Effects of woody plant species richness on mammal species richness in southern Africa

Aim To determine the relationship between the species richness of woody plants and that of mammals after accounting for the effect of environmental variables. Location Southern Africa, including Namibia, South Africa, Lesotho, Swaziland, Botswana, Zimbabwe, and part of Mozambique. Methods We used a comprehensive dataset including the species richness of mammals and of woody plants and environmental variables for 118 quadrats (each of 25,000 km²) across southern Africa, and used structural equation models (SEMs) and spatial regressions to examine the relationship between the species richness of woody plants and of mammal trophic guilds (herbivores, insectivores, carni/omnivores) and habitat guilds (aquatic/fossorial, ground‐living, climbers, aerial), after controlling for environment. We compared the results of SEMs with those of single‐predictor regressions (without controlling for environment) and of spatial regressions (controlling for both environment and residual spatial autocorrelation). Results The geographical variation of mammal species richness in southern Africa was strongly and positively related to that of woody plant species richness, and this relationship held for most mammal guilds even when the influence of environment and spatial autocorrelation had been accounted for. However, the effect of woody plant species richness on the richness of aquatic/fossorial species almost disappeared after controlling for environment, suggesting that the congruence in species richness patterns between these two groups results from similar responses to the same environmental variables. For many mammal guilds, the relative role of environmental predictors as measured by standardized partial regression coefficients changed depending on whether non‐spatial single‐predictor regressions, non‐spatial SEMs, or spatial regressions were used. Main conclusions Woody plants are important determinants of the species richness of most mammal guilds in southern Africa, even when controlling for environment and residual spatial autocorrelation. Environmental correlates with animal species richness as measured by simple correlations or single‐predictor regressions might not always reflect direct effects; they might, at least to some degree, result from indirect effects via woody plants. Interpretations of the strength of the effect of environmental variables on mammal species richness in southern Africa depend largely on whether spatial or non‐spatial models are used. We therefore stress the need for caution when interpreting environmental ‘effects’ on broad‐scale patterns of species richness if spatial and non‐spatial methods yield contrasting results.     

Spatial autocorrelation and red herrings in geographical ecology

Aim Spatial autocorrelation in ecological data can inflate Type I errors in statistical analyses. There has also been a recent claim that spatial autocorrelation generates ‘red herrings’, such that virtually all past analyses are flawed. We consider the origins of this phenomenon, the implications of spatial autocorrelation for macro‐scale patterns of species diversity and set out a clarification of the statistical problems generated by its presence. Location To illustrate the issues involved, we analyse the species richness of the birds of western/central Europe, north Africa and the Middle East. Methods Spatial correlograms for richness and five environmental variables were generated using Moran's I coefficients. Multiple regression, using both ordinary least‐squares (OLS) and generalized least squares (GLS) assuming a spatial structure in the residuals, were used to identify the strongest predictors of richness. Autocorrelation analyses of the residuals obtained after stepwise OLS regression were undertaken, and the ranks of variables in the full OLS and GLS models were compared. Results Bird richness is characterized by a quadratic north–south gradient. Spatial correlograms usually had positive autocorrelation up to c. 1600 km. Including the environmental variables successively in the OLS model reduced spatial autocorrelation in the residuals to non‐detectable levels, indicating that the variables explained all spatial structure in the data. In principle, if residuals are not autocorrelated then OLS is a special case of GLS. However, our comparison between OLS and GLS models including all environmental variables revealed that GLS de‐emphasized predictors with strong autocorrelation and long‐distance clinal structures, giving more importance to variables acting at smaller geographical scales. Conclusion Although spatial autocorrelation should always be investigated, it does not necessarily generate bias. Rather, it can be a useful tool to investigate mechanisms operating on richness at different spatial scales. Claims that analyses that do not take into account spatial autocorrelation are flawed are without foundation.     

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.     

