Extended dissimilarity: a method of robust estimation of ecological distances from high beta diversity data

It is widely accepted that reliable ordination of ecological data requires a strong linear or ordinal relationship between the dissimilarity of sites, based on species composition, and the ecological distance between them. Certain dissimilarity measures, having the property that they take a fixed maximum value when sites have no species in common, have been shown to be strongly correlated with ecological distance. For ecological gradients of moderate length (moderate beta diversity), such measures, in conjunction with non-metric multidimensional scaling, will reliably yield successful ordinations. However, as beta diversity increases, more sites have no species in common, and such measures invariably under-estimate ecological distance for such sites. Thus ordinations of data with high species turnover (high beta diversity) may fail.Extended dissimilarities are defined using an iterative adaptation of flexible shortest path adjustment applied to the matrix of dissimilarities with fixed maximum values. By means of theoretical argument and simulations, this is shown to lead to far stronger correlations between the adjusted site dissimilarity and ecological distance for ecological gradients of greater length than previously considered. Hence ordinations of extended dissimilarities, by means of either metric or non-metric scaling techniques, are shown to outperform corresponding ordinations of unadjusted dissimilarities, with the difference increasing with increasing beta diversity.     

Gradient structure of forest vegetation in the central Washington cascades

Summary Forest vegetation located in three areas of the central Washington Cascades, arrayed along a gradient of increasing continentality and decreasing rainfall, were compared using ordination methods. Within each region, lower and upper elevation sites were analyzed separately and for each set of sites, trees and ground story vegetation were analyzed independently. Principal components analysis, reciprocal averaging, weighted averaging, and polar ordination were applied to each set. The characteristics of the data determined which method gave the most readily interpretable results, but RA and WA usually best ordered the stands along a complex, combined coenocline, while PO often decomposed the gradient into moisture and temperature components. PCA was of little use, even with relatively low beta diversity.Results are presented in the form of stand by species tables for each data set and the most appropriate two dimensional ordination. Both are correlated to the classification. A mosaic diagram for each region is synthesized from these analyses and habitat data. These diagrams indicate that community types occupy a smaller portion of the habitat as continentality increases.The ordination results were in close agreement with our earlier classification of these stands. The maritime (west) region contains stands of low richness and with relatively little coenocline differentiation. In contrast, the continental (east) region has high alpha and beta diversity. Species in the west tend to occupy a broad portion of the available habitat range, while species in the east do not. The analyses reveal that ordinations by different strata may produce similar stand sequences if both strata are responding directly to the same factors, but that the correspondence degenerates where the understory responds primarily to the nature of the canopy dominant species. Thus stand sequence correlations are highest in the lowland eastern region and lowest in the lowland western region.This study demonstrates that none of these metric ordination methods is fool-proof and that none should be used exclusively or in isolation. RA and PO are demonstrated to be useful general methods; WA gave results similar to those of RA. PCA never produced uniquely superior results. Analyses with too few species and moderate beta diversity often produce distortions as pronounced as those with many species and high beta diversity.Nomenclature follows Hitchcock & Conquist (1973).Funds provided by the Graduate School Research Fund, University of Washington and by grants GB-20963 and GB-36810X to the Coniferous Forest Biome, U.S.I.B.P. This is contribution # 318 to the Coniferous Forest Biome. M.F. Denton, R.S. Fleming, A.R. Kruckeberg, and R.H. Whittaker each made significant suggestions. We thank R.S. Fleming, S.G. Fleming, C. Brewer, B.C. Cannon, K.E. Wade, K. Loughney, and J.E. Canfield for their assistance with field work and data analysis.     

