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A SIMULATION MODEL OF NORTHERN CORN ROOTWORM, DIABROTICA BARBERI SMITH AND LAWRENCE (COLEOPTERA: CHRYSOMELIDAE), POPULATION DYNAMICS AND OVIPOSITION: SIGNIFICANCE OF HOST PLANT PHENOLOGY

Published online by Cambridge University Press:  31 May 2012

Steven E. Naranjo
Affiliation:
Department of Entomology, Comstock Hall, Cornell University, Ithaca, New York, USA14853
Alan J. Sawyer
Affiliation:
USDA-ARS, Plant Protection Research Unit, U.S. Plant, Soil and Nutrition Laboratory, Tower Road, Ithaca, New York, USA14853

Abstract

Based on field and laboratory research, a simulation model was developed that describes the within-season population dynamics and oviposition of adult northern corn rootworm beetles, Diabrotica barberi Smith and Lawrence, in field corn, Zea mays L. Particular emphasis was placed on the role of host plant phenology. Overall goals were to examine the contribution of insect dispersal to the dynamics of single fields, and provide a means of examining the factors influencing insect/plant synchrony and the relationship between adult abundance, oviposition, and crop phenology. The model is process-oriented and integrates component models for corn phenology, and adult emergence, mortality, dispersal, reproductive development, and oviposition.

Comparison of field data with simulations excluding dispersal generally indicated a net emigration of beetles from corn fields on a season-long basis; however, the timing and magnitude of dispersal from fields were strongly influenced by the relative timing of corn flowering, beetle sex, and the reproductive maturity of females. Simulation and field data were used to describe and estimate the parameters of a component model for dispersal incorporating these features. Various component models and the overall system model were validated against independent field data. The model provided adequate prediction of adult emergence and crop phenology for three varieties on which it was based, but consistently underpredicted total oviposition and poorly predicted the phenology of two different corn varieties. Overall, the model accurately predicted seasonal population trends, the relative abundance of mature females, and the relationship between adult abundance and oviposition.

Résumé

On a mis au point un modèle de simulation décrivant la dynamique des populations saisonnière et la ponte de la chrysomèle des racines du maïs, Diabrotica barberi Smith et Lawrence, sur le maïs, Zea mays L., sur la base de résultats de terrain et de laboratoire. On s’est concentré sur le rôle de la phénologie de la plante hôte. Les objectifs d’ensemble étaient d’examiner la contribution de la dispersion sur la dynamique des champs individuels, et de développer des outils permettant d’étudier les facteurs qui influencent la synchronie insecte–plante et la relation entre l’abondance des adultes, la ponte et la phénologie de la culture. Le modèle est axé sur la description des processus et intègre des sous-modèles de la phénologie du maïs, de l’émergence des adultes, de la mortalité, de la dispersion, du développement reproducteur, et de la ponte.

La comparaison de données du terrain et des résultats de simulation faisant abstraction de la dispersion a généralement indiqué une émigration nette des chrysomèles des champs de maïs sur une base saisonnière; cependant, le moment et l’amplitude de l’émigration des champs étaient fortement influencés par le moment relatif de la floraison, le sexe des chrysomèles, et la maturité reproductive des femelles. Les résultats de simulation et les données du terrain ont été utilisés pour décrire et estimer les paramètres d’un sous-modèle de dispersion incorporant ces caractéristiques. Plusieurs sous-modèles et le modèle de l’ensemble du système ont pu être validés sur la base de données de terrain indépendantes. Le modèle a permis des prévisions correctes de l’émergence des adultes et de la phénologie du maïs pour les trois variétés qui ont servi au développement, mais il a généralement sous-estimé la ponte totale et a donné des prévisions incorrectes de la phénologie pour deux variétés différentes de maïs. Dans l’ensemble, le modèle a prédit correctement les tendances saisonnières des populations, l’abondance relative des femelles matures, et la relation entre l’abondance des adultes et la ponte.

