Large language models (LLMs) are a significant advancement in artificial intelligence (AI) and are being increasingly integrated into the workflows of researchers and practitioners in various fields, including ecology and conservation science.

In a new study published in Conservation Biology on April 13, researchers from the Xishuangbanna Tropical Botanical Garden (XTBG) of the Chinese Academy of Sciences warned that, while LLMs provide revolutionary tools for ecological research, they require prudent implementation and regulatory management to maximize benefits and mitigate potential risks.

The researchers reviewed the emerging applications of LLMs, drawing from the broader scientific literature and practical use cases. They found that LLMs such as GPT-5, LLaMA 2, and DeepSeek-V2 are already being used to extract ecological information from unstructured sources, enable natural-language queries of structured databases, and perform large-scale literature syntheses.

Practical use cases include the automated monitoring of news reports to generate ecological insights, the integration of edge devices such as camera traps to enhance biodiversity monitoring, and the provision of assistance with analytical tasks. Custom models trained on domain-specific information are also being deployed to improve scientific communication and outreach. Other emerging applications extend to policy analysis and decision support, including simulating stakeholder interactions using multiagent systems.

These advanced artificial intelligence systems could dramatically streamline research workflows, enhance biodiversity monitoring, and accelerate evidence-based conservation efforts. However, the researchers caution that technical and ethical challenges must be addressed to ensure equitable and effective adoption, including inaccuracies, biases, and environmental impacts.

The researchers recommended a set of best practices, including careful model selection, effective prompt engineering, and retrieval-augmented generation to improve factual accuracy and representation. They also recommended human-in-the-loop validation, and broader efforts to promote inclusive development, capacity building, and appropriate governance.

"When applied thoughtfully, large language models can serve as a valuable addition to the ecologists' toolkits, enhancing their scientific capacity and supporting their efforts to achieve global biodiversity goals," said Christos Mammides of XTBG.

He added that training and support for researchers, especially those in under-resourced settings, are essential to ensuring equitable access and meaningful participation in this technological shift.