From sampling stations to archipelagos: investigating aspects of the assemblage of insular biota

Aim To investigate the formation of nestedness and species co‐occurrence patterns at the local (sampling station), the intermediate (island group), and the archipelago scale. Location The study used data on the distribution of terrestrial isopods on 20 islands of the central Aegean (Greece). These islands are assigned to two distinct subgroups (Kyklades and Eastern islands). Methods The Nestedness Temperature Calculator was used to obtain nestedness values and maximally nested matrices, the EcoSim7 software and a modified version of Sanderson (2000 ) method were used for the analysis of species co‐occurrences. Idiosyncratic temperatures of species and the order of species placement in the maximally nested matrices were used for further comparisons among spatial scales. The relationships of nestedness values with beta‐diversity, habitat diversity and a number of ecological factors recorded for each sampling station were also investigated. Results Significant nestedness was found at all spatial scales. Levels of nestedness were not related to beta‐diversity or habitat diversity. Nestedness values were similar among spatial scales, but they were affected by matrix size. The species that contributed most to the nested patterns within single islands were not the same as those that produce nestedness at the archipelago scale. There was significant variation in the frequency of species occurrence among islands and among spatial scales. There was no direct effect of ecological factors on the shaping of patterns of nestedness within individual islands, but habitat heterogeneity was crucial for the existence of such patterns. Positive associations among species prevailed at all scales when species per station were considered, while negative associations prevailed in the species per island matrices. All associations resulted from the habitat structure of sampling stations and from particularities of geographical distributions. Conclusions There was no clear‐cut distinction between nestedness patterns among spatial scales, even though different species, and partially different factors, contributed to the formation of these patterns in each case. There was a core of species that contributed to the formation of nested patterns at all spatial scales, while the patterns of species associations suggested that biotic interactions are not an important causal factor. The results of this study suggest that locally rare species cannot be widespread at a higher spatial scale, while locally common species can have a restricted distribution.     

Relationship between horizontal pattern and vertical structure in a chalk grassland

Our objective was to examine the relationships between patterns of vertical structure and species microassociations at various scales in a species-rich chalk grassland.Transect data were analyzed for species microassociations by multiscale ordination of presence data from the Gerendal Nature Reserve, The Netherlands. Results indicated microassociations at scales of 30 cm, 2 m, 3 m, and 10 or more m. The microassociations at each scale comprised different but overlapping constellations of species.For the same transect, profile photographs were taken through the vegetation against a white background, and image analysis was used to provide indices of vegetation vertical cover, height and vertical center of biomass. Pattern analysis of these data indicated a regular pattern at a scale of 3 m.Standardized cross-variograms of the patterns of microassociations and vertical structure revealed only very weak relationships, even though one microassociation pattern and the vertical structure patterns occurred at the scale of 3 m. This is because the two 3 m patterns fell in and out of phase.Abbreviations PCA principal components analysis     

Scale‐dependence of vegetation‐environment relationships in semi‐natural grasslands

Questions: Which environmental and management factors determine plant species composition in semi‐natural grasslands within a local study area? Are vegetation and explanatory factors scale‐dependent? Location: Semi‐natural grasslands in Lærdal, Sognog Fjordane County, western Norway. Methods: We recorded plant species composition and explanatory variables in six grassland sites using a hierarchically nested sampling design with three levels: plots randomly placed within blocks selected within sites. We evaluated vegetation‐environment relationships at all three levels by means of DCA ordination and split‐plot GLM analyses. Results: The most important complex gradient determining variation in grassland species composition showed a broad‐scale relationship with management. Soil moisture conditions were related to vegetation variation on block scale, whereas element concentrations in the soil were significantly related to variation in species composition on all spatial scales. Our results show that vegetation‐environment relationships are dependent on the scale of observation. We suggest that scale‐related (and therefore methodological) issues may explain the wide range of vegetation‐environment relationships reported in the literature, for semi‐natural grassland in particular but also for other ecosystems. Conclusions: Interpretation of the variation in species composition of semi‐natural grasslands requires consideration of the spatial scales on which important environmental variables vary.     

