Large Landscape Conservation - Synthetic and Real-World Datasets
Large Landscape Conservation - Synthetic and Real-World Datasets
复制标题
大型景观保护 - 合成和真实世界数据集
DOI：
--
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Claire A. Montgomery
中科院分区：
文献类型：
--
作者：
B. Dilkina;Katherine J. Lai;Ronan Le Bras;Yexiang Xue;C. Gomes;Ashish Sabharwal;Jordan F. Suter;K. McKelvey;M. Schwartz;Claire A. Montgomery
生物多样性是生态系统产品和服务的基础，因此保护生物多样性是实现可持续性的关键。然而，由于人类土地利用和气候变化，许多物种的持久性受到栖息地丧失和破碎化的威胁。保护工作在有限的经济资源下实施，因此设计可扩展、成本效益和系统的保护规划方法是一项重要而具有挑战性的计算任务。特别是，保护良好栖息地之间的景观连通性已成为近年来保护的重点。我们给出了景观连通性保护的概述和一些潜在的图论优化问题。我们提出了一个合成生成器，能够创建随机结构化问题族，捕获现实世界实例的基本特征，但允许对不同解决方法进行彻底的典型案例性能评估。我们还提供了两个大规模的现实世界数据集，包括土地成本的经济数据，以及灰熊、狼獾和猞猁的物种数据。
Biodiversity underpins ecosystem goods and services and hence protecting it is key to achieving sustainability. However, the persistence of many species is threatened by habitat loss and fragmentation due to human land use and climate change. Conservation efforts are implemented under very limited economic resources, and therefore designing scalable, cost-efficient and systematic approaches for conservation planning is an important and challenging computational task. In particular, preserving landscape connectivity between good habitat has become a key conservation priority in recent years. We give an overview of landscape connectivity conservation and some of the underlying graph-theoretic optimization problems. We present a synthetic generator capable of creating families of randomized structured problems, capturing the essential features of real-world instances but allowing for a thorough typical-case performance evaluation of different solution methods. We also present two large-scale real-world datasets, including economic data on land cost, and species data for grizzly bears, wolverines and lynx.