Limited sampling hampers “big data” estimation of species richness in a tropical biodiversity hotspot

Macro‐scale species richness studies often use museum specimens as their main source of information. However, such datasets are often strongly biased due to variation in sampling effort in space and time. These biases may strongly affect diversity estimates and may, thereby, obstruct solid inference on the underlying diversity drivers, as well as mislead conservation prioritization. In recent years, this has resulted in an increased focus on developing methods to correct for sampling bias. In this study, we use sample‐size‐correcting methods to examine patterns of tropical plant diversity in Ecuador, one of the most species‐rich and climatically heterogeneous biodiversity hotspots. Species richness estimates were calculated based on 205,735 georeferenced specimens of 15,788 species using the Margalef diversity index, the Chao estimator, the second‐order Jackknife and Bootstrapping resampling methods, and Hill numbers and rarefaction. Species richness was heavily correlated with sampling effort, and only rarefaction was able to remove this effect, and we recommend this method for estimation of species richness with “big data” collections.     

Influences of interpolation of species ranges on elevational species richness gradients

Interpolation of species ranges has been a common approach to compensate for the unevenness or incompleteness in sampling effort in studies of geographic species richness gradients. However, potential biases introduced by this estimation method remain unclear. Here, we presented an explicit examination of the influences of one‐dimensional interpolation on elevational species richness gradients, and discussed potential causes and processes of these influences. We conducted intensive surveys of birds along the elevational gradients of the Ailao Mountains, southwestern China, and compared richness patterns based on interpolation with raw data as well as estimated data from rarefaction and Chao1 non‐parametric estimator; we also compared results of multiple linear regressions and hierarchical partitioning analyses explaining these four measures of richness. Actual evapotranspiration (AET) and the mid‐domain effect (MDE) were highly correlated and separately provided a good potential explanation for the unimodal richness pattern in the Ailao Mountains, with modifying and suppressive effects of other variables such as area. Interpolation consistently and significantly increased the effects of AET/MDE, while it reduced contributions of area and human disturbance. Our results demonstrated that while compensating for biases in sampling effort, interpolation may also spuriously fill genuine distribution gaps, and tend to underestimate the effects of the non‐monotonic or discontinuous influencing factors that are responsible for these gaps, and overestimate the effects of other factors actually suppressed by these factors. These influences were most strong for species with relatively medium elevational ranges. We conclude that at the regional scale, interpolation method is a potential source of bias in identifying and explaining species richness gradients and should be used with careful consideration. It may be advantageous to adopt other robust estimation methods besides interpolation to gain a more accurate assessment of species richness and a more objective understanding of their underlying mechanisms.     

Models and estimators linking individual-based and sample-based rarefaction,extrapolation and comparison of assemblages

Aims In ecology and conservation biology, the number of species counted in a biodiversity study is a key metric but is usually a biased underestimate of total species richness because many rare species are not detected. Moreover, comparing species richness among sites or samples is a statistical challenge because the observed number of species is sensitive to the number of individuals counted or the area sampled. For individual-based data, we treat a single, empirical sample of species abundances from an investigator-defined species assemblage or community as a reference point for two estimation objectives under two sampling models: estimating the expected number of species (and its unconditional variance) in a random sample of (i) a smaller number of individuals (multinomial model) or a smaller area sampled (Poisson model) and (ii) a larger number of individuals or a larger area sampled. For sample-based incidence (presence–absence) data, under a Bernoulli product model, we treat a single set of species incidence frequencies as the reference point to estimate richness for smaller and larger numbers of sampling units.Methods The first objective is a problem in interpolation that we address with classical rarefaction (multinomial model) and Coleman rarefaction (Poisson model) for individual-based data and with sample-based rarefaction (Bernoulli product model) for incidence frequencies. The second is a problem in extrapolation that we address with sampling-theoretic predictors for the number of species in a larger sample (multinomial model), a larger area (Poisson model) or a larger number of sampling units (Bernoulli product model), based on an estimate of asymptotic species richness. Although published methods exist for many of these objectives, we bring them together here with some new estimators under a unified statistical and notational framework. This novel integration of mathematically distinct approaches allowed us to link interpolated (rarefaction) curves and extrapolated curves to plot a unified species accumulation curve for empirical examples. We provide new, unconditional variance estimators for classical, individual-based rarefaction and for Coleman rarefaction, long missing from the toolkit of biodiversity measurement. We illustrate these methods with datasets for tropical beetles, tropical trees and tropical ants.Important findings Surprisingly, for all datasets we examined, the interpolation (rarefaction) curve and the extrapolation curve meet smoothly at the reference sample, yielding a single curve. Moreover, curves representing 95% confidence intervals for interpolated and extrapolated richness estimates also meet smoothly, allowing rigorous statistical comparison of samples not only for rarefaction but also for extrapolated richness values. The confidence intervals widen as the extrapolation moves further beyond the reference sample, but the method gives reasonable results for extrapolations up to about double or triple the original abundance or area of the reference sample. We found that the multinomial and Poisson models produced indistinguishable results, in units of estimated species, for all estimators and datasets. For sample-based abundance data, which allows the comparison of all three models, the Bernoulli product model generally yields lower richness estimates for rarefied data than either the multinomial or the Poisson models because of the ubiquity of non-random spatial distributions in nature.     

