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

Abstract

AbstractBiodiversity monitoring is undergoing a revolution, with fauna observations 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, aiming to closely mirror real-world conditions. We assessed the performance of five occupancy models: a standard simple detection/non detection model, a model based on count data, a continuous-time Poisson process, and two types of modulated Poisson processes. Our goal was to assess their respective abilities to estimate occupancy probability with continuously collected data.We found that, in most scenarios, both discrete and continuous models performed similarly, accurately estimating occupancy probability. Additionally, variation in discretisation intervals had minimal impact on the discrete models’ capacity to estimate occupancy accurately.Our study underscores that when the sole aim is to accurately estimate occupancy, opting for complex continuous models, with an increased number of parameters aiming to closely mimic ecological conditions, may not offer substantial advantages over simpler models. Therefore, choosing between continuous and discrete occupancy models should be driven by practical considerations such as data availability or implementation time, and the specific study objectives. For example modulated Poisson processes may be useful to better understand temporal variations in detection, which may reflect specific species behaviour. We hope that our findings offer valuable guidance for researchers and practitioners working with continuously collected data in wildlife monitoring and modelling.