Format: ORAL

Gabriela Ramrez-Ojeda1, Iris E. Peralta2,3, Juan E. Rodrguez-Prez4, Jaime Sahagn-Castellanos4, Eduardo Rodrguez-Guzmn5, Tulio C. Medina-Hinostroza6, Jorge R. Rijalba-Vela 7, Leopoldo P. Vsquez-Nez7, Jos L. Chvez-Servia8

1 Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Jalisco, México. 2 Universidad Nacional de Cuyo (UNCUYO), Mendoza, Argentina. 3 Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Mendoza, Argentina. 4 Universidad Autónoma Chapingo (UACh), Estado de México, México. 5 Universidad de Guadalajara (UdG), Zapopan, México. 6 Ministerio del Ambiente del Perú (MINAM), Lima, Perú. 7 Universidad Nacional Pedro Ruiz Gallo (UNPRG), Lambayeque, Perú. 8 Instituto Politécnico Nacional (IPN), Oaxaca. México.

Ecogeographic analysis allows identifying species adaptive ranges and most relevant variables explaining their patterns of distribution. Additionally, it is possible to predict environmental conditions, using ecological descriptors derived from the geographical location of collected germplasm and variables obtained through Geographical Information Systems (GIS). Wild tomato species are distributed in Colombia, Ecuador, the Galapagos Islands, Peru, Bolivia and Chile, in various ecosystems from sea level to approximately 3995 m. In Central America S. lycopersicum L. is expanded and naturalized in different regions. A predictive classification of tomatoes and closely related groups were considered as a framework for ecogeographic characterization. The objectives were to determine ecological descriptors and edaphoclimatic diversity along the distribution range of 12 species of wild tomatoes (Solanum sect. Lycopersicon) and four wild species of phylogenetically related groups (Solanum sect. Juglandifolia and sect. Lycopersicoides), using 4228 wild tomato accessions in South America, and 1296 accessions of S. lycopersicum in Mxico. An environmental information system was created with 34 edaphoclimatic variables with 1 km2 resolution. Multivariate techniques (Principal Component Analysis, PCA; Cluster Analysis, CA) and GIS tools, established most relevant variables for accession distribution, as well as the groups formed according to their environmental similarity. Three Principal Components (PCs) of PCA explained more than 75% of total variation for all species. The most relevant related to seasonal variables of temperature and precipitation. CA revealed five statistically significant clusters for Mexican species and six for wild South American species. Potential distribution analysis with Maxent model (10 replicates by cross-validation) determined areas with higher probability of tomato species occurrence. This approach reveals phylogenetic patterns of adaptation, allowing promissory strategies to test predictive models involving identification of abiotic and biotic resistances in populations, associating functional traits and species diversification towards the conservation and uses of valuable genetic resources.