A comparison of methods for mapping species ranges and species richness

Aim  Maps of species richness are the basis for applied research and conservation planning as well as for theoretical research investigating patterns of richness and the processes shaping these patterns. The method used to create a richness map could influence the results of such studies, but differences between these methods have been insufficiently evaluated. We investigate how different methods of mapping species ranges can influence patterns of richness, at three spatial resolutions.
Location  California, USA.
Methods  We created richness maps by overlaying individual species range maps for terrestrial amphibians and reptiles. The methods we used to create ranges included: point-to-grid maps, obtained by overlaying point observations of species occurrences with a grid and determining presence or absence for each cell; expert-drawn maps; and maps obtained through species distribution modelling. We also used a hybrid method that incorporated data from all three methods. We assessed the correlation and similarity of the spatial patterns of richness maps created with each of these four methods at three different resolutions.
Results  Richness maps created with different methods were more correlated at lower spatial resolutions than at higher resolutions. At all resolutions, point-to-grid richness maps estimated the lowest species richness and those derived from species distribution models the highest. Expert-drawn maps and hybrid maps showed intermediate levels of richness but had different spatial patterns of species richness from those derived with the other methods.
Main conclusions  Even in relatively well-studied areas such as California, different data sources can lead to rather dissimilar maps of species richness. Evaluating the strengths and weaknesses of different methods for creating a richness map can provide guidance for selecting the approach that is most appropriate for a given application and region.     

Disparity between range map- and survey-based analyses of species richness: patterns, processes and implications

Species richness patterns are characterized either by overlaying species range maps or by compiling geographically extensive survey data for multiple local communities. Although, these two approaches are clearly related, they need not produce identical richness patterns because species do not occur everywhere in their geographical range. Using North American breeding birds, we present the first continent‐wide comparison of survey and range map data. On average, bird species were detected on 40.5% of the surveys within their range. As a result of this range porosity, the geographical richness patterns differed markedly, with the greatest disparity in arid regions and at higher elevations. Environmental productivity was a stronger predictor of survey richness, while elevational heterogeneity was more important in determining range map richness. In addition, range map richness exhibited greater spatial autocorrelation and lower estimates of spatial turnover in species composition. Our results highlight the fact that range map richness represents species coexistence at a much coarser scale than survey data, and demonstrate that the conclusions drawn from species richness studies may depend on the data type used for analyses.     

The effect of the taxon and geographic range size of host eucalypt species on the species richness of gall-forming insects

Abstract This field study was designed to test whether the taxonomic group and geographic range size of a host plant species, usually found to influence insect species richness in other parts of the world, affected the number of gall species on Australian eucalypts. We assessed the local and regional species richness of gall-forming insects on five pairs of closely related eucalypt species. One pair belonged to the subgenus Corymbia, one to Monocalyptus, and three to different sections of Symphyomyrtus. Each eucalypt pair comprised a large and a small geographic range species. Species pairs were from coastal or inland regions of eastern Australia. The total number of gall species on eucalypt species with large geographic ranges was greater than on eucalypt species with small ranges, but only after the strong effect of eucalypt taxonomic grouping was taken into account. There was no relationship between the geographic range size of eucalypt species and the size of local assemblages of gall species, but the variation in insect species composition between local sites was higher on eucalypt species with large ranges than on those with small ranges. Thus the effect of host plant range size on insect species richness was due to greater differentiation between more widespread locations, rather than to greater local species richness. This study confirms the role of the geographic range size of a host plant in the determination of insect species richness and provides evidence for the importance of the taxon of a host plant.     

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.     

Determinants of avian species richness at different spatial scales

ABSTRACT. Studies of factors influencing avian biodiversity yield very different results depending on the spatial scale at which species richness is calculated. Ecological studies at small spatial scales (plot size 0.0025–0.4 km²) emphasize the importance of habitat diversity, whereas biogeographical studies at large spatial scales (quadrat size 400–50,000 km²) emphasize variables related to available energy such as temperature. In order to bridge the gap between those two approaches the bird atlas data set of Lake Constance was used to study factors determining avian species diversity at the intermediate spatial scales of landscapes (quadrat size 4–36 km²). At these spatial scales bird species richness was influenced by habitat diversity and not by variables related to available energy probably because, at the landscape scale, variation in available energy is small. Changing quadrat size between 4 and 36 km², but keeping the geographical extension of the study constant resulted in profound changes in the degree to which the amount of different habitat types was correlated with species richness. This suggests that high species diversity is achieved by different management regimes depending on the spatial scale at which species richness is calculated. However, generally, avian species diversity seems to be determined by spatial heterogeneity at the corresponding spatial scale. Thus, protecting the diversity of landscapes and ecosystems appears to ensure also high levels of species diversity.     

