协惯量分析与典范对应分析在植物群落排序中的应用比较

采用协惯量分析(PCA-CA COIA)和典范对应分析(CCA)两种排序方法, 对北京小龙门林场的黄檗 (Phellodendron amurense)群落进行了分析, 并用Spearman秩相关系数检验了对应排序轴的相关性。两种排序方法得出的结果基本一致, 两者的第一排序轴都反映了海拔高度和坡向对群落分布的影响, 而各自第二、第三排序轴所代表的环境意义有所差异, 并出现了交叉, 但是两者的前3个排序轴均反映了海拔、坡位、土壤厚度和凋落物层厚度的变化趋势, 说明在环境因子个数较少或共线性效应不明显的情况下, 协惯量分析也能达到CCA的分析效果, 并且在排序轴特征值解释量上高于典范对应分析。     

典范相关分析技术在植被生态信息中的应用研究

典范相关分析是一种检验两组变量间最大相关的多元统计技术。本文运用此技术结合Pearson's相关系数、PCA分析,对植物群落中植物重要值与土壤组分的相关研究表明:典范相关分析能极好地定量解释生态学中两组变量的相关,并能指示出多个因子的复合作用。同时强调,由于典范相关分析技术对原始数据的线性要求,从而有必要对数据进行标准化和预先的PCA分析。     

植物群落分布与环境的多元线性模型

 本文根据植物群落主成分分析三维排序与植物群落类型的关系,应用多元线性模型理论建立了江苏省盐城珍禽自然保护区盐土植物群落类型与环境的多元线性模型。研究结果表明该模型较好地描述了该保护区植物群落分布的格式,同时,也有助于更明确地揭示植物群落主成分分析各排序轴所反映的环境梯度。     

排序轴分类法及其应用

排序轴分类法及其应用张金屯（山西大学生物系,太原０３０００６）ＯｒｄｉｎａｔｉｏｎＡｘｅｓＰａｒｔｉｔｉｏｎｉｎｇａｎｄＩｔｓＡｐｐｌｉｃａｔｉｏｎ．　￥ＺｈａｎｇＪｉｎｔｕｎ（ＤｅｐｏｒｔｍｅｎｔｏｆＢｉｏｌｏｇｙ,ＳｈａｎｘｉＵｎｉｖｅｒｓｉ－ｔ...     

稀有种不同处理对典范对应分析排序结果影响的比较

运用吕梁山南段植物群落及其环境调查数据, 比较研究不同稀有种处理(剔除稀有种、稀有种不做处理与降低稀有种权重3种方法处理)对典范对应分析(CCA)排序结果的影响, 并用Spearman秩相关系数检验对应排序轴的相关性。结果表明3种方法的分析效果基本一致, 但它们对环境因子的解释趋势有差异。基于环境数据、物种数据和样方数据的排序轴相关分析结果显示: 未处理稀有种的CCA与降低稀有种权重的CCA吻合度高于剔除稀有种的CCA与降低稀有种权重的CCA的吻合度, 未处理稀有种的CCA与降低稀有种权重的CCA的前4轴呈极显著的一一对应关系; 剔除稀有种的CCA和降低稀有种权重的CCA仅在基于环境数据和样方数据分析时前3轴呈极显著的一一对应关系, 而在基于物种数据的相关分析时前4轴的对应相关性不显著。从物种-环境关系的解释量上来看, 降低稀有种权重的CCA最优, 剔除稀有种的CCA和未处理稀有种的CCA次之。结合对应排序轴的相关性分析和物种-环境关系累计解释量来看, 这3种稀有种处理方法在准确地揭示物种与环境关系时的顺序依次为: 降低稀有种权重>对稀有种不做处理>剔除稀有种。     

人工植被对崇明东滩滨海盐土水盐空间异质性的影响

运用经典统计学和地统计学相结合的方法,于2012年6月研究了上海市崇明东滩滨海盐土上3种人工植被表层土壤(0～20cm)水盐的空间异质性.结果表明:不同样地的土壤含水量大小顺序为:落羽杉林>狗牙根草丛>夹竹桃灌丛,其变异系数分别为13.9%、13.4%和12.9%;土壤电导率为:夹竹桃灌丛>落羽杉林>狗牙根草丛,其变异系数分别为79.2%、55.4%和15.9%;二者均属中等程度变异.不同样地土壤水盐的理论变异模型各不相同,其中土壤电导率的拟合效果更好,R2在0.97～0.99.当植被从夹竹桃灌丛→狗牙根草丛→落羽杉林,土壤含水量的空间异质性程度由弱变强,其中夹竹桃的变异性是随机的;植被从狗牙根草丛→夹竹桃灌丛→落羽杉林,土壤电导率的空间异质性由中等相关变为强相关.不同植被下土壤电导率空间自相关性多为正相关,土壤水分多呈负相关.狗牙根草丛的土壤水盐均呈条带状分布,落羽杉林地土壤水盐均呈大斑块连续状分布,而夹竹桃灌丛的土壤水分呈小斑块破碎状分布,盐分呈条带状分布.     