Species and plant community distribution in a Mojave-Great Basin desert transition

Multivariate analyses were used to describe the vegetation characteristics of a transition from lowelevation Mojave desert to higher-elevation Great Basin desert. Vegetation data used were from Plutonium Valley in the Nevada Test Site. Data from forty nine releves were analyzed with two classifications (two-day indicator analysis or TWINSPAN and unweighted paried group cluster analysis or CLUSTER). Three ordinations, reciprocal averaging (RA), detrended reciprocal averaging (DCA) and non-metric multidimensional scaling (MNDS), were also used. A rotational correlation analysis was used to determine the vector direction of environmental gradients that correlate best with ordination results. Only token correspondence was found between multivariate classes generated by TWINSPAN and CLUSTER, and seven classes (plant communities) identified from field reconnaissance. The latter seven communities were based on differences in dominant species. Distribution of the vegetation was related more to beta diversity than alpha diversity. Individual species were much less diagnostic than the amount of plant cover, groups or guilds of species or differences in elevation and steepness of slope. Because of the high beta diversity the NMDS ordination gave results with the greatest ease of interpretation.     

Does phylogenetic distance aid in detecting environmental gradients related to species composition?

Questions: How should we evaluate the success of new distance measures combining community abundance and phylogenetic information? How do we interpret ordinations using these metrics? Methods: We generated synthetic data along a known environmental gradient with two hypothetical underlying phylogenetic structures: niche phylogenetically conserved or dispersed along a gradient. We also examined tree species composition associated with gradients in elevation and longitude in Oregon, USA. NMS ordinations of plots in species space from phylogenetic (PD) and Sørensen distance (SD) matrices allowed comparison of the use of PD in different scenarios. Results: PD caused plots to cluster based on the clades that they contained, reducing stress with the synthetic data but not with the real example. Phylogenetic distance highlighted clades related to gradients when these were associated. When phylogeny was not conserved along a gradient, that gradient was less strong. Regardless of phylogenetic conservation, NMS using SD consistently extracted the strongest gradients in species composition. Conclusions: The success of PD should be evaluated on how well it extracts gradients in species composition and allows community ecologists to determine which gradients are partially explained by phylogeny and not based on its ability to reduce ordination stress. PD ordinations can help community ecologists interpret niche conservation but may obscure gradients related to species composition when niches are not conserved along the gradient of interest at the scale of the study.     

Compositional dissimilarity as a robust measure of ecological distance

The robustness of quantitative measures of compositional dissimilarity between sites was evaluated using extensive computer simulations of species' abundance patterns over one and two dimensional configurations of sample sites in ecological space. Robustness was equated with the strength over a range of models, of the linear and monotonic (rank-order) relationship between the compositional dissimilarities and the corresponding Euclidean distances between sites measured in the ecological space. The range of models reflected different assumptions about species' response curve shape, sampling pattern of sites, noise level of the data, species' interactions, trends in total site abundance, and beta diversity of gradients.The Kulczynski, Bray-Curtis and Relativized Manhattan measures were found to have not only a robust monotonic relationship with ecological distance, but also a robust linear (proportional) relationship until ecological distances became large. Less robust measures included Chord distance, Kendall's coefficient, Chisquared distance, Manhattan distance, and Euclidean distance.A new ordination method, hybrid multidimensional scaling (HMDS), is introduced that combines metric and nonmetric criteria, and so takes advantage of the particular properties of robust dissimilarity measures such as the Kulczynski measure.We thank M. P. Austin for encouraging this study, and I. C. Prentice, E. Van der Maarel, and an anonymous reviewer for helpful comments. E. M. Adomeit provided technical assistance.     

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.     

Effects of sample distribution along gradients on eigenvector ordination

In general, disproportionately heavy sampling of the ends of a gradient increases the interpretability of eigenvector ordinations. More specifically, correspondence analysis (CA) and detrended correspondence analysis (DCA) best reproduce the original positions of samples in simulated coenoclines when samples are clustered toward the ends of the axis. Principal components analysis (PCA) reproduces the original sample positions less well than either CA or DCA and shows no improvement as samples are increasingly clustered toward the ends of the axis. PCA and CA show less curvature of one dimensional data into the second axis when sampling favors the ends of the axis.     