Challenging urban species diversity: contrasting phylogenetic patterns across plant functional groups in Germany

Cities are hotspots of plant species richness, harboring more species than their rural surroundings, at least over large enough scales. However, species richness does not necessarily cover all aspects of biodiversity such as phylogenetic relationships. Ignoring these relationships, our understanding of how species assemblages develop and change in a changing environment remains incomplete. Given the high vascular plant species richness of urbanized areas in Germany, we asked whether these also have a higher phylogenetic diversity than rural areas, and whether phylogenetic diversity patterns differ systematically between species groups characterized by specific functional traits. Calculating the average phylogenetic distinctness of the total German flora and accounting for spatial autocorrelation, we show that phylogenetic diversity of urban areas does not reflect their high species richness. Hence, high urban species richness is mainly due to more closely related species that are functionally similar and able to deal with urbanization. This diminished phylogenetic information might decrease the flora's capacity to respond to environmental changes.     

Breeding bird diversity in relation to environmental gradients in China

Geographic variation in species richness has been explained by different theories such as energy, productivity, energy–water balance, habitat heterogeneity, and freezing tolerance. This study determines which of these theories best account for gradients of breeding bird richness in China. In addition, we develop a best-fit model to account for the relationship between breeding bird richness and environment in China. Breeding bird species richness in 207 localities (3271 km² per locality on average) from across China was related to thirteen environmental variables after accounting for sampling area. The Akaike's information criterion (AIC) was used to evaluate model performance. We used Moran's I to determine the magnitude of spatial autocorrelation in model residuals, and used simultaneous autoregressive model to determine coefficients of determination and AIC of explanatory variables after accounting for residual spatial autocorrelation. Of all environmental variables examined, normalized difference vegetation index, a measure of plant productivity, is the best variable to explain the variance in breeding bird richness. We found that species richness of breeding birds at the scale examined is best predicted by a combination of plant productivity, elevation range, seasonal variation in potential evapotranspiration, and mean annual temperature. These variables explained 47.3% of the variance in breeding bird richness after accounting for sampling area; most of the explained variance in richness is attributable to the first two of the four variables.     

The biogeography of invasive alien plants in California: an application of GIS and spatial regression analysis

The spatial distribution of invasive alien plants has been poorly documented in California. However, with the increased availability of GIS software and spatially explicit data, the distribution of invasive alien plants can be explored. Using bioregions as defined in Hickman (1993 ), I compared the distribution of invasive alien plants (n = 78) and noninvasive alien plants (n = 1097). The distribution of both categories of alien plants was similar with the exception of a higher concentration of invasive alien plants in the North Coast bioregion. Spatial autocorrelation analysis using Moran's I indicated significant spatial dependence for both invasive and noninvasive alien plant species. I used both ordinary least squares (OLS) and spatial autoregressive (SAR) models to assess the relationship between alien plant species distribution and native plant species richness, road density, population density, elevation, area of sample unit, and precipitation. The OLS model for invasive alien plants included two significant effects; native plant species richness and elevation. The SAR model for invasive alien plants included three significant effects; elevation, road density, and native plant species richness. The SAR model for noninvasive alien plants resulted in the same significant effects as invasive alien plants. Both invasive and noninvasive alien plants are found in regions with low elevation, high road density, and high native‐plant species richness. This is in congruity with previous spatial pattern studies of alien plant species. However, the similarity in effects for both categories of alien plants alludes to the importance of autecological attributes, such as pollination system, dispersal system and differing responses to disturbance in the distribution of invasive plant species. In addition, this study emphasizes the critical importance of testing for spatial autocorrelation in spatial pattern studies and using SAR models when appropriate.     

