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Large Language Models for Marine Protected Area Design and Management Planning
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Authors
Ysobel Sims 
,
Joseph M Maina ,
Arthur Tuda
Abstract
Achieving biodiversity targets requires advances in marine conservation planning and action. We make three arguments about large language models (LLMs): they add more value when embedded within decision-support systems; they integrate fragmented expertise, data, and tools; they can increase capacity in under-resourced settings through co-design. We illustrate these arguments with a conceptual LLM-enabled marine protected area design and management framework, and address concerns about data, governance, and knowledge equity.
DOI
https://doi.org/10.32942/X2RT2G
Subjects
Artificial Intelligence and Robotics, Biodiversity, Bioinformatics, Natural Resources and Conservation
Keywords
marine conservation, marine biodiversity, machine learning, life below water, global biodiversity framework, conservation informatics
Dates
Published: 2026-10-12 11:19
Last Updated: 2026-10-12 11:19
License
CC-By Attribution-NonCommercial-NoDerivatives 4.0 International
Additional Metadata
Conflict of interest statement:
None
Data and Code Availability Statement:
Not applicable
Language:
English
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