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

AimTo analyse the diversity and distribution of oceanic pelagic cephalopods along the eastern Pacific Ocean assessing the existence of biogeographic structuring, and the role of physical variables in generating geographical patterns. We hypothesized that the control by environmental factors, and the effect of geometric constraints, determine the range size and limits of distribution of oceanic cephalopods along the eastern Pacific Ocean, generating a latitudinal gradient in species richness.LocationEastern Pacific Ocean (60°N – 60°S), from the Gulf of Alaska to the Southern Ocean.MethodsBased on a literature review and >5,000 records obtained from collections, we constructed a presence–absence matrix including 61 latitudinal bands (2° each) along the Eastern Pacific, and estimated species richness and range endpoints at each band. Biogeographic units were determined by means of multivariate analyses. Species richness was compared with null model predictions in order to test for the existence of geometric constraints using the Mid‐Domain Null model. The effects on species richness of environmental variables (temperature, salinity and oxygen) were evaluated separately for surface and depth (0–1,000 m) data, by means of ordinary least squares regression and simultaneous autoregressive models. Rapoport's pattern was assessed by applying the Stevens’ method and the range midpoint method.ResultsSpecies richness was high across the tropics and decreased towards the both poles. We identified five biogeographic units, highlighting two major distribution breaks at 40°N and 42°S. Species richness was strongly related with environmental variables, although the combined variables accounted for a large fraction of the variance between 0 and 1,000 m (R2 = 0.99), while temperature was the best single predictor at the surface (R2 = 0.98). Species richness curves showed a mid‐domain effect (MDE), and the mean latitudinal range was higher in the tropics and at warm latitudes, generating an inverse Rapoport's pattern.Main conclusionsAlong the eastern Pacific Ocean oceanic cephalopods exhibit both a clear biogeographic patterning and an interhemispheric (poleward decreasing) diversity gradient, which appear strongly related with physical factors and external forcing, as well as with a MDE as a seemingly consequence of the naturally bounded domain of the Eastern Pacific.

The ‘mid‐domain effect’ (MDE) has received much attention recently as a candidate explanation for patterns in species richness over large geographic areas. Mid‐domain models generate a central peak in richness when species ranges are randomly placed within a bounded geographic area (i.e. the domain). The most common terrestrial mid‐domain models published to date have been 1‐D latitude or elevation models and 2‐D latitude‐longitude models. Here, we test 1‐D, 2‐D and 3‐D mid‐domain models incorporating latitude, longitude and elevation, and assess independent and concurrent effects of geometric constraints and climatic variables on species richness of North American trees. We use both the traditional ‘global’ regression models as well as geographically weighted regressions (‘local’ models) to examine local variation in the contribution of MDE and climatic variables to species richness across the domain. Our results show that in some dimensions the contribution of MDE to patterns of species richness can be quite substantial, and we show that in most cases a combination of MDE and climate predicted empirical species richness best in both local and global models. For the North American domain, MDE in the elevation dimension is clearly important in describing patterns of empirical species richness. We also show that the assumption of stationarity in global models is not met in the North American domain and that results of these models mask complex patterns in both the effect of MDE on richness and the response of species richness to climate. In particular we show the increased explanatory role of MDE in predicting species richness as domain edges are approached. Our results support the hypothesis that geometric constraints contribute to species richness patterns and we suggest the mid‐domain effect should be considered alongside more traditional environmental correlates in understanding patterns of species diversity.

In a recent article (Colwell et al. 2004; hereafter CRG), we reviewed all studies of the mid-domain effect (MDE) published up to that time and responded to earlier critiques of MDE by (among others) Hawkins and Diniz-Filho (2002) and Zapata et al. (2003). The discussion continues here with comments on our article by Hawkins, DinizFilho, and Weiss (2005; hereafter HDW) and by Zapata, Gaston, and Chown (2005; hereafter ZGC). In this article, we respond to HDW and ZGC. Null models and null hypotheses. ZGC defend their view of MDE models as null hypotheses subject to falsification. In contrast, proponents of MDE theory have always held that richness patterns have multiple causes. For example, referring to the effect of geometric (boundary) constraints on geographic patterns of species richness, Colwell and Lees (2000, p. 79) stated that “the question is not whether geometry affects such patterns, but by how much.” We view any assessment of the role of geometric constraints as a problem of estimating the magnitude of the contribution (if any) of MDE to richness patterns (“how much”). Just as with other candidate causes of richness patterns (e.g., climatic, topographic, or historical drivers), the appropriate null hypothesis (if any) is that MDE makes no contribution to richness patterns, not that MDE uniquely and fully accounts for richness patterns. It is for this reason that we distinguish between MDE as a model and the hypotheses that can be framed regarding its predictions.

