基于熵权灰色关联分析的喀斯特石漠化地区植被恢复效应评价
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摘要
以广西平果县喀斯特石漠化地区7种主要人工植被恢复模式为研究对象,采用主成分分析、相关性分析和敏感性分析方法,对涵盖林分生长、凋落物和土壤3类23个参数进行分析,选取对质量变异敏感、能最大限度代表所有候选参数的最小数据集,应用熵权灰色关联分析法对其植被恢复效应进行定量分析与评价。结果表明:1)研究区植被恢复效应评价最小数据集由胸径、最大拦蓄量、土壤自然含水量、全氮、速效磷和速效钾等6个参数组成。2)植被恢复效应加权关联度由大至小依次为银合欢0.795>任豆0.631>柚木0.583>香樟0.541>顶果木0.433>乌墨0.391>苏木0.381。3)不同模式植被恢复效应存在一定差异,银合欢属Ⅰ级(优),任豆属Ⅱ级(良),柚木和香樟属Ⅲ级(中),顶果木、乌墨和苏木属Ⅳ级(差)。4)银合欢和任豆属固氮类先锋树种占据着演替早期生态位,更能适应演替早期的环境,在生理特征上有明显优势。5)在喀斯特石漠化地区植被恢复初期筛选配置适生先锋树种,率先恢复植物群落的基本结构和功能,有利于演替后续优势物种的入侵和定居,加快植被恢复演替进程。6)实例验证了基于熵权灰色关联分析法用于植被恢复效应评价的可行性,为植被恢复效应定量化评价提供了新的方法,具有一定的实用价值和推广意义。
To assess the effect of vegetation restoration of karst rocky desertification in Pingguo County, Guangxi, China, seven primary vegetation restoration models there were surveyed. Three kinds of factors such as stand growth, litters and soil(including 23 parameters)were analyzed, and then the minimum data set that is sensitive to mass variation and can maximally represent all candidate parameters was selected. Finally, entropy weighted grey relational analysis was applied to quantitatively analyze and evaluate the effect of vegetation restoration. The results are as follows. 1) The minimum data set was composed of diameter at breast height, maximum interception amount of litter, soil natural water content, total nitrogen, available phosphorus, and available potassium. 2) The weighted incidence degree of vegetation restoration effects were ordered as followjng: Leucaena leucocephala(0.795) > Zenia insignis(0.631) > Tectona grandis(0.583) > Cinnamomum camphora(0.541) > Acrocarpus fraxinifolius(0.433) > Syzygium cumini(0.391) > Caesalpinia sappan(0.381). 3) There were some differences in the effects of different vegetation restoration modes, Leucaena was classified to 1 level(optimal), legume 2-level(good), teak and camphor 3 level(middle), fruit tree, black ink tree 4 level(poor). 4) Leucaena and legume were the pioneer species of nitrogen fixation, occupying the ecological niche in the early stage of succession and adapting to the environment in the early stage of succession, it had obvious advantages in physiological characteristics. 5) Therefore, in the early stage of vegetation restoration in karst rocky desertification areas, the selection and allocation of suitable pioneer species should be carried out to take the lead in restoring the basic structure and function of plant community, which is conducive to the invasion and settlement of subsequent dominant species in succession and the acceleration of the process of vegetation restoration and succession. 6) An example verifies the feasibility of using entropy weighted grey relational analysis method for evaluation of vegetation restoration effect, and provides a new method for quantitative evaluation of vegetation restoration effect, which has certain practical value and popularization significance.
