Cohort-structured tree populations

Within-population genetic structure in beech (Fagus sylvatica L.) stands characterized by different disturbance histories: does forest management simplify population substructure?

The fine-scale assessment of both spatially and non-spatially distributed genetic variation is crucial to preserve forest genetic resources through appropriate forest management. Cryptic within-population genetic structure may be more common than previously thought in forest tree populations, which has strong implications for the potential of forests to adapt to environmental change. The present study was aimed at comparing within-population genetic structure in European beech (Fagus sylvatica L.) plots experiencing different disturbance levels. Five plot pairs made up by disturbed and undisturbed plots having the same biogeographic history were sampled throughout Europe. Overall, 1298 individuals were analyzed using four highly polymorphic nuclear microsatellite markers (SSRs). Bayesian clustering within plots identified 3 to 11 genetic clusters (within-plot θ_ST ranged from 0.025 to 0.124). The proportion of within-population genetic variation due to genetic substructuring (F_CluPlot = 0.067) was higher than the differentiation among the 10 plots (F_PlotTot = 0.045). Focusing on the comparison between managed and unmanaged plots, disturbance mostly explains differences in the complexity of within-population genetic structure, determining a reduction of the number of genetic clusters present in a standardized area. Our results show that: i) genetic substructuring needs to be investigated when studying the within-population genetic structure in forest tree populations, and ii) indices describing subtle characteristics of the within-population genetic structure are good candidates for providing early signals of the consequences of forest management, and of disturbance events in general.

Association genetics of the loblolly pine (Pinus taeda, Pinaceae) metabolome

The metabolome of a plant comprises all small molecule metabolites, which are produced during cellular processes. The genetic basis for metabolites in nonmodel plants is unknown, despite frequently observed correlations between metabolite concentrations and stress responses. A quantitative genetic analysis of metabolites in a nonmodel plant species is thus warranted. Here, we use standard association genetic methods to correlate 3563 single nucleotide polymorphisms (SNPs) to concentrations of 292 metabolites measured in a single loblolly pine (Pinus taeda) association population. A total of 28 single locus associations were detected, representing 24 and 20 unique SNPs and metabolites, respectively. Multilocus Bayesian mixed linear models identified 2998 additional associations for a total of 1617 unique SNPs associated to 255 metabolites. These SNPs explained sizeable fractions of metabolite heritabilities when considered jointly (56.6% on average) and had lower minor allele frequencies and magnitudes of population structure as compared with random SNPs. Modest sets of SNPs (n = 1-23) explained sizeable portions of genetic effects for many metabolites, thus highlighting the importance of multi-SNP models to association mapping, and exhibited patterns of polymorphism consistent with being linked to targets of natural selection. The implications for association mapping in forest trees are discussed.