幼龄红树生物量模型及幼林红树林碳储量研究*

doi:10.11978/2024141

Abstract

Abstract:

As global climate change intensifies, mangroves—a vital coastal blue carbon ecosystem—have garnered increasing attention. This study aimed to develop biomass models for juvenile mangroves and assess the carbon storage of young mangrove ecosystems in Kaozhouyang Bay. The results provide empirical methods and a scientific basis for the rapid and accurate assessment of carbon stocks in artificially planted young mangroves. The study focuses on five artificially planted juvenile mangrove species in Kaozhouyang Bay: Avicennia marina, Rhizophora stylosa, Kandelia obovata, Bruguiera gymnorhiza, and Aegiceras corniculatum. Various factors derived from basal diameter (D) and tree height (H) were used to construct optimal allometric equations between biomass and dendrometric parameters. Furthermore, the best-performing biomass models were applied to estimate vegetation carbon storage and ecosystem carbon storage in the artificially planted mangroves of Kaozhouyang Bay. The findings indicated that multivariable models generally outperformed univariable models, except for the below-ground biomass model of Aegiceras corniculatum. The optimal biomass models for Avicennia marina, Rhizophora stylosa, Bruguiera gymnorhiza, and Aegiceras corniculatum were power function models, whereas linear models best fit Kandelia obovata. The carbon density of the artificially planted young mangrove ecosystems in Kaozhouyang Bay was (91.26±17.32) Mg C·hm^-2, with a total carbon stock of approximately 35964.65 Mg C. Soil carbon constituted 78.3% to 98.5% of the total carbon stock in these ecosystems. Among the different mangrove communities, vegetation carbon density ranked as follows (from highest to lowest): Aegiceras corniculatum + Kandelia obovata communities, Rhizophora stylosa + Avicennia marina communities, Avicennia marina community, and Bruguiera gymnorhiza community. These results offer valuable insights for assessing carbon storage and guiding ecological restoration efforts in artificially planted mangroves in Guangdong Province and nationwide.

Key words: juvenile mangroves, biomass models, carbon density, artificial mangrove, Kaozhouyang Bay

CLC Number:

HU Xin, XIONG Lanlan, CHEN Shunyang, ZHANG Huangchen, ZOU Yiyang, ZHANG Jichao, LIU Dongxi, HE Jialu, WU Yuqi, ZHU Zhenjie. Study on biomass models of juvenile mangroves and carbon storage in young mangrove ecosystems*[J].Journal of Tropical Oceanography, 2025, 44(4): 187-199.

Figures/Tables 7

Fig. 1

Fig. 2

Tab. 1

Tab. 2

Biomass model development for juvenile mangrove plants"

