Jump to Main Content
Adaptive variation in natural Alpine populations of Norway spruce (Picea abies [L.] Karst) at regional scale: Landscape features and altitudinal gradient effects
- Di Pierro, Erica A., Mosca, Elena, González-Martínez, Santiago C., Binelli, Giorgio, Neale, David B., La Porta, Nicola
- Forest ecology and management 2017 v.405 pp. 350-359
- Bayesian theory, Picea abies, alleles, climate change, climatic factors, conifers, forest management, gene flow, genetic variation, humans, landscapes, loci, longevity, models, provenance, single nucleotide polymorphism, temperate forests, trees, variance, viability, Alps region, Italy
- Conifers are key species of many boreal and temperate forests, providing crucial resources to human activities. Adaptation to local climate and long life span make tree species highly susceptible to both short- and long-term environmental changes. Thus, a substantial effect of ongoing climate change on forest viability and production potential is expected. A deeper understanding of the genetic mechanisms that control the adaptive ability of forests can be used for management strategies to assist forests through climate change. Indeed, a reliable and systematic genetic assessment of Norway spruce provenances for their response to extreme climatic conditions could be a prerequisite for a successful assisted migration of this species in a changing environment. The purpose of this study is to investigate, by mean of Single Nucleotide Polymorphisms (SNPs), the genetic bases of local adaptation along altitudinal gradients in 18 natural Alpine populations of Norway spruce (Picea abies [L.] Karst), sampled on a regional scale in Trentino-South Tyrol (Eastern Italian Alps). To account for patterns of gene flow and spatial genetic structure due to alpine landscape features, sampled stands were subdivided into three geographical groups, each including at least one sampled altitudinal gradient. As expected, hierarchical analyses of molecular variance revealed that most of the genetic variability was found within stands, though a little but significant variation was found among geographical groups. To detect potentially adaptive markers, two distinct approaches were used. First, classical outlier loci detection was applied along the altitudinal gradients using both a Bayesian analysis and a Hierarchical Island Model (HIM), which showed contrasting results. Subsequently, Moran’s Eigenvector Maps (MEM) variables, which may account for spatial variation and un-accounted environmental factors, were applied to an allele distribution model, and 19 loci significantly associated to environmental variation were identified. Four of these loci were also detected as outliers by the HIM. The combined approach of selection scan and spatial analysis method allowed for a parallel investigation of Norway spruce adaptive potential in alpine stands, providing evidence for selective forces acting on adaptive loci that could be relevant in forest management.