With the increasing use of sensors such as camera traps and autonomous recording units for biodiversity monitoring, there is a growing trend towards collecting fauna observations in continuous-time. This questions traditional analysis with discrete-time models, which, when data is collected in continuous-time, require data discretisation. Discretisation is an aggregation of data into arbitrarily chosen non-independent discrete time intervals, producing information loss. To overcome the limitations of discretisation, ecologists are increasingly turning to continuous-time models. Focusing on occupancy models, a type of species distribution models, we asked ourselves: Should we dedicate time and effort to learning and using these continuous-time models, or can we go on using discrete-time models?

We conducted a comparative simulation study, using detection data generated within a continuous-time framework. We aimed to evaluate the ability of different models to retrieve the simulated occupancy probability. We compared five static occupancy models with varying detection processes: discrete detection/non-detection process, discrete count process, continuous Poisson process, and two types of modulated Poisson processes. All models accurately estimated occupancy when we simulated detection of easily detectable animals, however all models struggled with highly elusive ones. Variations in discretisation intervals had minimal impact on the discrete models' capacity to estimate occupancy accurately. Our findings suggest that opting for models with a complex detection process may not be advantageous over simpler models when the sole aim is to accurately estimate occupancy. However, such models can offer valuable insights into specific species behaviour and broader ecological inquiries.