Data from: Plant dispersal in the sub-Antarctic inferred from anisotropic genetic structure
Born, Céline et al. (2011), Data from: Plant dispersal in the sub-Antarctic inferred from anisotropic genetic structure, Dryad, Dataset, https://doi.org/10.5061/dryad.4f1r5vg8
Climatic conditions and landscape features often strongly affect species’ local distribution patterns, dispersal, reproduction and survival, and may therefore have considerable impacts on species' fine-scale spatial genetic structure (SGS). In this paper we demonstrate the efficacy of combining fine-scale SGS analyses with isotropic and anisotropic spatial autocorrelation techniques to infer the impact of wind patterns on plant dispersal processes. We genotyped 1304 Azorella selago (Apiaceae) specimens, a wind-pollinated and wind-dispersed plant, from four populations distributed across sub-Antarctic Marion Island. SGS was variable with Sp values ranging from 0.001 to 0.014, suggesting notable variability in dispersal distance and wind velocities between sites. Nonetheless, the data supported previous hypotheses of a strong NW – SE gradient in wind strength across the island. Anisotropic autocorrelation analyses further suggested that dispersal is strongly directional, but varying between sites depending on the local prevailing winds. Despite the high frequency of gale-force winds on Marion Island, gene dispersal distance estimates (σ) were surprisingly low (< 10 m), most likely because of a low pollen dispersal efficiency. An SGS approach in association with isotropic and anisotropic analyses provides a powerful means to assess the relative influence of abiotic factors on dispersal, and allow inferences that would not be possible without this combined approach.