Plant composition associated with environmental gradients in tropical montane forests (Cueva de Los Guacharos National Park,Huila, Colombia)

Niche differentiation among tropical forest plants can generate species turnover along gradients of soil, topography, climate, and land use history. In this study we explore the relative importance of these variables as drivers of floristic composition in Cueva de Los Guacharos National Park. We established twenty 0.1‐ha plots, within which trees, lianas, and shrubs (diameter ≥ 2.5 cm) were censused. We selected plot locations in primary and disturbed forests, and we measured topography and soil variables. Despite their structural similarity, primary and disturbed forests differed floristically, and also differed in environmental variables measured. A NMDS ordination showed that variation in the floristic composition across plots is highly correlated to the exchangeable acidity, elevation, temperature, and magnesium availability. Variance partitioning analysis shows that together spatial and environmental variables explain 24.2 percent of the variation in species composition. ‘Pure environmental’ variables were more important in explaining compositional variability than ‘pure spatial’ processes (9.8% and 1.4%, respectively). Residual variance may be attributed to stochastic process or non‐measured biotic effects.     

Analyzing floristic inventories with multiple maps

Spatial observations of plant occurrences contain a wealth of information on relations among species and on the relation between species and environmental conditions. Typically, inventory data of this kind are large co-occurrence matrices, and hence, direct ecological interpretations based on expert knowledge are often very difficult. Hitherto, ordination approaches have been used to construct a virtual ordination space (represented as one or multiple scatter plots) in which species that often co-occur are situated close together, whereas species that hardly co-occur are found far apart. In this study, we investigate a recently proposed ordination approach, multiple maps t-SNE, that constructs multiple, independent ordination spaces in order to reveal and visualize complementary structure in the data. We compare multiple maps t-SNE to several conventional ordination approaches, exploring a large inventory of vascular plant occurrences (FLORKART). Our results reveal that multiple maps t-SNE is well suited for the analysis of floristic inventories. In particular, multiple maps t-SNE uncovers the major dependencies of species co-occurrences on climate and soil biogeochemical preconditions.     

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.     

Mapping gradual landscape-scale floristic changes in Amazonian primary rain forests by combining ordination and remote sensing

Aim We present a new method to economically map gradual changes in plant species composition in lowland rain forests using field data and satellite images. Such a method will be a useful tool in planning the sustainable use and conservation of Amazonian rain forests. Location The study covered an area of c. 700 km² of primary rain forest in Amazonian Ecuador. Methods We field inventoried the species composition of pteridophytes and Melastomataceae in 340 inventory plots (5 m × 50 m), described the prevailing topography and analysed soil cation concentration and texture. We used non‐metric multidimensional scaling (NMDS) to summarize the floristic variation among the inventory plots in three ordination dimensions. The scores of the three ordination axes were predicted to non‐visited places using a Landsat TM (thematic mapper) satellite image and the k nearest neighbours (knn) estimation method. To avoid extrapolation, we excluded from the analysis those pixel windows whose spectral values were not represented in the areas covered by field sampling. The accuracy of the predictions was evaluated by cross‐validation and by comparing the predictions based on spectrally nearest neighbours to the predictions based on random neighbours. Results The floristic gradients presented by NMDS ordination were interpretable in terms of topography, drainage and soil cation content. Thirteen percent of the cloud‐free pixels were excluded from the knn analysis to avoid extrapolation. The estimates of the floristic ordination scores based on spectrally nearest neighbours were always more accurate than estimates based on random neighbours. Main conclusions The presented method needs a relatively small input of work and resources, is mechanistic and produces maps that give relevant information on floristic variation over forest areas that are traditionally considered essentially homogeneous. Therefore, the method appears to have a great potential for use in mapping large areas of Amazonian rain forests.     