Woody riparian plant distributions in western Oregon,USA: comparing landscape and local scale factors

We studied riparian forests along mountain streams in four large watersheds of western Oregon and far northern California, USA, to better understand the multiscale controls on woody riparian vegetation in a geographically complex region. In each of the four-study watersheds, we sampled woody riparian vegetation in161-ha sampling reaches that straddled the stream channel. Within each hectare, we sampled riparian vegetation and local environmental factors in 40 m² sampling plots arrayed along topographic transects. We also surveyed natural disturbance gaps in 6 ha in each watershed to explore the effects of fine scale disturbance on species distributions. We compared species composition across our study watersheds and used Nonmetric Multidimensional Scaling (NMS) and chi-squared analyses to compare the relative importance of landscape scale climate variables and local topographic and disturbance variables in explaining species distributions at sampling plot and hectare scales. We noted substantial turnover in the riparian flora across the region, with greatest numbers of unique species in watersheds at the ends of the regional gradient. In NMS ordinations at both scales, variation in woody riparian species composition showed strongest correlations with climatic variables and Rubus spectabilis cover, but the latter was only an important factor in the two northern watersheds. At the smaller scale, topographic variables were also important. Chi-squared analyses confirmed that more species showed landscape scale habitat preferences (watershed associations) than associations with topographic position (94.7% vs. 42.7% of species tested) or gap versus forest setting (94.7% vs. 24.6% of species tested). The woody riparian flora of western Oregon shows important biogeographic variation; species distributions showed strong associations with climatic variables, which were the primary correlates of compositional change between riparian sites at both scales analyzed. Additional local variation in composition was explained by measures of topography and disturbance.     

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     

Canonical Correspondence Analysis with variation partitioning: some comments and an application

Abstract. This study presents an alternative treatment of data from a comprehensive vegetation study in which the main gradient structure of boreal coniferous forest vegetation in southern Norway was investigated by ordination techniques. The data sets include vegetation samples of different plot sizes, supplied with measurements of 33 environmental explanatory variables (classified in four groups) and nine spatial explanatory variables derived from geographical coordinates. Partitioning the variation of the species-sample plot matrices on different sets of explanatory variables is performed by use of (partial) Canonical Correspondence Analysis. Several aspects of vegetation-environment relationships in the investigation area are discussed on the basis of results obtained by the new method. Generally, ca. 35% of the variation in species abundances are explained by environmental and spatial variables. The results indicate support for the hypothesis of macro-scale topographic control over the differentiation of the vegetation, more strongly so in pine than in spruce forest where soil nutrients play a major role. Towards finer scales, the primary topographical and topographically dependent factors lose importance, and vegetational differentiation is more strongly affected by the accumulated effects of the vegetation (including the tree stand) on soils, shading, litter fall, etc. The fraction of variation in species abundance explained by significant environmental variables was found to be ca. twice as large as the fraction explained by spatial variables. The fraction of variation explained by the supplied variables differed between data sets; it was lower for cryptogams than for vascular plants, and lower for smaller than for larger sample plots. Possible reasons for these patterns are discussed. Some methodological aspects of CCA with variation partitioning are discussed: improvements, necessary precautions, and the advantages over alternative methods.     

A multiscale analysis of herbaceous species richness in a Mediterranean ecosystem