The altitudinal distribution patterns of angiosperms in Mt. Taibai, Qinling Mountains, China, were examined based on data of regional flora and Digital Elevation Model. Possible factors determining the distribution, such as geographical area effect and mid-domain effect (MDE) were assessed. The richness and density at species, genus, and family levels showed a hump-shaped pattern along the altitudinal gradient with peak at low-elevations. Similar patterns were identified for four life-forms (trees, lianas, shrubs and herbs), three biogeographical species groups (cosmopolitan, tropic and temperate), and two different range sizes (small- and large-ranged species). The endemic richness displayed a bimodal pattern with increasing altitude, but the proportion of endemism increased rapidly from low to high altitudes. The area of each altitudinal band was highly associated with the species richness, whereas the MDE could explain 87.0 and 50.3% of the richness variance for overall and endemic species, respectively. The collinearity between MDE and area could explain a large proportion (83.3%) of variance in species richness, implying the substitution between the two factors. Moreover, the explanatory power of MDE was stronger for large-ranged species than for small-ranged species. Our results confirmed that both MDE and area were all crucial factors to explain altitudinal richness patterns in Taibai Mountain, China. Key words: Species richness, range sizes, mid-domain effect, area effect, altitudinal gradient.

Although the mid-domain effect (MDE) model for species richness in bound- ed geographical domains has proved controversial, several studies have revealed its ex- planatory potential for patterns of species richness. This paper investigates unexplored aspects of one-dimensional MDE, based on a data set of latitudinal distributions of New World birds (3706 species) on a 10 scale. Two previously published data sets for other taxa are also considered. We adjusted band sums (number of species per latitudinal band) for longitudinal area by constructing species-area curves for each band. Area-corrected richness patterns differed substantially from raw band sums, although both confirmed a strong, mid- tropical peak in richness. An MDE model accounted for 47% of the adjusted pattern, whereas area alone explained 13% of variation. Area-adjusted band sum data proved preferable to coastal transect data from the same data set. MDE was relatively more important in smaller latitudinal domains and/or for taxa with relatively large ranges. On fundamental grounds, we concluded that MDE randomizations based on empirical ranges are more appropriate than those based on theoretical range size distributions. Models that, like MDE, produce quantitative richness predictions should be evaluated statistically against the null hypothesis of equality, not simply correlation, of empirical vs. predicted richness values.

BackgroundIn biological systems, biological diversity often displays a rapid turn-over across elevations. This defining feature has made mountains classic systems for studying the spatial variation in diversity. Because patterns of elevational diversity can vary among lineages and mountain systems it remains difficult to extrapolate findings from one montane region to another, or among lineages. In this study, we assessed patterns and drivers of avian diversity along an elevational gradient on the eastern slope of Mt. Gongga, the highest peak in the Hengduan Mountain Range in central China, and a mountain where comprehensive studies of avian diversity are still lacking.MethodsWe surveyed bird species in eight 400-m elevational bands from 1200 to 4400 m a.s.l. between 2012 and 2017. To test the relationship between bird species richness and environmental factors, we examined the relative importance of seven ecological variables on breeding season distribution patterns: land area (LA), mean daily temperature (MDT), seasonal temperature range (STR), the mid-domain effect (MDE), seasonal precipitation (SP), invertebrate biomass (IB) and enhanced vegetation index (EVI). Climate data were obtained from five local meteorological stations and three temperature/relative humidity smart sensors in 2016.ResultsA total of 219 bird species were recorded in the field, of which 204 were recorded during the breeding season (April–August). Species richness curves (calculated separately for total species, large-ranged species, and small-ranged species) were all hump-shaped. Large-ranged species contributed more to the total species richness pattern than small-ranged species. EVI and IB were positively correlated with total species richness and small-ranged species richness. LA and MDT were positively correlated with small-ranged species richness, while STR and SP were negatively correlated with small-ranged species richness. MDE was positively correlated with large-ranged species richness. When we considered the combination of candidate factors using multiple regression models and model-averaging, total species richness and large-ranged species richness were correlated with STR (negative) and MDE (positive), while small-ranged species richness was correlated with STR (negative) and IB (positive).ConclusionsAlthough no single key factor or suite of factors could explain patterns of diversity, we found that MDE, IB and STR play important but varying roles in shaping the elevational richness patterns of different bird species categories. Model-averaging indicates that small-ranged species appear to be mostly influenced by IB, as opposed to large-ranged species, which exhibit patterns more consistent with the MDE model. Our data also indicate that the species richness varied between seasons, offering a promising direction for future work.

