  Estimating animal abundance and density are fundamental goals of many
wildlife monitoring programs. Camera trapping has become an increasingly
popular tool to achieve these monitoring goals due to recent advances in
modeling approaches and the capacity to simultaneously collect data on multiple
species. However, estimating the density of unmarked populations continues to
be problematic due to the difficulty in implementing complex modeling
approaches, low precision of estimates, and absence of rigor in testing of
model assumptions and their influence on results. Here, we describe a novel
approach that uses still image camera traps to estimate animal density without
the need for individual identification, based on the Time spent In Front of the
Camera (TIFC). Using results from a large-scale multi-species monitoring
program with nearly 3,000 cameras deployed over six years in Alberta, Canada,
we provide a reproducible methodology to estimate parameters and we test key
assumptions of the TIFC model. We compare moose (Alces alces) density estimates
from aerial surveys and TIFC, including incorporating correction factors for
known TIFC assumption violations. The resulting corrected TIFC density
estimates are comparable to aerial density estimates. We discuss the
limitations of the TIFC method and areas needing further investigation,
including the need for long-term monitoring of assumption violations and the
number of cameras necessary to provide precise estimates. Despite the
challenges of assumption violations and high measurement error, cameras and the
TIFC method can provide useful alternative or complementary animal density
estimates for multi-species monitoring when compared to traditional monitoring
methods.