Aims Studies of species distribution patterns traditionally have been conducted at a single scale, often overlooking species–environment relationships operating at finer or coarser scales. Testing diversity-related hypotheses at multiple scales requires a robust sampling design that is nested across scales. Our chief motivation in this study was to quantify the contributions of different predictors of herbaceous species richness at a range of local scales.Methods Here, we develop a hierarchically nested sampling design that is balanced across scales, in order to study the role of several environmental factors in determining herbaceous species distribution at various scales simultaneously. We focus on the impact of woody vegetation, a relatively unexplored factor, as well as that of soil and topography. Light detection and ranging (LiDAR) imaging enabled precise characterization of the 3D structure of the woody vegetation, while acoustic spectrophotometry allowed a particularly high-resolution mapping of soil CaCO 3 and organic matter contents.Important findings We found that woody vegetation was the dominant explanatory variable at all three scales (10, 100 and 1000 m 2), accounting for more than 60% of the total explained variance. In addition, we found that the species richness–environment relationship was scale dependent. Many studies that explicitly address the issue of scale do so by comparing local and regional scales. Our results show that efforts to conserve plant communities should take into account scale dependence when analyzing species richness–environment relationships, even at much finer resolutions than local vs. regional. In addition, conserving heterogeneity in woody vegetation structure at multiple scales is a key to conserving diverse herbaceous communities.     

The evaluation of multi-species pattern

Abstract. Using artificial data, techniques that have been proposed for analyzing multiple species pattern in vegetation, are compared. No single method was capable of detecting, clearly and unambiguously, all scales of pattern in all cases, and the effectiveness of the different methods was found to depend on the scales of pattern of the component species and on how the patterns of individual species are combined. Some improvements for the application of the popular multi-scale ordination method are suggested. Several sets of field data are analyzed and the results used to illustrate a discussion of the existence and nature of multi-species pattern in vegetation and how it is to be evaluated.     

Classification of macroinvertebrate communities across drainage basins in Victoria, Australia: consequences of sampling on a broad spatial scale for predictive modelling

1. Spatial scale may influence the interpretation of environmental gradients that underlie classification and ordination analyses of lotic macroinvertebrate communities. This could have important consequences for the spatial scale over which predictive models derived from these multivariate analyses can be applied. 2. Macroinvertebrate community data (identified to genus or species) from edge and main-channel habitats were obtained for sites on rivers from 25 of the 29 drainage basins in Victoria. Trends in community similarity were analysed by carrying out separate multivariate analyses on data from the edge habitats (199 sites) and the main-channel habitats (163 sites). 3. Hierarchical classification (UPGMA) showed that the edge data could be placed into 11 site groups and the main-channel data into 12 site groups. 4. Ordination analysis (hybrid multidimensional scaling) showed no sharp disjunctions between site groups in either habitat; overlap was frequent. Correlation of the ordination patterns with environmental variables showed that edge communities varied longitudinally within a drainage basin and from the east to the west of Victoria. These two trends were superimposed on one another to form a single gradient on the ordination. The taxon richness of edge communities was also related to the species richness of macrophytes at a site. Main-channel communities also displayed a longitudinal and a geographic gradient, but these two gradients were uncorrelated on the ordination. 5. Community similarity only weakly reflected geographic proximity in either habitat. A preliminary subdivision of Victoria into a series of biogeographic regions did not match the pattern of distribution of site groups for the edge habitat, illustrating the difficulties of applying to lotic communities a priori regionalizations based on terrestrial features of the landscape. 6. The longitudinal gradients in the two data sets were commonly observed in data gathered at smaller spatial scales in Victoria. The other gradients (geographic, macrophyte), however, were either not consistently repeated or not evident at smaller spatial scales. At small spatial scales (i.e. within a single drainage basin) gradients were related to variables that varied over restricted ranges, e.g. mean particle size of the substratum. 7. Species richness was very variable when plotted against river slope or distance of site from source; both of these are measures of position on the longitudinal gradients. In contrast to suggestions in the literature, species richness did not show a unimodal trend on these gradients, or any other trend. 8. Environmental gradients (apart from longitudinal gradients) that underlie predictive models of macroinvertebrate distribution are reflections of the spatial scale on which the model has been constructed and cannot be extrapolated to different scales. Models must be suited to the spatial scale over which predictions are required.     