To assess the effect of vegetation restoration of karst rocky desertification in Pingguo County, Guangxi, China, seven primary vegetation restoration models there were surveyed. Three kinds of factors such as stand growth, litters and soil(including 23 parameters)were analyzed, and then the minimum data set that is sensitive to mass variation and can maximally represent all candidate parameters was selected. Finally, entropy weighted grey relational analysis was applied to quantitatively analyze and evaluate the effect of vegetation restoration. The results are as follows. 1) The minimum data set was composed of diameter at breast height, maximum interception amount of litter, soil natural water content, total nitrogen, available phosphorus, and available potassium. 2) The weighted incidence degree of vegetation restoration effects were ordered as followjng: Leucaena leucocephala(0.795) > Zenia insignis(0.631) > Tectona grandis(0.583) > Cinnamomum camphora(0.541) > Acrocarpus fraxinifolius(0.433) > Syzygium cumini(0.391) > Caesalpinia sappan(0.381). 3) There were some differences in the effects of different vegetation restoration modes, Leucaena was classified to 1 level(optimal), legume 2-level(good), teak and camphor 3 level(middle), fruit tree, black ink tree 4 level(poor). 4) Leucaena and legume were the pioneer species of nitrogen fixation, occupying the ecological niche in the early stage of succession and adapting to the environment in the early stage of succession, it had obvious advantages in physiological characteristics. 5) Therefore, in the early stage of vegetation restoration in karst rocky desertification areas, the selection and allocation of suitable pioneer species should be carried out to take the lead in restoring the basic structure and function of plant community, which is conducive to the invasion and settlement of subsequent dominant species in succession and the acceleration of the process of vegetation restoration and succession. 6) An example verifies the feasibility of using entropy weighted grey relational analysis method for evaluation of vegetation restoration effect, and provides a new method for quantitative evaluation of vegetation restoration effect, which has certain practical value and popularization significance.
引文
[1]蒋勇军,刘秀明,何师意,等.喀斯特槽谷土地石漠化与综合治理技术研发[J].生态学报,2016,36(22):7092-7097.
[2]中国治沙暨沙业学会.中国石漠化状况公报[EB/OL].http://zsxh.forestry.gov.cn2012.
[3]规划编写组,张菁.广西壮族自治区岩溶地区石漠化综合治理规划[J].草业科学,2008,25(9):93-102.
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[5]宋同清,彭晚霞,杜虎,等.中国西南喀斯特石漠化时空演变特征、发生机制与调控对策[J].生态学报,2014,34(18):5328-5341.
[6]司彬,何丙辉,姚小华,等.喀斯特石漠化形成原因及植被恢复途径探讨[J].江西农业大学学报,2006,28(3):392-396.
[7]司彬,姚小华,任华东,等.黔中喀斯特植被自然演替过程中物种组成及多样性研究--以贵州省普定县为例[J].林业科学,2008,21(5):669-674.
[8]田秀玲,吕娜,朱飞鸽,等.贵州省喀斯特山区植被恢复的物种选择现状分析[J].亚热带资源与环境学报,2012,7(2):17-26.
[9]郭柯,刘长成,董鸣.我国西南喀斯特植物生态适应性与石漠化治理[J].植物生态学报,2011,35(10):991-999.
[10]苏维词.中国西南岩溶山区石漠化的现状成因及治理的优化模式[J].水土保持学报,2002,16(2):29-32.
[11]张俊佩,张建国,段爱国,等.中国西南喀斯特地区石漠化治理[J].林业科学,2008,44(7):84-89.
[12]袁道先.岩溶石漠化问题的全球视野和我国的治理对策与经验[J].草业科学,2008,25(9):19-24.
[13]侯远瑞,黄大勇,郝海坤,等.广西岩溶石漠化地区典型造林模式及营建技术[J].广西林业科学,2010,39(4):211-213.
[14]彭玉华,欧芷阳,曹艳云,等.桂西南喀斯特山地主要植被类型凋落物累积量及其持水特性[J].中南林业科技大学学报,2013,33(2):81-85.
[15]国家林业局.中华人民共和国林业行业标准LY/T 1213 12151239 1237-1999森林土壤分析方法[S].北京:中国标准出版社,1999.
[16]国家林业局.中华人民共和国林业行业标准LY/T 1228 12321234-2015森林土壤分析方法[S].北京:中国标准出版社,2015.
[17]李桂林,陈杰,孔志超,等.基于土壤特征和土地利用变化的土壤质量评价最小数据集确定[J].生态学报,2007,27(7):2715-2724.