How to describe species richness patterns for bryophyte conservation?

A large amount of data for inconspicuous taxa is stored in natural history collections; however, this information is often neglected for biodiversity patterns studies. Here, we evaluate the performance of direct interpolation of museum collections data, equivalent to the traditional approach used in bryophyte conservation planning, and stacked species distribution models (S‐SDMs) to produce reliable reconstructions of species richness patterns, given that differences between these methods have been insufficiently evaluated for inconspicuous taxa. Our objective was to contrast if species distribution models produce better inferences of diversity richness than simply selecting areas with the higher species numbers. As model species, we selected Iberian species of the genus Grimmia (Bryophyta), and we used four well‐collected areas to compare and validate the following models: 1) four Maxent richness models, each generated without the data from one of the four areas, and a reference model created using all of the data and 2) four richness models obtained through direct spatial interpolation, each generated without the data from one area, and a reference model created with all of the data. The correlations between the partial and reference Maxent models were higher in all cases (0.45 to 0.99), whereas the correlations between the spatial interpolation models were negative and weak (−0.3 to −0.06). Our results demonstrate for the first time that S‐SDMs offer a useful tool for identifying detailed richness patterns for inconspicuous taxa such as bryophytes and improving incomplete distributions by assessing the potential richness of under‐surveyed areas, filling major gaps in the available data. In addition, the proposed strategy would enhance the value of the vast number of specimens housed in biological collections.     

Constant tree species richness along an elevational gradient of Mt. Bokor,a table-shaped mountain in southwestern Cambodia

Some previous studies along an elevational gradient on a tropical mountain documented that plant species richness decreases with increasing elevation. However, most of studies did not attempt to standardize the amount of sampling effort. In this paper, we employed a standardized sampling effort to study tree species richness along an elevational gradient on Mt. Bokor, a table-shaped mountain in southwestern Cambodia, and examined relationships between tree species richness and environmental factors. We used two methods to record tree species richness: first, we recorded trees taller than 4 m in 20 uniform plots (5 × 100 m) placed at 266–1048-m elevation; and second, we collected specimens along an elevational gradient from 200 to 1048 m. For both datasets, we applied rarefaction and a Chao1 estimator to standardize the sampling efforts. A generalized linear model (GLM) was used to test the relationship of species richness with elevation. We recorded 308 tree species from 20 plots and 389 tree species from the general collections. Species richness observed in 20 plots had a weak but non-significant correlation with elevation. Species richness estimated by rarefaction or Chao1 from both data sets also showed no significant correlations with elevation. Unlike many previous studies, tree species richness was nearly constant along the elevational gradient of Mt. Bokor where temperature and precipitation are expected to vary. We suggest that the table-shaped landscape of Mt. Bokor, where elevational interval areas do not significantly change between 200 and 900 m, may be a determinant of this constant species richness.     