Environmental,Spatial and Structural Components in the Composition of Mountain Forest in the Bavarian Alps

A combined systematic and stratified sampling design was conducted in mountain forests of the Bavarian Alps to find the principal dimensions of compositional variation of vegetation and their environmental drivers. In 1,505 plots species composition, forest types and soil profiles were recorded. Data from 14 climate stations were included. As we hypothesized that the tree layer is more influenced by management than the understorey and that the former modifies the habitat of the latter, the two matrices were analysed separately and the species composition of the tree layer was used as a structural predictor variable for the understorey. We applied constrained ordination to reveal the main gradients in floristic composition and variance partitioning to examine the portions of climatic, edaphic, spatial and structural components. Ellenberg indicator values and a generalized linear model were used to test whether a significant spatial gradient exists from east to west, the main spatial extent of the investigation area. Forest types were used as an overlay to assess the underlying environmental factors. It turned out that explained variance of the tree layer was considerably lower than in the understorey. Tree layer composition was more influenced by climatic variables than by soil. In the understorey, edaphic and climatic variables contributed almost equally to explained variance, but the tree layer had an additional explanatory power. No continentality gradient could be detected within the investigation area. Plant communities were well separated along gradients of acidity, moisture, nutrients and climate, which broadly confirms the known gradients for montane and subalpine zonal forests in the region. The study provides a quantitative synthesis of the knowledge on a diverse set of community types, which has so far been subject to disparate and sectorial treatment in the Bavarian Alps.     

Detection of multi-species patterns in plant populations

An analytical method for the detection of multi-species spatial patterns in grasslands was investigated. Several data sets of grasslands from the granitic pediment of the Sierra de Guadarrama (Central Spain) were used. The application of correspondence analysis to sequential abundance data of several species allowed the ordination of quadrats in an axis of floristic variation. Coordinate data were then subjected to pattern analysis through the use of variance tests, the results showing the existence of multi-species patches having variable dimensions.     

宜昌百里荒草山草坡群落物种分布的空间趋势分析

通过基于CCA的趋势面分析和空间插值方法,研究了宜昌百里荒山地草场的群落结构空间变化,以及群落结构空间趋势与主要环境因子的相关性。结果表明,该群落物种空间中的群落结构面和物理空间中的空间趋势面可以很好地吻合,说明该群落的结构由一种具有强烈空间结构化特征的机制控制。对群落结构和空间趋势影响最显著的环境因素是土壤有效磷。     

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.     

Modelling geographical patterns in species richness using eigenvector-based spatial filters

Aim To test the mechanisms driving bird species richness at broad spatial scales using eigenvector‐based spatial filtering. Location South America. Methods An eigenvector‐based spatial filtering was applied to evaluate spatial patterns in South American bird species richness, taking into account spatial autocorrelation in the data. The method consists of using the geographical coordinates of a region, based on eigenanalyses of geographical distances, to establish a set of spatial filters (eigenvectors) expressing the spatial structure of the region at different spatial scales. These filters can then be used as predictors in multiple and partial regression analyses, taking into account spatial autocorrelation. Autocorrelation in filters and in the regression residuals can be used as stopping rules to define which filters will be used in the analyses. Results Environmental component alone explained 8% of variation in richness, whereas 77% of the variation could be attributed to an interaction between environment and geography expressed by the filters (which include mainly broad‐scale climatic factors). Regression coefficients of environmental component were highest for AET. These results were unbiased by short‐scale spatial autocorrelation. Also, there was a significant interaction between topographic heterogeneity and minimum temperature. Conclusion Eigenvector‐based spatial filtering is a simple and suitable statistical protocol that can be used to analyse patterns in species richness taking into account spatial autocorrelation at different spatial scales. The results for South American birds are consistent with the climatic hypothesis, in general, and energy hypothesis, in particular. Habitat heterogeneity also has a significant effect on variation in species richness in warm tropical regions.     

