Macro‐scale bird species richness patterns of the East Asian mainland and islands: energy,area and isolation

Aim To create a map of bird species richness (BSR) in East Asia and to examine the effect of area, isolation, primary productivity, topographic heterogeneity, and human population density on BSR. Location East Asia (from 70° E to 180° E longitude), including the eastern half of the Palaearctic Region, the entire Oriental Region, and the entire Wallacea Subregion. Methods The breeding ranges of 2406 terrestrial bird species were mapped and overlaid to create a species richness map. The BSR map was transformed into a 100 × 100 km quadrat system, and BSR was analysed in relation to land area, average normalized difference vegetation index (NDVI), elevation range, and average population density. Results In general, BSR declined from the Tropics to the Arctic. In mainland East Asia, however, BSR was highest around the Tropic of Cancer, and fluctuated between 30° and 50° N. Islands had lower BSR than adjacent mainland areas. The NDVI was strongly positively correlated with BSR in mainland areas and on islands. For mainland areas, NDVI explained 65% of the BSR variation, and topographic heterogeneity explained an additional 6% in ordinary least‐squares regression. On islands, NDVI explained 66% of BSR variation, island area explained 13%, and distance to mainland accounted for 1%. Main conclusions In East Asia, we suggest that primary productivity is the key factor underpinning patterns of BSR. Primary productivity sets the upper limits of the capacity of habitats to support bird species. In isolated areas such as islands and peninsulas, however, BSR might not reach the richness limits set by primary productivity because the degree of isolation and area size also can affect species richness. Other factors, such as spatial heterogeneity, biotic interactions, and perturbations, may also affect species richness. However, their effects are secondary and are not as strong as primary productivity, isolation, and area size.     

Breeding bird species richness in Taiwan: distribution on gradients of elevation, primary productivity and urbanization

Aim To examine the richness of breeding bird species in relation to elevation, primary productivity and urbanization. Location The island of Taiwan (120°–122° E, 22°–25° N). Methods We arranged bird species richness (BSR) data from 288 bird censuses undertaken in Taiwan into a 2 × 2 km quadrat system and calculated average values of elevation, primary productivity [surrogated by normalized difference vegetation index (NDVI)], and urbanization (surrogated by road density and percentage of built area) for each 2 × 2 km quadrat. Results Bird species richness showed a hump‐shaped relationship with elevation. It increased with elevation from sea level (10–64 species per 2 × 2 km quadrat), peaked around 2000 m (43–76 species), and then decreased with elevation towards its minimum at the highest elevation. Road density and percentage of built area decreased with elevation, and NDVI showed a hump‐shaped relationship with elevation and inverse relationships with road density and percentage of built area. BSR increased with NDVI and decreased with road density and percentage of built area. Linear and cubic terms of elevation together explained 31.3% of the variance in BSR, and road density explained additional 3.4%. The explanatory power of NDVI on BSR was insignificant after the effects of elevation and road density had been justified. Main conclusions We argue that urbanization plays an important role in the BSR of Taiwan. Urbanization might indirectly decrease BSR through decreasing primary productivity and therefore change the hypothetical inverse relationship between BSR and elevation into a hump‐shaped relationship. We also propose a time hypothesis that the biotic communities in the mid‐elevation zone of Taiwan had relatively longer periods of existence during the Pleistocene glacial cycles, which might be one underlying process of the observed hump‐shaped relationship between species diversity and elevation.     

The effect of energy and seasonality on avian species richness and community composition

We analyzed geographic patterns of richness in both the breeding and winter season in relation to a remotely sensed index of seasonal production (normalized difference vegetation index [NDVI]) and to measures of habitat heterogeneity at four different spatial resolutions. The relationship between avian richness and NDVI was consistent between seasons, suggesting that the way in which available energy is converted to bird species is similar at these ecologically distinct times of year. The number and proportion of migrant species in breeding communities also increased predictably with the degree of seasonality. The NDVI was a much better predictor of seasonal richness at finer spatial scales, whereas habitat heterogeneity best predicted richness at coarser spatial resolutions. While we find strong support for a positive relationship between available energy and species richness, seasonal NDVI explained at most 61% of the variation in richness. Seasonal NDVI and habitat heterogeneity together explain up to 69% of the variation in richness.     

