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Updated: Jan 17, 2026

Field Collection and Laboratory Maintenance of Canopy-Forming Giant Kelp to Facilitate Restoration

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Published on: June 7, 2024

Developing new technologies to protect ecosystems: Planning with adaptive management.

Luz Valerie Pascal^1,2,3, Iadine Chadès^3,4, Matthew P Adams^1,2,5

¹School of Mathematical Sciences and Centre for Data Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD 4000, Australia.

Proceedings of the National Academy of Sciences of the United States of America

|September 24, 2025

Summary

Policymakers can use AI-driven adaptive management to set time limits for technology development, balancing risks and uncertainties. This approach helps optimize investments in critical areas like climate change and biodiversity conservation.

Keywords:

Great Barrier Reef POMDPs adaptive management technology development

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Area of Science:

Ecology
Environmental Science
Artificial Intelligence

Background:

Policymakers face challenges investing limited resources in new technologies for global crises like climate change and biodiversity loss.
Technology development and deployment involve inherent unpredictability in timelines, outcomes, and long-term effects.
Managing dynamic systems, such as threatened ecological systems, requires informed decisions on resource allocation for technology development.

Purpose of the Study:

To determine optimal time limits for technology development investments.
To provide clear, transparent rules for investing in new technologies.
To balance costs, benefits, and uncertainties in technology development and deployment.

Main Methods:

Utilized an adaptive management approach from Artificial Intelligence (AI).
Employed an analytical approximation to derive general investment rules.
Applied the approach to a case study of Australia's Great Barrier Reef.

Main Results:

A development time limit ranging from 0 to 45 years before ceasing investment was identified.
Demonstrated how ecological system characteristics influence optimal investment strategies.
Developed general rules for investing in new technologies across various domains.

Conclusions:

The AI-driven adaptive management framework offers a method to optimize technology development investments.
The findings provide practical guidance for policymakers in managing risks and uncertainties.
The approach is applicable to diverse fields including conservation, public health, and energy production.