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DISTRIBUTION MODEL OF HELIOTHIS ZEA (LEPIDOPTERA: NOCTUIDAE) DEVELOPMENT TIMES1

Published online by Cambridge University Press:  31 May 2012

P. J. H. Sharpe ,
R. M. Schoolfield  and
G. D. Butler Jr.
P. J. H. Sharpe
Affiliation:
Biosystems Research Division, Department of Industrial Engineering, Texas A&M University, College Station, Texas 77843
R. M. Schoolfield
Affiliation:
Biosystems Research Division, Department of Industrial Engineering, Texas A&M University, College Station, Texas 77843
G. D. Butler Jr.
Affiliation:
Western Cotton Research Laboratory, Agricultural Research, SEA, USDA, Phoenix, Arizona 85040
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Abstract

Two geographical biotypes (G and A) of Heliothis zea Boddie were identified in a constant temperature laboratory study. The G (Georgia) biotype was found to have a mean development rate 5 ± 1% faster and coefficient of variability 44 ± 9% higher than the comparable A (Arizona) biotype. Each geographical biotype was described by a biophysically based, nonlinear development function with an R2 equal to 0.995. At temperature ranging from 15.6° to 35.6°C, the adult emergence distributions transformed to a physiological age scale were shown statistically to be independent of temperature. They could be described by a “same shape” distribution function. The empirical same shape distribution for rate was not significantly different from a hypothetical normal distribution.

Résumé

Deux biotypes géographiques (G et A) d’Heliothis zea Boddie ont été identifiés lors d’une étude de laboratoire à température constante. Le biotype G (Georgia) a une vitesse de développement de 5 ± 1% plus élevée, et uncoefficient de variation de 44 ± 9% plus élevé que le biotype A (Arizona). Chaque biotype géographique est décrit par une fonction non-linéaire du développement, ayant une base biophysique, et dont le R2 est de 0.995. A des températures allant de 15.6 à 35.6°C, les distributions de l’émergence des adultes adaptées à une échelle d’âge physiologique se sont révélées statistiquement indépendantes de la température. Elles peuvent être décrites par une fonction de distribution “uniforme”. La distribution empirique uniforme du taux de développement n’est pas significativement différente d’une distribution normale hypothétique.

Type
Articles
Information
The Canadian Entomologist , Volume 113 , Issue 9 , September 1981 , pp. 845 - 856
Copyright
Copyright © Entomological Society of Canada 1981

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References

Barfield, C. S., Sharpe, P. J. H., and Bottrell, D. G.. 1977. A temperature-driven developmental model for parasite Bracon mellitor (Hymenoptera: Braconidae). Can. Ent. 109: 1503–1514.CrossRefGoogle Scholar
Butler, G. D. Jr., 1976. Bollworm: Development in relation to temperature and larval food. Environ. Ent. 5: 520–522.CrossRefGoogle Scholar
Curry, G. L., Feldman, R. M., and Sharpe, P. J. H.. 1978. Foundations of stochastic development. J. theor. Biol. 74: 397–410.CrossRefGoogle ScholarPubMed
Curry, G. L., Sharpe, P. J. H., DeMichele, D. W., and Cate, J. R.. 1980. Towards a management model of the cotton-boll weevil ecosystem. J. environ. Mgmt. 11: 187–223.Google Scholar
Hartstack, A. W. Jr., Witz, J. W., Hollingsworth, J. P., Ridgeway, R. L., and Lopez, J. D.. 1976. MOTHZV-2: A computer simulation of Heliothis zea and Heliothis virescens population dynamics (user manual). USDA, ARG. RES. SERV. ARS-S-127.Google Scholar
Henneberry, T. J. and Kishaba, A. N.. 1966. Pupal size and mortality, longevity and reproduction of cabbage loopers reared at several densities. J. econ. Ent. 59: 1490–1493.CrossRefGoogle ScholarPubMed
Holtzer, T. O., Bradley, J. R. Jr., and Rabb, R. L.. 1976. Geographic and genetic variation in time required for emergence of diapausing Heliothis zea. Ann. ent. Soc. Am. 69: 261–265.CrossRefGoogle Scholar
Howe, R. W. 1967. Temperature effects on embryonic development in insects. A. Rev. Ent. 12: 15–42.CrossRefGoogle ScholarPubMed
Lindgren, B. W. 1968. Statistical Theory. Macmillan, New York.Google Scholar
Marquardt, D. W. 1963. An algorithm for least-squares estimation of nonlinear parameters. J. Soc. ind. Appl. Math. 11: 431–441.CrossRefGoogle Scholar
Menke, W. W. 1973. A computer simulation model: the velvetbean caterpillar in the soybean agroecosystem. Fla Ent. 56: 92–102.CrossRefGoogle Scholar
Menke, W. W. 1974. Identification of viable biological strategies for pest management by simulation studies. IEEE Transactions on Systems, Man, and Cybernetics. Vol. SMC-4, No. 4. July 1974: 379–386.Google Scholar
Schoolfield, R. M., Sharpe, P. J. H., and Magnuson, C. E.. 1981. Nonlinear regression of biological temperature-dependent rate models based on absolute reaction-rate theory. J. theor. Biol. 88: 719–731.CrossRefGoogle ScholarPubMed
Sharpe, P. J. H. and DeMichele, D. W.. 1977. Reaction kinetics of poikilotherm development. J. theor. Biol. 64: 649–670.CrossRefGoogle ScholarPubMed
Sharpe, P. J. H., Curry, G. L., DeMichele, D. W., and Cole, C. L.. 1977. Distribution model of organism development times. J. theor. Biol. 66: 21–38.CrossRefGoogle ScholarPubMed
Sharpe, P. J. H. and Hu, L. C.. 1980. Reaction kinetics of nutrition dependent poikilotherm development. J. theor. Biol. 82: 317–333.CrossRefGoogle ScholarPubMed
Stephens, M. A. 1974. EDF statistics for goodness-of-fit and some comparisons. J. Am. stat. Ass. 69: 730–737.CrossRefGoogle Scholar
Starks, K. J., Wood, E. A. Jr., and Teetes, G. L.. 1973. Effects of temperature on the preference of two greenbug biotypes for sorghum selections. Environ. Ent. 2: 351–354.CrossRefGoogle Scholar
Stinner, R. E., Butler, G. D. Jr., Bacheler, J. S., and Tuttle, C.. 1975. Simulation of temperature-dependent development in population dynamics models. Can. Ent. 107: 1167–1174.CrossRefGoogle Scholar