Three novel methods to estimate abundance of unmarked animals using remote cameras

Abstract Abundance and density estimates are central to the field of ecology and are an important component of wildlife management. While many methods exist to estimate abundance from individually identifiable animals, it is much more difficult to estimate abundance of unmarked animals. One step toward noninvasive abundance estimation is the use of passive detectors such as remote cameras or acoustic recording devices. However, existing methods for estimating abundance from cameras for unmarked animals are limited by variable detection probability and have not taken full advantage of the information in camera trapping rate. We developed a time to event ( TTE ) model to estimate abundance from trapping rate. This estimate requires independent estimates of animal movement, so we collapsed the sampling occasions to create a space to event ( STE ) model that is not sensitive to movement rate. We further simplified the STE model into an instantaneous sampling ( IS ) estimator that applies fixed‐area counts to cameras. The STE and IS models utilize time‐lapse photographs to eliminate the variability in detection probability that comes with motion‐sensor photographs. We evaluated the three methods with simulations and performed a case study to estimate elk ( Cervus canadensis ) abundance from remote camera trap data in Idaho. Simulations demonstrated that the TTE model is sensitive to movement rate, but the STE and IS methods are unbiased regardless of movement. In our case study, elk abundance estimates were comparable to those from a recent aerial survey in the area, demonstrating that these new methods allow biologists to estimate abundance from unmarked populations without tracking individuals over time.