LAUSR

Cost-effective land conservation techniques, such as optimization, have the potential to contribute substantially to the provision of many important environmental benefits, such as biodiversity protection, flood control, food security, water quality, and reduction of greenhouse gas emissions. There has been a recent push for conservation organizations to adopt project selection optimization approaches such as binary linear programming. The metrics used to measure the benefits of a project however, are often poorly defined in that they do not directly compute a value. These scores represent normalized measurements of underlying values that are likely log-normally distributed. Applying such metrics in optimization will tend to undervalue high-benefit projects and select a suboptimal portfolio of projects relative to simpler approaches. This suboptimal performance can lead to losses in efficiency as high as 30%. We propose a hybrid optimization heuristic that can improve performance and, additionally, provide conservation professionals with more flexibility and freedom to select conservation projects at their discretion—potentially overcoming a substantial real-world adaptation hurdle.