Patterns of density and structure of natural populations ofTaxus baccatain the Hyrcanian forests of Iran

Abstract

Iran's Hyrcanian forests cover a relatively narrow strip in the northeastern part of the country, and are among the most important and valuable ecosystems inscribed in United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage List. European yew Taxus baccata L. is a Tertiary relict in the region and a long‐lived dioecious tree with high ecological and economic importance in the Hyrcanian forests. To study the structure and analysing the survivorship of yew stands, we selected two forest reserves (Gazou and Afratakhteh) with European yew. In the two study areas, we established 165 0.1 ha circular sample plots (75 of the sample plots were in Gazou and 90 sample plots were in Afratakhteh) and measured three characteristics of each tree. The structure of the stands was quantified by means of 1) size distributions by diameter at breast height (DBH), 2) height classes, 3) and stand basal area. Comparison of the diameter distribution by DBH classes in two the forest reserves showed that there was no statistical difference between the two populations. The highest number of yew trees in the Afratakhteh and Gazou populations were in the 12–16 and 18–24 m height classes, respectively. In Gazou, abundance was greatest at 1000 m a.s.l. on mesic exposures and intermediate slopes (40–50%), whereas in Afratakhteh it was found at 1600–1620 m a.s.l. on east‐facing areas with 55–65% slopes. Static life tables indicated that the first two age classes have particularily high mortality. Based on these results, we conclude that forest managers should support the regeneration of stands and increase survival rates by applying treatments such as thinning and selective removal of shrubs and saplings of other tree species.

Plants in Anticancer Drug Discovery: From Molecular Mechanism to Chemoprevention

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A noteworthy case of rewilding Chinese yew from a garden population in eastern China

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Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

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