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.

Microevolutionary processes shape adaptive responses to heterogeneous environments, where these effects vary both among and within species. However, the degree to which signatures of adaptation to environmental drivers can be detected based on spatial scale and genomic marker remains largely unknown. We studied signatures of local adaptation across different spatial extents, investigating complementary types of genomic variants–single nucleotide polymorphisms (SNPs) and polymorphic transposable elements (TEs)–in populations of the alpine model plant species Arabis alpina. We coupled high-resolution (0.5m) environmental factors, derived from remote sensing digital elevation models, with whole-genome sequenced data of 304 individuals across four populations. We demonstrate that responses of A. alpina to similar amounts of abiotic variation are largely governed by local evolutionary processes and find minimally overlapping signatures of local adaptation between SNPs and polymorphic TEs. Notably, functional annotations of high-impact genomic variants revealed several defence-related genes associated with the abiotic factors studied, which could indicate indirect selective pressure of biotic agents. Our results highlight the importance of considering different spatial extents and types of genomic polymorphisms when searching for signatures of adaptation to environmental variation. Such insights provide key information on microevolutionary processes and could guide management decisions to mitigate negative impacts of climate change on alpine plant populations.