Floristic composition across a climatic gradient in a neotropical lowland forest

Abstract. This study deals with the floristic composition of lowland tropical forest in the watershed of the Panama Canal. The floristic composition of large trees in 54 forest plots was analysed with respect to environmental factors, including precipitation, geologic parent material, stand age, topography, and soils. The plots contain 824 species of trees with a diameter at breast height ≥10 cm and represent a regional flora with exceptional β‐diversity. Plot data indicate that the Panamanian forest is strongly spatially structured at the landscape scale with floristic similarity decreasing rapidly as a function of inter‐plot geographic distance, especially for distances <5 km. The ordinations and patterns of endemism across the study area indicate broad floristic associations well correlated with Holdridge life zones. The results indicate the positive aspects of life zone classification at regional scales, while simultaneously highlighting its inadequacy for finer scales of analysis and resource management. Multivariate gradient analysis techniques (Non‐metric Multidimensional Distance Scaling and Detrended Correspondence Analysis) show clear patterns of floristic variability correlated with regional precipitation trends, surficial geology, and local soil attributes. Geologic and edaphic conditions, such as acidic soils or excessively drained limestone substrates, appear to override the effects of precipitation and modify forest composition. We conclude that the Panamanian forest shows clear patterns of spatial organization along environmental gradients, predominantly precipitation. The rapid decline in floristic similarity with distance between stands also suggests a role for dispersal limitation and stochastic events.     

Gradient analysis of dry grassland vegetation in Denmark

Abstract. We present a gradient analysis of 620 vegetation samples covering most of the floristic and environmental variation in semi‐natural grassland vegetation on well‐drained soils in Denmark. Vegetation was sampled using frequency in subplots. Explanatory variables were surface inclination, aspect, pH, geographical co‐ordinates together with indications of soil type. Detrended Correspondence Analysis revealed four floristic gradients that could be interpreted in ecological terms by measured variables supplemented with site calibrations based on weighted averaging of Ellenberg's indicator values. All four axes were interpreted using rank correlation statistics, and linear and non‐linear multiple regression of sample scores on explanatory variables. The first gradient was from dry calcareous to humid acidic grasslands; the second reflected an underlying gradient in fertility; the third reflected regional differentiation and the fourth was associated with variation in intensity of competition as indicated by association with calibrated Grime‐CSR values for the plots. We applied subset ordination to the data as a supplement to traditional permutation and correlation statistics to assess the consistency of ordination results. DCA axes 1 and 2 were consistent in space and time. This gradient analysis is discussed in a context of plant strategy theory and species diversity models. Ecocline patterns lend support to the view that grazing not only favours the ruderal strategy but also the stress‐tolerant strategy. The low rank of competition as an explanatory variable for the floristical gradients supports the notion that competitive effects play a subordinate role for species composition compared to microclimate and soil conditions in infertile semi‐natural grasslands.     

Environmental filters and patterns of tree regeneration in high altitude sub-tropical Quercus-dominated forests

We investigated how environmental variables explain patterns of tree regeneration in high altitude sub-tropical Quercus-dominated forests by: (1) determining whether the seedling and sapling communities show non-random spatial patterns of floristic composition; (2) identifying which environmental variables explain the observed patterns of floristic composition; (3) examining if similarity in floristic composition is related to similarity in environmental variables. We used data gathered in permanent plots established across 10 km in high altitude sub-tropical Quercus-dominated forests. Our analyses consisted of unconstrained ordination analyses (Non-metric Multidimensional Scaling) to characterize the spatial patterns of floristic composition; constrained ordination analyses (Canonical correspondence analysis) to assess the contribution of environmental variables in explaining patterns of floristic composition and, the simple and partial Mantel test to correlate the floristic composition similarity to environmental similarity. Our results provided evidence of non-random spatial patterns of floristic distribution due to structured environmental filters such as canopy-related variables, litter, grazing and aspect. Floristic compositional similarity did not depend on geographical distance between sites or on differences in their environment; therefore a number of plots were similar in floristic composition, in both seedlings and saplings, but have no environmental similarity.     

