Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-19T15:12:17.509Z Has data issue: false hasContentIssue false

Temperature-based model for predicting univoltine brood proportions in spruce beetle (Coleoptera: Scolytidae)

Published online by Cambridge University Press:  31 May 2012

E. Matthew Hansen*
Affiliation:
USDA Forest Service, Rocky Mountain Research Station, 860 N 1200 E, Logan, Utah, United States 84321
Barbara J. Bentz
Affiliation:
USDA Forest Service, Rocky Mountain Research Station, 860 N 1200 E, Logan, Utah, United States 84321
David L. Turner
Affiliation:
USDA Forest Service, Rocky Mountain Research Station, 860 N 1200 E, Logan, Utah, United States 84321
*
1 Author to whom all correspondence should be addressed (E-mail: [email protected]).

Abstract

The spruce beetle, Dendroctonus rufipennis (Kirby), has possible life cycles of 1 or 2 years. Empirical and experimental evidence suggest that temperature is the primary regulator of these life-history pathways. These different life cycles potentially result in substantial differences in population dynamics and subsequent spruce mortality. A multiyear field study was conducted in Utah, Colorado, and Alaska, to monitor spruce beetle development under a variety of field conditions with concurrent air temperature measurements. This information was used to model the tree- or stand-level proportion of univoltine beetles as a function of air temperature. Temperatures were summarized as averages, cumulative time, and accumulated heat units above specified thresholds over various seasonal intervals. Sampled proportions of univoltine insects were regressed against the summarized temperature values in logistic models. The best predictive variable, as evaluated by Akaike’s Information Criterion, was found to be cumulative hours above a threshold of 17 °C elapsed from 40 to 90 days following peak adult funnel-trap captures. Because the model can be used to forecast trends in spruce beetle populations and associated spruce mortality, it is a tool for forest planning.

Résumé

Le Dendroctone de l’épinette, Dendroctonus rufipennis (Kirby), a un cycle biologique de 1 ou 2 ans. Des données empiriques et des résultats d’expériences laissent croire que la température est le principal facteur de contrôle du développement. Ces cycles différents peuvent donner lieu à des différences importantes dans la dynamique des populations et éventuellement dans la mortalité des épinettes. Une étude de plusieurs années en nature, en Utah, au Colorado et en Alaska, a permis de suivre le développement de l’insecte dans des conditions diverses; la température de l’air a été relevée en même temps que les données. Cette information a été utilisée pour créer un modèle pour évaluer la proportion des individus univoltins à l’échelle d’un arbre ou d’un boisé en fonction de la température de l’air. Les températures sont exprimées par des moyennes, des durées cumulatives et des unités de chaleur accumulées au-dessus de seuils spécifiques au cours de divers intervalles saisonniers. Des droites de régression mettent en relation les pourcentages d’insectes univoltins dans les échantillons et les températures résumées dans les modèles logistiques. La variable la plus prédictive, d’après le critère d’information d’Akaike, s’est avérée être le nombre cumulatif d’heures au-dessus d’un seuil de 17 °C, de 40 à 90 jours après la capture maximale d’adultes dans des pièges à entonnoirs. Comme le modèle permet de prédire les tendances des populations de dendroctones et la mortalité des épinettes qui en dépend, il peut s’avérer utile en aménagement des forêts.

[Traduit par la Rédaction]

