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Environmental Effects on Pine Tree Carbon Budgets and Resistance to Bark Beetles

  • Richard T. Wilkens
  • Matthew P. Ayres
  • Peter L. LorioJr.
  • John D. Hodges
Part of the Ecological Studies book series (ECOLSTUD, volume 128)

Abstract

Pine trees are a dominant component of primary production in natural and man- aged ecosystems throughout the southeastern United States. Because of the economic importance of pines in the Southeast, the southern pine beetle (Dendroctonus frontalis Zirnmerman, Coleoptera: Scolytidae) can cause losses in excess of $236 million per year by attacking and killing pine trees (Price et al., 1992), and is arguably the greatest source of natural disturbance in ecosystems of the southeast. Interactions between pine trees and bark beetles have become a focus of global change research because it has long been hypothesized that bark beetle outbreaks are linked to climatic patterns (Beal 1927; Beal, 1933; Berryman and Ferrel, 1988; Christiansen and Bakke, 1988; Craighead, 1925; Gregoire, 1988; Kalkstein, 1976; King, 1972; Kroll and Reeves, 1978; Michaels, 1984; Raffa, 1988; St. George, 1930; Wyman, 1924). This implies that climate change will probably alter the frequency and intensity of forest disturbance from pest outbreaks. However, the mechanisms by which climatic patterns impact bark beetle population dynamics have remained obscure (Martinat, 1987; Mattson, 1980; Reeve et al., 1995). The development of accurate, physiologically explicit models is an essential first step in assessing the ecological risks associated with global change (Ayres, 1993). The research reported in this chapter was designed to test and refine a model of environmental effects on tree carbon budgets that provides a promising tool for understanding and predicting the effects of global change on interactions between pine trees and the southern pine beetle (SPB) (Figure 32.1).

Keywords

Secondary Metabolism Bark Beetle Resin Flow Resin Production Moderate Water Stress 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1998

Authors and Affiliations

  • Richard T. Wilkens
  • Matthew P. Ayres
  • Peter L. LorioJr.
  • John D. Hodges

There are no affiliations available

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