No evidence of intrinsic spatial processes driving Neotropical savanna vegetation on different substrates

The relationships between floristic patterns and environmental variation in tropical savannas have been the focus of many studies worldwide. However, important aspects of these relationships, such as the role of geographic distance in structuring plant communities, have received little attention. We investigated the individual and combined influences of substrate, climatic, and spatial factors on the floristic‐structural dissimilarity between two savanna physiognomies in the core region of Brazilian savannas: one on plain relief with deep soils and another on steep relief with shallow rocky soils. Ten 1‐ha plots were sampled in each physiognomy. We modeled species abundance using multiple linear models and variance partitioning. Our results indicated that spatial processes that are intrinsically related to species variation have negligible effects on floristic variation. The most important predictors in our models were related to soil characteristics (mainly nutrient availability) and topography (relief and elevation). Consequently, the substrate component exhibited the greatest power (14%) in explaining the floristic‐structural variation in the overall variance partitioning. Our results provide the first demonstration of the individual and combined contributions of substrate, climatic, and spatial factors to the occurrence and abundance of woody species in the most diverse and threatened savanna in the world. We also provide evidence that neutral processes might not be strong predictors of vegetation structure where savanna substrates differ greatly; instead, community structure may be primarily regulated by environmental filters.     

Approaching the serpentine factor at a local scale—a study in an ultramafic area in northern Greece

We explore factors responsible for vegetation differentiation in a small-scale serpentine area, and attempt to provide new insights in the complexity of the serpentine factor at community level. We sampled 49 quadrats. From each quadrat physical and chemical soil parameters were measured and species composition, altitude, inclination, aspect and coordinates were recorded. Quadrats were classified and ordination analyses were used to explore the environmental gradients and to estimate the explanatory power of the variables. Generalized linear models were used to investigate the response of species to environmental factors. Variance partitioning was applied to calculate the proportion of variance attributed to different groups of explanatory variables. The gradients revealed were related to soil texture, nutrient contents, calcium deficiency, chromium content, climatic parameters and grazing and disturbance intensity. Variance partitioning showed that the highest proportions of variance were attributed to the nutrients and physiographic (including soil texture) variables, while smaller but notable proportions of variance were attributed to geographical coordinates and to metal contents. Our study shows that vegetation differentiation at a local scale is determined by a complex factor of soil properties and climatic parameters, together with variation in disturbance and succession.     

Nonlinear dimensionality reduction: Alternative ordination approaches for extracting and visualizing biodiversity patterns in tropical montane forest vegetation data

Ecological patterns are difficult to extract directly from vegetation data. The respective surveys provide a high number of interrelated species occurrence variables. Since often only a limited number of ecological gradients determine species distributions, the data might be represented by much fewer but effectively independent variables. This can be achieved by reducing the dimensionality of the data. Conventional methods are either limited to linear feature extraction (e.g., principal component analysis, and Classical Multidimensional Scaling, CMDS) or require a priori assumptions on the intrinsic data dimensionality (e.g., Nonmetric Multidimensional Scaling, NMDS, and self organized maps, SOM).In this study we explored the potential of Isometric Feature Mapping (Isomap). This new method of dimensionality reduction is a nonlinear generalization of CMDS. Isomap is based on a nonlinear geodesic inter-point distance matrix. Estimating geodesic distances requires one free threshold parameter, which defines linear geometry to be preserved in the global nonlinear distance structure. We compared Isomap to its linear (CMDS) and nonmetric (NMDS) equivalents. Furthermore, the use of geodesic distances allowed also extending NMDS to a version that we called NMDS-G. In addition we investigated a supervised Isomap variant (S-Isomap) and showed that all these techniques are interpretable within a single methodical framework.As an example we investigated seven plots (subdivided in 456 subplots) in different secondary tropical montane forests with 773 species of vascular plants. A key problem for the study of tropical vegetation data is the heterogeneous small scale variability implying large ranges of β-diversity. The CMDS and NMDS methods did not reduce the data dimensionality reasonably. On the contrary, Isomap explained 95% of the data variance in the first five dimensions and provided ecologically interpretable visualizations; NMDS-G yielded similar results. The main shortcoming of the latter was the high computational cost and the requirement to predefine the dimension of the embedding space. The S-Isomap learning scheme did not improve the Isomap variant for an optimal threshold parameter but substantially improved the nonoptimal solutions.We conclude that Isomap as a new ordination method allows effective representations of high dimensional vegetation data sets. The method is promising since it does not require a priori assumptions, and is computationally highly effective.     

