Multivariate dispersion as a measure of beta diversity

Beta diversity can be defined as the variability in species composition among sampling units for a given area. We propose that it can be measured as the average dissimilarity from individual observation units to their group centroid in multivariate space, using an appropriate dissimilarity measure. Differences in beta diversity among different areas or groups of samples can be tested using this approach. The choice of transformation and dissimilarity measure has important consequences for interpreting results. For kelp holdfast assemblages from New Zealand, variation in species composition was greater in smaller holdfasts, while variation in relative abundances was greater in larger holdasts. Variation in community structure of Norwegian continental shelf macrobenthic fauna increased with increases in environmental heterogeneity, regardless of the measure used. We propose a new dissimilarity measure which allows the relative weight placed on changes in composition vs. abundance to be specified explicitly.     

Studying beta diversity: ecological variation partitioning by multiple regression and canonical analysis

Aims: Beta diversity is the variation in species composition amongsites in a geographic region. Beta diversity is a key conceptfor understanding the functioning of ecosystems, for the conservationof biodiversity and for ecosystem management. The present reportdescribes how to analyse beta diversity from community compositionand associated environmental and spatial data tables. Methods: Beta diversity can be studied by computing diversity indicesfor each site and testing hypotheses about the factors thatmay explain the variation among sites. Alternatively, one cancarry out a direct analysis of the community composition datatable over the study sites, as a function of sets of environmentaland spatial variables. These analyses are carried out by thestatistical method of partitioning the variation of the diversityindices or the community composition data table with respectto environmental and spatial variables. Variation partitioningis briefly described herein. Important findings: Variation partitioning is a method of choice for the interpretationof beta diversity using tables of environmental and spatialvariables. Beta diversity is an interesting ‘currency’for ecologists to compare either different sampling areas ordifferent ecological communities co-occurring in an area. Partitioningmust be based upon unbiased estimates of the variation of thecommunity composition data table that is explained by the varioustables of explanatory variables. The adjusted coefficient ofdetermination provides such an unbiased estimate in both multipleregression and canonical redundancy analysis. After partitioning,one can test the significance of the fractions of interest andplot maps of the fitted values corresponding to these fractions.     

Statistical methods for temporal and space–time analysis of community composition data

This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination, whereas others are recent, including the methods of temporal eigenfunction analysis developed for multiscale exploration (i.e. addressing several scales of variation) of univariate or multivariate response data, reviewed, to our knowledge for the first time in this review. These methods are illustrated with ecological data from 13 years of benthic surveys in Chesapeake Bay, USA. The following methods are applied to the Chesapeake data: distance-based Moran''s eigenvector maps, asymmetric eigenvector maps, scalogram, variation partitioning, multivariate correlogram, multivariate regression tree, and two-way MANOVA to study temporal and space–time variability. Local (temporal) contributions to beta diversity (LCBD indices) are computed and analysed graphically and by regression against environmental variables, and the role of species in determining the LCBD values is analysed by correlation analysis. A tutorial detailing the analyses in the R language is provided in an appendix.     

How Does Landscape Modification Induce Biological Homogenization in Tropical Stream Metacommunities?

Habitat modification can homogenize biological communities. Beta diversity analyses provide key information for understanding biotic homogenization, especially given recent conceptual and methodological advances. Here, we investigated if landscape modification was associated with taxonomic homogenization in 32 stream insect communities from the Brazilian Atlantic Forest. We investigated: (1) if the way we defined landscape affected our estimates of beta diversity; (2) to what extent changes in species composition versus relative abundance caused the observed homogenization; and (3) if environmental heterogeneity among modified habitats influenced homogenization. We detected taxonomic homogenization caused by landscape modification only when we used refined landscape categorizations and abundance‐based diversity measures. For forested streams, changes in relative abundance rather than absolute taxonomic composition increased the biological variation. Forested streams were generally more heterogeneous, with a variable set of abundant genera; by contrast, non‐forested streams were more homogeneous, with the same set of genera being more or less abundant. We suggest that landscape modification by agriculture, pasture, and silviculture reduces beta diversity by limiting the colonization of potential species, and, ultimately, causing taxonomic homogenization. Studies investigating biotic homogenization should include multiple dissimilarity measures representing changes in relative species abundance and community composition.     

