北热带喀斯特森林优势树种细根生物量估算模型构建

doi:10.17521/cjpe.2023.0087

植物生态学报 ›› 2024, Vol. 48 ›› Issue (10): 1312-1325.DOI: 10.17521/cjpe.2023.0087 cstr: 32100.14.cjpe.2023.0087

北热带喀斯特森林优势树种细根生物量估算模型构建

庞榆¹^,², 贺同鑫¹^,³, 孙建飞¹^,³^,^*(), 宁文彩¹, 裴广廷¹^,³, 胡宝清¹^,³, 王斌³

¹南宁师范大学北部湾环境演变与资源利用教育部重点实验室, 广西地表过程与智能模拟重点实验室, 南宁 530001
²南宁师范大学地理科学与规划学院, 南宁 530001
³弄岗喀斯特生态系统广西野外科学观测研究站, 广西崇左 532499

收稿日期:2023-03-29 接受日期:2024-04-08 出版日期:2024-10-20 发布日期:2024-04-12
通讯作者: 孙建飞
基金资助:
广西自然科学基金(2020GXNSFDA238010);广西自然科学基金(2021GXNSFBA220028);国家自然科学基金(42061009);国家自然科学基金(42277468);国家自然科学基金(42071135)

Construction of fine root biomass estimation models of dominant tree species in a north tropic karst forest

PANG Yu¹^,², HE Tong-Xin¹^,³, SUN Jian-Fei¹^,³^,^*(), NING Wen-Cai¹, PEI Guang-Ting¹^,³, HU Bao-Qing¹^,³, Wang Bin³

¹Ministry of Education Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China
²School of Geography and Planning, Nanning Normal University, Nanning 530001, China
³Nonggang Karst Ecosystem Observation and Research Station of Guangxi, Chongzuo, Guangxi 532499, China

Received:2023-03-29 Accepted:2024-04-08 Online:2024-10-20 Published:2024-04-12
Contact: SUN Jian-Fei
Supported by:
Guangxi Natural Science Foundation(2020GXNSFDA238010);Guangxi Natural Science Foundation(2021GXNSFBA220028);National Natural Science Foundation of China(42061009);National Natural Science Foundation of China(42277468);National Natural Science Foundation of China(42071135)

摘要/Abstract

摘要： 细根生物量是量化细根周转及其养分归还等动态特征的基础。喀斯特基岩裸露、土壤多碎石的生境, 决定了传统根钻法测定细根生物量耗时耗力、破坏性大且难做到定点取样, 样本异质性大。因此, 有必要建立以细根形态数据获取手段为基础的细根生物量估算模型, 定点、准确地估算地下根系生物量及其细根周转速率。以广西弄岗国家级自然保护区5种优势树种广西牡荆(Vitex kwangsiensis)、金丝李(Garcinia paucinervis)、米扬噎(Streblus tonkinensis)、山榄叶柿(Diospyros siderophylla)和蚬木(Excentrodendron tonkinense)为研究对象, 综合不同径级和土层, 获取1-3级与直径≤2 mm两种细根分类样本, 分别构建细根长度和直径或根表面积与细根生物量的关联方程, 并检验模型的稳健性和精确度, 筛选最优细根生物量估算模型。结果表明: (1)细根直径、长度或表面积均与细根生物量显著正相关, 能以其为变量构建细根形态指标与生物量的关联方程; (2)基于直径和长度构建的二变量细根生物量估算模型比以细根表面积构建的单变量模型模拟结果更为精确; (3)树种间细根直径、比根长和组织密度存在差异, 导致不同树种细根生物量最优估算模型不同。该研究基于细根形态指标构建细根生物量估算模型, 为今后通过微根管等可视化技术实现喀斯特定点监测的细根形态数据向生物量数据转化奠定基础, 有助于更加精确地估算特殊石山生境细根周转速率及其养分归还。

关键词: 细根, 生物量估算模型, 喀斯特, 热带季雨林

Abstract:

Aims Fine root biomass is the basis for quantifying fine root dynamic characteristics, such as fine root turnover and nutrient return. The special karst habitat with high-rock outcrops and gravel soil indicates that the traditional root drilling method for measuring fine root biomass is time-consuming, labor-intensive, destructive, and difficult to achieve site-specific sampling, and the sample heterogeneity is large. Therefore, it is necessary to establish fine root biomass estimation models based on morphological characteristics to accurately estimate fine root biomass and its turnover rate.

Methods Five dominant tree species, Vitex kwangsiensis, Garcinia paucinervis, Streblus tonkinensis, Diospyros siderophylla, and Excentrodendron tonkinense in Nonggang National Nature Reserve, Guangxi, were selected to establish fine root biomass estimation models. Two types of fine root classification were defined as 1-3 orders and diameters ≤2 mm. Quantitative correlations between length and diameter, root surface area, and fine root biomass were established based on the collected data. Subsequently, the robustness and accuracy of different models were tested, and optimal fine root biomass estimation models were selected.