Elevational gradients and species richness: do methods change pattern perception?

Aim Species richness patterns along elevational gradients have been documented extensively. Yet, the implications of differences in how the data are compiled are seldom explored. We investigate the effect of grain size on the richness–elevation relationship. Grain size varies among the principal methods used to collect or aggregate species occurrences: localized sites, elevational ‘bins’ and interpolation of species ranges. Assumptions of sampling and species distributions also vary among these methods. Methodology can influence the pattern that is perceived and comparability of results. We compare patterns from all three methods explicitly using the same suite of observations, based on museum records and field surveys of non‐flying small mammals. Our assessment is enhanced by comparing patterns resulting from each method for each of six adjacent mountain ranges. Location Utah, North America. Methods We document elevational species richness patterns using generalized linear models (GLMs), comparing the general shape of the trend as well as curvature, location and magnitude of peak richness across methods, both within and among gradients. We also introduce a new procedure to test for richness peaks using site‐based occurrences. Results We find a general congruence of the richness–elevation relationship, depicting a hump‐shaped pattern with a second‐order polynomial GLM showing a significant fit to nearly all gradient‐methodology combinations. However, underlying characteristics of the trend may vary with grain size. As grain size coarsens, maximum species richness increases and elevation of the mode slightly decreases. Results for curvature vary, but degree of curvature tends to increase as grain size coarsens. The richness–elevation patterns are independent of sampling effects. Main conclusions The perceived elevational diversity pattern for small mammals along these mountain ranges is not scale‐dependent. Differences in how the data are compiled are not reflected in major differences in patterns, even when local samples are neither uniformly spaced nor sampled with the same intensity. This result lends confidence to the assertion that patterns documented in similar studies with different methodologies and for which sampling is sufficiently comprehensive are good indicators of diversity. However, consistency of results from more than one compilation method may help to address issues of scale‐dependence, more so when these comparisons are made explicit.     

Could temperature and water availability drive elevational species richness patterns? A global case study for bats

Aim A global meta‐analysis was used to elucidate a mechanistic understanding of elevational species richness patterns of bats by examining both regional and local climatic factors, spatial constraints, sampling and interpolation. Based on these results, I propose the first climatic model for elevational gradients in species richness, and test it using preliminary bat data for two previously unexamined mountains. Location Global data set of bat species richness along elevational gradients from Old and New World mountains spanning 12.5° S to 38° N latitude. Methods Bat elevational studies were found through an extensive literature search. Use was made only of studies sampling  70% of the elevational gradient without significant sampling biases or strong anthropogenic disturbance. Undersampling and interpolation were explicitly examined with three levels of error analyses. The influence of spatial constraints was tested with a Monte Carlo simulation program, Mid‐Domain Null. Preliminary bat species richness data sets for two test mountains were compiled from specimen records from 12 US museum collections. Results Equal support was found for decreasing species richness with elevation and mid‐elevation peaks. Patterns were robust to substantial amounts of error, and did not appear to be a consequence of spatial constraints. Bat elevational richness patterns were related to local climatic gradients. Species richness was highest where both temperature and water availability were high, and declined as temperature and water availability decreased. Mid‐elevational peaks occurred on mountains with dry, arid bases, and decreasing species richness occurred on mountains with wet, warm bases. A preliminary analysis of bat richness patterns on elevational gradients in western Peru (dry base) and the Olympic Mountains, WA (wet base), supported the predictions of the climate model. Main conclusions The relationship between species richness and combined temperature and water availability may be due to both direct (thermoregulatory constraints) and indirect (food resources) factors. Abundance was positively correlated with species richness, suggesting that bat species richness may also be related to productivity. The climatic model may be applicable to other taxonomic groups with similar ecological constraints, for instance certain bird, insect and amphibian clades.     

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.     

