芦芽山亚高山草甸优势种群和群落的二维格局分析

种群和群落的二维空间格局研究能够更好地揭示群落的空间和结构特征,但在分析方法上有较大的困难。用垂直相交的两条样带在两个方向上同时取样的二维取样法,获得数据,用一维格局分析方法分别分析,可以得到各个种不同格局规模斑块的长、宽及面积,实现二维格局研究。用DCA排序和格局分析方法相结合,可以完成群落的二维格局分析。在山西芦芽山亚高山草甸应用的结果表明这样的垂直样带二维取样及分析方法较好地反映了种群和群落的空间特性,是非常有效的,并且该方法简单易做,具有较大的可操作性。所研究的草甸主要优势种格局斑块的形状比较规则,面积也较大。次要种斑块多为不规则形,面积也较小。群落格局与主要优势种的格局关系密切。     

宜昌百里荒草山草坡群落物种分布的空间趋势分析

通过基于CCA的趋势面分析和空间插值方法,研究了宜昌百里荒山地草场的群落结构空间变化,以及群落结构空间趋势与主要环境因子的相关性。结果表明,该群落物种空间中的群落结构面和物理空间中的空间趋势面可以很好地吻合,说明该群落的结构由一种具有强烈空间结构化特征的机制控制。对群落结构和空间趋势影响最显著的环境因素是土壤有效磷。     

Two-dimensional pattern analysis on dominant species and community in subalpine meadow of Luya Mountain,Shanxi Province,China

The spatial pattern analysis of population and community is important to understand community structure and has become one key topic in modern plant ecology.There are many techniques for analyzing one-dimensional pattern in ecological literature.Two-dimensional pattern analysis is better than one-dimensional analysis in the study on community spatial characteristics and structure.However,it is hard to analyze these two-dimensional patterns due to poor effective methodology.The two-dimensional sampling using two transects that meet at right angles was applied to get quadrat data in this work.And then the data from the two transects were analyzed separately by one-dimensional pattern analysis method,two-term local quadrat variance.The length,width,and area of patches at different scales of pattern for populations were obtained from the analysis.For community pattern,detrended correspondence analysis (DCA)was employed to summarize the species information firstly,and then the first DCA axis scores were analyzed to check its pattern.The application of this method to the pattern analysis on dominant populations and community for subalpine meadow (Comm.Polygonum viviparum+Carex rigescens+Kobresia bellardii)in the Luya mountains showed that it could release the characteristics of spatial pattern clearly and was a very effective technique.The method is easy to use and saves time with obvious advantages,compared with the twodimensional pattern analysis methods presented in the literatures.In the study meadow,the patterns of the main dominant species,Polygonum viviparum,Carex rigescens,and Kobresia bellardii,were apparent and comparatively regular in shape with large areas of patches at the same scale compared with other species such as Festuca sp.and Thalictrum petaloideum.There were two or three scales of patterns for each plant population studied.This was related to population features,the interaction with environmental factors,and their dominant position in the community.The two scales of patterns for the meadow community were clear.The pattern of community was closely correlated with that of the main dominant species.The patches of dominant species were inter-distributed and overlapped,and formed the community pattern together.This is beneficial for utilization of resources,and keeping the community stable.     

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。     

Two-dimensional pattern analysis on dominant species and community in subalpine meadow of Luya Mountain, Shanxi Province, China

The spatial pattern analysis of population and community is important to understand community structure and has become one key topic in modern plant ecology. There are many techniques for analyzing one-dimensional pattern in ecological literature. Two-dimensional pattern analysis is better than one-dimensional analysis in the study on community spatial characteristics and structure. However, it is hard to analyze these two-dimensional patterns due to poor effective methodology. The two-dimensional sampling using two transects that meet at right angles was applied to get quadrat data in this work. And then the data from the two transects were analyzed separately by one-dimensional pattern analysis method, two-term local quadrat variance. The length, width, and area of patches at different scales of pattern for populations were obtained from the analysis. For community pattern, detrended correspondence analysis (DCA) was employed to summarize the species information firstly, and then the first DCA axis scores were analyzed to check its pattern. The application of this method to the pattern analysis on dominant populations and community for subalpine meadow (Comm. Polygonum viviparum + Carex rigescens + Kobresia bellardii) in the Luya mountains showed that it could release the characteristics of spatial pattern clearly and was a very effective technique. The method is easy to use and saves time with obvious advantages, compared with the two-dimensional pattern analysis methods presented in the literatures. In the study meadow, the patterns of the main dominant species, Polygonum viviparum, Carex rigescens, and Kobresia bellardii, were apparent and comparatively regular in shape with large areas of patches at the same scale compared with other species such as Festuca sp. and Thalictrum petaloideum. There were two or three scales of patterns for each plant population studied. This was related to population features, the interaction with environmental factors, and their dominant position in the community. The two scales of patterns for the meadow community were clear. The pattern of community was closely correlated with that of the main dominant species. The patches of dominant species were inter-distributed and overlapped, and formed the community pattern together. This is beneficial for utilization of resources, and keeping the community stable. __________ Translated from Acta Ecologica Sinica, 2005, 25(6): 1264–1268 [译自: 生态学报]     

