Topography, energy and the global distribution of bird species richness

A major goal of ecology is to determine the causes of the latitudinal gradient in global distribution of species richness. Current evidence points to either energy availability or habitat heterogeneity as the most likely environmental drivers in terrestrial systems, but their relative importance is controversial in the absence of analyses of global (rather than continental or regional) extent. Here we use data on the global distribution of extant continental and continental island bird species to test the explanatory power of energy availability and habitat heterogeneity while simultaneously addressing issues of spatial resolution, spatial autocorrelation, geometric constraints upon species' range dynamics, and the impact of human populations and historical glacial ice-cover. At the finest resolution (1 degree), topographical variability and temperature are identified as the most important global predictors of avian species richness in multi-predictor models. Topographical variability is most important in single-predictor models, followed by productive energy. Adjusting for null expectations based on geometric constraints on species richness improves overall model fit but has negligible impact on tests of environmental predictors. Conclusions concerning the relative importance of environmental predictors of species richness cannot be extrapolated from one biogeographic realm to others or the globe. Rather a global perspective confirms the primary importance of mountain ranges in high-energy areas.     

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.     

Environmental correlates of leguminosae species richness in Mexico: Quantifying the contributions of energy and environmental seasonality

Explaining species richness patterns is a central issue in ecology, but a general explanation remains elusive. Environmental conditions have been proposed to be important drivers of these patterns, but we still need to better understand the relative contribution of environmental factors. Here, we aim at testing two environmental hypotheses for richness gradients: energy availability and environmental seasonality using diversity patterns of the family Leguminosae across Mexico. We compiled a data base of 502 species and 32,962 records. After dividing Mexico into 100 × 100 km grid cells, we constructed a map of variation in species richness that accounts for heterogeneity in sampling effort. We found the cells with the highest species richness of legumes are in the Neotropical region of Pacific coastal and southern Mexico, where the legume family dominates the tropical rain forests and seasonally dry tropical forests. Regression models show that energy and seasonality predictors can explain 25% and 49% of the variation in richness, respectively. Spatial autocorrelation analysis showed that richness has a strong spatial structure, but that most of this structure disappears when both energy and seasonality are used to account for richness gradient. Our study demonstrates multiple environmental conditions contribute complementarily to explain diversity gradients. Moreover, it shows that in some regions, environmental seasonality can be more important than energy availability, contradicting studies at coarser spatial scales. More basic taxonomic and floristic work is needed to help describe patterns of diversity for many groups to allow for testing the underlying mechanisms responsible for diversity gradients. Abstract in Spanish is available with online material.     

中国蚂蚁丰富度地理分布格局及其与环境因子的关系

物种丰富度分布格局及其形成机制的研究对于生物多样性保护具有重要意义。为了了解中国蚂蚁物种丰富度分布格局,利用中国省级尺度蚂蚁物种分布数据和环境信息,结合GIS和数理统计方法,探讨蚂蚁物种丰富度的地理分布格局与环境因子之间的关系。研究结果表明:(1)蚂蚁丰富度随纬度增加呈逐渐递减趋势,但缺乏显著的经度梯度。丰富度最高的地区主要集中在南方省份,我国北方、西北干旱区和青藏高原北部地区丰富度较低;(2)简单线性回归分析表明,能量、水分和季节性因素中,影响蚂蚁物种丰富度最强的因子分别为最冷月均温(TEMmin)(R2adj=0.532)、年均降水量(PREC)(R2adj=0.376)和年温度变化范围(TEMvar)(R2adj=0.539),而单个生境异质性因子对蚂蚁物种丰富度的影响均不显著;(3)最优模型由年均温(TEM)、海拔变化范围(ELErange)和年温度变化范围(TEMvar)组成,能够解释68.4%的蚂蚁丰富度地理分异。鉴于海拔变化范围更多地反映与温度相关的生境异质性,因此温度是限制中国蚂蚁分布的最重要因素。另外,分析结果还表明,海南、贵州、江西、四川、安徽和山西等6省蚂蚁区系调查最不充分,是未来发现蚂蚁新分布的热点地区。     

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.     