β-多样性的研究：应用多元回归和典范分析研究生态方差的分解

 β-多样性刻画了地理区域中不同地点物种组成的变化,是理解生态系统功能、生物多样性保护和生态系统管理的一个重要概念。该文介绍了如何从群落组成,相关环境和空间数据角度去分析β-多样性。β-多样性可以通过计算每个地点的多样性指数,进而对可能解释点之间差异的因子所作的假设进行检验来研究。也可以将涵盖所有点的群落组成数据表看作是一系列环境和空间变量的函数,进行直接分析。这种分析应用统计方法将多样性指数或群落组成数据表的方差进行关于环境和空间变量的分解。该文对方差分解进行阐述。方差分解是利用环境和空间变量来解释β-多样性的一种方法。β-多样性是生态学家用来比较不同地点或同一地点不同生态群落的一种手段。方差分解就是将群落组成数据表的总方差无偏分解成由各个解释变量所决定的子方差。调整的决定系数提供了针对多元回归和典范冗余分析的无偏估计。 方差分解后,可以对感兴趣的方差解释部分进行显著性检验,同时绘出基于这部分方差解释的预测图。     

Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment

Generalized dissimilarity modelling (GDM) is a statistical technique for analysing and predicting spatial patterns of turnover in community composition (beta diversity) across large regions. The approach is an extension of matrix regression, designed specifically to accommodate two types of nonlinearity commonly encountered in large-scaled ecological data sets: (1) the curvilinear relationship between increasing ecological distance, and observed compositional dissimilarity, between sites; and (2) the variation in the rate of compositional turnover at different positions along environmental gradients. GDM can be further adapted to accommodate special types of biological and environmental data including, for example, information on phylogenetic relationships between species and information on barriers to dispersal between geographical locations. The approach can be applied to a wide range of assessment activities including visualization of spatial patterns in community composition, constrained environmental classification, distributional modelling of species or community types, survey gap analysis, conservation assessment, and climate-change impact assessment.     

地理距离及环境差异对云南元江干热河谷植物群落beta多样性的影响

beta多样性反映了群落间物种组成的差异, 是生物多样性研究的热点之一。本研究通过对云南元江干热河谷41个植物群落样方进行调查, 用Jaccard相异系数表征物种beta多样性, 用样方之间的最近谱系距离(mean nearest taxon distance, MNTD)及平均谱系距离(mean pairwise distance, MPD)表征谱系beta多样性, 采用基于距离矩阵的多元回归和方差分解方法, 探讨了该区域干热河谷典型植物群落的物种beta多样性和谱系beta多样性与样方间环境差异(主要是气候)及地理距离之间的关系。结果表明: (1)群落间的地理距离和年平均温度差异对干热河谷植物群落的物种beta多样性和谱系beta多样性有显著影响; (2)地理距离对物种beta多样性和MNTD的影响最大; 地理距离和年平均温度差异对MPD的影响均较大; (3)样方间年平均温度与年平均降水量的差异和地理距离能够解释群落间beta多样性及谱系beta多样性11-13%的变异。以上结果表明, 生态位分化和扩散限制对该地区植物群落的beta多样性均有显著影响, 其中扩散限制的影响可能更大。此外, 人类活动等其他因素也很可能对元江干热河谷的群落组成具有非常重要的影响。     

Of beta diversity,variance, evenness,and dissimilarity

The amount of variation in species composition among sampling units or beta diversity has become a primary tool for connecting the spatial structure of species assemblages to ecological processes. Many different measures of beta diversity have been developed. Among them, the total variance in the community composition matrix has been proposed as a single‐number estimate of beta diversity. In this study, I first show that this measure summarizes the compositional variation among sampling units after nonlinear transformation of species abundances. Therefore, it is not always adequate for estimating beta diversity. Next, I propose an alternative approach for calculating beta diversity in which variance is substituted by a weighted measure of concentration (i.e., an inverse measure of evenness). The relationship between this new measure of beta diversity and so‐called multiple‐site dissimilarity measures is also discussed.     

