Assessing spatial genetic structure from molecular marker data via principal component analyses: A case study in a <i>Prosopis</i>sp. forest

Advances in genotyping technology, such as molecular markers, have noticeably improved our capacity to characterize genomes at multiple loci. Concomitantly, the methodological framework to analyze genetic data has expanded, and keeping abreast with the latest statistical developments to analyze molecular marker data in the context of spatial genetics has become a difficult task. Most methods in spatial statistics are devoted to univariate data whereas the nature of molecular marker data is highly dimensional. Multivariate methods are aimed at finding proximities between entities characterized by multiple variables by summarizing information in few synthetic variables. In particular, Principal Component analysis (PCA) has been used to study genetic structure of geo-referenced allele frequency profiles, incorporating spatial information with a posteriori analysis. Conversely, the recently developed spatially restricted PCA (sPCA) explicitly includes spatial data in the optimization criterion. In this work, we compared the results of the application of PCA and sPCA in the study of the spatial genetic structure at fine scale of a Prosopis flexuosa and P. chilensis hybrid swarm. Data consisted in the genetic characterization of 87 trees sampled in Córdoba, Argentina and genotyped at six microsatellites, which yielded 72 alleles. As expected, principal components explained more variance than sPCA components, but were less spatially autocorrelated. The maps obtained by the interpolation of sPC1 values allowed a better visualization of a patchy spatial pattern of genetic variability than the PC1 synthetic map. We also proposed a PC-sPC scatter plot of allele loadings to better understand the allele contributions to spatial genetic variability.

Pure and mixed plantations of Persian walnut (Juglans regia L.) for high quality timber production in Chile, South America

Aims Persian walnut(Juglans regia L.),an interesting forest species for the veneering industry,requires adequate management to produce valuable high-quality logs.Since species associations and management level can improve stand productivity,the novelty of this work was to assess Persian walnut performance in different planting mixtures and in pure plantations conditioned to management intensity.Methods Growth,straightness and survival measurements were taken annually for 7 years after planting pure and mixed plantations under two contrasting management scenarios.Diseases were recorded at Age 7 in all plantations.Under each management intensity,besides the monoculture,three mixtures were tested:a mixture of only main forest species,main forest species plus one arboreal companion species,Black alder(Alnus glutinosa L.)and main species plus the shrub Russian olive(Elaeagnus angustifolia L.)as nurse species.A test of interaction between plantation type and management scenario was conducted using repeated growth data.Important Findings The interaction was significant,indicating the presence of different mechanisms underlying plantation effects under high and low management level.Compared with pure plantations,Persian walnut associated with the nurse shrub exhibited 78%higher height and 53%higher diameter growth in plantations under low management.Health benefits(lower presence of walnut blight than in the monoculture)and better straightness were also found in the association including the shrub when the management intensity was not high.These beneficial effects in the presence of Russian olive were not present under high management intensity(irrigation,fertilization,tutoring and frequent pruning).Site-specific designs for Persian walnut plantations would depend on the foreseen management intensity.