卧龙自然保护区亚高山草甸的数量分类与排序

在野外植被调查的基础上,采用植被数量分析方法对岷江流域卧龙自然保护区亚高山草甸进行TWINSPAN分类和DCA排序,研究了植物种、植物群落与环境之间的关系.结果表明,该地区亚高山草甸共有植物139种,隶属于31科88属.应用数量分类方法将114个样方分为12个群落类型.在分析不同环境因子间的关系基础上,从定量的角度揭示了影响群落分布的主要因素是海拔梯度和土壤含水量.DCA排序图反映出排序轴的生态意义,第1轴反映了各群落类型所在环境的海拔梯度,从上到下,随着海拔的升高,植物群落或植物种的耐寒性越来越强;第2轴基本上反映了各群落类型所在环境的土壤含水量.排序结果与分类结果比较吻合,反映出植物群落类型和物种分布随环境因子梯度变化的趋势.     

西藏阿里植物群落的间接梯度分析、数量分类与环境解释

根据对西藏阿里地区163个植物群落样地资料进行的多元分析——排序、数量分类与环境解释,给出了该地区植被的基本类型、生态梯度及其与环境因子的定量关系。基本分析方法包括3个步骤：1)通过无倾向对应分析(DCA)的两个排序向量揭示了阿里植被的两个主要生态梯度;2)由该梯度的二维散点图及二元指示种分析(TWINSPAN)分别产生非等级制与等级制的植物群落分类系统;3)以多元回归分析将排序值与环境及地理参数相联系而给出各类型的环境指标——定量环境解释。分析表明,阿里植被类型及其分布主要取决于热量与湿度梯度,前者可通过地理参数,后者则通过土壤特征的数学表达式来定量地确定。两梯度包含的类型、种类与生境差异颇大,由低山暖性荒漠直到高山冰缘植被,从隐带性沼泽与盐生草甸到高原地带性荒漠与草原均各得其位,各有其值。表明该数量分析法对于处理高度生态多样性的植物群落生态信息是十分有效的。     

Multivariate analysis of plant communities in the Narok district, Kenya: The influence of environmental factors and human disturbance

Species type in 42 samples was examined using ordination analysis to establish whether the vegetation pattern displays gradation due to environmental factors, and whether the pattern was being altered by human disturbance. The analysis displayed two patterns. First, the ordination on axis 1 demonstrated vegetation gradation from xeric communities on the plains to humid forests on the highland. This pattern is explained on the basis of moisture and nutrient variations. Vegetation vectors based on species presence-absence were correlated with average rainfall, carbon, nitrogen and zinc.The second was separation of disturbed and undisturbed forest and bushland site categories on axis 2 of the ordination space. This pattern suggests that disturbance response is interactive with moisture so that only in moist communities does a significant vegetation response to disturbance occur.     

秦岭山地天然次生林群落MRT数量分类、CCA排序及多样性垂直格局

采用多元回归树(MRT)对秦岭山地天然次生林群落进行数量分类,采用典范对应分析(CCA)进行排序,分析了秦岭山地天然次生林群落物种多样性沿海拔梯度的变化规律。结果表明:(1) 275个样方共有种子植物195种,隶属61科128属。乔、灌、草3个层次物种多样性变化沿海拔梯度的变化趋势基本一致,呈单峰模型;(2)经交叉验证认为秦岭山地天然次生林群落可分为2类,Ⅰ冬瓜杨(Populus purdomii)+陇东海棠(Malus kansuensis)+蛇莓(Duchesnea indica)群落,Ⅱ锐齿槲栎(Quercus aliena var. acuteserrata)+黄栌(Cotinus coggygria)+茜草(Rubia cordifolia)群落;(3) CCA排序结果揭示了群落生境的分布范围,反映出生态轴的排序意义,较好地反映秦岭山地天然次生林群落与环境因子的关系,其结果表明,海拔、坡向、凋落层厚度和干扰情况4个变量对该地区次生林群落的分布有较大的影响。     