中国西南干旱河谷植物群落的数量分类和排序分析

中国西南干旱河谷植被是我国西南横断山区特有的植被类型, 目前关于西南干旱河谷植被还没有整体性的群落类型划分研究。根据对甘肃、四川、云南三省九条主要河流的干旱河谷段野外调查和文献来源的1,339个植物群落样方数据, 采用自适应仿射传播聚类方法, 对我国西南干旱河谷的植物群落进行数量分类, 并采用典范对应分析方法进行排序分析。结果表明: (1)调查样方的植物群落分为7个植被型(稀树草原、肉质灌丛、常绿阔叶灌丛、暖性落叶阔叶灌丛、常绿硬叶林、落叶阔叶林和暖性针叶林), 24个群系, 31个群丛类型。暖性落叶阔叶灌丛是本植被区的代表性植被类型; 分布最广的群系为鞍叶羊蹄甲灌丛(Form. Bauhinia brachycarpa, 样方比例50.9%)、黄茅灌草丛(Form. Heteropogon contortus, 样方比例11.9%)、孔颖草灌草丛(Form. Bothriochloa pertusa, 样方比例5.6%)、黄荆灌丛(Form. Vitex negundo, 样方比例4.2%)、知风草灌草丛(Form. Eragrostis ferruginea, 样方比例3.8%)、车桑子灌丛(Form. Dodonaea viscosa, 样方比例3.4%)、云南松疏林(Form. Pinus yunnanensis, 样方比例3.3%)。(2)冬季低温和降水的季节性是限制干旱河谷植物群落分布的主要气候因子。稀树草原、肉质灌丛是典型的干热河谷植被类型; 暖性落叶阔叶灌丛、常绿硬叶林、常绿阔叶灌丛是干暖河谷植被的优势类型; 暖性针叶林、落叶阔叶林则主要在干温河谷环境占优势。     

典范趋势面分析及其在山西省沙棘灌丛水平格局分析中的应用

介绍一种综合趋势面的分析方法——典范趋势面分析。典范趋势面分析是典范相关分析与趋势面分析的结合,它克服了趋势面分析的缺陷,能同时对单母体空间上的多变量进行分析,拟合一个综合趋势面。通过对山西沙棘灌丛水平格局的分析介绍了典范趋势面分析的应用,并比较了趋势面分析与典范趋势面分析。     

植被生态信息的典范相关分析技术理论

植被生态信息分析中对两（多）组取样集间的相关分析的需求导致了典范相关分析技术的应用和发展。本文对典范相关分析技术的原理、典范参数的生态学意义进行了提示,比较了典范相关分析与ＰＣＡ之间的差异,产在典范相关分析技术的应用过程中,原始数据的预处理提出了建议。     

应用典范对应分析探讨长白山金发藓科植物的生态位分化

应用典范对应分析(Canonical Co rrespondence Analysis, CCA)对长白山主要生态系统30个样点中的13种金发藓科植物分布与环境因子间的关系进行了研究。发现在长白山地区,金发藓科植物在海拔高度、土壤含砂量、含水量、酸度、光照条件等资源维上存在明显的生态分化现象。13种金发藓科植物中,球蒴金发藓(Polytrichum sphaerothecium(Besch.)C.Mull.、高山异发藓短叶变种(Polytrichastrum alpinum var. brevifolium(R.Br.)Brid.)在海拔上,拟异发藓(Polytrichastrum formosum(Hedw.)G.L.Smith)、变形异发藓(Polytrichastrum dicipensLimpr.)、毛尖金发藓(Polytrichum piliferum Schred.ex Hedw.)、桧叶金发藓(Polytrichum juniperinum Willd.ex Hedw.)在光照上以及大金发藓(Polytrichum commune Hedw.)、直叶金发藓(Polytrichum juniperinum ssp. strictum(Brid.)Nhy.&Sael.)在土壤酸度与水分上的生态要求比较接近。     

