Assessment of the relationships of geographic variation in species richness to climate and landscape variables within and among lineages of North American freshwater fishes

Aim Geographic variation in species richness is a well‐studied phenomenon. However, the unique response of individual lineages to environmental gradients in the context of general patterns of biodiversity across broad spatial scales has received limited attention. The focus of this research is to examine relationships between species richness and climate, topographic heterogeneity and stream channel characteristics within and among families of North American freshwater fishes. Location The United States and Canada. Methods Distribution maps of 828 native species of freshwater fishes were used to generate species richness estimates across the United States and Canada. Variation in species richness was predicted using spatially explicit models incorporating variation in climate, topography and/or stream channel length and stream channel diversity for all 828 species as well as for the seven largest families of freshwater fishes. Results The overall gradient of species richness in North American freshwater fishes is best predicted by a model incorporating variables describing climate and topography. However, the response of species richness to particular climate or landscape variables differed among families, with models possessing the highest predictive ability incorporating data on climate, topography and/or stream channel characteristics within a region. Main conclusions The correlations between species richness and abiotic variables suggest a strong influence of climate and physical habitat on the structuring of regional assemblages of North American freshwater fishes. However, the relationship between these variables and species richness varies among families, suggesting the importance of phylogenetic constraints on the regulation of geographic distributions of species.     

Using unclassified continuous remote sensing data to improve distribution models of red-listed plant species

Remote sensing (RS) data may play an important role in the development of cost-effective means for modelling, mapping, planning and conserving biodiversity. Specifically, at the landscape scale, spatial models for the occurrences of species of conservation concern may be improved by the inclusion of RS-based predictors, to help managers to better meet different conservation challenges. In this study, we examine whether predicted distributions of 28 red-listed plant species in north-eastern Finland at the resolution of 25 ha are improved when advanced RS-variables are included as unclassified continuous predictor variables, in addition to more commonly used climate and topography variables. Using generalized additive models (GAMs), we studied whether the spatial predictions of the distribution of red-listed plant species in boreal landscapes are improved by incorporating advanced RS (normalized difference vegetation index, normalized difference soil index and Tasseled Cap transformations) information into species-environment models. Models were fitted using three different sets of explanatory variables: (1) climate-topography only; (2) remote sensing only; and (3) combined climate-topography and remote sensing variables, and evaluated by four-fold cross-validation with the area under the curve (AUC) statistics. The inclusion of RS variables improved both the explanatory power (on average 8.1 % improvement) and cross-validation performance (2.5 %) of the models. Hybrid models produced ecologically more reliable distribution maps than models using only climate-topography variables, especially for mire and shore species. In conclusion, Landsat ETM+ data integrated with climate and topographical information has the potential to improve biodiversity and rarity assessments in northern landscapes, especially in predictive studies covering extensive and remote areas.     

宏生态尺度上景观破碎化对物种丰富度的影响

生物多样性的地理格局及其形成机制是宏生态学与生物地理学的研究热点。大量研究表明,景观尺度上的生境破碎化对物种多样性的分布格局具有重要作用,但目前尚不清楚这种作用是否足以在宏生态尺度上对生物多样性地理格局产生显著影响。利用中国大陆鸟类和哺乳动物的物种分布数据,在100 km×100 km网格的基础上生成了这两个类群生物的物种丰富度地理格局,进一步利用普通最小二乘法模型和空间自回归模型研究了物种丰富度与气候、生境异质性、景观破碎化的相关关系。结果表明,景观破碎化因子与鸟类和哺乳动物的物种丰富度都具有显著的关联关系,其方差贡献率可达约30%—50%(非空间模型)和60%—80%(空间模型),略低于或接近于气候和生境异质性因子。方差分解结果显示,景观破碎化因子与气候和生境异质性因子的方差贡献率的重叠部分达20%—40%。相对鸟类而言,景观破碎化对哺乳动物物种丰富度的地理格局具有更高的解释率。     

Statistical approaches to interpreting diversity patterns in the Norwegian mountain flora

The richness of Norwegian mountain plants in 75 grid squares is mapped from published distributional data for 109 species. Eleven explanatory variables representing bedrock geology, geography and topography, climate, and history (relative abundance of unglaciated areas) Tor each square are used in multiple regression analysis with associated Monte Carlo permutation tests to find statistically significant predictor variables for species richness. The variance in richness explained by the four major groups or explanatory variables is established by (partial) multiple regression analysis in which the groups of predictors are entered in different orders. The variance in species richness explained by the predictor variables is partitioned into four independent components. A predictive model for species richness using partial least squares regression and all explanatory variables has a coefficient of determination (R²) of 0.79. The statistical results consistently show that species-richness patterns are well explained by modern-day factors such as climate, geology, elevation, and geography without recourse to historical variables. The nunatak hypothesis of plant survival on unglaciated areas within Norway does not explain the observed richness patterns when modern ecological factors are considered first. The nunatak hypothesis thus appears to be redundant, a view supported by recent palaeobotanical. biosystematical, and evolutionary studies.     

