Large landscape conservation: synthetic and real-world datasets

摘要: Biodiversity underpins ecosystem goods and services hence protecting it is key to achieving sustainability. However, the persistence of many species threatened by habitat loss fragmentation due human land use climate change. Conservation efforts are implemented under very limited economic resources, therefore designing scalable, cost-efficient systematic approaches for conservation planning an important challenging computational task. In particular, preserving landscape connectivity between good has become a priority in recent years. We give overview some underlying graph-theoretic optimization problems. present synthetic generator capable creating families randomized structured problems, capturing essential features real-world instances but allowing thorough typical-case performance evaluation different solution methods. also two large-scale datasets, including data on cost, grizzly bears, wolverines lynx.