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European Journal of Forest Research

, Volume 135, Issue 3, pp 465–481 | Cite as

Assessing the relationship between height growth and molecular genetic variation in Douglas-fir (Pseudotsuga menziesii) provenances

  • Charalambos Neophytou
  • Anna-Maria Weisser
  • Daniel Landwehr
  • Muhidin Šeho
  • Ulrich Kohnle
  • Ingo Ensminger
  • Henning WildhagenEmail author
Original Paper

Abstract

Douglas-fir (Pseudotsuga menziesii) is a conifer tree native to western North America. In central Europe, it shows superior growth performance and is considered a suitable substitute for tree species impaired in vitality due to climate change. Maintenance and improvement of growth performance in a changing environment is a main challenge for forest tree breeders. In this context, genetic variation as a factor underlying phenotypic variation, but also as the basis for future adaptation, is of particular interest. The aims of this study were to analyse (1) genetic diversity of selected Douglas-fir provenances, (2) variation in height growth among provenances, and (3) to assess the link between genetic and phenotypic variation in height growth. Genotyping was done on microsatellite loci. Effects of ‘provenance’, ‘genotype’, and ‘site’ on height growth were assessed by fitting mixed linear models. The most significant genetic differentiation was observed between provenances of the coastal variety, versus a provenance of the interior variety originating from British Columbia. Although genetic differentiation among provenances of the coastal variety was lower, genetic structures within this variety were identified. Moreover, genetic diversity showed a latitudinal gradient with the southernmost provenances being more diverse, probably reflecting the species’ evolutionary history. The modelling approach revealed that height growth differed significantly by ‘provenance’, ‘site’, and the interaction between ‘site’ and ‘provenance’. Additionally, this analysis showed that genetic variation captured by the genotyped microsatellite loci was significantly related to variation in height growth, providing statistical evidence for a genetic component in the observed phenotypic variation.

Keywords

Douglas-fir Conifer Microsatellite marker SSR marker Provenance Height growth Genetic diversity Genetic differentiation Climate Mixed linear modelling 

Notes

Acknowledgments

The study sites are part of the International Douglas-fir provenance trial, initiated by Prof. R. Schober after a decision of the Unit of Forest Growth of the German Association of Forest Research Stations (DFFVA). The German federal state of Baden-Württemberg joined this provenance trial series in 1961 with test plantations at 15 locations, of which ten are currently still active and supervised by the Forest Research Institute of Baden-Württemberg (FVA). Seeds for the experiments were collected in 1955 by B. Strehlke from autochthonous stands in North America and from selected cone-bearing stands growing in south-western Germany judged by forest practitioners to display vigorous growth. We thank Dr. Axel Albrecht for helpful discussions. This study was financially supported by the Forest Research Institute Baden-Württemberg with funding to I.E., U.K., H.W., and by the German Science Foundation (DFG) with grants to U.K. (KO 931/8-1) and I.E. (EN 829/4-1) as part of the collaborative project DougAdapt (PAK 583/468).

Compliance with ethical standards

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationship that could be construed as a potential conflict of interest.

Supplementary material

10342_2016_946_MOESM1_ESM.pdf (1.2 mb)
Supplementary material 1 (PDF 1205 kb)

