The mid‐domain effect matters: simulation analyses of range‐size distribution data from Mount Kinabalu,Borneo

Aim In simulation exercises, mid‐domain peaks in species richness arise as a result of the random placement of modelled species ranges within simulated geometric constraints. This has been called the mid‐domain effect (MDE). Where close correspondence is found between such simulations and empirical data, it is not possible to reject the hypothesis that empirical species richness patterns result from the MDE rather than being the outcome (wholly or largely) of other factors. To separate the influence of the MDE from other factors we therefore need to evaluate variables other than species richness. The distribution of range sizes gives different predictions between models including the MDE or not. Here, we produce predictions for species richness and distribution of range sizes from one model without the MDE and from two MDE models: a classical MDE model encompassing only species with their entire range within the domain (range‐restricted MDE), and a model encompassing all species with the theoretical midpoint within the domain (midpoint‐restricted MDE). These predictions are compared with observations from the elevational pattern of range‐size distributions and species richness of vascular plants. Location Mount Kinabalu, Borneo. Methods The data set analysed comprises more than 28,000 plant specimens with information on elevation. Species ranges are simulated with various assumptions for the three models, and the species simulated are subsequently subjected to a sampling that simulates the actual collection of species on Mount Kinabalu. The resulting pattern of species richness and species range‐size distributions are compared with the observed pattern. Results The comparison of simulated and observed patterns indicates that an underlying monotonically decreasing trend in species richness with elevation is essential to explain fully the observed pattern of richness and range size. When the underlying trend is accounted for, the MDE model that restricts the distributions of theoretical midpoints performs better than both the classical MDE model and the model that does not incorporate geometric constraints. Main conclusions Of the three models evaluated here, the midpoint‐restricted MDE model is found to be the best for explaining species richness and species range‐size distributions on Mount Kinabalu.     

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

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

Richness patterns, species distributions and the principle of extreme deconstruction

Aim To analyse the global patterns in species richness of Viperidae snakes through the deconstruction of richness into sets of species according to their distribution models, range size, body size and phylogenetic structure, and to test if environmental drivers explaining the geographical ranges of species are similar to those explaining richness patterns, something we called the extreme deconstruction principle. Location Global. Methods We generated a global dataset of 228 terrestrial viperid snakes, which included geographical ranges (mapped at 1° resolution, for a grid with 7331 cells world‐wide), body sizes and phylogenetic relationships among species. We used logistic regression (generalized linear model; GLM) to model species geographical ranges with five environmental predictors. Sets of species richness were also generated for large and small‐bodied species, for basal and derived species and for four classes of geographical range sizes. Richness patterns were also modelled against the five environmental variables through standard ordinary least squares (OLS) multiple regressions. These subsets are replications to test if environmental factors driving species geographical ranges can be directly associated with those explaining richness patterns. Results Around 48% of the total variance in viperid richness was explained by the environmental model, but richness sets revealed different patterns across the world. The similarity between OLS coefficients and the primacy of variables across species geographical range GLMs was equal to 0.645 when analysing all viperid snakes. Thus, in general, when an environmental predictor it is important to model species geographical ranges, this predictor is also important when modelling richness, so that the extreme deconstruction principle holds. However, replicating this correlation using subsets of species within different categories in body size, range size and phylogenetic structure gave more variable results, with correlations between GLM and OLS coefficients varying from –0.46 up to 0.83. Despite this, there is a relatively high correspondence (r = 0.73) between the similarity of GLM‐OLS coefficients and R² values of richness models, indicating that when richness is well explained by the environment, the relative importance of environmental drivers is similar in the richness OLS and its corresponding set of GLMs. Main conclusions The deconstruction of species richness based on macroecological traits revealed that, at least for range size and phylogenetic level, the causes underlying patterns in viperid richness differ for the various sets of species. On the other hand, our analyses of extreme deconstruction using GLM for species geographical range support the idea that, if environmental drivers determine the geographical distribution of species by establishing niche boundaries, it is expected, at least in theory, that the overlap among ranges (i.e. richness) will reveal similar effects of these environmental drivers. Richness patterns may be indeed viewed as macroecological consequences of population‐level processes acting on species geographical ranges.     

