Conservation agencies worldwide must make the most efficient use of their limited resources to protect natural resources from over-harvesting and animals from poaching. Predictive modeling, a tool to increase efficiency, is seeing increased usage in conservation domains such as to protect wildlife from poaching. Many works in this wildlife protection domain, however, fail to train their models on real-world data or test their models in the real world. My thesis proposes novel poacher behavior models that are trained on real-world data and are tested via first-of-their-kind tests in the real world. First, I proposed a paradigm shift in traditional adversary behavior modeling techniques from Quantal Response-based models to decision tree-based models. Based on this shift, I proposed an ensemble of spatially-aware decision trees, INTERCEPT, that outperformed the prior stateof-the-art and then also presented results from a one-month pilot field test of the ensemble’s predictions in Uganda’s Queen Elizabeth Protected Area (QEPA). This field test represented the first time that a machine learning-based poacher behavior modeling application was tested in the field. Second, I proposed a hybrid spatio-temporal model that led to further performance improvements. To validate this model, I designed and conducted a large-scale, eight-month field test of this model’s predictions in QEPA. This field test, where rangers patrolled over 450 km in the largest and longest field test of a machine learning-based poacher behavior model to date in this domain, successfully demonstrated the selectiveness of the model’s predictions; the model successfully predicted, with statistical significance, where rangers would find more snaring activity and also where rangers would not find as much snaring activity. I also conducted detailed analysis of the behavior of my predictive model. Third, beyond wildlife poaching, I also provided novel graph-aware models for modeling human adversary behavior in wildlife or other contraband smuggling networks and tested them against human subjects. Lastly, I examined human considerations of deployment in new domains and the importance of easily-interpretable models and results. While such interpretability has been a recurring theme in all my thesis work, I also created a game-theoretic inspection strategy application that generated randomized factory inspection schedules and also contained visualization and explanation components for users.