Optimal Learning and Management of Threatened Species

Jue Wang (), Xueze Song (), Roozbeh Yousefi () and Zhigang Jiang ()
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Jue Wang: Smith School of Business, Queen’s University, Kingston, Ontario K7L 3N6, Canada; and SC Johnson School of Management, Cornell University, Ithaca, New York 14853
Xueze Song: Culverhouse College of Business, The University of Alabama, Tuscaloosa, Alabama 35487
Roozbeh Yousefi: Smith School of Business, Queen’s University, Kingston, Ontario K7L 3N6, Canada
Zhigang Jiang: Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; and Hainan Institute of National Park, Haikou, Hainan 570100, China

Management Science, 2025, vol. 71, issue 6, 4757-4776

Abstract: Amid an unprecedented loss of biodiversity, a pressing issue is how to improve the efficiency of conservation with limited resources and information. Collecting data on species with a small population is costly and time consuming, and many high-stakes decisions need to be made based on limited data. We develop a partially observable Markov decision processes model with unknown parameters to jointly optimize the information collection and protection efforts for threatened species. The model takes into account uncertainties about the state, detectability, and dynamics of the species, and it adaptively adjusts the efforts of surveying and protection in real time. Although the standard formulation is intractable, we exploit the structure of ecological problems to identify a hybrid belief state in low dimensions, and we reformulate the stochastic dynamic program as a piecewise deterministic optimal control problem. This enables us to obtain structural insights into the optimal policy (some are in closed form) and find a near-optimal approximate policy with performance guarantee. In certain situations, areas where the species has never been found may be more likely to contain the species than areas where it has been previously found. We also conduct a case study on the conservation of the Hainan gibbon, the rarest primate species, in which we extend the model to optimize the spatiotemporal allocation of limited resources.

Keywords: biodiversity; conservation planning; partially observable Markov decision processes (POMDP); Bayesian reinforcement learning; optimal control (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:71:y:2025:i:6:p:4757-4776

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