Species richness in Atlantic deep-sea fishes assessed in terms of the mid-domain effect and Rapoport's rule

Acknowledgement to reviewers

BackgroundThe distribution of resources can affect animal range sizes, which in turn may alter infectious disease dynamics in heterogenous environments. The risk of pathogen exposure or the spatial extent of outbreaks may vary with host range size. This study examined the range sizes of herbivorous anthrax host species in two ecosystems and relationships between spatial movement behavior and patterns of disease outbreaks for a multi-host environmentally transmitted pathogen.MethodsWe examined range sizes for seven host species and the spatial extent of anthrax outbreaks in Etosha National Park, Namibia and Kruger National Park, South Africa, where the main host species and outbreak sizes differ. We evaluated host range sizes using the local convex hull method at different temporal scales, within-individual temporal range overlap, and relationships between ranging behavior and species contributions to anthrax cases in each park. We estimated the spatial extent of annual anthrax mortalities and evaluated whether the extent was correlated with case numbers of a given host species.ResultsRange size differences among species were not linearly related to anthrax case numbers. In Kruger the main host species had small range sizes and high range overlap, which may heighten exposure when outbreaks occur within their ranges. However, different patterns were observed in Etosha, where the main host species had large range sizes and relatively little overlap. The spatial extent of anthrax mortalities was similar between parks but less variable in Etosha than Kruger. In Kruger outbreaks varied from small local clusters to large areas and the spatial extent correlated with case numbers and species affected. Secondary host species contributed relatively few cases to outbreaks; however, for these species with large range sizes, case numbers positively correlated with outbreak extent.ConclusionsOur results provide new information on the spatiotemporal structuring of ranging movements of anthrax host species in two ecosystems. The results linking anthrax dynamics to host space use are correlative, yet suggest that, though partial and proximate, host range size and overlap may be contributing factors in outbreak characteristics for environmentally transmitted pathogens.

A global analysis of elevational diversity trends for nonvolant small mammals revealed a clear pattern of mid-elevational peaks in species richness. Fifty-six data sets were used to test the predictions of a null model (the mid-domain effect) and climatic hypotheses. Very few data sets fit entirely within the predictions of the null model, and the average predictive power of the null model was low. Regional (gamma) diversity fit the null model better than did local (alpha) diversity. Diversity peaked at higher elevations on taller mountains, consistent with climatic factors producing elevationally correlated habitat bands (Massenerhebung effect). This positive, linear relationship was documented for all data sets but was particularly pronounced for alpha diversity. Gamma diversity, which is generally highly influenced by area, exhibited a trend of highest diversity shifting toward lower elevations, and higher elevational peaks in species diversity at higher latitudes. The elevation of temperate diversity peaks exhibited a negative association with latitude. These results are evidence for the importance of a suite of interacting climatic, area, and geometric factors on elevational diversity patterns, apparent in spite of noise associated with different sampling techniques, localities, and historical pressures.

AimThe aim of the present study was to describe the diversity patterns along elevational gradients that are crucial for conservation management and for understanding diversification processes in montane environments. This article analyses the distributional range and elevation patterns of endemic and non‐endemic (NE) vascular plants living on a unique set of Neotropical table mountains (tepuis). To investigate the potential causes of the high degree of diversity and endemism we tested the role of elevation, area and the mid‐domain effect (MDE). We also aimed to discuss the origin of the current tepuian biota.LocationThe Guayana Highlands (northern South America). Emphasis is placed on the mountaintops above 1500 m elevation, which form the highly biodiverse Pantepui biogeographical province.MethodsWe examined the distribution patterns of vascular plant species richness in relation to elevation, area, and the MDE using generalized additive models. We used Range Model for the MDE.ResultsWe found that regional endemics richness show a hump‐shaped curve in relation to elevation. Single‐tepui endemics (STE) increase with elevation, whereas NE and total species richness decrease. Area and MDE influence this pattern for Pantepui and STE, but not for NE. We also observed that the spatial distribution of endemic richness displays a left‐skewed distribution pattern due to the dominance of STE.Main conclusionsOur results demonstrate that a combination of elevation, area and MDE provide a basic explanation for the diversity of vascular plants in Pantepui. In addition, the present study indicates that maxima of STE are located at the highest altitudes, where the possibility of biotic connection (via migration) and gene flux has been minimal, even during glacial phases when most migration pathways amongst the tepui mountains were open. We also suggest that climatic filtering due to the extreme conditions atop the tepuis and low dispersal capacity stand out as the main drivers of the decline in NE species richness with elevation.

