Identifying landscape features that affect functional con- nectivity among populations is a major challenge in fundamental and applied sciences. Landscape genetics combines landscape and genetic data to address this issue, with the main objective of disentangling direct and indirect relationships among an intricate set of variables. Causal modeling has strong potential to address the complex nature of land- scape genetic data sets. However, this statistical approach was not ini- tially developed to address the pairwise distance matrices commonly used in landscape genetics. Here, we aimed to extend the applicability of two causal modeling methods—that is, maximum-likelihood path analysis and the directional separation test—by developing statistical approaches aimed at handling distance matrices and improving func- tional connectivity inference. Using simulations, we showed that these approaches greatly improved the robustness of the absolute (using a frequentist approach) and relative (using an information-theoretic ap- proach) ␣ts of the tested models. We used an empirical data set combin- ing genetic information on a freshwater ␣sh species (Gobio occitaniae) and detailed landscape descriptors to demonstrate the usefulness of causal modeling to identify functional connectivity in wild populations. Speci␣cally, we demonstrated how direct and indirect relationships in- volving altitude, temperature, and oxygen concentration in␣uenced within- and between-population genetic diversity of G. occitaniae.