Запасы и структура фитомассы древостоев северотаежных сосняков Республики Коми

Abstract

The research aims at estimating phytomass reserves of northern taiga green-moss and sphagnum pine forests growing in the Komi Republic. The study was carried out in pine forests at the Zelenoborsk forest research station of the Institute of Biology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences (IB Komi SC UB RAS) in 2016–2019. We analyzed sample trees data and derived power equations of the dependence of the individual fractions weight on the stem diameter at the breast height (1.3 m) for the main forest-forming species (pine, spruce, larch, and birch) of green-moss and sphagnum forest are characterized by rather large phytomass reserves, 136–211 t/ha. While in sphagnum pine forests there are 89–96 t/ha of phytomass. Despite the admixture of other wood species, the leading pool is represented by pine trees, and the main fraction (46–56 %) is wood of stem. The input of stem wood and stem as a whole (bark and wood) is greater in green-moss pine forests compared to sphagnum pine forests, while the share of roots in these two types is approximately the same. Participation of tree crowns (needles/leaves and branches) in the total phytomass reserves of pine stands on automorphic soils is significantly lower (17 % on average) than in communities on semihydromorphic and hydromorphic soils (22 % on average). The relatively greater mass of needles and leaves resulted in a high LAI, which varied from 8.8 to 17.8 and from 7.7 to 9.8 ha/ha, respectively, in green-moss and sphagnum pine forests. We found a high reliable (R = 0.88; p = 0.004) relationship between LAI and the tree basal areas sum, whereas it is statistically insignificant with density and wood supply. The conversion factors were calculated to convert timber volume into phytomass reserves of both individual fractions and species as a whole. The presented data are useful for assessing the productivity of pine ecosystems in different growing conditions during forest monitoring (including remote sensing methods) and also when planning forest management measures in order to increase the productivity of pine forests.