Disparity between range map- and survey-based analyses of species richness: patterns, processes and implications

Species richness patterns are characterized either by overlaying species range maps or by compiling geographically extensive survey data for multiple local communities. Although, these two approaches are clearly related, they need not produce identical richness patterns because species do not occur everywhere in their geographical range. Using North American breeding birds, we present the first continent‐wide comparison of survey and range map data. On average, bird species were detected on 40.5% of the surveys within their range. As a result of this range porosity, the geographical richness patterns differed markedly, with the greatest disparity in arid regions and at higher elevations. Environmental productivity was a stronger predictor of survey richness, while elevational heterogeneity was more important in determining range map richness. In addition, range map richness exhibited greater spatial autocorrelation and lower estimates of spatial turnover in species composition. Our results highlight the fact that range map richness represents species coexistence at a much coarser scale than survey data, and demonstrate that the conclusions drawn from species richness studies may depend on the data type used for analyses.     

The performance of range maps and species distribution models representing the geographic variation of species richness at different resolutions

Aim The method used to generate hypotheses about species distributions, in addition to spatial scale, may affect the biodiversity patterns that are then observed. We compared the performance of range maps and MaxEnt species distribution models at different spatial resolutions by examining the degree of similarity between predicted species richness and composition against observed values from well‐surveyed cells (WSCs). Location Mexico. Methods We estimated amphibian richness distributions at five spatial resolutions (from 0.083° to 2°) by overlaying 370 individual range maps or MaxEnt predictions, comparing the similarity of the spatial patterns and correlating predicted values with the observed values for WSCs. Additionally, we looked at species composition and assessed commission and omission errors associated with each method. Results MaxEnt predictions reveal greater geographic differences in richness between species rich and species poor regions than the range maps did at the five resolutions assessed. Correlations between species richness values estimated by either of the two procedures and the observed values from the WSCs increased with decreasing resolution. The slopes of the regressions between the predicted and observed values indicate that MaxEnt overpredicts observed species richness at all of the resolutions used, while range maps underpredict them, except at the finest resolution. Prediction errors did not vary significantly between methods at any resolution and tended to decrease with decreasing resolution. The accuracy of both procedures was clearly different when commission and omission errors were examined separately. Main conclusions Despite the congruent increase in the geographic richness patterns obtained from both procedures as resolution decreases, the maps created with these methods cannot be used interchangeably because of notable differences in the species compositions they report.     

Effects of geographical extent on the determinants of woody plant diversity

Despite a long history of study, the mechanisms underlying the geographical patterns of species richness are still controversial. Patterns and determinants of species richness are well‐known to vary with spatial scale. However, most studies on the effects of scale have focused on grain size whereas the quantitative effects of geographical extent are rarely tested. Here, using distribution maps of 11 405 woody species found in China and associated environmental data to the domain, we investigated the influence of geographical extent on the determinants of species richness patterns. Our results revealed consistent extent dependence of all species, narrow‐ and wide‐ranged species: with the expansion of geographical extents, the explanatory power of climate (i.e. environmental energy, water availability and climatic seasonality) increased, while the explanatory power of habitat heterogeneity and human activities decreased. Although the primary determinant of species richness patterns varied significantly at small to meso‐geographical extent, we showed that species richness was predominantly determined by environmental energy at large extent. Our findings indicate that differences in geographical extent may have led to the controversies regarding the primary determinants of richness patterns in previous studies, and that a multi‐scale perspective not only with regard to grain‐size but also extent is likely to shed new light on this old debate of what determines richness patterns.     

A comparison of methods for mapping species ranges and species richness

Aim  Maps of species richness are the basis for applied research and conservation planning as well as for theoretical research investigating patterns of richness and the processes shaping these patterns. The method used to create a richness map could influence the results of such studies, but differences between these methods have been insufficiently evaluated. We investigate how different methods of mapping species ranges can influence patterns of richness, at three spatial resolutions.
Location  California, USA.
Methods  We created richness maps by overlaying individual species range maps for terrestrial amphibians and reptiles. The methods we used to create ranges included: point-to-grid maps, obtained by overlaying point observations of species occurrences with a grid and determining presence or absence for each cell; expert-drawn maps; and maps obtained through species distribution modelling. We also used a hybrid method that incorporated data from all three methods. We assessed the correlation and similarity of the spatial patterns of richness maps created with each of these four methods at three different resolutions.
Results  Richness maps created with different methods were more correlated at lower spatial resolutions than at higher resolutions. At all resolutions, point-to-grid richness maps estimated the lowest species richness and those derived from species distribution models the highest. Expert-drawn maps and hybrid maps showed intermediate levels of richness but had different spatial patterns of species richness from those derived with the other methods.
Main conclusions  Even in relatively well-studied areas such as California, different data sources can lead to rather dissimilar maps of species richness. Evaluating the strengths and weaknesses of different methods for creating a richness map can provide guidance for selecting the approach that is most appropriate for a given application and region.     