Patterns of woody plant species richness in the Iberian Peninsula: environmental range and spatial scale

Aim Climate‐based models often explain most of the variation in species richness along broad‐scale geographical gradients. We aim to: (1) test predictions of woody plant species richness on a regional spatial extent deduced from macro‐scale models based on water–energy dynamics; (2) test if the length of the climate gradients will determine whether the relationship with woody species richness is monotonic or unimodal; and (3) evaluate the explanatory power of a previously proposed ‘water–energy’ model and regional models at two grain sizes. Location The Iberian Peninsula. Methods We estimated woody plant species richness on grid maps with c. 2500 and 22,500 km² cell size, using geocoded data for the individual species. Generalized additive models were used to explore the relationships between richness and climatic, topographical and substrate variables. Ordinary least squares regression was used to compare regional and more general water–energy models in relation to grain size. Variation partitioning by partial regression was applied to find how much of the variation in richness was related to spatial variables, explanatory variables and the overlap between these two. Results Water–energy dynamics generate important underlying gradients that determine the woody species richness even over a short spatial extent. The relationships between richness and the energy variables were linear to curvilinear, whereas those with precipitation were nonlinear and non‐monotonic. Only a small fraction of the spatially structured variation in woody species richness cannot be accounted for by the fitted variables related to climate, substrate and topography. The regional models accounted for higher variation in species richness than the water–energy models, although the water–energy model including topography performed well at the larger grain size. Elevation range was the most important predictor at all scales, probably because it corrects for ‘climatic error’ due to the unrealistic assumption that mean climate values are evenly distributed in the large grid cells. Minimum monthly potential evapotranspiration was the best climatic predictor at the larger grain size, but actual evapotranspiration was best at the smaller grain size. Energy variables were more important than precipitation individually. Precipitation was not a significant variable at the larger grain size when examined on its own, but was highly significant when an interaction term between itself and substrate was included in the model. Main conclusions The significance of range in elevation is probably because it corresponds to several aspects that may influence species diversity, such as climatic variability within grid cells, enhanced surface area, and location for refugia. The relative explanatory power of energy and water variables was high, and was influenced by the length of the climate gradient, substrate and grain size of the analysis. Energy appeared to have more influence than precipitation, but water availability is also determined by energy, substrate and topographic relief.     

Plant community composition and species richness in the High Arctic tundra: From the present to the future

Arctic plant communities are altered by climate changes. The magnitude of these alterations depends on whether species distributions are determined by macroclimatic conditions, by factors related to local topography, or by biotic interactions. Our current understanding of the relative importance of these conditions is limited due to the scarcity of studies, especially in the High Arctic. We investigated variations in vascular plant community composition and species richness based on 288 plots distributed on three sites along a coast‐inland gradient in Northeast Greenland using a stratified random design. We used an information theoretic approach to determine whether variations in species richness were best explained by macroclimate, by factors related to local topography (including soil water) or by plant‐plant interactions. Latent variable models were used to explain patterns in plant community composition. Species richness was mainly determined by variations in soil water content, which explained 35% of the variation, and to a minor degree by other variables related to topography. Species richness was not directly related to macroclimate. Latent variable models showed that 23.0% of the variation in community composition was explained by variables related to topography, while distance to the inland ice explained an additional 6.4 %. This indicates that some species are associated with environmental conditions found in only some parts of the coast–inland gradient. Inclusion of macroclimatic variation increased the model's explanatory power by 4.2%. Our results suggest that the main impact of climate changes in the High Arctic will be mediated by their influence on local soil water conditions. Increasing temperatures are likely to cause higher evaporation rates and alter the distribution of late‐melting snow patches. This will have little impact on landscape‐scale diversity if plants are able to redistribute locally to remain in areas with sufficient soil water.     

