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.     

Spatial patterns of plant association in grazed and ungrazed shrublands in the semi‐arid Karoo,South Africa

Abstract. The investigation of vegetation pattern and plant association by spatial statistics has become increasingly popular among plant ecologists. Recently, Individual‐centered analysis (ICA) has been introduced as a new tool for analysis of multi‐species co‐occurrence patterns. We tested this new technique by applying it to spatial data from grazed and ungrazed shrub communities in the semi‐arid Great Karoo, South Africa. There were substantial but complex and scale‐dependent differences in pattern between grazed and ungrazed vegetation. Unpalatable species that increase in abundance in grazed vegetation possibly play a key role in the change of vegetation pattern. At small scales we found indications of aggregation (< 30 cm) at the ungrazed, but of repulsion (30 – 40 cm) at the grazed site. An additional non‐random pattern at 60 – 170 cm at the grazed site was probably due to the clumped distributions of some species on broader scales. We show that the interpretability of ICA results is improved when the actual observed and expected frequencies of species combinations are added to the program output. The main strength of ICA is that it has the potential to detect association patterns that involve more than two species.     

Two-step vegetation analysis based on very large data sets

A two-step method for the classification of very large phytosociological data sets is demonstrated. Stratification of the set is suggested either by area in the case of a large and geographically heterogeneous region, or by vegetation type in the case of a set covering all the plant communities of an area. First, cluster analysis is performed on each subset. The resulting basic clusters are summarized by calculating a ‘synoptic coverabundance value’ for each species in each cluster. All basic clusters are then subjected to the same procedure. Second order clusters are interpreted as community types. The synoptic value proposed reflects both frequency and average cover-abundance. It is emphasized that a species should have a high frequency to be used as a diagnostic species. The method is demonstrated with a set of 1138 relevés and 250 species of coastal sand dune vegetation in Yucatan treated with the programs TWINSPAN and TABORD. Some problems and perspectives of the approach are discussed in the light of hierarchy theory and classification theory.     

红松存在/不存在数据的多尺度空间分布格局

红松属小兴安岭地区地带性植被优势种,该地区也是其分布的北缘。在景观尺度上开展红松的分布格局研究有利于进一步了解红松分布机理、未来迁移过程等问题,对其经营和保护有重要意义。将景观指数法与点格局分析法结合,设定8个空间尺度,利用红松存在/不存在数据,通过计算各空间尺度上红松聚集程度和景观指数,分析小兴安岭地区红松种群在多尺度上的分布格局。研究结果表明,小尺度上红松聚集分布明显,随机分布区多处于其聚集分布区的边缘,均匀分布区则散布在其聚集分布区内。景观指数研究表明,通过景观指数可判断红松聚集分布格局趋势,而不能判断均匀分布、随机分布格局趋势,因为它们在多尺度下景观指数波动大,不能用景观指数来描述分布格局。研究得出如下结论:1)红松主要分布在其分布区的核心区域内,在分布区边缘和过渡带上呈随机分布,2)存在/不存在数据能够用来分析种群的多尺度空间分布格局,3)空间尺度的变化会引起树种分布格局的变化,随机分布随尺度增加,边缘化程度加强,4)单一尺度上,景观格局指数不能完全描述种群分布格局;而在多尺度上,变化趋势稳定的景观指数表明聚集分布存在,而波动剧烈的景观指数常与随机分布和均匀分布联系在一起,5)地形因子中,红松对坡度和海拔两个因子变化敏感。     

Clade composition of a plant community indicates its phylogenetic diversity

Phylogenetic diversity quantification is based on indices computed from phylogenetic distances among species, which are derived from phylogenetic trees. This approach requires phylogenetic expertise and available molecular data, or a fully sampled synthesis‐based phylogeny. Here, we propose and evaluate a simpler alternative approach based on taxonomic coding. We developed metrics, the clade indices, based on information about clade proportions in communities and species richness of a community or a clade, which do not require phylogenies. Using vegetation records from herbaceous plots from Central Europe and simulated vegetation plots based on a megaphylogeny of vascular plants, we examined fit accuracy of our proposed indices for all dimensions of phylogenetic diversity (richness, divergence, and regularity). For real vegetation data, the clade indices fitted phylogeny‐based metrics very accurately (explanatory power was usually higher than 80% for phylogenetic richness, almost always higher than 90% for phylogenetic divergence, and often higher than 70% for phylogenetic regularity). For phylogenetic regularity, fit accuracy was habitat and species richness dependent. For phylogenetic richness and divergence, the clade indices performed consistently. In simulated datasets, fit accuracy of all clade indices increased with increasing species richness, suggesting better precision in species‐rich habitats and at larger spatial scales. Fit accuracy for phylogenetic divergence and regularity was unreliable at large phylogenetic scales, suggesting inadvisability of our method in habitats including many distantly related lineages. The clade indices are promising alternative measures for all projects with a phylogenetic framework, which can trade‐off a little precision for a significant speed‐up and simplification, such as macroecological analyses or where phylogenetic data is incomplete.     