Latitudinal patterns of tree β-diversity and relevant ecological processes vary across spatial extents in forests of southeastern China

AimTo map and analyse range size variation in the terrestrial seed‐plant flora of Australia in relation to latitude, habitat availability, climate and soils, and to compare mean range size between biomes and growth forms.LocationAustralia.MethodsRange sizes were estimated from herbarium records using alpha‐hulls for 19,227 species and mapped into a set of 0.5° × 0.5° grid cells across Australia. Ordinary‐least squares regressions were used to test for relationships between mean range size, latitude and habitat availability. Simultaneous autoregressive models (SAR) with spatial error terms were used in a multi‐model framework to assess the role of aridity, mean annual temperature (MAT), soil pH, depth and total P concentration in shaping range size variation. Species‐level differences between growth forms (graminoids, herbs, trees, shrubs, climbers) and biomes were assessed using one‐way analysis of variance (ANOVA).Results68% of Australian seed‐plant species have ranges which cover < 1% of the continent. Generally, large ranges are characteristic of graminoids and herbs and are associated with arid and grassland biomes. The smallest ranges were found in Mediterranean ecosystems in the SW corner of the continent, and were typically shrub species. Range size peaked at mid‐latitudes and was not consistently smaller at lower latitudes (contrary to Rapoport's Rule). Increasing aridity, MAT and soil pH were strong predictors of large range size; however, soil depth and P content had little influence in SAR models.Main conclusionsAridity and temperature are the primary drivers of range size variation in the Australian flora. Trait syndromes which promote colonization and survival in arid and grassland habitats (e.g. C4 photosynthesis, wind pollination) have allowed grasses and herbs to occupy large areas of the continent. Ranges were not smaller, on average, in the tropics due to the concentration of large‐ranged species in the widespread arid biome in the centre of the continent.

1. Knowledge of global patterns of biodiversity and regulating variables is indispensable to develop predictive models. 2. The present study used predictive modelling approaches to investigate hypotheses that explain the variation in fish species richness between estuaries over a worldwide spatial extent. Ultimately, such models will allow assessment of future changes in ecosystem structure and function as a result of environmental changes. 3. A comprehensive worldwide data base was compiled of the fish assemblage composition and environmental characteristics of estuaries. Generalized Linear Models were used to quantify how variation in species richness among estuaries is related to historical events, energy dynamics and ecosystem characteristics, while controlling for sampling effects. 4. At the global extent, species richness differed among marine biogeographic realms and continents and increased with mean sea surface temperature, terrestrial net primary productivity and the stability of connectivity with a marine ecosystem (open vs. temporarily open estuaries). At a smaller extent (within a marine biogeographic realm or continent), other characteristics were also important in predicting variation in species richness, with species richness increasing with estuary area and continental shelf width. 5. The results suggest that species richness in an estuary is defined by predictors that are spatially hierarchical. Over the largest spatial extents, species richness is influenced by the broader distributions and habitat use patterns of marine and freshwater species that can colonize estuaries, which are in turn governed by history contingency, energy dynamics and productivity variables. Species richness is also influenced by more regional and local parameters that can further affect the process of community colonization in an estuary including the connectivity of the estuary with the adjacent marine habitat, and, over smaller spatial extents, the size of these habitats. In summary, patterns of species richness in estuaries across large spatial extents seem to reflect from global to local processes acting on community colonization. The importance of considering spatial extent, sampling effects and of combining history and contemporary environmental characteristics when exploring biodiversity is highlighted.

A recent paper (1) purported to document negligible climatic determination among European bird species, with implications for forecasting range shifts in changing climates. However, only 12 of 100 species analyzed were endemic—thus, for the remaining 88% of test species, key limits with likely climatic determination were excluded, particularly eastward and southward (2). Second, the authors developed null distributions conserving the same prevalence and semivariogram as real species' distributions and showed that real distributions were not modeled better than “null” distributions. However, most variation in null and real distributions at broad geographic scales is explained by spatially structured climate variation that is difficult to disentangle. The authors' manipulation fails to eliminate climate as a correlate of null ranges exactly because their occurrences were spatially clumped: as climate is autocorrelated, null distributions have climate signatures just like real distributions. Producing null models removing effects of climate but keeping the spatial cohesion of the distribution is unlikely, as in recent debates regarding mid-domain effects as appropriate null models for diversity (3). Null distributions will show climate signatures similar to those of real species that are not eliminated in their randomization algorithm, so the authors cannot reject the hypothesis of a climate association in European birds. Nonetheless, we agree that, because distributions can be modeled by using current spatially structured climatic predictors without necessity of direct causal linkages (4), more studies oriented at testing the robustness of correlative methods in predicting species' distributions under future climate scenarios are needed (5).