Important findings The results showed that: (1) fine root diameter, length, and surface area were significantly positively correlated with fine root biomass; thus, the fine root morphological characteristics can be used to construct the correlation equation with fine root biomass; and (2) the bivariate fine root biomass estimation model based on diameter and length is more accurate than that based on fine root surface area; (3) the optimal estimation models of fine root biomass are different among tree species, due to the differences in fine root diameter, specific root length, and root tissue density among tree species. The construction of a fine root biomass estimation model based on fine root morphological indicators is beneficial for converting the obtained fine root morphological data using visualization technology, such as micro-root canal in a fixed location, into biomass data. Moreover, it is also helpful to estimate the fine root turnover rate and nutrient return of special rock mountain habitats more accurately.

Key words: fine root, biomass estimation model, karst, tropical seasonal forest

庞榆, 贺同鑫, 孙建飞, 宁文彩, 裴广廷, 胡宝清, 王斌. 北热带喀斯特森林优势树种细根生物量估算模型构建. 植物生态学报, 2024, 48(10): 1312-1325. DOI: 10.17521/cjpe.2023.0087

PANG Yu, HE Tong-Xin, SUN Jian-Fei, NING Wen-Cai, PEI Guang-Ting, HU Bao-Qing, Wang Bin. Construction of fine root biomass estimation models of dominant tree species in a north tropic karst forest. Chinese Journal of Plant Ecology, 2024, 48(10): 1312-1325. DOI: 10.17521/cjpe.2023.0087

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2023.0087

https://www.plant-ecology.com/CN/Y2024/V48/I10/1312

图/表 7

表1 广西弄岗北热带喀斯特森林土壤基本理化性质概况(平均值±标准误, n = 210)

Table 1 Basic information about soil physicochemical properties in a north tropic karst forest in Nonggang, Guangxi (mean ± SE, n = 210)

土壤性质 Soil properties		土壤性质 Soil properties
pH	7.35 ± 0.02	有效磷含量 Available phosphorus content (mg·kg^-1)	6.60 ± 0.46
土壤有机碳含量 SOC content (g·kg^-1)	34.16 ± 1.20	交换性钾含量 Exchangeable K⁺content (mg·kg^-1)	76.21 ± 1.60
总氮含量 TN content (g·kg^-1)	3.71 ± 0.07	交换性钙含量 Exchangeable Ca²⁺ content (g·kg^-1)	15.85 ± 0.07
铵态氮含量 NH₄⁺-N content (mg·kg^-1)	12.42 ± 0.62	交换性钠含量 Exchangeable Na⁺ content (mg·kg^-1)	14.18 ± 0.08
硝态氮含量 NO₃^--N content (mg·kg^-1)	9.40 ± 0.67	交换性镁含量 Exchangeable Mg²⁺content (g·kg^-1)	1.21 ± 0.01

表2 广西弄岗北热带喀斯特森林5个树种基本概况

Table 2 Basic information about five tree species in a north tropic karst forest in Nonggang, Guangxi

树种 Tree species	立木密度 Stand density (plant·hm^-2)	胸径 Diameter at breast height (cm)		树高 Height of tree (m)
树种 Tree species	立木密度 Stand density (plant·hm^-2)	平均 Mean	范围 Range	平均 Mean	范围 Range
广西牡荆 Vitex kwangsiensis	2.22	14.9	1.9-20.0	7.0	3.0-13.1
金丝李 Garcinia paucinervis	2.19	8.5	3.0-18.5	11.0	5.2-21.4
米扬噎 Streblus tonkinensis	3.81	6.9	1.5-12.2	9.3	3.6-17.8
山榄叶柿 Diospyros siderophylla	6.61	9.0	2.3-18.4	11.8	3.2-23.7
蚬木 Excentrodendron tonkinense	3.69	7.9	1.5-23.5	11.0	4.6-21.8

图1 广西弄岗北热带喀斯特森林5个树种间和根序间细根形态变化(平均值±标准误)。不同小写字母表示种内1-3级根序间差异显著, 不同大写字母表示种间同一根序差异显著(p < 0.05)。

Fig. 1 Changes of fine root morphology among different tree species and root orders of five tree species in a north tropic karst forest in Nonggang, Guangxi (mean ± SE). Different lowercase letters indicate significant differences among 1-3 root orders within species, and different uppercase letters indicate significant differences among species with the same root order (p < 0.05).

