植物生态学报 ›› 2026, Vol. 50 ›› Issue (2): 388-399.DOI: 10.17521/cjpe.2025.0202 cstr: 32100.14.cjpe.2025.0202
谭聪1, 石亮2,*(
), 赵常提1, 甘沛钦1, 陈冰瑞1, 谭深1, 卜燕华3, 田地1
收稿日期:2025-06-05
接受日期:2025-11-29
出版日期:2026-02-28
发布日期:2026-04-01
通讯作者:
*石亮 (shiliang0330@igsnrr.ac.cn)基金资助:
TAN Cong1, SHI Liang2,*(
), ZHAO Chang-Ti1, GAN Pei-Qin1, CHEN Bing-Rui1, TAN Shen1, BU Yan-Hua3, TIAN Di1
Received:2025-06-05
Accepted:2025-11-29
Online:2026-02-28
Published:2026-04-01
Contact:
*SHI Liang (shiliang0330@igsnrr.ac.cn)Supported by:摘要:
城市森林是缓解城市热岛效应、提升生态服务功能的重要生态屏障, 其结构与功能备受关注。植物功能性状既可表征植物生态策略, 也是揭示生态系统功能调控机制的重要指标。然而, 现有研究多集中于自然生态系统, 对于城市森林主要树种的生态策略及其生态功能效应仍缺乏深入探索。该研究以北京平原林主要树种刺槐(Robinia pseudoacacia)、杜仲(Eucommia ulmoides)、国槐(Styphnolobium japonicum)、元宝槭(Acer truncatum)、油松(Pinus tabuliformis)和银杏(Ginkgo biloba)为对象, 测定叶片形态、元素、化学防御和水力4类功能性状, 旨在揭示不同树种生态策略差异及平原林性状网络特征。结果表明, 不同树种的叶片功能性状及其生态策略差异显著: 油松比叶面积和膨压丧失点的水势最低, 体现其资源保守和抗旱优先的策略; 元宝槭比叶面积较高且总酚含量最高, 具有快速资源获取与高化学防御的特征; 刺槐氮含量最高, 总酚含量居中, 表现为快速生长和基础化学防御; 国槐磷含量最高, 总酚含量最低, 属于快速生长但化学防御低的类型; 杜仲叶干物质含量和碳含量较高, 偏向资源适度保守和结构性防御的策略; 银杏生长和化学防御相关性状表现为中等偏低, 凸显出均衡生长和化学防御较低的特征。此外, 基于6个树种构建的叶片功能性状网络整体参数(边密度0.37、直径4、平均路径长度1.90、平均聚类系数0.59、模块度0.26)显示网络结构较为简单, 仅以比叶面积为核心枢纽性状连接不同功能模块, 反映了北京平原林“速生高效”导向下的初步功能分化, 但同时也潜藏着生态系统脆弱性的特征。这些结果为理解城市森林树种的功能定位提供了新视角, 并为平原林多功能提升提供了理论依据。
谭聪, 石亮, 赵常提, 甘沛钦, 陈冰瑞, 谭深, 卜燕华, 田地. 基于叶片功能性状多维特征的北京平原林生态优化策略解析. 植物生态学报, 2026, 50(2): 388-399. DOI: 10.17521/cjpe.2025.0202
TAN Cong, SHI Liang, ZHAO Chang-Ti, GAN Pei-Qin, CHEN Bing-Rui, TAN Shen, BU Yan-Hua, TIAN Di. Analysis of ecological optimization strategies for Beijing plain forests based on multidimensional characteristics of leaf functional traits. Chinese Journal of Plant Ecology, 2026, 50(2): 388-399. DOI: 10.17521/cjpe.2025.0202
| 树种 Tree species | 密度 Density (plant·m-2) | 胸径 Diameter at breast height (cm) | 土壤总碳含量 Soil total carbon content (mg·g-1) | 土壤总氮含量 Soil total nitrogen content (mg·g-1) | 土壤总磷含量 Soil total phosphorus content (mg·g-1) |
|---|---|---|---|---|---|
| 刺槐 Robinia pseudoacacia | 0.04 | 21.49 ± 0.89 | 13.162 | 1.050 | 0.616 |
| 杜仲 Eucommia ulmoides | 0.07 | 16.63 ± 0.44 | 16.468 ± 0.761 | 1.422 ± 0.005 | 0.589 ± 0.004 |
| 国槐 Styphnolobium japonicum | 0.04 | 22.03 ± 0.74 | 10.689 | 1.233 | 0.576 |
| 元宝槭 Acer truncatum | 0.06 | 19.83 ± 0.61 | 13.491 ± 0.269 | 1.029 ± 0.009 | 0.557 ± 0.001 |