Primary productivity and species richness: relationships among functional guilds, residency groups and vagility classes at multiple spatial scales

One of the major determinants of species richness is the amount of energy available, often measured as primary productivity. Heterogeneity of environmental variables has also been found to influence species richness. Predicting species distributions across landscapes and identifying areas that have high species richness, or vulnerable groups of species, is useful for land management. Remotely sensed data may help identify such areas, with the Normalized Difference Vegetation Index (NDVI) providing an estimate of primary productivity. We examined the relationship between maximum productivity (NDVI), heterogeneity of productivity, and species richness of birds and butterflies at multiple spatial scales. We also explored relationships between productivity, functional guilds and residency groups of birds, and vagility classes of butterflies. Positive linear relationships between maximum NDVI and number of functional guilds of birds were found at two spatial scales. We also found positive linear relationships between maximum NDVI and species richness of neotropical migrant birds at two scales. Heterogeneity of NDVI, by contrast, was negatively associated with number of functional guilds of birds and species richness of resident birds. Maximum NDVI was associated with species richness of all butterflies and of the most vagile butterflies. No association was found between heterogeneity of NDVI and species richness of butterflies. In the Great Basin, where high greenness and availability of water correspond to areas of high species richness and maximum NDVI, our results suggest that NDVI can provide a reliable basis for stratifying surveys of biodiversity, by highlighting areas of potentially high biodiversity across large areas. Measures of heterogeneity of NDVI appear to be less useful in explaining species richness.     

Energy–water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperm species richness in China

Studying the pattern of species richness is crucial in understanding the diversity and distribution of organisms in the earth. Climate and human influences are the major driving factors that directly influence the large‐scale distributions of plant species, including gymnosperms. Understanding how gymnosperms respond to climate, topography, and human‐induced changes is useful in predicting the impacts of global change. Here, we attempt to evaluate how climatic and human‐induced processes could affect the spatial richness patterns of gymnosperms in China. Initially, we divided a map of the country into grid cells of 50 × 50 km² spatial resolution and plotted the geographical coordinate distribution occurrence of 236 native gymnosperm taxa. The gymnosperm taxa were separated into three response variables: (a) all species, (b) endemic species, and (c) nonendemic species, based on their distribution. The species richness patterns of these response variables to four predictor sets were also evaluated: (a) energy–water, (b) climatic seasonality, (c) habitat heterogeneity, and (d) human influences. We performed generalized linear models (GLMs) and variation partitioning analyses to determine the effect of predictors on spatial richness patterns. The results showed that the distribution pattern of species richness was highest in the southwestern mountainous area and Taiwan in China. We found a significant relationship between the predictor variable set and species richness pattern. Further, our findings provide evidence that climatic seasonality is the most important factor in explaining distinct fractions of variations in the species richness patterns of all studied response variables. Moreover, it was found that energy–water was the best predictor set to determine the richness pattern of all species and endemic species, while habitat heterogeneity has a better influence on nonendemic species. Therefore, we conclude that with the current climate fluctuations as a result of climate change and increasing human activities, gymnosperms might face a high risk of extinction.     

Influence of settlement time, human population, park shape and age, visitation and roads on the number of alien plant species in protected areas in the USA

Abstract. I examined a data set of 77 protected areas in the USA (including national and state parks) to determine which of the following variables most strongly influence alien plant species richness: park area, climate (temperature and precipitation), native species richness, visitation rate, local human population size, total road length, park shape and duration of European settlement. Many of these predictor variables are intercorrelated, so I used multiple regression to help separate their effects. In support of previous studies, native species richness was the best single predictor of alien species richness, probably because it was a good estimator of both park area and habitat diversity available for establishment of alien species. Other significant predictors of alien species richness were years of occupation of the area by European settlers and the human population size of adjacent counties. Climate, visitation rate, road length and park shape did not influence alien species richness. The proportion of alien species (alien richness/native richness) is inversely related to park area, in agreement with a previous study. By identifying which variables are most important in determining alien species richness, such findings suggest ways to reduce alien species establishment.     

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.     

