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New Forests

, Volume 50, Issue 4, pp 605–627 | Cite as

Estimation of genetic parameters using spatial analysis of Pinus elliottii Engelm. var. elliottii second-generation progeny trials in Argentina

  • Ector C. BelaberEmail author
  • María E. Gauchat
  • Gustavo H. Rodríguez
  • Nuno M. Borralho
  • Eduardo P. Cappa
Article
  • 164 Downloads

Abstract

Non-spatial and spatial analyses were carried out to estimate genetic parameters for growth and stem straightness traits between ages 4 and 7 in two series of eight progeny trials of Pinus elliottii Engelm. var. elliottii (PEE) established in 2004 and 2008, comprising 429 open-pollinated families from Instituto Nacional de Tecnología Agropecuaria (INTA) tree breeding population in Argentina. A first-order autoregressive spatial mixed model was found to significantly improve the fit of the model, compared with the standard non-spatial mixed model approach, due to substantial spatial heterogeneity within sites. The average individual-tree narrow-sense heritability estimates based on this spatial analysis were 0.49 for diameter at breast height, 0.36 for total height, and 0.48 for volume, with stem straightness being low to moderate (average = 0.11). The additive genetic correlation estimates between growth traits were positive (0.51 ≤ \(\hat{r}_{a}\) ≤ 0.99) and statistically significant from zero. In contrast, the genetic correlations between growth traits and stem straightness although in general also positive were not significantly different from zero (− 0.22 ≤ \(\hat{r}_{a}\) ≤ 0.59). Age-to-age genetic correlations were consistently higher for growth traits (\(\hat{r}_{a}\) ≥ 0.81) than for stem straightness (\(\hat{r}_{a}\) ≥ 0.33). The average additive genetic correlation estimated between sites within test series was high for all traits evaluated (average > 0.72). In contrast, average additive genetic correlation estimated between sites across series was slightly lower (average < 0.63). Implications of all these parameter estimates for genetic improvement of PEE in Argentina are discussed.

Keywords

Slash pine Spatial model Genetic parameters Tree breeding 

Notes

Acknowledgements

The authors are grateful to the companies PINDO S.A., BDP S.A., EVASA S.A., POMERA S.A., and INTA Montecarlo and Cerro Azul Experimental Stations, which made land and logistical support available for establishing and measuring these trials. We thank Diego Bogado, Lucas Gimenez, and Cristian Schoffen, who assisted with field work and data collections. We would also like to thank Alejandra Von Wallis for providing soil description and Fidelina Silva for providing climatic data.

Funding

This study was funded by Instituto Nacional de Tecnología Agropecuaria (grant number PNFOR-01104062).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11056_2018_9682_MOESM1_ESM.docx (81 kb)
Supplementary material 1 (DOCX 81 kb)

