结合多个环境因子的模糊数学排序

模糊数学排序是近年来引入植被分析的一个新方法,它可以结合环境因子,综合生态信息,受到生态学者的喜爱。最早的模糊数学排序用一个环境因子,后来可以将两个或三个环境因子结合在一个排序图上。但当环境因子多于三个时,排序难以完成。这就限制了该方法的实际应用,因为植被研究所得到的环境数据往往是一个庞大的矩阵。本文仅用除趋势对应分析（ＤＣＡ）综合环境因子信息,然后再进行模糊数学排序,这样环境因子的数目理论上讲没有限制,模糊数学排序的功能得以扩展。我们用该方法对英国北威尔士山地草甸１６６个样方４９个种的植被数据和８个环境因子数据进行了分析。结果较好地描述了草甸植被和环境因子之间的关系,生态意义明确．     

应用模糊数学评价观叶植物的观赏性

应用模糊数学方法评价52种观叶植物的观赏性,确定各种植物所处等级及又同一级别的排序,讨论模糊数学方法对于评价多指标且指标难以量化的多样品的优劣等级与名次的先进性与不足。     

应用模糊数学评价观叶植物的观赏性

应用模糊数学方法评价５２种观叶植物的观赏性,确定各种植物所处等级及同一级别的排序,讨论模糊数学方法对于评价多指标且指标难以量化的多样品的优劣等级与名次的先进性与不足。     

六种阔叶树叶片解剖结构特征及其耐旱性比较

以元宝枫、山桃、皂荚、黄栌、迎春、南蛇藤等6种阔叶树为研究对象,对各种类叶片的主要解剖构造特征进行观察和测定,并综合分析它们在控制水分上的能力。结果表明,不同树种控制水分的主要解剖特征各异;应用模糊数学分析,选择角质层厚度、栅/海比值、上、下表皮厚度、气孔的长度等5个指标进行综合评价,6种阔叶树叶片解剖构造在耐旱性上的能力大小排序为:元宝枫>黄栌>山桃>皂荚>南蛇藤>迎春。     

广东森林群落排序分析

本文应用极点排序和位置向量排序的方法对广东13个森林群落进行排序分析,并分别用极点排序图和位置向量排序的二维图和三维图表示排序的结果。同时对排序图的生态学意义及排序方法的优缺点进行讨论。结果表明,三维位置向量排序图能较好地把性质相近的群落类型聚在一起,可作为植被分类的辅助方法;积点排序图从一定程度上反映了植被的连续变化;极点排序与位置向量排序虽然取得一定结果,但由于同属线性排序,损失的信息量较多,寻求非线性排序方法是研究的方向。     

水深梯度下湿地植被空间分布与生态适应

采用模糊数学排序方法对黄河三角洲国家级自然保护区不同水深梯度下芦苇湿地植被进行了．研究,揭示了水深对植被空间分布的影响。结果表明,不同水深梯度下植物生境和群落类型都表现出较大差异,水深-30～40cm,为水陆过渡地带,旱生、水生植物并存,物种最为丰富,该段水深上植被盖度最大;水深在-30-50cm,由于地下水深较低,该段水深是研究区盐碱化程度最大处;水深低于-50cm时,地表较为干旱,盐碱化程度有所降低,植被类型被耐干旱植被代替。不同水深梯度影响了土壤水分、空气和土壤的生物、物理、化学过程,引起植被生长环境中土壤水分、盐碱化程度的改变,进而对植被空间分布和植被生态特征产生影响。     

生物进化论中的模糊问题初探

生物进化论中的模糊问题初探阎锡海１９６５年,美国著名的应用数学家、加利福尼亚大学教授扎德在研究事物之间隶属关系过程中创立了模糊集合论,即诞生了模糊数学。这一理论不仅吸引了国内外许多数学家为建造模糊数学这座大厦继续增砖添瓦,亦引起了众多的学者们对自然科...     