Macroecological explanations for differences in species richness gradients: a canonical analysis of South American birds

Aim To evaluate how spatial variation of species richness in different bird orders responds to environmental gradients and determine which order level trait best predicts these relationships. Location South America. Methods A canonical correlation analysis was performed between the species richness in each of 17 bird orders and eight environmental variables in 374, 220 × 220 km cells. Loadings associated with the first two canonical variables were regressed against six order‐level predictors, including diversification level (number of species in each order), body size, median geographical range size and characteristics included in the model to control Type I error rates (the phylogenetic relationship among orders and levels of local‐scale spatial autocorrelation). Results Richness patterns of 14 bird orders were highly correlated with the first canonical axis, indicating that most orders respond similarly to energy‐water gradients (primarily actual evapotranspiration, minimum temperature and potential evapotranspiration). In contrast, species richness within Trochiliformes, Apodiformes and Galliformes were also correlated with the second canonical variable, representing measures of mesoscale climatic variation (range in elevation within cells, minimum temperature, and the interaction term between them) and landcover (habitat diversity). We also found that total diversification within orders was the best predictor of the loadings associated with the first canonical axis, whereas body size of each order best predicted loadings on the second axis. Conclusion Our results broadly support climatic‐related hypotheses as explanations for spatial variation in species richness of different orders. However, both historical (order‐specific variation in speciation rates) and ecological (dispersal of species that evolved by independent processes into areas amenable to birds) processes can explain the relationship between order level traits, such as body size and diversification level, and magnitude of response to current environment, furnishing then guidelines for a further and deeper understanding of broad‐scale diversity gradients.     

Lizard species richness patterns in China and its environmental associations

Many ecological hypotheses have been widely used to explain species richness variation across the globe. We investigated lizard species richness patterns in China, and identified areas of high species richness. Furthermore, we tested hypotheses concerning the relationships between lizard richness and environmental variables. A large data including 30,902 records of point locality data for 151 lizard species occurring in China were retrieved from Herpetology museums of CIB/CAS and other museums through HerpNET, and published sources, and then predicted distributions maps were generated using ecological niche modeling. We overlaid all species prediction maps into a composite map to describe species richness patterns. A multiple regression analysis using eigenvector-based spatial filtering (SEVM) was performed to examine the best environmental predictors of species richness. Richness peaked mainly in southern China located in the Oriental realm. Our best multiple regression models explained a total of 80.1% variance of lizard richness (r² = 0.801; F = 203.47; P < 0.001). Among related factors in shaping species richness distribution, the best environmental predictors of species richness were: frost-day frequency, elevation, vegetation, and wet-day frequency. Based on models selection, our results revealed that underlying mechanisms related to different ecological hypotheses might work together and best explain lizard richness in China. We are in an initial step to develop a large data set on species richness, and provide the necessary conservation implications from habitat loss. Additional studies that test species richness at different geographical scale are required to better understand the factors that may influence the species richness distribution in East Asia.     

Hot,dry and different: Australian lizard richness is unlike that of mammals,amphibians and birds