Patterns of species richness for vascular plants in China's nature reserves

Explaining the heterogeneous distribution of biodiversity across the Earth has long been a challenge to ecologists and biogeographers. Here, we document the patterns of plant species richness for different taxonomic groups in China's nature reserves, and discuss their possible explanations at national and regional scales, using vascular plant richness data coupled with information on climate and topographical variables. We found that water deficit, energy and elevation range (a surrogate of habitat heterogeneity) represent the primary explanations for variation in plant species richness of the nature reserves across China. There are consistent relationships between species richness and climate and habitat heterogeneity for different taxonomic vascular plant groups at the national scale. Habitat heterogeneity is strongly associated with plant richness in all regions, whereas climatic constraints to plant diversity vary regionally. In the regions where energy is abundant or water is scarce, plant richness patterns were determined by water and habitat heterogeneity, whereas in the region with low energy inputs, water interacting with energy, and habitat heterogeneity determined its species richness pattern. Our results also suggest that energy variables alone do not represent the primary predictor of plant richness.     

Determinants of species richness patterns in the Netherlands across multiple taxonomic groups

We examined the species richness patterns of five different species groups (mosses, reptiles and amphibians, grasshoppers and crickets, dragonflies, and hoverflies) in the Netherlands (41,500 km²) using sampling units of 5 × 5 km. We compared the spatial patterns of species richness of the five groups using Spearman’s rank correlation and used a stepwise multiple regression generalized linear modelling (GLM) approach to assess their relation with a set of 36 environmental variables, selected because they can be related to the several hypotheses on biodiversity patterns. Species richness patterns of the five groups were to a certain extent congruent. Our data suggest that environmental heterogeneity (in particular habitat heterogeneity) is one of the major determinants of variation in species richness within these five groups. We found that for taxonomic groups comprising a low number of species, our regression model explained more of the variability in species richness than for taxonomic groups with a large number of species.     

Environmental variables drive spatial patterns of trophic diversity in mammals

Understanding environmental drivers of species diversity has become increasingly important under climate change. Different trophic groups (predators, omnivores and herbivores) interact with their environments in fundamentally different ways and may therefore be influenced by different environmental drivers. Using random forest models, we identified drivers of terrestrial mammals' total and proportional species richness within trophic groups at a global scale. Precipitation seasonality was the most important predictor of richness for all trophic groups. Richness peaked at intermediate precipitation seasonality, indicating that moderate levels of environmental heterogeneity promote mammal richness. Gross primary production (GPP) was the most important correlate of the relative contribution of each trophic group to total species richness. The strong relationship with GPP demonstrates that basal-level resource availability influences how diversity is structured among trophic groups. Our findings suggest that environmental characteristics that influence resource temporal variability and abundance are important predictors of terrestrial mammal richness at a global scale.     

The mid‐domain effect and habitat complexity applied to elevational gradients: Moss species richness in a temperate semihumid monsoon climate mountain of China

The utility of elevational gradients as tools to test either ecological hypotheses and delineate elevation‐associated environmental factors that explain the species diversity patterns is critical for moss species conservation. We examined the elevational patterns of species richness and evaluated the effects of spatial and environmental factors on moss species predicted a priori by alternative hypotheses, including mid‐domain effect (MDE), habitat complexity, energy, and environment proposed to explain the variation of diversity. Last, we assessed the contribution of elevation toward explaining the heterogeneity among sampling sites. We observed the hump‐shaped distribution pattern of species richness along elevational gradient. The MDE and the habitat complexity hypothesis were supported with MDE being the primary driver for richness patterns, whereas little support was found for the energy and the environmental factors.     

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.     