Fine spatial grain,large spatial extent and biogeography of macrophyte‐associated cladoceran communities across Neotropical floodplains

相似文献   

The relationship between species replacement,dissimilarity derived from nestedness,and nestedness

Aim Beta diversity can be partitioned into two components: dissimilarity due to species replacement and dissimilarity due to nestedness ( Baselga, 2010 , Global Ecology and Biogeography, 19 , 134–143). Several contributions have challenged this approach or proposed alternative frameworks. Here, I review the concepts and methods used in these recent contributions, with the aim of clarifying: (1) the rationale behind the partitioning of beta diversity into species replacement and nestedness‐resultant dissimilarity, (2) how, based on this rationale, numerators and denominators of indices have to match, and (3) how nestedness and nestedness‐resultant dissimilarity are related but different concepts. Innovation The rationale behind measures of species replacement (turnover) dictates that the number of species that are replaced between sites (numerator of the index) has to be relativized with respect to the total number of species that could potentially be replaced (denominator). However, a recently proposed partition of Jaccard dissimilarity fails to do this. In consequence, this partition underestimates the contribution of species replacement and overestimates the contribution of richness differences to total dissimilarity. I show how Jaccard dissimilarity can be partitioned into meaningful turnover and nestedness components, and extend these new indices to multiple‐site situations. Finally the concepts of nestedness and nestedness‐resultant dissimilarity are discussed. Main conclusions Nestedness should be assessed using consistent measures that depend both on paired overlap and matrix filling, e.g. NODF, whereas beta‐diversity patterns should be examined using measures that allow the total dissimilarity to be separated into the components of dissimilarity due to species replacement and dissimilarity due to nestedness. In the case of multiple‐site dissimilarity patterns, averaged pairwise indices should never be used because the mean of the pairwise values is unable to accurately reflect the multiple‐site attributes of dissimilarity.     

Thirty-year recovery of mollusc communities after nuclear experimentations on Fangataufa atoll (Tuamotu,French Polynesia)

A 30-year study of temporal changes in gastropod community structure on the reefs of a Pacific Ocean atoll (Fangataufa, Tuamotu Archipelago) subjected to atmospheric nuclear tests during the 1960s offered the opportunity for an otherwise impossible field experiment that could help ecologists understand mollusc primary succession. Reef molluscs were partly or entirely wiped out by the heat of the nuclear tests and the reefs were recolonized by ocean larvae. On all reefs, community composition before the tests was very different from what it evolved to afterwards. A new method of analysis was developed to study the temporal variation in community composition before versus after the tests (temporal beta diversity). Analyses showed that community compositions diverged through time among the reefs. Only some species can survive the harsh conditions of supralittoral zones, so the same species recolonized them; environmental filtering controlled the development of the new communities. In the reef flat and edge zones, differences in community composition seem to be the result of neutral stochastic colonization by larvae coming from the open ocean. All reefs developed a community composition quite different from that before the nuclear tests.     

Phylogenetic beta diversity: linking ecological and evolutionary processes across space in time

A key challenge in ecological research is to integrate data from different scales to evaluate the ecological and evolutionary mechanisms that influence current patterns of biological diversity. We build on recent attempts to incorporate phylogenetic information into traditional diversity analyses and on existing research on beta diversity and phylogenetic community ecology. Phylogenetic beta diversity (phylobetadiversity) measures the phylogenetic distance among communities and as such allows us to connect local processes, such as biotic interactions and environmental filtering, with more regional processes including trait evolution and speciation. When combined with traditional measures of beta diversity, environmental gradient analyses or ecological niche modelling, phylobetadiversity can provide significant and novel insights into the mechanisms underlying current patterns of biological diversity.     