References

  1. Aas G (2008) Die Douglasie (Pseudotsuga menziesii) in Nordamerika: Verbreitung, Variabilität und Ökologie. Die Douglasie-Perspektiven im Klimawandel. LWF Wissen 59:7–11Google Scholar
  2. Aitken SN, Yeaman S, Holliday JA, Wang T, Curtis-McLane S (2008) Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol Appl 1:95–111CrossRefPubMedPubMedCentralGoogle 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 19:1645–1661CrossRefPubMedPubMedCentralGoogle Scholar
  4. Albrecht A, Hanewinkel M, Bauhus J, Kohnle U (2012) How does silviculture affect storm damage in forests of south-western Germany? Results from empirical modeling based on long-term observations. Eur J For Res 131:229–247CrossRefGoogle Scholar
  5. Albrecht A, Kohnle U, Hanewinkel M, Bauhus J (2013) Storm damage in Douglas-fir unexpectedly high compared to Norway spruce. Ann For Sci 70:195–207CrossRefGoogle Scholar
  6. Andolfatto P (2001) Adaptive hitchhiking effects on genome variability. Curr Opin Genet Dev 11:635–641CrossRefPubMedGoogle Scholar
  7. Belaj A, Muñoz-Diez C, Baldoni L, Porceddu A, Barranco D, Satovic Z (2007) Genetic diversity and population structure of wild olives from the north-western Mediterranean assessed by SSR Markers. Ann Bot 100:449–458CrossRefPubMedPubMedCentralGoogle Scholar
  8. Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F (2004) GENETIX 4.05.2, Windows™ Software for Population Genetics. Laboratoire génome, populations, interactions, CNRS UMR, 5000, University of MontpellierGoogle Scholar
  9. Bryja J, Smith C, Konevcnỳ A, Reichard M (2010) Range-wide population genetic structure of the European bitterling (Rhodeus amarus) based on microsatellite and mitochondrial DNA analysis. Mol Ecol 19:4708–4722CrossRefPubMedGoogle Scholar
  10. Chakraborty D, Wang T, Andre K, Konnert M, Lexer MJ, Matulla C, Schueler S (2015) Selecting populations for non-analogous climate conditions using universal response functions: the case of Douglas-fir in central Europe. PLoS ONE 10(8):e0136357CrossRefPubMedPubMedCentralGoogle Scholar
  11. Darychuk N, Hawkins BJ, Stoehr M (2012) Trade-offs between growth and cold and drought hardiness in submaritime Douglas-fir. Can J For Res 42:1530–1541CrossRefGoogle Scholar
  12. Duplantier JM, Granjon L, Mathieu E, Bonhomme F (1990) Structures génétiques comparées de trois espèces de rongeurs africains du genre Mastomys au Sénégal. Genetica 81:179–192CrossRefGoogle Scholar
  13. Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361CrossRefGoogle Scholar
  14. Eckert AJ, Bower AD, Wegrzyn JL, Pande B, Jermstad KD, Krutovsky KV, St. Clair JB, Neale DB (2009) Association genetics of coastal Douglas fir (Pseudotsuga menziesii var. menziesii, Pinaceae). I. Cold-hardiness related traits. Genetics 182:1289–1302CrossRefPubMedPubMedCentralGoogle Scholar
  15. Ehring A, Klädtke J, Yue C (1999) Ein interaktives Programm zur Erstellung von Bestandeshöhenkurven. Centralblatt für das gesamte Forstwesen 116:47–52Google Scholar
  16. Eilmann B, Rigling A (2012) Tree-growth analyses to estimate tree species’ drought tolerance. Tree Phys 32:178–187CrossRefGoogle Scholar
  17. Eilmann B, de Vries SMG, den Ouden J, Mohren GMJ, Sauren P, Sass-Klaasen U (2013) Origin matters! Difference in drought tolerance and productivity of coastal Douglas-fir (Pseudotsuga menziesii (Mirb.)) provenances. For Ecol Manag 302:133–143CrossRefGoogle Scholar
  18. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620CrossRefPubMedGoogle Scholar
  19. González-Martínez SC, Wheeler NC, Ersoz E, Nelson CD, Neale DB (2007) Association genetics in Pinus taeda L. I. Wood property traits. Genetics 175:399–409CrossRefPubMedPubMedCentralGoogle Scholar
  20. Goudet J (2001) FSTAT (version 2.9.3.2): FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3.2). Available at http://www2.unil.ch/popgen/softwares/fstat.html