Aims (1) To map the species richness of Australian lizards and describe patterns of range size and species turnover that underlie them. (2) To assess the congruence in the species richness of lizards and other vertebrate groups. (3) To search for commonalities in the drivers of species richness in Australian vertebrates. Location Australia. Methods We digitized lizard distribution data to generate gridded maps of species richness and β‐diversity. Using similar maps for amphibians, mammals and birds, we explored the relationship between species richness and temperature, actual evapotranspiration, elevation and local elevation range. We used spatial eigenvector filtering and geographically weighted regression to explore geographical patterns and take spatial autocorrelation into account. We explored congruence between the species richness of vertebrate groups whilst controlling for environmental effects. Results Lizard richness peaks in the central deserts (where β‐diversity is low) and tropical north‐east (where β‐diversity is high). The intervening lowlands have low species richness and β‐diversity. Generally, lizard richness is uncorrelated with that of other vertebrates but this low congruence is strongly spatially structured. Environmental models for all groups also show strong spatial heterogeneity. Lizard richness is predicted by different environmental factors from other vertebrates, being highest in dry and hot regions. Accounting for environmental drivers, lizard richness is weakly positively related to richness of other vertebrates, both at global and local scales. Main conclusions Lizard species richness differs from that of other vertebrates. This difference is probably caused by differential responses to environmental gradients and different centres of diversification; there is little evidence for inter‐taxon competition limiting lizard richness. Local variation in habitat diversity or evolutionary radiations may explain weak associations between taxa, after controlling for environmental variables. We strongly recommend that studies of variation in species richness examine and account for non‐stationarity.     

Reconciling topographic and climatic effects on widespread and range-restricted species richness

Aim To identify the reasons behind differing geographical species richness patterns of range‐restricted and widespread species. Location The Western Hemisphere. Methods We used regression to determine the strongest environmental predictors of richness for widespread and range‐restricted mammal species in 10,000 km² quadrats in the continental Americas. We then used range‐placement models to predict the expected correlation between range‐restricted and widespread species richness were they to be determined by identical, random, or contrasting environmental factors. Finally, to determine the reasons underlying deviations from these predictions, we divided the Americas into 5% quantiles based on temperature and topographic heterogeneity and correlated richness of these two assemblages across quantiles – an approach that avoids constraints on statistical testing imposed by low potential for range overlap among range‐restricted species. Results Minimum annual temperature was the strongest predictor of widespread species richness while topographic heterogeneity was the best, although weak, predictor of range‐restricted species richness in conventional regression analysis. Our models revealed that the observed correlation between range‐restricted and widespread species richness was similar to what would be observed if both range‐restricted and widespread species richness were determined by temperature. Patterns of range‐restricted and widespread species richness were highly correlated across temperature quantiles, but range‐restricted species uniquely showed an increasing pattern across heterogeneity quantiles. Main conclusions Species richness gradients among range‐restricted species differ from those of widespread species, but not as extensively or for the reasons reported previously. Instead, these assemblages appear to share some but not all underlying environmental determinants of species richness. Our new approach to examining species richness patterns reveals that range‐restricted and widespread species richnesses share a common response to temperature that conventional analyses have not previously revealed. However, topographic heterogeneity has assemblage‐specific effects on range‐restricted species.     

Spatial patterns of woody plant and bird diversity: functional relationships or environmental effects?

Aim To understand cross‐taxon spatial congruence patterns of bird and woody plant species richness. In particular, to test the relative roles of functional relationships between birds and woody plants, and the direct and indirect environmental effects on broad‐scale species richness of both groups. Location Kenya. Methods Based on comprehensive range maps of all birds and woody plants (native species > 2.5 m in height) in Kenya, we mapped species richness of both groups. We distinguished species richness of four different avian frugivore guilds (obligate, partial, opportunistic and non‐frugivores) and fleshy‐fruited and non‐fleshy‐fruited woody plants. We used structural equation modelling and spatial regressions to test for effects of functional relationships (resource–consumer interactions and vegetation structural complexity) and environment (climate and habitat heterogeneity) on the richness patterns. Results Path analyses suggested that bird and woody plant species richness are linked via functional relationships, probably driven by vegetation structural complexity rather than trophic interactions. Bird species richness was determined in our models by both environmental variables and the functional relationships with woody plants. Direct environmental effects on woody plant richness differed from those on bird richness, and different avian consumer guilds showed distinct responses to climatic factors when woody plant species richness was included in path models. Main conclusions Our results imply that bird and woody plant diversity are linked at this scale via vegetation structural complexity, and that environmental factors differ in their direct effects on plants and avian trophic guilds. We conclude that climatic factors influence broad‐scale tropical bird species richness in large part indirectly, via effects on plants, rather than only directly as often assumed. This could have important implications for future predictions of animal species richness in response to climate change.     