Type
Articles
Copyright
Copyright © Entomological Society of Canada 1989

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References

Branson, T.F., and Krysan, J.L.. 1981. Feeding and oviposition behavior and life cycle strategies of Diabrotica: an evolutionary view with implications for pest management. Environ. Ent. 10: 826831.CrossRefGoogle Scholar
Cinereski, J.E., and Chiang, H.C.. 1968. The pattern of movements of adults of the northern corn rootworm inside and outside of corn fields. J. econ. Ent. 61: 826831.CrossRefGoogle Scholar
Clark, W.C., Jones, D.D., and Holling, C.S.. 1979. Lessons for ecological policy design: a case study of ecosystem management. Ecol. Modelling 7: 826831.CrossRefGoogle Scholar
Conover, W.J. 1980. Practical Nonparametric Statistics, 2nd ed. John Wiley and Sons, New York.Google Scholar
Curry, G.L., Feldman, R.M., and Sharpe, P.J.H.. 1978 a. Foundations of stochastic development. J. Theor. Biol. 74: 826831.CrossRefGoogle ScholarPubMed
Curry, G.L., Feldman, R.M., and Smith, K.C.. 1978 b. A stochastic model of a temperature dependent population. Theor. Pop. Biol. 13: 826831.CrossRefGoogle ScholarPubMed
Dennis, B., Kemp, W.P., and Beckwith, R.C.. 1986. Stochastic model of insect phenology: estimation and testing. Environ. Ent. 15: 826831.CrossRefGoogle Scholar
Dominique, C.R., Yule, W.N., and Martel, P.. 1983. Influence of soil type, soil moisture, and soil surface conditions on oviposition preferences of the northern corn rootworm, Diabrotica longicornis (Coleoptera: Chrysomelidae). Can. Ent. 115: 826831.Google Scholar
Eichers, T.R., Andrilenas, P.A., and Anderson, T.W.. 1978. Farmer's use of pesticides in 1976. USDA Agric. Econ. Rep. 418. 58 pp.Google Scholar
Foster, R.E., Ruesink, W.G., and Luckmann, W.H.. 1979. Northern corn rootworm egg sampling. J. econ. Ent. 72: 826831.CrossRefGoogle Scholar
Foster, R.E., Tollefson, J.J., Nyrop, J.P., and Hein, G.L.. 1986. Value of adult corn rootworm population estimates in pest management decision making. J. econ. Ent. 79: 826831.CrossRefGoogle Scholar
Gage, S.H., and Haynes, D.L.. 1975. Emergence under natural and manipulated conditions of Tetrastichus julis, an introduced larval parasite of the cereal leaf beetle, with reference to regional population management. Environ. Ent. 4: 826831.CrossRefGoogle Scholar
Haddock, R.C. 1984. Orientation and movement of the northern corn rootworm, Diabrotica barberi over large and small distances. Ph.D. dissertation, Cornell Univ., Ithaca, New York.Google Scholar
Harrison, S.R., Fick, G.W., and McCulloch, C.E.. 1988. Statistical validation of simulation models. In Shoemaker, C.A. (Ed.), Insect Pest Management Modelling. Wiley Interscience, New York. In press.Google Scholar
Hill, R.E., and Mayo, Z.B.. 1980. Distribution and abundance of corn rootworm species as influenced by topography and crop rotation in eastern Nebraska. Environ. Ent. 9: 826831.CrossRefGoogle Scholar
Hunter, R.B., Hunt, L.A., and Kannenberg, L.W.. 1974. Photoperiod and temperature effects on corn. Can. J. Plant Sci. 54: 826831.CrossRefGoogle Scholar
Johnson, I.R., and Thornley, J.H.M.. 1985. Temperature dependence of plant and crop processes. Ann. Botany 55: 826831.CrossRefGoogle Scholar
Lance, D.R., and Fisher, J.R.. 1987. Food quality of various plant tissues for adults of the northern corn rootworm (Coleoptera: Chrysomelidae). J. Kans. ent. Soc. 60: 826831.Google Scholar
Logan, J.A., Stinner, R.E., Rabb, R.L., and Bachelor, J.S.. 1979. A descriptive model for predicting spring emergence of Heliothis zea populations in North Carolina. Environ. Ent. 8: 826831.CrossRefGoogle Scholar
Luckmann, W.H. 1978. Insect control in corn; practices and prospects. pp. 137155in Smith, E.H., and Pimentel, D. (Eds.), Pest Control Strategies. Academic Press, New York.CrossRefGoogle Scholar
Mederski, H.J., Miller, M.E., and Weaver, C.R.. 1973. Accumulated heat units for classifying corn hybrid maturity. Agron. J. 65: 826831.CrossRefGoogle Scholar
Mooney, E., and Turpin, F.T.. 1976. ROWSIM corn rootworm simulator. Purdue Univ. Agric. Exp. Stn. Res. Bull. 938.Google Scholar