Pteridophyte richness, climate and topography in the Iberian Peninsula: comparing spatial and nonspatial models of richness patterns

Aim To describe the spatial variation in pteridophyte species richness; evaluate the importance of macroclimate, topography and within‐grid cell range variables; assess the influence of spatial autocorrelation on the significance of the variables; and to test the prediction of the mid‐domain effect. Location The Iberian Peninsula. Methods We estimated pteridophyte richness on a grid map with c. 2500 km² cell size, using published geocoded data of the individual species. Environmental data were obtained by superimposing the grid system over isoline maps of precipitation, temperature, and altitude. Mean and range values were calculated for each cell. Pteridophyte richness was related to the environmental variables by means of nonspatial and spatial generalized least squares models. We also used ordinary least squares regression, where a variance partitioning was performed to partial out the spatial component, i.e. latitude and longitude. Coastal and central cells were compared to test the mid‐domain effect. Results Both spatial and nonspatial models showed that pteridophyte richness was best explained by a second‐order polynomial of mean annual precipitation and a quadratic elevation‐range term, although the relative importance of these two variables varied when spatial autocorrelation was accounted for. Precipitation range was weakly significant in a nonspatial multiple model (i.e. ordinary regression), and did not remain significant in spatial models. Richness is significantly higher along the coast than in the centre of the peninsula. Main conclusions Spatial autocorrelation affects the statistical significance of explanatory variables, but this did not change the biological interpretation of precipitation and elevation range as the main predictors of pteridophyte richness. Spatial and nonspatial models gave very similar results, which reinforce the idea that water availability and topographic relief control species richness in relatively high‐energy regions. The prediction of the mid‐domain effect is falsified.     

Regional scale relationships between ecosystem structure and functioning: the case of the Patagonian steppes

Aims 1. To characterize ecosystem functioning by focusing on above‐ground net primary production (ANPP), and 2. to relate the spatial heterogeneity of both functional and structural attributes of vegetation to environmental factors and landscape structure. We discuss the relationship between vegetation structure and functioning found in Patagonia in terms of the capabilities of remote sensing techniques to monitor and assess desertification. Location Western portion of the Patagonian steppes in Argentina (39°30′ S to 45°27′ S). Methods We used remotely‐sensed data from Landsat TM and AVHRR/NOAA sensors to characterize vegetation structure (physiognomic units) and ecosystem functioning (ANPP and its seasonal and interannual variation). We combined the satellite information with floristic relevés and field estimates of ANPP. We built an empirical relationship between the Landsat TM‐derived normalized difference vegetation index (NDVI) and field ANPP. Using stepwise regressions we explored the relationship between ANPP and both environmental variables (precipitation and temperature surrogates) and structural attributes of the landscape (proportion and diversity of different physiognomic classes (PCs)). Results PCs were quite heterogeneous in floristic terms, probably reflecting degradation processes. Regional estimates of ANPP showed differences of one order of magnitude among physiognomic classes. Fifty percent of the spatial variance in ANPP was accounted for by longitude, reflecting the dependency of ANPP on precipitation. The proportion of prairies and semideserts, latitude and, to a lesser extent, the number of PCs within an 8 × 8 km cell accounted for an additional 33% of the ANPP variability. ANPP spatial heterogeneity (calculated from Landsat TM data) within an 8 × 8 km cell was positively associated with the mean AVHRR/NOAA NDVI and with the diversity of physiognomic classes. Main conclusions Our results suggest that the spatial and temporal patterns of ecosystem functioning described from ANPP result not only from water availability and thermal conditions but also from landscape structure (proportion and diversity of different PCs). The structural classification performed using remotely‐sensed data captured the spatial variability in physiognomy. Such capability will allow the use of spectral classifications to monitor desertification.     