Fine-scale spatial variation in plant species richness and its relationship to environmental conditions in coastal marshlands

Previous studies have shown that variations in environmental conditions play a major role in explaining variations in plant species richness at community and landscape scales. In this study, we considered the degree to which fine-scale spatial variations in richness could be related to fine-scale variations in abiotic and biotic factors. To examine spatial variation in richness, grids of 1 m² plots were laid out at five sites within a coastal riverine wetland landscape. At each site, a 5 × 7 array of plots was established adjacent to the river’s edge with plots one meter apart. In addition to the estimation of species richness, environmental measurements included sediment salinity, plot microelevation, percent of plot recently disturbed, and estimated community biomass. Our analysis strategy was to combine the use of structural equation modeling (path modeling) with an assessment of spatial association. Mantel’s tests revealed significant spatial autocorrelation in species richness at four of the five sites sampled, indicating that richness in a plot correlated with the richness of nearby plots. We subsequently considered the degree to which spatial autocorrelations in richness could be explained by spatial autocorrelations in environmental conditions. Once data were corrected for environmental correlations, spatial autocorrelation in residual species richness could not be detected at any site. Based on these results, we conclude that in this coastal wetland, there appears to be a fine-scale mapping of diversity to microgradients in environmental conditions.     

Analysis of determinants of mammalian species richness in South America using spatial autoregressive models

Classically, hypotheses concerning the distribution of species have been explored by evaluating the relationship between species richness and environmental variables using ordinary least squares (OLS) regression. However, environmental and ecological data generally show spatial autocorrelation, thus violating the assumption of independently distributed errors. When spatial autocorrelation exists, an alternative is to use autoregressive models that assume spatially autocorrelated errors. We examined the relationship between mammalian species richness in South America and environmental variables, thereby evaluating the relative importance of four competing hypotheses to explain mammalian species richness. Additionally, we compared the results of ordinary least squares (OLS) regression and spatial autoregressive models using Conditional and Simultaneous Autoregressive (CAR and SAR, respectively) models. Variables associated with productivity were the most important at determining mammalian species richness at the scale analyzed. Whereas OLS residuals between species richness and environmental variables were strongly autocorrelated, those from autoregressive models showed less spatial autocorrelation, particularly the SAR model, indicating its suitability for these data. Autoregressive models also fit the data better than the OLS model (increasing R² by 5–14%), and the relative importance of the explanatory variables shifted under CAR and SAR models. These analyses underscore the importance of controlling for spatial autocorrelation in biogeographical studies.     

毛乌素沙地南缘沙漠化临界区域土壤水分和植被空间格局

应用地统计学和经典统计学方法,对毛乌素沙地南缘沙漠化临界区域土壤水分和植被特征的空间分布格局及其相互关系进行研究,结果表明: 0-5 cm和5-10 cm土壤水分符合指数模型,10-15 cm土壤水分和植物群落物种数、植被盖度、植被密度都符合球状模型;0-5 cm土壤水分、植物群落物种数和植被盖度都具有强空间自相关性,5-10 cm、10-15 cm土壤水分和植被密度都具有中等程度的空间自相关性;从牛枝子群落到黑沙蒿群落,各层土壤水分与植物群落物种数之间具有相似的空间格局,都呈先升高后降低的变化趋势,而植被盖度和植被密度呈逐渐减小的变化趋势;0-5 cm土壤水分与植物群落物种数之间具有显著的正相关,是制约植被物种空间分布的关键因素。     

黄河流域被子植物和陆栖脊椎动物丰富度格局及其影响因子

生物多样性的大尺度空间分布格局及其形成机制一直是生态学和生物地理学的核心内容。黄河流域是我国重要的生态屏障, 明确该区域动植物多样性分布格局及其影响因素, 对我国黄河流域生态保护和高质量发展具有重要意义。本研究通过收集黄河流域被子植物和陆栖脊椎动物分布数据, 结合气候、环境异质性和人类活动等信息, 探讨了黄河流域被子植物和陆栖脊椎动物物种丰富度格局及其主要影响因素。结果表明, 黄河流域被子植物和陆栖脊椎动物物种丰富度在区域尺度具有相似的分布格局: 南部山地动植物物种丰富度最高, 而东部高寒区和北部干旱区物种丰富度最低。回归树模型表明, 冠层高度范围和净初级生产力范围分别是黄河流域被子植物和陆栖脊椎动物物种丰富度最重要的预测因子; 当移除空间自相关影响后, 环境异质性和气候因子依然对区域尺度的动植物物种丰富度具有较高且相似的解释度。表明环境异质性和气候共同决定了黄河流域被子植物和陆栖脊椎动物物种丰富度格局, 而人类使用土地面积并不是影响黄河流域动植物物种丰富度格局的主要因子。因此, 在未来的研究中若针对不同区域筛选出更精准的环境驱动因子或选用更多不同类别的环境异质性因子进行分析, 将有助于更深入理解物种多样性格局的成因。     

