InforLorV4, Hal, Curation, bibRecord, 000B20

Adaptive Management of Migratory Birds Under Sea Level Rise

Identifieur interne : 000B20 ( Hal/Curation ); précédent : 000B19; suivant : 000B21

Adaptive Management of Migratory Birds Under Sea Level Rise

Auteurs : Samuel Nicol [Australie] ; Olivier Buffet [France] ; Takuya Iwamura [États-Unis] ; Iadine Chadès [Australie]

Source :

RBID : Hal:hal-00907334

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.

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<front><div type="abstract" xml:lang="en">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.</div>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Adaptive Management of Migratory Birds Under Sea Level Rise</title>
<author role="aut"><persName><forename type="first">Samuel</forename>
<surname>Nicol</surname>
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<email>snicol@alaska.edu</email>
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<author role="aut"><persName><forename type="first">Olivier</forename>
<surname>Buffet</surname>
</persName>
<email>olivier.buffet@loria.fr</email>
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<author role="aut"><persName><forename type="first">Takuya</forename>
<surname>Iwamura</surname>
</persName>
<email>takuya@stanford.edu</email>
<idno type="halAuthorId">946005</idno>
<orgName ref="#struct-73500"></orgName>
<affiliation ref="#struct-237267"></affiliation>
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<author role="aut"><persName><forename type="first">Iadine</forename>
<surname>Chadès</surname>
</persName>
<email>iadine.chades@csiro.au</email>
<ptr type="url" target="http://www.csiro.au/Organisation-Structure/Divisions/Ecosystem-Sciences/IadineChades.aspx"></ptr>
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<monogr><meeting><title>IJCAI - 23rd International Joint Conference on Artificial Intelligence - 2013</title>
<date type="start">2013-08-03</date>
<date type="end">2013-08-09</date>
<settlement>Pékin</settlement>
<country key="CN">China</country>
</meeting>
<editor>Francesca Rossi</editor>
<imprint><publisher>AAAI Press</publisher>
<biblScope unit="serie">Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence</biblScope>
<biblScope unit="pp">2955-2957</biblScope>
<date type="datePub">2013-08</date>
</imprint>
</monogr>
<ref type="publisher">dl.acm.org/ft_gateway.cfm?id=2540555&ftid=1410309&dwn=1&CFID=265861503&CFTOKEN=96725518</ref>
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<abstract xml:lang="en">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.</abstract>
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Adaptive Management of Migratory Birds Under Sea Level Rise

Adaptive Management of Migratory Birds Under Sea Level Rise

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