This review article, authored by a team of researchers from various institutions, aims to bridge the gap between geneticists and landscape ecologists in the field of landscape genetics. Landscape genetics integrates population genetics, landscape ecology, and spatial statistics to evaluate how landscape variables influence genetic diversity and population structure. The authors define landscape genetics as the study of how landscape composition, configuration, and matrix quality affect gene flow and spatial genetic variation. They highlight the importance of appropriate sampling design and spatial analysis techniques, such as assignment tests, matrix correlations, dispersal route analysis, and autocorrelation, to effectively study these relationships. The review also discusses future directions, including improving the representation of genetic data for spatial analysis, handling noisy ecological data, expanding multivariate analyses, and predicting landscape and species distribution changes. The authors emphasize the need for interdisciplinary collaboration to advance the field and suggest workshops and courses to foster communication among experts in different disciplines.This review article, authored by a team of researchers from various institutions, aims to bridge the gap between geneticists and landscape ecologists in the field of landscape genetics. Landscape genetics integrates population genetics, landscape ecology, and spatial statistics to evaluate how landscape variables influence genetic diversity and population structure. The authors define landscape genetics as the study of how landscape composition, configuration, and matrix quality affect gene flow and spatial genetic variation. They highlight the importance of appropriate sampling design and spatial analysis techniques, such as assignment tests, matrix correlations, dispersal route analysis, and autocorrelation, to effectively study these relationships. The review also discusses future directions, including improving the representation of genetic data for spatial analysis, handling noisy ecological data, expanding multivariate analyses, and predicting landscape and species distribution changes. The authors emphasize the need for interdisciplinary collaboration to advance the field and suggest workshops and courses to foster communication among experts in different disciplines.