图2 广西弄岗北热带喀斯特5个树种径级间和土层间细根形态变化(平均值±标准误)。不同小写字母表示同一物种在径级间或土层间差异显著(p < 0.05)。DBH, 胸径。

Fig. 2 Changes of fine root morphology among different DBH levels and soil layers of five tree species in a north tropic karst forest in Nonggang, Guangxi (mean ± SE). Different lowercase letters indicate significant differences among different DBH levels or soil layers within species (p < 0.05). DBH, diameter at breast height.

表3 广西弄岗北热带喀斯特5种树种细根生物量与形态指标相关性分析

Table 3 Correlation analysis between fine root biomass and morphological indexes of five tree species in a north tropic karst forest in Nonggang, Guangxi

	比根长 Specific root length (m·g^-1)	直径 Diameter (mm)	长度 Length (cm)	表面积 Surface area (cm²)	组织密度 Tissue density (g·cm^-3)
GXMJ	-0.20^**	0.67^**	0.67^**	0.83^**	0.20^**
JSL	-0.52^**	0.88^**	0.53^**	0.91^**	-0.09
MYY	-0.24^**	0.80^**	0.32^**	0.81^**	0.32^**
SLYS	-0.50^**	0.85^**	0.38^**	0.84^**	0.26^**
XM	-0.23^**	0.73^**	0.60^**	0.93^**	-0.03
Total	-0.19^**	0.76^**	0.50^**	0.85^**	0.15^**

表4 广西弄岗北热带喀斯特5种树种细根生物量模型决定系数

Table 4 Determination coefficients of fine root biomass model of five tree species in a north tropic karst forest in Nonggang, Guangxi

树种 Tree species	决定系数 Determination coefficient	单变量(A) Single variable (A)		二变量(D、L) Two variables (D and L)
树种 Tree species	决定系数 Determination coefficient	(1)	(2)	(3)	(4)	(5)	(6)	(7)
广西牡荆 Vitex kwangsiensis	R²_(Z)	0.80	0.84	0.93	0.91	0.92	0.95	0.91
	R²_(D)	0.86	0.94	0.98	0.97	0.98	0.98	0.97
	R²_(J)	0.69	0.76	0.87	0.85	0.84	0.41	0.84
金丝李 Garcinia paucinervis	R²_(Z)	0.87	0.92	0.97	0.97	0.96	0.97	0.96
	R²_(D)	0.90	0.95	0.99	0.98	-	0.96	0.97
	R²_(J)	0.82	0.90	0.96	0.95	0.95	0.96	0.95
米扬噎 Streblus tonkinensis	R²_(Z)	0.74	0.88	0.15	0.85	0.84	0.91	0.85
	R²_(D)	0.82	0.93	-	-	0.87	0.68	0.86
	R²_(J)	0.65	0.86	0.90	0.86	0.84	0.94	0.85
山榄叶柿 Diospyros siderophylla	R²_(Z)	0.79	0.90	0.94	0.93	0.93	0.89	0.93
	R²_(D)	0.91	0.93	0.95	0.96	0.96	0.93	0.96
	R²_(J)	0.71	0.88	-	0.91	0.92	0.93	0.92
蚬木 Excentrodendron tonkinense	R²_(Z)	0.84	0.92	0.96	0.94	0.95	0.20	0.94
	R²_(D)	0.86	0.94	0.96	0.96	0.95	0.62	0.95
	R²_(J)	0.86	0.90	0.96	0.94	0.94	0.96	0.94

图3 广西弄岗北热带喀斯特森林5个树种细根生物量实测值与模型估算值相关分析。A、B, 广西牡荆。C、D, 金丝李。E、F, 米扬噎。G、H、I, 山榄叶柿。J、K, 蚬木。

Fig. 3 Correlation analysis between measured and estimated fine root biomass of five tree species in a north tropic karst forest in Nonggang, Guangxi. A, B, Vitex kwangsiensis. C, D, Garcinia paucinervis. E, F, Streblus tonkinensis. G, H, I, Diospyros siderophylla. J, K, Excentrodendron tonkinense.

