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Vegetation Type Effects on Nutrient Status and Stoichiometry of the Forest Floor in Southern Korea

  • Choonsig Kim (Division of Environmental and Forest Science, Gyeongsang National University) ;
  • Nam Jin Noh (Division of Forest Science, Kangwon National University)
  • 투고 : 2024.01.12
  • 심사 : 2024.02.15
  • 발행 : 2024.03.31

초록

Quantitative evaluation of nutrient status and stoichiometry on the forest floor is a good indicator of litter quality in various vegetation types. This study was conducted to determine the effects of vegetation type on the nutrient concentration and stoichiometry of forest floors at a regional scale. Forest floor samples were collected from four vegetation types of evergreen coniferous forests including Cryptomeria japonica, Chamaecyparis obtusa, evergreen broadleaf, and bamboo spp. forests in southern Korea. The dry weight of the forest floor was higher in the C. japonica and C. obtusa forests than in the evergreen broadleaf and bamboo forests. The mean carbon (C) concentrations of the forest floor were highest in the broadleaf forest, followed by the bamboo forest, C. japonica and C. obtusa forests. Mean nitrogen (N) and phosphorous (P) concentrations in the the coniferous forests were lower than those in the broadleaf and bamboo forests. The mean C:N ratio was the highest in C. obtusa forest (118±25), followed by C. japonica (66±6), evergreen broadleaf (41±1), and bamboo (30±1) forests. However, C:P and N:P ratios were lower in the coniferous forests than in the broadleaf forest indicating that the stoichiometry of the forest floor varies across vegetation types. The C, N, and P stocks on the forest floor were higher in the C. obtusa forest than in the broadleaf or bamboo forests. These results highlight that vegetation type-dependent stoichiometric ratio is an useful indicator for understanding interspecific difference in quality and quantity of the forest floor.

키워드

과제정보

This work was partially supported by the National Research Foundation of Korea Grant funded by the Korean government (2020R1A2C1005791; RS-2023-00213945). We thank our students for their great support in data collection.

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