大别山山地次生林鸟类群落集团结构的季节变化

2007年12月—2008年12月,在大别山鹞落坪,对落叶阔叶次生林鸟类的集团结构的季节变化特征进行了研究。采用连续取样法采集鸟类取食行为数据,用聚类分析法对繁殖和非繁殖季节鸟类群落进行集团划分,通过无倾向对应分析（DCA）对32种森林鸟类的两个季节资源利用特点进行了研究。结果表明,鸟类群落在非繁殖季节可以分为地面、灌丛、树干（枝）、冠层等4个取食集团,而在繁殖季节还出现空中取食集团;候鸟影响鹞落坪次生林鸟类群落取食集团的结构,产生新的取食集团。DCA的第一轴主要代表鸟类取食高度信息,第二轴主要代表鸟类取食位置信息,第三轴代表鸟类取食方式和取食基质信息;用DCA前两轴对32种鸟类排序表明,有6种鸟主要在第一轴发生变化,有4种鸟主要在第二轴发生变化。     

中国东北西部地区沙质荒漠化过程与植被动态关系的生态学研究：植被的排序

根据东北沙质荒漠化地质呼伦贝尔沙地,科尔沁沙地的72个沙地植物群落样地,276种植物的调查数据进行DCA排序,排序结果充分反映了沙地植被与地理因子,气候因子,地形因子,水文因子,干扰因子的关系,DCA排序的第1轴反映沙地植被分布的水分梯度;第2轴反映土壤梯度;第4轴主要反映热量梯度;DCA的1、4轴排序图和1,2,4轴排序图,反映沙地植被与沙质荒漠化的生态规律,采用定性因子评价赋值与定量因子相结合的方法,经双重筛选逐步回归分析,建立了沙地植被DCA排序坐标与地理,气候,地形,水文,干扰等诸多生态因子的线性回归模型。     

科尔沁沙地植物群落分布与土壤特性关系的DCA、CCA及DCCA分析

基于科尔沁沙地流动沙丘、半固定沙丘、固定沙丘和草地群落类型24个样地的野外调查,选取11个土壤因子,应用除趋势对应分析法(DCA)、典范对应分析法(CCA)和除趋势典范对应分析法(DCCA)分析沙地植物群落分布与环境因子的关系。结果表明:DCA、CCA和DCCA的物种排序第1轴代表的土壤特性梯度一致,其解释总方差超过33%,即土壤碳氮含量、p H、电导率、容重、粘粉粒等共同的梯度决定了群落生境的变化,影响着沙地植物群落类型的分布格局;3种排序法的物种排序第2轴土壤因子的相关性有较大差异,DCA第2轴仅与土壤细沙含量显著正相关,CCA第2轴与土壤碳氮比和细沙含量显著负相关,DCCA第2轴与土壤碳氮比和细沙含量显著正相关,而与粗沙含量显著负相关。Shannon指数和Simpson指数分别与DCA、CCA和DCCA前2个排序轴存在显著二元线性关系,且Shannon指数的拟合程度好于Simpson指数。3种排序分析方法中,CCA物种排序前两轴的累计解释方差(58.6%)高于DCA和DCCA,因此CCA排序法更适合于沙地植被分布格局研究及其环境的解释。     

桂林岩溶石山青冈栎群落的数量分析

应用双向指示种分析(TWINSPAN)和除趋势对应分析(DCA)方法对桂林岩溶石山青冈栎群落进行数量分类与排序。通过TWINSPAN分类,将青冈栎群落60个样方划分为8个群丛类型,探讨了各群丛类型的基本特征。结果表明:DCA排序与TWINSPAN分类结果较一致,DCA排序较好地体现了各群丛类型与环境因子的相互关系,DCA第一排序轴主要反映了坡度的变化梯度;DCA排序图的对角线基本体现了坡向的变化梯度。坡度的变化是影响岩溶石山青冈栎群落物种组成与分布的重要生态因子。     

黄山短尾猴（Macaca thibetana）的主要食性分析

2011年11月至2012年10月,作者采用粪便显微分析法研究了黄山短尾猴的四季食性.结果表明：黄山短尾猴共取食植物26科50种;不同季节间黄山短尾猴食性具有明显变化,冬季取食13科25种植物,春季取食23科46种植物,夏季取食14科32种植物,秋季取食17科37种植物;壳斗科、樟科是黄山短尾猴全年的主要食物,占短尾猴总取食量的51.26%～59.75%,其他科属植物也占有重要地位;甜槠在四季均为黄山短尾猴的主要取食食物,豹皮樟在冬季取食量相对较高,秋季最低,红楠、杉木在冬春季节的取食比例差异最大;黄山短尾猴采取与其他灵长类相似的觅食策略;黄山短尾猴四季食物生态位宽度差异不明显,受人为影响较为明显.     