| 油松 Pinus tabuliformis | 0.05 | 16.07 ± 0.38 | 13.892 ± 0.675 | 0.729 ± 0.010 | 0.522 ± 0.000 |
| 银杏 Ginkgo biloba | 0.05 | 15.09 ± 0.51 | 16.021 ± 0.682 | 0.994 ± 0.007 | 0.573 ± 0.002 |
表1 北京平原林样地基本信息
Table 1 Basic characteristics of sample plots across Beijing plain forests
| 树种 Tree species | 密度 Density (plant·m-2) | 胸径 Diameter at breast height (cm) | 土壤总碳含量 Soil total carbon content (mg·g-1) | 土壤总氮含量 Soil total nitrogen content (mg·g-1) | 土壤总磷含量 Soil total phosphorus content (mg·g-1) |
|---|---|---|---|---|---|
| 刺槐 Robinia pseudoacacia | 0.04 | 21.49 ± 0.89 | 13.162 | 1.050 | 0.616 |
| 杜仲 Eucommia ulmoides | 0.07 | 16.63 ± 0.44 | 16.468 ± 0.761 | 1.422 ± 0.005 | 0.589 ± 0.004 |
| 国槐 Styphnolobium japonicum | 0.04 | 22.03 ± 0.74 | 10.689 | 1.233 | 0.576 |
| 元宝槭 Acer truncatum | 0.06 | 19.83 ± 0.61 | 13.491 ± 0.269 | 1.029 ± 0.009 | 0.557 ± 0.001 |
| 油松 Pinus tabuliformis | 0.05 | 16.07 ± 0.38 | 13.892 ± 0.675 | 0.729 ± 0.010 | 0.522 ± 0.000 |
| 银杏 Ginkgo biloba | 0.05 | 15.09 ± 0.51 | 16.021 ± 0.682 | 0.994 ± 0.007 | 0.573 ± 0.002 |
图1 不同树种叶片功能性状的差异比较。Cleaf, 叶水容; C:N, 碳氮比; C:P, 碳磷比; FLA, 黄酮含量, LA, 叶面积; LC, 叶碳含量; LDMC, 叶干物质含量; LN, 叶氮含量; LP, 叶磷含量; N:P, 氮磷比; RWCtlp, 膨压丧失点的相对含水量; SLA, 比叶面积; TA, 单宁含量; TP, 总酚含量; εmax, 最大细胞弹性模量; Ψsat, 饱和渗透势; Ψtlp, 膨压丧失点水势。CH, 刺槐; DZ, 杜仲; GH, 国槐; YBQ, 元宝槭; YS, 油松; YX, 银杏。不同小写字母代表树种间差异显著(p < 0.05)。
Fig. 1 Interspecific variation in leaf functional traits across tree species. Cleaf, leaf water capacity; C:N, carbon-to-nitrogen ratio; C:P, carbon-to-phosphorus ratio; FLA, flavonoid content; LA, leaf area; LC, leaf carbon content; LDMC, leaf dry matter content; LN, leaf nitrogen content; LP, leaf phosphorus content; N:P, nitrogen-to-phosphorus ratio; RWCtlp, relative water content at turgor loss point; SLA, specific leaf area; TA, tannin content; TP, total phenolic content; εmax, maximum bulk elastic modulus; Ψsat, saturated osmotic potential; Ψtlp, water potential at turgor loss point. CH, Robinia pseudoacacia; DZ, Eucommia ulmoides; GH, Styphnolobium japonicum; YBQ, Acer truncatum; YS, Pinus tabuliformis; YX, Ginkgo biloba. Different lowercase letters indicate statistically significant differences among species (p < 0.05, Kruskal-Wallis test with Dunn’s post hoc comparison).
