Elena Mosca

The geographical and environmental determinants of genetic diversity for four alpine conifers of the European Alps

Climate is one of the most important drivers of local adaptation in forest tree species. Standing levels of genetic diversity and structure within and among natural populations of forest trees are determined by the interplay between climatic heterogeneity and the balance between selection and gene flow. To investigate this interplay, single nucleotide polymorphisms (SNPs) were genotyped in 24 to 37 populations from four subalpine conifers, Abies alba Mill., Larix decidua Mill., Pinus cembra L. and Pinus mugo Turra, across their natural ranges in the Italian Alps and Apennines. Patterns of population structure were apparent using a Bayesian clustering program, STRUCTURE, which identified three to five genetic groups per species. Geographical correlates with these patterns, however, were only apparent for P. cembra. Multivariate environmental variables [i.e. principal components (PCs)] were subsequently tested for association with SNPs using a Bayesian generalized linear mixed model. The majority of the SNPs, ranging from six in L. decidua to 18 in P. mugo, were associated with PC1, corresponding to winter precipitation and seasonal minimum temperature. In A. alba, four SNPs were associated with PC2, corresponding to the seasonal minimum temperature. Functional annotation of those genes with the orthologs in Arabidopsis revealed several genes involved in abiotic stress response. This study provides a detailed assessment of population structure and its association with environment and geography in four coniferous species in the Italian mountains.

Environmental versus geographical determinants of genetic structure in two subalpine conifers

Alpine ecosystems are facing rapid human-induced environmental changes, and so more knowledge about tree adaptive potential is needed. This study investigated the relative role of isolation by distance (IBD) versus isolation by adaptation (IBA) in explaining population genetic structure in Abies alba and Larix decidua, based on 231 and 233 single nucleotide polymorphisms (SNPs) sampled across 36 and 22 natural populations, respectively, in the Alps and Apennines. Genetic structure was investigated for both geographical and environmental groups, using analysis of molecular variance (AMOVA). For each species, nine environmental groups were defined using climate variables selected from a multiple factor analysis. Complementary methods were applied to identify outliers based on these groups, and to test for IBD versus IBA. AMOVA showed weak but significant genetic structure for both species, with higher values in L. decidua. Among the potential outliers detected, up to two loci were found for geographical groups and up to seven for environmental groups. A stronger effect of IBD than IBA was found in both species; nevertheless, once spatial effects had been removed, temperature and soil in A. alba, and precipitation in both species, were relevant factors explaining genetic structure. Based on our findings, in the Alpine region, genetic structure seems to be affected by both geographical isolation and environmental gradients, creating opportunities for local adaptation.

Micro- and macro-geographic scale effect on the molecular imprint of selection and adaptation in Norway spruce

Forest tree species of temperate and boreal regions have undergone a long history of demographic changes and evolutionary adaptations. The main objective of this study was to detect signals of selection in Norway spruce (Picea abies [L.] Karst), at different sampling-scales and to investigate, accounting for population structure, the effect of environment on species genetic diversity. A total of 384 single nucleotide polymorphisms (SNPs) representing 290 genes were genotyped at two geographic scales: across 12 populations distributed along two altitudinal-transects in the Alps (micro-geographic scale), and across 27 populations belonging to the range of Norway spruce in central and south-east Europe (macro-geographic scale). At the macrogeographic scale, principal component analysis combined with Bayesian clustering revealed three major clusters, corresponding to the main areas of southern spruce occurrence, i.e. the Alps, Carpathians, and Hercynia. The populations along the altitudinal transects were not differentiated. To assess the role of selection in structuring genetic variation, we applied a Bayesian and coalescent-based F ST-outlier method and tested for correlations between allele frequencies and climatic variables using regression analyses. At the macro-geographic scale, the F ST-outlier methods detected together 11 F ST-outliers. Six outliers were detected when the same analyses were carried out taking into account the genetic structure. Regression analyses with population structure correction resulted in the identification of two (micro-geographic scale) and 38 SNPs (macro-geographic scale) significantly correlated with temperature and/or precipitation. Six of these loci overlapped with F ST-outliers, among them two loci encoding an enzyme involved in riboflavin biosynthesis and a sucrose synthase. The results of this study indicate a strong relationship between genetic and environmental variation at both geographic scales. It also suggests that an integrative approach combining different outlier detection methods and population sampling at different geographic scales is useful to identify loci potentially involved in adaptation.

