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Abstract
Addressing resource allocation problems in a decision making framework allows for resources to be optimally allocated in such a way that addresses all objectives relevant to the decision. Private land incentive programs pursue conservation objectives by allocating financial resources to landowners to promote specific management practices, but fixed program budgets usually require hard choices to be made among potential enrollments. Such incentive programs are used heavily to bolster grassland bird species such as the northern bobwhite (Colinus virginianus), but often programs targeting this declining species are vague about their objectives and how they allocate resources on private lands for the species. I cast the problem of resource allocation into the PrOACT cycle and describe a proposed framework for making decisions about which landowners to enroll in conservation incentive programs. I then simulate the proposed, model based framework and compared it to a rank and scoring approach and a randomized approach of selecting applicants. I find that, depending on resource constraints, the model based framework outperforms both the rank and scoring approach as well as the randomized approach. The model based framework returns a greater number of species and is more cost effective. These results strengthen the argument for employing a decision analytics based approaches when distributing public resources for conservation.