Plant species diversity and environmental heterogeneity: spatial scale and competing hypotheses

Questions: What is the observed relationship between plant species diversity and spatial environmental heterogeneity? Does the relationship scale predictably with sample plot size? What are the relative contributions to diversity patterns of variables linked to productivity or available energy compared to those corresponding to spatial heterogeneity? Methods: Observational and experimental studies that quantified relationships between plant species richness and within‐sample spatial environmental heterogeneity were reviewed. Effect size in experimental studies was quantified as the standardized mean difference between control (homogeneous) and heterogeneous treatments. For observational studies, effect sizes in individual studies were examined graphically across a gradient of plot size (focal scale). Relative contributions of variables representing spatial heterogeneity were compared to those representing available energy using a response ratio. Results: Forty‐one observational and 11 experimental studies quantified plant species diversity and spatial environmental heterogeneity. Observational studies reported positive species diversity‐spatial heterogeneity correlations at all points across a plot size gradient from ～1.0 × 10^?1 to ～1.0 × 10¹¹ m², although many studies reported spatial heterogeneity variables with no significant relationships to species diversity. The cross‐study effect size in experimental studies was not significantly different from zero. Available energy variables explained consistently more of the variance in species richness than spatial heterogeneity variables, especially at the smallest and largest plot sizes. Main conclusions: Species diversity was not related to spatial heterogeneity in a way predictable by plot size. Positive heterogeneity‐diversity relationships were common, confirming the importance of niche differentiation in species diversity patterns, but future studies examining a range of spatial scales in the same system are required to determine the role of dispersal and available energy in these patterns.     

Factors controlling the spatial species richness pattern of four groups of terrestrial vertebrates in an area between two different biogeographic regions in northern Spain

Aim To examine the influence of environmental variables on species richness patterns of amphibians, reptiles, mammals and birds and to assess the general usefulness of regional atlases of fauna. Location Navarra (10,421 km²) is located in the north of the Iberian Peninsula, in a territory shared by Mediterranean and Eurosiberian biogeographic regions. Important ecological patterns, climate, topography and land‐cover vary significantly from north to south. Methods Maps of vertebrate distribution and climatological and environmental data bases were used in a geographic information systems framework. Generalized additive models and partial regression analysis were used as statistical tools to differentiate (A) the purely spatial fraction, (B) the spatially structured environmental fraction and (C) the purely environmental fraction. In this way, we can evaluate the explanatory capacity of each variable, avoiding false correlations and assessing true causality. Final models were obtained through a stepwise procedure. Results Energy‐related features of climate, aridity and land‐cover variables show significant correlation with the species richness of reptiles, mammals and birds. Mammals and birds exhibit a spatial pattern correlated with variables such as aridity index and vegetation land‐cover. However, the high values of the spatially structured environmental fraction B and the low values of the purely environmental fraction A suggest that these predictor variables have a limited causal relationship with species richness for these vertebrate groups. An increment in land‐cover diversity is correlated with an increment of specific richness in reptiles, mammals and birds. No variables were found to be statistically correlated with amphibian species richness. Main conclusions Although aridity and land‐cover are the best predictor variables, their causal relationship with species richness must be considered with caution. Historical factors exhibiting a similar spatial pattern may be considered equally important in explaining the patterns of species richness. Also, land‐cover diversity appears as an important factor for maintaining biological diversity. Partial regression analysis has proved a useful technique in dealing with spatial autocorrelation. These results highlight the usefulness of coarsely sampled data and cartography at regional scales to predict and explain species richness patterns for mammals and birds. The accuracy of models appears to be related to the range perception of each group and the scale of the information.     