树种	自变量	因变量	R²	P	回归模型	RSS	AIC	RMSE
红海榄地上生物量模型	D	W_t	0.88	<0.01	W_t = 0.0094×D^3.0878	0.62	-114.33	0.14
	D	W_t	0.53	<0.01	W_t = 0.2358×D-0.3383	0.88	-103.79	0.17
	H	W_t	0.61	<0.01	W_t = 0.1037×H^3.072	0.84	-105.40	0.17
	H	W_t	0.54	<0.01	W_t = 0.6918×H-0.493	0.86	-104.52	0.17
	DH	W_t	0.88	<0.01	W_t = 0.0269×(DH)^1.8155	0.28	-135.97	0.10
	DH	W_t	0.77	<0.01	W_t = 0.1608×(DH)-0.1843	0.44	-122.53	0.12
	D²H	W_t	0.90	<0.01	W_t = 0.0175×(D²H)^1.1779	0.23	-136.10	0.11
	D²H	W_t	0.83	<0.01	W_t = 0.0388×(D²H)-0.0554	0.33	-131.72	0.10
	DH²	W_t	0.82	<0.01	W_t = 0.0434×(DH²)^1.197	0.34	-130.33	0.11
	DH²	W_t	0.89	<0.01	W_t = 0.1037×(DH²)-0.0841	0.29	-136.58	0.11
红海榄地下生物量模型	D	W_d	0.88	<0.01	W_d = 0.0022×D^3.4548	0.61	-115.09	0.14
	D	W_d	0.41	<0.01	W_d = 0.1651×D-0.2747	0.63	-113.83	0.15
	H	W_d	0.56	<0.01	W_d = 0.0356×H^2.4809	0.88	-103.94	0.17
	H	W_d	0.34	<0.01	W_d = 0.3549×H-0.2487	0.70	-110.59	0.15
	DH	W_d	0.84	<0.01	W_d = 0.0097×(DH)^1.752	0.59	-113.90	0.14
	DH	W_d	0.62	<0.01	W_d = 0.108×(DH)-0.1492	0.71	-124.90	0.12
	D²H	W_d	0.90	<0.01	W_d = 0.0056×(D²H)^1.2153	0.53	-146.91	0.13
	D²H	W_d	0.76	<0.01	W_d = 0.0289×(D²H)-0.0791	0.75	-139.11	0.09
	DH²	W_d	0.76	<0.01	W_d = 0.0164×(DH²)^0.7605	0.93	-100.30	0.18
	DH²	W_d	0.78	<0.01	W_d = 0.0718×(D²H)-0.0876	0.74	-141.31	0.09
白骨壤地上生物量模型	D	W_t	0.92	<0.01	W_t = 0.0182×D^3.7886	2.35	-74.44	0.28
	D	W_t	0.68	<0.01	W_t = 0.4464×D-0.4502	1.56	-86.62	0.23
	H	W_t	0.74	<0.01	W_t = 0.2047×H^3.722	0.87	-104.18	0.17
	H	W_t	0.73	<0.01	W_t = 0.9049×H-0.4899	1.30	-92.09	0.21
	DH	W_t	0.93	<0.01	W_t = 0.0595×(DH)^2.1007	1.12	-94.54	0.19
	DH	W_t	0.88	<0.01	W_t = 0.28×(DH)-0.1703	0.59	-113.80	0.14
	D²H	W_t	0.95	<0.01	W_t = 0.0383×(D²H)^1.3822	0.71	-131.98	0.14
	D²H	W_t	0.84	<0.01	W_t = 0.0844×(D²H)-0.0159	0.75	-106.71	0.16
	DH²	W_t	0.89	<0.01	W_t = 0.0931×(DH²)^1.3826	0.63	-112.00	0.14
	DH²	W_t	0.91	<0.01	W_t = 0.1705×(DH²)-0.0315	0.43	-123.03	0.12
白骨壤地下生物量模型	D	W_d	0.93	<0.01	W_d = 0.01×D^3.145	0.20	-148.92	0.08
	D	W_d	0.61	<0.01	W_d = 0.1179×D-0.0983	0.17	-152.36	0.08
	H	W_d	0.80	<0.01	W_d = 0.1062×H^3.4719	0.18	-176.96	0.05
	H	W_d	0.75	<0.01	W_d = 0.2913×H-0.1313	0.11	-165.76	0.06
	DH	W_d	0.93	<0.01	W_d = 0.031×(DH)^1.7634	0.14	-156.55	0.07
	DH	W_d	0.79	<0.01	W_d = 0.0803×(DH)-0.0281	0.10	-168.47	0.06
	D²H	W_d	0.94	<0.01	W_d = 0.0206×(D²H)^1.144	0.12	-163.05	0.09
	D²H	W_d	0.74	<0.01	W_d =0.0239×(D²H)+0.0099	0.12	-162.04	0.06
	DH²	W_d	0.90	<0.01	W_d = 0.0473×(DH²)^1.1905	0.17	-150.86	0.08
	DH²	W_d	0.83	<0.01	W_d =0.0526×(D²H)+0.0067	0.08	-175.10	0.05
桐花树地上生物量模型	D	W_t	0.90	<0.01	W_t = 0.011×D^2.3301	0.03	-208.12	0.03
	D	W_t	0.57	<0.01	W_t = 0.0704×D-0.0731	0.04	-196.34	0.04
	H	W_t	0.33	<0.01	W_t = 0.1184×H^2.1268	0.07	-179.13	0.05
	H	W_t	0.24	<0.01	W_t = 0.1877×H-0.0587	0.13	-161.70	0.07
桐花树地上生物量模型	DH	W_t	0.84	<0.01	W_t = 0.0378×(DH)^1.5403	0.02	-210.19	0.03
	DH	W_t	0.67	<0.01	W_t = 0.0825×(DH)-0.0402	0.03	-202.75	0.03
	D²H	W_t	0.90	<0.01	W_t = 0.0227×(D²H)^0.9647	0.02	-214.66	0.03
	D²H	W_t	0.84	<0.01	W_t = 0.0275×(D²H)-0.0109	0.02	-203.74	0.02
	DH²	W_t	0.71	<0.01	W_t = 0.065×(DH²)^1.0197	0.03	-199.39	0.03
	DH²	W_t	0.68	<0.01	W_t = 0.0881×(DH²)-0.0127	0.03	-203.57	0.03