简讯

据2004年版中国科技期刊引证报告统计（1576种期刊）,2003年《应用生态学报》总被引频次为2253次,总排序为第19名,在生物学类期刊排序为第3名;影响因子为0．990,总排序为第70名,在生物学类期刊排序为第3名。2003年《生态学杂志》总被引频次为836次,总排序为第150名,在生物学类期刊排序为第lO名;影响因子为0．607,总排序为第198名,在生物学类期刊排序为第13名。     

简讯

据2004年版中国科技期刊引证报告统计(1576种期刊),2003年《应用生态学报》总被引频次为2253次,总排序为第19名,在生物学类期刊排序为第3名;影响因子为0．990,总排序为第70名,在生物学类期刊排序为第3名．2003年《生态学杂志》总被引频次为836次,总排序为第150名,在生物学类期刊排序为第10名;影响因子为0．607,总排序为第198名,在生物学类期刊排序为第13名．     

一种天敌控制多种害虫作用的模糊数学评价方法

从生物防治的基本思想出发,利用模糊数学和生态经济的原理,探讨天敌的作用,建立了一种天敌控制多种害虫的模糊数学评判方法,文中给出了一种天敌对害虫的影响率和影响强度、一种天敌对害虫影响的大小以及天敌对害虫的控制能力,并进行了控制分析。     

Herb-rich spruce forests in W Nordland, N Norway: an ecological and methodological study

The vegetation of herb-rich spruce forests in three localities in Brønnøy municipality, W Nordland, N Norway, has been analysed using 120 sample plots, each 25 m², distributed by a restricted random method. In connection with every sample plot a set of ecological variables have been measured. The most important gradients for the differentiation of the vegetation were identified by DCA ordination and statistical analysis of the vegetational and the ecological data sets. The gradients were: (1) the nutrient gradient, (2) the soil moisture gradient and (3) the microclimate gradient. The importance of choice of ordination technique (DCA or LNMDS) relative to the importance of the choice of some parameters in DCA and LNMDS has been evaluated. Indicating from this evaluation were (1) the choice of weighting function prior to DCA ordination can be as important as the choice of ordination technique when the data set is small; (2) choice of dimensionality in LNMDS is normally not as decisive for the ordination result as the choice of ordination technique and (3) when the data set is larger, the choice of scale range is less decisive for the ordination result than the choice of dimensionality in LNMDS.     

Finding a common ordination for several data sets by individual differences scaling

Individual differences scaling is a multidimensional scaling method for finding a common ordination for several data sets. An individual ordination for each data set can then be derived from the common ordination by adjusting the axis lengths so as to maximize the correlations between observed proximities and individual ordination distances. The importance of the various axes for each data set and the mutual similarities and goodness of fit for the individual data sets are described by weight plots. As an example, 46 soft-water lakes in eastern Finland are ordinated on two dimensions according to 3 chemical data sets (water in summer and autumn, sediment) and 4 biological sets (major phytoplankton groups, phytoplankton, surface sediment diatom and cladoceran assemblages). The method seems to be effective as a means of ordination for obtaining the common ordination for the data sets. The major taxonomic groups gave the ordination which differed most clearly from the ordinations of the other data sets. Phytoplankton was most poorly ordinated in all the analyses. The other data sets were fairly coherent. When only biological data sets were ordinated, the diatoms and cladocerans showed rather different patterns. It seems that the cladocerans are best correlated with water chemistry, both according to weights in the joint analysis, and according to correlation between the axes from the biological data sets and the chemical variables.Abbreviations CCA = Canonical correspondence analysis - IDS = Individual differences scaling - MDS = multidimensional scaling - PCA = Principal components analysis     

On the variation explained by ordination and constrained ordination axes

Abstract. Total inertia (TI), the sum of eigenvalues for all ordination axes, is often used as a measure of total variation in a data set. By use of simulated data sets, I demonstrate that lack-of-fit of data to the response model implicit in any eigenvector ordination method results in polynomial distortion ordination axes, with eigenvalues that normally contribute 30–70% to TI (depending on data set properties). The amount of compositional variation extracted on ecologically interpretable ordination axes (structure axes) is thus underestimated by the eigenvalue-to-total-inertia ratio. I recommend that the current use of total inertia as a measure of compositional variation is discontinued. Eigenvalues of structure axes can, however, be used with some caution to indicate their relative importance. I also demonstrate that when the total inertia is partitioned on different sets of explanatory variables and unexplained variation by use of (partial) constrained ordination, (35) 50–85% of the variation ‘unexplained’ by the supplied explanatory variables represents lack-of-fit of data to model. Thus, the common interpretation of ‘unexplained variation’ as random variation (‘noise’) or coenoclinal variation caused by unmeasured explanatory variables, is generally inappropriate. I recommend a change of focus from the variation-explained-to-total inertia ratio and ‘unexplained’ variation to relative amounts of variation explained by different sets of explanatory variables.     