Continental and regional ranges of North American mammals: Rapoport's rule in real and null worlds

Aim To assess the relationship between species richness and distribution within regions arranged along a latitudinal gradient we use the North American mammalian fauna as a study case for testing theoretical models. Location North America. Methods We propose a conceptual framework based on a fully stochastic mid‐domain model to explore geographical patterns of range size and species richness that emerge when the size and position of species ranges along a one‐dimensional latitudinal gradient are randomly generated. We also analyse patterns for the mammal fauna of North America by comparing empirical results from a biogeographical data base with predictions based on randomization null models. Results We confirmed the validity of Rapoport's rule for the mammals of North America by documenting gradients in the size of the continental ranges of species. Additionally, we demonstrated gradients of mean regional range size that parallel those of continental range. Our data also demonstrated that mean range size, measured both as a continental or a regional variable, is significantly correlated with the geographical pattern in species richness. All these patterns deviated sharply from null models. Main conclusions Rapoport's statement of an areographic relationship between species distribution and richness is highly relevant in modern discussions about ecological patterns at the geographical scale.     

When does diversity fit null model predictions? Scale and range size mediate the mid‐domain effect

Aim  Recently, a flurry of studies have focused on the extent to which geographical patterns of diversity fit mid-domain effect (MDE) null models. While some studies find strong support for MDE null models, others find little. We test two hypotheses that might explain this variation among studies: small-ranged groups of species are less likely than large-ranged species to show mid-domain peaks in species richness, and mid-domain null model predictions are less robust for smaller spatial extents than for larger spatial extents.
Location  We analyse data sets from elevational, riverine, continental and other domains from around the world.
Methods  We use a combination of Spearman rank correlations and binomial tests to examine whether differences within and among studies and domains in the predictive power of MDE null models vary with spatial scale and range size.
Results  Small-ranged groups of species are less likely to fit mid-domain predictions than large-ranged groups of species. At large spatial extents, diversity patterns of taxonomic groups with large mean range sizes fit MDE null model predictions better than did diversity patterns of groups with small mean range sizes. MDE predictions were more explanatory at larger spatial extents than at smaller extents. Diversity patterns at smaller spatial extents fit MDE predictions poorly across all range sizes. Thus, MDE predictions should be expected to explain patterns of species richness when ranges and the scale of analysis are both large.
Main conclusions  Taken together, the support for these hypotheses offers a more sophisticated model of when MDE predictions should be expected to explain patterns of species richness, namely when ranges and the scale of analysis are both large. Thus the circumstances in which the MDE is important are finite and apparently predictable.     

Spatial diversity patterns of Pristimantis frogs in the Tropical Andes

Although biodiversity gradients have been widely documented, the factors governing broad‐scale patterns in species richness are still a source of intense debate and interest in ecology, evolution, and conservation biology. Here, we tested whether spatial hypotheses (species–area effect, topographic heterogeneity, mid‐domain null model, and latitudinal effect) explain the pattern of diversity observed along the altitudinal gradient of Andean rain frogs of the genus Pristimantis. We compiled a gamma‐diversity database of 378 species of Pristimantis from the tropical Andes, specifically from Colombia to Bolivia, using records collected above 500 m.a.s.l. Analyses were performed at three spatial levels: Tropical Andes as a whole, split in its two main domains (Northern and Central Andes), and split in its 11 main mountain ranges. Species richness, area, and topographic heterogeneity were calculated for each 500‐m‐width elevational band. Spatial hypotheses were tested using linear regression models. We examined the fit of the observed diversity to the mid‐domain hypothesis using randomizations. The species richness of Pristimantis showed a hump‐shaped pattern across most of the altitudinal gradients of the Tropical Andes. There was high variability in the relationship between area and species richness along the Tropical Andes. Correcting for area effects had little impact in the shape of the empirical pattern of biodiversity curves. Mid‐domain models produced similar gradients in species richness relative to empirical gradients, but the fit varied among mountain ranges. The effect of topographic heterogeneity on species richness varied among mountain ranges. There was a significant negative relationship between latitude and species richness. Our findings suggest that spatial processes partially explain the richness patterns of Pristimantis frogs along the Tropical Andes. Explaining the current patterns of biodiversity in this hot spot may require further studies on other possible underlying mechanisms (e.g., historical, biotic, or climatic hypotheses) to elucidate the factors that limit the ranges of species along this elevational gradient.     

