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Citizen scientists, who are volunteers from the community that participate as field assistants in scientific studies, enable research to be performed at much larger spatial and temporal scales than trained scientists can cover. Species distribution modeling, which involves understanding species-habitat relationships, is a research area that can benefit greatly from citizen science. The eBird project is one of the largest citizen science programs in existence. By allowing birders to upload observations of bird species to an online database, eBird can provide useful data for species distribution modeling. However, since birders vary in their levels of expertise, the quality of data submitted to eBird is often questioned. In this paper, we develop a probabilistic model called the Occupancy-Detection-Expertise (ODE) model that incorporates the expertise of birders submitting data to eBird. We show that modeling the expertise of birders can improve the accuracy of predicting observations of a bird species at a site. In addition, we can use the ODE model for two other tasks: predicting birder expertise given their history of eBird checklists and identifying bird species that are difficult for novices to detect.