参考文献 57

[1]	Bardgett RD, Mommer L, de Vries FT (2014). Going underground: root traits as drivers of ecosystem processes. Trends in Ecology & Evolution, 29, 692-699.
[2]	Cai MS (2009). Study on aboveground and underground biomass allocation of Chinese fir forest in China. Forestry Prospect and Design, (1), 95-98.
	[ 蔡梅生 (2009). 中国杉木林地上地下生物量分配研究. 林业勘察设计, (1), 95-98.]
[3]	Chang WJ, Guo DL (2008). Variation in root diameter among 45 common tree species in temperate, subtropical and tropical forests in China. Chinese Journal of Plant Ecology, 32, 1248-1257.
	[ 常文静, 郭大立 (2008). 中国温带、亚热带和热带森林45个常见树种细根直径变异. 植物生态学报, 32, 1248-1257.] DOI
[4]	Chen FQ, Luo Y, Li QH (2013). Allometric equations for estimating biomass of dominant shrub species in subtropical forests in eastern Guangdong Province, China. Journal of Central South University of Forestry & Technology, 33(2), 5-10.
	[ 陈富强, 罗勇, 李清湖 (2013). 粤东地区森林灌木层优势植物生物量估算模型. 中南林业科技大学学报, 33(2), 5-10.]
[5]	Cheng YH, Han YZ, Wang QC, Wang ZQ (2005). Seasonal dynamics of fine root biomass, root length density, specific root length and soil resource availability in a Larix gmelinii plantation. Acta Phytoecologica Sinica, 29, 303-310.
	[ 程云环, 韩有志, 王庆成, 王政权 (2005). 落叶松人工林细根动态与土壤资源有效性关系研究. 植物生态学报, 29, 303-310.]
[6]	Comas L, Eissenstat D (2004). Linking fine root traits to maximum potential growth rate among 11 mature temperate tree species. Functional Ecology, 18, 388-397.
[7]	Cornelissen JHC, Lavorel S, Garnier E, Díaz S, Buchmann N, Gurvich DE, Reich PB ter Steege H, Morgan HD, van der Heijden MGA, Pausas JG, Poorter H (2003). A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Australian Journal of Botany, 51, 335-380.
[8]	Curt T, Prévosto B (2003). Rooting strategy of naturally regenerated beech in silver birch and Scots pine woodlands//Abe J. Roots: The Dynamic Interface Between Plants and the Earth. Springer, Dordrecht, the Netherlands. 265-279.
[9]	Dong LH, Li FR (2016). Additive stand-level biomass models for natural larch forest in the east of Daxing’an Mountains. Scientia Silvae Sinicae, 52(7), 13-21.
	[ 董利虎, 李凤日 (2016). 大兴安岭东部天然落叶松林可加性林分生物量估算模型. 林业科学, 52(7), 13-21.]
[10]	Dong LH, Li FR, Song YW (2015). Error structure and additivity model of individual tree biomass model for four natural conifer species in Northeast China. Chinese Journal of Applied Ecology, 26, 704-714. PMID
	[ 董利虎, 李凤日, 宋玉文 (2015). 东北林区4个天然针叶树种单木生物量模型误差结构及可加性模型. 应用生态学报, 26, 704-714.]
[11]	Freschet GT, Valverde-Barrantes OJ, Tucker CM, Craine JM, McCormack ML, Violle C, Fort F, Blackwood CB, Urban-Mead KR, Iversen CM, Bonis A, Comas LH, Cornelissen JHC, Dong M, Guo D, et al. (2017). Climate, soil and plant functional types as drivers of global fine-root trait variation. Journal of Ecology, 105, 1182-1196.
[12]	Gao J, Wang JN, Xu B, Xie Y, He JD, Wu Y (2016). Plant leaf traits, height and biomass partitioning in typical ephemerals under different levels of snow cover thickness in an alpine meadow. Chinese Journal of Plant Ecology, 40, 775-787. DOI
	[ 高景, 王金牛, 徐波, 谢雨, 贺俊东, 吴彦 (2016). 不同雪被厚度下典型高山草地早春植物叶片性状、株高及生物量分配的研究. 植物生态学报, 40, 775-787.] DOI
[13]	Guo DL, Li H, Mitchell RJ, Han WX, Hendricks JJ, Fahey TJ, Hendrick RL (2008). Fine root heterogeneity by branch order: exploring the discrepancy in root turnover estimates between minirhizotron and carbon isotopic methods. New Phytologist, 177, 443-456. DOI PMID
[14]	Guo K, Liu CC, Dong M (2011). Ecological adaptation of plants and control of rocky-desertification on karst region of Southwest China. Chinese Journal of Plant Ecology, 35, 991-999. DOI
	[ 郭柯, 刘长成, 董鸣 (2011). 我国西南喀斯特植物生态适应性与石漠化治理. 植物生态学报, 35, 991-999.] DOI
