Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-24T19:27:06.075Z Has data issue: false hasContentIssue false

A METHOD OF DESCRIBING AND USING VARIABILITY IN DEVELOPMENT RATES FOR THE SIMULATION OF INSECT PHENOLOGY

Published online by Cambridge University Press:  31 May 2012

Jacques Régnière
Affiliation:
Great Lakes Forest Research Centre, Canadian Forestry Service, Sault Ste. Marie, Ontario P6A 5M7

Abstract

An analytical method for the description of intrinsic variability in insect development rates for incorporation in phenology models is presented. Two sets of experimental data are used as examples. The method is easy to apply, can describe data accurately, produces highly realistic simulations of insect development, and is amenable to the simulation of age-dependent mortality, feeding, and reproduction. The method was developed for univoltine insects with discrete generations, but it can also be applied to multivoltine species with overlapping generations. A modification of the method to handle small samples is also discussed and applied to data.

Résumé

Une méthode analytique pour la description de la variabilité intrinsèque des taux de développement des insectes pour incorporation dans des modèles de phénologie est présentée. Deux ensembles de données expérimentales sont utilisés comme exemples. Cette méthode s'avère facile d'application, peut décrire des données avec précision, produit des simulations hautement réalistes, et se prête bien à la simulation de la mortalité, de l'alimentation et de la reproduction basées sur l'âge physiologique. La méthode a été développée pour des insectes univoltins à générations distinctes, mais peut aussi s'appliquer aux espèces multivoltines. Une modification de la méthode pour échantillons de petite taille est discutée et appliquée à des données.

Type
Articles
Copyright
Copyright © Entomological Society of Canada 1984

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Chen, W. and Allen, J. C.. 1983. Mathematical simulation technique for mortality in an insect population. Ent. Soc. Am. Bull. 29(3): 3136.Google Scholar
Goudriaan, J. 1973. Dispersion in simulation models of population growth and salt movement in the soil. Neth. J. agric. Sci. 21: 269281.Google Scholar
Grisdale, D. 1970. An improved laboratory method for rearing large numbers of spruce budworm, Choristoneura fumiferana (Lepidoptera: Tortricidae). Can. Ent. 102: 11111117.CrossRefGoogle Scholar
Logan, J. A., Stinner, R. E., Rabb, R. L., and Bacheler, J. S.. 1979. A descriptive model for predicting spring emergence of Heliothis zea populations in North Carolina. Environ. Ent. 8: 141146.CrossRefGoogle Scholar
Logan, J. A., Woolkind, D. J., Hoyt, S. C., and Tanigoshi, L. K.. 1976. An analytic model for description of temperature dependent rate phenomena in arthropods. Environ. Ent. 5: 11331140.CrossRefGoogle Scholar
Manetsch, T. J. 1976. Time-varying distributed delays and their use in aggregative models of large systems. Trans. Systems, Man, and Cybern., SMC–6: 547553.Google Scholar
McMorran, A. 1965. A synthetic diet for the spruce budworm, Choristoneura fumiferana (CLem.) (Lepidoptera: Tortricidae). Can. Ent. 97: 5862.CrossRefGoogle Scholar
Miller, C. A. 1958. The measurement of spruce budworm populations and mortality during the first and second larval instars. Can. J. Zool. 36: 409422.CrossRefGoogle Scholar
Régnière, J. 1982. A process-oriented model of spruce budworm phenology (Lepidoptera: Tortriciade). Can. Ent. 114: 811825.CrossRefGoogle Scholar
Régnière, J. 1983. An oviposition model for the spruce budworm (Lepidoptera: Tortricidae). Can. Ent. 115: 13711382.CrossRefGoogle Scholar
Régnière, J., Rabb, R. L., and Stinner, R. E.. 1981. Popillia japonica: Simulation of temperature-dependent development of the immatures, and prediction of adult emergence. Environ. Ent. 10: 290296.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: 719731.CrossRefGoogle ScholarPubMed
Sharpe, P. J. H. and DeMichele, D. W.. 1977. Reaction kinetics of poikilotherm development. J. theor. Biol. 64: 649670.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: 2138.CrossRefGoogle ScholarPubMed
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: 11671174.CrossRefGoogle Scholar
Stinner, R. E., Gutierrez, A. P., and Butler, G. D. Jr., 1974. An algorithm for temperature-dependent growth rate simulation. Can. Ent. 106: 519524.CrossRefGoogle Scholar