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DETERMINISTIC AND STOCHASTIC MODELS SIMULATING THE GROWTH OF INSECT POPULATIONS OVER A RANGE OF TEMPERATURES UNDER MALTHUSIAN CONDITIONS

Published online by Cambridge University Press:  31 May 2012

J. M. Hardman
J. M. Hardman
Affiliation:
CSIRO, Division of Entomology, Canberra City, A.C.T., Australia
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Abstract

The concept of degree-day summation was employed in three models of malthusian growth predictive over a range of temperatures. When supplied input data from life table studies conducted at five constant temperatures, the models were able to predict the magnitude and pattern of growth of populations of Tribolium confusum Duval reared under malthusian conditions. The stochastic model, moreover, revealed that the series of chance events found in the course of population growth could explain differences between one population and the next. When computer experiments on the importance of various life table parameters were run, the models revealed the overwhelming importance of time taken to mature on the rate of population growth. The level of fecundity was next in order of importance while population growth was least sensitive to changes in survivorship.

Type
Articles
Information
The Canadian Entomologist , Volume 108 , Issue 9 , September 1976 , pp. 907 - 924
Copyright
Copyright © Entomological Society of Canada 1976

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References

Birch, L. C. 1945 a. A contribution to the ecology of Calandra oryzae L. and Rhizopertha dominica Fab. (Coleoptera) in stored wheat. Trans. roy. Soc. S. Aust. 69: 140–151.Google Scholar
Birch, L. C. 1945 b. The influence of temperature, humidity and density on the oviposition of the small strain of Calandra oryzae L. and Rhizopertha dominica Fab. (Coleoptera). Aust. J. exp. Biol. med Sci. 23: 197–203.CrossRefGoogle ScholarPubMed
Birch, L. C. 1948. The intrinsic rate of natural increase of an insect population. J. Anim. Ecol. 17: 15–26.CrossRefGoogle Scholar
Cole, L. C. 1954. The population consequences of life history phenomena. Q. Rev. Biol. 29: 103–137.CrossRefGoogle ScholarPubMed
Gilbert, N. and Gutierrez, A. P.. 1973. A plant-aphid-parasite relationship. J. Anim. Ecol. 42: 323–340.CrossRefGoogle Scholar
Hardman, J. M. 1976. Life table data for use in deterministic and stochastic simulation models predicting the growth of insect populations under malthusian conditions. Can. Ent. 108: 897–906.CrossRefGoogle Scholar
Holling, C. S. 1966. The functional response of invertebrate predators to prey density. Mem. ent. Soc. Can. 48: 86 pp.Google Scholar
Howe, R. W. 1952. The biology of the rice weevil, Calandra oryzae (L.). Ann. appl. Biol. 39: 168–180.CrossRefGoogle Scholar
Howe, R. W. 1953. Studies on beetles of the family Ptinidae. VIII. The intrinsic rate of increase of some ptinid beetles. Ann. appl. Biol. 40: 121–133.CrossRefGoogle Scholar
Howe, R. W. 1956. The effect of temperature and humidity on the rate of development and mortality of Tribolium castaneum (Herbst) (Coleoptera, Tenebrionidae). Ann. appl. Biol. 44: 356–368.CrossRefGoogle Scholar
Howe, R. W. 1960. The effects of temperature and humidity on the rate of development and the mortality of Tribolium confusum Duval (Coleoptera, Tenebrionidae). Ann. app. Biol. 48: 363–376.CrossRefGoogle Scholar
Howe, R. W. 1962 a. The effect of temperature and relative humidity on the rate of development and the mortality of Tribolium madens (Charp.) (Coleoptera, Tenebrionidae). Ann. appl. Biol. 50: 649–660.CrossRefGoogle Scholar
Howe, R. W. 1962 b. The effects of temperature and humidity on the oviposition rate of Tribolium castaneum (Hbst.) (Coleoptera, Tenebrionidae). Bull. ent. Res. 53: 301–310.CrossRefGoogle Scholar
Howe, R. W. 1963. The prediction of the status of a pest by means of laboratory experiments. Wld. Rev. Pest Control, Spring '63, Vol. 2, Part 1.Google Scholar
Howe, R. W. 1965. A summary of estimates of optimal and minimal conditions for population increase of some stored products insects. J. stored Prod. Res. 1: 177–184.CrossRefGoogle Scholar
Howe, R. W. 1967. Temperature effects on embryonic development in insects. A. Rev. Ent. 12: 15–42.CrossRefGoogle ScholarPubMed
Howe, R. W. 1968. A further consideration of the heterogeneity of the developmental period of Tribolium castaneum (Herbst) (Col. Tenebrionidae) in constant environmental conditions. J. stored Prod. Res. 4: 221–231.CrossRefGoogle Scholar
Hubbell, S. P. 1971. Of sowbugs and systems: the ecological bioenergetics of a terrestrial isopod, pp. 269–324. In Patten, B.C. (Ed.), Systems analysis and simulation in ecology. I. Academic Press, New York.CrossRefGoogle Scholar
Hughes, R. D. 1973. Computer simulations of aphid populations, pp. 85–91. In Lowe, A. D. (Ed.), Perspectives in aphid biology. Bull. ent. Soc. N.Z. 2.Google Scholar
King, C. E. and Dawson, P. S.. 1971. Population biology and the Tribolium model. Evol. Biol. 5: 133–237.Google Scholar
Kitching, R. L. 1971. A simple simulation model of dispersal of animals among units of discrete habitats. Oecologia 7: 95–116.CrossRefGoogle ScholarPubMed
Kowal, N. E. 1971. A rationale for modelling dynamic ecological systems, pp. 123–196. In Patten, B.C. (Ed.), Systems analysis and simulation in ecology. I. Academic Press, New York.CrossRefGoogle Scholar
Krebs, C. J. 1972. Ecology: the experimental analysis of distribution and abundance. Harper and Row, New York.Google Scholar
Leslie, P. H. 1958. A stochastic model for studying the properties of certain biological systems by numerical methods. Biometrika 45: 16–37.CrossRefGoogle Scholar
May, R. M. 1973. Stability and complexity in model ecosystems. Princeton University Press, Princeton, N.J.Google ScholarPubMed
Mertz, D. B. 1972. The Tribolium model and the mathematics of population growth. Ann. Rev. ecol. Syst. 3: 51–78.CrossRefGoogle Scholar
Niven, B. S. 1967. The stochastic simulation of Tribolium populations. Physiol. Zool. 40: 67–82.CrossRefGoogle Scholar
Odum, E. P. 1971. Fundamentals of ecology. W. B. Saunders, Toronto.Google Scholar
Skellam, J. G. 1955. The mathematical approach to population dynamics, pp. 31–46. In Cragg, J. B. and Pirie, N. W. (Eds.), The numbers of man and animals. Oliver and Boyd, Edinburgh.Google Scholar
Smith, L. B. 1966. Effect of crowding on oviposition, development and mortality of Cryptolestes ferrugineus (Stephens). (Coleoptera, Cucujidae). J. stored Prod. Res.: 91–104.Google Scholar
Solomon, M. E. 1953. The population dynamics of storage pests. Proc. 12th int. Congr. Ent. (Montreal), Vol. 2, pp. 235–248.Google Scholar
Watt, K. E. F. 1968. Ecology and resource management: a quantitative approach. McGraw-Hill, New York.Google Scholar
Zettler, J. L. and LeCato, G. L.. 1974. Sublethal doses of malathion and dichlorvos: effects on fecundity of the black carpet beetle. J. econ. Ent. 67: 19–21.CrossRefGoogle Scholar