Tree Genetics & Genomes

, 12:9 | Cite as

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

  • Elena Mosca
  • Felix Gugerli
  • Andrew J. Eckert
  • David B. Neale
Original Article
Part of the following topical collections:
  1. Adaptation


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.


Climate change Elevation Regional scale Single nucleotide polymorphisms Pinus cembra Pinus mugo 



We thank Erica Di Pierro for preparing the elevational gradient sampling map and Luca Delucchi for providing the environmental data. We thank Christian Rellstab for the help with the LFMM data analysis. Two anonymous reviewers made valuable suggestions on an earlier version of the article. The ACE-SAP project was partially funded by the Autonomous Province of Trento (Italy), with regulation No. 23, June 12, 2008, of the University and Scientific Research Service. This project was partially realized in the framework of Cost Action FP1202 MaP-FGR. FG acknowledges the support from the Swiss National Science Foundation (31003A_152664).

Compliance with ethical standards

Data archiving statement

Data for this study are available in the Supplementary materials. SNP by sample matrix and flanking sequences of the genotyped SNPs are stored in Dryad Digital Repository (Mosca et al. 2012c).

Supplementary material

11295_2015_964_MOESM1_ESM.xlsx (175 kb)
ESM 1 SNP annotation and estimates of He (gene diversity corrected for sample size, Nei 1978), h (gene diversity with unordered alleles, Pons and Petit 1996), maf (minor allele frequency), MAF (major allele frequency) in P. cembra and P. mugo. (XLSX 174 kb)
11295_2015_964_MOESM2_ESM.xlsx (110 kb)
ESM 2 Global F-statistics and pairwise F ST, calculated using the AMOVA approach in Pinus cembra and P. mugo. (XLSX 110 kb)
11295_2015_964_MOESM3_ESM.xlsx (22 kb)
ESM 3 Pairwise spatial and genetic distances written as matrices in all sampled sites of Pinus cembra and P. mugo. (XLSX 21 kb)
11295_2015_964_MOESM4_ESM.xlsx (218 kb)
ESM 4 Differences in allele frequencies between populations along elevational gradients in Pinus cembra and P. mugo. (XLSX 217 kb)
11295_2015_964_MOESM5_ESM.docx (422 kb)
ESM 5 (DOCX 422 kb)


