Genetic data are increasingly used in landscape ecology for the indirect assessment of functional connectivity, that is, the permeability of landscape to movements of organ- isms. Among available tools, matrix correlation analyses (e.g. Mantel tests or mixed models) are commonly used to test for the relationship between pairwise genetic dis- tances and movement costs incurred by dispersing individuals. When organisms are spatially clustered, a population-based sampling scheme (PSS) is usually performed, so that a large number of genotypes can be used to compute pairwise genetic distances on the basis of allelic frequencies. Because of financial constraints, this kind of sam- pling scheme implies a drastic reduction in the number of sampled aggregates, thereby reducing sampling coverage at the landscape level. We used matrix correlation analy- ses on simulated and empirical genetic data sets to investigate the efficiency of an individual-based sampling scheme (ISS) in detecting isolation-by-distance and isola- tion-by-barrier patterns. Provided that pseudo-replication issues are taken into account (e.g. through restricted permutations in Mantel tests), we showed that the use of inter- individual measures of genotypic dissimilarity may efficiently replace interpopulation measures of genetic differentiation: the sampling of only three or four individuals per aggregate may be sufficient to efficiently detect specific genetic patterns in most situa- tions. The ISS proved to be a promising methodological alternative to the more con- ventional PSS, offering much flexibility in the spatial design of sampling schemes and ensuring an optimal representativeness of landscape heterogeneity in data, with few aggregates left unsampled. Each strategy offering specific advantages, a combined use of both sampling schemes is discussed.