Uncovering temporal patterns of wildlife activity, habitat use and habitat preference from camera‐trap data

Abstract An understanding of habitat use and levels of active behaviour is foundational to wildlife behaviour, ecology, conservation and management. These variables are commonly measured by tracking individuals in space and time using biologging. In principle, camera‐trap data also contain information about both activity level and habitat use; however, because motion‐triggered cameras capture only active animals, the raw rates of capture in different habitats give us an indication of habitat use by active animals only, missing patterns of use and preference by inactive individuals. Furthermore, capture rates are readily confounded by spatially imbalanced survey effort (either by design or by accident), and by habitat‐related variation in other determinants of trap rate (animal speed and camera sensitivity). Here, we show how camera‐traps can be used to infer patterns of both activity and habitat use by deconfounding these effects. Using simulation, we show that the method is minimally biased when the key underlying assumption (spatially invariant activity pattern) is met, but can otherwise be moderately biased. It also understandably fails to provide reliable information for parts of the day when there is very little or no activity, working best for populations that are at least partly available for detection throughout the day. We demonstrate the use of the method by applying it to 7 years of springtime data on seven mammal populations from habitat‐stratified camera‐trap monitoring in Hoge Veluwe National Park, the Netherlands. The method recovered credible patterns of habitat‐specific activity levels in all species, and credible patterns of habitat use and preference in all but one strictly nocturnal species. The method uncovered hidden diurnal migration patterns between habitats, preference for resource‐rich habitat, risk avoidance during public opening hours, and spatiotemporal segregation in some species. The new method allows analysis of habitat relationships for entire communities of larger mammals without the need for trapping and tagging.