群落二维格局分析的两种方法

群落二维空间格局研究能够更好地揭示群落的特征,但在分析方法上有较大的困难。用垂直相交的两条样带在两个方向上同时取样的二维取样法,获得数据,用DCA排序与格局分析相结合的方法,得到群落不同格局规模斑块的长、宽及面积,实现二维格局研究。在亚高山草甸群落格局研究中应用表明,两个方法均是有效的。     

Community structure of epilithic and endolithic algae and cyanobacteria on cliffs of the Niagara Escarpment

Abstract. In order to understand how communities of rock-inhabiting micro-organisms are spatially organized, rock samples and environmental variables were collected from five cliff sites in Ontario, Canada. Algae and cyanobacteria were extracted from the epilithic zone (rock surface) and endolithic zone (subsurface) of 180 samples, and the frequency of each taxon (genus or larger) was determined. Multivariate statistical analyses were used to determine (1) the variation in community composition on different spatial scales; (2) differences in community composition between surface and subsurface; and (3) the environmental constraints of community structure. 13 taxa of cyanobacteria and 12 taxa of eukaryotic algae were identified in the endolithic zone, and 17 and 14 in the epilithic zone, respectively. The most frequent taxa were similar in both zones, but MANOVA of species frequencies showed significant differences between endolithic and epilithic communities. DCA and CCA showed that geographic-scale variation in community composition was the dominant trend in the data for both surface and subsurface. Cyanobacteria and eukaryotic algae showed a clear separation on the first ordination axis. When the geographic-scale variation was removed in a partial ordination, the separation of organisms by kingdoms disappeared and epi- and endolithic ordinations were no longer similar. Light then appeared to control the endolithic species composition, while other factors, possibly moisture-related, were suggested for the epilithic community. Despite biomass and productivity that varies mostly at scales of < 1m², we conclude here that species composition and its environmental constraints vary at small as well as larger scales.     

塔里木河流域荒漠河岸林群落的多元分析与环境解释

应用无偏对应分析（DCA）及二歧指示种分析（TWINSPAN）对塔里木河流域荒漠河岸林33个植物群落方资料进行了多元分析－排序和数量分类,并应用植物群落的排序值与环境参数的多元回归分析给出各群落类型的定量环境解释,探讨了该区植物群落的基本类型,生态梯度及其与环境因子的定量关系,结果表明,塔里木河流域漠河岸林的群落类型及其分布主要取决于土壤湿度和盐分含量,并可通过环境参数的数字表达式定量的确定。     

甘南玛曲植物群落的多元分析与环境解释

应用无偏对应分析（ＤＣＡ）及二岐指示种分析（ＴＷＩＮＳＰＡＮ）对玛曲１７８个植物群落样方资料进行了多斐邓和数量分类,并应用植物群落的排序值与环境参数的多元回归分析出各群类型的定量环境解释,探讨了该区植物群的基本类型生态梯度及其与环境因子的定量关系。结果表明,玛曲的植物群落类型及基分布主要取惟地热量和湿度,并可通过环境参数地数字表达式定量地确定。     

Multiscale ordination: a method for detecting pattern at several seales

Multivariate analyses of vegetation data have been restricted to a single scale of sampling, or multiscale sampling has been restricted to a single species. However, vegetation scientists need to be able to explore spatial relationships of many species over many scales. We present a modification of Noy-Meir & Anderson's (1971) method of multiscale ordination by summing two-term local covariance matrices and smoothing the component profiles. The advantages of our method are: 1) results are less subject to the starting position of the transect, 2) matrices may be added at any block size, and 3) plots of factor scores are smoothed by a moving weighted average to better reveal patterns at a prescribed scale.This procedure provides statistical associations of species over a range of scales. The scales which exhibit the association to the maximum extent are then determined from multiscale ordination. The relationships of different associations and their scales can then be examined. The application of the method to fabricated data proved successful in recovering the structure built into the data. When used on real vegetation data, from a community and a landscape, the method revealed the details of species associations over a range of scales, and of the relationships among associations.Abbreviations PCA = Principal Components Analysis - DCA = Detrended Correspondence Analysis - TTLC = Two-Term Local Covariance     

Utility of Marine Benthic Associations as a Multivariate Proxy of Paleobathymetry: A Direct Test from Recent Coastal Ecosystems of North Carolina