云南龙马山滇金丝猴(Rhinopithecus bieti)冬季食性分析

2006年11月-2007年12月,对云南云龙县龙马山滇金丝猴(Rhinopithecus bieti)冬季食性进行了研究.采用粪便显微组织学分析技术分析龙马山滇金丝猴冬季采食植物的种类组成和比例,并测定了该猴群主要取食的15种植物(包括两种松萝科植物)(取食食物百分比>1%)和次要取食的10种植物(取食食物百分比<1%)的化学成分.研究结果表明,龙马山滇金丝猴冬季取食的植物共计26科45种,其中云龙箭竹、长松萝、花松萝、实竹、空心箭竹分别占19.78%、10.28%、8.37%、4.93%、4.46%,是滇金丝猴冬季主要取食的植物种类,占取食植物的47.99%.木本植物和草本植物的叶是滇金丝猴的主要食物,在冬季食物中所占百分比最高,达75%.冬季滇金丝猴比较喜欢采食P/F值较高、单宁含量较低的食物,其主要食物比次要食物含有较高的P/F值和较低的单宁(P<0.05).     

崇明东滩冬季水鸟生态位分析

依据2003年冬季对崇明东滩自然保护区越冬水鸟的种类、数量、生境类型分布的最新调查数据,以及上海农林局10年来积累的越冬水鸟食性与形态的数据,采取聚类分析方法对崇明东滩冬季鸟类的群落生态进行研究,从鸟类的取食空间生态位、食性生态位以及形态生态位三个维度确认其生态资源分配状况,并由此确认了占据优势种群地位的鸟类在不同生态位维度上的分离是群落结构处于稳定状态的主要原因。     

贺兰山高山麝冬春季的食性

利用粪便显微分析法对贺兰山高山麝冬春季的食性进行了研究。采集高山麝活动区域内粪样和植物样本,采用频率转换法对数据进行处理,得到高山麝冬春季的食性组成及比例。结果表明:高山麝冬季共取食植物19科30种(属),其中蔷薇科(17.16%)、忍冬科(16.64%)、豆科(15.64%)和莎草科(10.93%)组成了高山麝冬季的主要食物;春季共取食植物20科31种(属),其中蔷薇科(27.37%)、杨柳科(13.28%)和豆科(12.84%)为主要食物;秦氏黄芪(Astragalus chingianus)为冬春季共同的主要食物,分别占冬春季食物的11.33%和11.04%;此外,高山麝也取食乔木类植物,取食量从冬季的9.53%上升至春季的18.67%;根据高山麝粪样镜检结果,计算其取食植物的Shannon指数、均匀度指数、生态位宽度指数,分析高山麝食物组成及其多样性,结果显示,3种指数冬季均高于春季。     

松山自然保护区森林群落的数量分类和排序

根据68个森林群落样方数据,采用双向指示种分析(TW INSPAN)方法,对松山自然保护区的森林群落进行分类和采用除趋势对应分析(DCA)、典范对应分析(CCA)方法进行排序。结果表明:(1)TW INSPAN将该区的森林群落分为13类型;(2)样方的DCA排序及样方和优势种的CCA排序较好地揭示了该区森林群落的分布格局与环境梯度的关系;CCA第一轴明显地反映出森林群落的海拔梯度、枯枝落叶层厚度和土壤深度变化,沿CCA第一轴从左到右,海拔逐渐升高,枯枝落叶层越厚和土壤越深;第二轴与海拔高度和坡度成正相关,而与土壤紧实度成负相关。其中海拔梯度是环境因子中对森林群落起决定性作用的因子。(3)与DCA相比,CCA的排序轴更有利于生态意义的解释,后者能同时反映样方间在种类组成上及环境因子组成上的相似性,表现在排序图中样方较集中,群落间的界线变得较模糊,因此如果排序同分类结合使用,DCA的效果要好于CCA。(4)TW INSPAN分类与DCA和CCA排序的结果,同时表明了该地区森林群落的垂直分布格局。     