Environmental correlates of species richness of European springtails (Hexapoda: Collembola)

Our knowledge about environmental correlates of the spatial distribution of animal species stems mostly from the study of well known vertebrate and a few invertebrate taxa. The poor spatial resolution of faunistic data and undersampling prohibit detailed spatial modeling for the vast majority of arthropods. However, many such models are necessary for a comparative approach to the impact of environmental factors on the spatial distribution of species of different taxa. Here we use recent compilations of species richness of 35 European countries and larger islands and linear spatial autocorrelation modeling to infer the influence of area and environmental variables on the number of springtail (Collembola) species in Europe. We show that area, winter length and annual temperature difference are major predictors of species richness. We also detected a significant negative longitudinal gradient in the number of springtail species towards Eastern Europe that might be caused by postglacial colonization. In turn, environmental heterogeneity and vascular plant species richness did not significantly contribute to model performance. Contrary to theoretical expectations, climate and longitude corrected species–area relationships of Collembola did not significantly differ between islands and mainlands.     

The relative importance of environment, human activity and space in explaining species richness of South African bird orders

Aim To assess the relative importance of environmental (climate, habitat heterogeneity and topography), human (population density, economic prosperity and land transformation) and spatial (autocorrelation) influences, and the interactions between these predictor groups, on species richness patterns of various avifaunal orders. Location South Africa. Methods Generalized linear models were used to determine the amount of variation in species richness, for each order, attributable to each of the different predictor groups. To assess the relationships between species richness and the various predictor groups, a deviance statistic (a measure of goodness of fit for each model) and the percentage deviation explained for the best fitting model were calculated. Results Of the 12 avifaunal orders examined, spatially structured environmental deviance accounted for most of the variation in species richness in 11 orders (averaging 28%), and 50% or more in seven orders. However, orders comprising mostly water birds (Charadriiformes, Anseriformes, Ciconiformes) had a relatively large component of purely spatial deviance compared with spatially structured environmental deviance, and much of this spatial deviance was due to higher‐order spatial effects such as patchiness, as opposed to linear gradients in species richness. Although human activity, in general, offered little explanatory power to species richness patterns, it was an important correlate of spatial variation in species of Charadriiformes and Anseriformes. The species richness of these water birds was positively related to the presence of artificial water bodies. Main conclusions Not all bird orders showed similar trends when assessing, simultaneously, the relative importance of environmental, human and spatial influences in affecting bird species richness patterns. Although spatially structured environmental deviance described most of the variation in bird species richness, the explanatory power of purely spatial deviance, mostly due to nonlinear geographical effects such as patchiness, became more apparent in orders representing water birds. This was especially true for Charadriiformes, where the strong anthropogenic relationship has negative implications for the successful conservation of this group.     

Environmental determinants of geographic butterfly richness pattern in eastern China

A long-standing task for ecologists and biogeographers is to reveal the underlying mechanisms accounting for the geographic pattern of species diversity. The number of hypotheses to explain geographic variation in species diversity has increased dramatically during the past half century. The oldest and the most popular one is environmental determination. However, seasonality, the intra-annual variability in climate variables has been rarely related to species richness. In this study, we assessed the relative importance of three environmental hypotheses: energy, seasonality and heterogeneity in explaining species richness pattern of butterflies in Eastern China. In addition, we also examined how environmental variables affect the relationship between species richness of butterflies and seed plants at geographic scale. All the environmental factors significantly affected butterfly richness, except sampling area and coefficient of variation of mean monthly precipitation. Energy and seasonality hypotheses explained comparable variation in butterfly richness (42.3 vs. 39.3 %), higher than that of heterogeneity hypothesis (25.9 %). Variation partitioning indicated that the independent effect of seasonality was much lower (0.0 %) than that of energy (5.5 %) and heterogeneity (6.3 %). However, seasonality performed better in explaining butterfly richness in topographically complex areas, reducing spatial autocorrelation in butterfly richness, and more strongly affect the association between butterflies and seed plants. The positive relationship between seed plant richness and butterfly richness was most likely the result of environmental variables (especially seasonality) influencing them in parallel. Insufficient sampling may partly explain the low explanatory power of environmental model (52.1 %) for geographic butterfly richness pattern. Our results have important implications for predicting the response of butterfly diversity to climate change.     

