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Estimating animal abundance and density are fundamental goals of manywildlife monitoring programs. Camera trapping has become an increasinglypopular tool to achieve these monitoring goals due to recent advances inmodeling approaches and the capacity to simultaneously collect data on multiplespecies. However, estimating the density of unmarked populations continues tobe problematic due to the difficulty in implementing complex modelingapproaches, low precision of estimates, and absence of rigor in testing ofmodel assumptions and their influence on results. Here, we describe a novelapproach that uses still image camera traps to estimate animal density withoutthe need for individual identification, based on the Time spent In Front of theCamera (TIFC). Using results from a large-scale multi-species monitoringprogram with nearly 3,000 cameras deployed over six years in Alberta, Canada,we provide a reproducible methodology to estimate parameters and we test keyassumptions of the TIFC model. We compare moose (Alces alces) density estimatesfrom aerial surveys and TIFC, including incorporating correction factors forknown TIFC assumption violations. The resulting corrected TIFC densityestimates are comparable to aerial density estimates. We discuss thelimitations of the TIFC method and areas needing further investigation,including the need for long-term monitoring of assumption violations and thenumber of cameras necessary to provide precise estimates. Despite thechallenges of assumption violations and high measurement error, cameras and theTIFC method can provide useful alternative or complementary animal densityestimates for multi-species monitoring when compared to traditional monitoringmethods.

Applying and testing a novel method to estimate animal density from motion-triggered cameras