The demand for methods to translate information between spatial scales (i.e. size of observational units and the total area of study) has intensified given increased recognition that empirical data collection and practical applications occur at scales ranging from individual organisms to landscapes. For example, there has been considerable interest in “scaling-down” methods that have been successful at predicting fine-scale species’ distributions from coarse-scale distributional maps. Here, we describe the application of scaling-down methods to the estimation of colonization and extinction rates in metapopulations using long-term, large-scale data sets of two roadside plant species, Helianthus annuus and Silene latifolia. Fine-scale data collected from roadside populations were aggregated to generate data at several increasingly coarse scales. The relationships between occupancy, colonization, or extinction and the scale of measurement (scale-curves) were determined using the standard “fully-nested” method and the “stratified random sampling” method. Both methods were successful at predicting not only occupancy, but also the dynamic metapopulation processes of extinction and colonization (R2 values, averaged across species and methods, were 88.5, 69.3, and 88.8%, respectively, for occupancy, extinction, and colonization). Scaling-down generated more accurate predictions in Helianthus (average R2=88.4) compared to Silene (average R2=63.4), and in both species, scaling-down generated more accurate predictions for occupancy and colonizations compared to extinctions. This is one of the first demonstrations that dynamic processes are scalable, and provides a useful methodology for dealing with the logistical challenges of collecting fine-scale data over large geographic areas when studying metapopulation processes or range limits.