The ability to determine species distributions based on limited sampling efforts is important for studies of conservation, biogeography and ecology. Current methods of predicting distributions can be divided into those that a) use presence and absence data and those that b) use presence-only data. Because many studies of species distributions commonly start with collection data acquired from museum specimens, absence data is generally unavailable. We used the ecological niche modeling algorithm Maximum Entropy, which utilizes presence-only data, to predict the distribution of Mycotrupes gaigei in Florida. GIS data layers of environmental variables were converted into grid maps. With this data along with collection localities (presence data), MaxEnt produced a distribution likelihood map. The most significant variables were determined. The effectiveness of this model was tested by collecting in sites that were and were not predicted to have M. gaigei.