Multiscale ordination: a method for detecting pattern at several seales

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

Characterization of spatial scaling relationships between vegetation pattern and topography at different directions in Gurbantunggut desert,China

Vegetation striped pattern is a common feature in semiarid and arid landscapes, which is seen as mosaics including vegetated and non-vegetated patches. Identifying scales of pattern in ecological systems and referring patterns to multi-scaled processes that create them are ongoing challenges. The aim of this paper is to study the vegetation patterns and their across-scale relationships between the vegetation and anisotropic topography (W–E and N–S) in 12 transects at Gurbantunggut desert. We used wavelet-based across-scale analysis for extracting information on scales of pattern for those transect data, evaluating their inherent structure, and inferring characteristics of the processes that imposed those patterns at across scales. The results show that, in W–E direction, the scales of vegetation pattern (C. ewersmanniana is at the scale 40 m, H. ammodendron, at 35 m) correspond to the dune ridge/dune valley sequences (appearing at distance of 40 m), and vegetation on mesoscale and large scale are significant cross-scale correlation with topography on mesoscale and large scale in all W–E transects. In N–S direction, there is an irregular pattern of vegetation along the N–S irregular topography, and no unified cross-scale relationships between topography and vegetation on different scales in different transects. Moreover, cross-scale correlation analysis between topography and vegetation provides further detail on hierarchical structure and specific scales in space that strongly influenced the larger patterns. Knowledge of the cross-scale relationships between topography and vegetation could lead to better understanding and management of biological resources in that region.     

A new measure of fidelity and its application to defining species groups

Abstract. The first objective of this paper is to define a new measure of fidelity of a species to a vegetation unit, called u. The value of u is derived from the approximation of the binomial or the hypergeometric distribution by the normal distribution. It is shown that the properties of u meet the requirements for a fidelity measure in vegetation science, i.e. (1) to reflect differences of a species’relative frequency inside a certain vegetation unit and its relative frequency in the remainder of the data set; (2) to increase with increasing size of the data set. Additionally (3), u has the property to be dependent on the proportion of the vegetation unit's size to the size of the whole data set. The second objective is to present a method of how to use the value of u for finding species groups in large data bases and for defining vegetation units. A species group is defined by possession of species that show the highest value of u among all species in the data set with regard to the vegetation unit defined by this species group. The vegetation unit is defined as comprising all relevés that include a minimum number of the species in the species group. This minimum number is derived statistically in such a way that fewer relevés always belong to a species group than would be expected if the differential species were distributed randomly among the relevés. An iterative algorithm is described for detecting species groups in data bases. Starting with an initial species group, species composition of this group and the vegetation unit defined by this group are mutually optimized. With this algorithm species groups are formed in a data set independently of each other. Subsequently, these species groups can be combined in such a way that they are suited to define commonly known syntaxa a posteriori.     

黄河三角洲植被指数与地形要素的多尺度分析

结合地理信息系统和遥感技术, 研究了黄河三角洲植被指数NDVI与一系列地形要素间的尺度依赖关系, 从而检验在较大尺度上滨海生态系统植被分布格局是否存在水分再分配的调控作用。结果表明: 1)NDVI值在4种主要群落类型间差异显著, 这种显著差异是由滨海盐生植物的生境特点决定的; 2)地表高程在所有的10个粒度尺度上均与NDVI相关关系显著, 这种显著关系在小尺度上与地下水埋深有关, 而在大尺度上可能参与水分再分配过程; 3)在750 m粒度尺度附近存在水分再分配的调控作用, 在该尺度附近地形湿润度指数(TWI)和坡度与NDVI相关达到极显著, 而且其Moran’sI指数突然增大。黄河三角洲的植被地形关系表现为不同尺度上对土壤水分和盐分的调控, 在小尺度上地形因素通过土壤表面蒸发过程影响土壤水分与盐分, 而在大尺度上地形因素主要通过地表径流对土壤水分与盐分进行再分配。     