桐花树地下生物量模型	D	W_d	0.90	<0.01	W_d = 0.0038×D^2.4588	0.00	-299.13	0.01
	D	W_d	0.65	<0.01	W_d = 0.0241×D-0.0234	0.00	-269.91	0.01
	H	W_d	0.36	<0.01	W_d = 0.0462×H^2.2983	0.00	-250.07	0.01
	H	W_d	0.31	<0.01	W_d = 0.0662×H-0.0201	0.00	-249.42	0.02
	DH	W_d	0.84	<0.01	W_d = 0.0143×(DH)^1.5553	0.00	-288.83	0.01
	DH	W_d	0.74	<0.01	W_d = 0.0272×(DH)-0.0104	0.00	-276.19	0.01
	D²H	W_d	0.88	<0.01	W_d = 0.0084×(D²H)^0.9858	0.00	-296.93	0.01
	D²H	W_d	0.88	<0.01	W_d = 0.009×(D²H)-0.0008	0.00	-299.49	0.01
	DH²	W_d	0.70	<0.01	W_d = 0.0247×(DH²)^1.0295	0.00	-275.08	0.01
	DH²	W_d	0.74	<0.01	W_d = 0.0293×(D²H)-0.0017	0.00	-276.52	0.01
木榄地上生物量模型	D	W_t	0.73	<0.01	W_t = 0.0043×D^3.6814	0.05	-191.85	0.04
	D	W_t	0.87	<0.01	W_t = 0.1475×D-0.1976	0.04	-199.76	0.03
	H	W_t	0.86	<0.01	W_t = 0.2561×H^3.4544	0.12	-164.76	0.06
	H	W_t	0.58	<0.01	W_t = 0.3593×H-0.1392	0.12	-163.59	0.06
	DH	W_t	0.95	<0.01	W_t = 0.0353×(DH)^2.1235	0.04	-199.35	0.04
	DH	W_t	0.88	<0.01	W_t = 0.1151×(DH)-0.0631	0.04	-198.12	0.03
	D²H	W_t	0.91	<0.01	W_t = 0.0158×(D²H)^1.413	0.06	-184.64	0.04
	D²H	W_t	0.93	<0.01	W_t = 0.0333×(D²H)-0.0128	0.06	-200.10	0.03
	DH²	W_t	0.95	<0.01	W_t = 0.0767×(DH²)^1.3609	0.06	-184.47	0.04
	DH²	W_t	0.83	<0.01	W_t = 0.1054×(DH²)-0.0152	0.05	-188.28	0.04
木榄地下生物量模型	D	W_d	0.57	<0.01	W_d = 0.0013×D^3.8032	0.01	-228.58	0.02
	D	W_d	0.78	<0.01	W_d = 0.0452×D-0.0568	0.01	-249.71	0.02
	H	W_d	0.92	<0.01	W_d = 0.1168×H^4.2022	0.01	-226.94	0.02
	H	W_d	0.67	<0.01	W_d = 0.1263×H-0.049	0.01	-238.25	0.02
	DH	W_d	0.88	<0.01	W_d = 0.0106×(DH)^2.4009	0.02	-216.43	0.03
	DH	W_d	0.84	<0.01	W_d = 0.0369×(DH)-0.0177	0.00	-258.62	0.01
	D²H	W_d	0.80	<0.01	W_d = 0.0044×(D²H)^1.5536	0.02	-213.67	0.03
	D²H	W_d	0.83	<0.01	W_d = 0.0102×(D²H)-0.0004	0.01	-255.70	0.01
	DH²	W_d	0.93	<0.01	W_d = 0.026×(DH²)^1.5795	0.02	-251.75	0.02
	DH²	W_d	0.80	<0.01	W_d =0.0338×(D²H)-0.0025	0.01	-251.02	0.01
秋茄地上生物量模型	D	W_t	0.91	<0.01	W_t = 0.0243×D^2.0144	4.83	-52.82	0.40
	D	W_t	0.82	<0.01	W_t = 0.274×D-0.4659	3.23	-64.85	0.33
	H	W_t	0.87	<0.01	W_t = 0.2833×H^3.4024	6.38	-44.44	0.46
	H	W_t	0.66	<0.01	W_t = 1.7532×H-1.1884	6.51	-43.82	0.47
	DH	W_t	0.95	<0.01	W_t = 0.0569×(DH)^1.3385	1.21	-92.22	0.20
	DH	W_t	0.96	<0.01	W_t = 0.1588×(DH)-0.1618	0.83	-103.62	0.17
	D²H	W_t	0.94	<0.01	W_t = 0.04×(D²H)^0.8104	2.54	-70.04	0.29
	D²H	W_t	0.84	<0.01	W_t = 0.011×(D²H)+0.1818	3.03	-64.78	0.32
	DH²	W_t	0.95	<0.01	W_t = 0.0882×(DH²)^0.9796	0.58	-114.47	0.14
	DH²	W_t	0.97	<0.01	W_t = 0.085×(DH²) + 0.0237	0.54	-116.38	0.13
秋茄地下生物量模型	D	W_d	0.95	<0.01	W_d = 0.0073×D^2.3728	4.02	-58.33	0.37
	D	W_d	0.87	<0.01	W_d = 0.1571×D-0.2736	0.80	-106.59	0.16
	H	W_d	0.85	<0.01	W_d = 0.1209×H^3.8117	2.58	-71.63	0.29
	H	W_d	0.60	<0.01	W_d = 0.9006×H-0.5968	2.38	-74.00	0.28
	DH	W_d	0.96	<0.01	W_d = 0.0202×(DH)^1.5373	0.71	-108.20	0.15
	DH	W_d	0.97	<0.01	W_d = 0.0887×(DH)-0.1029	0.20	-146.39	0.08
	D²H	W_d	0.96	<0.01	W_d = 0.0134×(D²H)^0.9392	1.80	-80.48	0.24
	D²H	W_d	0.89	<0.01	W_d = 0.0063×(D²H)-0.0678	1.20	-92.60	0.20
	DH²	W_d	0.94	<0.01	W_d = 0.0334×(DH²)^1.1147	0.20	-146.77	0.08
	DH²	W_d	0.98	<0.01	W_d = 0.0473×(D²H)-0.0102	0.14	-156.31	0.07

