Abundance estimation of unmarked animals based on camera-trap data

Gilbert and colleagues reviewed the growing array of methods for estimating abundance and density of animals that cannot be individually identified from camera-trap images, a category that includes many cats and most prey species. They compared approaches such as the Random Encounter Model, distance sampling, time-to-event and space-to-event estimators, the REST model, and N-mixture and spatial-count models, laying out each method's assumptions, data requirements, strengths, and weaknesses. The review helps practitioners choose an estimator appropriate to their study constraints and warns against misapplying methods whose assumptions are violated. By synthesizing a fast-evolving subfield, it became an essential reference for recognition-free abundance estimation and is widely cited when justifying method choice for unmarked-species density work.