[15]	Guo LB, Wang MB, Gifford RM (2007). The change of soil carbon stocks and fine root dynamics after land use change from a native pasture to a pine plantation. Plant and Soil, 299, 251-262.
[16]	Han C, Song M, Du H, Zeng FP, Peng WX, Wang H, Chen L, Su L (2017). Biomass and carbon storage in roots of Cunninghamia lanceolata and Pinus massoniana plantations at different stand ages in Guangxi. Acta Ecologica Sinica, 37, 2282-2289.
	[ 韩畅, 宋敏, 杜虎, 曾馥平, 彭晚霞, 王华, 陈莉, 苏樑 (2017). 广西不同林龄杉木、马尾松人工林根系生物量及碳储量特征. 生态学报, 37, 2282-2289.]
[17]	He TX, Hu BQ, Zhang JB, Zhang SM, Pang Y, Pei GT, Hu G, Zhang W, Sun JF (2020). Fine root effects on the retention and availability of soil carbon and nitrogen after ten years of vegetation restoration in a karst slope ecosystem. Acta Ecologica Sinica, 40, 8638-8648.
	[ 贺同鑫, 胡宝清, 张建兵, 张诗萌, 庞榆, 裴广廷, 胡刚, 张伟, 孙建飞 (2020). 植被恢复十年喀斯特坡地细根对土壤碳氮存留与可利用性的影响. 生态学报, 40, 8638-8648.]
[18]	Hendricks JJ, Nadelhoffer KJ, Aber JD (1993). Assessing the role of fine roots in carbon and nutrient cycling. Trends in Ecology & Evolution, 8, 174-178.
[19]	Hu QJ, Wang LJ, Sheng MY (2019). Research progress of plant fine root production and turnover in plants. World Forestry Research, 32(2), 29-34.
	[ 胡琪娟, 王霖娇, 盛茂银 (2019). 植物细根生产和周转研究进展. 世界林业研究, 32(2), 29-34.]
[20]	Huang CH, Zhou GY, Zhao HB, Zhou ZP, Qiu ZJ (2013). Determination of root system biomass of matured Cunninghamia lanceolata plantation in Tianjingshan forest farm, Guangdong Province. Journal of Central South University of Forestry & Technology, 33(9), 80-86.
	[ 黄春华, 周光益, 赵厚本, 周志平, 邱治军 (2013). 广东天井山成熟杉木人工林根系生物量的测定. 中南林业科技大学学报, 33(9), 80-86.]
[21]	Huang YS, Wu WH, Jiang RH, Liu SY, Liu Y, Li XK (2013). Primary study on plant species diversity of plant in Longgang National Nature Reserve of Guangxi. Guihaia, 33, 346-355.
	[ 黄俞淞, 吴望辉, 蒋日红, 刘晟源, 刘演, 李先琨 (2013). 广西弄岗国家级自然保护区植物物种多样性初步研究. 广西植物, 33, 346-355.]
[22]	Iversen CM, McCormack ML, Powell AS, Blackwood CB, Freschet GT, Kattge J, Roumet C, Stover DB, Soudzilovskaia NA, Valverde-Barrantes OJ, van Bodegom PM, Violle C (2017). A global Fine-Root Ecology Database to address below-ground challenges in plant ecology. New Phytologist, 215, 15-26.
[23]	Jia MK (2018). Research progress and protection measures of biodiversity in Guangxi Nonggang National Nature Reserve. South China Agriculture, 12(36), 119-120.
	[ 贾梦可 (2018). 广西弄岗国家级自然保护区生物多样性的研究进展及保护措施. 南方农业, 12(36), 119-120.]
[24]	Lan J, Xiao ZQ, Li JM, Zhang YT (2020). Biomass allocation and allometric growth of Picea schrenkiana in Tianshan Mountains. Journal of Zhejiang A&F University, 37, 416-423.
	[ 兰洁, 肖中琪, 李吉玫, 张毓涛 (2020). 天山雪岭云杉生物量分配格局及异速生长模型. 浙江农林大学学报, 37, 416-423.]
[25]	Lan Y, Wei X, Jia B, Zhai YQ, Wang YJ, Yun BY (2022). Establishment and optimization of maize root biomass model under mulching and drip irrigation in Ningxia. Soil and Fertilizer Sciences in China, (5), 184-193.
	[ 兰宇, 魏雪, 贾彪, 翟勇全, 王艺君, 运彬媛 (2022). 宁夏覆膜滴灌玉米根系生物量模型构建与优选. 中国土壤与肥料, (5), 184-193.]
[26]	Lee WK, von Gadow K, Chung DJ, Lee JL, Shin MY (2004). DBH growth model for Pinus densiflora and Quercus variabilis mixed forests in central Korea. Ecological Modelling, 176, 187-200.
[27]	Li W, Shi YF, Zhu DD, Wang WQ, Liu HW, Li JY, Shi NN, Ma L, Fu SL (2021). Fine root biomass and morphology in a temperate forest are influenced more by the nitrogen treatment approach than the rate. Ecological Indicators, 130, 108031. DOI: 10.1016/j.ecolind.2021.108031.
[28]	Li W, Wang CK, Zhang QZ (2015). Differentiation of stand individuals impacts allometry and biomass allocation of Larix gmelinii trees. Acta Ecologica Sinica, 35, 1679-1687.
