Quantitative genetic architecture and evolutionary potential of lodgepole pine: insights from a multi-zone provenance trial

Abstract

Lodgepole pine (Pinus contorta ssp. latifolia) is the most widely distributed and commercially valuable subspecies among the four recognized subspecies of P. contorta. This study explored the genetic potential of seven quantitative traits using a comprehensive 14-provenance trial that included family structure, with collections from five geographic zones across British Columbia. The analysis focused on wood specific gravity, branch characteristics (angle, diameter, and length) as well as growth parameters (height, diameter, and volume). While inter-provenance differences within zones remained generally modest at age 12, substantial variation at the family level within provenances became apparent, showing significant genetic diversity within populations. Heritability estimates showed considerable variation across both traits and geographic regions, reflecting the complex genetic basis of these characteristics. Quantitative genetic differentiation (Q_st) for all traits except branch angle indicated that natural selection rather than genetic drift drove the evolution of these traits. The lack of significant isolation-by-distance effects indicated that patterns of genetic variation were not geographically structured in a straightforward linear way. Correlation analyses among traits uncovered important evolutionary trade-offs, with specific gravity showing negative associations with growth traits and branch length, but no link to branch angle. Path analysis pinpointed branch length as a key mediating factor directly decreasing wood density while also enhancing growth performance. These results highlight the hierarchical nature of trait co-evolution, where branch angle develops independently, and branch length plays a central role in mediating covariations among other features. The comprehensive results improve our understanding of lodgepole pine’s genetic potential and offer valuable insights for breeding programs aiming to balance growth performance with wood quality objectives.

Keywords

Lodgepole pine / Heritability / Quantitative trait differentiation / Correlation / Path analysis

References

[1] Aitken SN, Yeaman S, Holliday JA, Wang TL, Curtis-McLane S. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol Appl, 2008, 1(1): 95-111

[2] Anderson RL, Bancroft TA. Statistical theory in research, 1952, New York, McGraw-Hill

[3] Aspinwall MJ, King JS, Domec JC, McKeand SE, Isik F. Genetic effects on transpiration, canopy conductance, stomatal sensitivity to vapour pressure deficit, and cavitation resistance in loblolly pine. Ecohydrology, 2011, 4(2): 168-182

[4] Barnett JR, Jeronimidis G. Wood quality and its biological basis, 2003, Oxford, Blackwell Publishing

[5] Barton N, Partridge L. Limits to natural selection. BioEssays, 2000, 22(12): 1075-1084

[6] Benito Garzón M, Alía R, Robson TM, Zavala MA. Intra-specific variability and plasticity influence potential tree species distributions under climate change: intra-specific variability and plasticity. Glob Ecol Biogeogr, 2011, 20(5): 766-778

[7] Cannell MGR, Grace J. Competition for light: detection, measurement, and quantification. Can J for Res, 1993, 23(10): 1969-1979

[8] Ceulemans R, Stettler RF, Hinckley TM, Isebrands JG, Heilman PE. Crown architecture of Populus clones as determined by branch orientation and branch characteristics. Tree Physiol, 1990, 7(1–4): 157-167

[9] Cornelius J. Heritabilities and additive genetic coefficients of variation in forest trees. Can J for Res, 1994, 24(2): 372-379

[10] Cown DJ, Young GD, Burdon RD. Variation in wood characteristics of 20-year-old half-sib families of Pinus radiata. N Z J for Sci, 1992, 21(1): 63-76

[11] Critchfield WB (1957) Geographic variation in Pinus contorta. Maria Moors Cabot Foundation, Publication No. 3. Harvard University, Cambridge, MA.

[12] Davis MB, Shaw RG. Range shifts and adaptive responses to quaternary climate change. Science, 2001, 292(5517): 673-679

[13] Dickmann DI, Isebrands JG, Eckenwalder JE, Richardson J. Poplar culture in North America, 2001, Ottawa, NRC Research Press

[14] Epperson BK, Allard RW. Allozyme analysis of the mating system in lodgepole pine populations. J Hered, 1984, 75(3): 212-214

[15] Falconer DS, Mackay TFC. Introduction to quantitative genetics, 19964London, Longman

[16] Fisher RA. The genetical theory of natural selection, 1930, Oxford, The Clarendon Press