[18]欧芷阳,苏志尧,朱剑云.东莞主要森林群落的土壤基本性质和综合评价[J].中南林业科技大学学报,2013,33(8):96-102.
[19]唐菲菲,邓艳林,郑茂,等.基于灰色关联分析的湘西北石漠化区土壤质量评价[J].中南林业科技大学学报,2016,36(9):36-43.
[20]王明全,王金达,刘景双,等.基于物元模型的吉林省西部生态环境脆弱性评价[J].生态学杂志,2007,26(2):291-295.
[21]周文华,王如松.基于熵权的北京城市生态系统健康模糊综合评价[J].生态学报,2005,25(12):3244-3251.
[22]段锦,康慕谊,戴诚,等.基于格网的东江流域生态安全空间综合评价[J].生态学杂志,2012,31(8):2075-2081.
[23]李帅,魏虹,倪细炉,等.基于层次分析法和熵权法的宁夏城市人居环境质量评价[J].应用生态学报,2014,25(9):2700-2708.
[24]张凤太,苏维词,周继霞.基于熵权灰色关联分析的城市生态安全评[J].生态学杂志,2008,27(7):1249-1254.
[25]唐柄哲,何丙辉,闫建梅.川中丘陵区土地利用方式对土壤理化性质[J].应用生态学报,2016,27(5):1445-1452.
[26]吕刚,傅昕阳,李叶鑫,等.露天煤矿排土场复垦区不同植被类型枯落物持水特性研究[J].水土保持学报,2017,31(1):146-152.
[27]宋成军,马克明,傅伯杰,等.固氮类植物在陆地生态系统中的作用研究进展[J].生态学报,2009,29(2):869-877.
[28]许明祥,刘国彬,赵允格.黄土丘陵土壤质量评价指标研究[J].应用生态学报,2005,16(10):1843-1848.
[29]贺祥.基于熵权灰色关联法的贵州岩溶山区人地耦合系统脆弱性分析[J].水土保持研究,2014,21(1):283-289.
[30]张显松,姚健,薛建辉,等.喀斯特地区人工林凋落物及表层土壤水源涵养功能[J].林业科技开发,2010,24(4):32-35.
[31]赵娜,孟平,张劲松,等.华北低丘山地不同退耕年限刺槐人工林土壤质量评价[J].应用生态学报,2014,25(3):351-358.
[32]PASTOR J,BINKLEY D N.Nitrogen fixa tion and the mass balances of carbon and nitrogen in ecosystems[J].Biogeochemistry,1998,43:63-78.
[33]米锋,李吉跃,杨家伟.森林生态效益评价的研究进展[J].北京林业大学学报,2003,25(6):77-83.
[34]邓聚龙.农业灰色理论与方法[M].济南:山东科技出版社,1988.
[2]中国治沙暨沙业学会.中国石漠化状况公报[EB/OL].http://zsxh.forestry.gov.cn2012.
[3]规划编写组,张菁.广西壮族自治区岩溶地区石漠化综合治理规划[J].草业科学,2008,25(9):93-102.
[4]王世杰,李阳兵,李瑞玲.喀斯特石漠化的形成背景、演化与治理[J].第四纪研究,2003,23(6):657-666.
[5]宋同清,彭晚霞,杜虎,等.中国西南喀斯特石漠化时空演变特征、发生机制与调控对策[J].生态学报,2014,34(18):5328-5341.
[6]司彬,何丙辉,姚小华,等.喀斯特石漠化形成原因及植被恢复途径探讨[J].江西农业大学学报,2006,28(3):392-396.
[7]司彬,姚小华,任华东,等.黔中喀斯特植被自然演替过程中物种组成及多样性研究--以贵州省普定县为例[J].林业科学,2008,21(5):669-674.
[8]田秀玲,吕娜,朱飞鸽,等.贵州省喀斯特山区植被恢复的物种选择现状分析[J].亚热带资源与环境学报,2012,7(2):17-26.
[9]郭柯,刘长成,董鸣.我国西南喀斯特植物生态适应性与石漠化治理[J].植物生态学报,2011,35(10):991-999.