Relative importance of habitat area, connectivity, management and local factors for vascular plants: spring ephemerals in boreal semi-natural grasslands

Using species and environmental data from an extensive grassland area in south-western Finland, we investigated the effect of patch area and connectivity, management and local habitat variables on the occurrence of spring-flowering vascular plants and their richness in boreal agricultural landscapes. Generalized linear models (GLM) and variation partitioning were used to study the explanatory power of the three groups of variables and their combined contributions on the richness and occurrence of six spring-flowering plant species. Generalized additive models (GAMs) and associated cross-validation tests were used to evaluate the predictability of the species occurrence and richness patterns. Present-day grassland patch area and connectivity were important predictors for occurrence and richness of the studied plant species. In addition, local habitat factors, especially radiation, accounted for major fractions of occurrence patterns of the studied species. Hybrid models including variables from all three variable groups had higher explanatory power and predictive capability than partial models. However, performance of the separate single-species models varied considerably between the six study species. Exclusion of radiation or connectivity from the hybrid models decreased their predictive performance, suggesting that these factors are of particular importance for grassland plant species at their northern range margins. When developing conservation and management planning for grassland plant species in Northern Europe, attention should be paid to well-connected networks of grassland patches including large, steeply-sloped patches with a favorable microclimate.     

Biotic interactions boost spatial models of species richness

Biotic interactions are known to affect the composition of species assemblages via several mechanisms, such as competition and facilitation. However, most spatial models of species richness do not explicitly consider inter‐specific interactions. Here, we test whether incorporating biotic interactions into high‐resolution models alters predictions of species richness as hypothesised. We included key biotic variables (cover of three dominant arctic‐alpine plant species) into two methodologically divergent species richness modelling frameworks – stacked species distribution models (SSDM) and macroecological models (MEM) – for three ecologically and evolutionary distinct taxonomic groups (vascular plants, bryophytes and lichens). Predictions from models including biotic interactions were compared to the predictions of models based on climatic and abiotic data only. Including plant–plant interactions consistently and significantly lowered bias in species richness predictions and increased predictive power for independent evaluation data when compared to the conventional climatic and abiotic data based models. Improvements in predictions were constant irrespective of the modelling framework or taxonomic group used. The global biodiversity crisis necessitates accurate predictions of how changes in biotic and abiotic conditions will potentially affect species richness patterns. Here, we demonstrate that models of the spatial distribution of species richness can be improved by incorporating biotic interactions, and thus that these key predictor factors must be accounted for in biodiversity forecasts.     

Geographical patterns of community‐based tree species richness in Chinese mountain forests: the effects of contemporary climate and regional history

The relationship between climate/productivity and historical/regional contingency and their relative influence on geographical patterns of species richness (GPSR) are still unresolved. Based on field data from 1494 plots from forests on 63 mountains across China, we document the GPSR for forest communities. Regression tree and generalized linear models were used to explore the discreteness and gradient of the distribution of tree species richness (α‐diversity), and to estimate the correlations of climate, historical floristic region, and local habitat with species richness. The collinearity between climatic variables and region were further disentangled; and the spatial autocorrelation in the patterns of α‐diversity and the residuals of alternative predictive models were compared. Overall, 75% of variation in plot‐based α‐diversity of trees was accounted for by all variables included, and about 66.5%, 64.5% and 27.9% by climate, region, and local habitat respectively. Importantly, the explanatory power of these variables differed in particular for coniferous, deciduous broadleaved and evergreen broadleaved species. Ambient temperature was more important for α‐diversity of trees than were the other climatic variables across China. Spatial autocorrelation in the pattern of α‐diversity could be accounted for mainly by spatial variation climate. The concordance between tree α‐diversity, historical flora, contemporary climate, and Quaternary climate change mode suggests the climate/productivity and historical/regional contingency both contribute to the GPSR in a complimentary manner. Taken together, our results provide unique evidence to link of the effects of contemporary climate and historical climate change on species richness across scales.     

Quantifying island isolation – insights from global patterns of insular plant species richness

