The distribution of living organisms, habitats and ecosystems is primarily driven by abiotic environmental factors that are spatially structured. Assessing the spatial structure of environmental factors through spatial autocorrelation analyses (SAC) can therefore help understand their scale of influence on the distribution of organisms, habitats, and ecosystems. Here we propose a methodological framework that combines SAC and statistical clustering to identify scales of spatial patterning of environmental factors, which can then be interpreted as the scales at which those factors influence the geographic distribution of biological and ecological features. We then illustrate the framework with two case studies of contrasting size and biophysical settings: one in Northwest Portugal and the other in the Western Swiss Alps. The framework proposed is conceptually and statistically robust and provides a valuable approach for ecological and environmental research, particularly when building predictors for ecological models. By combining SAC and statistical clustering, the framework can significantly promote fundamental research on spatially-structured ecological patterns while fostering research in fields such as global change ecology, conservation planning, and landscape management.