Tab. 2

Fig. 3

Tab. 3

Fig. 4

References 57

[1]	董利虎, 李凤日, 2018. 大兴安岭东部主要林分类型乔木层生物量估算模型[J]. 应用生态学报, 29(9): 2825-2834.
	DONG LIHU, LI FENGRI, 2018. Stand-level biomass estimation models for the tree layer of main forest types in East Daxing’an Mountains, China[J]. Chinese Journal of Applied Ecology, 29(9): 2825-2834 (in Chinese with English abstract).
[2]	范航清, 王文卿, 2017. 中国红树林保育的若干重要问题[J]. 厦门大学学报(自然科学版), 56(3): 323-330.
	FAN HANGQING, WANG WENQING, 2017. Some thematic issues for mangrove conservation in China[J]. Journal of Xiamen University (Natural Science), 56(3): 323-330 (in Chinese with English abstract).
[3]	高天伦, 管伟, 毛静, 等, 2017. 广东省雷州附城主要红树林群落碳储量及其影响因子[J]. 生态环境学报, 26(6): 985-990.
	GAO TIANLUN, GUAN WEI, MAO JING, et al, 2017. Carbon storage and influence factors of major mangrove communities in Fucheng, Leizhou Peninsula, Guangdong Province[J]. Ecology and Environmental Sciences, 26(6): 985-990 (in Chinese with English abstract).
[4]	管丽娟, 廖静, 2018. 惠州考洲洋: 用红树林捍卫美丽海湾[J]. 海洋与渔业, (4): 41-42 (in Chinese).
[5]	何琴飞, 郑威, 黄小荣, 等, 2017. 广西钦州湾红树林碳储量与分配特征[J]. 中南林业科技大学学报, 37(11): 121-126.
	HE QINFEI, ZHENG WEI, HUANG XIAORONG, et al, 2017. Carbon storage and distribution of mangroves at Qinzhou bay[J]. Journal of Central South University of Forestry & Technology, 37(11): 121-126 (in Chinese with English abstract).
[6]	胡平, 向雪莲, 黄子健, 等, 2024. 外来与乡土红树植物群落生物量动态[J]. 应用与环境生物学报, 30(5): 929-934.
	HU PING, XIANG XUELIAN, HUANG ZIJIAN, et al, 2024. Biomass dynamics in exotic and native mangrove species[J]. Chinese Journal of Applied and Environmental Biology, 30(5): 929-934 (in Chinese with English abstract).
[7]	胡懿凯, 徐耀文, 薛春泉, 等, 2019. 广东省无瓣海桑和林地土壤碳储量研究[J]. 华南农业大学学报, 40(6): 95-103.
	HU YIKAI, XU YAOWEN, XUE CHUNQUAN, et al, 2019. Studies on carbon storages of Sonneratia apetala forest vegetation and soil in Guangdong Province[J]. Journal of South China Agricultural University, 40(6): 95-103 (in Chinese with English abstract).
[8]	黄润霞, 吴卓翎, 彭江炜, 等, 2019. 广东红树植物木榄生物量模型[J]. 西北农林科技大学学报(自然科学版), 47(12): 86-94, 103.
	HUANG RUNXIA, WU ZHUOLING, PENG JIANGWEI, et al, 2019. Biomass model of Bruguiera gymnorrhiza in Guangdong[J]. Journal of Northwest A & F University (Natural Science Edition), 47(12): 86-94, 103 (in Chinese with English abstract).
[9]	金川, 王金旺, 郑坚, 等, 2012. 异速生长法计算秋茄红树林生物量[J]. 生态学报, 32(11): 3414-3422.
	JIN CHUAN, WANG JINWANG, ZHENG JIAN, et al, 2012. An assessment method of Kandelia obovata population biomass[J]. Acta Ecologica Sinica, 32(11): 3414-3422 (in Chinese with English abstract).
[10]	李海奎, 雷渊才, 曾伟生, 2011. 基于森林清查资料的中国森林植被碳储量[J]. 林业科学, 47(7): 7-12.
	LI HAIKUI, LEI YUANCAI, ZENG WEISHENG, 2011. Forest carbon storage in China estimated using forestry inventory data[J]. Scientia Silvae Sinicae, 47(7): 7-12 (in Chinese with English abstract).
[11]	李林锋, 吴小凤, 刘素青, 2015. 湛江5种红树林树种光合作用特性及光合固碳能力研究[J]. 广西植物, 35(6): 825-832.
	LI LINFENG, WU XIAOFENG, LIU SUQING, 2015. Characteristics of photosynthesis and photosynthetic carbon fixation capacity of five mangrove tree species in Zhanjiang City[J]. Guihaia, 35(6): 825-832 (in Chinese with English abstract).
[12]	宁世江, 蒋运生, 邓泽龙, 等, 1996. 广西龙门岛群桐花树天然林生物量的初步研究[J]. 植物生态学报, (1): 57-64.