Isolation is a driving factor of species richness and other island community attributes. Most empirical studies have investigated the effect of isolation measured as distance to the nearest continent. Here we expanded this perspective by comparing the explanatory power of seventeen isolation metrics in sixty‐eight variations for vascular plant species richness on 453 islands worldwide. Our objectives were to identify ecologically meaningful metrics and to quantify their relative importance for species richness in a globally representative data set. We considered the distances to the nearest mainland and to other islands, stepping stone distances, the area of surrounding landmasses, prevailing wind and ocean currents and climatic similarity between source and target areas. These factors are closely linked to colonization and maintenance of plant species richness on islands. We tested the metrics in spatial multi‐predictor models accounting for area, climate, topography and island geology. Besides area, isolation was the second most important factor determining species richness on the studied islands. A model including the proportion of surrounding land area as the isolation metric had the highest predictive power, explaining 86.1% of the variation. Distances to large islands, stepping stone distances and distances to climatically similar landmasses performed slightly better than distance to the nearest mainland. The effect of isolation was weaker for large islands suggesting that speciation counteracts the negative effect of isolation on immigration on large islands. Continental islands were less affected by isolation than oceanic islands. Our results suggest that a variety of immigration mechanisms influence plant species richness on islands and we show that this can be detected at macro‐scales. Although the distance to the nearest mainland is an adequate and easy‐to‐calculate measure of isolation, accounting for stepping stones, large islands as source landmasses, climatic similarity and the area of surrounding landmasses increases the explanatory power of isolation for species richness.     

Modelling the species richness distribution of French dung beetles (Coleoptera, Scarabaeidae) and delimiting the predictive capacity of different groups of explanatory variables

Aim To predict French Scarabaeidae dung beetle species richness distribution, and to determine the possible underlying causal factors. Location The entire French territory has been studied by dividing it into 301 grid cells of 0.72 × 0.36 degrees. Method Species richness distribution was predicted using generalized linear models to relate the number of species with spatial, topographic and climate variables in grid squares previously identified as well sampled (n = 66). The predictive function includes the curvilinear relationship between variables, interaction terms and the significant third‐degree polynomial terms of latitude and longitude. The final model was validated by a jack‐knife procedure. The underlying causal factors were investigated by partial regression analysis, decomposing the variation in species richness among spatial, topographic and climate type variables. Results The final model accounts for 86.2% of total deviance, with a mean jack‐knife predictive error of 17.7%. The species richness map obtained highlights the Mediterranean as the region richest in species, and the less well‐explored south‐western region as also being species‐rich. The largest fraction of variability (38%) in the number of species is accounted for by the combined effect of the three groups of explanatory variables. The spatially structured climate component explains 21% of variation, while the pure climate and pure spatial components explain 14% and 11%, respectively. The effect of topography was negligible. Conclusions Delimiting the adequately inventoried areas and elaborating forecasting models using simple environmental variables can rapidly produce an estimate of the species richness distribution. Scarabaeidae species richness distribution seems to be mainly influenced by temperature. Minimum mean temperature is the most influential variable on a local scale, while maximum and mean temperature are the most important spatially structured variables. We suggest that species richness variation is mainly conditioned by the failure of many species to go beyond determined temperature range limits.     

西双版纳种子植物物种多样性的垂直格局及机制

物种多样性海拔分布格局及其形成机制的研究是生物地理学和宏观生态学的重要议题之一。本文利用西双版纳植物专著资料, 结合高分辨率的地形和气候等数据, 探讨了面积、边界限制和现代气候对西双版纳野生种子植物物种丰富度及物种密度海拔分布格局的影响。结果表明: (1)物种丰富度呈单峰分布格局, 面积(81.9%)、边界限制(17.5%)和气候(60.0-69.3%)都不同程度地解释了物种丰富度的单峰格局; (2)利用幂函数种-面积关系计算的物种密度沿海拔大致呈减小的分布趋势, 气候的解释率降低为32.6-40.6%, 与边界限制无显著相关关系; (3)利用等面积高度带划分得到的物种密度沿海拔呈单峰变化趋势, 物种密度与边界限制无显著相关性, 但气候对物种密度的解释率为81.6-89.9%。研究结果有助于准确全面地理解物种多样性的海拔分布格局及其成因机制, 为西双版纳生物多样性保护提供理论支撑和实践指导。     

On the relationship between a resource based measure of geodiversity and broad scale biodiversity patterns

