Integrating Geostatistical Approaches into Landscape Genetics: Assessing Spatial Patterns of Genetic Variation in Fern Species Pteridium aquilinum (L.) Kuhn and Pteris cretica L.
摘要
Integrating geostatistical methods and landscape genetics analyses can enhance understanding of the spatial structuring of genetic diversity, gene flow, and continuity of geographically restricted plant populations. These insights may inform conservation strategies for endangered species. Pteridium aquilinum (L.) Kuhn and Pteris cretica L. are two fern species with highly confined distributions in Iran, forming patchy, isolated populations at risk of extinction. Previous studies using spatial principal component analysis (sPCA) and landscape genetics revealed spatial genetic structuring. Here, we analyzed key genetic parameters—including genetic diversity (He), effective number of alleles (Ne), and genetic differentiation (Fst)—alongside morphological traits to investigate genetic and phenotypic variation. Our framework combines geostatistical methods such as variogram analysis (e.g., range values of 88–258 km), Moran’s I (up to 0.41, p < 0.01), and spatial dependency indices (SDI ranging from 3 to 87%) for assessing spatial autocorrelation, as well as kriging for interpolating and predicting genetic diversity in unsampled locations. We aimed to assess spatial autocorrelation in genetic variation, identify clustering patterns in genetic and morphological traits, and generate predictive spatial maps to support conservation. To our knowledge, this is among the first studies applying a comprehensive geostatistical and landscape genetics approach in fern species. Results showed pronounced spatial structuring of genetic variability, differentiation, and phenotypic traits. Kriging predicted spatial trends in genetic diversity, providing insights into unsampled areas. Geostatistical results aligned with landscape genetic analyses, underscoring the value of integrating these methods in conservation research.