Summary1. Understanding the distribution and ecology of episodic or mobile species requires us to address multiple potential biases, including spatial clustering of survey locations, imperfect detectability and partial availability for detection. These challenges have been addressed individually by previous modelling approaches, but there is currently no extension of the occupancy modelling framework that accounts for all three problems while estimating occupancy (w), availability for detection (i.e. use; h) and detectability (P). 2. We describe a hierarchical Bayes multi-scale occupancy model that simultaneously estimates site occupancy, use, and detectability, while accounting for spatial dependence through a state-space approach based on repeated samples at multiple spatial or temporal scales. As an example application, we analyse the spatiotemporal distribution of the Louisiana waterthrush Seiurus motacilla with respect to catchment size and availability of potential prey based on data collected along Appalachian streams of southern West Virginia, USA. In spring 2009, single observers recorded detections of Louisiana waterthrush (henceforth, waterthrush) within 75 m of point-count stations (i.e. sites) during four 5-min surveys per site, with each survey broken into 1-min intervals. 3. Waterthrushes were widely distributed (w range: 0AE6-1AE0) and were regularly using (h range: 0AE4-0AE6) count circles along forested mountain streams. While accounting for detection biases and spatial dependence among nearby sampling sites, waterthrushes became more common as catchment area increased, and they became more available for detection as the per cent of the benthic macroinvertebrates that were of the orders Ephemeroptera, Plecoptera or Trichoptera (EPT) increased. These results lend some support to the hypothesis that waterthrushes are influenced by instream conditions as mediated by watershed size and benthic macroinvertebrate community composition. 4. Synthesis and applications. Although several available modelling techniques provide estimates of occupancy at one scale, hierarchical Bayes multi-scale occupancy modelling provides estimates of distribution at two scales simultaneously while accounting for detection biases and spatial dependencies. Hierarchical Bayes multi-scale occupancy models therefore hold significant potential for addressing complex conservation threats that operate at a landscape scale (e.g. climate change) and probably influence species distributions over multiple scales.

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