  1. Akey JM (2009) Constructing genomic maps of positive selection in humans: where do we go from here? Genome Res 19:711–722PubMedCentralCrossRefPubMedGoogle Scholar
  2. Alberto FJ, Niort J, Derory J, Lepais O, Vitalis R, Galop D, Kremer A (2010) Population differentiation of sessile oak at the altitudinal front of migration in the French Pyrenees. Mol Ecol 19:2626–2639CrossRefPubMedGoogle Scholar
  3. Alberto FJ, Aitken SN, Alía R, González-Martínez SC, Hänninen H, Kremer A, Lefèvre F, Lenormand T, Yeaman S, Whetten R, Savolainen O (2013) Potential for evolutionary responses to climate change—evidence from tree populations. Glob Chang Biol 19:1645–1661PubMedCentralCrossRefPubMedGoogle Scholar
  4. Anfodillo T, Rento S, Carraro V, Furlanetto L, Urbinati C, Carrer M (1998) Tree water relations and climatic variations at the alpine timberline: seasonal changes of sap flux and xylem water potential in Larix decidua Miller, Picea abies (L.) Karst, and Pinus cembra L. Ann Sci For 55:159–172CrossRefGoogle Scholar
  5. Balducci L, Deslauriers A, Giovannelli A, Beaulieu M, Delzon S, Rossi S, Rathgeber CBK (2014) How do drought and warming influence survival and wood traits of Picea mariana saplings? J Exp Bot 4:1–13Google Scholar
  6. Beaumont MA, Balding DJ (2004) Identifying adaptive genetic divergence among populations from genome scans. Mol Ecol 13:969–980CrossRefPubMedGoogle Scholar
  7. Beaumont MA, Nichols RA (1996) Evaluating loci for use in the genetic analysis of population structure. Proc R Soc Lond 263:1619–1626CrossRefGoogle Scholar
  8. Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F (2004) GENETIX 4.05, logiciel sous Windows pour la génétique des populations. Laboratoire Génome, Populations, Interactions, CNRS UMR 5000. Université de Montpellier II; MontpellierGoogle Scholar
  9. Brodribba TJ, McAdama SAM, Jordana GJ, Martins SCV (2014) Conifer species adapt to low-rainfall climates by following one of two divergent pathways. PNAS 111:14489–14493CrossRefGoogle Scholar
  10. Coop G, Witonsky D, Di Rienzo A, Pritchard JK (2010) Using environmental correlations to identify loci underlying local adaptation. Genetics 185:1411–1423PubMedCentralCrossRefPubMedGoogle Scholar
  11. De Mita S, Thuillet AC, Gay L, Ahmadi N, Manel S, Ronfort J, Vigouroux Y (2013) Detecting selection along environmental gradients: analysis of eight methods and their effectiveness for outbreeding and selfing populations. Mol Ecol 22:1383–1399CrossRefPubMedGoogle Scholar
  12. Dirnböck T, Dullinger S, Grabherr G (2003) A regional impact assessment of climate and land-use change on alpine vegetation. J Biogeogr 30:401–417CrossRefGoogle Scholar
  13. Eckert AJ, Bower AD, González-Martínez SC, Wegrzyn JL, Coop G, Neale DB (2010) Back to nature: ecological genomics of loblolly pine (Pinus taeda, Pinaceae). Mol Ecol 19:3789–3805CrossRefPubMedGoogle Scholar
  14. Eveno E, Collada C, Guevara MA, Léger V, Soto A, Díaz L, Léger P, González-Martínez SC, Cervera MT, Plomion C et al (2008) Contrasting patterns of selection at Pinus pinaster Ait. drought stress candidate genes as revealed by genetic differentiation analyses. Mol Biol Evol 25:417–437CrossRefPubMedGoogle Scholar
  15. Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567CrossRefPubMedGoogle Scholar
  16. Excoffier L, Hofer T, Foll M (2009) Detecting loci under selection in a hierarchically structured population. Heredity 103:285–298CrossRefPubMedGoogle Scholar
  17. Foll M, Gaggiotti OE (2008) A genome scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180:977–993PubMedCentralCrossRefPubMedGoogle Scholar
  18. Frichot E, Schoville SD, Bouchard G, François O (2013) Testing for associations between loci and environmental gradients using Latent Factor Mixed Models. Mol Biol Evol 30:1687–1699PubMedCentralCrossRefPubMedGoogle Scholar
  19. Gapare WJ, Aitken SN, Ritland CE (2005) Genetic diversity of core and peripheral Sitka spruce (Picea sitchensis (Bong.) Carr) populations: implications for conservation of widespread species. Biol Conserv 123:113–123CrossRefGoogle Scholar
  20. García-Lorenzo M, Sjödin A, Jansson S, Funk C (2006) Protease gene families in Populus and Arabidopsis. BMC Plant Biol 6:30PubMedCentralCrossRefPubMedGoogle Scholar
  21. Garcia-Ramos G, Kirkpatrick M (1997) Genetic models of adaptation and gene flow in peripheral populations. Evolution 51:21–28CrossRefGoogle Scholar
  22. Goslee SC, Urban DL (2007) The ecodist package for dissimilarity-based analysis of ecological data. J Stat Softw 22:1–19CrossRefGoogle Scholar
