Adaptive management of migratory birds under sea level rise

Samuel Nicol, Olivier Buffet, Takuya Iwamura, Iadine Chadès

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The best practice method for managing ecological systems under uncertainty is adaptive management (AM), an iterative process of reducing uncertainty while simultaneously optimizing a management objective. Existing solution methods used for AM problems assume that the system dynamics are stationary, i.e., described by one of a set of pre-defined models. In reality ecological systems are rarely stationary and evolve over time. Importantly, the effects of climate change on populations are unlikely to be captured by stationary models. Practitioners need efficient algorithms to implement AM on real-world problems. AM can be formulated as a hidden model Markov Decision Process (hmMDP), which allows the state space to be factored and shows promise for the rapid resolution of large problems. We provide an ecological dataset and performance metrics for the AM of a network of shorebird species utilizing the East Asian-Australasian flyway given uncertainty about the rate of sea level rise. The non-stationary system is modelled as a stationary POMDP containing hidden alternative models with known probabilities of transition between them. We challenge the POMDP community to exploit the simplifications allowed by structuring the AM problem as an hmMDP and improve our benchmark solutions.

Original languageEnglish
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Pages2955-2957
Number of pages3
StatePublished - 2013
Externally publishedYes
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: 3 Aug 20139 Aug 2013

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
Country/TerritoryChina
CityBeijing
Period3/08/139/08/13

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