Landscape genetics aims to assess the effect of the landscape on intraspecific genetic structure. To quantify interdeme landscape structure, landscape genetics mostly uses landscape resistance surfaces and least-cost paths or straight-line transects. However, both approaches have drawbacks. Parameterization of resistance surfaces is a subjective process, and least-cost paths represent a single migration route. A transect-based approach might oversimplify migration patterns by assuming rectilinear migration. To overcome these limitations, we combined these two methods in a new landscape genetic approach: least-cost transect analysis (LCTA). Habitat-matrix resistance surfaces were used to create least-cost paths, which were subsequently buffered to form transects in which the abundance of several landscape elements was quantified. To maintain objectivity, this analysis was repeated so that each landscape element was in turn regarded as migration habitat. The relationship between landscape predictor variables and genetic distances was then assessed following a mixed modeling approach to account for the non-independence of values in distance matrices. Subsequently, predictor variables were selected making use of the R_β^2 statistic. We applied LCTA and the mixed model approach to an empirical genetic dataset on the endangered damselfly, Coenagrion mercuriale. We compared the results to those obtained from traditional least-cost, effective and resistance distance analysis and showed that LCTA not only outperforms existing methods in a statistical way, but also provides more information about the migration ecology of the focal species. Although we believe the statistical approach to be an improvement for the analysis of distance matrices in landscape genetics, more stringent testing is needed.