Comparison of native and exotic distribution and richness models across scales reveals essential conservation lessons

Comparing native and exotic plant species distribution and richness models can help to reveal the causes of invasive exotic species proliferation and provide recommendations for preserving native‐dominated ecosystems. However, models may have limited applicability if potentially divergent patterns across scales, spatial autocorrelation and correspondence with community‐wide patterns such as species richness are not considered. I modeled the distributions of 20 dominant native and 20 dominant exotic species among and within patches in a heavily‐invaded and threatened ecosystem in western North America, examining the roles of scale and species origin on variable selection, spatial autocorrelation and model accuracy to determine conditions that favour native over exotic dominants, and derive recommendations for effective management. I also compared distribution models with native and exotic species richness models, to determine the extent to which dominant native and exotic species were representative of synoptic community patterns. Predictability was lower for exotic dominants, possibly because they are environmental generalists, and was lower within than among patches. Predictors were generally shared between distribution and richness models; however, species‐specific differences were common within both native and exotic species groups. Predictors for individual species across scales were frequently different and sometimes opposing. Distribution and richness models suggest that management assuming environmental affiliation at one scale may be ineffective at another; that site prioritization to maximize native versus exotic richness may not preserve the habitat of some common native species; and that intensive management to reduce exotics may be difficult due to low predictability and shared affiliations with natives. Comparing native and exotic distribution and richness models at two scales enabled scale‐specific conservation recommendations and elucidated trade‐offs between management for richness and representation that distribution models at an individual scale would not have allowed.     

Water–energy,land‐cover and heterogeneity drivers of the distribution of plant species richness in a mountain region of the European Alps

Aim To evaluate the relative importance of water–energy, land‐cover, environmental heterogeneity and spatial variables on the regional distribution of Red‐Listed and common vascular plant species richness. Location Trento Province (c. 6200 km²) on the southern border of the European Alps (Italy), subdivided regularly into 228 3′ × 5′ quadrants. Methods Data from a floristic inventory were separated into two subsets, representing Red‐Listed and common (i.e. all except Red‐Listed) plant species richness. Both subsets were separately related to water–energy, land‐cover and environmental heterogeneity variables. We simultaneously applied ordinary least squares regression with variation partitioning and hierarchical partitioning, attempting to identify the most important factors controlling species richness. We combined the analysis of environmental variables with a trend surface analysis and a spatial autocorrelation analysis. Results At the regional scale, plant species richness of both Red‐Listed and common species was primarily related to energy availability and land cover, whereas environmental heterogeneity had a lesser effect. The greatest number of species of both subsets was found in quadrants with the largest energy availability and the greatest degree of urbanization. These findings suggest that the elevation range within our study region imposes an energy‐driven control on the distribution of species richness, which resembles that of the broader latitude gradient. Overall, the two species subsets had similar trends concerning the relative importance of water–energy, land cover and environmental heterogeneity, showing a few differences regarding the selection of some predictors of secondary importance. The incorporation of spatial variables did not improve the explanatory power of the environmental models and the high original spatial autocorrelation in the response variables was reduced drastically by including the selected environmental variables. Main conclusions Water–energy and land cover showed significant pure effects in explaining plant species richness, indicating that climate and land cover should both be included as explanatory variables in modelling species richness in human‐affected landscapes. However, the high degree of shared variation between the two groups made the relative effects difficult to separate. The relatively low range of variation in the environmental heterogeneity variables within our sampling domain might have caused the low importance of this complex factor.     