Patterns of floristic richness in vegetation communities of California: regional scale analysis with multi-temporal NDVI

Aim Applying water‐energy dynamics and heterogeneity theory to explain species richness via remote sensing could allow for the regional characterization and monitoring of vegetation community assemblages and their environment. We assess the relationship of multi‐temporal normalized difference vegetation index (NDVI) to plant species richness in vegetation communities. Location California, USA. Methods Sub‐regions containing species inventories for chaparral, coastal sage scrub, foothill woodland, and yellow pine forest communities were intersected with a vegetation community map and an AVHRR NDVI time series for 1990, 1991, 1992, 1995 and 1996. Principal components analysis reduced the AVHRR data to three variables representing the sum and temporal trajectories of NDVI within each community. A fourth variable representing heterogeneity was tested using the standard deviation of the first component. Quadratic forms of these variables were also tested. Species richness was analysed by stepwise regression. Results Chaparral, coastal sage scrub, and yellow pine forest had the best relationships between species richness and NDVI. Richness of chaparral was related to NDVI heterogeneity and spring greenness (r² varied between 0.26 and 0.62 depending on year of NDVI data). Richness of coastal sage scrub was nonlinearly related to annual NDVI and heterogeneity (r² 0.63–0.81), with peak richness at intermediate values. Foothill woodland richness was related to heterogeneity in a monotonic curvilinear fashion (r² 0.28–0.35). Yellow pine forest richness was negatively related to spring greenness and positively related to heterogeneity (r² 0.40–0.46). Main Conclusions While NDVI's relationship to species richness varied, the selection of NDVI variables was generally consistent across years and indicated that spatial variability in NDVI may reflect important patterns in water‐energy use that affect plant species richness. The principal component axis that should correspond closely with annual mean NPP showed a less prominent role. We conclude that plant species richness for coarse vegetation associations can be characterized and monitored at a regional scale and over long periods of time using relatively coarse resolution NDVI data.     

Effects of spatial heterogeneity on butterfly species richness in Rocky Mountain National Park, CO, USA

We investigated butterfly responses to plot-level characteristics (plant species richness, vegetation height, and range in NDVI [normalized difference vegetation index]) and spatial heterogeneity in topography and landscape patterns (composition and configuration) at multiple spatial scales. Stratified random sampling was used to collect data on butterfly species richness from seventy-six 20 × 50 m plots. The plant species richness and average vegetation height data were collected from 76 modified-Whittaker plots overlaid on 76 butterfly plots. Spatial heterogeneity around sample plots was quantified by measuring topographic variables and landscape metrics at eight spatial extents (radii of 300, 600 to 2,400 m). The number of butterfly species recorded was strongly positively correlated with plant species richness, proportion of shrubland and mean patch size of shrubland. Patterns in butterfly species richness were negatively correlated with other variables including mean patch size, average vegetation height, elevation, and range in NDVI. The best predictive model selected using Akaike’s Information Criterion corrected for small sample size (AIC_c), explained 62% of the variation in butterfly species richness at the 2,100 m spatial extent. Average vegetation height and mean patch size were among the best predictors of butterfly species richness. The models that included plot-level information and topographic variables explained relatively less variation in butterfly species richness, and were improved significantly after including landscape metrics. Our results suggest that spatial heterogeneity greatly influences patterns in butterfly species richness, and that it should be explicitly considered in conservation and management actions.     

Relative influence of habitat heterogeneity, climate, human disturbance, and spatial structure on vertebrate species richness in Spain

Many factors affect the distribution of species richness. This study examines the relative influence of habitat heterogeneity, climate, human disturbance, and spatial structure on the species-richness distribution of terrestrial vertebrates (amphibians, reptiles, birds and mammals) in mainland Spain. The results indicate that spatial structure and environment exert similar influences on species richness. For all four taxa, species richness increases southward and northward, being lower in the center of the country, when controlled for other variables. This may be the result of a peninsular effect, as found in other studies, and reflect the importance of historical events on species richness in the Iberian Peninsula. Climate is more important than habitat heterogeneity in determining species richness. Temperature is positively correlated with amphibian, reptile, and bird species richness, while mammalian species richness is highest at intermediate temperatures. This effect is stronger in ectotherms than among endotherms, perhaps reflecting physiological differences. Precipitation positively correlates with bird and mammalian species richness, but has no effect on ectotherm species richness. Amphibian species richness increases with altitudinal range, and bird species richness with habitat diversity. Human population density is positively correlated with bird and mammalian species richness, but does not affect ectotherm species richness, while amphibian and bird species richness is highest at moderate levels of human land alteration (farmland). However, unexplained variance remains, and we discuss that the effects of environmental variables on species richness may vary geographically, causing different effects to be obscured on a national scale, diminishing the explanatory power of environmental variables.     