Species distribution modelling as a macroecological tool: a case study using New World amphibians

Although species distribution modelling (SDM) is widely accepted among the scientific community and is increasingly used in ecology, conservation biology and biogeography, methodological limitations generate potential problems for its application in macroecology. Using amphibian species richness in North and South America, we compare species richness patterns derived from SDM maps and ‘expert’ maps to evaluate if: 1) richness patterns derived from SDM are biased toward climate‐based explanations for diversity when compared to expert maps, since SDM methods are typically based on climatic variables; and 2) SDM is a reliable tool for generating richness maps in hyperrich regions where point occurrence data are limited for many species. We found that although three widely used SDM methods overestimated amphibian species richness in grid cells when compared to expert richness maps in both North and South America due to systematic overestimation of range sizes, diversity gradients were reasonably robust at broad scales. Further, climatic variables statistically explained patterns of richness at similar levels among the different richness sources, although climatic relationships were stronger in the much better known North America than in South America. We conclude that in the face of the high deforestation rates coupled with incomplete data on species distributions, especially in the tropics, SDM represents a useful macroecological tool for investigating broad‐scale richness patterns and the dynamics between species richness and climate.     

The diversity field of New World leaf‐nosed bats (Phyllostomidae)

Aim To analyse how the patterns of species richness for the whole family Phyllostomidae determine the structure of diversity fields (sets of species‐richness values) within the ranges of individual bat species. Location The range of the family Phyllostomidae in North and South America. Methods We generated a database of the occurrence of 143 phyllostomid bat species in 6794 quadrats, analysing the species‐richness frequency distribution for all sites, and for subsets of sites defined by the geographic ranges of species. Range–diversity plots, depicting simultaneously the size and the mean species richness of ranges, were built to explore the patterns of co‐occurrence in widespread and restricted species. We compared the empirical patterns against two null models: (1) with scattered (non‐cohesive) ranges, and (2) with cohesive ranges modelled with the spreading‐dye algorithm. Diversity fields were analysed with richness maps for individual species and with comparisons of species‐richness frequency distributions. Results Overall richness frequency distribution showed a multimodal pattern, whereas simulated distributions showed lower values of variance, and were unimodal (for model 1) and bimodal (for model 2). Range–diversity plots for the empirical data and for the cohesive‐ranges simulation showed a strong tendency of species to co‐occur in high‐diversity sites. The scattered‐ranges simulation showed no such tendency. Diversity fields varied according to idiosyncratic features of species generating particular geographic patterns and richness frequency distributions. Main conclusions Phyllostomid bats show a higher level of co‐occurrence than expected from null models. That tendency in turn implies a higher variance in species richness among sites, generating a wider species‐richness frequency distribution. The diversity field of individual species results from the size, shape and location of ranges, but also depends on the general pattern of richness for the whole family.     

Large‐scale distribution patterns of mangrove nematodes: A global meta‐analysis

Mangroves harbor diverse invertebrate communities, suggesting that macroecological distribution patterns of habitat‐forming foundation species drive the associated faunal distribution. Whether these are driven by mangrove biogeography is still ambiguous. For small‐bodied taxa, local factors and landscape metrics might be as important as macroecology. We performed a meta‐analysis to address the following questions: (1) can richness of mangrove trees explain macroecological patterns of nematode richness? and (2) do local landscape attributes have equal or higher importance than biogeography in structuring nematode richness? Mangrove areas of Caribbean‐Southwest Atlantic, Western Indian, Central Indo‐Pacific, and Southwest Pacific biogeographic regions. We used random‐effects meta‐analyses based on natural logarithm of the response ratio (lnRR) to assess the importance of macroecology (i.e., biogeographic regions, latitude, longitude), local factors (i.e., aboveground mangrove biomass and tree richness), and landscape metrics (forest area and shape) in structuring nematode richness from 34 mangroves sites around the world. Latitude, mangrove forest area, and forest shape index explained 19% of the heterogeneity across studies. Richness was higher at low latitudes, closer to the equator. At local scales, richness increased slightly with landscape complexity and decreased with forest shape index. Our results contrast with biogeographic diversity patterns of mangrove‐associated taxa. Global‐scale nematode diversity may have evolved independently of mangrove tree richness, and diversity of small‐bodied metazoans is probably more closely driven by latitude and associated climates, rather than local, landscape, or global biogeographic patterns.     

The river domain: why are there more species halfway up the river?