  21. Goudet J, Raymond M, de Meeüs T, Rousset F (1996) Testing differentiation in diploid populations. Genetics 144:1933–1940PubMedPubMedCentralGoogle Scholar
  22. Gould PJ, Harrington CA, St. Clair JB (2012) Growth phenology of coast Douglas-fir seed sources planted in diverse environments. Tree Phys 32:1482–1496CrossRefGoogle Scholar
  23. Grivet D, Sork VL, Westfall RD, Davis FW (2008) Conserving the evolutionary potential of California valley oak (Quercus lobata Née): a multivariate genetic approach to conservation planning. Mol Ecol 17:139–156CrossRefPubMedGoogle Scholar
  24. Gugger PF, Sugita S (2010) Glacial populations and postglacial migration of Douglas-fir based on fossil pollen and macrofossil evidence. Quaternary Sci Rev 29:2052–2070CrossRefGoogle Scholar
  25. Gugger PF, Sugita S, Cavender-Bares J (2010) Phylogeography of Douglas-fir based on mitochondrial and chloroplast DNA sequences: testing hypotheses from the fossil record. Mol Ecol 19:1877–1897CrossRefPubMedGoogle Scholar
  26. Guichoux E, Lagache L, Wagner S, Chaumiel P, Léger P, Lepais O, Lepoittevin C, Malausa T, Revardel E, Salin F, Petit J (2011) Current trends in microsatellite genotyping. Mol Ecol Res 11:591–611CrossRefGoogle Scholar
  27. Hermann RK, Lavender DP (1990) Pseudotsuga menziesii (Mirb.) Franco. In: Burns R M, Barbara H, Honkala BH (eds) Silvics of North America: 1. Conifers, USDA Forest Service Agriculture Handbook 654, Washington, DC, pp 527–540Google Scholar
  28. Hermann RK, Lavender DP (1999) Douglas-fir planted forests. New For 17:53–70CrossRefGoogle Scholar
  29. Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Res 9:1322–1332CrossRefGoogle Scholar
  30. Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23:1801–1806CrossRefPubMedGoogle Scholar
  31. Jansen K, Sohrt J, Kohnle U, Ensminger I, Gessler A (2013) Tree ring isotopic composition, radial increment and height growth reveal provenance-specific reactions of Douglas-fir towards environmental parameters. Trees 27:37–52CrossRefGoogle Scholar
  32. Kenk G, Thren M (1984) Ergebnisse verschiedener Douglasienprovenienzversuche in Baden-Württemberg. Teil I: Der Internationale Douglasien-Provenienzversuch 1958. Allg Forst Jagdztg 155:165–184Google Scholar
  33. Kohnle U, Hein S, Sorensen FC, Weiskittel AR (2012) Effects of seed source origin on bark thickness of Douglas-fir (Pseudotsuga menziesii) growing in south-western Germany. Can J For Res 42:382–399CrossRefGoogle Scholar
  34. Konnert M, Ruetz W, Schirmer R (2008) Fragen zum forstlichen Vermehrungsgut bei Douglasie. LWF-Wissen 59:22–26Google Scholar
  35. Krakowski J, Stoehr MU (2009) Coastal Douglas-fir provenance variation: patterns and predictions for British Columbia seed transfer. Ann For Sci 66:811CrossRefGoogle Scholar
  36. Kremer A, Potts BM, Delzon S (2014) Genetic divergence in forest trees: understanding the consequences of climate change. Funct Ecol 28:22–36CrossRefGoogle Scholar
  37. Krutovsky KV, Neale DB (2005) Nucleotide diversity and linkage disequilibrium in cold-hardiness- and wood quality-related candidate genes in Douglas fir. Genetics 171:2029–2041CrossRefPubMedPubMedCentralGoogle Scholar
  38. Krutovsky KV, St. Clair JB, Saich R, Hipkins VD, Neale DB (2009) Estimation of population structure in coastal Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco var. menziesii] using allozyme and microsatellite markers. Tree Genet Genomes 5:641–658CrossRefGoogle Scholar
  39. Leites LP, Robinson AP, Rehfeldt GE, Marshall JD, Crookston NL (2012) Height-growth response to climatic changes differs among populations of Douglas-fir: a novel analysis of historic data. Ecol Appl 22:154–165CrossRefPubMedGoogle Scholar
  40. Li P, Adams WT (1989) Range-wide patterns of allozyme variation in Douglas-fir (Pseudotsuga menziesii). Can J For Res 19:149–161CrossRefGoogle Scholar