Benthic marine fossil associations have been used in paleontological studies as multivariate environmental proxies, with particular focus on their utility as water depth estimators. To test this approach directly, we evaluated modern marine invertebrate communities along an onshore-offshore gradient to determine the relationship between community composition and bathymetry, compare the performance of various ordination techniques, and assess whether restricting community datasets to preservable taxa (a proxy for paleontological data) and finer spatial scales diminishes the applicability of multivariate community data as an environmental proxy. Different indirect (unconstrained) ordination techniques (PCoA, CA, DCA, and NMDS) yielded consistent outcomes: locality Axis 1 scores correlated with actual locality depths, and taxon Axis 1 scores correlated with actual preferred taxon depths, indicating that changes in faunal associations primarily reflect bathymetry, or its environmental correlatives. For datasets restricted to taxa with preservable hard parts, heavily biomineralized mollusks, open ocean habitats, and a single onshore-offshore gradient, the significant correlation between water depth and Axis 1 was still observed. However, for these restricted datasets, the correlation between Axis 1 and bathymetry was reduced and, in most cases, notably weaker than estimates produced by subsampling models. Consistent with multiple paleontological studies, the direct tests carried out here for a modern habitat using known bathymetry suggests that multivariate proxies derived from marine benthic associations may serve as a viable proxy of water depth. The general applicability of multivariate paleocommunity data as an indirect proxy of bathymetry is dependent on habitat type, intrinsic ecological characteristics of dominant faunas, taxonomic scope, and spatial and temporal scales of analysis, highlighting the need for continued testing in present-day depositional settings.     

应用间接排序识别盐沼植被的光谱特征:以崇明东滩为例

遥感技术已成为大尺度植被分类的重要手段,而地面植物群落特征与其光谱特征之间的关系是解译遥感影像的关键。该研究选择上海崇明东滩自然保护区的盐沼植物群落为对象,应用ASD地物光谱仪测定其植物群落的光谱反射率,并采用10个小型机载成像光谱仪(CASI)默认植被波段组,应用主分量分析法和相关分析分析了不同群落光谱特征与生态环境因子之间的关系。分析结果表明,间接排序法PCA能够识别盐沼植被中光滩、海三棱 草(Scirpus mariqueter)群落、芦苇(Phragmites australis)群落和互花米草(Spartina alterniflora)等群落的光谱特征,绝大多数盐沼湿地植物群落组成与光谱特征之间有显著的相关,识别效果最好的波段组是736~744 nm、746~753 nm、775~784 nm、815~824 nm和860~870 nm;对光谱反射率影响最大的生态环境因子分别是植物群落的高度和盖度,高程和其它环境因子的影响次之。研究成果可为遥感监测崇明东滩自然保护区内入侵种互花米草的空间分布和扩散规律提供技术支撑,为高光谱遥感影像的影像判读和解译分类以及盐沼湿地植被制图提供科学依据。     

微生物生态研究中BIOLOG方法数据分析及R语言实现

微生物生态研究中,对微生物群落结构、群落特征以及其与环境因素的关系的揭示,一直受到广泛关注;适当的数据分析方法有助于更清晰地刻画微生物群落结构特征,明确其与环境因素的关系。结合实例,对微生物生态研究中基于BIOLOG微平板技术的数据分析方法进行梳理,分别介绍数据读取整理、特征指数计算、非限制性排序、限制性排序、聚类分析、环境向量拟合、蒙特尔检验等常用数据操作及生态分析方法;针对不同方法结论,结合研究目标和生态理论给出具有统计学意义的解释,并评价不同方法特点及适用场景;分析过程以R语言实现,并提供全部代码。结果表明,BIOLOG方法产生数据能从多个角度表征微生物群落功能特征,并结合环境指标梯度进行分析;但BIOLOG数据可能不满足正态性分布,在基于正态分布的分析前应提前进行检验;排序分析时应慎用主成分分析,可优先采用其他基于距离矩阵的排序方法;R语言能够简化BIOLOG数据读取及操作,易于完成各类统计分析。本研究能够对微生物生态研究者科学选择应用统计分析方法、提高数据处理效率提供直接参考。     

Biological guidelines for freshwater sediment based on BEnthic Assessment of SedimenT (the BEAST) using a multivariate approach for predicting biological state

