When does temporal resolution matter? Including detection covariates in discrete- versus continuous-time occupancy and N-mixture models

Camera traps and other sensors allow continuous-time biodiversity observation, raising new questions and opportunities for modelling detection in hierarchical models such as occupancy (for species presence) and N-mixture models (for abundance). We focused on a rarely considered aspect: how the temporal treatment of detection covariates affects inference. Through simulations and a five-month case study on an research center, we examined the effects of covariate temporal resolution, discretisation scale in discrete-time (DT) models, and interpolation methods in continuous-time (CT) models. While occupancy and abundance estimates were largely unaffected by these choices, detection estimates were more sensitive to them. DT models with fine temporal discretisation closely matched CT models. Simulations showed that when detection covariates had no effect on detectability, the considered modelling choices had little impact. But when covariates did influence detection, bias and error increased if their temporal variation was not accurately retained. The case study revealed more complex patterns, highlighting the consequences of temporally simplifying both observations and detection covariates. Overall, our results suggest that when detectability is of ecological interest, exploring a range of temporal treatments of detection covariates, from fine-scale to coarser resolutions, can reveal complementary insights into scale-dependent patterns in detection.