  41. Little EL (1971) Atlas of United States trees, volume 1, conifers and important hardwoods: U.S. Department of Agriculture Miscellaneous Publication 1146Google Scholar
  42. Martin A, Herrera MA, Martin LM (2012) In situ conservation and landscape genetics in forest species. J Nat Resour Devel 2:1–5Google Scholar
  43. Matschiner M, Salzburger W (2009) TANDEM: integrating automated allele binning into genetics and genomics workflows. Bioinformatics 25:1982–1983CrossRefPubMedGoogle Scholar
  44. Messaoud Y, Chen HYH (2011) The influence of recent climate change on tree height growth differs with species and spatial environment. PLoS ONE 6(2):e14691. doi: 10.1371/journal.pone.0014691 CrossRefPubMedPubMedCentralGoogle Scholar
  45. 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
  46. Müller T, Ensminger I, Schmid KJ (2012) A catalogue of putative unique transcripts from Douglas-fir (Pseudotsuga menziesii) based on 454 transcriptome sequencing of genetically diverse, drought stressed seedlings. BMC Genom 13:673. doi: 10.1186/1471-2164-13-673 CrossRefGoogle Scholar
  47. Müller T, Freund F, Wildhagen H, Schmid KJ (2015) Targeted re-sequencing of five Douglas-fir provenances reveals population structure and putative target genes of positive selection. Tree Genet Genomes 11:816CrossRefGoogle Scholar
  48. Neale DB, Kremer A (2011) Forest tree genomics: growing resources and applications. Nat Rev Gen 12:111–122CrossRefGoogle Scholar
  49. Nei M (1972) Genetic distance between populations. Am Nat 106:283–292CrossRefGoogle Scholar
  50. Nosil P, Egan SP, Funk DJ (2008) Heterogeneous genomic differentiation between walking-stick ecotypes: “Isolation by adaptation” and multiple roles for divergent selection. Evolution 62:316–336CrossRefPubMedGoogle Scholar
  51. Nosil P, Funk DJ, Ortiz-Barrientos D (2009) Divergent selection and heterogeneous genomic divergence. Mol Ecol 18:375–402CrossRefPubMedGoogle Scholar
  52. O’Brien IEW, Smith DR, Gardner RC, Murray BG (1996) Flow cytometric determination of genome size in Pinus. Plant Sci 115:91–99CrossRefGoogle Scholar
  53. Oksanen J, Guillaume Blanchet F, Kindt R Legendre, P Minchin PR, O’Hara RB, Simpson GL, Solymos P, Henry H, Stevens H, Wagner H (2013) Community Ecology Package. R package version 2.0-10. http://CRAN.R-project.org/package=vegan
  54. Pinheiro J, Bates D, DebRoy S, Sarkar D, and the R Development Core Team (2012). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-103Google Scholar
  55. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedPubMedCentralGoogle Scholar
  56. Pritchard JK, Pickrell JK, Coop G (2010) The genetics of human adaptation: hard sweeps, soft sweeps and polygenic adaptation. Curr Biol 20:R208–R215. doi: 10.1016/j.cub.2009.11.055 CrossRefPubMedPubMedCentralGoogle Scholar
  57. Puettmann K, Coates KD, Messier C (2009) A critique of silviculture: managing for complexity. Island Press, Washington, DCGoogle Scholar
  58. R Development Core Team (2011) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org
  59. Ramírez-Valiente JA, Lorenzo Z, Soto A, Valladares F, Gil L, Aranda I (2010) Natural selection on cork oak: allele frequency reveals divergent selection in cork oak populations along a temperature cline. Evol Ecol 24:1031–1044CrossRefGoogle Scholar
  60. Rehfeldt GE (1989) Ecological adaptations in Douglas-fir (Pseudotsuga menziesii var. glauca): a synthesis. For Ecol Manag 28:203–215CrossRefGoogle Scholar
  61. Rosenberg NA (2004) Distruct: a program for the graphical display of population structure. Mol Ecol Notes 4:137–138CrossRefGoogle Scholar
  62. Schmidt M, Hanewinkel M, Kändler G, Kublin E, Kohnle U (2010) An inventory-based approach for modeling single-tree storm damage-experiences with the winter storm of 1999 in south-western Germany. Can J For Res 40:1636–1652CrossRefGoogle Scholar
  63. Schütt P, Schuck HJ, Stimm B (2002) Lexikon der Baum-und Straucharten: Das Standardwerk der Forstbotanik. Nikol Verlagsgesellschaft mbH & Co, KG, HamburgGoogle Scholar
