Introduction to Occupancy Modeling


Target Audience:

Target Audience: Biologists and resource managers who are seeking to gain experience developing and running occupancy models. All modeling will be done in R, so a working knowledge of R is required.


Documenting the presence or absence of species across sites is central to ecological studies and management, such as understanding changes in a species' distribution pattern. However, biologists cannot always perfectly know the "state" of a species at a site (present or absent) because survey methods are imperfect. Occupancy modeling is the de facto method for quantifying species distributions while correcting for imperfect detection. The primary R package for analyzing occupancy data is RPresence. This course introduces occupancy modeling through a series of learnR tutorials that include written, video, R exercises, and quiz elements. Topics include basic probability distributions (e.g., the binomial and multinomial distribution), likelihood, single season occupancy models, inclusion of site- and survey-level covariates, model selection, and basic principles of study design. Extensions include the multi-season occupancy, multi-state, and other models. The typical weekly format consists of learnR tutorials and homework that can be done individually or in small groups, posting to a discussion board, and a virtual classroom meeting to debrief homework. The course schedule runs over a 15 week time span (with one week off). The final week(s) of the course include a final project in which participants will analyze a hypothetical dataset and write a short paper that describes the analytical steps and primary results. This course was developed in collaboration between the U.S. Geological Survey (Eastern Ecological Science Center and Vermont Cooperative Fish and Wildlife Research Unit) and the U.S. Fish and Wildlife Service (National Conservation Training Center). Participants should be prepared to work an average of 5 hours per week. Upon completion of this course, participants will be able to: * Use basic probability distributions that are at the center of occupancy modeling. * Describe the basic occupancy model and its assumptions. * Use the R package, RPresence, to analyze occupancy data. * Present results of an occupancy analysis to stakeholders * Build more advanced R skills.

Course Short Name
Course Type
Training Tuition Cost
75.0 hours
Training Credit Hours
Semester Hours

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