Abstract This paper describes the first results for an alternative approach to the development of sediment quality criteria in the nearshore areas of the Laurentian Great Lakes. The approach is derived from methods developed in the United Kingdom for establishing predictive relationships between macroinvertebrate fauna and the physico-chemistry of riverine environments. The technique involves a multivariate statistical approach using (i) data on the structure of benthic invertebrate communities, (ii) functional responses (survival, growth and reproduction) in four sediment toxicity tests (bioassays) with benthic invertebrates; and (iii) selected environmental variables at 96 reference (‘clean’) sites in the nearshore areas of all five Great Lakes (Lakes Superior, Huron, Erie, Ontario and Michigan). Two pattern recognition techniques (using the computer software package PATN) are employed in the analysis: cluster analysis and ordination. The ordination vector scores from the original axes of the pattern analysis are correlated (using CORR in SAS) with environmental variables which are anticipated to be least affected by anthropogenic activities (e. g. alkalinity, depth, silt, sodium etc.). Multiple discriminant analysis (MDA) is used to relate the site groupings from the pattern analysis to the environmental variables and to generate a model that can be used to predict community assemblages and functional responses at new sites with unknown but potential contamination. The predicted community assemblages and functional responses are then compared with the actual benthic communities and responses at a site, and the need for remedial action is determined. The predictive capability of the discriminant model was confirmed by performing several validation runs on subsets of the data. An example of the use of the model for sediment in Collingwood Bay (an area of concern designated by the IJC in Georgian Bay, Lake Huron) is presented and the technique is shown to be more precise in determining the need for remediation than the currently used provincial sediment quality criteria based on Screening Level Concentration (SLC) and laboratory toxicity tests. The ultimate goal of the study is the development of a method to determine the need for, and the success of, remedial action and to predict what benthic communities should look like at a site if it were clean and what responses of organisms in sediment toxicity tests constitute an acceptable end-point.     

历山自然保护区猪尾沟森林群落植被格局及环境解释

应用TWINSPAN、DCA和DCCA,从植物种,植物群落与环境的生态关系方面,研究历山自然保护区猪尾沟森林群落的植被分布格局,并给予合理的环境解释。结果如下：（1）采用TWINSPAN数量分类方法,将植被划分为9个群落类型。（2）对于特定的研究区域猪尾沟,制约森林群落类型,植物种分布格局的主要因素是海拔梯度,即水、热两个环境因子。（3）DCCA排序图明显反映出排序轴的生态意义,第一轴基本上突出反映了各植物群落所在环境的海拔梯度,即热量因素,沿第一轴从左到右,海拔逐渐升高,植物群落或植物种对热量要求降低;第二轴主要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,沿第二轴从下到上,坡度渐缓,坡向渐向阳。     

Metacommunity analysis of Mexican bats: environmentally mediated structure in an area of high geographic and environmental complexity

Aim We tested the hypothesis that distributions of Mexican bats are defined by shared responses to environmental gradients for the entire Mexican bat metacommunity and for each of four metaensembles (frugivores, nectarivores, gleaning insectivores, and aerial insectivores). Further, we identified the main environmental factors to which bats respond for multiple spatial extents. Location Mexico. Methods Using bat presence–absence data, as well as vegetation composition for each of 31 sites, we analysed metacommunity structure via a comprehensive, hierarchical approach that uses reciprocal averaging (RA) to detect latent environmental gradients corresponding to each metacommunity structure (e.g. Clementsian, Gleasonian, nested, random). Canonical correspondence analysis (CCA) was used to relate such gradients to variation in vegetation composition. Results For all bat species and for each ensemble, the primary gradient of ordination from RA, which is based on species data only, recovered an axis of humidity that matched that obtained for the first axis of the CCA ordination, which is based both on vegetation attributes and on species composition of sites. For the complete assemblage as well as for aerial and gleaning insectivores, analyses revealed Clementsian or quasi‐Clementsian structures with discrete compartments (distinctive groups of species along portions of an environmental gradient) coincident with the humidity gradient and with the Nearctic–Neotropical divide. Within‐compartment analysis further revealed Clementsian or quasi‐Clementsian structures corresponding to a gradient of elevational complexity that matched the second ordination axis in CCA. Frugivores had quasi‐nested structure, whereas nectarivores had Gleasonian structure. Main conclusions Our hierarchical approach to metacommunity analysis detected complex metacommunity structures associated with multiple environmental gradients at different spatial extents. More importantly, the resulting structures and their extent along environmental gradients are determined by ensemble‐specific characteristics and not by arbitrarily circumscribed study areas. This property renders compartment‐level analyses particularly useful for large‐scale ecological analyses in areas where more than one gradient may exist and species sorting may occur at multiple scales.