The term Citizen Science refers to scientific research in which volunteers from the community participate in scientific studies as field assistants. Since citizen scientists can collect data cheaply, they 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, in theory, can benefit greatly from citizen science. The eBird project is one of the largest citizen science programs in existence that can provide useful data for species distribution modeling. eBird is an online database that allows birders to submit checklists summarizing their observations of bird species. However, since birders vary in their levels of expertise, the quality of data submitted to eBird, and to any citizen science program in general, 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 checklists 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.Keywords: Graphical Models,
Applications,
Species Distribution Modeling,
Contrast Mining,
Citizen Science,
Bayesian Network