Ordination on the basis of fuzzy set theory

Fuzzy set theory is an extension of classical set theory where elements of a set have grades of membership ranging from zero for non-membership to one for full membership. Exactly as for classical sets, there exist operators, relations, and mappings appropriate for these fuzzy sets. This paper presents the concepts of fuzzy sets, operations, relations, and mappings in an ecological context. Fuzzy set theory is then established as a theoretical basis for ordination, and is employed in a sequence of examples in an analysis of forest vegetation of western Montana, U.S.A. The example ordinations show how site characteristics can be analyzed for their effect on vegetation composition, and how different site factors can be synthesized into complex environmental factors using the calculus of fuzzy set theory.In contrast to current ordination methods, ordinations based on fuzzy set theory require the investigator to hypothesize an ecological relationship between vegetation and environment, or between different vegatation compositions, before constructing the ordination. The plotted ordination is then viewed as evidence to corroborate or discredit the hypothesis.I am grateful to Dr R. D. Pfister (formerly USDA Forest Service) for kind permission to publish data from a Forest Service study.I would like to gratefully acknowledge the helpful comments and criticisms of Drs. G. Cottam, J. D. Aber, T. F. H. Allen, E. W. Beals, I. C. Prentice, C. G. Lorimer, and two anonymous reviewers.Taxonomic nomenclature follows Hitchcock & Cronquist (1973).I would like to thank the Dean of the College of Letters and Sciences, University of Wisconsin—Madison, for a fellowship which supported this research, and the Department of Botany for computer funds to perform the analyses.     

A phytoecological study of the mire Northern Kisselbergmosen, SE Norway. II. Identification of gradients by detrended (canonical) correspondence analysis

The vegetation of a poor mire is sampled by two procedures; 800 randomly placed sample plots made up the R data set, 765 subjectively selected plots in 153 sample plot series made up the S data set. DCA ordination and constrained ordination by DCCA of the data sets and subsets showed the existence of three coenoclines in the material: (1) the coenocline along the mire expanse: low to high median depth to the water table—mire margin gradient, (2) the poor-rich coenocline, dependent on a complex-gradient in substrate chemistry, and (3) a coenocline attributed to variation in peat productivity. Thus the assumption of Fennoscandian mire scientists embedded in numerous systems for classifying mire vegetation, that three gradients are the most important in the mire ecosystem, is partly confirmed. In the investigated area, two of the gradients normally considered make up one complex coenocline (1), and a fourth coenocline (3) has to be added. The effects of sampling techniques on correlations between coenoclines and on ordination results are discussed, and an improved sampling technique is suggested. The major faults of DCA: (1) the tongue effect, and (2) the instability, are described and discussed. It is concluded that if due attention is taken to reveal effects of the faults of the method, DCA is among the best ordination methods currently available.     

A paired-quadrat method for use in multiscale ordination

Multiscale ordination is a technique for examining spatial patterns of several species at several scales. We present a paired-quadrat method (paired quadrat covariance; PQC) to be used in multiscale ordination and test it with artificial data. Multiscale ordination with PQC successfully extracted the salient features of the data set. The method appears to be more sensitive than blocked-quadrat techniques for extracting small-scale patterns. We suggest that PQC will be useful as a complement to existing procedures or as a tool for analysing data from scattered quadrat arrangements.Abbreviations PQC = Paired Quadrat Covariance - PQV = Paired Quadrat Variance - TTLC = Two-Term Local Covariance - TTLQV = Two-Term Local Quadrat Variance     

Effects of species partition on explanatory variables in direct gradient analysis - a case study from Senegal