中国北方典型草地物种丰富度与生产力的关系

利用2002–2004年内蒙古和甘肃南部几种典型草地的实测资料,研究了不同尺度物种丰富度与生产力的关系,并初步探讨了其形成机制。结果显示,温带草地的物种丰富度随生产力的增加而增加,但受空间尺度影响。在群落尺度(同一群落),在7种样方数大于15的群落中,仅沙生针茅(Stipaglareosa)群落物种丰富度与生产力呈现单峰型关系,其余均呈现线性正相关关系;在植被类型尺度,物种丰富度–生产力之间表现为显著的正相关关系;在研究区尺度,物种丰富度随生产力的增加而显著增加。研究还表明,研究区群落生产力的变化范围为13–368g·m–2·yr–1,物种丰富度为4–35种;生产力从高到低的顺序为:高寒草甸>草甸草原>典型草原>荒漠草原。     

使用大熊猫数据评估Biomod2和MaxEnt分布预测模型的表现

物种分布模型是物种研究和保护者常用的工具.不同模型的预测结果可能相差很大,对研究者选择模型造成一定的难度.本研究使用大熊猫的实际分布数据评估了两种常见物种分布模型Biomod2和最大熵模型(MaxEnt)的表现,运用ROC曲线下面积(area under the curve,AUC)、真实技巧统计值(true skill statistics,TSS)、KAPPA统计量3种指标综合评估了两种模型预测结果的准确度.结果表明: 当使用的物种分布数据和模拟重复次数足够多的时候,两者都能够给出相当准确的预测.相对于MaxEnt,Biomod2的预测准确度更高,尤其是在物种分布点稀少的情况下.然而,Biomod2使用难度较大,运行时间较长,数据处理能力有限.研究者应基于对预测结果的误差要求来选择模型.在误差要求明确且两个模型都能满足误差要求时,建议使用MaxEnt,否则应优先考虑使用Biomod2.     

Type and spatial structure of distribution data and the perceived determinants of geographical gradients in ecology: the species richness of African birds

Aim Studies exploring the determinants of geographical gradients in the occurrence of species or their traits obtain data by: (1) overlaying species range maps; (2) mapping survey‐based species counts; or (3) superimposing models of individual species’ distributions. These data types have different spatial characteristics. We investigated whether these differences influence conclusions regarding postulated determinants of species richness patterns. Location Our study examined terrestrial bird diversity patterns in 13 nations of southern and eastern Africa, spanning temperate to tropical climates. Methods Four species richness maps were compiled based on range maps, field‐derived bird atlas data, logistic and autologistic distribution models. Ordinary and spatial regression models served to examine how well each of five hypotheses predicted patterns in each map. These hypotheses propose productivity, temperature, the heat–water balance, habitat heterogeneity and climatic stability as the predominant determinants of species richness. Results The four richness maps portrayed broadly similar geographical patterns but, due to the nature of underlying data types, exhibited marked differences in spatial autocorrelation structure. These differences in spatial structure emerged as important in determining which hypothesis appeared most capable of explaining each map's patterns. This was true even when regressions accounted for spurious effects of spatial autocorrelation. Each richness map, therefore, identified a different hypothesis as the most likely cause of broad‐scale gradients in species diversity. Main conclusions Because the ‘true’ spatial structure of species richness patterns remains elusive, firm conclusions regarding their underlying environmental drivers remain difficult. More broadly, our findings suggest that care should be taken to interpret putative determinants of large‐scale ecological gradients in light of the type and spatial characteristics of the underlying data. Indeed, closer scrutiny of these underlying data — here the distributions of individual species — and their environmental associations may offer important insights into the ultimate causes of observed broad‐scale patterns.     

Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna

Aim This article aims to test for and explore spatial nonstationarity in the relationship between avian species richness and a set of explanatory variables to further the understanding of species diversity variation. Location Sub‐Saharan Africa. Methods Geographically weighted regression was used to study the relationship between species richness of the endemic avifauna of sub‐Saharan Africa and a set of perceived environmental determinants, comprising the variables of temperature, precipitation and normalized difference vegetation index. Results The relationships between species richness and the explanatory variables were found to be significantly spatially variable and scale‐dependent. At local scales > 90% of the variation was explained, but this declined at coarser scales, with the greatest sensitivity to scale variation evident for narrow ranging species. The complex spatial pattern in regression model parameter estimates also gave rise to a spatial variation in scale effects. Main conclusions Relationships between environmental variables are generally assumed to be spatially stationary and conventional, global, regression techniques are therefore used in their modelling. This assumption was not satisfied in this study, with the relationships varying significantly in space. In such circumstances the average impression provided by a global model may not accurately represent conditions locally. Spatial nonstationarity in the relationship has important implications, especially for studies of species diversity patterns and their scaling.     

