Genetic divergence of rubber tree estimated by multivariate techniques and microsatellite markers

Genetic diversity of 60 Hevea genotypes, consisting of Asiatic, Amazonian, African and IAC clones, and pertaining to the genetic breeding program of the Agronomic Institute (IAC), Brazil, was estimated. Analyses were based on phenotypic multivariate parameters and microsatellites. Five agronomic descriptors were employed in multivariate procedures, such as Standard Euclidian Distance, Tocher clustering and principal component analysis. Genetic variability among the genotypes was estimated with 68 selected polymorphic SSRs, by way of Modified Rogers Genetic Distance and UPGMA clustering. Structure software in a Bayesian approach was used in discriminating among groups. Genetic diversity was estimated through Nei's statistics. The genotypes were clustered into 12 groups according to the Tocher method, while the molecular analysis identified six groups. In the phenotypic and microsatellite analyses, the Amazonian and IAC genotypes were distributed in several groups, whereas the Asiatic were in only a few. Observed heterozygosity ranged from 0.05 to 0.96. Both high total diversity (HT' = 0.58) and high gene differentiation (G st' = 0.61) were observed, and indicated high genetic variation among the 60 genotypes, which may be useful for breeding programs. The analyzed agronomic parameters and SSRs markers were effective in assessing genetic diversity among Hevea genotypes, besides proving to be useful for characterizing genetic variability

The IUCN and the Red List

Systematic conservation planning aims to identify comprehensive protected area networks that together will minimize biodiversity loss. Importantly, conservation planners seek to determine where to allocate limited resources first, particularly given the uneven spread of, and threats to, biodiversity. The International Union for the Conservation of Nature (IUCN) Red List of Threatened Species incorporates data not only on threats to species, but also on species distributions and ecological requirements. These temporal and spatial attributes, when combined with other datasets, have proven useful for determining the most urgent priority areas for conserving biodiversity, from the global level down to the scale of individual sites. Although many challenges remain, the increasing reliability and comprehensiveness of the IUCN Red List suggests that its role as a source of biodiversity data in systematic conservation planning is certain to expand dramatically