Analysing biodiversity observation data collected in continuous time: Should we use discrete‐ or continuous‐time occupancy models?

Biodiversity monitoring is undergoing a revolution, with fauna observation data being increasingly gathered continuously over extended periods, through sensors like camera traps and acoustic recorders, or via opportunistic observations. These data are often analysed with discrete‐time ecological models, requiring the transformation of continuously collected data into arbitrarily chosen, non‐independent discrete‐time intervals. To overcome this issue, ecologists are increasingly turning to the existing continuous‐time models in the literature. Closer to the real detection process, they are lesser known than discrete‐time models, not always easily accessible and can be more complex. 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 data generated within a continuous‐time framework. We assessed the performance of five static occupancy models with varying detection processes: discrete detection/non‐detection process, discrete count process, continuous‐time Poisson process and two types of modulated Poisson processes. Our goal was to assess their abilities to estimate occupancy probability with continuously collected data. We applied all models to empirical lynx data as an illustrative example.

In scenarios with easily detectable animals, we found that all models accurately estimated occupancy. All models reached their limits with highly elusive animals. Variation in discretisation intervals had minimal impact on the discrete models' capacity to estimate occupancy accurately.

Our study underscores that opting for continuous‐time models with an increased number of parameters, aiming to get closer to the sensor detection process, may not offer substantial advantages over simpler models when the sole aim is to accurately estimate occupancy. Model choice can thus be driven by practical considerations such as data availability or implementation time. However, occupancy models can encompass goals beyond estimating occupancy probability. Continuous‐time models, particularly those considering temporal variations in detection, can offer valuable insights into specific species behaviour and broader ecological inquiries. We hope that our findings offer valuable guidance for researchers and practitioners working with continuously collected data in wildlife monitoring and modelling.