A phytosociological system based on multi-scaled pattern analysis: a first example

The aquatic and riparian vegetation of a small Belgian stream is used as a first example for a phytosociological system based on multi-scaled pattern analysis. Surveying is continuous, the stream being divided into ecologically homogeneous sections. Some sections were sampled meter by meter. A pattern analysis of the whole stream and of some of its reaches was then performed. This defined the preferential scales of analysis. The multivariate analyses were carried out on several scales, but on any one scale, only the species structured in the analysis are considered. The vegetation is described. Some concrete and theoretical points are discussed.     

科尔沁沙地植被恢复中差不嘎蒿种群不同龄级个体的点格局分析

差不嘎蒿(Artemisia halodendron)主要分布在呼伦贝尔沙地和科尔沁沙地的流动和半固定沙丘上, 是良好的乡土固沙半灌木, 也是退化沙地固定和植被恢复过程中的建群种。点格局分析方法是20世纪末发展起来的多尺度空间格局分析方法。通过对差不嘎蒿种群的点格局分析, 发现差不嘎蒿幼体的空间格局多为集群分布, 在各个尺度上都极为显著, 而随着差不嘎蒿龄级的增加, 其空间分布也逐渐显现为随机分布。差不嘎蒿相邻龄级的空间关系差异不显著, 而间隔龄级间则呈空间负相关。这与其幼体聚集, 成体随机分布的空间格局相一致。     

科尔沁沙地植被恢复中差不嘎蒿种群不同龄级个体的点格局分析

差不嘎蒿（Artemisia halodendron）主要分布在呼伦贝尔沙地和科尔沁沙地的流动和半固定沙丘上,是良好的乡土固沙半灌木,也是退化沙地固定和植被恢复过程中的建群种。点格局分析方法是20世纪末发展起来的多尺度空间格局分析方法。通过对差不嘎蒿种群的点格局分析,发现差不嘎蒿幼体的空间格局多为集群分布,在各个尺度上都极为显著,而随着差不嘎蒿龄级的增加,其空间分布也逐渐显现为随机分布。差不嘎蒿相邻龄级的空间关系差异不显著,而间隔龄级间则呈空间负相关。这与其幼体聚集,成体随机分布的空间格局相一致。     

Scale dependence of vegetation-environment correlations: A case study of a North Carolina piedmont woodland

Abstract. Vegetation and its correlation with environment has been traditionally studied at a single scale of observation. If different ecological processes are dominant at different spatial and temporal scales, the results obtained from such observations will be specific to the single scale of observation employed and will lack generality. Consequently, it is important to assess whether the processes that determine community structure and function are similar at different scales, or whether, how rapidly, and under what circumstances the dominant processes change with scale of observation. Indeed, early work by Greig-Smith and associates (Greig-Smith 1952; Austin & Greig-Smith 1968; see Greig-Smith 1979; Kershaw & Looney 1985; Austin & Nicholls 1988) suggested that plant-plant interactions are typically important at small scales, but that the physical environment dominates at large scales. Using a gridded and mapped 6.6 ha portion of the Duke Forest on the North Carolina piedmont for a case study, we examined the importance of scale in vegetation studies by testing four hypotheses. First, we hypothesized that the correlation between vegetation composition and environment should increase with increasing grain (quadrat) size. Our results support this hypothesis. Second, we hypothesized that the environmental factors most highly correlated with species composition should be similar at all grain sizes within the 6.6-ha study area, and should be among the environmental factors strongly correlated with species composition over the much larger extent of the ca. 3500 ha Duke Forest. Our data are not consistent with either portion of this hypothesis. Third, we hypothesized that at the smaller grain sizes employed in this study (< 256 m²), the composition of the tree canopy should contribute significantly to the vegetation pattern in the under-story. Our results do not support this hypothesis. Finally, we predicted that with increased extent of sampling, the correlation between environment and vegetation should increase. Our data suggest the opposite may be true. This study confirms that results of vegetation analyses can depend greatly on the grain and extent of the samples employed. Whenever possible, sampling should include a variety of grain sizes and a carefully selected sample extent so as to ensure that the results obtained are robust. Application of the methods used here to a variety of vegetation types could lead to a better understanding of whether different ecological processes typically dominate at different spatial scales.     

