Deforestation is the leading driver of biodiversity loss in the world today. Improved understanding of the factors that drive the rate and pattern of forest loss are critical to guide proactive conservation of forest ecosystems and the biodiversity they support. The island of Borneo in Southeast Asia has some the highest levels of biodiversity in the world and the highest deforestation rates. Samuel Cushman, a scientist at the Forest Service’s Rocky Mountain Research Station, developed a novel application of multi-scale machine-learning predictive modeling to identify the drivers and predict the rates and patterns of forest loss across the island of Borneo. The model produced much stronger and more accurate predictions than traditional approaches and has provided detailed and specific guidance for conservation prioritization and management efforts in Borneo. The project improved predictive modeling of deforestation with a novel application of multi-scale machine learning predictive modeling. The project provided highly accurate predictions of future deforestation risk across the full extent of Borneo, which provides critical guidance for proactive conservation and management efforts.