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| 浙江省三门县古树资源特征和空间分布格局分析∗ |
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朱弘1, 洪凌涛2, 任典挺3, 李贺鹏1, 岳春雷1, 舒红锁3
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1.浙江省林业科学研究院;2.衢州市柯城区公路管理中心;3.三门县自然资源和规划局
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| 摘要: |
| 为进一步掌握浙江省台州市三门县古树资源现状构成特征, 探索其空间分布格局, 基于 2019
年三门县古树普查基础数据, 分析了全县古树的优势树种、 物种多样性和结构特征, 并结合 ArcGIS 的空
间分析方法, 系统研究了全县古树的空间分布格局。 结果表明, (1) 三门县共有古树 681 株, 隶属 24 科
30 属 35 种, 其中针叶树种 7 种, 阔叶树种 28 种, 常绿 15 种, 落叶 20 种; 优势种包括樟 Camphora officinarum、 枫香树 Liquidambar formosana、 糙叶树 Aphananthe aspera、 朴树 Celtis sinensis 等 9 种, 占总株数
的 85. 46%, 构成古树资源的主体; 一级、 二级和三级古树分别占比 12%、 21%和 67%。 (2) 物种多样性
和灰色关联度分析显示: 海游街道的物种多样性综合排序最高 (H′ = 2. 265, D = 0. 854, J = 0. 507, 关联
度= 0. 978), 而蛇蟠乡的排序最低。 (3) 结构特征方面, 古树的树龄集中在 100~150 a、 胸径集中在 110~
160 cm、 树高集中在 15~20 m、 冠幅集中在 15~20 m; 胸径和冠幅呈极显著的线性关系, 拟合方程为 y =
0. 614 37x-0. 375 (R
2 = 0. 455, P<0. 01)。 (4) 空间分布方面, 各乡镇 (街道) 古树的核密度分布差异显
著, 高密集区主要位于西北部 (海游街道、 海润街道和健跳镇) 和中部 (横渡镇); 最近邻分析显示, 古
树整体 (NNI = 2. 968, Z = 42. 929, P<0. 000) 和二级古树 (NNI = 2. 968, Z = 42. 916, P<0. 000) 呈显著
的离散分布, 而一级、 三级古树及主要单一树种则呈显著的聚集分布。 |
| 关键词: 三门县 古树 空间分布格局 最近邻分析 核密度分析 |
| DOI： |
| 分类号: |
| 基金项目:浙江省省院合作林业科技项目（2022SY06）；浙江省省属科研院所扶持专项（2024F1065-1，2021F1065-6） |
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| Resource Characteristics and Spatial Distribution Pattern Analysis for Large Old Trees in Sanmen County, Zhejiang Province |
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Zhu Hong1, HONG Lingtao2, Ren Duanting3, Li Hepeng1, Yue Chunlei1, Shu Hongsuo3
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1.Zhejiang Academy of Forestry;2.Quzhou Kecheng District Highway Management Center;3.Sanmen Natural Resources and Planning Bureau
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| Abstract: |
| To further grasp the current compositional characteristics of large old trees (LOTs) resources i
Sanmen county, Taizhou c
n
ity, Zhejiang province, and explore their spatial distribution patterns. This study analyzed the dominant tree species, species diversity, and structural characteristics of LOTs in Sanmen county,
based on the basic data from the 2019 tree survey. Utilizing ArcGIS spatial analysis methods, the research systematically investigated the spatial distribution pattern of LOTs in the county. The results showed that (1) San- men county has a total of 681 LOTs belonging to 24 families, 30 genera, and 35 species, including 7 coniferous species, 28 broad-leaved species, 15 evergreen species, and 20 deciduous species. Dominant species like
Camphora officinarum, Liquidambar formosana, Aphananthe aspera, Celtis sinensis, and 9 other species, accounting for 85. 46% of the total tree count, forming the core resources of LOTs. The distribution of LOTs is as
follows: first-level trees account for 12%, second-level for 21%, and third-level for 67%. (2) Analysis of
species diversity and grey relational analysis (GRA) reveals that Haiyou Street exhibits the highest level of species diversity, with comprehensive indices of H′ = 2. 265, D = 0. 854, J = 0. 507, and relational degree =
0. 978. In contrast, Shepan Township demonstrates the lowest ranking. (3) In terms of structural characteristics, the age of LOTs primarily falls within the range of 100 - 150 years, with the diameter at breast height
(DBH) concentrated around 110-160 cm, the tree height is typically ranging from 15-20 m, and the crown
width concentrated between 15 - 20 m. A notable linear relationship exists between the DBH and the crown
width, represented by the equation y = 0. 614 37x-0. 375 (R
2 = 0. 455, P<0. 01). (4) Significant variations
are observed in the kernel density distribution of LOTs across different townships and streets, with dense clusters
predominantly situated in the northwest (including Haiyou street, Hairun street, and Jiantiao township) and the
central region (Hengdu town) of the county. The nearest neighbor analysis ( NNA) reveals that the overall
LOTs (NNI = 2. 968, Z = 42. 929, P<0. 000) and second-level LOTs (NNI = 2. 968, Z = 42. 916, P<0. 000)
exhibit a significant dispersed distribution, while first, third-level LOTs, along with major single species, are
notably clustered. The above research findings hold significance for the preservation and management of LOTs′s
resources and biodiversity in Sanmen county. |
| Key words: Sanmen county large old trees (LOTs) spatial distribution pattern nearest neighbor analsis (NNA) kernel density estimation (KED) |
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