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The Use of Mathematics and Computers to Determine Optimal Strategy and Tactics for a Given Insect Pest Control Problem

Published online by Cambridge University Press:  31 May 2012

K. E. F. Watt
Affiliation:
Statistical Research Service, Canada Department of Forestry, Ottawa, Ontario

Abstract

A computer program has been developed for use in evaluating various strategies of insect pest control. At its present stage of development, the program simulates the effects of various dosages of insecticide, parasite release, and spraying of virus, or any combination of these, weather and pest density on reproduction, dispersal and mortality in a pest population. Effects of changing pest densities on the parasite population and on tree growth and mortality in a hypothetical, 6.4 million-acre balsam fir forest are simulated. Also, the computer simulates dispersal of parasites and disease incidence. All computations are performed separately for each of 625 4-mile-by-4-milc hypothetical squares of forest area in the hypothetical 10,000 square mile area. All operating costs and losses due to lost tree growth and tree mortality are printed each year. The particular set of strategies to be used in each game is included in the input data for each 35-year computer experiment, along with data on physiological parameters, allowable pest thresholds for each type of control, genetic parameters, behavioristic and dispersal data and costs of control. One play of the game takes 50-120 seconds on an IBM 7090 computer, depending on the array of strategies selected.

There were a number of significant conclusions from the simulation study. Biological control agents can not be as effective as insecticides unless they keep the pest at very low levels indefinitely. If they only drop the pest to about 10% of the peak level it would have attained without control, this may not be good enough to save the trees, because lethal effects accumulate. That is, pest densities which are not quite high enough to kill a tree quickly can kill it if the pest persists at these densities for several consecutive years. However, by selecting biological control agents with optimal physiological parameters, biological control can be made to produce totally effective control.

For all kinds of control, control is vastly more effective if applied 10 years or more before peak pest densities. Therefore, potential pests should be subjected to preventive control at pre-pest densities, rather than being allowed to reach critical densities. The latter policy ignores the hazard of successive years of sublethal pest densities whose effects can cumulate to become lethal. It should be noted, however, that this conclusion follows from exploration of a hypothetical situation, and might not be applicable when considered relative to the exigencies of an actual field problem.

Type
Articles
Copyright
Copyright © Entomological Society of Canada 1964

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References

Belyea, R. M. 1952. Death and deterioration of balsam fir weakened by spruce budworm defoliation in Ontario. J. For. 50: 729738.Google Scholar
Bird, F. T., and Burk, J. M.. 1961. Artificially disseminated virus as a factor controlling the European spruce sawfly, Diprion hercyniae (Htg.), in the absence of introduced parasites. Canad. Ent. 93: 228238.Google Scholar
Dobzhansky, T., and Wright, S.. 1943. Genetics of natural populations X. Dispersion rates in Drosophila pseudoobscura. Genetics 28: 304340.CrossRefGoogle ScholarPubMed
Elliott, K. R. 1960. A history of recent infestations of the spruce budworm in northwestern Ontario, and an estimate of resultant timber losses. For. Chron. 36: 6182.CrossRefGoogle Scholar
Glendenning, R. 1933. A successful parasite introduction into British Columbia. Canad. Ent. 65: 169171.CrossRefGoogle Scholar
Holling, C. S. 1963. An experimental component analysis of population processes. Mem. ent. Soc. Can. 32: 2232.Google Scholar
Holling, C. S. 1963. An experimental and mathematical analysis of attack by invertebrate predators. (Unpublished.)Google Scholar
Holling, C. S., Brown, D. M. and Watt, K. E. F.. 1963. Simulation of attack by invertebrate predators. (Unpublished.)Google Scholar
Jarvis, J. M. 1960. Forty-five years growth on the Goulais River watershed. Tech. Note Can. Dep. N. Aff. Nat. Res., For. Res. Div. 84, 3 pp.Google Scholar
McGugan, B. M., and Blais, J. R.. 1959. Spruce budworm parasite studies in northwestern Ontario. Canad. Ent. 91: 758783.Google Scholar
Miller, C. A. 1959. The interaction of the spruce budworm, Choristoneura fumiferana (Clem.), and the parasite Apanteles fumiferanae (Vier.). Canad. Ent. 91: 457477.CrossRefGoogle Scholar
Miller, C. A. 1960. The interaction of the spruce budworm, Choristoneura fumiferana (Clem.), and the parasite Glypta fumiferanae (Vier.). Canad. Ent. 92: 839850.CrossRefGoogle Scholar
Morris, R. F. 1955. The development of sampling techniques for forest insect defoliators, with particular reference to the spruce budworm. Canad. J. Zool. 33: 225294.CrossRefGoogle Scholar
Morris, R. F. (ed.) 1963. The dynamics of epidemic spruce budworm populations. Mem. ent. Soc. Can. 31, 332 pp.Google Scholar
Mott, D. G., Nairn, L. D. and Cook, J. A.. 1957. Radial growth in forest trees and effects of insect defoliation. For. Sci. 3: 286304.Google Scholar
Smith, R. W. 1959. Status in Ontario of Collyria calcitrator (Grav.) (Hymenoptera: Ichneumonidae) and of Pediobius beneficus (Gahan.) (Hymenoptera: Eulophidae) as parasites of the European wheat stem sawfly, Cephus pygmaeus (L.) (Hymenoptera: Cephidae). Canad. Ent. 91: 697700.Google Scholar
Vincent, A. B. 1955. Development of a balsam fir and white spruce forest in northwestern New Brunswick. For. Res. Note Can. Dep. N. Aff. Nat. Res. 6, 27 pp.Google Scholar
Watt, K. E. F. 1959. A mathematical model for the effect of densities of attacked and attacking species on the number attacked. Canad. Ent. 91: 129144.CrossRefGoogle Scholar
Watt, K. E. F. 1963. Dynamic programming, “look ahead programming,” and the strategy of insect pest control. Canad. Ent. 95: 525536.Google Scholar