References

  1. Apiolaza LA (2011) Basic density of radiata pine in New Zealand: genetic and environmental factors. Tree Genetics and Genomes 8(1):87–96.  https://doi.org/10.1007/s11295-011-0423-1 CrossRefGoogle Scholar
  2. Banerjee S, Finley AO, Waldmann P, Ericsson T (2010) Hierarchical spatial process models for multiple traits in large genetic trials. J Am Stat Assoc 105(490):506–521.  https://doi.org/10.1198/jasa.2009.ap09068 CrossRefGoogle Scholar
  3. Barrett WH (1974) Variación geográfica en P. elliottii Engelm. y P. taeda L. II. Cinco años de crecimiento en el nordeste argentino. IDIA Suplemento For 8:18–39Google Scholar
  4. Barth S, Crechi E, Fassola H, Keller A (2006) Estimación de volúmenes individuales con corteza en Pinus elliottii Eng. Implantado en la zona norte de la provincia de Misiones, Argentina. En 12as Jornadas Técnicas Forestales y Ambientales—FCF, UNaM—EEA Montecarlo, INTA 8, 9 y 10 de Junio de 2006—Eldorado, Misiones. ArgentinaGoogle Scholar
  5. Bouffier L, Raffin A, Kremer A (2008) Evolution of genetic variation for selected traits in successive breeding populations of maritime pine. Heredity 101:156–216CrossRefGoogle Scholar
  6. Brawner J, Dieter MJ, Nikles DG (2005) Mid-rotation performance of Pinus caribaea var. hondurensis hybrids with both P. oocarpa and P. tecunumanii: hybrid superiority, stability of parental performance and potential for a multi-species synthetic breed. For Genet 12(1):1–13. Available at: https://kf.tuzvo.sk/sites/default/files/FG12-1_001-013_0.pdf
  7. Cappa EP, Stoehr MU (2017) A combined analysis in complementary progeny tests: effects on breeding value accuracies. Silvae Genet.  https://doi.org/10.1515/sg-2016-0005 Google Scholar
  8. Cappa EP, Marcó MA, Nikles DG, Last IA (2012a) Perfomance of Pinus elliottii, Pinus caribaea, their F1, F2 and backcross hybrids and Pinus taeda to 10 years in the Mesopotamia region, Argentina. New For 44:197–218CrossRefGoogle Scholar
  9. Cappa EP, Yanchuk AD, Cartwright CV (2012b) Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials. Ann For Sci 69:627–640.  https://doi.org/10.1007/s13595-011-0179-7 CrossRefGoogle Scholar
  10. Cappa EP, Yanchuk AD, Cartwright CV (2015) Estimation of genetic parameters for height using spatial analysis in Tsuga heterophylla full-sibling family trials in British Columbia. Silvae Genet 64:59–73.  https://doi.org/10.1515/sg-2015-0005 CrossRefGoogle Scholar
  11. Cornelius JP (1994) Heritabilities and additive genetic coefficients of variation in forest trees. Can J For Res 24:372–379CrossRefGoogle Scholar
  12. Costa e Silva J J, Dutkowski GW, Borralho NMG (2005) Across-site heterogeneity of genetic and environmental variances in the genetic evaluation of Eucalyptus globulus trials for height growth. Ann For Sci 62:183–191CrossRefGoogle Scholar
  13. Costa e Silva J J, Borralho NMG, Araújo JA, Vaillancourt RE, Potts BM (2009) Genetic parameters for growth, wood density and pulp yield in Eucalyptus globulus. Tree Genet Genomes 5:291–305CrossRefGoogle Scholar
  14. Costa e Silva J, Dutkowski GW, Gilmour AR (2001) Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual. Can J For Res 31:1887–1893CrossRefGoogle Scholar
  15. Cotterill PP, Dean CA, Van Wyk G (1987) Additive and dominance genetic effects in Pinus pinaster, P. radiata and P. elliottii and some implications for breeding strategy. Silvae Genet 36(5–6):221–232. Available at: https://www.thuenen.de/en/info-desk/publications/silvae-genetica/archive/
  16. de la Mata R, Zas R (2010) Transferring Atlantic maritime pine improved material to a region with marked Mediterranean influence in inland NW Spain: a likelihood-based approach on spatially adjusted field data. Eur J For Res 129(4):645–658CrossRefGoogle Scholar
  17. Dieters MJ (1996) Genetic parameters for slash pine (Pinus elliottii) grown in south-east Queensland, Australia: growth, steam straightness and crown defects. For Genet 3(1):27–36. Available at: https://kf.tuzvo.sk/sites/default/files/FG03-1_027-036.pdf
  18. Dieters MJ, White TL, Hodge GR (1995) Genetic parameter estimates for volume from full-sib tests of slash pine (Pinus elliottii). Can J For Res 25(8):1397–1408CrossRefGoogle Scholar
  19. Dieters MJ, Nikles DG, Toon PG, Pomroy P (1997) Genetic parameters for F1 hybrids of Pinus caribaea var. hondurensis with both Pinus oocarpa and Pinus tecunumanii. Can J For Res 27(7):1024–1031CrossRefGoogle Scholar
  20. Dutkowski GW, Costa e Silva J, Gilmour AR, Lopez GA (2002) Spatial analysis methods for forest genetic trials. Can J For Res 32:2201–2214CrossRefGoogle Scholar
  21. Dutkowski GW, Costa e Silva J, Gilmour AR, Wellendorf H, Aguiar A (2006) Spatial analysis enhances modeling of a wide variety of traits in forest genetic trials. Can J For Res 36:1851–1870CrossRefGoogle Scholar