	NING SHIJIANG, JIANG YUNSHENG, DENG ZELONG, et al, 1996. A preliminary study on biomass of Aegiceras corniculatum natural forest in Longmen islets of Guangxi[J]. Acta Phytoecologica Sinica, (1): 57-64 (in Chinese with English abstract).
[13]	覃国铭, 张靖凡, 周金戈, 等, 2023. 广东省红树林土壤碳储量及固碳潜力研究[J]. 热带地理, 43(1): 23-30.
	QIN GUOMING, ZHANG JINGFAN, ZHOU JINGE, et al, 2023. Soil carbon stock and potential carbon storage in the mangrove forests of Guangdong[J]. Tropical Geography, 43(1): 23-30 (in Chinese with English abstract).
[14]	汪珍川, 杜虎, 宋同清, 等, 2015. 广西主要树种(组)异速生长模型及森林生物量特征[J]. 生态学报, 35(13): 4462-4472.
	WANG ZHENCHUAN, DU HU, SONG TONGQING, et al, 2015. Allometric models of major tree species and forest biomass in Guangxi[J]. Acta Ecologica Sinica, 35(13): 4462-4472 (in Chinese with English abstract).
[15]	王友绍, 2021. 全球气候变化对红树林生态系统的影响、挑战与机遇[J]. 热带海洋学报, 40(3): 1-14.
	WANG YOUSHAO, 2021. Impacts, challenges and opportunities of global climate change on mangrove ecosystems[J]. Journal of Tropical Oceanography, 40(3): 1-14 (in Chinese with English abstract).
[16]	武高洁, 郭志华, 郭菊兰, 等, 2014. 红树林异速生长方程估算生物量研究进展[J]. 湿地科学与管理, 10(3): 61-65.
	WU GAOJIE, GUO ZHIHUA, GUO JULAN, et al, 2014. Progress in studies of biomass estimation using allometric equation of mangrove forest[J]. Wetland Science & Management, 10(3): 61-65 (in Chinese with English abstract).
[17]	吴雪, 赵鑫, 辜伟芳, 等, 2025. 浙南海岸带人工秋茄红树林与互花米草盐沼土壤碳汇对比研究[J]. 热带海洋学报, 44(1): 1-10.
	WU XUE, ZHAO XIN,GU WEIFANG et al, 2025. Comparative study on soil carbon sinks of artificial Kandelia obovate mangrove and Spartina alterniflora salt marsh in the southern Zhejiang coastal zone[J]. Journal of Tropical Oceanography, 44(1): 1-10 (in Chinese with English abstract).
[18]	辛琨, 颜葵, 李真, 等, 2014. 海南岛红树林湿地土壤有机碳分布规律及影响因素研究[J]. 土壤学报, 51(5): 1078-1086.
	XIN KUN, YAN KUI, LI ZHEN, et al, 2014. Distribution of soil organic carbon in mangrove wetlands of Hainan island and its influencing factors[J]. Acta Pedologica Sinica, 51(5): 1078-1086 (in Chinese with English abstract).
[19]	鄢春梅, 李文凤, 谢绍茂, 2021. 美丽海湾建设背景下考洲洋海岸带整治与生态修复实践[J]. 广东园林, 43(3): 61-65.
	YAN CHUNMEI, LI WENFENG, XIE SHAOMAO, 2021. Practice of coastal zone regulation and ecological restoration in Kaozhouyang under the background of Beautiful Bay construction[J]. Guangdong Landscape Architecture, 43(3): 61-65 (in Chinese with English abstract).
[20]	姚正阳, 刘建军, 2014. 西安市4种城市绿化灌木单株生物量估算模型[J]. 应用生态学报, 25(1): 111-116.
	YAO ZHENGYANG, LIU JIANJUN, 2014. Models for biomass estimation of four shrub species planted in urban area of Xi’an City, Northwest China[J]. Chinese Journal of Applied Ecology, 25(1): 111-116 (in Chinese with English abstract).
[21]	周治刚, 岳文, 李辉权, 等, 2024. 树种类型和潮滩高程对广东湛江高桥红树林碳储量的影响[J]. 热带海洋学报, 43(2): 108-120.
	ZHOU ZHIGANG, YUE WEN, LI HUIQUAN, et al, 2024. Influence of tree species and intertidal elevations on the carbon storage of the Gaoqiao mangrove area in Zhanjiang, Guangdong Province[J]. Journal of Tropical Oceanography, 43(2): 108-120 (in Chinese with English abstract).
[22]	朱可峰, 廖宝文, 章家恩, 2011. 广州市南沙红树植物无瓣海桑、木榄人工林生物量的研究[J]. 林业科学研究, 24(4): 531-536.
	ZHU KEFENG, LIAO BAOWEN, ZHANG JIAEN, 2011. Studies on the biomass of mangrove plantation of Sonneratia apetala and Bruguiera gymnorrhiza in the wetland of Nansha in Guangzhou city[J]. Forest Research, 24(4): 531-536 (in Chinese with English abstract).