Biologists have long noted higher levels of species diversity in the longitudinal middle‐courses of river systems and have proposed many explanations. As a new explanation for this widespread pattern, we suggest that many middle‐course peaks in richness may be, at least in part, a consequence of geometric constraints on the location of species’ ranges along river courses, considering river headwaters and mouths as boundaries for the taxa considered. We demonstrate this extension of the mid‐domain effect (MDE) to river systems for riparian plants along two rivers in Sweden, where a previous study found a middle‐course peak in richness of natural (non‐ruderal) species. We compare patterns of empirical richness of these species to null model predictions of species richness along the two river systems and to spatial patterns for six environmental variables (channel width, substrate fineness, substrate heterogeneity, ice scour, bank height, and bank area). In addition, we examine the independent prediction of mid‐domain effects models that species with large ranges, because the location of their ranges is more constrained, are more likely to produce a mid‐domain peak in richness than are species with small ranges. Species richness patterns of riparian plants were best predicted by models including both null model predictions and environmental variables. When species were divided into large‐ranged and small‐ranged groups, the mid‐domain effect was more prominent and the null model predictions were a better fit to the empirical richness patterns of large‐ranged species than those of small‐ranged species. Our results suggest that the peak in riparian plant species richness in the middle courses of the rivers studied can be explained by an underlying mid‐domain effect (driven by geometric constraints on large‐ranged species), together with environmental effects on richness patterns (particularly on small‐ranged species). We suggest that the mid‐domain effect may help to explain similar middle‐course richness peaks along other rivers.     

Assessing the accuracy of species distribution models to predict amphibian species richness patterns

1. Evaluating the distribution of species richness where biodiversity is high but has been insufficiently sampled is not an easy task. Species distribution modelling has become a useful approach for predicting their ranges, based on the relationships between species records and environmental variables. Overlapping predictions of individual distributions could be a useful strategy for obtaining estimates of species richness and composition in a region, but these estimates should be evaluated using a proper validation process, which compares the predicted richness values and composition with accurate data from independent sources. 2. In this study, we propose a simple approach to estimate model performance for several distributional predictions generated simultaneously. This approach is particularly suitable when species distribution modelling techniques that require only presence data are used. 3. The individual distributions for the 370 known amphibian species of Mexico were predicted using maxent to model data on their known presence (66,113 presence-only records). Distributions were subsequently overlapped to obtain a prediction of species richness. Accuracy was assessed by comparing the overall species richness values predicted for the region with observed and predicted values from 118 well-surveyed sites, each with an area of c. 100 km(2), which were identified using species accumulation curves and nonparametric estimators. 4. The derived models revealed a remarkable heterogeneity of species richness across the country, provided information about species composition per site and allowed us to obtain a measure of the spatial distribution of prediction errors. Examining the magnitude and location of model inaccuracies, as well as separately assessing errors of both commission and omission, highlights the inaccuracy of the predictions of species distribution models and the need to provide measures of uncertainty along with the model results. 5. The combination of a species distribution modelling method like maxent and species richness estimators offers a useful tool for identifying when the overall pattern provided by all model predictions might be representing the geographical patterns of species richness and composition, regardless of the particular quality or accuracy of the predictions for each individual species.     

Are stacked species distribution models accurate at predicting multiple levels of diversity along a rainfall gradient?

We use observed patterns of species richness and composition of ant communities along a 1000 mm rainfall gradient in northern Australian savanna to assess the accuracy of species richness and turnover predictions derived from stacked species distribution models (S‐SDMs) and constrained by macroecological models (MEMs). We systematically sampled ants at 15 sites at 50 km intervals along the rainfall gradient in 2012 and 2013. Using the observed data, we created MEMs of species richness, composition and turnover. We built distribution models for 135 of the observed species using data from museum collections and online databases. We compared two approaches of stacking SDMs and three modelling algorithms to identify the most accurate way of predicting richness and composition. We then applied the same beta diversity metrics to compare the observed versus predicted patterns. Stacked SDMs consistently over‐predicted local species richness, and there was a mismatch between the observed pattern of richness estimated from the MEM, and the pattern predicted by S‐SDMs. The most accurate richness and turnover predictions occurred when the stacked models were rank‐ordered by their habitat suitability and constrained by the observed MEM richness predictions. In contrast with species richness, the predictions obtained by the MEM of community similarity, composition and turnover matched those predicted by the S‐SDMs. S‐SDMs regulated by MEMs may therefore be a useful tool in predicting compositional patterns despite being unreliable estimators of species richness. Our results highlight that the choice of species distribution model, the stacking method used, and underlying macroecological patterns all influence the accuracy of community assembly predictions derived from S‐SDMS.     

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.     

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.     

