Harnessing Multiscale Topographic Environmental Variables for Regional Coral Species Distribution Models.

Effective biodiversity conservation requires knowledge of species' distributions across large areas, yet prevalence data for marine sessile species is scarce, with traditional variables often unavailable at appropriate temporal and spatial resolutions. As marine organism distributions generally depend on terrain heterogeneity, topographic variables derived from digital elevation models (DEMs) can be useful proxies in ecological modelling, given appropriate spatial resolutions. Here, we use three reef-building coral species across the Great Barrier Reef, Australia, in a case study to (1) assess high-resolution bathymetry DEM sources for accuracy, (2) harness their derived topographic variables for regional coral species distribution models (SDMs), and (3) develop a transferable framework to produce, select and integrate multi-resolution variables into marine spatial models. For this, we obtained and processed three distinct bathymetric digital depth models that we treat as DEMs, which are available across the GBR extent: (i) Allen Coral Atlas (ACA) at 10 m, (ii) DeepReef at 30 m and (iii) DeepReef at 100 m. We generalised the three DEMs to multiple nested spatial resolutions (15 m-120 m) and derived the same eight topographic variables to assess SDM sensitivity to bathymetry source and spatial resolution. The ACA and DeepReef DEMs shared similar vertical accuracies, each producing topographic variables relevant to marine SDMs. Slope and vector ruggedness measure (VRM), capturing hydrodynamic movement and shelter or exposure, were the most relevant variables in SDMs of all three species. Interestingly, variables at the finest resolution (15 m) were not always the most relevant for producing accurate coral SDMs, with optimal resolutions between 15 and 60 m depending on the variable type and species. Using multi-resolution topographic variables in SDMs provided nuanced insights into the multiscale drivers of regional coral distributions. Drawing from this case study, we provide a practical and transferable framework to facilitate the adoption of multiscale SDMs for better-informed conservation and management planning.