Determinants of regional species richness: an empirical analysis of the number of hawkmoth species (Lepidoptera: Sphingidae) on the Malesian archipelago

Aims Major patterns and determinants of the species richness of Sphingidae in the Malesian archipelago were investigated, including a distinction of richness patterns between subfamilies and range‐size classes. Location Southeast Asia, Malesia. Methods Using a compilation of specimen‐label data bases, geographic information system (GIS)‐supported estimates of distributional ranges for all Sphingidae species of Southeast Asia were used to assess the species richness of islands. Range maps for all species and checklists for 114 islands can be found at http://www.sphingidae‐sea.biozentrum.uni‐wuerzburg.de . Potential determinants of the species richness of islands were tested with general linear models. Results The estimated species richness of islands in the region is determined by biogeographical association, seasonality, availability of rain forest and island size. Species–area relationships are linear on a semi‐logarithmic representation, but not on a double‐logarithmic scale. Species richness of all sphingid subfamilies is influenced by biogeography. The presence of large rain‐forest areas affects mainly Smerinthinae, whereas distance from continental Asia is conspicuously irrelevant for this group. Widespread rather than geographically restricted species shape the overall distribution patterns of species richness. The altitudinal range of islands does not significantly affect species‐richness patterns, but its potential effects on geographically restricted species are discussed. Main conclusions As well as being affected by climatic and vegetation parameters, sphingid species richness is strongly influenced by a historical, directional dispersal process from continental Southeast Asia to the Pacific islands. This process did not apply equally to species of different taxonomic groups or range sizes. Widespread species decline in species richness towards the south‐east, whereas geographically restricted species exhibit an inverse pattern of species richness, probably because speciation becomes more important in this group within the more isolated island groups.     

Multiple environmental determinants of regional species richness and effects of geographic range size

Understanding patterns of species richness at broad geographic extents remains one of the most challenging yet necessary scientific goals of our time. Many hypotheses have been proposed to account for spatial variation in species richness; among them, environmental determinants have played a central role. In this study, we use data on regional bat species richness in the New World to study redundancy and complementarity of three environmental hypotheses: energy, heterogeneity and seasonality. We accomplish this by partitioning variation in species richness among components associated with unique and combined effects of variables from each hypotheses, and by partitioning the overall richness gradient into gradients of species with varying breadths of geographic distribution. These three environmental hypotheses explain most variation in the species richness gradient of all bats, but do not account for all positive spatial autocorrelation at short distances. Although environmental predictors are highly redundant, energy and seasonality explain different and complementary fractions of variation in species richness of all bats. On the other hand, heterogeneity variables contribute little to explain this gradient. However, results change dramatically when richness is estimated for groups of species with different sizes of geographic distribution. First, the amount of variation explained by environment decreases with a decrease in range size; this suggests that richness gradients of small‐ranged species can not be explained as easily as those of broadly distributed species, as has been implied by analyses that do not consider differences in range size among species. Second, the relative contribution of environmental predictors to explained variation also changes with change in range size. Seasonality and energy are good predictors of species with broad distributions, but they loose almost all explanatory power for richness of species with small ranges. In contrast, heterogeneity, which is a relatively poor predictor of richness of species with large ranges, becomes the main predictor of richness gradients of species with restricted distributions. This suggests that range size is a different dimension on which heterogeneity and other environmental characteristics are complementary to each other. Our results suggest that determinants of species richness gradients might be complex, or at least more complex than many studies have previously suggested.     

Breeding bird species richness in Spain: assessing diversity hypothesis at various scales

The variation of passerine species richness in Spain was studied at various spatial scales. Presence-absence data was resampled to construct three species richness maps in lattices of 10×10, 30×30, and 50×50 km UTM cells. The importance of habitat, species-energy, climatic variability, disturbance, history and geometric constraints hypotheses was assessed using geographical data. Stochastic, range-based models were used to simulate neutral colonization events from Europe or from Africa. The importance of small scale processes remained after the inclusion of environmental covariates, indicating a possible role of ecological interactions that was represented in the models by a conditional spatial autoregressive term. Historical effects and energy related measures explained most of the variation in regional species richness. Local and regional habitat structure measures explained the pattern only after large scale trends were considered. The differences when species richness was analyzed at each scale reveal the importance of spatial issues in diversity studies. The possible role of post glacial migration in shaping the observed patterns, and implications for conservation are discussed.     

