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. The landscape genetics approaches combines tools from molecular genetics, landscape ecology and spatial statistics and is decisive for proper management of genetic resources at population level. 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 19 C. sativa populations collected in five European countries (Italy, Greece, Turkey, Bulgaria and Slovakia) were analysed by means of 6 nuclear microsatellite markers. Population structure inference using STRUCTURE 2.3.3 software indicates K= 3 as the most likely number of clusters: 1) Turkey, 2) Greece and Bulgaria, 3) Italy and Slovakia. A substructure is observed for K=6. The combination of geostatistical analysis (ArcGIS 9.3 software) such as IDW interpolation of Q-membership and diversity indices (He, pRs, 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 (1972) 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.
POPULATION STRUCTURE AND GENETIC DIVERSITY OF C. SATIVA: A LANDSCAPE GENETICS APPROACH
C Mattioni;FChiocchini;P Pollegioni;M Cherubini;F Villani
2013
Abstract
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. The landscape genetics approaches combines tools from molecular genetics, landscape ecology and spatial statistics and is decisive for proper management of genetic resources at population level. 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 19 C. sativa populations collected in five European countries (Italy, Greece, Turkey, Bulgaria and Slovakia) were analysed by means of 6 nuclear microsatellite markers. Population structure inference using STRUCTURE 2.3.3 software indicates K= 3 as the most likely number of clusters: 1) Turkey, 2) Greece and Bulgaria, 3) Italy and Slovakia. A substructure is observed for K=6. The combination of geostatistical analysis (ArcGIS 9.3 software) such as IDW interpolation of Q-membership and diversity indices (He, pRs, 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 (1972) 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