哀牢山亚热带中山湿性常绿阔叶林树种beta多样性格局形成的驱动力

Beta多样性通常指群落在时间和空间上物种组成的差异, 包括物种周转组分和物种丰富度差异组分。驱动beta多样性格局形成的生态过程决定了群落的时空动态, 然而关于beta多样性及其两个组分格局形成的驱动力还存在较多争议。以往研究表明, beta多样性的格局存在取样尺度的依赖性, 驱动其形成的生态过程在不同取样尺度下的相对重要性也随之改变。本研究以哀牢山亚热带中山湿性常绿阔叶林20 ha动态监测样地为研究对象, 在不同取样尺度上, 将样方间的Bray-Curtis指数分解为物种周转组分和物种丰富度差异组分, 通过典范冗余分析和方差分解的方法揭示环境过滤和扩散限制对于beta多样性及其两个组分格局形成的相对重要性及其尺度依赖性。结果表明: (1) beta多样性、物种周转组分和物种丰富度差异组分均随取样尺度的增大而减小。在不同取样尺度下, 物种周转组分对于beta多样性的贡献始终占主导地位。(2)随着取样尺度的增大, 环境过滤驱动beta多样性格局形成的相对重要性逐渐增加, 而扩散限制的相对重要性逐渐降低。本研究进一步证实了取样尺度在beta多样性格局形成及其驱动力定量评价中的重要性, 今后的研究需要进一步解析上述尺度效应的形成机制。     

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.     

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     

Contrasting diversity patterns of crenarchaeal, bacterial and fungal soil communities in an alpine landscape

Background

The advent of molecular techniques in microbial ecology has aroused interest in gaining an understanding about the spatial distribution of regional pools of soil microbes and the main drivers responsible of these spatial patterns. Here, we assessed the distribution of crenarcheal, bacterial and fungal communities in an alpine landscape displaying high turnover in plant species over short distances. Our aim is to determine the relative contribution of plant species composition, environmental conditions, and geographic isolation on microbial community distribution.

Methodology/Principal Findings

Eleven types of habitats that best represent the landscape heterogeneity were investigated. Crenarchaeal, bacterial and fungal communities were described by means of Single Strand Conformation Polymorphism. Relationships between microbial beta diversity patterns were examined by using Bray-Curtis dissimilarities and Principal Coordinate Analyses. Distance-based redundancy analyses and variation partitioning were used to estimate the relative contributions of different drivers on microbial beta diversity. Microbial communities tended to be habitat-specific and did not display significant spatial autocorrelation. Microbial beta diversity correlated with soil pH. Fungal beta-diversity was mainly related to soil organic matter. Though the effect of plant species composition was significant for all microbial groups, it was much stronger for Fungi. In contrast, geographic distances did not have any effect on microbial beta diversity.

Conclusions/Significance

Microbial communities exhibit non-random spatial patterns of diversity in alpine landscapes. Crenarcheal, bacterial and fungal community turnover is high and associated with plant species composition through different set of soil variables, but is not caused by geographical isolation.     

Species richness and distortion in reciprocal averaging and detrended correspondence analysis

Gradients in beta diversity and species richness cause different forms of distortion in reciprocal averaging ordinations. Detrended correspondence analysis largely removes the beta diversity effect and reduces, but does not eliminate, the influence of species richness.     

On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray-Curtis coefficient for denuded assemblages

