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Predicted Evolutionary Response to Selection for Tolerance of Soybean (Glycine max) and Intraspecific Competition in a Nonweed Population of Poorjoe (Diodia teres)

Published online by Cambridge University Press:  12 June 2017

Nicholas Jordan
Nicholas Jordan*
Affiliation:
Dep. Bot., Duke Univ., Durham, NC 27706
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Abstract

Genetic variation within a nonweed (coastal) population of poorjoe was measured in an experimental population subjected to soybean and intraspecific competition for space and light. A sib analysis was used to estimate phenotypic and genetic variance/covariance components of aboveground biomass production and five independent measurements of growth defined by a path-coefficient model. A multivariate analysis of response to selection was applied to predict evolutionary change in the growth measurements in response to selection for performance under soybean and intraspecific space/light competition; sufficient genetic variation was present in the experimental population to allow a rapid response to this selection. Selection under competitive conditions was predicted to cause the nonweed population to emerge earlier, grow faster early in development, change in growth form, and grow faster under a soybean canopy. Most of these changes would increase the resemblance of the selected population to a weed population of poorjoe. However, the growth rate of the selected population under a soybean canopy was predicted to become greater than that actually observed in the present-day weed population. Multivariate analysis of response to selection may be generally useful in predicting evolutionary response of plant pest populations to control practices.

Keywords

Poorjoe, Diodia teres Walt. ♯3 DIQTEsoybean, Glycine max (L.) Merr. ‘Ransom’Weed biotypesweed evolutionmultivariate selection analysisDIQTE
Type
Special Topics
Information
Weed Science , Volume 37 , Issue 3 , May 1989 , pp. 451 - 457
Copyright
Copyright © 1989 by the Weed Science Society of America 