南京外秦淮河河岸带草本花卉群落分类与排序

探索南京外秦淮河河岸带草本花卉群落类型及其分布情况,确定影响草本花卉及群落分布的主要环境因子,为秦淮河生态恢复及河流生态廊道的构建提供科学依据。基于对外秦淮河草本植物群落的实际调查数据,采用平均聚合聚类(UPGMA)进行聚类分析,运用α多样性指数分析不同类型群落的多样性特征,运用除趋势对应分析(DCA)对样点分布进行分析,利用典范对应分析(CCA)对物种和样点进行排序,研究外秦淮河河岸带草本花卉群落组成与分布及其与环境因子的关系。结果表明：(1)利用UPGMA将外秦淮河河岸带调查的草本花卉划分为4种不同类型的群落,与样点DCA排序结果较为一致;(2)群落Ⅳ、群落Ⅲ和群落Ⅱ的物种丰富度指数、Simpson指数、Shannon指数、Pielou均匀度指数表现较好,而群落Ⅰ最差;(3)通过CCA排序可知,第一排序轴与枯落物厚度相关性最强,第二排序轴与坡度显著相关;(4)外秦淮河草本花卉分布与群落分布均受环境因子影响。     

Effects of detrending and rescaling on correspondence analysis: solution stability and accuracy

Detrending and non-linear axis rescaling potentially improve the accuracy of gradient recovery in correspondence analyses but also reduce the stability or consistency of solutions. Variation among bootstrapped ordination solutions was compared across methods in analyses of both field and simulated data. Solution accuracy, measured with mean squared errors from Procrustes analysis, was compared using simulated data with known structure.Standard detrending-by-segments combined with non-linear rescaling entailed some cost in solution stability, but could improve the accuracy of solutions for long gradients. Without non-linear rescaling these solutions were usually less stable and less accurate. Although detrending-by-polynomials might be preferable on other grounds, it did not produce more accurate or stable solutions than detrending-by-segments.Abbreviations CA = correspondence analysis - DCA = detrended correspondence analysis - MSE = Procrustes mean squared error statistic - SD = standard deviation units of species turnover - SRV = scaled variance in species ranks     

Bootstrapped ordination: a method for estimating sampling effects in indirect gradient analysis

Indirect gradient analysis, or ordination, is primarily a method of exploratory data analysis. However, to support biological interpretations of resulting axes as vegetation gradients, or later confirmatory analyses and statistical tests, these axes need to be stable or at least robust into minor sampling effects. We develop a computer-intensive bootstrap (resampling) approach to estimate sampling effects on solutions from nonlinear ordination.We apply this approach to simulated data and to three forest data sets from North Carolina, USA and examine the resulting patterns of local and global instability in detrended correspondence analysis (DCA) solutions. We propose a bootstrap coefficient, scaled rank variance (SRV), to estimate remaining instability in species ranks after rotating axes to a common global orientation. In analysis of simulated data, bootstrap SRV was generally consistent with an equivalent estimate from repeated sampling. In an example using field data SRV, bootstrapped DCA showed good recovery of the order of common species along the first two axes, but poor recovery of later axes. We also suggest some criteria to use with the SRV to decide how many axes to retain and attempt to interpret.Abbreviations DCA= detrended correspondence analysis - SRV= scaled rank variance     