	[ 李巍, 王传宽, 张全智 (2015). 林木分化对兴安落叶松异速生长方程和生物量分配的影响. 生态学报, 35, 1679-1687.]
[29]	Liu K, Cao L, Wang GB, Cao FL (2017). Biomass allocation patterns and allometric models of Ginkgo biloba. Journal of Beijing Forestry University, 39(4), 12-20.
	[ 刘坤, 曹林, 汪贵斌, 曹福亮 (2017). 银杏生物量分配格局及异速生长模型. 北京林业大学学报, 39(4), 12-20.]
[30]	Liu LB, Zhong QL, Ni J (2018). Allometric function-based root biomass estimate of woody plants in a karst evergreen and deciduous broadleaf and mixed forest in central Guizhou Province, southwestern China. Acta Ecologica Sinica, 38, 8726-8732.
	[ 刘立斌, 钟巧连, 倪健 (2018). 基于生物量回归方程估算黔中喀斯特常绿落叶阔叶混交林木本植物的根系生物量. 生态学报, 38, 8726-8732.]
[31]	Liu Y, Wang GL, Yu KX, Li P, Xiao L, Liu GB (2018). A new method to optimize root order classification based on the diameter interval of fine root. Scientific Reports, 8, 2960. DOI: 10.1038/s41598-018-21248-6. PMID
[32]	Lyu GH, Xie YB, Wen RH, Wang XY, Jia QY (2019). Modeling root biomass of maize in Northeast China. Chinese Journal of Eco-Agriculture, 27, 572-580.
	[ 吕国红, 谢艳兵, 温日红, 王笑影, 贾庆宇 (2019). 东北玉米根系生物量模型的构建. 中国生态农业学报, 27, 572-580.]
[33]	Ma Z, Guo D, Xu X, Lu M, Bardgett RD, Eissenstat DM, McCormack ML, Hedin LO (2018). Evolutionary history resolves global organization of root functional traits. Nature, 555, 94-97.
[34]	Makita N, Hirano Y, Mizoguchi T, Kominami Y, Dannoura M, Ishii H, Finér L, Kanazawa Y (2011). Very fine roots respond to soil depth: biomass allocation, morphology, and physiology in a broad-leaved temperate forest. Ecological Research, 26, 95-104.
[35]	Mandal G, Joshi SP (2015). Estimation of above-ground biomass and carbon stock of an invasive woody shrub in the subtropical deciduous forests of Doon Valley, western Himalaya, India. Journal of Forestry Research, 26, 291-305.
[36]	Mokany K, Raison RJ, Prokushkin AS (2006). Critical analysis of root:shoot ratios in terrestrial biomes. Global Change Biology, 12, 84-96.
[37]	Ni J, Luo DH, Xia J, Zhang ZH, Hu G (2015). Vegetation in karst terrain of southwestern China allocates more biomass to roots. Solid Earth, 6, 799-810.
[38]	Pei YM, Lei PF, Xiang WH, Ouyang S, Xu YY (2018). Effect of stand age on fine root biomass, production and morphology in Chinese fir plantations in subtropical China. Sustainability, 10, 2280. DOI: 10.3390/su10072280.
[39]	Pregitzer KS, DeForest JL, Burton AJ, Allen MF, Ruess RW, Hendrick RL (2002). Fine root architecture of nine North American trees. Ecological Monographs, 72, 293309. DOI: 10.1890/0012-9615(2002)072.
[40]	Qin LH, Zhang MZ, Zhong SH, Yu XH (2017). Model uncertainty in forest biomass estimation. Acta Ecologica Sinica, 37, 7912-7919.
	[ 秦立厚, 张茂震, 钟世红, 于晓辉 (2017). 森林生物量估算中模型不确定性分析. 生态学报, 37, 7912-7919.]
[41]	Song ZR, Qin JS, Li MJ, Ma JM, Zhong FY, Yang ZQ, Yan PD (2020). Study on root biomass of Pinus massoniana plantations in subtropical China. Journal of Guangxi Normal University (Natural Science Edition), 38(1), 149-156.
	[ 宋尊荣, 秦佳双, 李明金, 马姜明, 钟凤跃, 杨章旗, 颜培栋 (2020). 南亚热带马尾松人工林根系生物量分布格局. 广西师范大学学报(自然科学版), 38(1), 149-156.]
[42]	Wan W, He GQ, Tang HX, Jia FH (2020). Preliminary study on butterfly biodiversity in Nonggang National Nature Reserve, Guangxi. Journal of Chuzhou University, 22(2), 1-5.
	[ 万薇, 何桂强, 唐华兴, 贾凤海 (2020). 广西弄岗国家级自然保护区蝶类生物多样性初探. 滁州学院学报, 22(2), 1-5.]
[43]	Wang B, Huang YS, Li XK, Xiang WS, Ding T, Huang FZ, Lu SH, Han WH, Wen SJ, He LJ (2014). Species composition and spatial distribution of a 15 ha northern tropical karst seasonal rain forest dynamics study plot in Nonggang, Guangxi, Southern China. Biodiversity Science, 22, 141-156. DOI