Relative importance of water, energy, and heterogeneity in determining regional pteridophyte and seed plant richness in China

Environmental variables, such as ambient energy, water availability, and environmental heterogeneity have been frequently proposed to account for species diversity gradients. How taxon-specific functional traits define large-scale richness gradients is a fundamental issue in understanding spatial patterns of species diversity, but has not been well documented. Using a large dataset on the regional flora from China, we examine the contrast spatial patterns and environmental determinants between pteridophytes and seed plants which differ in dispersal capacity and environmental requirements. Pteridophyte richness shows more pronounced spatial variation and stronger environmental associations than seed plant richness. Water availability generally accounts for more spatial variance in species richness of pteridophytes and seed plants than energy and heterogeneity do, especially for pteridophytes which have high dependence on moist and shady environments. Thus, pteridophyte richness is disproportionally affected by water-related variables; this in turn results in a higher proportion of pteridophytes in regional vascular plant floras (pteridophyte proportion) in wet regions. Most of the variance in seed plant richness, pteridophyte richness, and pteridophyte proportion explained by energy is included in variation that water and heterogeneity account for, indicating the redundancy of energy in the study extent. However, heterogeneity is more important for determining seed plant distributions. Pteridophyte and seed plant richness is strongly correlated, even after the environmental effects have been removed, implying functional linkages between them. Our study highlights the importance of incorporating biological traits of different taxonomic groups into the studies of macroecology and global change biology.     

Human impacts on environment–diversity relationships: evidence for biotic homogenization from butterfly species richness patterns

Aim Broad‐scale spatial variation in species richness relates to climate and physical heterogeneity but human activities may be changing these patterns. We test whether climate and heterogeneity predict butterfly species richness regionally and across Canada and whether these relationships change in areas of human activity. Location Canada. Methods We modelled the ranges of 102 butterfly species using genetic algorithms for rule‐set production (GARP). We then measured butterfly species richness and potentially important aspects of human activity and the natural environment. These were included in a series of statistical models to determine which factors are likely to affect butterfly species richness in Canada. We considered patterns across Canada, within predominantly natural areas, human‐dominated areas and particular ecozones. We examined independent observations of butterfly species currently listed under Canada's endangered species legislation to test whether these were consistent with findings from statistical models. Results Growing season temperature is the main determinant of butterfly species richness across Canada, with substantial contributions from habitat heterogeneity (measured using elevation). Only in the driest areas does precipitation emerge as a leading predictor of richness. The slope of relationships between all of these variables and butterfly species richness becomes shallower in human‐dominated areas, but butterfly richness is still highest there. Insecticide applications, habitat loss and road networks reduce butterfly richness in human‐dominated areas, but these effects are relatively small. All of Canada's at‐risk butterfly species are located in these human‐dominated areas. Main conclusions Temperature affects butterfly species richness to a greater extent than habitat heterogeneity at fine spatial scales and is generally far more important than precipitation, supporting both the species richness–energy and habitat heterogeneity hypotheses. Human activities, especially in southern Canada, appear to cause surprisingly consistent trends in biotic homogenization across this region, perhaps through range expansion of common species and loss of range‐restricted species.     

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。     

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.     

Can we predict butterfly diversity along an elevation gradient from space?

An important challenge in ecology is to predict patterns of biodiversity across eco‐geographical gradients. This is particularly relevant in areas that are inaccessible, but are of high research and conservation value, such as mountains. Potentially, remotely‐sensed vegetation indices derived from satellite images can help in predicting species diversity in vast and remote areas via their relationship with two of the major factors that are known to affect biodiversity: productivity and spatial heterogeneity in productivity. Here, we examined whether the Normalized Difference Vegetation Index (NDVI) can be used effectively to predict changes in butterfly richness, range size rarity and beta diversity along an elevation gradient. We examined the relationship between butterfly diversity and both the mean NDVI within elevation belts (a surrogate of productivity) and the variability in NDVI within and among elevation belts (surrogates for spatial heterogeneity in productivity). We calculated NDVI at three spatial extents, using a high spatial resolution QuickBird satellite image. We obtained data on butterfly richness, rarity and beta diversity by field sampling 100 m quadrats and transects between 500 and 2200 m in Mt Hermon, Israel. We found that the variability in NDVI, as measured both within and among adjacent elevation belts, was strongly and significantly correlated with butterfly richness. Butterfly range size rarity was strongly correlated with the mean and the standard deviation of NDVI within belts. In our system it appears that it is spatial heterogeneity in productivity rather than productivity per se that explained butterfly richness. These results suggest that remotely‐sensed data can provide a useful tool for assessing spatial patterns of butterfly richness in inaccessible areas. The results further indicate the importance of considering spatial heterogeneity in productivity along elevation gradients, which has no lesser importance than productivity in shaping richness and rarity, especially at the local scale.     