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References

Literature Cited

1. Andersen, R. N., Menges, R. M., and Conn, J. S. 1985. Variability in velvetleaf (Abutilon theophrasti) and reproduction beyond its current range in North America. Weed Sci. 33:507–512.Google Scholar
2. Antlfinger, A. E. 1981. The genetic basis of microdifferentiation in natural and experimental populations of Borrichia frutescens in relation to salinity. Evolution 1056–1068.CrossRefGoogle Scholar
3. Antonovics, J. 1976. The nature of limits to natural selection. Ann. Mo. Bot. Gard. 63:224–247.Google Scholar
4. Barrett, S.C.H. 1981. Genetic variation in weeds. Pages 67–93 in Charudattan, R. C. and Walker, H. L., eds. Biological Control of Weeds by Plant Pathogens. John Wiley and Sons, New York.Google Scholar
5. Bradshaw, A. D. 1982. Evolution of heavy metal resistance — an analogue for herbicide resistance. Pages 293–309 in LeBaron, H. M. and Gressel, J., eds. Herbicide Resistance in Plants. John Wiley and Sons, New York.Google Scholar
6. Cavers, P. B. 1985. Intractable weeds — intraspecific variation must be considered in formulating control measures. 1985 Br. Crop Prot. Conf. — Weeds 367–376.Google Scholar
7. Clay, K. 1982. Environmental and genetic determinants of cleistogamy in a natural population of the grass Danthonia spicata . Evolution 36:734–741.CrossRefGoogle Scholar
8. Cohen, J. and Cohen, P. 1983. Applied Multiple Regression/ Correlation Analysis for the Behavioral Sciences. Chapter 8. L. Erlbaum Associates, Hillsdale.Google Scholar
9. Falconer, D. S. 1981. Introduction to Quantitative Genetics. 2d ed. Chapters 9–10. Longman, New York.Google Scholar
10. Grossman, M. 1970. Sampling variance of the correlation coefficients estimated from analyses of variance and covariance. Theor. Appl. Gen. 40:357–359.CrossRefGoogle ScholarPubMed
11. Haas, H. and Streibig, J. C. 1982. Changing patterns of weed distribution as a result of herbicide use and other agronomic factors. Pages 57–80 in Lebaron, H. M. and Gressel, J., eds. Herbicide Resistance in Plants. John Wiley and Sons, New York.Google Scholar
12. Hallauer, A. R. and Miranda, J. B., Fo, . 1981. Quantitative Genetics in Maize Breeding. Chapters 2 and 5. Iowa State Univ. Press, Ames.Google Scholar
13. Harper, J. L. 1977. Population Biology of Plants. Chapters 6–8. Academic Press, New York.Google Scholar
14. Harris, P. 1981. Stress as a strategy in the biological control of weeds. Pages 114–137 in Papavizas, G. C., ed. Biological Control in Crop Production (BARC Symp. No. 5). Allenheld, Osmun, London.Google Scholar
15. Hume, L. and Cavers, P. B. 1982. Geographic variation in a widespread perennial weed, Rumex crispus: the relative amounts of genetic and environmentally-induced variation among populations. Can. J. Bot. 60:1928–1937.CrossRefGoogle Scholar
16. Jordan, N. 1986. Mechanisms of selection leading to population differentiation in Diodia teres, a weedy annual plant. Ph.D. Dissertation, Duke Univ., Durham, NC.Google Scholar
17. Jordan, N. 1989. Path analysis of growth differences between weed and nonweed populations of poorjoe (Diodia teres) in competition with soybean (Glycine max). Weed Sci. 37:129–136.Google Scholar
18. Lande, R. 1979. Quantitative genetic analysis of multivariate evolution, applied to brain:body size allometry. Evolution 33:402–416.Google Scholar
19. Lande, R. and Arnold, S. J. 1983. The measurement of selection on correlated characters. Evolution 37:1210–1227.Google Scholar
20. Li, C. C. 1975. Path Analysis – A Primer. Boxwood Press, Pacific Grove.Google Scholar
21. Mitchell-Olds, T. 1986. Quantitative genetics of survival and growth in Impatiens capensis . Evolution 40:107–116.Google Scholar
22. Mitchell-Olds, T. and Rutledge, J. J. 1986. Quantitative genetics in natural plant populations: a review of the theory. Am. Nat. 127:379–402.CrossRefGoogle Scholar
23. Mitchell-Olds, T. and Shaw, R. G. 1987. Regression analysis of natural selection: statistical inference and biological interpretation. Evolution 41:1149–1161.CrossRefGoogle ScholarPubMed
24. Patterson, D. T. 1985. Comparative ecophysiology of weeds and crops. Pages 101–130 in Duke, S. O., ed. Weed Physiology. Vol. I. Reproduction and Ecophysiology. CRC Press, Boca Raton.Google Scholar
25. Price, S. C., Hill, J. E., and Allard, R. W. 1983. Genetic variation for herbicide reaction in plant populations. Weed Sci. 31:652–657.CrossRefGoogle Scholar
26. Radosevich, S. R. and Holt, J. S. 1984. Weed Ecology. Chapter 4. John Wiley and Sons, New York.Google Scholar
27. Roach, D. A. 1986. Life history variation in Geranium carolinianum. I. Covariation between characters at different stages of the life cycle. Am. Nat. 128:47–57.CrossRefGoogle Scholar
28. Schoen, D. J. 1982. The breeding system of Gilia achilleifolia: variation in floral characteristics and outcrossing rate. Evolution 36:352–360.Google Scholar
29. Sokal, R. R. and Rohlf, F. J. 1981. Biometry. 2nd ed. Ch. 10. W. H. Freeman, New York.Google Scholar
30. Sokal, R. R. and Rohlf, F. J. 1981. Biometry. 2nd ed. Page 583. W. H. Freeman, New York.Google Scholar
31. Tallis, G. M. 1959. Sampling errors of genetic correlation coefficients calculated from the analyses of variance and covariance. Aust. J. Stat. 10:35–43.Google Scholar
32. Thomas, R. L., Grafius, J. E., and Hahn, S. K. 1971. Transformation of sequential quantitative characters. Heredity 26:198–193.Google Scholar
33. Wright, S. 1934. The method of path coefficients. Ann. Mathematical Statistics 51:161–215.CrossRefGoogle Scholar
34. Via, S. 1984. The quantitative genetics of polyphagy in an insect herbivore. I. Genotype-environment interaction in larval performance on different host species. Evolution 38:881–895.Google Scholar