Patterns and causes of species richness: a general simulation model for macroecology

Understanding the causes of spatial variation in species richness is a major research focus of biogeography and macroecology. Gridded environmental data and species richness maps have been used in increasingly sophisticated curve‐fitting analyses, but these methods have not brought us much closer to a mechanistic understanding of the patterns. During the past two decades, macroecologists have successfully addressed technical problems posed by spatial autocorrelation, intercorrelation of predictor variables and non‐linearity. However, curve‐fitting approaches are problematic because most theoretical models in macroecology do not make quantitative predictions, and they do not incorporate interactions among multiple forces. As an alternative, we propose a mechanistic modelling approach. We describe computer simulation models of the stochastic origin, spread, and extinction of species’ geographical ranges in an environmentally heterogeneous, gridded domain and describe progress to date regarding their implementation. The output from such a general simulation model (GSM) would, at a minimum, consist of the simulated distribution of species ranges on a map, yielding the predicted number of species in each grid cell of the domain. In contrast to curve‐fitting analysis, simulation modelling explicitly incorporates the processes believed to be affecting the geographical ranges of species and generates a number of quantitative predictions that can be compared to empirical patterns. We describe three of the ‘control knobs’ for a GSM that specify simple rules for dispersal, evolutionary origins and environmental gradients. Binary combinations of different knob settings correspond to eight distinct simulation models, five of which are already represented in the literature of macroecology. The output from such a GSM will include the predicted species richness per grid cell, the range size frequency distribution, the simulated phylogeny and simulated geographical ranges of the component species, all of which can be compared to empirical patterns. Challenges to the development of the GSM include the measurement of goodness of fit (GOF) between observed data and model predictions, as well as the estimation, optimization and interpretation of the model parameters. The simulation approach offers new insights into the origin and maintenance of species richness patterns, and may provide a common framework for investigating the effects of contemporary climate, evolutionary history and geometric constraints on global biodiversity gradients. With further development, the GSM has the potential to provide a conceptual bridge between macroecology and historical biogeography.     

The relative roles of environment and history as controls of tree species composition and richness in Europe

Aim This study investigates the determinants of European‐scale patterns in tree species composition and richness, addressing the following questions: (1) What is the relative importance of environment and history? History refers to lasting effects of past large‐scale events and time‐dependent cumulative effects of ongoing processes, notably dispersal limited range dynamics. (2) Among the environmental determinants, what is the relative importance of climate, soils, and forest cover? (3) Do the answers to questions 1 and 2 differ between conifers and Fagales, the two major monophyletic groups of European trees? Location The study area comprises most of Europe (34° N–72° N and 11° W–32° E). Methods Atlas data on native distributions of 54 large tree species at 50 × 50 km resolution were linked with climatic, edaphic, and forest cover maps in a geographical information system. Unconstrained (principal components analysis using Hellinger distance transformation and detrended correspondence analysis) and constrained ordinations (redundancy analysis using Hellinger distance transformation and canonical correspondence analysis) and multiple linear regressions were used to investigate the determinants of species composition and species richness, respectively. History is expected to leave its mark as broad spatial patterns and was represented by the nine spatial terms of a cubic trend surface polynomial. Results The main floristic pattern identified by all ordinations was a latitude‐temperature gradient, while the lower axes corresponded mostly to spatial variables. Partitioning the floristic variation using constrained ordinations showed the mixed spatial‐environmental and pure spatial fractions to be much greater than the pure environmental fraction. Biplots, forward variable selection, and partial analyses all suggested climatic variables as more important floristic determinants than forest cover or soil variables. Tree species richness peaked in the mountainous regions of East‐Central and Southern Europe, except the Far West. Variation partitioning of species richness found the mixed spatial‐environmental and pure spatial fractions to be much greater than the pure environmental fraction for all species combined and Fagales, but not for conifers. The scaled regression coefficients indicated climate as a stronger determinant of richness than soils or forest cover. While the dominant patterns were similar for conifers and Fagales, conifers exhibited less predictable patterns overall, a smaller pure spatial variation fraction relative to pure environmental fraction, and a greater relative importance of climate; all differences being more pronounced for species richness than for species composition. Main conclusions The analyses suggest that history is at least as important as current environment in controlling species composition and richness of European trees, with the exception of conifer species richness. Strong support for interpreting the spatial patterns as outcomes of historical processes, notably dispersal limitation, came from the observation that many European tree species naturalize extensively outside their native ranges. Furthermore, it was confirmed that climate predominates among environmental determinants of distribution and diversity patterns at large spatial scales. Finally, the particular patterns exhibited by conifers probably reflect greater environmental specialization and greater human impact. These findings warn against expecting the European tree flora to be able track fast future climate changes on its own.     