Bray-Curtis similarity is widely employed in multivariate analysis of assemblage data, for sound biological reasons. This paper discusses two problems, however, with its practical application: its behaviour is erratic (or even undefined) for the vanishingly sparse samples that may be found as an end-point to a severe impact gradient, or a start-point in colonisation studies; and, in common with all similarity measures on species-level data, it is sensitive to inconsistency of taxonomic identification through time. It is shown that the latter problem is ameliorated by application of ‘taxonomic dissimilarity’ coefficients, a natural extension of the concept of taxonomic distinctness indices. Two previous suggestions for use with presence/absence data, denoted here by Γ⁺ and Θ⁺, are noted to be simple generalisations of the Bray-Curtis and Kulczynski measures, respectively. Also seen is their ability to permit ordinations of assemblages from wide geographic scales, with no species in common, and for which Bray-Curtis would return zero similarity for all pairs of samples.The primary problem addressed, however, is that of denuded or entirely blank samples. Where it can be convincingly argued that impoverished samples are near-blank from the same cause, rather than by random occurrences from inadequate sample sizes (tow length, core diameter, transect or quadrat size etc.), a simple adjustment to the form of the Bray-Curtis coefficient can generate meaningful MDS displays which would otherwise collapse, and can improve values of the ANOSIM R statistic (increased separation of groups in multivariate space). It is also shown to have no effect at all on the normal functioning of a Bray-Curtis analysis when at least a modest amount of data is present for all samples.Examination of the properties of this ‘zero-adjusted’ Bray-Curtis measure goes hand-in-hand with a wider discussion of the efficacy of competing similarity, distance or dissimilarity coefficients (collectively: resemblance measures) in community ecology. The inherent biological guidelines underlying the ‘Bray-Curtis family’ of measures (including Kulczynski, Sorenson, Ochiai and Canberra dissimilarity) are made explicit. These and other commonly employed measures (e.g. Euclidean, Manhattan, Gower and chi-squared distances) are calculated for several ‘classic’ data sets of impact events or gradients in space and time. Behaviour of particular coefficients is judged against the interpretability of the resulting ordination plots and an objective measure of the ability to discriminate between a priori defined hypotheses, representing impact conditions. A second-stage MDS plot of a set of resemblance coefficients, based on the respective similarities of the multivariate patterns each generates (an MDS of MDS plots, in effect), is seen to be useful in determining which coefficients are extracting essentially different information from the same assemblage matrix. This suggests a mechanism for practical classification of the plethora of resemblance measures defined in the literature. Similarity-based ANOSIM R statistics and Spearman ρ correlations, whose non-parametric structure make them absolutely comparable across different resemblance measures, answer questions about whether the different information extracted by some coefficients is more, or less, helpful to the final biological interpretation.     

Resemblance in phylogenetic diversity among ecological assemblages

Questions: How can a resemblance (similarity or dissimilarity) measure be formulated to include information on both the evolutionary relationships and abundances of organisms, and how does it compare to measures lacking such information? Methods: We extend the family of Phylogenetic Diversity (PD) measures to include a generalized method for calculating pair‐wise resemblance of ecological assemblages. Building on previous work, we calculate the matching/mismatching components of the 2 × 2 contingency table so as to incorporate information on both phylogeny and abundance. We refer to the class of measures so defined as “PD resemblance” and use the term “SD resemblance” for the traditional class of measures based on species diversity alone. As an illustration, we employ data on the diversity and stem density of shrubs of Toohey Forest, Australia, to compare PD resemblance to its SD resemblance equivalent for both incidence and abundance data. Results: While highly correlated, PD resemblance consistently measures assemblages as more similar than does SD resemblance, and tends to “smooth out” the otherwise skewed and truncated distribution of pair‐wise resemblance indices of our high‐turnover data set, resulting in nMDS ordinations with lower stress. Randomization of species distributions across assemblages indicates that phylogeny has made a significant contribution to the ordination pattern. Conclusions: PD resemblance measures, in addition to providing an evolutionary perspective, have great potential to improve distance‐based analyses of community patterns, particularly if species responses to ecological gradients are unimodal and phylogenetically conserved.     

Beta diversity as the variance of community data: dissimilarity coefficients and partitioning

Beta diversity can be measured in different ways. Among these, the total variance of the community data table Y can be used as an estimate of beta diversity. We show how the total variance of Y can be calculated either directly or through a dissimilarity matrix obtained using any dissimilarity index deemed appropriate for pairwise comparisons of community composition data. We addressed the question of which index to use by coding 16 indices using 14 properties that are necessary for beta assessment, comparability among data sets, sampling issues and ordination. Our comparison analysis classified the coefficients under study into five types, three of which are appropriate for beta diversity assessment. Our approach links the concept of beta diversity with the analysis of community data by commonly used methods like ordination and anova . Total beta can be partitioned into Species Contributions (SCBD: degree of variation of individual species across the study area) and Local Contributions (LCBD: comparative indicators of the ecological uniqueness of the sites) to Beta Diversity. Moreover, total beta can be broken up into within‐ and among‐group components by manova , into orthogonal axes by ordination, into spatial scales by eigenfunction analysis or among explanatory data sets by variation partitioning.     