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.     

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.     

Small mammal species richness and turnover along elevational gradient in Yulong Mountain,Yunnan, Southwest China

Understanding the species diversity patterns along elevational gradients is critical for biodiversity conservation in mountainous regions. We examined the elevational patterns of species richness and turnover, and evaluated the effects of spatial and environmental factors on nonvolant small mammals (hereafter “small mammal”) predicted a priori by alternative hypotheses (mid‐domain effect [MDE], species–area relationship [SAR], energy, environmental stability, and habitat complexity]) proposed to explain the variation of diversity. We designed a standardized sampling scheme to trap small mammals at ten elevational bands across the entire elevational gradient on Yulong Mountain, southwest China. A total of 1,808 small mammals representing 23 species were trapped. We observed the hump‐shaped distribution pattern of the overall species richness along elevational gradient. Insectivores, rodents, large‐ranged species, and endemic species richness showed the general hump‐shaped pattern but peaked at different elevations, whereas the small‐ranged species and endemic species favored the decreasing richness pattern. The MDE and the energy hypothesis were supported, whereas little support was found for the SAR, the environmental stability hypothesis, and the habitat complexity. However, the primary driver(s) for richness patterns differed among the partitioning groups, with NDVI (the normalized difference vegetation index) and MDE being the most important variables for the total richness pattern. Species turnover for all small mammal groups increased with elevation, and it supported a decrease in community similarity with elevational distance. Our results emphasized for increased conservation efforts in the higher elevation regions of the Yulong Mountain.     

Disentangling the effects of area, energy and habitat heterogeneity on boreal forest bird species richness in protected areas

Aim  One of the few general laws in ecology is that species richness is a positive function of area. However, it has been proposed that area would merely be a proxy for energy. Additionally, habitat heterogeneity has been found to be an important factor determining species richness. Yet the relative importance of those relationships is little known, and it is still unclear how they are brought about. We aimed to dissect which factors drive the species richness of boreal forest birds, and to identify the most probable mechanisms.
Location  Forested protected areas in Finland.
Methods  Using bird line census data collected in 104 protected areas, we ran simultaneous autoregressive models to explain the species richness of forest birds. We explored the value of forest area, tree volume, tree growth, mean degree days and habitat heterogeneity as explanatory variables and used the species richness within different species groups, based on the predictions of hypothesized mechanisms, as a response variable.
Results  Energy, rather than area or habitat heterogeneity, seems to be the main driver of species richness in boreal forest birds. More specifically, productive energy was a better predictor of total species richness than solar energy. Among the tested hypothetical mechanisms, the sampling hypothesis received strong support. After accounting for sampling, solar energy had an effect on species richness.
Main conclusions  As productive energy, such as tree volume, is associated with species richness, high-energy areas should be prioritized in forest conservation planning. Reductions in productive energy may first lead to the disappearance of the rarest species due to the random sampling process. Climate change may result in increased species richness due to increasing amount of productive and solar energy in forests. However, the range shifts of bird species may not be fast enough to keep up with the temperature increases.     

Species richness, environmental correlates, and spatial scale: a test using South African birds

Energy and habitat heterogeneity are important correlates of spatial variation in species richness, though few investigations have sought to determine simultaneously their relative influences. Here we use the South African avifauna to examine the extent to which species richness is related to these variables and how these relationships depend on spatial grain. Taking spatial autocorrelation and area effects into account, we find that primary productivity, precipitation, absolute minimum temperature, and, at coarser resolutions, habitat heterogeneity account for most of the variation in species richness. Species richness and productivity are positively related, whereas the relationship between potential evapotranspiration (PET) and richness is unimodal. This is largely because of the constraining effects of low rainfall on productivity in high-PET areas. The increase in the importance of vegetation heterogeneity as an explanatory variable is caused largely by an increase in the range of vegetation heterogeneity included at coarse resolutions and is probably also a result of the positive effects of environmental heterogeneity on species richness. Our findings indicate that species richness is correlated with, and hence likely a function of, several variables, that spatial resolution and extent must be taken into account during investigations of these relationships, and that surrogate measures for productivity should be interpreted cautiously.