Does climate determine broad‐scale patterns of species richness? A test of the causal link by natural experiment

Aim Broad‐scale spatial patterns of species richness are very strongly correlated with climatic variables. If there is a causal link, i.e. if climate directly or indirectly determines patterns of richness, then when the climatic variables change, richness should change in the manner that spatial correlations between richness and climate would predict. The present study tests this prediction using seasonal changes in climatic variables and bird richness. Location We used a grid of equal area quadrats (37 000 km²) covering North and Central America as far south as Nicaragua. Methods Summer and winter bird distribution data were drawn from monographs and field guides. Climatic data came from published sources. We also used remotely sensed NDVI (normalized difference vegetation index — a measure of greenness). Results Bird species richness changes temporally (between summer and winter) in a manner that is close to, but statistically distinguishable from, the change one would predict from models relating the spatial variation in richness at a single time to climatic variables. If one further takes into account the seasonal changes in NDVI and within‐season variability of temperature and precipitation, then winter and summer richness follow congruent, statistically indistinguishable patterns. Main conclusions Our results are consistent with the hypothesis that climatic variables (temperature and precipitation) and vegetation cover directly or indirectly influence patterns of bird species richness.     

Interpreting biogeographical boundaries among Afrotropical birds: spatial patterns in richness gradients and species replacement

Aim We analyse the geographical distribution of 1911 Afrotropical bird species using indices of three simple biogeographic patterns. The first index, the frequency of species with range edges (T_e), is formulated to map directly the density of species distribution limits, for comparison with the results of traditional biogeographical classification and ordination procedures, in order to show variations in the strength and breadth of transition zones. The other two indices are formulated to seek to distinguish as directly as possible between two components within these transition-zone patterns: contributions from gradients in species richness (T_g); and contributions from replacements among species (T_r). We test the ability of these indices to discover the same boundaries among Afrotropical bird faunas as one popular procedure for classifying areas (TWINSPAN) and then use them to look for geographical trends in the different kinds of transition zones. Location The analysis is restricted to the sub-Saharan or Afrotropical region, excluding the Arabian Peninsula, Madagascar and all offshore islands. Methods We record the presence of each species in 1961 1°×1° grid cells of the map. To apply the three indices, each (core) grid cell in turn is compared with its neighbouring eight cells in the grid. The range edges index (T_e) counts the number of species with range edges between the core cell and the surrounding cells. The richness gradients index (T_g) counts the largest difference in species richness measured diametrically across the core cell in any direction when there is a consistent trend in richness along this line of three cells. The species replacements index (T_r) counts the number of species pairs recorded within a nine-cell neighbourhood that are not corecorded within any of the cells. Values for each of the 1961 grid cells are calculated and used to produce colour-scale maps of transition zones. Results Large-scale spatial patterns of variation in density of range edges (T_e) are consistent with classifications of the same data and with most previous biogeographical classifications proposed for the region. Variation in richness gradients (T_g) and species replacements (T_r) explain different parts of this pattern, with transition zones around humid forests in the equatorial region being dominated by species replacement, and transition zones around deserts (most extensive in the north and south) being dominated by richness gradients. Main conclusions The three indices distinguish the spatial arrangement and intensity of different kinds of transition zones, thereby providing a first step towards a more rigorous mechanistic understanding of the different processes by which they may have arisen and are maintained. As an example of one such pattern shown by our analyses of Afrotropical birds, there is evidence for a broad latitudinal trend in the nature of transition zones in faunal composition (following the latitudinal distribution of the different kinds of habitat transitions), from being dominated by species replacements near the equator to being dominated by richness gradients further from the equator.