Mapping patterns of ferns species richness through the use of herbarium data

This paper aims to analyse the spatial patterns of sampling effort and species richness of pteridophyte in a well-investigated region as Tuscany, Italy, by using data stored from a geodatabase storing information on the specimens preserved in the main herbaria of the region. A total of 6,905 records about pteridophyte specimens were extracted from the geodatabase, and 5,638 of such specimens were studied through the use of spatial statistical techniques. The data about the sampling effort and species richness were analysed in relation to topographical variables to assess any significant relationship. Specimen-based rarefaction techniques were used to compare areas with different number of detected species. The analysis of the sampling effort data showed a nonhomogeneous distribution of herbarium data, with some areas being intensively sampled and others being almost unsampled. Thus, the geographical distribution of specimens was extremely clustered. The comparison across geographical areas through specimen-based rarefaction curves showed great differences in species richness and sampling completeness. The analysis of the residuals of species–area relationships evidenced that the distance to water bodies was the only significant topographical variable in controlling species diversity.     

Rarity, commonness, and patterns of species richness: the mammals of Mexico

Aim To determine whether rare or common species contribute most to overall patterns of spatial variation in extant species richness. Location Mexico. Methods Using data on the distribution of mammal species across Mexico at a quarter degree resolution, we ranked species from the most widespread to the most restricted (common‐to‐rare) within the study area, and from the most restricted to the most widespread (rare‐to‐common), and generated a sequence of patterns of species richness for increasing numbers of species. At each stage along both series of richness patterns, we correlated the species richness pattern for the subassemblage with that of the full assemblage. This allows comparison of subassemblages of the n most common with the n most rare species, in terms of how well they match the full assemblage richness pattern. Further analyses examined the effects on these patterns of correlation of the amount of raw information contained in the distributions of given numbers of rare and common species. Results For the mammals of Mexico the more widely distributed species contribute disproportionately to patterns of species richness compared with more restricted species, particularly for non‐volant species and endemic species. This is not simply a consequence of differences in the volumes of information contained in the distributions of rare and common species, with the disproportionate contribution of common species if anything being sharpened when these differences are taken into account. The pattern is most clearly demonstrated by endemic species, suggesting that the contribution of common species is clearest when the causes of rarity and commonness are limited to those genuinely resulting in narrow and widespread geographical ranges, respectively, rather than artificial (e.g. geopolitical) boundaries to the extents of study regions. Conclusions Perhaps surprisingly, an understanding of the determinants of overall patterns of species richness may gain most from consideration of why common species occur in some areas and are absent from others, rather than consideration of the distributions of rare species.     

The mid-domain effect in ectomycorrhizal fungi: range overlap along an elevation gradient on Mount Fuji,Japan

Mid-domain effect (MDE) models predict that the random placement of species'' ranges within a bounded geographical area leads to increased range overlap and species richness in the center of the bounded area. These models are frequently applied to study species-richness patterns of macroorganisms, but the MDE in relation to microorganisms is poorly understood. In this study, we examined the characteristics of the MDE in richness patterns of ectomycorrhizal (EM) fungi, an ecologically important group of soil symbionts. We conducted intensive soil sampling to investigate overlap among species ranges and the applicability of the MDE to EM fungi in four temperate forest stands along an elevation gradient on Mount Fuji, Japan. Molecular analyses using direct sequencing revealed 302 EM fungal species. Of 73 EM fungal species found in multiple stands, 72 inhabited a continuous range along the elevation gradient. The maximum overlap in species range and the highest species richness occurred at elevations in the middle of the gradient. The observed richness pattern also fit within the 95% confidence interval of the mid-domain null model, supporting the role of the MDE in EM fungal richness. Deviation in observed richness from the mean of the mid-domain null estimation was negatively correlated with some environmental factors, including precipitation and soil C/N, indicating that unexplained richness patterns could be driven by these environmental factors. Our results clearly support the existence of microbial species'' ranges along environmental gradients and the potential applicability of the MDE to better understand microbial diversity patterns.     