Deconstructing the mammal species richness pattern in Europe – towards an understanding of the relative importance of climate,biogeographic history,habitat heterogeneity and humans

Aim We deconstructed the mammal species richness pattern in Europe to assess the importance of large‐scale gradients in current macroclimate relative to biogeographic history, habitat heterogeneity and human influence (HHH variables) as richness determinants for total species, and for widespread and endemic species separately. Location Europe, west of 30° E. Methods We deconstructed total species richness (50‐km resolution) into its widespread and endemic species richness components. We used simultaneous autoregressive modelling (SAR) with information‐theoretic model selection and variation partitioning to assess the importance of macroclimate and HHH variables. The HHH variables included two historical factors, estimated by novel methodologies: (1) ice‐age‐driven dynamics, represented by accessibility to recolonization from hindcasting‐estimated glacial refugia, and (2) biogeographic peninsular dynamics, represented by distance to the entry region for the main European faunal source in western Asia. Results A large fraction of explained variation was shared between macroclimate and HHH in the SAR models. For total species richness, more variation could be uniquely attributed to macroclimate than to HHH, whereas for the deconstructed patterns (widespread and endemic species) the opposite was the case. Considering the individual factors, there was a strong peninsula effect on both widespread and endemic species richness but not on total richness. Main conclusions Both macroclimate and HHH variables (history, habitat heterogeneity and human influence) proved important predictors of species richness, but also difficult to disentangle. Notably, biogeographic history, in particular peninsular dynamics, is an important determinant of widespread and endemic species richness.     

Environment–richness relationships for mammals,birds, reptiles,and amphibians at global and regional scales

Climate has been routinely indicated as a major determinant of broad-scale species richness patterns for a variety of taxa, but studies vary widely in attributing richness variation to the broad-scale distribution of energy, water, ecosystem productivity, habitat heterogeneity, or some combination thereof. Here, I report global and regional environment–richness relationships for the four classes of terrestrial vertebrates (mammals, birds, reptiles, amphibians) using identical sample units and the same set of climate (temperature, precipitation, annual actual evapotranspiration), productivity (normalized difference vegetation index), and topographic (elevation range) variables. My results strongly support concomitant availability of energy and water as the principal constraint on global richness for all vertebrate groups except reptiles, which are largely constrained by temperature. However, environment–richness models for all taxonomic groups varied widely when applied to single (continental-scale) biogeographic realms. In particular, I found strong support for the ‘water–energy dynamics hypothesis’ that models richness as a function of ambient energy (temperature) in high latitudes and water availability (precipitation) at low latitudes, partially independent of productivity. Ectotherm groups were more constrained by temperature than endotherms, and amphibians were more constrained by water availability than other groups. Although habitat heterogeneity, measured as elevation range, was a consistent contributor to global and regional richness models for all groups, its contribution was always minor compared to other variables. I conclude that temperature and water availability are key variables for modeling broad-scale vertebrate richness, but there remains significant room for taxon-specific modeling approaches and for the inclusion of non-climate factors related to evolutionary history and faunal assembly in different regions.     

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.     

Bird diversity patterns in Neotropical temperate farmlands: The role of environmental factors and trophic groups in the spring and autumn

Productivity, habitat heterogeneity and environmental similarity are of the most widely accepted hypotheses to explain spatial patterns of species richness and species composition similarity. Environmental factors may exhibit seasonal changes affecting species distributions. We explored possible changes in spatial patterns of bird species richness and species composition similarity. Feeding habits are likely to have a major influence in bird–environment associations and, given that food availability shows seasonal changes in temperate climates, we expect those associations to differ by trophic group (insectivores or granivores). We surveyed birds and estimated environmental variables along line‐transects covering an E‐W gradient of annual precipitation in the Pampas of Argentina during the autumn and the spring. We examined responses of bird species richness to spatial changes in habitat productivity and heterogeneity using regression analyses, and explored potential differences between seasons of those responses. Furthermore, we used Mantel tests to examine the relationship between species composition similarity and both the environmental similarity between sites and the geographic distance between sites, also assessing differences between seasons in those relationships. Richness of insectivorous birds was directly related to primary productivity in both seasons, whereas richness of seed‐eaters showed a positive association with habitat heterogeneity during the spring. Species composition similarity between assemblages was correlated with both productivity similarity and geographic proximity during the autumn and the spring, except for insectivore assemblages. Diversity within main trophic groups seemed to reflect differences in their spatial patterns as a response to changes between seasons in the spatial patterns of food resources. Our findings suggest that considering different seasons and functional groups in the analyses of diversity spatial pattern could contribute to better understand the determinants of biological diversity in temperate climates.     

