Soil Microbial Biomass Carbon,Nitrogen and Nutrient Characteristics of Different Plant Communities in Dongzhai Port

doi:10.13466/j.cnki.lyzygl.2021.06.016

Abstract

Abstract:

This paper conducted the analysis of soil physical and chemical properties,soil microbial biomass,carbon and nitrogen,and nutrient content of different mangrove plant communities in Dongzhai Port. The results showed that:1) The variation ranges of soil microbial biomass carbon and nitrogen (MBC and MBN) were 135.83-1 171.24 mg/kg and 35.13-232.89 mg/kg,respectively. The variation laws of MBC and MBN contents among different plant communities were basically the same and there were significant differences. Among them,the soil MBC and MBN contents of Avicennia marina and Rhizophora stylosa communities were the lowest. 2)The average soil TC∶TN∶TP of Dongzhai Port was 76∶4∶1,average MBC∶MBN was 5∶1. There were significant differences in soil nutrients,microbial biomass carbon and nitrogen,and in their stoichiometric ratios among communities with different tree species at different locations.3)Correlation analysis showed that there was a close relationship between soil microbial biomass carbon and nitrogen and soil nutrients in Dongzhai Port mangrove wetland,which could be used as an early biological indicator of soil carbon and nitrogen change in this area.

Key words: mangrove wetland, soil microbial biomass carbon and nitrogen, stoichiometry, Dongzhai Port

CLC Number:

CHEN Xiaohua, CHEN Zongzhu, LEI Jinrui, WU Tingtian, LI Yuanling. Soil Microbial Biomass Carbon,Nitrogen and Nutrient Characteristics of Different Plant Communities in Dongzhai Port[J]. FOREST RESOURCES WANAGEMENT, 2021, 0(6): 97-104.

