Effective biodiversity conservation requires knowledge of species’ distributions across large areas, yet prevalence data for marine sessile species is scarce. As marine organism distributions generally depend on terrain heterogeneity, topographic variables derived from digital elevation models (DEMs) can be useful proxies in distribution modelling. However, the suitability of such variables depends on spatial resolution. Here, we (1) assess three high-resolution bathymetry DEMs for accuracy, (2) harness DEM-derived topographic variables for regional coral species distribution models (SDMs), and (3) develop a transferable framework to produce, select and integrate multi-resolution topographic variables into marine spatial modelling. We use a case study from three reef-building Acropora coral species sampled across 23 reefs of the Great Barrier Reef, Australia. Obtaining three open-source bathymetric DEMs (15m Allen Coral Atlas (ACA), 30m DeepReef, 100m DeepReef), we produce eight derived topographic variables generalised to multiple nested spatial resolutions (15m to 120m) to assess SDM sensitivity to bathymetry source and resolution. We found that the ACA and DeepReef DEMs had similar vertical accuracies, each producing topographic variables relevant to marine SDMs. Slope and vector ruggedness measure (VRM) explained most of the variance for all three species. Coral prevalence increased with slope to a moderate steepness before quickly decreasing with increasing steepness for two species, while the third species plateaued. The prevalence of all species was negatively associated with VRM. Topographic variables for coral SDMs were most relevant at 15–60m resolutions, where the optimal resolution depended on the variable type and species. Overall, we show that the finest resolution was unnecessary to achieve high-performing SDMs. Running SDMs with multi-resolution topographic variables provided insights into the importance of terrain attributes for distribution modelling of different species. We provide a transferrable framework to facilitate the adoption of multiscale SDMs for better-informed conservation and management planning.