Rarefaction and extrapolation of species richness using an area‐based Fisher's logseries

Fisher's logseries is widely used to characterize species abundance pattern, and some previous studies used it to predict species richness. However, this model, derived from the negative binomial model, degenerates at the zero‐abundance point (i.e., its probability mass fully concentrates at zero abundance, leading to an odd situation that no species can occur in the studied sample). Moreover, it is not directly related to the sampling area size. In this sense, the original Fisher's alpha (correspondingly, species richness) is incomparable among ecological communities with varying area sizes. To overcome these limitations, we developed a novel area‐based logseries model that can account for the compounding effect of the sampling area. The new model can be used to conduct area‐based rarefaction and extrapolation of species richness, with the advantage of accurately predicting species richness in a large region that has an area size being hundreds or thousands of times larger than that of a locally observed sample, provided that data follow the proposed model. The power of our proposed model has been validated by extensive numerical simulations and empirically tested through tree species richness extrapolation and interpolation in Brazilian Atlantic forests. Our parametric model is data parsimonious as it is still applicable when only the information on species number, community size, or the numbers of singleton and doubleton species in the local sample is available. Notably, in comparison with the original Fisher's method, our area‐based model can provide asymptotically unbiased variance estimation (therefore correct 95% confidence interval) for species richness. In conclusion, the proposed area‐based Fisher's logseries model can be of broad applications with clear and proper statistical background. Particularly, it is very suitable for being applied to hyperdiverse ecological assemblages in which nonparametric richness estimators were found to greatly underestimate species richness.     

新疆天山南坡中段种子植物区系垂直分布格局分析

对植物多样性垂直分布格局及其维持机制的研究可以有效揭示植物物种多样性分布特征及其环境影响因子。本文通过野外调查、查阅标本并结合相关文献资料,对天山南坡中段种子植物区系沿海拔梯度的分布格局进行了系统研究。结果显示,在大区域尺度上,科属种的物种丰富度随海拔升高均呈先增加后减少的趋势,且最高值出现在中低海拔1900~2000 m处;不同生活型植物沿海拔梯度的变化格局有所不同,其中,乔木、一年生草本、藤本及寄生植物表现出随海拔升高物种丰富度逐渐降低的趋势,灌木、多年生草本及二年生草本植物物种丰富度则呈先增加后减少的变化趋势;从植物区系地理成分来看,世界分布所占的比重沿海拔梯度升高呈先增加后减少的趋势;温带地理成分所占的比重沿海拔梯度升高呈缓慢上升趋势;古地中海地理成分所占的比重沿海拔梯度升高呈先增加后减少然后再增加的变化趋势;热带地理成分所占的比重沿海拔升高呈逐渐下降的趋势;东亚地理成分所占的比重沿海拔梯度升高呈先增加后减少然后再增加的变化趋势。对该分布格局与当地干旱的气候条件及海拔梯度上热量和水分条件的变化相适应。     

Species richness and altitude: a comparison between null models and interpolated plant species richness along the Himalayan altitudinal gradient, Nepal

We compare different null models for species richness patterns in the Nepalese Himalayas, the largest altitudinal gradient in the world. Species richness is estimated by interpolation of presences between the extreme recorded altitudinal ranges. The number of species in 100-m altitudinal bands increases steeply with altitude until 1,500 m above sea level. Between 1,500 and 2,500 m, little change in the number of species is observed, but above this altitude, a decrease in species richness is evident. We simulate different null models to investigate the effect of hard boundaries and an assumed linear relationship between species richness and altitude. We also stimulate the effect of interpolation when incomplete sampling is assumed. Some modifications on earlier simulations are presented. We demonstrate that all three factors in combination may explain the observed pattern in species richness. Estimating species richness by interpolating species presence between maximum and minimum altitudes creates an artificially steep decrease in species richness toward the ends of the gradient. The addition of hard boundaries and an underlying linear trend in species richness is needed to simulate the observed broad pattern in species richness along altitude in the Nepalese Himalayas.     