图2 6个树种14个叶片功能性状的主成分分析(PCA) (A)以及得分差异比较(B、C)。不同颜色代表不同树种。箱线图表示第一(PC1)和第二(PC2)主成分得分在不同树种间的差异, 黄色“ ”代表该组数据平均值点, 不同小写字母表示树种间差异显著(p < 0.05)。Cleaf, 叶水容; FLA, 黄酮含量, LA, 叶面积; LC, 叶碳含量; LDMC, 叶干物质含量; LN, 叶氮含量; LP, 叶磷含量; RWCtlp, 膨压丧失点的相对含水量; SLA, 比叶面积; TA, 单宁含量; TP, 总酚含量; εmax, 最大细胞弹性模量; Ψsat, 饱和渗透势; Ψtlp, 膨压丧失点水势。CH, 刺槐; DZ, 杜仲; GH, 国槐; YBQ, 元宝槭; YS, 油松; YX, 银杏。
Fig. 2 Principal component analysis (PCA) of leaf functional traits across six tree species (A) and comparisons of score differences (B, C). Different colors represent distinct tree species. Box plots display the variations in the first (PC1) and second (PC2) principal component scores among species, with different lowercase letters indicating significant differences (p < 0.05). Cleaf, leaf water capacity; FLA, flavonoid content; LA, leaf area; LC, leaf carbon content; LDMC, leaf dry matter content; LN, leaf nitrogen content; LP, leaf phosphorus content; RWCtlp, relative water content at turgor loss point; SLA, specific leaf area; TA, tannin content; TP, total phenolic content; εmax, maximum bulk elastic modulus; Ψsat, saturated osmotic potential; Ψtlp, water potential at turgor loss point. CH, Robinia pseudoacacia; DZ, Eucommia ulmoides; GH, Styphnolobium japonicum; YBQ, Acer truncatum; YS, Pinus tabuliformis; YX, Ginkgo biloba.
图3 叶片性状网络(A)及网络节点参数(B)。该网络为无方向无权重网络, 红色和蓝色连接线仅代表正相关和负相关关系, 线的宽度代表相关性强度, 同一颜色的节点(性状)代表属于同一功能模块。Cleaf, 叶水容; C:N, 碳氮比; C:P, 碳磷比; FLA, 黄酮含量, LA, 叶面积; LC, 叶碳含量; LDMC, 叶干物质含量; LN, 叶氮含量; LP, 叶磷含量; N:P, 氮磷比; RWCtlp, 膨压丧失点的相对含水量; SLA, 比叶面积; TA, 单宁含量; TP, 总酚含量; εmax, 最大细胞弹性模量; Ψsat, 饱和渗透势; Ψtlp, 膨压丧失点水势。
Fig. 3 Leaf trait network architecture (A) with node parameters (B). The network is undirected and unweighted. Red and blue edges represent positive and negative correlations, respectively, with edge width proportional to correlation strength. Nodes (traits) sharing identical colors belong to the same functional module. Cleaf, leaf water capacity; C:N, carbon-to-nitrogen ratio; C:P, carbon-to-phosphorus ratio; FLA, flavonoid content; LA, leaf area; LC, leaf carbon content; LDMC, leaf dry matter content; LN, leaf nitrogen content; LP, leaf phosphorus content; N:P, nitrogen-to-phosphorus ratio; RWCtlp, relative water content at turgor loss point; SLA, specific leaf area; TA, tannin content; TP, total phenolic content; εmax, maximum bulk elastic modulus; Ψsat, saturated osmotic potential; Ψtlp, water potential at turgor loss point.
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