Patterns in species associations in plant communities: the importance of scale

Abstract. Present discussions on competitive interactions and the occurrence of predictable patterns in species composition – including assembly rules – are likely to benefit from appropriate analyses of the spatial structure in plant communities. We suggest such an analysis when we specifically want to detect scale regions where fine-scale local processes may affect the spatial pattern of species composition. We combine indirect ordination in the form of Detrended Correspondence Analysis (DCA) and geostatistics in the form of variography. The species abundance data in the sampled quadrats are summarized as positions on the axes in the ordination. Each axis is used as a regionalized variable in the variography to obtain the spatial dependence of the quadrats. The spatial pattern found will suggest the relevant scale region in which to perform an analysis of species associations. A significant spatial dependence (the ‘range’ in geostatistical jargon) will define the size of a sampling plot that will minimize both the problem of being too small and thus having the risk of oversampling of e.g. clonal individuals and of being too large which will risk including individuals that do not interact. We also suggest that plots are spaced at least a ‘range’ apart to insure spatial and statistical independence. Comparisons of species compositions in such plots will reveal any positive or negative associations between species on a scale where these should reflect species-species interactions. To illustrate the method it is applied to three different data sets from two different plant communities.     

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

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

大尺度生物多样性评价

如何在减小采样付出和获取真实信息之间取得平衡,是大尺度生物多样性评价的一个主要问题。本文首先给出了大尺度评价的定义,然后回顾其历史并指出了其中存在的问题。接着从采样空间策略、代理物种、评价指标、快速评价技术、遥感技术5个方面对评价设计方法学作了总结,最后引入了多尺度生物多样性评价体系,作为解决问题的思路。该体系要求在一系列空间尺度上对生物多样性的特征值进行采样和计算,其核心是中间尺度的构建,可以采用自底向上和自顶向下两种思路构建中间尺度。对于多样性的保育来说,中间尺度至关重要,生物多样性的评价、管理规划和实践的整合需要在中间尺度上进行。大尺度生物多样性变化可以作为整合的背景,而小尺度是保育管理行动的合适尺度。     

Geomorphic predictors of riparian vegetation in small mountain watersheds

Aims Hydrogeomorphic processes operating at watershed, process zone and site scales influence the distribution of riparian vegetation. However, most studies examining the relationships between hydrogeomorphic processes and riparian vegetation are conducted at site scales. We quantified the relative importance of watershed, process zone and site geomorphic characteristics for predicting riparian plant community types and plant species abundances in four small mountain watersheds in central Nevada, USA.Methods We mapped riparian vegetation types and identified process zones (based on dominant geomorphic process and valley fill material) within the watersheds. We sampled sites in each combination of vegetation type and process zone (n = 184 sites) and collected data on watershed scale factors, valley and stream geomorphic characteristics and on plant cover of each geomorphic surface. Plant community types were defined by cluster and indicator species analyses of plant cover data, and related to geomorphic variables using ordination analysis (nonmetric multidimensional scaling). Linear mixed effects models were used to predict abundances of indicator species.Important findings Variables describing position in the watershed (elevation, contributing area) that are related to gradients of temperature, moisture and stream discharge were of primary importance in predicting plant community types. Variables describing local geomorphic setting (valley width, stream gradient, channel sediments, geomorphic surface height) were of secondary importance, but accurately described the geomorphic setting of indicator species. The process zone classification did not include position in the watershed or channel characteristics and only predicted plant community types with unique geomorphic settings. In small mountain watersheds, predicting riparian vegetation distribution requires explicit consideration of scale and geomorphic context within and among watersheds in addition to site variables.     

Finding essential scales of spatial variation in ecological data: a multivariate approach

The identification of spatial structures is a key step in understanding the ecological processes structuring the distribution of organisms. Spatial patterns in species distributions result from a combination of several processes occuring at different scales: identifying these scales is thus a crucial issue. Recent studies have proposed a new family of spatial predictors (PCNM: principal coordinates of neighbours matrices; MEMs: Moran's eigenvectors maps) that allow for modelling of spatial variation on different scales. To assess the multi-scale spatial patterns in multivariate data, these variables are often used as predictors in constrained ordination methods. However, the selection of the appropriate spatial predictors is still troublesome, and the identification of the main scales of spatial variation remains an open question. This paper presents a new statistical tool to tackle this issue: the multi-scale pattern analysis (MSPA). This ordination method uses MEMs to decompose ecological variability into several spatial scales and then summarizes this decomposition using graphical representations. A canonical form of MSPA can also be used to assess the spatial scales of the species-environment relationships. MSPA is compared to constrained ordination using simulated data, and illustrated using the famous oribatid mites dataset. The method is implemented in the free software R.     

