Large Landscape Conservation — Synthetic and Real-World Datasets

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.

Authors

• Bistra Dilkina University of Southern California
• Katherine Lai Cornell University
• Ronan Le Bras
• Yexiang Xue Department of Computer Science, Purdue University
• Carla Gomes
• Ashish Sabharwal
• Jordan Suter Oberlin College
• Kevin McKelvey US Forest Service
• Michael Schwartz US Forest Service
• Claire Montgomery Oregon State University

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