Type
Articles
Copyright
Copyright © Entomological Society of Canada 2001

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

Burnham, K.P., Anderson, D.R. 1998. Model selection and inference: a practical information-theoretic approach. New York: Springer-Verlag IncCrossRefGoogle Scholar
Dyer, E.D.A. 1969. Influence of temperature inversion on development of spruce beetle, Dendroctonus obesus (Mannerheim) (Coleoptera: Scolytidae). Journal of the Entomological Society of British Columbia 66: 41–5Google Scholar
Dyer, E.D.A. 1970. Larval diapause in Dendroctonus obesus (Mannerheim) (Coleoptera: Scolytidae). Journal of the Entomological Society of British Columbia 67: 1821Google Scholar
Dyer, E.D.A., Hall, P.M. 1977. Factors affecting larval diapause in Dendroctonus rufipennis (Mannerheim) (Coleoptera: Scolytidae). The Canadian Entomologist 109: 1485–90CrossRefGoogle Scholar
Dyer, E.D.A., Skovsgaard, J.P., McMullenm, L.H. 1968. Temperature in relation to development rates of two bark beetles. Bi-monthly Research Notes – Canada, Forestry Service 24: 15–6Google Scholar
Efron, B., Tibshirani, R.J. 1993. An introduction to the bootstrap. New York: Chapman and Hall IncCrossRefGoogle Scholar
Hansen, E.M., Bentz, B.J., Turner, D.L. 2001. Physiological basis for flexible voltinism in the spruce beetle (Coleoptera: Scolytidae). The Canadian Entomologist 133: 805–17CrossRefGoogle Scholar
Holsten, E.H., Thier, R.W., Munson, A.S., Gibson, K.E. 1999. The spruce beetle. United States Department of Agriculture Forest Service, Forest Insect and Disease Leaflet 127Google Scholar
Husch, B., Miller, C.I., Beers, T.W. 1982. Forest mensuration. New York: John Wiley and Sons, IncGoogle Scholar
Littell, R.C., Milliken, G.A., Stroup, W.W., Wolfinger, R.D. 1996. SAS system for mixed models. Cary, North Carolina: SAS Institute IncGoogle Scholar
Massey, C.L., Wygant, N.D. 1954. Biology and control of the Engelmann spruce beetle in Colorado. United States Department of Agriculture Circular 944Google Scholar
McCambridge, W.F., Knight, F.B. 1972. Factors affecting spruce beetles during a small outbreak. Ecology 53: 830–9CrossRefGoogle Scholar
Nagelkerke, N.J.D. 1991. A note on a general definition of the coefficient of determination. Biometrika 78: 691–2CrossRefGoogle Scholar
Newnham, R.M. 1992. Variable-form taper functions for four Alberta tree species. Canadian Journal of Forest Research 22: 210–23CrossRefGoogle Scholar
Reynolds, K.M., Holsten, E.H. 1994. Relative importance of risk factors for spruce beetle outbreaks. Canadian Journal of Forest Research 24: 2089–95CrossRefGoogle Scholar
Safranyik, L., Simmons, C., Barclay, H.J. 1990. A conceptual model of spruce beetle population dynamics. Forestry Canada Pacific and Yukon Region Information Report BC–X–316Google Scholar
Schaupp, W.C., Frank, M., Johnson, S. 1999. Evaluation of the spruce beetle in 1998 within the Routt Divide blowdown of October 1997, on the Hahns Peak and Bears Ears Ranger Districts, Routt National Forest, Colorado. United States Forest Service Biological Evaluation R2–99–08Google Scholar
Schmid, J.M., Frye, R.H. 1977. Spruce beetle in the Rockies. United States Forest Service General Technical Report RM–49Google Scholar
Veblen, T.T., Hadley, K.S., Nel, E.M., Kitzberger, T., Reed, M., Villalba, R. 1994. Disturbance regime and disturbance interactions in a Rocky Mountain subalpine forest. Journal of Ecology 82: 125–35CrossRefGoogle Scholar
Werner, R.A., Holsten, E.H. 1985. Factors influencing generation times of spruce beetles in Alaska. Canadian Journal of Forest Research 15: 438–43CrossRefGoogle Scholar
Werner, R.A., Holsten, E.H. 1995. Current status of research with the spruce beetle, Dendroctonus rufipennis. pp 23–9 in Salom, S.M. and Hobson, K.R. (Eds), Application of semiochemicals for management of bark beetle infestations: proceedings of an informal conference. United States Department of Agriculture Forest Service General Technical Report INT–318Google Scholar