Globally, land use change is the primary driver of biodiversity loss (IPBES, 2019). Land use change due to agricultural expansion is driving bird species to the brink of extinction in the Peruvian Amazon rainforest. Agriculture is one of the primary threats to bird species in the region, and agroforestry is being pursued in some communities as a potential solution to reduce agriculture's impacts on species, as agroforestry provides improved habitat for wildlife while also enabling livelihoods for people. Understanding how anthropogenic land use choices affect imperiled species is an important prerequisite for conservation policy and practice in the region. In this thesis, I develop a spatial model for quantifying expected threat abatement from shifting agricultural land use choices towards agroforestry. I used this model explored how agricultural land use impacts imperiled bird species in the Peruvian Amazon. My approach builds on the species threat abatement and restoration (STAR) metric to make the expected consequences of reducing agricultural threats spatially explicit. I then analyzed results of applying the metric to alternative scenarios with and without agroforestry conversion. I found that agroforestry could result in up to 18.68% reduction in mean bird projected population decline. I found that converting all terrestrial agriculture in the Peruvian Amazon to agroforestry could produce a benefit of up to 83% to imperiled birds in the region in terms of improvement in Red List status. This use of the STAR metric to model alternative scenarios presents a novel usage for the STAR metric and a promising approach to understand how to address terrestrial biodiversity challenges efficiently and effectively.