1. Camera traps are an essential tool to quantify the distribution, abundance and behavior of mobile species. As detection probabilities vary greatly among camera trap locations, they must be accounted for when analyzing such data, which is generally done using occupancy models. We introduces a Bayesian Time-dependent Occupancy Model for CameraTrap data (Tomcat), suited to estimate relative event densities in space and time. Tomcat allows to learn about the environmental requirements and daily activity patterns of species while accounting for imperfect detection. It further implements a sparse model that deals well will a large number of potentially highly correlated environmental variables. 3. By integrating both spatial and temporal information, we extend the notation of overlap coefficient between species to time and space to study niche partitioning. 4. We illustrate the power of Tomcat through an application to camera trap data of eight sympatrically occurring duiker species in the the savanna -rainforest ecotone in the Central African Republic and show that most species pairs show little overlap. Exceptions are those for which one species is very rare, likely as a result of direct competition.