Abstract Species occurrence is often influenced by changes in environmental conditions at multiple spatial and temporal scales working simultaneously in a hierarchical fashion. While previous dynamic multi‐scale occupancy modelling frameworks address dynamics at multiple spatial scales, they assume both large‐scale and small‐scale units are closed during the same period, potentially leading to bias in small‐scale estimates. We present an extended robust design that allows for the estimation of colonization and persistence probabilities across three spatiotemporal scales (large‐scale annual, small‐scale annual and small‐scale intra‐annual) simultaneously, while also accounting for imperfect detection in a Bayesian framework. We specified latent variables in a hierarchical manner, where occurrence at each scale was dependent on higher‐level latent variables. To test model performance under a variety of sampling scenarios, we simulated data across 22 data‐generating designs. As an example, we also fit the novel occupancy model to invasive Silver Carp ( Hypophthalmichthys molitrix ) detection/non‐detection data collected in the Ohio River Basin, USA. We found that the model generally performed well, even under limited replication across different scales. Model performance declined in more ‘extreme’ simulations where colonization or persistence was rare. We found that invasive carp turnover probability varied across a gradient of invasion as well as across all spatiotemporal scales, exemplifying the benefits of utilizing our multi‐scale approach. This modelling framework offers a powerful tool for disentangling the multi‐scale processes that drive species distributions across time. The model was able to identify trends in carp occurrence even with limited data and uneven sampling efforts that would have otherwise been masked by more traditional occupancy modelling approaches. Annual large‐scale and small‐scale turnover varied substantially by river section, but intra‐annual turnover was constantly low throughout the entire study area. By explicitly modelling dynamic parameters at multiple scales, this approach fills a critical gap in ecological modelling, providing the resolution to detect fine‐scale processes and the scope to inform broad‐scale management.
Shepta et al. (Tue,) studied this question.