千岛湖岛屿维管植物β多样性及其影响因素

通过样地调查方法、Jaccard相异性指数、Spearman回归分析和非度量多维标度(NMDS)排序分析,研究了千岛湖154个岛屿上不同植物群落β多样性及其主要影响因素。结果表明不同的景观参数对不同植物生长型有不同程度的影响,其中(1)藤本、灌木的β多样性形成的主导因素是面积,即面积差越大的区域间的β多样性越高;(2)乔木的β多样性主要受到岛屿间距离的限制,岛屿间距离越远,β多样性越高;(3)草本植物的β多样性分布与岛屿面积差及岛屿间距离并未呈现出显著相关,即其分布不受这两种因素限制;(4)NMDS分析结果显示岛屿面积、形状、边缘面积比和岛屿到大陆最小距离等特征对千岛湖岛屿上植物β多样性起决定性的作用。千岛湖陆桥岛屿组成的片段化生境中植物β多样性受扩散限制和生态位假说的共同影响。     

Evaluating and presenting uncertainty in model‐based unconstrained ordination

Variability in ecological community composition is often analyzed by recording the presence or abundance of taxa in sample units, calculating a symmetric matrix of pairwise distances or dissimilarities among sample units and then mapping the resulting matrix to a low‐dimensional representation through methods collectively called ordination. Unconstrained ordination only uses taxon composition data, without any environmental or experimental covariates, to infer latent compositional gradients associated with the sampling units. Commonly, such distance‐based methods have been used for ordination, but recently there has been a shift toward model‐based approaches. Model‐based unconstrained ordinations are commonly formulated using a Bayesian latent factor model that permits uncertainty assessment for parameters, including the latent factors that correspond to gradients in community composition. While model‐based methods have the additional benefit of addressing uncertainty in the estimated gradients, typically the current practice is to report point estimates without summarizing uncertainty. To demonstrate the uncertainty present in model‐based unconstrained ordination, the well‐known spider and dune data sets were analyzed and shown to have large uncertainty in the ordination projections. Hence to understand the factors that contribute to the uncertainty, simulation studies were conducted to assess the impact of additional sampling units or species to help inform future ordination studies that seek to minimize variability in the latent factors. Accurate reporting of uncertainty is an important part of transparency in the scientific process; thus, a model‐based approach that accounts for uncertainty is valuable. An R package, UncertainOrd , contains visualization tools that accurately represent estimates of the gradients in community composition in the presence of uncertainty.     

The influence of seasonal and taxonomic factors on the ordination and classification of running-water sites in Great Britain and on the prediction of their macro-invertebrate communities

SUMMARY. 1. Macro-invertebrate samples were collected from 268 running-water sites in Great Britain in each of three seasons (spring, summer and autumn). A combined seasons’treatment was generated by amalgamating the individual seasons’data. These four seasonal options were each subjected to four distinct taxonomic analyses differing in level of identification and whether the data were quantitative or qualitative. Thus sixteen data-sets were available for analysis. Environmental data on physical and chemical variables, macrophyte cover and date of sampling were also recorded for each site. 2. All sixteen data-sets were ordinated by detrended correspondence analysis and classified by two-way indicator species analysis. There were strong correlations between the sixteen ordinations and significant concordance between classifications. 3. The relationships between ordination scores and single environmental variables were investigated. Muhiple discriminant analysis was used to fit environmental data to eight selected classifications covering the full range of seasonal and taxonomic treatments. The environmental variables most useful in distinguishing between rivers were substratum characteristics, alkalinity and total oxidized nitrogen. Within-river differences were often highly correlated with discharge, distance from source, width and depth. Slope and altitude contributed strongly to both between-river and within-river distinctions. 4. Between-site variation (beta diversity), eigenvalues of ordination, the reliability of classifications, the proportion of sites correctly assigned to their biological group using environmental data and the standardized similarity between observed and predicted fauna were all higher when identifications were taken to species level, rather than one of three family treatments. Qualitative data on a reduced list of families gave comparable or better results than more detailed family treatments. 5. Combined seasons’data enabled better categorization and prediction than single season's. 6. The values of the Czekanowski Index of Similarity between the observed and predicted fauna of test sites were close to realistic maximum values. 7. Recommendations are made concerning potential usages of the various classifications. The species level classification has uses in the field of conservation and in the prediction of biological response to environmental change. The family level classifications have value in developing local site inventories and in the interpretation of pollution surveillance programmes.     

