Patterns in nature arise from processes interacting across a continuum of spatial scales, where new relationships emerge at each level of investigation. These patterns are nested features encompassing fine-scale local patterns, such as topography and geology, through to large-scale global climatic patterns. As scales vary, differences in selection pressures result in biodiversity at genetic, species, and ecosystem levels. Rapid environmental changes, however, put biodiversity at risk, calling for well-informed, effective management strategies. Evolutionary ecology modelling can advise conservation practitioners about the susceptibility of organisms to novel conditions, leading to more effective management decisions and prioritisation strategies, particularly when resources are limited. The reliability and accuracy of models depends on access to high-quality input data, particularly for environmental variables. Topographic variables derived from digital elevation models (DEMs) are becoming increasingly popular for biodiversity modelling. These variables serve as proxies for ecologically relevant environmental factors, offering high resolutions ranging from less than one centimetre through to hundreds of metres across local to global extents, generally at low costs for the end user. As topographic variables become available at ever finer resolutions, there comes a need to address which resolution to select. While some researchers advocate using the finest resolution variables, such small grain sizes may not be necessary, extending computational time and introducing noise into models without gaining ecological insights. Furthermore, spatially explicit modelling is required to gain a comprehensive understanding of how extrinsic pressures drive evolutionary processes across a continuum of scales. Consequently, the choice of spatial resolution for input variables must be carefully considered and well-justified. How to select spatial resolutions objectively and practically, however, is yet to be addressed. In this thesis, I propose practical methods to objectively obtain topographic variables at appropriate spatial resolutions tailored to specific ecological contexts. Using three case studies from alpine and marine ecosystems, I explore the relevance of high resolution topographic variables ranging from 5cm to 120m for modelling species distributions and detecting molecular signatures of local adaptation. The objective of this work is to investigate how spatial resolutions impact ecological modelling across heterogeneous environments. The successful implementation of an original three-step framework—encompassing the production, selection, and integration of multi-resolution variables—underscores the importance of multiscale analyses. The integration high resolution topographic variables at appropriate spatial scales can provide insights into the processes underlying local adaptation at individual (presence of a species), and molecular (presence of genetic variants) levels. This approach facilitates a more nuanced, objective, and comprehensive understanding of ecological processes, empowering the development of robust models for biodiversity research and conservation.