  23. Günther T, Coop G (2013) Robust identification of local adaptation from allele frequencies. Genetics 195:205–220PubMedCentralCrossRefPubMedGoogle Scholar
  24. Hancock AM, Witonsky DB, Gordon AS, Eshel G, Pritchard JK, Coop G, Di Rienzo A (2008) Adaptations to climate in candidate genes for common metabolic disorders. PLoS Genet 4:e32PubMedCentralCrossRefPubMedGoogle Scholar
  25. Hansen J, Sato M, Ruedy R, Lo K, Lea DW, Medina-Elizade M (2006) Global temperature change. PNAS 103:14288–14293PubMedCentralCrossRefPubMedGoogle Scholar
  26. Hantel M, Hirtl-Wielke L-M (2007) Sensitivity of Alpine snow cover to European temperature. Int J Climatol 27:1265–1275CrossRefGoogle Scholar
  27. Hardy OJ, Vekemans X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620CrossRefGoogle Scholar
  28. Heuertz M, Teufel J, González-Martínez SC, Soto Á, Fady B, Alía R, Vendramin GG (2010) Geography determines genetic relationships between species of mountain pine (Pinus mugo complex) in western Europe. J Biogeogr 37:541–556CrossRefGoogle Scholar
  29. Holliday JA, Ralph SG, White R, Bohlmann J, Aitken SN (2008) Global monitoring of autumn gene expression within and among phenotypically divergent populations of Sitka spruce (Picea sitchensis). New Phytol 178:103–122CrossRefPubMedGoogle Scholar
  30. Holliday JA, Ritland K, Aitken SN (2010) Widespread, ecologically relevant genetic markers developed from association mapping of climate-related traits in Sitka spruce (Picea sitchensis). New Phytol 188:501–514CrossRefPubMedGoogle Scholar
  31. Holliday JA, Suren H, Aitken SN (2012) Divergent selection and heterogeneous migration rates across the range of Sitka spruce (Picea sitchensis). Proc R Soc B 279:1675–1683PubMedCentralCrossRefPubMedGoogle Scholar
  32. Joost S, Bonin A, Bruford MW, Després L, Conord C, Erhardt G, Taberlet P (2007) A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation. Mol Ecol 16:3955–3969CrossRefPubMedGoogle Scholar
  33. Körner C (2003) Alpine plant life. Functional plant ecology of high mountain ecosystems. Springer, BerlinGoogle Scholar
  34. Kramer K, Vreugdenhil SJ, van der Werf DC (2008) Effects of flooding on the recruitment, damage and mortality of riparian tree species: a field and simulation study on the Rhine floodplain. For Ecol Manag 255:3893–3903CrossRefGoogle Scholar
  35. Le Gall H, Philippe F, Domon J-M, Gillet F, Pelloux J, Rayon C (2015) Cell wall metabolism in response to abiotic stress. Plants 24:112–166CrossRefGoogle Scholar
  36. Leonelli G, Pelfini M, Morra di Cella U, Garavaglia V (2011) Climate warming and the recent treeline shift in the European Alps: the role of geomorphological factors in high-altitude sites. AMBIO 40:264–273PubMedCentralCrossRefPubMedGoogle Scholar
  37. Lindner M, Maroschek M, Netherer S, Kremer A, Barbati A, Garcia-Gonzalo J, Seidl R, Delzon S, Corona P, Kolströma M, Lexer MJ, Marchetti M (2010) Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. For Ecol Manag 259:698–709CrossRefGoogle Scholar
  38. Manel S, Gugerli F, Thuiller W, Alvarez N, Legendre P, Holderegger R, Gielly L, Taberlet P, Intra Bio Div Consortium (2012) Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation. Mol Ecol 21:3729–3738PubMedCentralCrossRefPubMedGoogle Scholar
  39. Meirmans PG (2012) The trouble with isolation by distance. Mol Ecol 21:2839–2846CrossRefPubMedGoogle Scholar
  40. Mimura M, Aitken SN (2010) Local adaptation at the range peripheries of Sitka spruce. J Evol Biol 23:249–258CrossRefPubMedGoogle Scholar
  41. Mosca E, Eckert AJ, Liechty JD, Wegrzyn JL, La Porta N, Vendramin GG, Neale DB (2012a) Contrasting patterns of nucleotide diversity for four conifers of Alpine European forest. Evol Appl 5(7):762–775PubMedCentralCrossRefPubMedGoogle Scholar
  42. Mosca E, Eckert AJ, Di Pierro EA, Rocchini D, La Porta N, Belletti P, Neale DB (2012b) The geographical and environmental determinants of genetic diversity for four alpine conifers of the European Alps. Mol Ecol 21:5530–5545CrossRefPubMedGoogle Scholar
  43. Mosca E, Eckert AJ, Di Pierro EA, Rocchini D, La Porta N, Belletti P, Neale DB (2012c) Data from: The geographical and environmental determinants of genetic diversity for four alpine conifers of the European Alps. Dryad Digit Repository. doi: 10.5061/dryad.tm33d Google Scholar
  44. Mosca E, González-Martínez SC, Neale DB (2014) Environmental versus geographical determinants of genetic structure in two subalpine conifers. New Phytol 201:180–192CrossRefPubMedGoogle Scholar