[17] Forrester DI. Linking forest growth with stand structure: tree size inequality, tree growth or resource partitioning and the asymmetry of competition. For Ecol Manag, 2019, 447: 139-157

[18] Grattapaglia D, Resende MDV. Genomic selection in forest tree breeding. Tree Genet Genomes, 2011, 7(2): 241-255

[19] Grattapaglia D, Plomion C, Kirst M, Sederoff RR. Genomics of growth traits in forest trees. Curr Opin Plant Biol, 2009, 12(2): 148-156

[20] Groover A, Fontana JR, Dupper G, Ma CP, Martienssen R, Strauss S, Meilan R. Gene and enhancer trap tagging of vascular-expressed genes in poplar trees. Plant Physiol, 2004, 134(4): 1742-1751

[21] He ZH, Xiao Y, Lv YW, Yeh FC, Wang X, Hu XS. Prediction of genetic gains from selection in tree breeding. Forests, 2023, 14(3): 520

[22] Hill WG. Understanding and using quantitative genetic variation. Philos Trans R Soc Lond B Biol Sci, 2010, 365153773-85

[23] Hill WG. “Conversion” of epistatic into additive genetic variance in finite populations and possible impact on long-term selection response. J Anim Breed Genet, 2017, 134(3): 196-201

[24] Hoffmann AA, Merilä J. Heritable variation and evolution under favourable and unfavourable conditions. Trends Ecol Evol, 1999, 14(3): 96-101

[25] Hong Z, Fries A, Wu HX. High negative genetic correlations between growth traits and wood properties suggest incorporating multiple traits selection including economic weights for the future Scots pine breeding programs. Ann for Sci, 2014, 71(4): 463-472

[26] Hu XS, Li BL. Linking evolutionary quantitative genetics to conservation of genetic resources in natural forest populations. Silvae Genet, 2002, 51: 177-183

[27] Huang XH, Han B. Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol, 2014, 65: 531-551

[28] SAS Institute (2021) SAS (Version 9.4). Cary, NC.

[29] Isik K, Isik F. Genetic variation in Pinus brutia Ten in Turkey: II. Branching and crown traits. Silvae Genet, 1999, 48: 293-302

[30] Kassambara A (2023) ggcorrplot: visualization of a correlation matrix using 'ggplot2'. R package version 0.1.4.1. http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2

[31] Kaya Z, Steel F, Temerit A, Vurdu H. Genetic variation in wood specific gravity of half-sib families of Pinus nigra subsp pallasiana tested at the juvenile stage: implications for early selection. Silvae Genet, 2003, 52(3/4): 153-158

[32] Kendall MG, Stuart A. The advanced theory of statistics, Vol. 1. Distribution theory, 1969, London, Charles Griffin and Company Limited

[33] Kimura M. On the change of population fitness by natural selection. Heredity, 1958, 12(2): 145-167

[34] King DA. The adaptive significance of tree height. Am Nat, 1990, 135(6): 809-828

[35] Kovats M. Estimating juvenile tree volumes for provenance and progeny testing. Can J for Res, 1977, 7(2): 335-342

[36] Kruuk LEB. Estimating genetic parameters in natural populations using the ‘animal model’. Phil Trans R Soc Lond B, 2004, 359(1446): 873-890

[37] Kumar S, Lee J. Age-age correlations and early selection for end-of-rotation wood density in radiata pine. Forest Genet, 2002, 9(4): 323-330

[38] Kuuluvainen T. Crown architecture and stemwood production in Norway spruce (Picea abies (L.) Karst.). Tree Physiol, 1988, 4(4): 337-346

[39] Lamy JB, Bouffier L, Burlett R, Plomion C, Cochard H, Delzon S. Uniform selection as a primary force reducing population genetic differentiation of cavitation resistance across a species range. PLoS ONE, 2011, 6(8): e23476

[40] Lande R. The genetic covariance between characters maintained by pleiotropic mutations. Genetics, 1980, 941203-215

[41] Larson PR. The vascular cambium: development and structure, 1994, Berlin, Springer-Verlag

[42] Leinonen T, McCairns RJS, O’Hara RB, Merilä J. Q_ST–F_ST comparisons: evolutionary and ecological insights from genomic heterogeneity. Nat Rev Genet, 2013, 14(3): 179-190

[43] Li CC. Path analysis: a primer, 1975, Pacific Grove, CA, Boxwood Press

[44] Li H, Wen Z, Huang M, Wang M. A genetic study on characteristics of crown light interception in Populus deltoides. Can J for Res, 1997, 27(9): 1465-1470

[45] Li XD, Han GJ, Zhou YJ, Chen BH, Zhang J, Yang F, Wang LM, Liu GF, Li HY. Genetic variation and selection of half-sib families of Pinus sibirica in the Xinlin district of the Greater Khingan Range. J Forestry Res, 2025, 36: 86

[46] Little EL, Critchfield WB (1969) Subdivisions of the genus Pinus (pines). USDA Forest Service Miscellaneous Publication 1144. Washington, DC.