Morris, R.F. 1963. The development of a population model for the spruce budworm through analysis of survival rates. Mem. ent. Soc. Can. 31: 826831.Google Scholar
Naranjo, S.E., and Sawyer, A.J.. 1987. Reproductive biology and survival of Diabrotica barberi (Coleoptera: Chrysomelidae): effect of temperature, food, and seasonal time of emergence. Ann. ent. Soc. Am. 80: 826831.CrossRefGoogle Scholar
Naranjo, S.E., and Sawyer, A.J.. 1988 a. Impact of host plant phenology on the population dynamics and oviposition of the northern corn rootworn, Diabrotica barberi (Coleoptera: Chrysomelidae) in field corn. Environ. Ent. 17: 826831.CrossRefGoogle Scholar
Naranjo, S.E., and Sawyer, A.J.. 1988 b. A temperature and age-dependent simulation model of reproduction for the northern corn rootworm, Diabrotica barberi (Coleoptera: Chrysomelidae). Can. Ent. 120: 826831.CrossRefGoogle Scholar
Naranjo, S.E., and Sawyer, A.J.. 1989. Analysis of a simulation model of northern corn rootworm, Diabrotica barberi Smith and Lawrence (Coleoptera: Chrysomelidae), dynamics in field corn, with implications for population management. Can. Ent. 121: 826831.Google Scholar
Osawa, A., Shoemaker, C.A., and Stedinger, J.R.. 1983. A stochastic model of balsam fir bud phenology utilizing maximum likelihood parameter estimation. Forest Sci. 29: 826831.Google Scholar
Overton, W.S. 1977. A strategy of model construction. pp. 5073in Hall, C.S., and Day, J. W. (Eds.), Ecosystem Modeling in Theory and Practice. John Wiley and Sons, New York.Google Scholar
Russell, W.K., and Stuber, C.W.. 1985. Genotype × photoperiod and genotype × temperature interactions for maturity in maize. Crop Sci. 25: 826831.CrossRefGoogle Scholar
SAS Institute. 1985. SAS User's Guide: Statistics. SAS Institute Inc., Cary, North Carolina.Google Scholar
Sawyer, A.J. 1985. Efficient monitoring of the density of adult northern corn rootworm in field corn. Can. Ent. 117: 826831.CrossRefGoogle Scholar
Sawyer, A.J., and Haynes, D.L.. 1985. Simulating the spatiotemporal dynamics of the cereal leaf beetle in a regional crop system. Ecol. Modelling 30: 826831.CrossRefGoogle Scholar
Schoolfield, R.M., Sharpe, P.J.H., and Magnuson, C.E.. 1981. Non-linear regression of biological temperature-dependent rate models based on absolute reaction-rate theory. J. Theor. Biol. 88: 826831.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: 826831.CrossRefGoogle ScholarPubMed
Sharpe, P.J.H., and DeMichele, D.W.. 1977. Reaction kinetics of poikilotherm development. J. Theor. Biol. 64: 826831.CrossRefGoogle ScholarPubMed
Stamm, D.E., Mayo, Z.B., Campbell, J.B., Witkowski, J.F., Anderson, W., and Kozub, R.. 1985. Western corn rootworm (Coleoptera: Chrysomelidae) beetle counts as a means of making larval control recommendations in Nebraska. J. econ. Ent. 78: 826831.CrossRefGoogle Scholar
Stauber, M.S., Zuber, M.S., and Decker, W.L.. 1978. Estimation of the tasseling date of corn (Zea mays L.). Agron. J. 60: 826831.Google Scholar
Stinner, R.E., Barfield, C.S., Stimac, J.L., and Dohse, L.. 1983. Dispersal and movement of insect pests. A. Rev. Ent. 28: 826831.CrossRefGoogle Scholar
Stinner, R.E., Butler, G.D. Jr., Batcheler, J.S., and Tuttle, C.. 1975. Simulation of temperature-dependent development in population dynamics models. Can. Ent. 107: 826831.CrossRefGoogle Scholar
Turpin, F.T., Dumenil, L.C., and Peters, D.C.. 1972. Edaphic and agronomic characters that affect potential for rootworm damage to corn in Iowa. J. econ. Ent. 65: 826831.CrossRefGoogle Scholar
Turpin, F.T., and Maxwell, J.D.. 1976. Decision-making related to use of soil insecticides by Indiana corn farmers. J. econ. Ent. 69: 826831.CrossRefGoogle Scholar
Warrington, I.J., and Kanemasu, E.T.. 1983. Corn growth response to temperature and photoperiod I. Seedling emergence, tassel initiation, and anthesis. Agron. J. 75: 826831.Google Scholar
Watt, K.E.F. 1968. Ecology and Resource Management. McGraw Hill, New York. 450 pp.Google Scholar
Welch, S.M., Croft, B.A., and Michels, M.F.. 1981. Validation of pest management models. Environ. Ent. 10: 826831.CrossRefGoogle Scholar