Blog List

Friday 2 September 2016

A new approach to prediction of the age-age correlation for use in tree breeding

Published Date
Author

Original Paper
DOI: 10.1007/s13595-016-0570-5

Cite this article as: 
Rweyongeza, D.M. Annals of Forest Science (2016). doi:10.1007/s13595-016-0570-5

Abstract

Key message
Early selection in tree breeding requires a credible age-age correlation. Modelling height growth in provenance and progeny trials, we can predict age-age correlations suitable for use in operational breeding as described in this article.

Context

Tree breeding involves early selection, which is an indirect selection using a genetic correlation. This study describes a procedure of predicting an age-age phenotypic correlation as a surrogate for a genetic correlation. Although the predicted correlations are based on white spruce (Picea glauca) and lodgepole pine (Pinus contorta) data, they can be used in other coniferous species with similar mode of height growths.

Aims

The aim of the study is to predict a correlation coefficient used to adjust breeding values at a measurement age to breeding values at a rotation age. This correlation is derived from the observed height growth trajectories of trees in progeny and provenance trials.

Methods

Correlation prediction equations were developed using modelled height growth in provenance and progeny trials of lodgepole pine and white spruce. The time lag between successive tree ages was used as a correlation predictor variable.

Results

Correlations differed between spruce and pine but the differences narrowed as trees grew older. For example, a correlation between 20 and 100 years was 0.607 for spruce and 0.470 for pine, whereas that of 30 and 100 was 0.826 for spruce and 0.832 for pine. Based on the age-age correlation, the optimum selection age for a 100-year rotation age is 40–50 years. Parameters of the tree height growth function exhibited significant genetic variance and genotype × environment interaction.

Conclusion

After the age of 40 years, age-age correlation for height may be less important for selection and genetic gain prediction than the correlation between height and diameter, which is declining with tree age.