[23]	朱耀军, 郭菊兰, 武高洁, 2012. 红树林湿地有机碳研究进展[J]. 生态学杂志, 31(10): 2681-2687.
	ZHU YAOJUN, GUO JULAN, WU GAOJIE, 2012. Organic carbon in mangrove wetlands: A review[J]. Chinese Journal of Ecology, 31(10): 2681-2687 (in Chinese with English abstract).
[24]	ADAME M F, ZAKARIA R M, FRY B, et al, 2018. Loss and recovery of carbon and nitrogen after mangrove clearing[J]. Ocean & Coastal Management, 161: 117-126.
[25]	ALONGI D M, 2012. Carbon sequestration in mangrove forests[J]. Carbon Management, 3(3): 313-322.
[26]	ATWOOD T B, CONNOLLY R M, ALMAHASHEER H, et al, 2017. Global patterns in mangrove soil carbon stocks and losses[J]. Nature Climate Change, 7(7): 523-528.
[27]	BAI JIANKUN, MENG YUCHEN, GOU RUIKUN, et al, 2021. Mangrove diversity enhances plant biomass production and carbon storage in Hainan island, China[J]. Functional Ecology, 35(3): 774-786.
[28]	CHENG KAI, YANG HAITAO, TAO SHENGLI, et al, 2024. Carbon storage through China’s planted forest expansion[J]. Nature Communications, 15(1): 4106.
[29]	CHOU M Q, LIN W J, LIN C W, et al, 2022. Allometric equations may underestimate the contribution of fine roots to mangrove carbon sequestration[J]. Science of The Total Environment, 833: 155032.
[30]	COMLEY B W T, MCGUINNESS K A, 2005. Above-and below-ground biomass, and allometry, of four common northern Australian mangroves[J]. Australian Journal of Botany, 53(5): 431-436.
[31]	CORONADO-MOLINA C, DAY J W, REYES E, et al, 2004. Standing crop and aboveground biomass partitioning of a dwarf mangrove forest in Taylor River Slough, Florida[J]. Wetlands Ecology and Management, 12(3): 157-164.
[32]	DONATO D C, KAUFFMAN J B, MURDIYARSO D, et al, 2011. Mangroves among the most carbon-rich forests in the tropics[J]. Nature Geoscience, 4(5): 293-297.
[33]	FAYOLLE A, DOUCET J L, GILLET J F, et al, 2013. Tree allometry in Central Africa: Testing the validity of pantropical multi-species allometric equations for estimating biomass and carbon stocks[J]. Forest Ecology and Management, 305: 29-37.
[34]	FORRESTER D I, BAUHUS J, 2016. A review of processes behind diversity—productivity relationships in forests[J]. Current Forestry Reports, 2: 45-61.
[35]	GOLLEY F, ODUM H T, WILSON R F, 1962. The structure and metabolism of a Puerto Rican red mangrove forest in May[J]. Ecology, 43(1): 9-19.
[36]	HOWARD J, HOYT S, ISENSEE K, et al, 2014. Coastal blue carbon: methods for assessing carbon stocks and emissions factors in mangroves, tidal salt marshes, and seagrasses[R]. Arlington: Conservation International, Intergovernmental, Oceanographic Commission of UNESCO, International Union for Conservation of Nature.
[37]	KAUFFMAN J B, COLE T G, 2010. Micronesian mangrove forest structure and tree responses to a severe typhoon[J]. Wetlands, 30(6): 1077-1084.
[38]	KOMIYAMA A, ONG J E, POUNGPARN S, 2008. Allometry, biomass, and productivity of mangrove forests: A review[J]. Aquatic Botany, 89(2): 128-137.
[39]	KOMIYAMA A, POUNGPARN S, KATO S, 2005. Common allometric equations for estimating the tree weight of mangroves[J]. Journal of Tropical Ecology, 21(4): 471-477.
[40]	KRISTENSEN E, BOUILLON S, DITTMAR T, et al, 2008. Organic carbon dynamics in mangrove ecosystems: a review[J]. Aquatic Botany, 89(2): 201-219.
