D'Souza, JenniferLaubach, ZacharyMustafa, Tarek AlZarrieß, SinaFrühstückl, RobertIllari, PhyllisBasile, ValerioBosco, CristinaGrasso, FrancescaIbrahim, Muhammad OkkySkeppstedt, MariaStede, Manfred2025-02-172025-02-172025-03978-9908-53-114-4https://hdl.handle.net/10062/107178This 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.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/Article