Regional enrichment of predacious water beetles in temporary ponds at opposite east–west ends of the Palearctic

Regional and local species richness of temporary pond dytiscid water beetles were compared among three regions within the Palearctic: (1) Sweden in north west Europe, (2) Primorye and (3) Sakhalin Island in the Russian Far East. Both local and regional species richness were highest in Sweden and lowest in Sakhalin. Regional species richness was calculated from literature and collecting data for each region and for nested parts of regions. Local species richness was estimated from standardized net samples from fourteen or fifteen ponds in each region. Two different rarefaction techniques applied to the net-sample data confirmed the observed interregional differences in species richness. Partial least square regression showed that pond area, depth and temperature affected local species richness positively in each region, whereas increasing shade and drought frequency had negative effects. Residuals from the regression analysis were positive in Sweden, negative in Sakhalin, and near zero in Primorye ponds. Consequently, the local species richness was related positively to regional species richness also when compensated for differences in the local pond environment. This was verified when pond species richness of each region was correlated with principal component scores representing a combination of pond area, depth and temperature. The species' distributions among ponds displayed significantly nested patterns in Sweden and Sakhalin. However, species were significantly sorted along the pond area gradient only in Sweden. It is concluded that the observed interregional differences in local species richness are best explained by the accompanying gradient in regional species richness, lending support to the hypothesis of regional enrichment. Selected historical and ecological explanations for the observed differences in regional species richness are discussed.     

Quantifying regional biodiversity in the tropics: A case study of freshwater fish in Trinidad and Tobago

Extinction rates are predicted to accelerate during the Anthropocene. Quantifying and mitigating these extinctions demands robust data on distributions of species and the diversity of taxa in regional biotas. However, many assemblages, particularly those in the tropics, are poorly characterized. Targeted surveys and historical museum collections are increasingly being used to meet the urgent need for accurate information, but the extent to which these contrasting data sources support meaningful inferences about biodiversity change in regional assemblages remains unclear. Here, we seek to elucidate uncertainty surrounding regional biodiversity estimates by evaluating the performance of these alternative methods in estimating the species richness and assemblage composition of the freshwater fish of Trinidad & Tobago. We compared estimates of regional species richness derived from two freshwater fish datasets: a targeted two year survey of Trinidad & Tobago rivers and historical museum collection records submitted to The University of the West Indies Zoology Museum. Richness was estimated using rarefaction and extrapolation, and assemblage composition was benchmarked against a recent literature review. Both datasets provided similar estimates of regional freshwater fish species richness (50 and 46 species, respectively), with a large overlap (85%) in species identities. Regional species richness estimates based on survey and museum data are thus comparable, and consistent in the species they include. Our results suggest that museum collection data are a viable option for setting reliable baselines in many tropical systems, thereby widening options for meaningful monitoring and evaluation of temporal trends. Abstract in Spanish is available with online material.     

Elevational Gradient in Species Richness Pattern of Epigaeic Beetles and Underlying Mechanisms at East Slope of Balang Mountain in Southwestern China

We report on the species richness patterns of epigaeic beetles (Coleoptera: Carabidae and Staphylinidae) along a subtropical elevational gradient of Balang Mountain, southwestern China. We tested the roles of environmental factors (e.g. temperature, area and litter cover) and direct biotic interactions (e.g. foods and antagonists) that shape elevational diversity gradients. Beetles were sampled at 19 sites using pitfall traps along the studied elevational gradient ranging from 1500 m–4000 m during the 2004 growing season. A total of 74416 specimens representing 260 species were recorded. Species richness of epigaeic beetles and two families showed unimodal patterns along the elevational gradient, peaking at mid-elevations (c. 2535 m), and the ranges of most beetle species were narrow along the gradient. The potential correlates of both species richness and environmental variables were examined using linear and second order polynomial regressions. The results showed that temperature, area and litter cover had strong explanatory power of beetle species richness for nearly all richness patterns, of beetles as a whole and of Carabidae and Staphylinidae, but the density of antagonists was associated with species richness of Carabidae only. Multiple regression analyses suggested that the three environmental factors combined contributed most to richness patterns for most taxa. The results suggest that environmental factors associated with temperature, area and habitat heterogeneity could account for most variation in richness pattern of epigaeic beetles. Additionally, the mid-elevation peaks and the small range size of most species indicate that conservation efforts should give attention to the entire gradient rather than just mid-elevations.     