Detrended correspondence analysis: An improved ordination technique

Summary Detrended correspondence analysis (DCA) is an improvement upon the reciprocal averaging (RA) ordination technique. RA has two main faults: the second axis is often an arch or horseshoe distortion of the first axis, and distances in the ordination space do not have a consistent meaning in terms of compositional change (in particular, distances at the ends of the first RA axis are compressed relative to the middle). DCA corrects these two faults. Tests with simulated and field data show DCA superior to RA and to nonmetric multidimensional sealing in giving clear, interpretable results. DCA has several advantages. (a) Its performance is the best of the ordination techniques tested, and both species and sample ordinations are produced simultaneously. (b) The axes are scaled in standard deviation units with a definite meaning, (c) As implemented in a FORTRAN program called DECORANA, computing time rises only linearly with the amount of data analyzed, and only positive entries in the data matrix are stored in memory, so very large data sets present no difficulty. However, DCA has limitations, making it best to remove extreme outliers and discontinuities prior to analysis. DCA consistently gives the most interpretable ordination results, but as always the interpretation of results remains a matter of ecological insight and is improved by field experience and by integration of supplementary environmental data for the vegetation sample sites.This research was supported by the Institute of Terrestrial Ecology, Bangor, Wales, and by a grant from the National Science Foundation to R.H. Whittaker. We thank R.H. Whittaker for encouragement and comments, S.B. Singer for assistance with the Cornell computer, and H.J.B. Birks, S.R. Sabo, T.C.E. Wells, and R.H. Whittaker for data sets used for ordination tests.     

A comparison of detrended correspondence analysis and principal co-ordinates analysis using four sets of Tasmanian vegetation data

Indirect gradient analysis, which entails the elucidation of relationships between trends in community composition and underlying environmental or successional gradients, is a major objective of ordination in plant ecology. Two ordination techniques, detrended correspondence analysis (DCA) and principal co-ordinates analysis (PCOA), were compared using three sets of Tasmanian vegetation data having known gradients and one set where the vegetation was expected to respond to diverse environmental variables. In every case, the results obtained by DCA were considered superior to, or at least as good as, those of PCOA. Hence, DCA appears to be the more suitable of the two methods for indirect gradient analysis.     

Multivariate analysis of benthic invertebrate communities: the implication of choosing particular data standardizations,measures of association,and ordination methods

Benthic invertebrate data from thirty-nine lakes in south-central Ontario were analyzed to determine the effect of choosing particular data standardizations, resemblance measures, and ordination methods on the resultant multivariate summaries. Logarithmic-transformed, 0–1 scaled, and ranked data were used as standardized variables with resemblance measures of Bray-Curtis, Euclidean distance, cosine distance, correlation, covariance and chi-squared distance. Combinations of these measures and standardizations were used in principal components analysis, principal coordinates analysis, non-metric multidimensional scaling, correspondence analysis, and detrended correspondence analysis. Correspondence analysis and principal components analysis using a correlation coefficient provided the most consistent results irrespective of the choice in data standardization. Other approaches using detrended correspondence analysis, principal components analysis, principal coordinates analysis, and non-metric multidimensional scaling provided less consistent results. These latter three methods produced similar results when the abundance data were replaced with ranks or standardized to a 0–1 range. The log-transformed data produced the least consistent results, whereas ranked data were most consistent. Resemblance measures such as the Bray-Curtis and correlation coefficient provided more consistent solutions than measures such as Euclidean distance or the covariance matrix when different data standardizations were used. The cosine distance based on standardized data provided results comparable to the CA and DCA solutions. Overall, CA proved most robust as it demonstrated high consistency irrespective of the data standardizations. The strong influence of data standardization on the other ordination methods emphasizes the importance of this frequently neglected stage of data analysis.     

Plant-environment relationships on the Montlake wildlife area,Seattle, Washington,USA

Grassland vegetation on the Montlake fill was analyzed using TWINSPAN. Eight herb communities were recognized. Moisture, proximity to gas vents, and disturbance are the main factors that control species and community distributions. Binary discriminant analysis (BDA) and detrended correspondence analysis (DCA) were used to study species-environment relationships. BDA revealed complex species response patterns and the resultant indicator values were used to interpret the ordination axes. Species distributions are controlled primarily by moisture, but also influenced by soil pH. Multiple regressions revealed little about plant-environment relationships not discovered by BDA. Before robust nonlinear methods are available, BDA, metric ordination with data stratification and nonmetric ordination are methods that can yield satisfactory results in exploratory plant-environment studies. BDA alone is an efficient, useful first approach where response patterns of species are initially unknown.Abbreviations BDA Binary Discriminant Analysis - DCA Detrended Correspondence Analysis     