The influence of non‐climate predictors at local and landscape resolutions depends on the autecology of the species

Species distribution models have come under criticism for being too simplistic for making robust future forecasts, partly because they assume that climate is the main determinant of geographical range at large spatial extents and coarse resolutions, with non‐climate predictors being important only at finer scales. We suggest that this paradigm might be obscured by species movement patterns. To explore this we used contrasting kangaroo (family Macropodidae) case studies: two species with relatively small, stable home ranges (Macropus giganteus and M. robustus) and three species with more extensive, adaptive ranging behaviour (M. antilopinus, M. fuliginosus and M. rufus). We predicted that non‐climate predictors will be most influential to model fit and predictive performance at local spatial resolution for the former species and at landscape resolution for the latter species. We compared residuals autocovariate – boosted regression tree (RAC‐BRT) model statistics with and without species‐specific non‐climate predictors (habitat, soil, fire, water and topography), at local‐ and landscape‐level spatial resolutions (5 and 50 km). As predicted, the influence of non‐climate predictors on model fit and predictive performance (compared with climate‐only models) was greater at 50 compared with 5 km resolution for M. rufus and M. fuliginosus and the opposite trend was observed for M. giganteus. The results for M. robustus and M. antilopinus were inconclusive. Also notable was the difference in inter‐scale importance of climate predictors in the presence of non‐climate predictors. In conclusion, differences in autecology, particularly relating to space use, may contribute to the importance of non‐climate predictors at a given scale, not model scale per se. Further exploration of this concept across a range of species is encouraged and findings may contribute to more effective conservation and management of species at ecologically meaningful scales.     

珠三角河网浮游植物物种丰富度时空特征

对2012年珠三角河网浮游植物物种丰富度的时空特征进行了系统阐析。季节上,枯水期的物种丰度差异大,丰水期差异小;空间上,广州周边及河网中部个别站位的总种数高于其他站位。不同季节的空间特征显示,枯水期的物种丰度自西江沿线、河网中部、广州周边呈递增趋势;而丰水期呈现三角洲两侧的物种丰富度高于河网中部。各类群相对组成结果显示,硅藻在枯水季节占绝对优势,丰水期优势下降;空间上广州周边站位硅藻百分比明显低于其他站位。分析原因,径流相关的补充和稀释作用和水体搅动引起的底层藻类的悬浮补充不仅影响物种丰富度的季节变动,也影响不同类群的相对组成;水体交换能力和营养盐分别是决定丰水期和枯水期物种丰富度空间分布的关键因素。     

Tackling intraspecific genetic structure in distribution models better reflects species geographical range

Genetic diversity provides insight into heterogeneous demographic and adaptive history across organisms’ distribution ranges. For this reason, decomposing single species into genetic units may represent a powerful tool to better understand biogeographical patterns as well as improve predictions of the effects of GCC (global climate change) on biodiversity loss. Using 279 georeferenced Iberian accessions, we used classes of three intraspecific genetic units of the annual plant Arabidopsis thaliana obtained from the genetic analyses of nuclear SNPs (single nucleotide polymorphisms), chloroplast SNPs, and the vernalization requirement for flowering. We used SDM (species distribution models), including climate, vegetation, and soil data, at the whole‐species and genetic‐unit levels. We compared model outputs for present environmental conditions and with a particularly severe GCC scenario. SDM accuracy was high for genetic units with smaller distribution ranges. Kernel density plots identified the environmental variables underpinning potential distribution ranges of genetic units. Combinations of environmental variables accounted for potential distribution ranges of genetic units, which shrank dramatically with GCC at almost all levels. Only two genetic clusters increased their potential distribution ranges with GCC. The application of SDM to intraspecific genetic units provides a detailed picture on the biogeographical patterns of distinct genetic groups based on different genetic criteria. Our approach also allowed us to pinpoint the genetic changes, in terms of genetic background and physiological requirements for flowering, that Iberian A. thaliana may experience with a GCC scenario applying SDM to intraspecific genetic units.     

How species richness and total abundance constrain the distribution of abundance

The species abundance distribution (SAD) is one of the most intensively studied distributions in ecology and its hollow‐curve shape is one of ecology's most general patterns. We examine the SAD in the context of all possible forms having the same richness (S) and total abundance (N), i.e. the feasible set. We find that feasible sets are dominated by similarly shaped hollow curves, most of which are highly correlated with empirical SADs (most R² values > 75%), revealing a strong influence of N and S on the form of the SAD and an a priori explanation for the ubiquitous hollow curve. Empirical SADs are often more hollow and less variable than the majority of the feasible set, revealing exceptional unevenness and relatively low natural variability among ecological communities. We discuss the importance of the feasible set in understanding how general constraints determine observable variation and influence the forms of predicted and empirical patterns.