[10]苏维词.中国西南岩溶山区石漠化的现状成因及治理的优化模式[J].水土保持学报,2002,16(2):29-32.
[11]张俊佩,张建国,段爱国,等.中国西南喀斯特地区石漠化治理[J].林业科学,2008,44(7):84-89.
[12]袁道先.岩溶石漠化问题的全球视野和我国的治理对策与经验[J].草业科学,2008,25(9):19-24.
[13]侯远瑞,黄大勇,郝海坤,等.广西岩溶石漠化地区典型造林模式及营建技术[J].广西林业科学,2010,39(4):211-213.
[14]彭玉华,欧芷阳,曹艳云,等.桂西南喀斯特山地主要植被类型凋落物累积量及其持水特性[J].中南林业科技大学学报,2013,33(2):81-85.
[15]国家林业局.中华人民共和国林业行业标准LY/T 1213 12151239 1237-1999森林土壤分析方法[S].北京:中国标准出版社,1999.
[16]国家林业局.中华人民共和国林业行业标准LY/T 1228 12321234-2015森林土壤分析方法[S].北京:中国标准出版社,2015.
[17]李桂林,陈杰,孔志超,等.基于土壤特征和土地利用变化的土壤质量评价最小数据集确定[J].生态学报,2007,27(7):2715-2724.
[18]欧芷阳,苏志尧,朱剑云.东莞主要森林群落的土壤基本性质和综合评价[J].中南林业科技大学学报,2013,33(8):96-102.
[19]唐菲菲,邓艳林,郑茂,等.基于灰色关联分析的湘西北石漠化区土壤质量评价[J].中南林业科技大学学报,2016,36(9):36-43.
[20]王明全,王金达,刘景双,等.基于物元模型的吉林省西部生态环境脆弱性评价[J].生态学杂志,2007,26(2):291-295.
[21]周文华,王如松.基于熵权的北京城市生态系统健康模糊综合评价[J].生态学报,2005,25(12):3244-3251.
[22]段锦,康慕谊,戴诚,等.基于格网的东江流域生态安全空间综合评价[J].生态学杂志,2012,31(8):2075-2081.
[23]李帅,魏虹,倪细炉,等.基于层次分析法和熵权法的宁夏城市人居环境质量评价[J].应用生态学报,2014,25(9):2700-2708.
[24]张凤太,苏维词,周继霞.基于熵权灰色关联分析的城市生态安全评[J].生态学杂志,2008,27(7):1249-1254.
[25]唐柄哲,何丙辉,闫建梅.川中丘陵区土地利用方式对土壤理化性质[J].应用生态学报,2016,27(5):1445-1452.
[26]吕刚,傅昕阳,李叶鑫,等.露天煤矿排土场复垦区不同植被类型枯落物持水特性研究[J].水土保持学报,2017,31(1):146-152.
[27]宋成军,马克明,傅伯杰,等.固氮类植物在陆地生态系统中的作用研究进展[J].生态学报,2009,29(2):869-877.
[28]许明祥,刘国彬,赵允格.黄土丘陵土壤质量评价指标研究[J].应用生态学报,2005,16(10):1843-1848.
[29]贺祥.基于熵权灰色关联法的贵州岩溶山区人地耦合系统脆弱性分析[J].水土保持研究,2014,21(1):283-289.
[30]张显松,姚健,薛建辉,等.喀斯特地区人工林凋落物及表层土壤水源涵养功能[J].林业科技开发,2010,24(4):32-35.
[31]赵娜,孟平,张劲松,等.华北低丘山地不同退耕年限刺槐人工林土壤质量评价[J].应用生态学报,2014,25(3):351-358.
[32]PASTOR J,BINKLEY D N.Nitrogen fixa tion and the mass balances of carbon and nitrogen in ecosystems[J].Biogeochemistry,1998,43:63-78.
[33]米锋,李吉跃,杨家伟.森林生态效益评价的研究进展[J].北京林业大学学报,2003,25(6):77-83.
[34]邓聚龙.农业灰色理论与方法[M].济南:山东科技出版社,1988.
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