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

arXiv: https://arxiv.org/abs/2501.18287 · HTML: https://mlsanity.com/paper/2501.18287/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 13 of 13)

- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [Transformer models: an introduction and catalog](https://mlsanity.com/paper/2302.07730/) (2023)
- [Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation](https://mlsanity.com/paper/2402.08100/) (2024)
- [Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!](https://mlsanity.com/paper/2405.11706/) (2024)
- [SemEval-2021 Task 11: NLPContributionGraph -- Structuring Scholarly NLP Contributions for a Research Knowledge Graph](https://mlsanity.com/paper/2106.07385/) (2021)
- [Automated Mining of Leaderboards for Empirical AI Research](https://mlsanity.com/paper/2109.13089/) (2021)
- [Large Language Models for Scientific Information Extraction: An Empirical Study for Virology](https://mlsanity.com/paper/2401.10040/) (2024)
- [End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models](https://mlsanity.com/paper/1912.13415/) (2019)
- [Domain-independent Extraction of Scientific Concepts from Research Articles](https://mlsanity.com/paper/2001.03067/) (2020)
- [Computer Science Named Entity Recognition in the Open Research Knowledge Graph](https://mlsanity.com/paper/2203.14579/) (2022)
- [UIUC_BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions](https://mlsanity.com/paper/2105.05435/) (2021)

## Built on by (most cited first, 4 of 4)

- [MAviS: A Multimodal Conversational Assistant For Avian Species](https://mlsanity.com/paper/2603.07294/) (2026)
- [Taxonomic Reasoning for Rare Arthropods: Combining Dense Image Captioning and RAG for Interpretable Classification](https://mlsanity.com/paper/2503.10886/) (2025)
- [Diagnosing Structural Failures in LLM-Based Evidence Extraction for Meta-Analysis](https://mlsanity.com/paper/2602.10881/) (2026)
- [SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions](https://mlsanity.com/paper/2607.27955/) (2026)

## Abstract (arXiv)

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.