MAPPING GENETIC DIVERSITY OF CASTANEA SATIVA: APPLICATION OF SPATIAL ANALYSIS FOR BIOGEOGRAPHY AND CONSERVATION STUDIES

The utilization of strategies to understanding geographical pattern of genetic diversity and population structure has an important role in the management and conservation of species. Landscape genetics, combining molecular population genetics and landscape ecology, provides information about the interaction between landscape features and evolutionary processes within species such as gene flow or local adaptation. This is an appropriate approach for tree species that are increasingly vulnerable to losses of genetic diversity due to land use change and land degradation. Sweet chestnut, a multipurpose tree widely distributed around Europe, is a species of great economic importance for fruit and timber. The present distribution of this species is a result of both historical and contemporary processes; therefore the knowledge of the spatial structure of populations could be crucial in conservation genetic programs. In this study, we combined genetic analysis, spatial statistic tools and GIS technologies to identify the structure of populations as well as the genetic physical and environmental barriers. A total of 63 C. sativa populations collected in 10 European countries were analysed by means of 6 nuclear microsatellite markers. Set of measures of intra- and inter-populations genetic statistics, observed (Na) and effective (Ne) number of alleles, observed (Ho) and expected (He) heterozygosity, were calculated using the program GeneAlEx 6. Population structure analysis was conducted using the software STRUCTURE 2.3.3. The populations analyzed were grouped in three main clusters: the first pool includes populations from Russia, Azerbaijan, Georgia and East Turkey, the second western Turkey, Greece and Bulgaria, and the third Hungary, Slovakia, Italy and Spain. The combination of geostatistical analysis (ArcGIS 9.3 software) such as IDW interpolation of Q-membership and diversity indices as heterozygosity (He) and allelic richness (Rs) allowed us to visualize the spatial structure of chestnut populations. Statistically significant genetic barriers between European chestnut populations were identified by BARRIER software using 100 bootstrapped Nei's genetic distance matrices and Monmonier's maximum difference algorithm. The overlay approach allowed the identification of spatial coincidence between landscape features and genetic discontinues. The results of this work provide valuable base line data for more in-depth studies on chestnut landscape genetics that can contribute to its conservation. Keywords: Castanea sativa, microsatellite, genetic diversity, landscape genetics