Wildlife students as community scientists provide statewide detection data to help non‐invasively monitor Oklahoma bobcats

Abstract Community science projects are ideal for collecting species data at large spatial scales and can connect the public with the management and conservation of native wildlife. Wildlife students represent a section of the public that is highly motivated to conduct ecological research. To engage wildlife students in a monitoring program for an elusive mesocarnivore, we recruited volunteers from across Oklahoma's universities for 3 winter fieldwork periods: December–January during 2018–2019, 2019–2020, and 2020–2021. We worked with ≥60 volunteers who deployed ≥170 bobcat ( Lynx rufus ) hair‐snare cubbies in 38 Oklahoma counties for 3 consecutive weeks. We used detection/nondetection data from morphological and genetic identification of hair samples in an occupancy modeling framework to determine bobcat detection and space use patterns for the state of Oklahoma. Probability of use models showed land cover type was an important predictor of use, but suffered highly variable effects, likely due to the diversity of environments sampled, under‐sampled land cover types, and species identification uncertainty. Meanwhile, historic harvest records provided better estimates of bobcat probability of use, but predictions can be affected by external factors unrelated to bobcat ecology, such as trapping culture, access to land, and fur prices. Our study suggests that wildlife students can be an effective resource for managers conducting large‐scale occupancy and space‐use studies, but other noninvasive detection methods may result in improved data collection when targeting bobcats.