Predicting patterns of vascular plant species richness with composite variables: a meso-scale study in Finnish Lapland

This paper is an attempt, using statistical modelling techniques, to understand the patterns of vascular plant species richness at the poorly studied meso-scale within a relatively unexplored subarctic zone. Species richness is related to floristic-environmental composite variables, using occurrence data of vascular plants and environmental and spatial predictor variables in 362 1 km² grid squares in the Kevo Nature Reserve. Species richness is modelled in two different way. First, by detecting the major floristic-environmental gradients with the ordination procedure of canonical correspondence analysis, and subsequently relating these ordination axes to species richness by generalized linear modelling. Second, species richness is directly related to the composite environmental factors of explanatory variables, using partial least squares regression. The most important explanatory variables, as suggested by both approaches, are relatively similar, and largely reflect the influence of altitude or altitudinally related variables in the models. The most prominent floristic gradient in the data runs from alpine habitats to river valleys, and this gradient is the main source of variation in species richness. Some local environmental variables are also relatively important predictors; the grid squares rich in vascular plant taxa are mainly located in the lowlands of the reserve and are characterised by rivers and brooks, as well as by abundant cliff walls. The two statistical models account for approximately the same amount of variation in the species richness, with more than half of the variation unexplained. Potential reasons for the relatively modest fit are discussed, and the results are compared to the characteristics of the diversity-environment relationships at both broader- and finer-scales.     

Modelling of rare plant species richness by landscape variables in an agriculture area in Finland

A multivariate linear regression model is proposed for predicting and mapping rare vascular plant species richness in Finnish agricultural landscapes according to landscape variables. The data used in developing the model were derived from a floristic inventory from 105 0.5 km × 0.5 km grid squares. Using a stepwise multiple regression technique, four landscape variables were found to explain 71.8% of the variability in the number of rare plant species. The results suggest that the local `hotspots' of rare plants (squares with 5 rare taxa) are mainly found in heterogeneous river valleys, where extensive semi-natural grasslands and herb-rich forests occur on the steep slopes. According to other similar studies, intermediate human disturbance increases the number of rare species in agricultural landscapes. It appears that empirical models based on landscape variables derived from digital maps can provide relatively accurate surrogates for extensive field surveys and fine-scale observations on the distributions of rare taxa in agricultural landscapes. Potential reasons for the performance of the model and the ecology and habitats of the species concerned are discussed.     

Contrasting effects of biotic interactions on richness and distribution of vascular plants,bryophytes and lichens in an arctic–alpine landscape

Biotic interactions may strongly affect the distribution of individual species and the resulting patterns of species richness. However, the impacts can vary depending on the species or taxa examined, suggesting that the influences of interactions on species distributions and diversity are not always straightforward and can be taxon-contingent. The aim of this study was therefore to examine how the importance of biotic interactions varies within a community. We incorporated three biotic predictors (cover of the dominant vascular species) into two correlative species richness modelling frameworks to predict spatial variation in the number of vascular plants, bryophytes and lichens in arctic–alpine Fennoscandia, in N Europe. In addition, predictions based on single-species distribution models were used to determine the nature of the impact (negative vs. positive outcome) of the three dominant species on individual vascular plant, bryophyte and lichen species. Our results suggest that biotic variables can be as important as abiotic variables, but their relative contributions in explaining the richness of sub-dominant species vary among dominant species, species group and the modelling framework implemented. Similarly, the impacts of biotic interactions on individual species varied among the three species groups and dominant species, with the observed patterns partly reflecting species’ biogeographic range. Our study provides additional support for the importance of biotic interactions in modifying arctic–alpine biodiversity patterns and highlights that the impacts of interactions are not constant across taxa or biotic drivers. The influence of biotic interactions, including the taxon contingency and range-based impacts, should therefore be accounted for when developing biodiversity forecasts.     

Ants and plants as indicators of biodiversity,ecosystem services,and conservation value in constructed grasslands

Grasslands are constructed for soil and wildlife conservation in agricultural landscapes across Europe and North America. Constructed grasslands may mitigate habitat loss for grassland-dependent animals and enhance ecosystem services that are important to agriculture. The responses of animal species richness and abundance to grassland habitat quality are often highly variable, however, and monitoring of multiple taxa is often not feasible. We evaluated whether multiple animal taxa responded to variation in constructed grassland habitats of southwest Ohio, USA, in ways that could be predicted from indicators based on quality assessment indices, Simpson diversity, and the species richness of ants and plants. The quality assessment indices included a widely used Floristic Quality Assessment (FQA) index, and a new Ant Quality Assessment (AntQA) index, both based on habitat specificity and species traits. The ant and plant indicators were used as predictor variables in separate general linear models of four target taxa—bees, beetles, butterflies and birds—with response variables of overall species richness and abundance, and subsets of taxa that included the abundance of ecosystem-service providers and grassland-associated species. Plant Simpson diversity was the best-fitting predictor variable in models of overall bee and beetle abundance, and the abundance of bees classified as ecosystem-service (ES) providers. FQA and plant richness were the best predictors of overall butterfly species richness and abundance. Ant species richness was the best predictor of overall bird species richness and abundance as well as the abundance of ES birds, while the AntQA index was the best predictor for the abundance of grassland bird and butterfly species. Thus, plant Simpson diversity and ant species richness were the most effective indicators for complementary components of grassland animal communities, whereas quality assessment indices were less robust as indicators and require more knowledge on the habitat specificity of individual ant and plant species.     

