Harnessing Large Language Models for Ecological Literature Reviews: A Practical Pipeline

Hundreds of thousands of peer-reviewed articles and grey literature reports are published every year in ecology and conservation biology. This ever-growing body of knowledge presents new challenges. Indeed, it is becoming increasingly challenging for researchers to stay current on new information and to identify knowledge gaps. Here, we argue that Large Language Models (LLMs) such as OpenAI’s GPT-4o mini offer a powerful yet accessible solution to help overcome this challenge, as LLMs require only effective prompt engineering rather than specialized AI expertise. We present a streamlined LLM-driven pipeline for filtering and extracting information from large volumes of literature, illustrating its potential through two case studies. Our findings show that, by combining LLMs with short, iterative prompting workflows and targeted manual validation checks, researchers can rapidly obtain structured outputs—such as study locations, biome types, or quantitative measures—while minimizing model hallucinations and misinterpretations. We emphasize that domain experts remain integral for shaping prompts, verifying results, and ensuring the extracted information aligns with real-world research and conservation needs. Overall, this pipeline underscores the synergy between human expertise and LLM capabilities, promising more efficient literature reviews for a broad range of ecological and conservation applications.