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 Acropora 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 10m, ii) DeepReef at 30m and iii) DeepReef at 100m. We generalised the three DEMs to multiple nested spatial resolutions (15m to 120m) 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 (15m) were not always the most relevant for producing accurate coral SDMs, with optimal resolutions between 15–60m 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 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, it remains largely unknown to which degree signatures of adaptation to environmental drivers can be detected based on the choice of spatial scale and genomic marker. We studied signatures of local adaptation across two levels of 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 environmental factors, derived from remote sensing digital elevation models at very high resolution (0.5m), with whole-genome sequencing data of 304 individuals across four populations. By comparing putatively adaptive loci detected between each local population versus a regional assessment including all populations simultaneously, we demonstrate that responses of A. alpina to similar amounts of abiotic variation are largely governed by local evolutionary processes. Furthermore, we find minimally overlapping signatures of local adaptation between SNPs and polymorphic TEs. Notably, functional annotations of candidate genes for adaptation revealed several symbiosis-related genes associated with the abiotic factors studied, which could represent selective pressures from 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.

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.