Climate-related adaptive genetic variation and population structure in natural stands of Norway spruce in the South-Eastern Alps

Forest trees dominate many Alpine landscapes that are currently exposed to changing climate. Norway spruce is one of the most important conifer species of the Italian Alps, and natural populations are found across steep environmental gradients with large differences in temperature and moisture availability. This study seeks to determine and quantify patterns of genetic diversity in natural populations toward understanding adaptive responses to changing climate. Across the Italian species range, 24 natural stands were sampled with a major focus on the Eastern Italian Alps. Sampled trees were genotyped for 384 selected single nucleotide polymorphisms (SNPs) from 285 genes. A wide array of potential candidate genes was tested for correlation with climatic parameters. To minimize false-positive association between genotype and climate, population structure was investigated. Pairwise FST estimates between sampled populations ranged between 0.000 and 0.075, with the highest values involving the two disjoint populations, Valdieri, on the western Italian Alps, and Campolino, the most southern population on the Apennines. Despite considerable genetic admixture among populations, both Bayesian and multivariate approach identified four genetic clusters. Selection scans revealed five FST outliers, and the environmental association analysis detected ten SNPs associated to one or more climatic variables. Overall, 13 potentially adaptive loci were identified, three of which have been reported in a previous study on the same species conducted on a broader geographical scale. In our study, precipitation, more than temperature, was often associated with genotype; therefore, it appears as the most important environmental variable associated with the high sensitivity of Norway spruce to soil water supply. These findings provide relevant information for understanding and quantifying climate change effects on this species and its ability to genetically adapt.

Signatures of natural selection on Pinus cembra and P. mugo along elevational gradients in the Alps

Alpine regions represent an interesting biome for studying local adaptation in forest trees. Strong genetic differentiation is expected along elevational gradients in spite of extensive gene flow. We sampled 18 and 20 natural populations of Pinus cembra and Pinus mugo, in two subregions and four elevational gradients. To investigate the effects of elevation on genetic diversity and adaptation, 768 and 1152 single nucleotide polymorphisms (SNPs) were genotyped in P. cembra and P. mugo. We found low but significant genetic differentiation among populations in both species. To discover outliers, we applied Bayesian simulation and hierarchical island model analyses. A larger number of outliers were found using the first method. Some SNPs were detected with both analyses: one SNP in P. cembra and three in P. mugo when using two subregions and four SNPs in P. cembra and one in P. mugo when using four elevational gradients. The association between environmental and genetic variation was tested with Bayesian simulation (Bayenv) and a latent factor mixed model (LFMM). The first method, using all populations, detected 6 and 20 SNPs associated to temperature in P. cembra and in P. mugo, respectively, 3 SNPs associated to precipitation in P. cembra, and 14 SNPs to elevation in P. mugo. The LFMM found a higher number of SNPs associated to temperature in P. mugo than in P. cembra (37 vs. 27), with a stronger association with maximum temperature (April–June). In P. cembra, the majority of associations (51 SNPs) were found with precipitation (January–March). Five SNPs in common between species were found on genes potentially involved in plant response to abiotic stress. Using these results, we confirmed that temperature was an important driver of adaptive potential for each species so that continued changes to global temperatures will likely involve continued adaptation as ranges shift upwards.

Alpine forest genomics network (AForGeN): a report of the first annual meeting

The Alpine Forest Genomics Network was formed in 2011 and held its first annual meeting on June 24–26, 2012, in the Natural Park Adamello Brenta in Trentino Region, Italy. The meeting was attended by 30 researchers from the alpine region of Europe and had two primary goals: (1) for researchers to introduce each other to current and planned research activities in forest landscape genomics and (2) to develop a strategic vision for the network. A steering committee was elected and will develop a white paper over the next year. The next annual meeting will be held in Austria in June 2013.