Wild carrot differentiation in Europe and positive selection at DcAOX1 gene

By definition, the domestication process leads to an overall reduction of crop genetic diversity. This lead to the current search of genomic regions in wild crop relatives (CWR), an important task for modern carrot breeding. Nowadays massive sequencing possibilities can allow for discovery of novel genetic resources in wild populations, but this quest could be aided by the use of a surrogate gene (to first identify and prioritize novel wild populations for increased sequencing effort). Alternative oxidase (AOX) gene family seems to be linked to all kinds of abiotic and biotic stress reactions in various organisms and thus have the potential to be used in the identification of CWR hotspots of environment-adapted diversity. High variability of DcAOX1 was found in populations of wild carrot sampled across a West-European environmental gradient. Even though no direct relation was found with the analyzed climatic conditions or with physical distance, population differentiation exists and results mainly from the polymorphisms associated with DcAOX1 exon 1 and intron 1. The relatively high number of amino acid changes and the identification of several unusually variable positions (through a likelihood ratio test), suggests that DcAOX1 gene might be under positive selection. However, if positive selection is considered, it only acts on some specific populations (i.e. is in the form of adaptive differences in different population locations) given the observed high genetic diversity. We were able to identify two populations with higher levels of differentiation which are promising as hot spots of specific functional diversity.

A new approach for computing a flood vulnerability index using cluster analysis

A Flood Vulnerability Index (FloodVI) was developed using Principal Component Analysis (PCA) and a new aggregation method based on Cluster Analysis (CA). PCA simplifies a large number of variables into a few uncorrelated factors representing the social, economic, physical and environmental dimensions of vulnerability. CA groups areas that have the same characteristics in terms of vulnerability into vulnerability classes. The grouping of the areas determines their classification contrary to other aggregation methods in which the areas' classification determines their grouping. While other aggregation methods distribute the areas into classes, in an artificial manner, by imposing a certain probability for an area to belong to a certain class, as determined by the assumption that the aggregation measure used is normally distributed, CA does not constrain the distribution of the areas by the classes. FloodVI was designed at the neighbourhood level and was applied to the Portuguese municipality of Vila Nova de Gaia where several flood events have taken place in the recent past. The FloodVI sensitivity was assessed using three different aggregation methods: the sum of component scores, the first component score and the weighted sum of component scores. The results highlight the sensitivity of the FloodVI to different aggregation methods. Both sum of component scores and weighted sum of component scores have shown similar results. The first component score aggregation method classifies almost all areas as having medium vulnerability and finally the results obtained using the CA show a distinct differentiation of the vulnerability where hot spots can be clearly identified. The information provided by records of previous flood events corroborate the results obtained with CA, because the inundated areas with greater damages are those that are identified as high and very high vulnerability areas by CA. This supports the fact that CA provides a reliable FloodVI.