Geodiversity, (diversity of the geosphere) incorporates many of the environmental patterns and processes that are considered drivers of biodiversity. Components of geodiversity (climate, topography, geology and hydrology) can be considered in terms of their resource giving potential, where resources are taken as energy, water, space and nutrients. The total amount of these resources, along with their spatial and temporal variation, is herein proposed as a compound index of geodiversity that has the potential to model broad scale biodiversity patterns. This paper outlines potential datasets that could be used to represent geodiversity, and then reviews the theoretical links between each element of the proposed compound index of geodiversity (overall resource availability, temporal variation and spatial variation in those resources) and broad-scale patterns of biodiversity. Support for the influence of each of the elements of geodiversity on overall biodiversity patterns was found in the literature, although the majority of relevant research focuses on resource availability, particularly available energy. The links between temporal and spatial variation in resources and biodiversity have been less thoroughly investigated in the literature. For the most part, it was reported that overall resource availability, temporal variation and spatial variation in those resources do not act in isolation in terms of controlling biodiversity. Overall there are sufficient datasets to calculate the proposed compound index of geodiversity, and evidence in the literature for links between the geographical distribution of biodiversity and each of the elements of the compound index defined. Since data for measuring geodiversity is more spatially consistent and widely available (thanks to satellite remote sensing) geodiversity has potential as a conservation planning tool, especially where biological data are not available or sparsely distributed.     

Predicting patterns of plant species richness in megadiverse South Africa

Using new tools (boosted regression trees) in predictive biogeography, with extensive spatial 23 distribution data for >19 000 species, we developed predictive models for South African plant species richness patterns. Further, biome level analysis explored possible functional determinants of country‐wide regional species richness. Finally, to test model reliability independently, we predicted potential alien invasive plant species richness with an independent dataset. Amongst the different hypotheses generally invoked to explain species 30 diversity (energy, favorableness, topographic heterogeneity, irregularity and seasonality), results revealed topographic heterogeneity as the most powerful single explanatory variable for indigenous South African plant species richness. Some biome‐specific responses were observed, i.e. two of the five analyzed biomes (Fynbos and Grassland) had richness best explained by the “species‐favorableness” hypothesis, but even in this case, topographic heterogeneity was also a primary predictor. This analysis, the largest conducted on an almost exhaustive species sample in a species‐rich region, demonstrates the preeminence of topographic heterogeneity in shaping the spatial pattern of regional plant species richness. Model reliability was confirmed by the considerable predictive power for alien invasive species richness. It thus appears that topographic heterogeneity controls species richness in two main ways: firstly, by providing an abundance of ecological niches in contemporary space (revealed by alien invasive species richness relationships) and secondly, by facilitating the persistence of ecological niches through time. The extraordinary richness of the South African Fynbos biome, a world‐renowned hotspot of biodiversity with the steepest environmental gradients in South Africa, may thus have arisen through both mechanisms. Comparisons with similar regions of the world outside South Africa are needed to confirm the generality of topographic heterogeneity and favorableness as predictors of plant richness.     

Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico

Predicting broad-scale patterns of biodiversity is challenging, particularly in ecosystems where traditional methods of quantifying habitat structure fail to capture subtle but potentially important variation within habitat types. With the unprecedented rate at which global biodiversity is declining, there is a strong need for improvement in methods for discerning broad-scale differences in habitat quality. Here, we test the importance of habitat structure (i.e. fine-scale spatial variability in plant growth forms) and plant productivity (i.e. amount of green biomass) for predicting avian biodiversity. We used image texture (i.e. a surrogate for habitat structure) and vegetation indices (i.e., surrogates for plant productivity) derived from Landsat Thematic Mapper (TM) data for predicting bird species richness patterns in the northern Chihuahuan Desert of New Mexico. Bird species richness was summarized for forty-two 108 ha plots in the McGregor Range of Fort Bliss Military Reserve between 1996 and 1998. Six Landsat TM bands and the normalized difference vegetation index (NDVI) were used to calculate first-order and second-order image textures measures. The relationship between bird species richness versus image texture and productivity (mean NDVI) was assessed using Bayesian model averaging. The predictive ability of the models was evaluated using leave-one-out cross-validation. Texture of NDVI predicted bird species richness better than texture of individual Landsat TM bands and accounted for up to 82.3% of the variability in species richness. Combining habitat structure and productivity measures accounted for up to 87.4% of the variability in bird species richness. Our results highlight that texture measures from Landsat TM imagery were useful for predicting patterns of bird species richness in semi-arid ecosystems and that image texture is a promising tool when assessing broad-scale patterns of biodiversity using remotely sensed data.     

Climate-based models of spatial patterns of species richness in Egypt's butterfly and mammal fauna

Aim  Identifying areas of high species richness is an important goal of conservation biogeography. In this study we compared alternative methods for generating climate-based estimates of spatial patterns of butterfly and mammal species richness.
Location  Egypt.
Methods  Data on the occurrence of butterflies and mammals in Egypt were taken from an electronic database compiled from museum records and the literature. Using M axent , species distribution models were built with these data and with variables describing climate and habitat. Species richness predictions were made by summing distribution models for individual species and by modelling observed species richness directly using the same environmental variables.
Results  Estimates of species richness from both methods correlated positively with each other and with observed species richness. Protected areas had higher species richness (both predicted and actual) than unprotected areas.
Main conclusions  Our results suggest that climate-based models of species richness could provide a rapid method for selecting potential areas for protection and thus have important implications for biodiversity conservation.     