[41]	KUSMANA C, HIDAYAT T, TIRYANA T, et al, 2018. Allometric models for above- and below-ground biomass of Sonneratia spp.[J]. Global Ecology and Conservation, 15:e00417.
[42]	MCLEOD E, CHMURA G L, BOUILLON S, et al, 2011. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO₂[J]. Frontiers in Ecology and the Environment, 9(10): 552-560.
[43]	MENG YUCHEN, BAI JIANKUN, GOU RUIKUN, et al, 2021. Relationships between above- and below-ground carbon stocks in mangrove forests facilitate better estimation of total mangrove blue carbon[J]. Carbon Balance and Management, 16(1): 8.
[44]	MENSAH S, VELDTMAN R, DU TOIT B, et al, 2016. Aboveground biomass and carbon in a South African mistbelt forest and the relationships with tree species diversity and forest structures[J]. Forests, 7(4): 79.
[45]	O’CONNOR J J, FEST B J, SIEVERS M, et al, 2020. Impacts of land management practices on blue carbon stocks and greenhouse gas fluxes in coastal ecosystems—A meta‐analysis[J]. Global Change Biology, 26(3): 1354-1366.
[46]	OUÉDRAOGO K, DIMOBE K, THIOMBIANO A, 2020. Allometric models for estimating aboveground biomass and carbon stock for Diospyros mespiliformis in West Africa[J]. Silva Fennica, 54(1): 10215
[47]	PHIRI D, ACKERMAN P, WESSELS B, et al, 2015. Biomass equations for selected drought-tolerant eucalypts in South Africa[J]. Southern Forests: a Journal of Forest Science, 77(4): 255-262.
[48]	PURNOBASUKI H, SUZUKI M, 2005. Aerenchyma tissue development and gas-pathway structure in root of Avicennia marina (Forsk.) Vierh[J]. Journal of Plant Research, 118(4): 285-294.
[49]	SEGURA M, KANNINEN M, 2005. Allometric models for tree volume and total aboveground biomass in a tropical humid forest in costa Rica[J]. Biotropica, 37(1): 2-8.
[50]	SMITH T J, WHELAN K R T, 2006. Development of allometric relations for three mangrove species in South Florida for use in the Greater Everglades Ecosystem restoration[J]. Wetlands Ecology and Management, 14: 409-419.
[51]	SONG SHANSHAN, DING YALI, LI WEI, et al, 2023. Mangrove reforestation provides greater blue carbon benefit than afforestation for mitigating global climate change[J]. Nature Communications, 14(1): 756.
[52]	TAM N F Y, WONG Y S, 1998. Variations of soil nutrient and organic matter content in a subtropical mangrove ecosystem[J]. Water, Air, and Soil Pollution, 103: 245-261.
[53]	TAM N F Y, WONG Y S, LAN C Y, et al, 1995. Community structure and standing crop biomass of a mangrove forest in Futian Nature Reserve, Shenzhen, China[J]. Hydrobiologia, 295: 193-201.
[54]	TURNBULL L A, LEVINE J M, LOREAU M, et al, 2013. Coexistence, niches and biodiversity effects on ecosystem functioning[J]. Ecology Letters, 16(1): 116-127.
[55]	WOODROFFE C D, 1985. Studies of a mangrove basin, Tuff Crater, New Zealand: I. Mangrove biomass and production of detritus[J]. Estuarine, Coastal and Shelf Science, 20(3): 265-280.
[56]	ZANVO S M G, MENSAH S, SALAKO K V, et al, 2023. Tree height-diameter, aboveground and belowground biomass allometries for two West African mangrove species[J]. Biomass and Bioenergy, 176: 106917.
[57]	ZHANG ZIMING, WANG YING, ZHU YAKUN, et al, 2022. Carbon sequestration in soil and biomass under native and non-native mangrove ecosystems[J]. Plant and Soil, 479(1): 61-76.