Habitat associations of an insular Wallacean avifauna: A multi-scale approach for biodiversity proxies

The endemic avifauna of Wallacea is of high conservation significance, but remains poorly studied. Identifying priority conservation areas requires a greater understanding of the habitat associations of these bird communities, and of how spatial scale of analysis can influence the interpretation of these associations. This study aims to determine which proxy habitat measures, at which spatial scales of analysis, can provide useful inferential data on the composition of Wallacean forest avifauna. Research was conducted within the Lambusango forest reserve, South-East Sulawesi, using point count surveys to sample avifauna. Habitat properties were characterised in three ways: broad classification of forest type, canopy remotely-sensed response derived from satellite imagery, and in situ measures of vegetation composition and structure. Furthermore, we examined avifauna–habitat relationships at three spatial scales: area (c.400 ha per sample site), transect (c.10 ha) and point (c.0.2 ha). Results demonstrate that broad forest type classifications at an area scale can help to determine conservation value, indicating that primary and old secondary forests are important for supporting many species with lower ecological tolerances, such as large-bodied frugivores. At the transect-scale, significant congruence occurs between bird community composition and several habitat variables derived from vegetation sampling and satellite imagery, particularly tree size, undergrowth density, and Normalised Difference Vegetation Index (NDVI) values; this highlights the importance small scale habitat associations can have on determining α-diversity. Analysis at the point-scale was ineffective in providing proxy indications for avifauna. These findings should be considered when determining future priority conservation areas for Wallacean avifauna.     

Fractal geometry: a tool for describing spatial patterns of plant communities

Vegetation is a fractal because it exhibits variation over a continuum of scales. The spatial structure of sandrim, bryophyte, pocosin, suburban lawn, forest tree, and forest understory communities was analyzed with a combination of ordination and geostatistical methods. The results either suggest appropriate quadrat sizes and spacings for vegetation research, or they reveal that a sampling design compatible with classical statistics is impossible. The fractal dimensions obtained from these analyses are generally close to 2, implying weak spatial dependence. The fractal dimension is not a constant function of scale, implying that patterns of spatial variation at one scale cannot be extrapolated to other scales.     

Community pattern analysis: A method for quantifying community mosaic structure

A method of quantifying community spatial patterns, community pattern analysis, is described. It is proposed that ordination analysis is used to obtain an integrated score for each quadrat from transect data. For the data presented here, separate ordinations were made of both floristic and environmental (soils) data. The ordination axis scores are then analysed using two or three-term local variance analysis to quantify the scales of community pattern. Correlation analyses allow the relationship between the vegetation and soils data (as represented by ordination axis scores), and other environmental data to be investigated at defined scales. The advantages of this method, that employs the joint application of conventional methods, are that it includes the influence of all species in the analysis, and that multiple uncorrelated scales of pattern within a community are identified.     

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。     

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。     

以林相图为基础的不同取样方式对植被排序分析结果的影响

以6种不同方式对林相图中同一样带取样,采用CA、DCA和CCA 3种排序方法,研究了取样方式对排序轴的解释效果、物种与环境因子、环境因子之间、环境因子与坐标轴之间关系的影响.结果表明,样方大小和形状的变化在不同程度上改变了排序结果.大样方和长方形样方都增强了排序轴的解释效果,并对双序图中稀有种、独特种的位置有较大的影响;环境因子中土壤因子对样方的大小和形状都很敏感,坡度、经纬度只对样方大小敏感,坡位、海拔、温度和降水则对样方形状敏感;随着样方面积的增加,海拔、温度和降水的作用降低,而坡向的作用增加.