Figures/Tables 5

Fig.1

Fig.2

Fig.3

Fig.4

Tab.1

References 27

[1]	Arunachalam K, Arunachalam A, Melkania N P. Influence of soil properties on microbial populations,activity and biomass in humid subtropical mountainous ecosystems of India[J]. Biology & Fertility of Soils, 1999, 30(3):217-223.
[2]	Wardle D A. A comparative assessment of factors which influence microbial biomass carbon and nitrogen levels in soil[J]. Biological Reviews, 2008, 67(3):321-358. doi: 10.1111/brv.1992.67.issue-3
[3]	Kononen M, Jauhiainen J, Straková P, et al. Deforested and drained tropical peatland sites show poorer peat substrate quality and lower microbial biomass and activity than unmanaged swamp forest[J]. Soil Biology & Biochemistry, 2018, 123(8):229-241. doi: 10.1016/j.soilbio.2018.04.028
[4]	Torres I F, Bastida F, Hernández T, et al. The role of lignin and cellulose in the carbon-cycling of degraded soils under semiarid climate and their relation to microbial biomass[J]. Soil Biology & Biochemistry, 2014, 75(8):152-160. doi: 10.1016/j.soilbio.2014.04.007
[5]	He Z L, Yang X E, Baligar V C, et al. Microbiological and biochemical indexing systems for assessing quality of acid soils[J]. Advances in Agronomy, 2003, 78(2):89-138.
[6]	谢龙莲, 陈秋波, 王真辉, 等. 环境变化对土壤微生物的影响[J]. 热带农业科学, 2004, 24(3):39-47.
[7]	Barbier E B. The protective service of mangrove ecosystems:a review of valuation methods[J]. Marine Pollution Bulletin, 2016, 109(2):676-681. doi: 10.1016/j.marpolbul.2016.01.033 pmid: 26851868
[8]	Richards D R, Friess D A. Rates and drivers of mangrove deforestation in Southeast Asia,2000—2012[J]. Proceedings of the national academy of sciences of the United States of America, 2016, 113(2):344-349. doi: 10.1073/pnas.1510272113 pmid: 26712025
[9]	Cui Xiaowei, Liang Jie, Lu Weizhi, et al. Stronger ecosystem carbon sequestration potential of mangrove wetlands with respect to terrestrial forests in subtropical China[J]. Agricultural and Forest Meteorology, 2018, 249(2):71-80. doi: 10.1016/j.agrformet.2017.11.019
[10]	Pendleton L, Donato D C, Murray B C, et al. Estimating global “blue carbon” emissions from conversion and degradation of vegetated coastal ecosystems[J]. PloS One, 2012, 7(9):1-6.
[11]	Siikamäki J, Sanchirico J N, Jardine S L. Global economic potential for reducing carbon dioxide emissions from mangrove loss[J]. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(36):14369-14374. doi: 10.1073/pnas.1200519109 pmid: 22847435
[12]	Kelleway J J, Saintilan N, Macreadie P I, et al. Seventy years of continuous encroachment substantially increases 'blue carbon' capacity as mangroves replace intertidal salt marshes[J]. Global Change Biology, 2016, 22(3):1097-1109. doi: 10.1111/gcb.13158 pmid: 26670941
[13]	Li Shibo, Chen Pohung, Huang Jihsheng, et al. Factors regulating carbon sinks in mangrove ecosystems[J]. Global Change Biology, 2018, 24(9):4195-4210. doi: 10.1111/gcb.14322 pmid: 29790233
[14]	Osland M J, Gabler C A, Grace J B, et al. Climate and plant controls on soil organic matter in coastal wetlands[J]. Global Change Biology, 2018, 24(11):5361-5379. doi: 10.1111/gcb.2018.24.issue-11
[15]	鲁如坤. 土壤农业化学分析方法[M]. 北京: 中国农业科技出版社, 2000.
[16]	李延茂, 胡江春, 汪思龙, 等. 森林生态系统中土壤微生物的作用与应用[J]. 应用生态学报, 2004, 15(10):1943-1946.
[17]	张雅茜, 方晰, 冼应男, 等. 亚热带区4种林地土壤微生物生物量碳氮磷及酶活性特征[J]. 生态学报, 2019, 39(14):5326-5338.
[18]	李荣, 宋维峰. 基于地统计学的哈尼梯田土壤微生物生物量碳时空分布特征[J]. 土壤通报, 2020, 51(6):1366-1373.
[19]	贾培龙, 安韶山, 李程程, 等. 黄土高原森林带土壤养分和微生物量及其生态化学计量变化特征[J]. 水土保持学报, 2020, 34(1):315-321.
[20]	李万年, 黄则月, 赵春梅, 等. 望天树人工幼林土壤微生物量碳氮及养分特征[J]. 北京林业大学学报, 2020, 42(12):51-62.
[21]	Cleveland C C, Liptzin D. C∶N∶P stoichiometry in soil:is there a “Redfield ratio” for the microbial biomass?[J]. Biogeochemistry, 2007, 85(3):235-252. doi: 10.1007/s10533-007-9132-0
[22]	Tian H, Chen G, Zhang C, et al. Pattern and variation of C∶N∶P ratios in China's soils:a synjournal of observational data[J]. Biogeochemistry, 2010, 98(1-3):139-151. doi: 10.1007/s10533-009-9382-0
[23]	宋一凡, 卢亚静, 刘铁军, 等. 荒漠草原不同雨量带土壤-植物-微生物 C、N、P 及其化学计量特征[J]. 生态学报, 2020, 40(12):4011-4023.
[24]	习盼. 滩涂湿地典型植物群落土壤活性有机碳组分及酶活性特征[D]. 南京:南京大学, 2020.
[25]	涂志华, 范志平, 王善祥, 等. 大伙房水库流域不同水源涵养林土壤微生物量碳氮特征及其影响因素[J]. 中国水土保持科学, 2019, 17(4):130-140.
[26]	邓健, 张丹, 张伟, 等. 黄土丘陵区刺槐叶片-土壤-微生物碳氮磷化学计量学及其稳态性特征[J]. 生态学报, 2019, 39(15):5527-5535.
[27]	刘春华, 吴东梅, 刘雨晖, 等. 氮沉降对米槠天然林土壤有机碳及微生物群落结构的影响[J]. 林业科学研究, 2021, 34(2):42-49.

	MBN	TOC	TN	MBC∶MBN	TOC∶TN	TP	TK	pH	RS
MBC	0.859^**	0.494^**	0.526^**	0.475^**	0.253^*	0.358^**	0.425^**	-0.222	0.235^*
MBN	1	0.351^**	0.439^**	0.090	0.050	0.572^**	0.302^**	0.023	0.243^*
TOC	0.351^**	1	0.964^**	0.530^**	0.458^**	0.354^**	0.110	-0.484^**	0.455^**
TN	0.439^**	0.964^**	1	0.466^**	0.249^*	0.522^**	0.165	-0.370^**	0.429^**
MBC∶MBN	0.090	0.530^**	0.466^**	1	0.500^**	-0.043	0.302^**	-0.632^**	0.121
TOC∶TN	0.050	0.458^**	0.249^*	0.500^**	1	-0.196	-0.013	-0.578^**	0.231^*