Hierarchical clustering of perennial ryegrass populations with geographic contiguity constraint

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Abstract
An algorithm of automatic classification is proposed and applied to a large collection of perennial ryegrass wild populations from France. This method is based on an ascendant hierarchical clustering using the Euclidian distance from the principal components extracted from the variance-covariance matrix between 28 agronomic traits. A contiguity constraint is imposed: only those pairs of populations which are defined as contiguous are grouped together into a cluster. The definition of contiguity is based on a geostatistical parameter: the range of the variogramme, i.e. the largest distance above which the variance between pairs of population no longer increases. This method yields clusters that are generally more compact than those obtained without constraint. In most cases the contours of these clusters fit well with known ecogeographic regions, namely, for macroclimatic homogeneous conditions. This suggests that selective factors exert a major influence in the genetic differentiation of ryegrass populations for quantitatively inherited adaptive traits. It is proposed that such a method could provide useful genetic and ecogeographic bases for sampling a core collection in widespread wild species such as forage grasses.