The inselberg flora of Atlantic Central Africa. I. Determinants of species assemblages

Aims To identify the relative contributions of environmental determinism, dispersal limitation and historical factors in the spatial structure of the floristic data of inselbergs at the local and regional scales, and to test if the extent of species spatial aggregation is related to dispersal abilities. Location Rain forest inselbergs of Equatorial Guinea, northern Gabon and southern Cameroon (western central Africa). Methods We use phytosociological relevés and herbarium collections obtained from 27 inselbergs using a stratified sampling scheme considering six plant formations. Data analysis focused on Rubiaceae, Orchidaceae, Melastomataceae, Poaceae, Commelinaceae, Acanthaceae, Begoniaceae and Pteridophytes. Data were investigated using ordination methods (detrended correspondence analysis, DCA; canonical correspondence analysis, CCA), Sørensen's coefficient of similarity and spatial autocorrelation statistics. Comparisons were made at the local and regional scales using ordinations of life‐form spectra and ordinations of species data. Results At the local scale, the forest‐inselberg ecotone is the main gradient structuring the floristic data. At the regional scale, this is still the main gradient in the ordination of life‐form spectra, but other factors become predominant in analyses of species assemblages. CCA identified three environmental variables explaining a significant part of the variation in floristic data. Spatial autocorrelation analyses showed that both the flora and the environmental factors are spatially autocorrelated: the similarity of species compositions within plant formations decreasing approximately linearly with the logarithm of the spatial distance. The extent of species distribution was correlated with their a priori dispersal abilities as assessed by their diaspore types. Main conclusions At a local scale, species composition is best explained by a continuous cline of edaphic conditions along the forest‐inselberg ecotone, generating a wide array of ecological niches. At a regional scale, these ecological niches are occupied by different species depending on the available local species pool. These subregional species pools probably result from varying environmental conditions, dispersal limitation and the history of past vegetation changes due to climatic fluctuations.     

Studies of a vegetation transect through brigalow (Acacia harpophylla) forest in central Queensland

Vegetation and environmental data were collected in 182 contiguous plots along a belt transect, 3.7 km long, in central Queensland through a relatively undisturbed forest dominated by brigalow (Acacia harpophylla). A subset of eighty-nine plots using percentage cover of 128 species was classified using a polythetic agglomerative approach. Dual stand and species ordinations by principal component analysis and reciprocal averaging were also undertaken. The cluster analysis and ordination of unstandardized cover data, grouped stands on the basis of abundance of the predominant canopy species, but only where these species were true dominants such as Macropteranthes leichhardtii did these same groupings appear in the ordination of standardized data. The latter ordination was ecologically more satisfying, but the complementary species ordination was unsatisfactory. The vegetation-species complex was best explained by dual species and stand ordinations using presence-absence data. Reciprocal averaging appeared to produce a marginally better ordination than principal component analysis. An ordination of eleven environmental factors indicated soil profile and presence of gilgai were the most important environmental variables. The ordination was enhanced by varimax rotation which focused on a more homogeneous environmental gradient and coincided more closely with the vegetation ordinations. An ordination using both species and environmental factors substantiated the explanation of the vegetation-environmental complex derived from separate ordinations. The main gradient revealed from the ordinations appeared to be a mesic-xeric gradient stretching from Macropteranthes leichhardtii semi-evergreen vine thicket at the mesic end diverging through various A. harpophylla - dominant communities to A. harpophylla - Eucalyptus melanophloia woodland on duplex soils and Dichanthium affine grassland on clay soils. Six plant communities are defined and described and each related to a particular set of environmental conditions. These communities are bonewood (Macropteranthes leichhardtii) - semi-evergreen vine thicket, brigalow (Acacia harpophylla) - semi-evergreen vine thicket, brigalow (A. harpophylla) continuum (clay soils), Dichanthium affine grassland, brigalow (A. harpophylla) continuum (duplex soils) and brigalow (A. harpophylla) - silver-leaved ironbark (Eucalyptus melanophloia) woodland.