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Dynamic optimization of landscape connectivity embedding spatial-capture-recapture information

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Abstract

Maintaining landscape connectivity is increasingly important in wildlife conservation, especially for species experiencing the effects of habitat loss and fragmentation. We propose a novel approach to dynamically optimize landscape connectivity. Our approach is based on a mixed integer program formulation, embedding a spatial capture-recapture model that estimates the density, space usage, and landscape connectivity for a given species. Our method takes into account the fact that local animal density and connectivity change dynamically and non-linearly with different habitat protection plans. In order to scale up our encoding, we propose a sampling scheme via random partitioning of the search space using parity functions. We show that our method scales to realworld size problems and dramatically outperforms the solution quality of an expectation maximization approach and a sample average approximation approach.

Additional publication details

Publication type Conference Paper
Publication Subtype Conference Paper
Title Dynamic optimization of landscape connectivity embedding spatial-capture-recapture information
Year Published 2017
Language English
Publisher Association for the Advancement of Artificial Intelligence
Contributing office(s) Coop Res Unit Leetown
Description 7 p.
Larger Work Type Book
Larger Work Subtype Conference publication
Larger Work Title Proceedings of the Thirty-First Conference on Artificial Intelligence (AAAI-17)
Conference Title Thirty-First Conference on Artificial Intelligence (AAAI-17)
Conference Location San Francisco, CA