  22. Dutkowski GW, Ivkovic M, Gapare WJ, McRae TA (2016) Defining breeding and deployment regions for radiata pine in southern Australia. New For.  https://doi.org/10.1007/s11056-016-9544-6 Google Scholar
  23. Fu YB, Yanchuk AD, Namkoong G (1999) Spatial patterns of tree height variations in a series of Douglas-fir progeny trials: implications for genetic testing. Can J For Res 29:714–723CrossRefGoogle Scholar
  24. Gapare WJ, Baltunis BS, Ivković M, Low CB, Jefferson P, Wu HX (2010) Performance differences among ex situ native-provenance collections of Pinus radiata D. Don. 1: potential for infusion into breeding populations in Australia and New Zealand. Tree Genet Genomes 7(2):409–419CrossRefGoogle Scholar
  25. Garcia-Gonzalez F, Simmons LW, Tomkins JL, Kotiaho JS, Evans JP (2012) Comparing evolvabilities: common errors surrounding the calculation and use of coefficients of additive genetic variation. Evolution 66(8):2341–2349CrossRefGoogle Scholar
  26. Gianola D, Norton HW (1981) Scaling threshold characters. Genetics 99:357–364Google Scholar
  27. Gilmour AR, Thompson R, Cullis BR (1995) Average information, an efficient algorithm for REML estimation in linear mixed models. Biometrics 51:1440–1450CrossRefGoogle Scholar
  28. Gilmour AR, Cullis BR, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 2:269–293CrossRefGoogle Scholar
  29. Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ASReml User Guide Release 3.0. VSN International Ltd., Hemel HempsteadGoogle Scholar
  30. Gilmour AR, Gogel BJ, Cullis BR, Welham SJ, Thompson R (2015) ASReml User Guide Release 4.1. VSN International Ltd: Hemel Hempstead, HP1 1ESGoogle Scholar
  31. Hardner CM, Dieters M, Dale G, De Lacy I, Basford KE (2010) Patterns of genotype-by-environment interaction in diameter at breast height at age 3 for eucalypt hybrid clones grown for reafforestation of lands affected by salinity. Tree Genet Genomes 6:833–851CrossRefGoogle Scholar
  32. Henderson CR (1984) Applications of linear models in animal breeding. University of Guelph, GuelphGoogle Scholar
  33. Hodge GR, White TL (1992) Genetic parameter estimates for growth traits at different ages in slash pine and some implications for breeding. Silvae Genet 41(4):252–262. Available at: https://www.thuenen.de/en/info-desk/publications/silvae-genetica/archive/
  34. Houle D (1992) Comparing evolvability and variability of quantitative traits. Genetics 130(1):195–204Google Scholar
  35. Huber DA, White TL, Powell GL (1996) Genetic analysis of Pinus elliottii var. elliottii: Estimate of genetic parameters, breeding value predictions and forward selection candidates. Not published INTA internal reportGoogle Scholar
  36. Huber DA, White TL, Powell GL (2003) Age-Five Results from the Cooperative Forest Genetics Research Program Slash Pine Polymix Trials. Proceedings of 27th southern forest tree improvement conference, Jun 24–27, Oklahoma State University, Stillwater, Oklahoma, USA, 38-43. Available at: https://www.researchgate.net/publication/237251536
  37. Ivković M, Gapare W, Yang H, Dutkowski G, Buxton P, Wu H (2015) Pattern of genotype by environment interaction for radiata pine in southern Australia. Ann For Sci 72(3):391–401.  https://doi.org/10.1007/s13595-014-0437-6 CrossRefGoogle Scholar
  38. Kenward MG, Roger JH (1997) Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53:983–997.  https://doi.org/10.2307/2533558 CrossRefGoogle Scholar
  39. Kerr RJ, Dutkowski GW, Jansson G, Persson T, Westin J (2015) Connectedness among test series in mixed linear models of genetic evaluation for forest trees. Tree Genet Genomes 11(4):1–13CrossRefGoogle Scholar
  40. Lande R (1977) On comparing coefficients of variation. Syst Zool 26(2):214–217CrossRefGoogle Scholar
  41. Larguía A (1992) Historia de los pinos resinosos en Misiones. En: Jornadas sobre pinos subtropicales. Actas, tomo II, pp 400–402. Eldorado, Misiones, Argentina. 5, 6 y 7 de agosto. Ed. Centro de Investigaciones y Experiencias ForestalesGoogle Scholar
  42. Li XB, Huber DA, Powell GL, White TL, Peter GF (2007) Breeding for improved growth and juvenile corewood stiffness in slash pine. Can J For Res 37:1886–1893CrossRefGoogle Scholar
  43. López JA (2006) “Control genético del volumen y eficiencia de la selección temprana en Pinus elliottii Engelm. var. elliottii”. 12 Jornadas Técnicas, Forestales y Ambientales. Eldorado, Misiones. ArgentinaGoogle Scholar
  44. López JA, Staffieri GM (2003) Interacción genotipo-ambiente y heredabilidad de la densidad de la madera de Pinus elliottii var. elliottii en el noreste de Argentina. XVIII Jornadas Forestales de Entre Ríos. Concordia, 23–24 de Octubre de 2003. Actas en CDGoogle Scholar