  45. Ndimba BK, Chivasa S, Simon WJ, Slabas AR (2005) Identification of Arabidopsis salt and osmotic stress responsive proteins using two-dimensional difference gel electrophoresis and mass spectrometry. Proteomics 5:4185–4196CrossRefPubMedGoogle Scholar
  46. Nei M (1978) Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89:583–590PubMedCentralPubMedGoogle Scholar
  47. Nishizawa A, Yabuta Y, Shigeoka S (2008) Galactinol and raffinose constitute a novel function to protect plants from oxidative damage. Plant Physiol 147:1251–1263PubMedCentralCrossRefPubMedGoogle Scholar
  48. Novembre J, Di Rienzo A (2009) Spatial patterns of variation due to natural selection in humans. Nat Rev Genet 10:745–755PubMedCentralCrossRefPubMedGoogle Scholar
  49. Oberhuber W (2004) Influence of climate on radial growth of Pinus cembra within the alpine timberline ecotone. Tree Physiol 29:291–301CrossRefGoogle Scholar
  50. Pons O, Petit RJ (1996) Measuring and testing genetic differentiation with ordered versus unordered alleles. Genetics 144:1237–1245PubMedCentralPubMedGoogle Scholar
  51. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedCentralPubMedGoogle Scholar
  52. Prunier J, Laroche J, Beaulieu J, Bousquet J (2011) Scanning the genome for gene SNPs related to climate adaptation and estimating selection at the molecular level in boreal black spruce. Mol Ecol 20:1702–1716CrossRefPubMedGoogle Scholar
  53. Quiroga MP, Premoli AC (2007) Genetic patterns in Podocarpus parlatorei reveal the long-term persistence of cold-tolerant elements in the southern Yungas. J Biogeogr 34:447–455CrossRefGoogle Scholar
  54. R Development Core Team (2011) R: a language and environment for statistical computing. Vienna, Austria: R foundation for Statistical Computing. [www document] URL: Accessed 8 Jul 2011
  55. Rebetez M, Reinhard M (2008) Monthly air temperature trends in Switzerland 1901–2000 and 1975–2004. Theor Appl Climatol 91:27–34CrossRefGoogle Scholar
  56. Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R (2015) A practical guide to environmental association analysis in landscape genomics. Mol Ecol 24:4348–4370CrossRefPubMedGoogle Scholar
  57. Senn J (1999) Tree mortality caused by Gremmeniella abietina in a subalpine afforestation in the central Alps and its relationship with duration of snow cover. Eur J For Pathol 29:65–74CrossRefGoogle Scholar
  58. Storey JD (2003) The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Stat 31:2013–2035CrossRefGoogle Scholar
  59. Storz JF, Wheat CW (2010) Integrating evolutionary and functional approaches to infer adaptation at specific loci. Evolution 64:2489–2509PubMedCentralCrossRefPubMedGoogle Scholar
  60. Theurillat JP, Guisan A (2001) Potential impact of climate change on vegetation in the European Alps: a review. Clim Chang 50:77–109CrossRefGoogle Scholar
  61. Tian M, Lou L, Liu L, Yu F, Zhao Q, Zhang H, Wu Y, Tang S, Xia R, Zhu B, Serino G, Xie Q (2008) The RING finger E3 ligase STRF1 is involved in membrane trafficking and modulates salt-stress response in Arabidopsis thaliana. Plant J 82:81–92CrossRefGoogle Scholar
  62. Tollefsrud MM, Sonstebo JH, Brochmann C, Johnsen O, Skroppa T, Vendramin GG (2009) Combined analysis of nuclear and mitochondrial markers provide new insight into the genetic structure of North European (Picea abies). Heredity 102:549–562CrossRefPubMedGoogle Scholar
  63. Tranquillini W (1979) Physiological ecology of the Alpine timberline: tree existence at high altitudes with special reference to European Alps. Springer, BerlinCrossRefGoogle Scholar
  64. Wang IJ, Bradburd GS (2014) Isolation by environment. Mol Ecol 23:5649–5662CrossRefPubMedGoogle Scholar
  65. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CrossRefGoogle Scholar
  66. Zwiazek JJ, Renault S, Croser C, Hansen J, Beck E (2001) Biochemical and biophysical changes in relation to cold hardiness. In: Bigras FJ, Colombo SJ (eds) Conifer cold hardiness. Kluwer Academic, Dordrecht, pp 165–186CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Elena Mosca
    • 1
  • Felix Gugerli
    • 2
  • Andrew J. Eckert
    • 3
  • David B. Neale
    • 1
    • 4
  1. 1.Research and Innovation CentreFondazione Edmund Mach (FEM)S. Michele all’AdigeItaly
  2. 2.Snow and Landscape ResearchWSL Swiss Federal Institute for ForestBirmensdorfSwitzerland
  3. 3.Department of BiologyVirginia Commonwealth UniversityRichmondUSA
  4. 4.Department of Plant SciencesUniversity of California at DavisDavisUSA

Personalised recommendations