	[ 王斌, 黄俞淞, 李先琨, 向悟生, 丁涛, 黄甫昭, 陆树华, 韩文衡, 文淑均, 何兰军 (2014). 弄岗北热带喀斯特季节性雨林15 ha监测样地的树种组成与空间分布. 生物多样性, 22, 141-156.] DOI
[44]	Wang CK (2006). Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests. Forest Ecology and Management, 222, 9-16.
[45]	Wang H, Niu SK, Sun W, Zhang ZX (2014). Study on biomass estimation model for woody plant of main forest types in Beijing mountainous area. Ecological Science, 33, 741-748.
	[ 王欢, 牛树奎, 孙武, 张志旭 (2014). 北京山区主要林下木本植物生物量估测模型. 生态科学, 33, 741-748.]
[46]	Wang JS, Zhang CY, Fan XH, Zhao YZ (2011). Biomass allocation patterns and allometric models of Abies nephrolepis Maxim. Acta Ecologica Sinica, 31, 3918-3927.
	[ 汪金松, 张春雨, 范秀华, 赵亚洲 (2011). 臭冷杉生物量分配格局及异速生长模型. 生态学报, 31, 3918-3927.]
[47]	Wang N, Wang BT, Wang RJ, Cao XY, Wang WJ, Chi L (2013). Biomass allocation patterns and allometric models of Populus davidiana and Pinus tabuliformis Carr. in west of Shanxi Province. Bulletin of Soil and Water Conservation, 33, 151-155.
	[ 王宁, 王百田, 王瑞君, 曹晓阳, 王文静, 迟璐 (2013). 晋西山杨和油松生物量分配格局及异速生长模型研究. 水土保持通报, 33, 151-155.]
[48]	Wang ZC, Du H, Song TQ, Peng WX, Zeng FP, Zeng ZX, Zhang H (2015). Allometric models of major tree species and forest biomass in Guangxi. Acta Ecologica Sinica, 35, 4462-4472.
	[ 汪珍川, 杜虎, 宋同清, 彭晚霞, 曾馥平, 曾昭霞, 张浩 (2015). 广西主要树种(组)异速生长模型及森林生物量特征. 生态学报, 35, 4462-4472.]
[49]	Wang ZQ, Guo DL, Wang XR, Gu JC, Mei L (2006). Fine root architecture, morphology, and biomass of different branch orders of two Chinese temperate tree species. Plant and Soil, 288, 155-171.
[50]	Wu J, Sheng MY (2020). Research progress in root ecology of karst vegetation in China. Plant Science Journal, 38, 565-573.
	[ 吴静, 盛茂银 (2020). 我国喀斯特植被根系生态学研究进展. 植物科学学报, 38, 565-573.]
[51]	Wu J, Sheng MY, Xiao HL, Guo C, Wang LJ (2022). Fine root architecture of adaptive plants and its correlation with nutrient stoichiometric characteristics of fine root and rhizosphere soils in karst rocky desertification environments, SW China. Acta Ecologica Sinica, 42, 677-687.
	[ 吴静, 盛茂银, 肖海龙, 郭超, 王霖娇 (2022). 西南喀斯特石漠化环境适生植物细根构型及其与细根和根际土壤养分计量特征的相关性. 生态学报, 42, 677-687.]
[52]	Xiang WS, Li DX, Wang B, Li XK, Guo YL, Wen SJ, Lu SH, Liang SC (2019). Characteristics and spatial distribution of forest gap in a northern tropical karst seasonal rainforest in Nonggang, Guangxi, South China. Guihaia, 39, 87-97.
	[ 向悟生, 李冬兴, 王斌, 李先琨, 郭屹立, 文淑均, 陆树华, 梁士楚 (2019). 广西弄岗北热带喀斯特季节性雨林林隙特征与空间分布. 广西植物, 39, 87-97.]
[53]	Xiao YF, Ou GL, Wang JF, Xu H, Nong SX (2014). Biomass estimation models of individual root for Pinus kesiya var. langbianensis. Journal of Northeast Forestry University, 42(1), 57-60.
	[ 肖义发, 欧光龙, 王俊峰, 胥辉, 农世新 (2014). 思茅松单木根系生物量的估算模型. 东北林业大学学报, 42(1), 57-60.]
[54]	Yu J, Hu QL, Zhang ZM, Zhao Y, Zhao H, Zhao CD, Liu YC (2018). Study on root biomass and morphological characteristics of Quercus variabilis Bl. forest of different age in the south foot of Taihang Mountain. Journal of Henan Agricultural University, 52, 533-539.
	[ 余洁, 胡启立, 张志铭, 赵勇, 赵河, 赵琛迪, 刘雅辰 (2018). 太行山南麓地区不同林龄栓皮栎林根系生物量及形态特征研究. 河南农业大学学报, 52, 533-539.]
[55]	Yuan ZY, Chen HYH (2012). Fine root dynamics with stand development in the boreal forest. Functional Ecology, 26, 991-998.
[56]	Zeng WS, Chen XY, Yang XY (2019). Biomass modeling and productivity analysis of planted Populus spp. in China. Scientia Silvae Sinicae, 55(11), 1-8.
	[ 曾伟生, 陈新云, 杨学云 (2019). 我国人工杨树生物量建模和生产力分析. 林业科学, 55(11), 1-8.]
[57]	Zeng WS, Tang SZ (2011). Establishment of below-ground biomass equations for larch in northeastern and masson pine in southern China. Journal of Beijing Forestry University, 33(2), 1-6.
	[ 曾伟生, 唐守正 (2011). 东北落叶松和南方马尾松地下生物量模型研建. 北京林业大学学报, 33(2), 1-6.]