Vegetation-environment relations of a Middle Zambezi floodplain

Detrended correspondence analysis was used to study the relationships between environmental factors and the species composition of vegetation on Zambezi River alluvium downstream of the Kariba hydroelectric dam. Grass, sedge and woody species were recorded in 73 stands in Mana Pools National Park, Zimbabwe. Grass and sedge species composition was related to the soil moisture regime, as indexed by soil texture and flooding frequency. The first woody plant ordination axis was related to a stand development gradient; Acacia albida was a pioneer species on lowlying sandbanks and the woody species richness of stands increased with their height above the Zambezi River. Two-way indicator species analysis identified 7 vegetation types which could be separated on the basis of their topsoil texture and flooding frequency. The types were: sandbanks; young A. albida woodland; A. albida woodland; A. albida dominated mixed woodland; mixed riverine woodland with understory; mixed riverine woodland; and grassland on clay soils.     

Correlations among vegetation strata and environmental variables in subalpine spruce-fir forests,southeastern British Columbia

A set of forest vegetation-environment data consisting of 110 plots from subalpine spruce-fir forests in southeastern British Columbia was analyzed with multivariate methods. The aims were to describe and compare relationships among plots using species composition data from four strata of the forest (trees, shrubs, herbs, bryophytes), and to compare patterns in the separate strata with patterns in the environmental data. Weak but statistically significant correlations were detected among PCA axes from all four strata, with a tendency for correlations to be stronger between axes of adjacent strata. Relationships between stratal patterns and environmental variation were examined by correlating PCA axes with environmental variables separately, and jointly with canonical correlation analysis. Linear composites of the environmental data identified by the first canonical axis were similar for shrub, herb, and bryophyte strata implying similar responses to environmental variation in understorey strata; however, < 16% of the total variance was explained. No clear relationships emerged between tree stratum and environmental variation. Probable determinants of pattern in the tree stratum include historical events such as fire and winter snowpack, as well as possible interactions between species. Collectively, these events bear little resemblance to existing environmental conditions in the forest.Nomenclature used by BCMF follows Crum, Steere & Anderson (1973), Hale & Culberson (1970), and Stottler & Crandall-Stottler (1977) for non-vascular plants, and Taylor & MacBryde (1977) for vascular plants.The British Columbia Ministry of Forests provided the data on which this paper is based, as well as a grant to pay for the computer time used. We are grateful to G. Utzig for initially suggesting that a project of this nature be done, and to S. Phelps for helping with the retrieval of computer data files from tape. This paper has greatly benefited from discussions with P. Comeau and R. K. Scagel, and the comments of two anonymous reviewers.     

并殖吸虫成虫形态结构特征排序

对9种并殖吸虫成虫形态特征进行主成分分析及聚类分析,结果表明:在并殖吸虫成虫的6项主要特征中,体形因素及卵巢类型是重要的分类依据。在被分析的9个种中,怡乐村并殖吸虫、斯氏狸殖吸虫、巨睾狸殖吸虫、三平正并殖吸虫、异盘并殖吸虫、陈氏并殖吸虫与卫氏并殖吸虫7个种具有种的独立地位,而泡囊狸殖吸虫和扁囊并殖吸虫未能分别从斯氏狸殖吸虫和卫氏并殖吸虫中分开。     

汾河河口湿地植被数量分类与排序

在群落样方调查基础上,采用双向指示种分析（TWINSPAN）和除趋势对应分析（DCA）对汾河入河口湿地植被群落进行了数量分类和排序。TWINSPAN将78个样方分为18个群丛,并论述了各群丛的群落学特征。DCA排序结果反映了植物群落类型与环境梯度水分之间的关系,表明影响群丛分布格局的主导生态因子为土壤水分。     

Syntaxonomy of the Australian mangal refined through iterative ordinations

Mangal (tidal forest) communities vary with respect to position in the littoral sequence, and latitude. A previous classification of mangal from the southern and western coastlines of Australia has been subjected to a series of ordinations, in an attempt to produce a robust syntaxonomy. An iterative ordination process allowed for the progressive refinement of the syntaxonomic units. This process allows an ecologist to use critically a numerical technique to refine ecological assumptions developed from field work, and/or non-numerical analyses. Ordination analysis suggested a need for the classification to be refined, and allowed the construction of a final syntaxonomy. In conjunction with broad environmental data collected during the investigation, the ordinations allowed the development of some environmental perspectives. These suggested that littoral position is a stronger environmental influence on mangal communities than balance between salt and freshwater.