Process-based models of species distributions and the mid-domain effect

Null models that place species ranges at random within a bounded geographical domain produce hump-shaped species richness gradients (the "mid-domain effect," or MDE). However, there is debate about the extent to which these models are a suitable null expectation for effects of environmental gradients on species richness. Here, I present a process-based framework for modeling species distributions within a bounded geographical domain. Analysis of null models consistent with the mid-domain hypothesis shows that MDEs are indeed likely to be ubiquitous consequences of geographical domain boundaries. Comparing the probability distributions of range locations for the process-based and randomization-based models reveals that randomization models probably overestimate the contribution of MDEs to empirical patterns of species richness, but it also indicates that other testable predictions from randomization models are likely to be robust. I also show how this process-based framework can be extended beyond null models to incorporate effects of environmental gradients within the domain. This study provides a first step toward an ecological theory of species distributions in geographical space that can incorporate both "geometric constraints" and effects of environmental gradients, and it shows how such a theory can inform our understanding of species richness gradients in nature.     

Midpoint attractors and species richness: Modelling the interaction between environmental drivers and geometric constraints

We introduce a novel framework for conceptualising, quantifying and unifying discordant patterns of species richness along geographical gradients. While not itself explicitly mechanistic, this approach offers a path towards understanding mechanisms. In this study, we focused on the diverse patterns of species richness on mountainsides. We conjectured that elevational range midpoints of species may be drawn towards a single midpoint attractor – a unimodal gradient of environmental favourability. The midpoint attractor interacts with geometric constraints imposed by sea level and the mountaintop to produce taxon‐specific patterns of species richness. We developed a Bayesian simulation model to estimate the location and strength of the midpoint attractor from species occurrence data sampled along mountainsides. We also constructed midpoint predictor models to test whether environmental variables could directly account for the observed patterns of species range midpoints. We challenged these models with 16 elevational data sets, comprising 4500 species of insects, vertebrates and plants. The midpoint predictor models generally failed to predict the pattern of species midpoints. In contrast, the midpoint attractor model closely reproduced empirical spatial patterns of species richness and range midpoints. Gradients of environmental favourability, subject to geometric constraints, may parsimoniously account for elevational and other patterns of species richness.     

Species richness and evolutionary niche dynamics: a spatial pattern-oriented simulation experiment

Evolutionary processes underlying spatial patterns in species richness remain largely unexplored, and correlative studies lack the theoretical basis to explain these patterns in evolutionary terms. In this study, we develop a spatially explicit simulation model to evaluate, under a pattern-oriented modeling approach, whether evolutionary niche dynamics (the balance between niche conservatism and niche evolution processes) can provide a parsimonious explanation for patterns in species richness. We model the size, shape, and location of species' geographical ranges in a multivariate heterogeneous environmental landscape by simulating an evolutionary process in which environmental fluctuations create geographic range fragmentation, which, in turn, regulates speciation and extinction. We applied the model to the South American domain, adjusting parameters to maximize the correspondence between observed and predicted patterns in richness of about 3,000 bird species. Predicted spatial patterns, which closely resemble observed ones (r2=0.795), proved sensitive to niche dynamics processes. Our simulations allow evaluation of the roles of both evolutionary and ecological processes in explaining spatial patterns in species richness, revealing the enormous potential of the link between ecology and historical biogeography under integrated theoretical and methodological frameworks.     

An evolutionary tolerance model explaining spatial patterns in species richness under environmental gradients and geometric constraints

In this study, we developed a simulation model based on the ecological and evolutionary dynamics of geographical ranges, to understand the role of species' environmental tolerances and the strength of the environmental gradient in determining spatial patterns in species richness. Using an one-dimensional space, we present the model and dissect its parameters. Also, we test the ability of the model to simulate richness in complex two-dimensional domains and to fit real patterns in species richness, using South American Tyrannidae as an example. We found that a mid-spatial peak in species richness arises spontaneously under conditions of high environmental tolerances and/or a weak environmental gradient, since this condition causes wide species' geographic ranges, which are constrained by domain's boundary and tend to overlap in the middle. Our model was also a good predictor of real patterns in species richness, especially under conditions of high environmental strength and small species' tolerance. We conclude that this kind of spatial simulation models based on species' physiological tolerance may be an important tool to understand the evolutionary dynamics of species' geographic ranges and in spatial patterns of species richness.     

