Dynamic Optimization of Landscape Connectivity Embedding Spatial-Capture-Recapture Information
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 real-world size problems and dramatically outperforms the solution quality of an expectation maximization approach and a sample average approximation approach.
Cite
Text
Xue et al. "Dynamic Optimization of Landscape Connectivity Embedding Spatial-Capture-Recapture Information." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11175Markdown
[Xue et al. "Dynamic Optimization of Landscape Connectivity Embedding Spatial-Capture-Recapture Information." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/xue2017aaai-dynamic/) doi:10.1609/AAAI.V31I1.11175BibTeX
@inproceedings{xue2017aaai-dynamic,
title = {{Dynamic Optimization of Landscape Connectivity Embedding Spatial-Capture-Recapture Information}},
author = {Xue, Yexiang and Wu, XiaoJian and Morin, Dana and Dilkina, Bistra and Fuller, Angela and Royle, J. Andrew and Gomes, Carla P.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {4552-4558},
doi = {10.1609/AAAI.V31I1.11175},
url = {https://mlanthology.org/aaai/2017/xue2017aaai-dynamic/}
}