Environmental correlates for tropical tree diversity and distribution patterns in Borneo

Aim Identify environmental correlates for tropical tree diversity and composition. Location Borneo, Southeast Asia. Methods A GIS‐environmental database with 5 arc minute (c. 10 × 10 km) resolution was combined with tree inventory data. Tree diversity, phylogenetic diversity (PD) and the two main compositional gradients were determined for 46 tree inventories. Akaike's information criterion and a data jackknifing procedure were used to select 50 explanatory models for diversity and composition gradients. The average of these models was used as our final diversity and compositional model. We applied Moran's I to detect spatial autocorrelation of residuals. Results Tree diversity, PD and the two main compositional gradients in Borneo were all significantly correlated with the environment. Tree diversity correlated negatively with elevation, soil depth, soil coarseness (texture) and organic carbon content, whereas it correlated positively with soil C:N ratio, soil pH, moisture storage capacity and annual rainfall. Tree PD was correlated positively with elevation and temperature seasonality and was largely determined by gymnosperms. However, angiosperm PD also correlated positive with elevation. Compositional patterns were strongly correlated with elevation but soil texture, cation‐exchange‐capacity, C:N ratio, C and N content and drainage were also important next to rainfall seasonality and El Niño Southern Oscillation drought impact. Main conclusions Although elevation is the most important correlate for diversity and compositional gradients in Borneo, significant additional variability is explained by soil characteristics (texture, carbon content, pH, depth, drainage and nutrient status) and climate (annual rainfall, rainfall seasonality and droughts). The identified environmental correlates for diversity and composition gradients correspond to those found in other tropical regions of the world. Differences between the regions are mainly formed by differences in the relative importance of the environmental variables in explaining diversity and compositional gradients.     

Multi-extent analysis of the relationship between pteridophyte species richness and climate

Aim To determine the relationship between the distribution of climate, climatic heterogeneity and pteridophyte species richness gradients in Australia, using an approach that does not assume potential relationships are spatially invariant and allows for scale effects (extent of analysis) to be explicitly examined. Location Australia, extending from 10° S to 43° S and 112° E to 153° E. Method Species richness within 50 × 50 km grid cells was determined using point distribution data. Climatic surfaces representing the distribution and availability of water and energy at 1 km and 5 km cell resolutions were obtained. Climate at the 50 km resolution of analysis was represented by their mean and standard deviation in that area. Relationships were assessed using geographically weighted linear regression at a range of spatial bandwidths to investigate scale effects. Results The parameters and the predictive strength of all models varied across space at all extents of analysis. Overall, climatic variables representing water availability were more highly correlated to pteridophyte richness gradients in Australia than those representing energy. Their variance in cells further increased the strength of the relationships in topographically heterogeneous regions. Relationships with water were strong across all extents of analysis, particularly in the tropical and subtropical parts of the continent. Water availability explained less of the variation in richness at higher latitudes. Main conclusions This study brings into question the ability of aspatial and single‐extent models, searching for a unified explanation of macro‐scaled patterns in gradients of diversity, to adequately represent reality. It showed that, across Australia, there is a positive relationship between pteridophyte species richness and water availability but the strength and nature of the relationship varies spatially with scale in a highly complex manner. The spatial variance, or actual complexity, in these relationships could not have been demonstrated had a traditional aspatial global regression approach been used. Regional scale variation in relationships may be at least as important as more general relationships for a true understanding of the distribution of broad‐scale diversity.     

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.