树种	基径/cm	株高/m	树龄/a	地上生物量/kg	地下生物量/kg	地上碳密度系数/%	地下碳密度系数/%
红海榄	1.05~4.14	0.46~1.73	1~5	0.012~1.416	0.002~1.069	43.79~44.66	31.66~40.61
白骨壤	0.70~3.60	0.36~1.63	1~5	0.004~1.531	0.001~0.570	41.87~44.55	35.47~38.79
桐花树	0.60~3.30	0.32~0.91	1~3	0.002~0.326	0.001~0.104	36.67~40.39	35.96~42.20
木榄	0.96~3.76	0.32~1.03	1~3	0.006~0.437	0.001~0.131	43.06~43.93	31.28~37.26
秋茄	0.65~14.32	0.36~2.15	1~5	0.006~3.822	0.003~2.215	39.28~46.54	32.45~40.60

站位	物种	密度/(ind·hm^-2)	基径/cm	株高/m
C1	白骨壤	11067	3.17±0.54	1.34±0.26
C1	红海榄	12667	2.41±0.58	0.70±0.24
C2	木榄	10667	2.46±1.14	1.02±0.37
C3	桐花树	11200	2.43±0.51	0.87±0.11
C3	秋茄	3600	3.80±2.90	0.85±0.45
C4	白骨壤	26000	2.08±0.80	1.09±0.19
C5	白骨壤	11200	2.49±0.38	0.77±0.11
C5	红海榄	22000	2.09±0.42	0.77±0.08

Study on biomass models of juvenile mangroves and carbon storage in young mangrove ecosystems*

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