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2013), and failing to account for spatial dependencies will lead to overconfidence in estimated uncertainties, and might in some cases lead to bias in estimated effects of predictor variables (Guélat & Kéry 2018). However, this assumption is rarely met in the field (Johnson et al. Initial formulations of occupancy models estimated species occupancy across multiple sites that were assumed to be spatially independent (MacKenzie et al. Due to their ability to model species occurrences while accounting for imperfect detection, occupancy models have become widely used in ecology (Bailey et al. Much of the data available to ecologists consists of species occurrences, which in turn have sparked the development of statistical models to analyse such data (Bailey et al. More broadly, our model framework creates opportunities to explicitly account for the spatial structure found in many spatially nested study designs, and to study interacting species that have contrasting movement ranges with camera traps.Supplementary materials accompanying this paper appear online. Most parameters are estimated with low uncertainty (i.e. We further evaluate the model’s ability to deal with sparse field data by applying it to a multi-scale camera trapping dataset on a mustelid-rodent predator–prey system. With a simulation study, we demonstrate that the model is able to estimate most parameters without marked bias under low, medium and high average occupancy probabilities, as well as low, medium and high detection probabilities, with only a small bias for some parameters in low-detection scenarios. it estimates initial occupancy, colonization and extinction probabilities-including probabilities conditional to the other species’ presence. Here we bridge these two model frameworks by developing a multi-scale, two-species occupancy model. However, as interacting species (e.g., predators and prey) often operate at different spatial scales, including nested spatial structure might be especially relevant to models of interacting species. Occupancy models have been extended to account for either multiple spatial scales or species interactions in a dynamic setting.