Optimal Learning and Management of Threatened Species
86 Pages Posted: 18 Jun 2023 Last revised: 9 Feb 2024
Date Written: June 8, 2023
In the face of an unprecedented loss of biodiversity, a pressing issue is how to improve the effectiveness of conservation with limited resources and information. We develop a Bayes-adaptive POMDP framework to optimize the learning and management of threatened species in a partially observable, dynamic, and uncertain environment. This framework takes into account uncertainties about the state, dynamics, and detectability of a species and optimizes the efforts of protection and surveying in real time. We exploit the structure of conservation problems to identify a low-dimensional hybrid belief state and 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 an efficient near-optimal approximation policy with performance guarantee. Interestingly, 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 conduct a case study on the conservation of the Hainan Gibbon, in which we extend the model to multiple sites to optimize the spatiotemporal allocation of resources.
Keywords: Biodiversity conservation, Partially observable Markov decision processes (POMDP), Parameter uncertainty, Bayesian reinforcement Learning
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