BIODIVERSITY RESEARCH: Native and introduced fish species richness in Mediterranean streams: the role of multiple landscape influences

Aim To examine the role of multiple landscape factors on the species richness patterns of native and introduced freshwater fish. Location Mediterranean streams, south‐western Iberian Peninsula, Europe (c. 87,000 km²). Methods We used a dataset of fish occurrences from 436 stream sites. We quantified the incremental explanatory power of multiple landscape factors in native, introduced, and overall local species richness using regression analysis. First, we related variation in local species richness across river basins to regional species richness (here, the basin species pool), area and factors of climate and topography. Second, we related within‐river basin local species richness to site’s climate and topography, and spatial structure derived from Principal Coordinates of Neighbour Matrices approach, after testing for species richness spatial autocorrelation; predicted local richness was mapped. Results Patterns of local species richness across river basins were strongly associated with regional species richness for overall, native and introduced species; annual rainfall showed a significant incremental contribution to variation in introduced species richness only. Within river basins, environmental factors were associated with local richness for the three species groups, though their contributions to the total explained variation were inferior to those of spatial factors; rainfall seasonality and stream slope were the most consistent environmental correlates for all species groups, while the influence of spatial factors was most prevalent for native species. Main conclusions Landscape factors operating among and within river basins seem to play a relevant role in shaping local species richness of both native and introduced species, and may be contingent on basin‐specific contexts. Nevertheless, local factors, such as habitat characteristics and biotic interactions and human‐induced disturbances may also be at play. Multiscale approaches incorporating a multitude of factors are strongly encouraged to facilitate a deeper understanding of the biodiversity patterns of Mediterranean streams, and to promote more effective conservation and management strategies.     

A globally consistent richness-climate relationship for angiosperms

Species richness, the simplest index of biodiversity, varies greatly over broad spatial scales. Richness-climate relationships often account for >80% of the spatial variance in richness. However, it has been suggested that richness-climate relationships differ significantly among geographic regions and that there is no globally consistent relationship. This study investigated the global patterns of species and family richness of angiosperms in relation to climate. We found that models relating angiosperm richness to mean annual temperature, annual water deficit, and their interaction or models relating richness to annual potential evapotranspiration and water deficit are both globally consistent and very strong and are independent of the diverse evolutionary histories and functional assemblages of plants in different parts of the world. Thus, effects of other factors such as evolutionary history, postglacial dispersal, soil nutrients, topography, or other climatic variables either must be quite minor over broad scales (because there is little residual variation left to explain) or they must be strongly collinear with global patterns of climate. The correlations shown here must be predicted by any successful hypothesis of mechanisms controlling richness patterns.     

The unusual suspect: Land use is a key predictor of biodiversity patterns in the Iberian Peninsula

Although land use change is a key driver of biodiversity change, related variables such as habitat area and habitat heterogeneity are seldom considered in modeling approaches at larger extents. To address this knowledge gap we tested the contribution of land use related variables to models describing richness patterns of amphibians, reptiles and passerines in the Iberian Peninsula. We analyzed the relationship between species richness and habitat heterogeneity at two spatial resolutions (i.e., 10 km × 10 km and 50 km × 50 km). Using both ordinary least square and simultaneous autoregressive models, we assessed the relative importance of land use variables, climate variables and topographic variables. We also compare the species–area relationship with a multi-habitat model, the countryside species–area relationship, to assess the role of the area of different types of habitats on species diversity across scales. The association between habitat heterogeneity and species richness varied with the taxa and spatial resolution. A positive relationship was detected for all taxa at a grain size of 10 km × 10 km, but only passerines responded at a grain size of 50 km × 50 km. Species richness patterns were well described by abiotic predictors, but habitat predictors also explained a considerable portion of the variation. Moreover, species richness patterns were better described by a multi-habitat species-area model, incorporating land use variables, than by the classic power model, which only includes area as the single explanatory variable. Our results suggest that the role of land use in shaping species richness patterns goes beyond the local scale and persists at larger spatial scales. These findings call for the need of integrating land use variables in models designed to assess species richness response to large scale environmental changes.