Habitat is more important than climate and animal richness at shaping latitudinal variation in plant diversity in China

Species data from 249 National Nature Reserves in China were used to identify potential underlying drivers of latitudinal gradients in plant diversity. We used generalized linear models to assess correlations between predictor and plant species richness. Variance partitioning was then used to decompose the variation in plant richness into different taxonomic levels among the three groups of predictors (i.e., climate, habitat and animal). We found that species richness showed significant latitudinal trends in richness (p?<?0.001). This remained true when examining gymnosperms, angiosperms and ferns individually. Climate and habitat variables explained more variation in richness across different plant groups than did animal richness. Annual precipitation was the best climate variable across different taxonomic plants groups, and soil pH and elevation range were the best habitat variables across different taxonomic plant groups. The independent effects of habitat variables were higher than that of climate and animal variables across different taxonomic plant groups. Finally, climate, habitat heterogeneity, and animal richness explain 48.8% of the variation in total species richness, 28.2% in gymnosperm richness, 44.2% in angiosperm richness, and 38.9% in fern richness.     

Latitudinal diversity gradients in bryophytes and woody plants: Roles of temperature and water availability

It remains unclear whether the latitudinal diversity gradients of micro- and macro-organisms are driven by the same macro-environmental variables. We used the newly completed species catalog and distribution information of bryophytes in China to explore their spatial species richness patterns, and to investigate the underlying roles of energy availability, climatic seasonality, and environmental heterogeneity in shaping these patterns. We then compared these patterns to those found for woody plants. We found that, unlike woody plants, mosses and liverworts showed only weakly negative latitudinal trends in species richness. The spatial patterns of liverwort richness and moss richness were overwhelmingly explained by contemporary environmental variables, although explained variation was lower than that for woody plants. Similar to woody plants, energy and climatic seasonality hypotheses dominate as explanatory variables but show high redundancy in shaping the distribution of bryophytes. Water variables, that is, the annual availability, intra-annual variability and spatial heterogeneity in precipitation, played a predominant role in explaining spatial variation of species richness of bryophytes, especially for liverworts, whereas woody plant richness was affected most by temperature variables. We suggest that further research on spatial patterns of bryophytes should incorporate the knowledge on their ecophysiology and evolution.     

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.     

Species Richness-Environment Relationships of European Arthropods at Two Spatial Grains: Habitats and Countries

We study how species richness of arthropods relates to theories concerning net primary productivity, ambient energy, water-energy dynamics and spatial environmental heterogeneity. We use two datasets of arthropod richness with similar spatial extents (Scandinavia to Mediterranean), but contrasting spatial grain (local habitat and country). Samples of ground-dwelling spiders, beetles, bugs and ants were collected from 32 paired habitats at 16 locations across Europe. Species richness of these taxonomic groups was also determined for 25 European countries based on the Fauna Europaea database. We tested effects of net primary productivity (NPP), annual mean temperature (T), annual rainfall (R) and potential evapotranspiration of the coldest month (PET_min) on species richness and turnover. Spatial environmental heterogeneity within countries was considered by including the ranges of NPP, T, R and PET_min. At the local habitat grain, relationships between species richness and environmental variables differed strongly between taxa and trophic groups. However, species turnover across locations was strongly correlated with differences in T. At the country grain, species richness was significantly correlated with environmental variables from all four theories. In particular, species richness within countries increased strongly with spatial heterogeneity in T. The importance of spatial heterogeneity in T for both species turnover across locations and for species richness within countries suggests that the temperature niche is an important determinant of arthropod diversity. We suggest that, unless climatic heterogeneity is constant across sampling units, coarse-grained studies should always account for environmental heterogeneity as a predictor of arthropod species richness, just as studies with variable area of sampling units routinely consider area.     

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.