Motion‐sensitive cameras track population abundance changes in a boreal mammal community in southwestern Yukon, Canada

Abstract Motion‐sensitive cameras are commonly used to monitor wildlife occupancy rates; however, few studies have assessed whether data from cameras are correlated with density estimates obtained from more traditional labor‐intensive methods such as those based on capture‐mark‐recapture. We used data from a boreal forest community to test whether camera data were correlated with densities estimated from independent monitoring methods. We placed 72 covert cameras in the forest around Lhù'ààn Mân' (Kluane Lake), Yukon, Canada, for 7 years and tracked changes in population densities by camera hit rates. We independently estimated population densities of snowshoe hares ( Lepus americanus ) and red squirrels ( Tamiasciurus hudsonicus ) using capture‐mark‐recapture via live trapping, and Canada lynx ( Lynx canadensis ), coyotes ( Canis latrans ), and moose ( Alces americanus ) by snow track transects. Density estimates obtained from conventional aerial surveys were also periodically available for moose. Except for red squirrels, camera hit rates were highly correlated with population density estimates obtained by traditional methods, including across a large range of estimated densities corresponding to cyclic population dynamics in several species. Accordingly, we infer that motion‐sensitive cameras could supplement or replace traditional methods for monitoring key species in boreal forest food webs. Using cameras to monitor population change has several advantages; they require less effort in the field, are non‐invasive compared to live‐trapping, include multiple species at the same time, and rely less on weather than either aerial surveys or snow track transects. Tracking changes across the vast boreal forest is becoming increasingly necessary because of climate and landscape change and our data validate the use of motion‐sensitive cameras to provide a useful quantitative method for state‐of‐the‐environment reporting.