Alien and native birds in South Africa: patterns, processes and conservation

The spatial distribution of alien species richness often correlates positively with native species richness, and reflects the role of human density and activity, and primary productivity and habitat heterogeneity, in facilitating the establishment and spread of alien species. Here, we investigate the relationship between the spatial distribution of alien bird species, human density, and anthropogenic and natural environmental conditions. Next, we examined the relationship between the spatial distribution of alien bird species and native bird species richness. We examined alien species richness as a response variable, using correlative analyses that take spatial autocorrelation into account. Further, each alien bird species was examined as a response variable, using logistic regression procedures based on binary presence–absence data. A combination of human density and natural habitat heterogeneity best explained the spatial distribution of alien species richness. This contrasts with the results for individual alien species and with previous studies on other non-native taxa showing the importance of primary productivity and anthropogenic habitat modification as explanatory variables. In general, native species richness is an important correlate of the spatial distribution of alien species richness and individual alien species, with alien species being more similar to common species than to rare species.     

Plant composition,not richness,drives occurrence of specialist herbivores

1. How herbivore plant diversity relationships are shaped by the interplay of biotic and abiotic environmental variables is only partly understood. For instance, plant diversity is commonly assumed to determine abundance and richness of associated specialist herbivores. However, this relationship can be altered when environmental variables such as temperature covary with plant diversity. 2. Using gall‐inducing arthropods as focal organisms, biotic and abiotic environmental variables were tested for their relevance to specialist herbivores and their relationship to host plants. In particular, the hypothesis that abundance and richness of gall‐inducing arthropods increase with plant richness was addressed. Additionally, the study asked whether communities of gall‐inducing arthropods match the communities of their host plants. 3. Neither abundance nor species richness of gall‐inducing arthropods was correlated with plant richness or any other of the tested environmental variables. Instead, the number of gall species found per plant decreased with plant richness. This indicates that processes of associational resistance may explain the specialised plant herbivore relationship in our study. 4. Community composition of gall‐inducing arthropods matched host plant communities. In specialised plant herbivore relationships, the presence of obligate host plant species is a prerequisite for the occurrence of its herbivores. 5. It is concluded that the abiotic environment may only play an indirect role in shaping specialist herbivore communities. Instead, the occurrence of specialist herbivore communities might be best explained by plant species composition. Thus, plant species identity should be considered when aiming to understand the processes that shape diversity patterns of specialist herbivores.     

Variation in small-scale spatial heterogeneity of soil properties and vegetation with different land use in semiarid grassland ecosystem

Soil properties (i.e. soil organic carbon, SOC; soil organic nitrogen, SON; and soil C/N ratio) and vegetation in a semiarid grassland of Inner Mongolia, northern China, were studied with the method of geostatistical analysis. We examined the spatial heterogeneity of soil and plants, and possible impacts of land use on their heterogeneity and on the relationship between soil resources and plant richness. Land use affected small scale spatial heterogeneity in plants and soil. SOC, SON and C/N ratio displayed autocorrelation over a range of ～2 m under most circumstances on sites where livestock grazing had been excluded. The uncontrolled grazing site (UG, i.e. unregulated grazing by excessive livestock) displayed an increased range of spatial autocorrelation and the total amount of variability in soil nitrogen over the other land use types. Plant life forms and plant species exhibited spatial autocorrelation over a range of about 2 m on the grazing exclusion (GE) and mowed (MW) sites, while pattern of spatial autocorrelation for several less common species on the UG site were difficult to predict. Plant species richness was positively related with spatial heterogeneity of SOC, SON and C/N on both GE and MW sites, and with only SOC heterogeneity on the UG site. These suggest that spatial soil heterogeneity plays an active role in maintaining plant species richness. However, we call for caution in generalization of the control of spatial soil heterogeneity over plant richness when multiple modes of disturbances are present, as we found in this study that higher total amount of variation in soil nitrogen and C/N ratio on the over-grazed UG site did not lead to increased plant species richness, and that land use had apparent effects on the patterns of spatial heterogeneity in both vegetation and soil.