Ivanova G. A., Zhila S. V., Ivanov V. A., Kovaleva N. M., Kukavskaya E. A. Post-Fire Transformation of Basic Components of Pine Forests in Central Siberia
How to cite: Ivanova G. A.1, Zhila S. V.1, Ivanov V. A.2, Kovaleva N. M.1, Kukavskaya E. A.1 Post-fire transformation of basic components of pine forests in Central Siberia // Sibirskij Lesnoj Zurnal (Sib. J. For. Sci.). 2018. N. 3: 30–41 (in Russian with English abstract).
© Ivanova G. A., Zhila S. V., Ivanov V. A., Kovaleva N. M., Kukavskaya E. A., 2018
The paper is devoted to the effects of various fire intensities on components of pine forests. Based on experiments of forest fire modeling conducted in 2002-2003 for the first time in Siberia, long-term monitoring of impact of various intensities fires on Scots pine stand components and post-fire succession was conducted. Significant post-fire transformation of all Scots pine stands components was revealed after high-intensity fires. The relationship between tree mortality and fire intensity was established. The estimation of initial post-fire succession, change of composition and structure of grasses and small shrubs as well as pine regeneration was provided. The relationship between phytomass consumption and fire intensity was established. Aboveground phytomass of trees, grasses and small shrubs decreased after fires of low and moderate intensities by 5–10 % and 20 %, respectively, and after high-intensity fires – by 74 %. Due to tree mortality, mortmass of duff and down woody debris increased after high-intensity fires more than two times. After the fires, the redistribution of the biomass of vegetation in mortmass, especially pronounced after high-intensity fires. The regression equations of phytomass accumulation depending on fire intensity and time since fire were obtained for southern and central taiga Scots pine stands. The investigation conducted allows to forecast impact of fires of various intensities on forest components, post-fire succession and reforestation of Siberian Scots pine stands based on data on prefire ecosystem state and fire intensity.