[47] Lotan JE, Critchfield WB (1990) Pinus contorta Dougl. ex. Loud. In: Burns, R.M. and Honkala, B.H. (coords.) Silvics of North America, Vol. 1. Conifers. USDA Forest Service Agriculture Handbook 654, Washington, DC, pp. 302–315.

[48] Lynch M, Hill WG. Phenotypic evolution by neutral mutation. Evolution, 1986, 40(5): 915-935

[49] Lynch M, Walsh B. Genetics and analysis of quantitative traits, 1998, Sunderland, MA, Sinauer Associates

[50] MacKay TFC. The genetic architecture of quantitative traits. Annu Rev Genet, 2001, 35: 303-339

[51] MacKay TFC, Stone EA, Ayroles JF. The genetics of quantitative traits: challenges and prospects. Nat Rev Genet, 2009, 10(8): 565-577

[52] Mäkinen H, Colin F. Predicting the number, death, and self-pruning of branches in Scots pine. Can J for Res, 1999, 29(8): 1225-1236

[53] Mäkinen H, Saranpää P, Linder S. Wood-density variation of Norway spruce in relation to nutrient optimization and fibre dimensions. Can J for Res, 2002, 32(2): 185-194

[54] Martínez-Alonso C, Villar-Salvador P, Pardos M, Gonzalez-Rodriguez AM. Crown architecture influences the water use efficiency of young Pinus nigra (Arn.) trees. For Ecol Manage, 2020, 464: 118066

[55] McKay JK, Latta RG. Adaptive population divergence: markers, QTL and traits. Trends Ecol Evol, 2002, 17(6): 285-291

[56] Merilä J, Crnokrak P. Comparison of genetic differentiation at marker loci and quantitative traits. J Evol Biol, 2001, 14(6): 892-903

[57] Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001, 157(4): 1819-1829

[58] Namkoong G. Introduction to quantitative genetics in forestry, 1981, Kent, Castle House Publications

[59] Neale DB, Kremer A. Forest tree genomics: growing resources and applications. Nat Rev Genet, 2011, 12(2): 111-122

[60] Nordborg M, Tavaré S. Linkage disequilibrium: what history has to tell us. Trends Genet, 2002, 18(2): 83-90

[61] O'Hara RB, Merilä J. Bias and precision in QST estimates: problems and some solutions. Genetics, 2005, 171(3): 1331-1339

[62] Panshin AJ, de Zeeuw C. Textbook of wood technology, 19804New York, McGraw-Hill

[63] Pinyopich A, Ditta GS, Savidge B, Liljegren SJ, Baumann E, Wisman E, Yanofsky MF. Assessing the redundancy of MADS-box genes during carpel and ovule development. Nature, 2003, 424(6944): 85-88

[64] Pretzsch H. The social drift of trees. Consequence for growth trend detection, stand dynamics, and silviculture. Eur J Forest Res, 2021, 140(3): 703-719

[65] Pretzsch H, Forrester DI. Stand dynamics of mixed-species forest ecosystems. Mixed-species forests, 2017, Berlin, Heidelberg, Springer107-164

[66] Reed DH, Frankham R. How closely correlated are molecular and quantitative measures of genetic variation? A meta-analysis. Evolution, 2001, 55(6): 1095

[67] Rehfeldt GE, Ying CC, Spittlehouse DL, Hamilton DA. Genetic responses to climate in Pinus contorta: niche breadth, climate change, and reforestation. Ecol Monogr, 1999, 693375

[68] Rehfeldt GE, Crookston NL, Warwell MV, Evans JS. Empirical analyses of plant-climate relationships for the western United States. Int J Plant Sci, 2006, 16761123-1150

