Hostname: page-component-76fb5796d-zzh7m Total loading time: 0 Render date: 2024-04-26T21:03:27.588Z Has data issue: false hasContentIssue false
  • English
  • Français

FORECASTING THE EFFICACY OF OPERATIONAL BACILLUS THURINGIENSIS BERLINER APPLICATIONS AGAINST SPRUCE BUDWORM, CHORISTONEURA FUMIFERANA CLEMENS (LEPIDOPTERA: TORTRICIDAE), USING DOSE INGESTION DATA: INITIAL MODELS

Published online by Cambridge University Press:  31 May 2012

Richard A. Fleming  and
Kees van Frankenhuyzen
Richard A. Fleming
Affiliation:
Forest Pest Management Institute, Forestry Canada, PO Box 490, Sault Ste. Marie, Ontario, Canada P6A 5M7
Kees van Frankenhuyzen
Affiliation:
Forest Pest Management Institute, Forestry Canada, PO Box 490, Sault Ste. Marie, Ontario, Canada P6A 5M7
Get access Rights & Permissions [Opens in a new window]

Abstract

Single aerial applications of Bacillus thuringiensis Berliner (Bt) to control infestations of the eastern spruce budworm (Choristoneura fumiferana Clemens) have had varied operational success. Double applications are too expensive for general use, but might prove useful if directed to areas where the initial application was unsuccessful. This requires forecasts of the efficacy of the initial application in operational spray blocks within 4–5 days.

Data were collected in 30 spray blocks in 1989 in a feasibility study to determine if such forecasts of spray efficacy could be made from the prespray budworm population density, N0, and from the proportion of the population that had ingested a lethal dose Bt within 2 days of application, M. A mathematical model forecasting the postspray budworm population density, NF, was derived from population-dynamic considerations and fitted (r2 = 0.48, p < 0.0001):

The proportion of current foliage defoliated, D, depended (r = 0.81) on N0 and on whether the block was sprayed (I = 0) or not (I = 1):

Only one measure of defoliation involved M in any statistically significant way. The predicted (from values of N0) proportion of defoliation prevented by Bt application, dD, was weakly (r2 = 0.25, p = 0.002) related to M:

The large proportion of the variation in efficacy that remains unexplained by the models involving M limits the operational utility of this approach as it now stands for specific sites. The potential for further development of these models as decision support tools for fairly large spray blocks is discussed in terms of improving the sampling plan and including additional predictor variables.

Methods are also presented that reduce bias in calculations of population reduction (Abbott 1925) and foliage protection when data are available from few control and many treatment blocks.

Résumé

Les arrosages aériens uniques du bacille Bacillus thuringiensis Berliner (Bt) en vue de contenir les infestations de la Tordeuse des bourgeons de l’épinette (Choristoneura fumiferana Clemens) ont des taux de succès très variés. Les traitements doubles sont trop coûteux pour être employés couramment, mais pourraient être utiles dans des régions où le premier traitement s’est avéré inefficace. Il faudrait dans ce cas pouvoir préévaluer l’efficacité du traitement initial durant 4–5 jours dans des carrés d’arrosage spécifiques.

Des données ont été recueillies en 1989 dans 30 carrés d’arrosage au cours d’une étude de faisabilité, dans le but de déterminer si l’efficacité de l’arrosage pouvait être préévaluée en fonction de la densité initiale de la population de tordeuses avant l’arrosage, N0, et en fonction de la proportion de la population qui avait consommé une dose létale de Bt en moins de 2 jours après le traitement, M. Un modèle mathématique permettant de prédire la densité de la population de tordeuses après l’arrosage, NF, a été élaboré à partir de considérations démographiques et adjusté (r2 = 0.48, p < 0,0001):

La proportion de feuillage présent défolié, D, était fonction (r2 = 0,81) de N0, et variait selon que le carré avait été traité (I = 0) ou non (I = 1) :

Une seule mesure de la défoliation était fonction de M de façon statistiquement significative. La proportion prédite (à partir des valeurs de N0) de défoliation empêchée par l’application du bacille, dD, n’était que faiblement (r2 = 0,25, p = 0,002) reliée à M:

La proportion importante de la variation dans l’efficacité qui reste inexpliquée selon les modèles basés sur M limite l’utilité opérationnelle de cette approche comme elle existe actuellement dans le cas de sites spécifiques. La possibilité d’élargir ces modèles pour pouvoir les utiliser dans la planification de l’arrosage de carrés de grande taille est examinée en vue de l’amélioration du plan d’échantillonnage et de l’utilisation de variables additionnelles de prévision.

D’autres méthodes permettant de réduire le biais dans le calcul de la réduction de la population (Abbott 1925) et de la protection du feuillage sont examinées dans les cas où il y a peu de carrés témoins et plusieurs carrés traités.