神农架川金丝猴栖息地森林群落的数量分类与排序

在神农架川金丝猴生境典型地段设立样方58块,根据样方资料对神农架川金丝猴栖息地的森林群落用组平均法分类和DCA排序.用组平均法将58块样地分为9个群系,依据《中国植被》的分类原则和系统将研究区植物群落划归为7个植被型.样地的DCA排序较好地揭示了该区森林群落的分布格局与环境梯度的关系;DCA第二轴明显地反映出森林群落的海拔梯度变化,沿DCA第二轴从右到左,海拔逐渐升高;第一轴表现了各植物群落或植物种所在环境的坡度、坡向,即水分和光照因素,沿第一轴从下到上,坡度渐缓、坡向渐向阳.其中海拔梯度是环境因子中对森林群落起决定性作用的因子.研究表明,巴山冷杉+糙皮桦-大齿槭+尾萼蔷薇-高原露珠草+星果草群系发育较好,高大树木占有较大的比例,是神农架川金丝猴最适宜栖息地.7个植被型物种丰富度指数在群落梯度上呈规律性波动.其中针叶林和针叶-阔叶混交林中,物种丰富度指数在群落梯度上的总体趋势表现为灌木层＞草木层＞乔木层;在常绿阔叶林和常绿-落叶阔叶混交林中,该趋势为灌木层＞乔木层＞草本层;在落叶阔叶林中,其丰富度指数的趋势为灌木层＞草本层和乔木层.不同植被类型川金丝猴食源植物种类在群落梯度上的变化趋势与物种丰富度指数相同,但地衣类植物作为川金丝猴冬季的重要食物只在针叶林和针叶-阔叶混交林中生长.本研究为制定栖息地保护计划,更好地保护神农架川金丝猴提供了科学理论依据.     

Ordination methods and chironomid species in stony bottom streams

Stony bottom streams in Italy were sampled in 369 sites in different years, and 174 chironomid species (or species groups) were identified.The species by sites matrix was submitted to different ordination methods, with the aim of outlining the major factors that are responsible of the observed species composition.The methods considered were principal component analysis (PCA), detrended correspondence analysis (DCA) and non-metric multidimensional scaling (MDSCAL). These methods assume different models describing the species-responses to the environmental gradients.PCA gave poor results, the best species ordination was given by MDSCAL. DCA gave results in agreement with MDSCAL. DCA results were useful as a starting point to perform MDSCAL. MDSCAL was performed with different options. At last Procrustean analysis was carried out to have a single fitted configuration, that summarized the results given by the different MDSCAL methods.All multivariate analyses emphasized that the first ordination axis can be interpreted as a crenonrithron-potamon gradient, probably an oxygen availability and/or a water temperature gradient, whereas the second axis can be considered a water speed gradient, separating lotic from lentic sites.The 3rd and 4th ordination axes had apparently no ecological meaning.Separated clusters of species were never evident, so well defined chironomid species assemblages could not be outlined in this analysis.     

A combination of fuzzy set ordination with detrended correspondence analysis: One way to combine multi-environmental variables with vegetation data

This paper examines one possible way of Fuzzy Set Ordination by using multi-environmental variables. FSO's function is improved through combination with Detrended Correspondence Analysis which is used to summarize environmental information. It can be used to analyse the relationships between vegetation and environment no matter how many environmental variables are involved. An example with vegetation and environmental data collected from upland grasslands in Northern Snowdonia, Wales, is presented. Its results are consistent with that of CCA and DCCA.Abbreviations FSO Fuzzy set ordination - DCA Detrended correspondence analysis - CCA Canonical correspondence analysis - DCCA Detrended canonical correspondence analysis     

On the relationship between sample plot size and beta diversity in boreal coniferous forests

Nested sample plots of three sizes (16, 1, and 1/16 sq. m) from three different studies of Norwegian coniferous forests have been subjected to DCA ordination using the same choice of options. At each sample plot size, species quantities are recorded as frequency in 16 subplots. Beta diversity, measured as length of the first DCA axis, invariably increased upon lowering of sample plot size. The same applied to the eigenvalues of the axes. This is explained as a consequence of the weakening of structure in the data matrices when the fine-grained patterns of the vegetation are emphasized.