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Biology, Chemistry and Structure of Tension Wood

  • Judith Felten
  • Björn SundbergEmail author
Chapter
Part of the Plant Cell Monographs book series (CELLMONO, volume 20)

Abstract

Trees maintain and adjust their stature by developing reaction wood in stems and branches. The physical properties of reaction wood result in a higher strain than in normal wood. Because reaction wood is only formed at one side of the stem, this unilateral strain creates a force and hence a movement of the stem or branches towards a more favorable position. The spectacular modification of cambial growth, cell shape, cell-wall chemistry, and ultrastructure observed in reaction wood has attracted generations of scientists to study its features and molecular regulation. In the early literature, the physiology of reaction wood induction was much studied, especially the relative importance of positional and mechanical sensing for its induction. Even today this is still a matter of debate and confusion, as discussed in the first part of this chapter. In angiosperm trees, reaction wood is denoted tension wood (TW), and in many tree species TW fibers develop an inner cellulose-rich gelatinous layer (G-fibers). Much research has been devoted to understand the chemistry and ultrastructure of the gelatinous layer and its function in creating tension stress in the wood. Less attention has been paid to TW without G-fibers, although it has similar physical properties and function as TW with G-fibers. The chemistry and structural variation of TW, and their importance for TW function, are discussed in the second part of this chapter. Not much is known about the molecular control of TW formation. However, some information has been gained about the role of plant hormones as signaling components in TW induction. The last part of the chapter summarizes this knowledge.

Keywords

Compression Wood Tension Wood Hybrid Aspen Reaction Wood Lumen Side 
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.

Notes

Acknowledgments

We kindly thank Kjell Olofsson and Dr. Melissa Roach for providing graphical material for Fig. 1 and Drs Urs Fischer and Totte Niittylä for reviewing this chapter. We also thank FORMAS, Swedish Research Council, VINNOVA, and Bio4Energy (the Swedish Programme for renewable energy) for funding.

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Forest Genetics and Plant Physiology, Umeå Plant Science CenterSwedish University of Agricultural SciencesUmeåSweden

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