北热带喀斯特森林优势树种细根生物量估算模型构建

Construction of fine root biomass estimation models of dominant tree species in a north tropic karst forest

全文

PDF (PC)

附录附件

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 57

相关文章 15

编辑推荐

Metrics

本文评价

[1]	廖苏慧, 倪隆康, 秦佳双, 谭羽, 顾大形. 中亚热带喀斯特森林不同演替阶段树种水力调节策略差异[J]. 植物生态学报, 2024, 48(9): 1223-1231.
[2]	蔡慧颖, 梁亚涛, 娄虎, 杨光, 孙龙. 白桦细根功能性状和根际细菌群落随火后时间的变化[J]. 植物生态学报, 2024, 48(7): 828-843.
[3]	郑莉莉, 余林兰, 戴萍, 薛跃规, 龙萍. 广西大石围天坑群植物叶片养分特征及其适应性[J]. 植物生态学报, 2024, 48(7): 872-887.
[4]	刘瑶, 钟全林, 徐朝斌, 程栋梁, 郑跃芳, 邹宇星, 张雪, 郑新杰, 周云若. 不同大小刨花楠细根功能性状与根际微环境关系[J]. 植物生态学报, 2024, 48(6): 744-759.
[5]	徐子怡, 金光泽. 阔叶红松林不同菌根类型幼苗细根功能性状的变异与权衡[J]. 植物生态学报, 2024, 48(5): 612-622.
[6]	曲泽坤, 朱丽琴, 姜琦, 王小红, 姚晓东, 蔡世锋, 罗素珍, 陈光水. 亚热带常绿阔叶林丛枝菌根树种养分觅食策略及其与细根形态间的关系[J]. 植物生态学报, 2024, 48(4): 416-427.
[7]	杜旭龙, 黄锦学, 杨智杰, 熊德成. 增温对植物叶片和细根氧化损伤与防御特征及其相互关联影响的研究进展[J]. 植物生态学报, 2024, 48(2): 135-146.
[8]	舒韦维, 杨坤, 马俊旭, 闵惠琳, 陈琳, 刘士玲, 黄日逸, 明安刚, 明财道, 田祖为. 氮添加对红锥不同序级细根形态和化学性状的影响[J]. 植物生态学报, 2024, 48(1): 103-112.
[9]	孙佳慧, 史海兰, 陈科宇, 纪宝明, 张静. 植物细根功能性状的权衡关系研究进展[J]. 植物生态学报, 2023, 47(8): 1055-1070.
[10]	吴晨, 陈心怡, 刘源豪, 黄锦学, 熊德成. 增温对森林细根生长、死亡及周转特征影响的研究进展[J]. 植物生态学报, 2023, 47(8): 1043-1054.
[11]	吴帆, 吴晨, 张宇辉, 余恒, 魏智华, 郑蔚, 刘小飞, 陈仕东, 杨智杰, 熊德成. 增温对成熟杉木人工林不同季节细根生长、形态及生理代谢特征的影响[J]. 植物生态学报, 2023, 47(6): 856-866.
[12]	祝维, 周欧, 孙一鸣, 古丽米热·依力哈木, 王亚飞, 杨红青, 贾黎明, 席本野. 混交林内毛白杨和刺槐根系吸水的动态生态位划分[J]. 植物生态学报, 2023, 47(3): 389-403.
[13]	陈心怡, 吴晨, 黄锦学, 熊德成. 增温对林木细根物候影响的研究进展[J]. 植物生态学报, 2023, 47(11): 1471-1482.
[14]	万春燕, 余俊瑞, 朱师丹. 喀斯特与非喀斯特森林乔木叶性状及其相关性网络的差异[J]. 植物生态学报, 2023, 47(10): 1386-1397.
[15]	孙彩丽, 仇模升, 黄朝相, 王艺伟. 黔西南石漠化过程中土壤胞外酶活性及其化学计量变化特征[J]. 植物生态学报, 2022, 46(7): 834-845.