Using AI enhanced agent-based models to support management of wild populations

Abstract

Managing wild populations in rapidly changing, human-dominated landscapes requires models that accommodate complex interactions among climate, land use, disease, and evolution. Agent-based models (ABMs) are well suited to this task but are often difficult to parameterize, calibrate, and interpret at management-relevant scales. We discuss how artificial-intelligence (AI) techniques, including machine-learning regression, data-mining diagnostics, geospatial informatics, and large-language-model code aides, can streamline ABM parameter estimation and scenario testing, enhance extraction of decision-support metrics, and broaden the accessibility of ABMs for conservation planning.

Aniruddha Belsare

Assistant Professor of Disease Ecology

My research interests include wildlife disease ecology, disease modeling and wildlife medicine.