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     

The Historic Square Foot Dataset – Outstanding small-scale richness in Swiss grasslands around the year 1900

Grasslands host a significant share of Europe's species diversity but are among the most threatened vegetation types of the continent. Resurvey studies can help to understand patterns and drivers of changes in grassland diversity and species composition. However, most resurveys are based on local or regional data, and hardly reach back more than eight decades. Here, we publish and describe the Historic Square Foot Dataset, comprising 580 0.09-m² and 43 1-m² vegetation plots carefully sampled between 1884 and 1931, covering a wide range of grassland types across Switzerland. We provide the plots as an open-access data set with coordinates, relocation accuracy and fractional aboveground biomass per vascular plant species. We assigned EUNIS habitat types to most plots. Mean vascular plant species richness in 0.09 m² was 19.7, with a maximum of 47. This is considerably more than the present-day world record of 43 species for this plot size. Historically, species richness did not vary with elevation, differing from the unimodal relationship found today. The data set provides unique insight into how grasslands in Central Europe looked more than 100 years ago, thus offering manifold options for studies on the development of grassland biodiversity and productivity.     

毛乌素沙地油蒿种群点格局分析

油蒿(Artemisia ordosica)是我国北方农牧交错带的重要固沙植物,研究其种群格局对理解种群生态过程和改善流沙治理技术具有重要意义。点格局分析法是20世纪末发展起来的多尺度空间格局分析方法。通过研究油蒿种群的点格局,发现油蒿种群的空间分布格局和空间关联性同空间尺度、植株形体大小以及生境3种因素有密切联系。在较小的空间尺度上,油蒿种群倾向于非随机分布(集群分布比均匀分布常见),个体间有较强的空间关联(正关联比负关联常见);当空间尺度大于临界值后,油蒿种群倾向于服从随机分布,同时种群的空间关联性减弱。幼小油蒿植株具有明显的集群分布趋势,高大植株则表现出聚集强度的降低趋势;形体大小的差异越大,植株间的正关联关系越弱,或者负关联关系越强。与固定沙地相比,半固定沙地油蒿种群的集群分布现象更加明显,同时种群的空间正关联关系更强。研究结果表明,当通过移栽油蒿成体治理流动沙地时,应尽量将其栽种成集群分布而非均匀分布的形式,以提高植株成活率。     

Species groups can be transferred across different scales

Aim To test whether species groups (i.e. assemblages of species co‐occurring in nature) that are statistically derived at one scale (broad, medium, or fine scale) can be transferred to another scale, and to identify the driving forces that determine species groups at the various scales. Location Northern Bohemia (Czech Republic, central Europe) in the Je?tědský h?bet mountain range and its neighbourhood. Methods Three data sets were sampled: a floristic data set at the broad scale, another floristic data set at the intermediate scale, and a vegetation data set at the habitat scale. First, in each data set, species groups were produced by the COCKTAIL algorithm, which ensures maximized joint occurrence in the data set using a fidelity coefficient. Corresponding species groups were produced in the individual data sets by employing the same species for starting the algorithm. Second, the species groups formed in one data set, i.e. at a particular scale, were applied crosswise to the other data sets, i.e. to the other scales. Correspondence of a species group formed at a particular scale with a species group at another scale was determined. Third, to highlight the driving factors for the distribution of the plant species groups at each scale, canonical correspondence analysis was carried out. Results Twelve species groups were used to analyse the transferability of the groups across the three scales, but only six of them were found to be common to all scales. Correspondence of species groups derived from the finest scale with those derived at the broadest scale was, on average, higher than in the opposite direction. Forest (tree layer) cover, altitude and bedrock type explained most of the variability in canonical correspondence analysis across all scales. Main conclusions Transferability of species groups distinguished at a fine scale to broader scales is better than it is in the opposite direction. Therefore, a possible application of the results is to use species groups to predict the potential occurrence of missing species in broad‐scale floristic surveys from fine‐scale vegetation‐plot data.     

