biomedical domain for network biology @xcite , gene prioritization @xcite , drug repositioning @xcite , and curated database creation @xcite , their application in invasion biology remains underexplored. to the best of our knowledge, the small-scale inas dataset @xcite is the only invasion biologyspecific resource with annotated hypotheses for scientific abstracts. ## information extraction with llms an ie task requires two prerequisites: 1) a collection of papers for processing, and 2) a schema defining the extraction targets. ## recommendations for future work future work should explore integrating ontologies with llms to enhance information extraction (ie) and linked data creation, addressing key research questions: how llms can assist in ontology and knowledge graph construction @xcite , improve question answering through ontology support @xcite , enable ontology learning from text @xcite , and enhance representation learning @xcite . ontologies, as formal specifications of shared conceptualizations @xcite , enable structured knowledge representation, yet their adoption is hindered by expertise barriers. future research should investigate schema-driven ie, optimizing the information provided to llms, refining structured guidance @xcite , and assessing how llm-derived knowledge aligns with expert consensus. ontologies can improve llms by supplying domain-specific definitions, guiding semantic modeling, enhancing entity and relation extraction, and integrating with retrievalaugmented generation (rag) to reduce hallucinations @xcite . however, constraints must be considered in rapidly evolving fields, where rigid ontological structures may limit adaptability to emerging knowledge. balancing structured knowledge integration with flexibility will be crucial for leveraging llms effectively across diverse domains. ## conclusion this study highlights the potential of llms for advancing ie in invasion biology by extracting species, locations, habitats, and ecosystems from scientific literature. through a standardized semantic schema, we demonstrated how llms can structure complex ecological data, enhancing research workflows. our two-stage approach first extracts detailed, context-specific structures (specialize stage) and then integrates them into a flexible schema (generalize stage) balancing specificity and generality. this method enables structured representation of ecological complexity. the released dataset and schema support refining extraction methods, integrating ontologies, and broader ecological applications, underscoring llms' role in bridging unstructured data and structured knowledge in ecology.