The role of environment and mid-domain effect on moth species richness along a tropical elevational gradient

Aim The biodiversity of geometrid moths (Lepidoptera) along a complete tropical elevational gradient was studied for the first time. The patterns are described, and the role of geometric constraints and environmental factors is explored. Location The study was carried out along the Barva Transect (10° N, 84° W), a complete elevational gradient ranging from 40 to 2730 m a.s.l. in Braulio Carrillo National Park, Costa Rica, and adjacent areas. Methods Moths were sampled manually in 2003 and 2004 at 12 rain forest sites using light ‘towers’, each with two 15 W ultraviolet fluorescent tubes. We used abundance‐based rarefaction, statistical estimation of true richness (Chao 1), geographically interpolated observed richness and Fisher's alpha as measures of local diversity. Results A total of 13,765 specimens representing 739 species were analysed. All four measures showed a hump‐shaped pattern with maxima between 500 and 2100 m elevation. The two subfamilies showed richness and diversity maxima at either lower (Ennominae) or higher (Larentiinae) elevation than Geometridae as a whole. Among the four environmental factors tested, relative humidity yielded the highest correlation over the transect with the rarefaction‐based richness estimates as well as with estimated true species richness of Geometridae as a whole and of Larentiinae, while rainfall explained the greatest variation of Ennominae richness. The elevational pattern of moth richness was discordant with both temperature and with tree species richness. A combination of all environmental factors in a stepwise multiple regression produced high values of r² in Geometridae. The potential effects of geometric constraints (mid‐domain effect, MDE) were investigated by comparing them with observed, interpolated richness. Overall, models fitted very well for Geometridae as a whole and for Ennominae, but less well for Larentiinae. Small‐ranged species showed stronger deviations from model predictions than large‐ranged species, and differed strikingly between the two subfamilies, suggesting that environmental factors play a more pronounced role for small‐ranged species. We hypothesize that small‐ranged species (at least of the Ennominae) may tend to be host specialists, whereas large‐ranged species tend to be polyphagous. Based on interpolated ranges, mean elevational range for these moths was larger with increasing elevation, in accordance with Rapoport's elevational rule, although sampling effects may have exaggerated this pattern. The underlying mechanism remains unknown because Rapoport's ‘rescue’ hypothesis could not explain the observed pattern. Conclusions The results clearly show that moth diversity shows a hump‐shaped pattern. However, remarkable variation exists with regard to taxon and range size. Both environmental and geometric factors are likely to contribute to the observed patterns.     

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.     

What drives the species richness patterns of non‐volant small mammals along a subtropical elevational gradient?

The biodiversity of non‐volant small mammals along an extensive subtropical elevational gradient was studied for the first time on Gongga Mountain, the highest mountain in Hengduan Mountain ranges in China, located in one of the 25 global biodiversity hotspots. Non‐volant small mammals were replicate sampled in two seasons at eight sampling sites between 1000 and 4200 m elevation on the eastern slope of Gongga Mountain. In all, 726 individual small mammals representing 25 species were documented in 28 800 trap nights. The species richness pattern for non‐volant small mammals along the elevational gradients was hump‐shaped with highest richness at mid‐elevations. However, different richness patterns emerged between endemic and non‐endemic species, between larger‐ranged and smaller‐ranged species and between rodents and insectivores. Temperature, precipitation, plant species richness and geometric constraints (mid‐ domain effect) were most significant in explaining species richness patterns. Based on the analysis of simple ordinary least squares (OLS) and stepwise multiple regressions, the overall richness pattern, as well as the pattern of insectivores, endemic species and larger‐ranged species showed strong correlation with geometric constraint predictions. However, non‐endemic species richness was more strongly correlated with temperature, while rodent richness was correlated with plant species richness. Our study shows that no single key factor can explain all richness patterns of non‐volant small mammals. We need to be cautious in summarizing a general richness pattern of large species groups (e.g. small mammals or mammals) from species in smaller groups having different ecological distributions and life histories. Elevational richness patterns and their driving factors for small mammals are more likely dependent on what kind of species we study.