  64. Scotti-Saintagne C, Mariette S, Porth I, Goicoechea PG, Barreneche T, Bodénès C, Burg K, Kremer A (2004) Genome scanning for interspecific differentiation between two closely related oak species [Quercus robur L. and Q. petraea (Matt.) Liebl.]. Genetics 168:1615–1626CrossRefPubMedPubMedCentralGoogle Scholar
  65. Šeho M, Kohnle U (2014) Der Internationale Douglasien-Provenienzversuch 1958: Unterschiede in der Ausprägung von Ast- und Stammmerkmalen auf den südwestdeutschen Versuchsflächen. Allg Forst Jagdztg 185:27–42Google Scholar
  66. Selkoe KA, Toonen RJ (2006) Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol Lett 9:615–629CrossRefPubMedGoogle Scholar
  67. She JX, Autem M, Kotulas G, Pasteur N, Bonhomme F (1987) Multivariate analysis of genetic exchanges between Solea aegyptiaca and Solea senegalensis (Teleosts, Soleidae). Biol J Linn Soc 32:357–371CrossRefGoogle Scholar
  68. Slavov GT, Howe GT, Yakovlev I, Edwards KJ, Krutovsky KV, Tuskan GA, Carlson JE, Strauss SH, Adams WT (2004) Highly variable SSR markers in Douglas-fir: Mendelian inheritance and map locations. Theor Appl Genet 108:873–880CrossRefPubMedGoogle Scholar
  69. Sork VL, Davis FW, Westfall R, Flint A, Ikegami M, Wang H, Grivet D (2010) Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change. Mol Ecol 19:3806–3823CrossRefPubMedGoogle Scholar
  70. Strehlke B (1959) Die Ernte von Douglasiensamen in USA und Kanada. Folgerungen für die deutsche Forstwirtschaft. Der Forst- und Holzwirt 14:295–300Google Scholar
  71. Takezaki N, Nei M, Tamura K (2010) POPTREE2: Software for Constructing Population Trees from Allele Frequency Data and Computing Other Population Statistics with Windows Interface. Mol Biol Evol 27:747–752CrossRefPubMedPubMedCentralGoogle Scholar
  72. Temunović M, Franjić J, Satovic Z, Grgurev M, Frascaria-Lacoste N, Fernández-Manjarrés JF (2012) Environmental heterogeneity explains the genetic structure of continental and mediterranean populations of Fraxinus angustifolia Vahl. PLoS ONE 7:e42764. doi: 10.1371/journal.pone.0042764 CrossRefPubMedPubMedCentralGoogle Scholar
  73. Tsukada M (1982) Pseudotsuga menziesii (Mirb.) Franco: Its pollen dispersal and late Quaternary history in the Pacific Northwest [USA]. Jpn J Ecol 32:159–187Google Scholar
  74. Tsumura Y, Uchiyama K, Moriguchi Y, Ueno S, Ihara-Ujino T (2012) Genome scanning for detecting adaptive genes along environmental gradients in the Japanese conifer, Cryptomeria japonica. Heredity 109:349–360CrossRefPubMedPubMedCentralGoogle Scholar
  75. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) Micro-Checker: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4:535–538CrossRefGoogle Scholar
  76. Vasemägi A, Gross R, Paaver T, Kangur M, Nilsson J, Eriksson LO (2001) Identification of the origin of an Atlantic salmon (Salmo salar L.) population in a recently recolonized river in the Baltic Sea. Mol Ecol 10:2877–2882CrossRefPubMedGoogle Scholar
  77. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CrossRefGoogle Scholar
  78. Zuur A, Ieno EN, Walker N, Saveliev AA, Smith GM (2007) Mixed effects models and extensions in ecology with R. Springer, New YorkGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Charalambos Neophytou
    • 1
  • Anna-Maria Weisser
    • 1
  • Daniel Landwehr
    • 1
  • Muhidin Šeho
    • 1
  • Ulrich Kohnle
    • 1
  • Ingo Ensminger
    • 1
    • 2
  • Henning Wildhagen
    • 1
    • 3
    Email author
  1. 1.Forest Research Institute Baden-Württemberg (FVA)FreiburgGermany
  2. 2.Department of Biology, Graduate Programs in Cell & Systems Biology and Ecology and Evolutionary BiologyUniversity of TorontoMississaugaCanada
  3. 3.Büsgen-Institut, Department of Forest Botany and Tree PhysiologyGeorg-August-Universität GöttingenGöttingenGermany

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