Emerging Applications of Large Language Models in Ecology and Conservation Science

Christos Mammides, Hao Gu, Thilina Nimalrathna, Naufal Rahman Avicena, Harris Papadopoulos, Ahimsa Campos-Arceiz

The emergence of large language models (LLMs) marks a major development in artificial intelligence, with potentially transformative implications for ecology and conservation science. Built on advanced deep-learning architectures, these models can support a wide range of tasks, from analysing unstructured texts to enhancing biodiversity monitoring and generating policy-relevant insights. This article synthesises emerging applications of LLMs across ecology and conservation, drawing on the wider literature and practical use cases. We highlight the potential of LLMs to streamline ecological workflows and accelerate evidence-based conservation, while also discussing key technical and ethical challenges, such as inaccurate and biased outputs, and unequal access. We offer recommendations for addressing these challenges to support the reliable and responsible use of LLMs, including strategies for improving output accuracy and ensuring proper validation. When implemented thoughtfully, LLMs can serve as a valuable addition to the ecologists’ toolkit, enhancing scientific capacity and supporting broader efforts towards achieving biodiversity goals.