  45. Magnussen S (1993) Bias in genetic variance estimates due to spatial autocorrelation. Theor Appl Genet 86:349–355.  https://doi.org/10.1007/BF00222101 CrossRefGoogle Scholar
  46. Marcó MA (2012) Avances en los programas de INTA de Pinos en Región Mesopotámica. En actas: Jornadas de Actualización Técnica “Mejoramiento Genético de Pinos y Eucaliptos Subtropicales”. 02-03 de Agosto. Concordia, Entre Ríos. ArgentinaGoogle Scholar
  47. Meuwissen T, Engel B, Van Der Werf J (1995) Maximizing selection efficiency for categorical traits. J Animal Sci 73:1933–1939CrossRefGoogle Scholar
  48. Ministry of Agriculture-Industry, Secretariat of Agriculture, Livestock and Fisheries (MAGyP). 2016. Available at: http://www.minagri.gob.ar/site/institucional/prensa/forestal.php. Accessed 15 may 2016
  49. Patterson HD, Thompson R (1971) Recovery of inter-block information when block sizes are unequal. Biometrika 58:545–554CrossRefGoogle Scholar
  50. Pswarayi IZ, Barnes RD, Birks JS, Kanowski PJ (1996) Genetic parameter estimate for production and quality traits of Pinus elliottii Engelm. var. elliottii in Zimbabwe. Silvae Genet 45(4):216–222. Available at: https://www.thuenen.de/en/info-desk/publications/silvae-genetica/archive/
  51. Raymond C, Cotterill P (1990) Methods of assessing crown form of Pinus radiata. Silvae Genet 39(2):67–71. Available at: https://www.thuenen.de/en/info-desk/publications/silvae-genetica/archive/
  52. Rockwood DL, Huber DA, White TL (2001) Provenance and family variability in slash pine (Pinus elliottii var. elliottii Engelm.) growth in southern Brazil and northeastern Argentina. New For 21:115–125CrossRefGoogle Scholar
  53. Rodríguez GH, Gauchat ME (2005) Sub-capítulo I: Subprograma Pinos en Región Mesopotámica Pinus elliottii, Pinus taeda. Capítulo III Subprogramas de Producción de Material de Propagación Mejorado, Mejora Genética. Mejores Árboles para más Forestadores, El programa de Producción de Material de Propagación Mejorado y el Mejoramiento Genético en el Proyecto Forestal de Desarrollo. SAGPyA-INTA. pp 23–41Google Scholar
  54. Schmidtling RC, Marco MA, La Farge T (1997) Performance of select slash pine families in Argentina and USA Proc. 24th biennial southern forest tree improvement conference 384–386Google Scholar
  55. Stram D, Lee JW (1994) Variance components testing in the longitudinal mixed effects model. Biometrics 50(3):1171–1177CrossRefGoogle Scholar
  56. White TL (1996) Genetic parameter estimates and breeding value predictions: issues and implications in tree improvement programs. In: Dieters MJ, Matheson AC, Nikles DG, Harwood CE, Walker SM (eds) Proceedings of the QFRI-IUFRO Conference Tree Improvement for Sustainable Tropical Forestry. Caloundra, Queensland, Australia, pp 110–117Google Scholar
  57. White TL, Byram TD (2004) Slash pine tree improvement. 2004. Proceedings: Dickens, E.D.; Barnett, J.P.; Hubbard, W.G.; Jokela, E.J., eds. 2004. Slash pine: still growing and growing!. Proceedings of the slash pine symposium. Gen. Tech. Rep. SRS-76. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 148 pGoogle Scholar
  58. White TL, Hodge GR, Powell GL (1993) An advanced-generation tree improvement plan for slash pine in the southeastern United States. Silvae Genet 42:359–371. Available at: https://www.thuenen.de/en/info-desk/publications/silvae-genetica/archive/
  59. Wu HX (1999) Study of early selection in tree breeding: 2. Advantage of early selection through shortening of breeding cycle. Silvae Genet 48(2):78–83. Available at: https://www.thuenen.de/en/info-desk/publications/silvae-genetica/archive/
  60. Xiao Y, Jokela EJ, White TL (2003) Species differences in crown structure and growth performance of juvenile loblolly and slash pine. For Ecol Manag 174:295–313CrossRefGoogle Scholar
  61. Ye TZ, Jayawickrama KJS (2008) Efficiency of using spatial analysis in first-generation coastal Douglas-fir progeny tests in the US Pacific Northwest. Tree Genet Genomes 4:677–692CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Ector C. Belaber
    • 1
    Email author
  • María E. Gauchat
    • 1
  • Gustavo H. Rodríguez
    • 1
  • Nuno M. Borralho
    • 2
    • 3
  • Eduardo P. Cappa
    • 4
    • 5
  1. 1.Instituto Nacional de Tecnología Agropecuaria (INTA)MontecarloArgentina
  2. 2.CartaxoPortugal
  3. 3.Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade Técnica de Lisboa, Tapada da AjudaLisbonPortugal
  4. 4.Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, De Los Reseros y Dr. Nicolás Repetto s/nHurlingham, Buenos AiresArgentina
  5. 5.Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Buenos AiresArgentina

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