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.
Suggested Citation
Xue, Y., Wu, X., Morin, D.J., Dilkina, B., Fuller, A.K., Royle, J., Gomes, C.P., 2017, Dynamic optimization of landscape connectivity embedding spatial-capture-recapture information, in Proceedings of the Thirty-First Conference on Artificial Intelligence (AAAI-17), San Francisco, CA, 7 p.
| 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 |