Drivers of beta diversity of macroinvertebrate communities in tropical forest streams

1. There has recently been increasing interest in patterns of beta diversity but we still lack a comprehensive understanding of these patterns in various regions (e.g. the tropics), ecosystems (e.g. streams) and organism groups (e.g. invertebrates). 2. Our aim was to investigate the patterns of beta diversity of stream macroinvertebrates in relation to key environmental (i.e. stream size, pH and habitat degradation) and geographical variables (i.e. latitude, longitude, altitude) in a tropical region. We surveyed a total of 8–10 riffle sites in each of 34 streams (altogether 337 riffle sites were sampled) in Peninsular Malaysia to examine variation in macroinvertebrate community composition at within‐stream and among‐stream scales. 3. Based on test of homogeneity of dispersion, we found that the streams studied differed significantly in within‐stream variation in community composition (i.e. among‐site variation of within stream beta diversity). The patterns were similar based on Bray–Curtis coefficient on abundance data, Sorensen coefficient on presence–absence data and Simpson coefficient on presence–absence data. We also found that within‐stream beta diversity was significantly related to stream size, pH and latitude, with each of these variables individually accounting for around 20% of the variation in beta diversity in simple regressions, while the total variation explained by the three significant variables amounted to around 50% in multiple regressions. By contrast, habitat degradation, longitude and altitude were not significantly related to beta diversity. We also found that the factor drainage basin accounted for much of the variation in beta diversity in general linear models, suppressing the effects of environmental variables. 4. We concluded that within‐stream beta diversity is mainly related to a combination of the identity of a drainage basin and stream environmental factors. Our findings provide important background for stream environmental assessment and conservation planning by emphasising that (i) macroinvertebrate communities within streams are not homogeneous, but show considerable beta diversity, (ii) streams differ in their degree of within‐stream beta diversity, (iii) stream size and water pH should be considered in applied contexts related to within‐stream beta diversity and (iv) historical effects may be different in different drainage basins and may affect present‐day patterns of within‐stream beta diversity.     

北京东灵山辽东栎林植物物种多样性的多尺度分析

多尺度分析物种多样性格局能够为有效保护生物多样性提供重要信息.利用物种多样性的加法分配法则分析了样方-坡位-坡面等级尺度系统辽东栎林植物物种多样性(gamma多样性)的alpha多样性和beta多样性在各尺度上的分配关系.结果表明以物种丰富度为指标的区域物种多样性的最大贡献来自坡面尺度,表明坡面尺度是维持辽东栎林物种多样性的有效尺度;而对Simpson多样性和Shannon多样性的最大贡献则来自样方内,这决定于群落物种优势度和稀有度格局;各尺度间beta多样性组分随尺度的增大而增大可能是环境异质性和扩散作用的综合结果.各尺度间Shannon多样性对总体多样性的贡献大于Simpson多样性的贡献是偶见种在各尺度间分配的结果.物种多样性分配的加法法则为物种多样性格局的多尺度分析提供了理论框架,是检验物种多样性格局形成机制的有效方法.     

The spatial scaling of beta diversity

Beta diversity is an important concept used to describe turnover in species composition across a wide range of spatial and temporal scales, and it underpins much of conservation theory and practice. Although substantial progress has been made in the mathematical and terminological treatment of different measures of beta diversity, there has been little conceptual synthesis of potential scale dependence of beta diversity with increasing spatial grain and geographic extent of sampling. Here, we evaluate different conceptual approaches to the spatial scaling of beta diversity, interpreted from ‘fixed’ and ‘varying’ perspectives of spatial grain and extent. We argue that a ‘sliding window’ perspective, in which spatial grain and extent covary, is an informative way to conceptualize community differentiation across scales. This concept more realistically reflects the varying empirical approaches that researchers adopt in field sampling and the varying scales of landscape perception by different organisms. Scale dependence in beta diversity has broad implications for emerging fields in ecology and biogeography, such as the integration of fine‐resolution ecogenomic data with large‐scale macroecological studies, as well as for guiding appropriate management responses to threats to biodiversity operating at different spatial scales.     