Climatic and trophic processes drive long‐term changes in functional diversity of freshwater invertebrate communities

When investigating the fields of biogeography and macroecology, climate‐ and productivity‐related variables are frequently identified as the strongest correlates of species‐diversity patterns. These variables have been usually merged under the climate/productivity hypothesis and describe the direct and indirect actions of climate on species. Being among the most vulnerable ecosystems to climate change, streams and rivers are expected to be influenced both by climatic and trophic (i.e. productivity‐related) factors. We propose here to distinguish the relative influence of the two processes on large‐scale, long‐term changes in the functional diversity of freshwater invertebrate communities over two decades in France. To this end, we designed two functional indices using invertebrate traits to surrogate the respective mechanisms: climate vulnerability and feeding specialisation. Using geographically weighted regression (GWR) models, we showed that trends in both indices, along with the initial regional species‐pools, have significantly contributed to the overall long‐term increase in functional diversity of invertebrate communities. In addition, we highlighted a strong geographical differentiation in the contribution patterns with the climate vulnerability effect decreasing with latitude and the feeding specialisation effect being higher in headwaters than in large rivers. Finally, taking into account this non‐stationarity in the ecological processes and responses using GWR models allowed explaining about 75% of the long‐term changes in the community diversity. Consequently, this study offers sound perspectives in predicting the future patterns of trends in functional diversity of communities under different scenarios of environmental changes, like climate and/or land‐use.     

Biogeography of Great Basin butterflies: revisiting patterns, paradigms, and climate change scenarios

We used comprehensive data on butterfly distributions from six mountain ranges in the Great Basin to explore three connected biogeographic issues. First, we examined species richness and occurrence patterns both within and among mountain ranges. Only one range had a significant relationship between species richness and area. Relationships between species richness and elevation varied among mountain ranges. Species richness decreased as elevation increased in one range, increased as elevation increased in three ranges, and was not correlated in two ranges. In each range, distributional patterns were nested, but less vagile species did not always exhibit greater nestedness. Second, we compared our work with similar studies of montane mammals. Results from both taxonomic groups suggest that it may be appropriate to modify existing general paradigms of the biogeography of montane faunas in the Great Basin. Third, we revisited and refined previous predictions of how butterfly assemblages in the Great Basin may respond to climate change. The effects of climate change on species richness of montane butterflies may vary considerably among mountain ranges. In several ranges, few if any species apparently would be lost. Neither local species composition nor the potential order of species extirpations appears to be generalizable among ranges.     

Relative role of contemporary environment versus history in shaping diversity patterns of China's woody plants

What determines large‐scale patterns of species diversity is a central and controversial topic in biogeography and ecology. In this study, we compared the effects of contemporary environment and historical contingencies on species richness patterns of woody plants in China, using fine‐resolution geographic databases of the distributions of 11 405 woody species and climate, topography, and vegetation information. Residuals of species richness‐environment generalized linear models were significantly different from 0 in the majority of seven biogeographical regions, and also differed significantly between these regions, indicating significant deviation from the predicted species richness based on contemporary environment. Additionally, species richness of a given biogeographical region deviated substantially from the predictions of species richness‐environment models developed for the remaining regions combined. This suggests different richness‐environment relationships among regions. These results indicate important historical signals in the species richness patterns of woody plants across China. The signals are especially pronounced in the eastern Himalayas, the Mongolian Plateau, and the Tibetan Plateau, perhaps reflecting their special geological features and history. Nevertheless, partial regression indicated that historical effects were less important relative to contemporary environment. In conclusion, contemporary environment (notably climate) determines the general trend in woody‐plant species richness across China, while historical contingencies generate regional deviations from this trend. Our findings imply that both species diversity and regional evolutionary and ecological histories should be taken into account for future nature conservation.     

Ensemble modelling of species distribution: the effects of geographical and environmental ranges

The aim of this study was to analyse the effects of species geographical and environmental ranges on the predictive performances of species distribution models (SDMs). We explored the usefulness of ensemble modelling approaches and tested whether species attributes influenced the outcomes of such approaches. Eight SDMs were used to model the current distribution of 35 fish species at 1110 stream sections in France. We first quantified the consensus among the resulting set of predictions for each fish species. Next, we created an average model by taking the average of the individual model predictions and tested whether the average model improved the predictive performances of single SDMs. Lastly, we described the ranges of fish species along four gradients: latitudinal, thermal, stream gradient (i.e. upstream‐downstream) and elevation. After accounting for the effects of phylogenetic relatedness and species prevalence, these four species attributes were related to the observed variations in both consensus among SDMs and predictive performances by using generalized estimation equations. Our results highlight the usefulness of ensemble approaches for identifying geographical areas of agreement among predictions. Although the geographical extent of species had no effect on the performances of SDMs, we demonstrated that more consensual and accurate predictions were obtained for species with low thermal and elevation ranges, validating the hypothesis that specialist species yield models with higher accuracy than generalist ones. We emphasized that significant improvements in the accuracy of SDMs can be achieved by using an average model. Furthermore, these improvements were higher for species with smaller ranges along the four gradients studied. The geographical extent and ranges of species along environmental gradients provide promising insights into our understanding of uncertainties in species distribution modelling.     

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

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