[Traduit par la rédaction]

Type
Articles
Information
The Canadian Entomologist , Volume 124 , Issue 6 , December 1992 , pp. 1101 - 1113
Copyright
Copyright © Entomological Society of Canada 1992

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

Abbott, W.S. 1925. A method for computing the effectiveness of an insecticide. Journal of Economic Entomology 18: 265–267.CrossRefGoogle Scholar
Carter, N.E. 1989. Protection spraying against spruce budworm in New Brunswick 1988. Department of Natural Resources and Energy, Fredericton, N.B. NRE/RNE-89-02-001.Google Scholar
Carter, N.E. 1990. Protection spraying against spruce budworm in New Brunswick 1989. Department of Natural Resources and Energy, Fredericton, N.B.Google Scholar
Draper, N.R., and Smith, H.. 1981. Applied Regression Analysis. Wiley, New York, NY. 709 pp.Google Scholar
Fast, P.G., and Régnière, J.. 1984. Effect of exposure time to Bacillus thuringiensis on mortality and recovery of the spruce budworm (Lepidoptera: Tortricidae). The Canadian Entomologist 116: 123–130.CrossRefGoogle Scholar
Fettes, J.J. 1950. Investigations of sampling techniques for population studies of the spruce budworm on balsam fir in Ontario. Forest Insect Laboratory Sault Ste. Marie Ont. Annual Technical Report 4: 163–401.Google Scholar
Fleming, R., and Retnakaran, A.. 1985. Evaluating single treatment data using Abbott's formula with reference to insecticides. Journal of Economic Entomology 78: 1179–1181.CrossRefGoogle Scholar
Fleming, R.A., Shoemaker, C.A., and Stedinger, J.R.. 1983. Analysis of the regional dynamics of unsprayed spruce budworm (Lepidoptera: Tortricidae) populations. Environmental Entomology 12: 707–713.CrossRefGoogle Scholar
Fleming, R.A., Shoemaker, C.A., and Stedinger, J.R.. 1984. An assessment of the impact of large scale spraying operations on the regional dynamics of spruce budworm (Lepidoptera: Tortricidae) populations. The Canadian Entomologist 116: 633–644.CrossRefGoogle Scholar
Frane, J. 1988. All possible subsets regression. pp. 919–939 in Dixon, W.J. (Ed.), BMDP Statistical Software Manual. University of California Press, Berkeley, CA. 1234 pp.Google Scholar
Grisdale, D. 1970. An improved method for rearing large numbers of spruce budworm, Choristoneura fumiferana (Lepidoptera: Tortricidae). The Canadian Entomologist 102: 1111–1117.CrossRefGoogle Scholar
Holling, C.S. 1965. The functional response of predators to prey density and its role in mimicry and population regulation. Memoirs of the Entomological Society of Canada 45: 1–60.Google Scholar
Irland, L.C., and Rumpf, T.A.. 1987. Cost trends for Bacillus thuringiensis in the Maine spruce budworm control program. Bulletin of the Entomological Society of America 33: 86–90.CrossRefGoogle Scholar
Jones, D.D. 1979. The budworm site model. pp. 91–155 in Norton, G.A., and Holling, C.S. (Eds.), Pest Management, Pergamon Press, Oxford. 349 pp.Google Scholar
Lysyk, T.J. 1990. Relationships between spruce budworm (Lepidoptera: Tortricidae) egg mass density and resultant defoliation of balsam fir and white spruce. The Canadian Entomologist 122: 253–262.CrossRefGoogle Scholar
Ralston, M. 1988. Derivative-free nonlinear regression. pp. 389–417 in Dixon, W.J. (Ed.), BMDP Statistical Software Manual. University of California Press, Berkeley, CA. 1234 pp.Google Scholar
Régnière, J., and You, M.. 1991. A process-oriented model of spruce budworm (Lepidoptera: Tortricidae) feeding on balsam fir and white spruce. Ecological Modelling 54: 277–297.CrossRefGoogle Scholar
Stedinger, J.R. 1984. A spruce budworm – forest model and its implications for suppression programs. Forest Science 30: 597–615.Google Scholar
Stelzer, M.J., and Beckwith, R.C.. 1988. Comparison of two isolates of Bacillus thuringiensis in a field test on western spruce budworm (Lepidoptera: Tortricidae). Journal of Economic Entomology 81: 880–886.CrossRefGoogle Scholar
van Frankenhuyzen, K. 1990 a. Development and current status of Bacillus thuringiensis for control of defoliating forest insects. Forestry Chronicle 66: 498–507.CrossRefGoogle Scholar
van Frankenhuyzen, K. 1990 b. Effect of temperature and exposure time on toxicity of Bacillus thuringiensis spray deposits to spruce budworm, Choristoneura fumiferana (Lepidoptera: Tortricidae). The Canadian Entomologist 122: 69–75.CrossRefGoogle Scholar
van Frankenhuyzen, K., and Nystrom, C.W.. 1987. Effect of temperature on mortality and recovery of spruce budworm (Lepidoptera: Tortricidae) exposed to Bacillus thuringiensis. The Canadian Entomologist 119: 941–954.CrossRefGoogle Scholar
van Frankenhuyzen, K., and Nystrom, C.W.. 1989. Residual toxicity of a high-potency formulation of Bacillus thuringiensis to spruce budworm (Lepidoptera: Tortricidae). Journal of Economic Entomology 82: 868–872.CrossRefGoogle Scholar