[69] Ritland K. Marker-inferred relatedness as a tool for detecting heritability in nature. Mol Ecol, 2000, 9(9): 1195-1204

[70] Roff DA. Evolutionary quantitative genetics, 1997, Boston, MA, Springer, US

[71] Rweyongeza DM, Dhir NK, Barnhardt LK, Hansen C, Yang RC. Population differentiation of the lodgepole pine (Pinus contorta) and jack pine (Pinus banksiana) complex in Alberta: growth, survival, and responses to climate. Can J Bot, 2007, 85(6): 545-556

[72] Savolainen O, Pyhäjärvi T, Knürr T. Gene flow and local adaptation in trees. Annu Rev Ecol Evol Syst, 2007, 38: 595-619

[73] South A (2023) rnaturalearth: World map data from Natural Earth (Version 1.0.1) [Computer software]. https://CRAN.R-project.org/package=rnaturalearth

[74] Spitze K. Population structure in Daphnia obtusa: quantitative genetic and allozymic variation. Genetics, 1993, 135(2): 367-374

[75] Stam LF, Laurie CC. Molecular dissection of a major gene effect on a quantitative trait: the level of alcohol dehydrogenase expression in Drosophila melanogaster. Genetics, 1996, 144(4): 1559-1564

[76] Tucker GF, Maguire DA, Tupinambá-Simões F. Associations between shade tolerance and wood specific gravity for conifers in contrast to angiosperm trees: foundations of the conifer fitness-enhancing shade tolerance hypothesis. Plant Enviro Interact, 2024, 5: e10131

[77] van Buijtenen JP. Genomics and quantitative genetics. Can J for Res, 2001, 31(4): 617-622

[78] Visscher PM, Hill WG, Wray NR. Heritability in the genomics era—concepts and misconceptions. Nat Rev Genet, 2008, 9(4): 255-266

[79] Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet, 2017, 101(1): 5-22

[80] Vitti JJ, Grossman SR, Sabeti PC. Detecting natural selection in genomic data. Annu Rev Genet, 2013, 47: 97-120

[81] Walsh B, Lynch M. Evolution and selection of quantitative traits, 2018, Oxford, Oxford University Press

[82] Wheeler NC, Guries RP. Population structure, genic diversity, and morphological variation in Pinus contorta Dougl. Can J for Res, 1982, 12(3): 595-606

[83] White TL, Adams WT, Neale DB. Forest genetics, 2007, UK, CABI

[84] Wright S. Evolution and the genetics of populations. Vol. 2. The theory of gene frequencies, 1969, Chicago, University of Chicago Press

[85] Wu HX, Matheson AC. Genotype by environment interactions in an Australia-wide radiata pine diallel mating experiment: implications for regionalized breeding. For Sci, 2005, 51(1): 29-40

[86] Wu HX, Ying CC. Geographic pattern of local optimality in natural populations of lodgepole pine. For Ecol Manag, 2004, 194(1–3): 177-198

[87] Xie CY, Ying CC. Genetic architecture and adaptive landscape of interior lodgepole pine (Pinus contorta ssp. latifolia) in Canada. Can J for Res, 1995, 25(12): 2010-2021

[88] Yang RC, Yeh FC, Yanchuk AD. A comparison of isozyme and quantitative genetic variation in Pinus contorta ssp. latifolia by FST. Genetics, 1996, 142(3): 1045-1052

[89] Yeh FC, Layton C. The organization of genetic variability in central and marginal populations of lodgepole pine Pinus contorta ssp. latifolia. Can J Genet Cytol, 1979, 21(4): 487-503

[90] Ying CC. Performance of lodgepole pine provenances at sites in southwestern British Columbia. Silvae Genet, 1991, 40: 215-223

[91] Zhang SY, Morgenstern EK. Genetic variation and inheritance of wood density in black spruce (Picea mariana) and its relationship with growth: implications for tree breeding. Wood Sci Technol, 1995, 30(1): 63-75

[92] Zhang H, Zhang SK, Chen SP, Xia DA, Yang CP, Zhao XY. Genetic variation and superior provenances selection for wood properties of Larix olgensis at four trials. J Forestry Res, 2022, 33(6): 1867-1879

[93] Zobel BJ, van Buijtenen JP. Wood variation: its causes and control, 1989, Berlin, Springer-Verlag

RIGHTS & PERMISSIONS

Northeast Forestry University