Modelling the spatial distribution of beta diversity in Australian subtropical rainforest

Investigation of the spatial distribution of biodiversity among communities or across habitats (beta diversity) is often hampered by a scarcity of biological survey data. This is particularly the case in communities of high floristic diversity, such as the subtropical rainforests of eastern Australia. In contrast, there is excellent spatial coverage of environmental data for this region, such as geology, elevation and climate data. Generalized dissimilarity modelling was used in this study to combine biological survey data and environmental data grids for the investigation and prediction of floristic turnover among vegetation communities at a regional scale. Generalized dissimilarity modelling identified four environmental predictors of floristic turnover in the study region, all of which are linked with moisture stress: radiation of the driest quarter, precipitation of the driest period of the year, slope and aspect. Ten land classes representing largely homogeneous floristics and environment were identified and mapped for the region, allowing significantly greater discrimination than currently available mapping for this region. With increases in evapotranspiration and moisture stress predicted as a result of climate change, these results may allow future floristic shifts to be assessed in relation to regional‐scale gradients in floristic turnover.     

Latitudinal gradients of associations between beta and gamma diversity of trees in forest communities in the New World

Aims We analyze two continental data sets of forest communities from across the New World to examine the latitudinal gradients of beta diversity after accounting for gamma diversity and the latitudinal gradient of gamma diversity after accounting for beta diversity.Methods Correlation and regression analyses were used to relate beta and gamma diversity to latitude along two latitudinal gradients in the New World (one including 72 forest sites located south of the equator and the other including 79 forest sites located north of the equator).Important findings Beta diversity and gamma diversity were negatively correlated with latitude. Beta diversity was strongly and positively correlated with gamma diversity (Pearson's correlation coefficient: 0.783 for New World North and 0.848 for New World South). When beta diversity was regressed on latitude and gamma diversity, 69.8 and 85.7% of the variation in beta diversity were explained, respectively, for New World North and New World South. When gamma diversity was regressed on latitude and beta diversity, 81.8 and 84.3% of the variation in gamma diversity were explained, respectively, for New World North and New World South. After statistically removing the relationship between beta and gamma diversity, latitude has weak or no relationships with beta and gamma diversity. However, strong positive correlations between beta and gamma diversity may not be considered as evidence of one driving the other along a latitudinal gradient.     

西藏横断山区溪流细菌beta多样性组分对气候和水体环境的响应

理解沿环境或空间梯度的群落组成变化(即beta多样性)一直是生态学和保护生物学的中心问题, 且beta多样性的形成机制及其对环境的响应已成为当前生物多样性研究的热点问题。本文以西藏横断山区怒江和澜沧江两个流域入江溪流中的细菌为研究对象, 使用Baselga的beta多样性分解方法, 基于Sørensen相异性指数将细菌的beta多样性分解为周转(turnover)和嵌套(nestedness)两个组分, 探究了细菌beta多样性及其分解组分随海拔距离的分布模式, 并且衡量了环境、气候和空间因子的相对重要性。结果表明, 两个流域中细菌的群落结构显著不同。两个流域的细菌总beta多样性和周转组分随海拔距离的增加而增加, 周转组分占总beta多样性的比例较大。气候和环境因子是两个流域中细菌总beta多样性及周转过程的重要预测因子, 并且所有的显著因子均为正相关, 其中环境因子中相关性最高的为海拔距离(R ²= 0.408, P < 0.001), 而气候因子中相关性最高的为年均温差(R ²= 0.417, P < 0.001)。方差分解结果暗示嵌套组分主要受空间扩散的影响; 总beta多样性和周转组分在环境较恶劣的澜沧江主要受环境过滤的影响, 而在环境较温和的怒江主要受空间扩散和环境过滤的共同影响。此外, 较为恶劣的环境条件会增加细菌的总beta多样性和周转率, 并且会形成更强的环境筛选作用去影响细菌群落的物种组成。我们的研究表明对西藏横断山区水体细菌多样性的保护需要从整个流域入手, 而非少量的生物多样性热点地区。     

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。