A dynamic multi‐scale occupancy model to estimate temporal dynamics and hierarchical habitat use for nomadic species

Distribution models are increasingly being used to understand how landscape and climatic changes are affecting the processes driving spatial and temporal distributions of plants and animals. However, many modeling efforts ignore the dynamic processes that drive distributional patterns at different scales, which may result in misleading inference about the factors influencing species distributions. Current occupancy models allow estimation of occupancy at different scales and, separately, estimation of immigration and emigration. However, joint estimation of local extinction, colonization, and occupancy within a multi‐scale model is currently unpublished. We extended multi‐scale models to account for the dynamic processes governing species distributions, while concurrently modeling local‐scale availability. We fit the model to data for lark buntings and chestnut‐collared longspurs in the Great Plains, USA, collected under the Integrated Monitoring in Bird Conservation Regions program. We investigate how the amount of grassland and shrubland and annual vegetation conditions affect bird occupancy dynamics and local vegetation structure affects fine‐scale occupancy. Buntings were prevalent and longspurs rare in our study area, but both species were locally prevalent when present. Buntings colonized sites with preferred habitat configurations, longspurs colonized a wider range of landscape conditions, and site persistence of both was higher at sites with greener vegetation. Turnover rates were high for both species, quantifying the nomadic behavior of the species. Our model allows researchers to jointly investigate temporal dynamics of species distributions and hierarchical habitat use. Our results indicate that grassland birds respond to different covariates at landscape and local scales suggesting different conservation goals at each scale. High turnover rates of these species highlight the need to account for the dynamics of nomadic species, and our model can help inform how to coordinate management efforts to provide appropriate habitat configurations at the landscape scale and provide habitat targets for local managers.     

Diversity and productivity of plant communities across the Inland Northwest,USA

No definitive explanation for the form of the relationship between species diversity and ecosystem productivity exists nor is there agreement on the mechanisms linking diversity and productivity across scales. Here, we examine changes in the form of the diversity–productivity relationship within and across the plant communities at three observational scales: plots, alliances, and physiognomic vegetation types (PVTs). Vascular plant richness data are from 4,760 20 m² vegetation field plots. Productivity estimates in grams carbon per square meter are from annual net primary productivity (ANPP) models. Analyses with generalized linear models confirm scale dependence in the species diversity–productivity relationship. At the plot focus, the observed diversity–productivity relationship was weak. When plot data were aggregated to a focus of vegetation alliances, a hump-shaped relationship was observed. Species turnover among plots cannot explain the observed hump-shaped relationship at the alliance focus because we used mean plot richness across plots as our index of species richness for alliances and PVTs. The sorting of alliances along the productivity gradient appears to follow regional patterns of moisture availability, with alliances that occupy dry environments occurring within the increasing phase of the hump-shaped pattern, alliances that occupy mesic to hydric environments occurring near the top or in the decreasing phase of the curve, and alliances that occupy the wettest environments having the fewest species and the highest ANPP. This pattern is consistent with the intermediate productivity theory but appears to be inconsistent with the predictions of water–energy theory.     

Considerations for ecological reconstruction of historic vegetation: Analysis of the spatial uncertainties in the California Vegetation Type Map dataset

Historical ecological data are valuable for reconstructing early environmental and vegetation community conditions and examining change to vegetation communities and disturbance regimes over decadal and longer temporal scales, but these data are not free from error. We examine the spatial uncertainties associated with 18,000 vegetation plots in the decades-old California Vegetation Type Mapping (VTM) dataset that has been digitized for use in modern ecological analysis. We examine the relationship between plot location error and basemap year, basemap scale, plot elevation, plot slope, and general plot habitat type. Bivariate plots and classification and regression tree analysis (CART) confirm that basemap scale and age are the strongest explanation of total error. Total error in spatial location for all plots ranged from 126.9 m to 462.3 m; plots drawn on 15-min (1:62,500-scale) basemaps had total error ranging from 126 m to 199.7 m, and plots drawn on coarser-scale basemaps (1:125,000-scale) had total errors ranging from 241 m to 461.2 m. Relocation of individual VTM plots is considerably easier for plots originally marked on 1:62,500-scale maps produced after 1904, and more difficult for plots originally marked on 1:125,000-scale maps produced before 1898. Biogeographical analyses that rely less on relocating individual plots, such as environmental niche modeling or multivariate analyses can alleviate some of these concerns, but all researchers using these kinds of data need to consider errors in spatial location of plots. The paper also discusses ways in which the differing spatial error might be reported and visualized by those using the dataset, and how the data might be used in modern environmental niche models.