Abstract. Species-environment data from Senegal, West Africa, are used to study the effects of partition of a large species data set into subsets corresponding to rare and common species respectively. The original data set contains 129 woody plant species from 909 plots and 60 explanatory variables. By applying Canonical Correspondence Analysis to data subsets, marked differences in the forward-selected variables were detected. The highest resemblance was found between the complete species set and the common species subset. Only one of eight selected variables was common to all species and the rare species groups. These findings were tested with partial ordination, applying the selected variables from the original species group (Vb), as variables and covariables to the analyses of common and rare species. For the common species this application resulted in a constrained ordination with higher eigenvalues as compared to the set of variables selected with reference to the common species group. Using the rare species group, the application of Vb gave a much lower sum of eigenvalues than did the ordination with selected variables based on the rare species group only. Evidently, the set of variables selected on the basis of the rare species data were more significant. Hence, the resulting gradients depend on the frequency of the species. Gradient analysis is apparently only valid for groups of species with closely resembling characteristics. This implies that different functional types of species, with different distributions and abundances, respond individually to environmental variation. Extrapolating deduced gradients from one species group to another maybe risky, particularly when used in vegetation modelling.     

Effects of plot size on the ordination of vegetation samples

Questions: Do ordination patterns differ when based on vegetation samples recorded in plots of different size? If so, how large is the effect of plot size relative to the effects of data set heterogeneity and of using presence/absence or cover‐abundance data? Can we combine plots of different size in a single ordination? Methods: Two homogeneous and two heterogeneous data sets were sampled in Czech forests and grasslands. Cover‐abundances of plant species were recorded in series of five or six nested quadrats of increasing size (forest 49‐961 m2; grassland 1‐49 m²). Separate ordinations were computed for plots of each size for each data set, using either species presences/absences or cover‐abundances recorded on an ordinal scale. Ordination patterns were compared with Procrustean analysis. Also, ordinations of data sets jointly containing plots of different size were calculated; effects of plot size were evaluated using a Monte Carlo test in constrained ordination. Results: The results were consistent between forest and grassland data sets. In homogeneous data sets, the effect of presence/absence vs. cover‐abundance was similar to, or larger than, the effect of plot size; for presence/absence data the differences between ordinations of differently sized plots were smaller than for cover‐abundance data. In heterogeneous data sets, the effect of plot size was larger than the effect of presence‐absence vs. cover‐abundance. The plots of smaller size (= 100 m2 in forests, = 4 m² in grasslands) yielded the most deviating ordination patterns. Joint ordinations of differently sized plots mostly did not yield patterns that would be artifacts of different plot size, except for plots from the homogeneous data sets that differed in size by a factor of four or higher. Conclusions: Variation in plot size does influence ordination patterns. Smaller plots tend to produce less stable ordination patterns, especially in data sets with low ß‐diversity and species cover‐abundances. Data sets containing samples from plots of different sizes can be used for ordination if they represent vegetation with large ß‐diversity. However, if data sets are homogeneous, i.e. with low ß‐diversity, the differences in plot sizes should not be very large, in order to avoid the danger of plot size differences distorting the real vegetation differentiation in ordination patterns.     

Comparison of species ordinations resulting from alternative indices of interspecific association and different numbers of included species

Summary Several measures of interspecific association are compared. Dispersion and covariance are limited in value because they respond to the commonness of the species compared. Correlation is not so limited but it responds to discrepancies in commonness among the species. The practical result of these relationships between commonness and association is that only the most common species can occupy periferal positions in a species ordination. Rare species are relegated to positions near the center not on the basis of their phytosociological pattern but simply because of their rarity. Both Cole's index of association and the tetrachoric correlation overcome the problem imposed by the relationship between ordination position and species commonness and they both produce very similar results. The effect of differing numbers of species on the ordination configuration is examined using both Pearson's correlation and Cole's index. The basic pattern of the ordination is set with the first few species when Cole's index is used, however, since rare species are given more weight in the analysis with this index, the addition of several very rare species can change the configuration of the ordination.Nomenclature of species is given in Table 1.Research supported by the Eastern Deciduous Forest Biome Project, US-IBP, funded by the National Science Foundation under Interagency Agreement AG-199, BMS69-01147 A09 with the Energy Research and Development Administration — Oak Ridge National Laboratory. Research also supported by the U.S. Energy Research and Development Administration under contract with the Union Carbide Corporation. Contribution No. 240 from the EDFB, US-IBP. Publication No. 790. Environmental Sciences Division, ORNL.     

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.