References

  1. Beckley TM (1989) Moving toward consensus-based forest management: a comparison of industrial, co-managed, community and small private forests in Canada. For Chron 74:736–744. doi:10.5558/tfc74736-5CrossRef
  2. Bond BJ, Czarnomski NM, Cooper C, Day ME, Greenwood MS (2007) Developmental decline in height growth in Douglas-fir. Tree Physiol 27:441–453. doi:10.1093/treephys/27.3.441
  3. Chang SJ (1984) Determination of the optimum rotation age: a theoretical analysis. Forest Ecol Manag 8:137–147CrossRef
  4. Costa P, Durel CE (1996) Time trends in genetic control over height and diameter in maritime pine. Can J For Res 26:1209–1217. doi:10.1139/x26-135CrossRef
  5. Cotterill PP, Dean CA (1988) Changes in genetic control of growth of radiata pine to 16 years and efficiencies of early selection. Silvae Genet 37:138–146
  6. Danjon F (1994) Heritability and genetic correlations for estimated growth curve parameters in maritime pine. Theor Appl Genet 89:911–921. doi:10.1007/BF00224517PubMed
  7. de Sousa GP, Bortoletto N, Cardinal ABB, Gouvêa LRL, Da Costa RB, De Moraes MLT (2005) Age-age correlation for early selection of rubber tree genotypes in Sao Paulo State, Brazil. Genetics Mol Biol 28:758–764. doi:10.1590/S1415-47572005000500018
  8. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman Group Ltd, London
  9. Gill JGS (1987) Juvenile-mature correlations and trends of genetic variances in Sitka spruce in Britain. Silvae Genet 36:189–194
  10. Grattapaglia D, Resende MDV (2011) Genomic selection in forest tree breeding. Tree Genet Genomes 7:241–255. doi:10.1007/s11295-010-0328-4CrossRef
  11. Gwaze DP, Wooliams JA, Kanowski PJ (1997) Optimum selection age for height in Pinus taeda L. in Zimbabwe. Silvae Genet 46:358–364
  12. Hodge GR, White TL (1992) Genetic parameter estimates for growth traits at different ages in slash pine and some implications for breeding. Silvae Genet 41:252–262
  13. Huang S, Titus SJ, Weins DP (1992) Comparison of nonlinear height-diameter functions for major Alberta tree species. Can J Forest Res 22:1297–1304. doi:10.1139/x92-172CrossRef
  14. SAS Institute (2004) SAS System for Windows. Version 9.2. Carry, NC.
  15. Isik F (2014) Genomic selection in forest tree breeding: the concept and an outlook to the future. New Forest. doi:10.1007/s11056-014-9422-z
  16. Jansson G, Li B, Hannrup B (2003) Time trends in genetic parameters for height and optimal age for parental selection in Scots pine. Forest Sci 49:696–705
  17. Kramer PJ, Kozlowski TT (1979) Physiology of woody plants. Academic Press Inc, San Diego
  18. Kremer A (1992) Prediction of age-age correlations of total height based on serial correlations between height increments in maritime pine (Pinus pinaster Ait.). Theor Appl Genet 85:152–158. doi:10.1007/BF00222853PubMed
  19. Kroon J, Andersson B, Mullin TJ (2008) Genetic variation in the diameter-height relationship in Scots pine (Pinus sylvestris). Can J Forest Res 38:1493–1503. doi:10.1139/X07-233CrossRef
  20. Kung FH (1973) Development and use of juvenile-mature correlations in a black walnut tree improvement program. In: Proceedings of the 12th Southern Forest Tree Improvement conference. P 243–249. Available at http://www.rngr.net/publications/tree-improvement-proceedings/sftic/1973. Accessed 20 December 2015
  21. Kung FH (1993) Modeling loblolly pine age-age correlation for height using the degree of non-determination. In. Proceedings of the 22nd Southern Forest Tree Improvement conference. P 334–340. Available at http://www.rngr.net/publications/tree-improvement-proceedings/sftic/1993 Accessed 20 December 2015
  22. Lambeth CC (1980) Juvenile-mature correlations in Pinaceae and implications for early selection. For Sci 26:571–580
  23. Lambeth C, Dill LA (2001) Prediction models for juvenile-mature correlations for loblolly pine growth traits within, between and across sites. For Genet 8:101–108
  24. Lambeth CC, Van Buijtenen JP, Duke SD, McCullough RB (1983) Early selection if effective in 20-year-old genetic tests of loblolly pine. Silvae Genet 32:210–215
  25. Lotan JE, Critchfield WB (1990) Pinus contorta Dougl. ex Loud. In: Burns RM, Honkala BH (eds) Silvics of North America, vol 1, Agriculture Handbook 654. United States Department of Agriculture, Washington DC, pp pp 302–pp 315
  26. Luckert MK, Haley D (1995) The allowable cut effect as a policy instrument in Canadian forestry. Can J For Res 25:1821–1829. doi:10.1139/x95-197CrossRef
  27. Meng SX, Huang S (2010) Incorporating correlated error structure into mixed forest growth models: prediction and inference implications. Can J For Res 40:977–990. doi:10.1139/X10-032CrossRef
  28. Mullin TJ, Park YS (1994) Genetic parameters and age-age correlations in clonally replicated test of black spruce after 10 years. Can J For Res 24:2330–2341. doi:10.1139/x94-301CrossRef
  29. Nair KR (1954) The fitting of growth curves. In: Kempthorne O (ed) Statistics and mathematics in biology. Iowa State University, Ames, pp p 119–p 133
  30. Namkoong G, Conkle MT (1976) Time trends in genetic control of height growth in ponderosa pine. Forest Sci 22:2–12
  31. Namkoong G, Kang H (1990) Quantitative genetics of forest trees. In: Janick J (ed) Plant breeding reviews, vol 8. Timber Press Inc, Portland OR
  32. Namkoong G, Usanis RA, Silen RR (1972) Age-related variation in genetic control of height growth in Douglas-fir. Theor Appl Genet 42:151–159. doi:10.1007/BF00280791PubMedCrossRef
  33. Natural Resources Canada. 2014. The State of Canada’s Forests Annual Report. ISSN 1488–2736
  34. Newton PF (2015) Genetic worth effect models for boreal conifers and their utility when integrated into density management decision-support system. Open J For 5:105–115. doi:10.4236/ojf.2015.51011
  35. Niklas KJ (2007) Maximum plant height and biophysical factors that limit it. Tree Physiol 27:433–440. doi:10.1093/treephys/27.3.433PubMedCrossRef
  36. Resende MFR Jr, Munoz P, Acosta JJ, Peter GG, Davis JM, Grattapaglia D, Resende MDV, Kist M (2012) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 193:617–624PubMedCrossRef
  37. Richards FJ (1959) A flexible growth function for empirical use. J Exp Bot 10:290–300. doi:10.1093/jxb/10.2.290CrossRef
  38. Rweyongeza DM (2011) Pattern of genotype-environment interaction in Picea glauca(Moench) Voss in Alberta, Canada. Ann For Sci 68:245–253. doi:10.1007/s13595-011-0032-zCrossRef
  39. Rweyongeza DM, Yeh FC, Dhir NK (2004) Genetic parameters for seasonal height and height growth curves of white spruce seedlings and their implications to early selection. Forest Ecol Manag 187:159–172. doi:10.1016/s0378-1127(03)00329-3CrossRef
  40. Rweyongeza DM, Yang R-C, Dhir NK, Barnhardt LK, Hansen C (2007) Genetic variation and climatic impacts on survival and growth of white spruce in Alberta, Canada. Silvae Genet 56:117–127
  41. Rweyongeza DM, Barnhardt LK, Dhir NK, Hansen C (2010) Population differentiation and climatic adaptation for growth potential of white spruce (Picea glauca) in Alberta, Canada. Silvae Genet 59:158–169
  42. Tauer CG, McNew RW (1985) Inheritance and correlation of growth of shortleaf pine in two environments. Silvae Genet 34:5–11
  43. Vanderklein D, Martinez-Vilalta J, Lee S, Mencuccini M (2007) Plant size, not age, relates growth and gas exchange in grafted Scots pine trees. Tree Physiol 27:71–79. doi:10.1093/treephys/27.1.71PubMedCrossRef
  44. White TL, Adams WT, Neal DB (2007) Forest genetics. CABI Publishing, Cambridge, MACrossRef
  45. Xie CY, Yanchuk AD (2003) Breeding values of parental trees, genetic worth of seed orchard seedlots, and yields of improved stocks in British Columbia. West J Appl For 18:88–100
  46. Xie C–Y, Ying CC (1996) Heritabilities, age-age correlations and early selection in lodgepole pine (Pinus contorta spp. latifolia). Silvae Genet 45:101–105
  47. Ye TZ, Jayawickrama KJS (2012) Early selection for improving volume growth in coastal Douglas-fir breeding programs. Silvae Genet 61:186–198
  48. Zobel B, Talbert J (1984) Applied forest tree improvement. John Wiley & Sons, New York


For further details log on website :
http://link.springer.com/article/10.1007/s13595-016-0570-5

No comments:

Post a Comment

Advantages and Disadvantages of Fasting for Runners

Author BY   ANDREA CESPEDES  Food is fuel, especially for serious runners who need a lot of energy. It may seem counterintuiti...