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.     

Statistical approaches to interpreting diversity patterns in the Norwegian mountain flora

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

Threat spots and environmental determinants of red-listed plant,butterfly and bird species in boreal agricultural environments

The aims of this study were (1) to examine the geographic distribution of red-listed species of agricultural environments and identify their national threat spots (areas with high diversity of threatened species) in Finland and (2) to determine the main environmental variables related to the richness and occurrence patterns of red-listed species. Atlas data of 21 plant, 17 butterfly and 11 bird species recorded using 10 km grid squares were employed in the study. Generalized additive models (GAMs) were constructed separately for species richness and occurrence of individual species of the three species groups using climate and land cover predictor variables. The predictive accuracy of models, as measured using correlation between the observed and predicted values and AUC statistics, was generally good. Temperature-related variables were the most important determinants of species richness and occurrence of all three taxa. In addition, land cover variables had a strong effect on the distribution of species. Plants and butterflies were positively related to the cover of grasslands and birds to small-scale agricultural mosaic as well as to arable land. Spatial coincidence of threat spots of plants, butterflies and birds was limited, which emphasizes the importance of considering the potentially contrasting environmental requirements of different taxa in conservation planning. Further, it is obvious that the maintenance of various non-crop habitats and heterogeneous agricultural landscapes has an essential role in the preservation of red-listed species of boreal rural environments.     

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.     

Comparison of predictor sets for species richness and the number of rare species of butterflies and birds

Aim Accurate inventories of biota are typically restricted to few locations within an extensive region. Accordingly, effective planning must involve some form of surrogate measures coupled with spatial modelling. We conducted a simultaneous comparison of models of both species richness and the number of rare species using three types of surrogates (indicator species, vegetation composition and structure, and topoclimate) as predictors. We evaluated each type of surrogate alone and in combination with others. Location Data for our analyses were collected from 1996–2004 in three adjacent mountain ranges in the central Great Basin (Lander and Nye counties, Nevada, USA), the Shoshone Mountains, Toiyabe Range and Toquima Range. Methods Data on species richness and species composition of butterflies and birds and measures of vegetation composition and structure were obtained in the field. Topoclimatic variables were derived by GIS from digital sources and satellite images. We used Poisson regression with Bayesian model averaging to predict species richness and the number of rare species. We compared the expected prediction success of all models on the basis of internal and external validation trials. Results Same‐taxon indicator species were the most accurate predictors of species richness and of the number of rare species of butterflies and birds. Cross‐taxon indicator species and topoclimate variables were reasonably accurate predictors of species richness of butterflies and birds and of the number of rare butterfly species. Although vegetation variables were more effective for predicting species richness and number of rare species of birds than of butterflies, they were the least accurate predictors overall. Main conclusions Although indicator species may provide the most accurate predictions of species richness, their practical value, like any surrogate measure, depends greatly on ecological considerations and land‐use context. In general, the ability to predict numbers of rare species based on any set of candidate predictors was weaker than the ability to predict species richness, which may result from the high degree of stochasticity that often characterizes distributions of rare species. Our statistical approach for objective examination of different candidate predictors can help ensure that selection of species‐richness surrogates in any system is scientifically reliable and cost‐effective.     

Local‐ to continental‐scale variation in the richness and composition of an aquatic food web

Aim We investigated patterns of species richness and composition of the aquatic food web found in the liquid‐filled leaves of the North American purple pitcher plant, Sarracenia purpurea (Sarraceniaceae), from local to continental scales. Location We sampled 20 pitcher‐plant communities at each of 39 sites spanning the geographic range of S. purpurea– from northern Florida to Newfoundland and westward to eastern British Columbia. Methods Environmental predictors of variation in species composition and species richness were measured at two different spatial scales: among pitchers within sites and among sites. Hierarchical Bayesian models were used to examine correlates and similarities of species richness and abundance within and among sites. Results Ninety‐two taxa of arthropods, protozoa and bacteria were identified in the 780 pitcher samples. The variation in the species composition of this multi‐trophic level community across the broad geographic range of the host plant was lower than the variation among pitchers within host‐plant populations. Variation among food webs in richness and composition was related to climate, pore‐water chemistry, pitcher‐plant morphology and leaf age. Variation in the abundance of the five most common invertebrates was also strongly related to pitcher morphology and site‐specific climatic and other environmental variables. Main conclusions The surprising result that these communities are more variable within their host‐plant populations than across North America suggests that the food web in S. purpurea leaves consists of two groups of species: (1) a core group of mostly obligate pitcher‐plant residents that have evolved strong requirements for the host plant and that co‐occur consistently across North America, and (2) a larger set of relatively uncommon, generalist taxa that co‐occur patchily.     

Do Stacked Species Distribution Models Reflect Altitudinal Diversity Patterns?

The objective of this study was to evaluate the performance of stacked species distribution models in predicting the alpha and gamma species diversity patterns of two important plant clades along elevation in the Andes. We modelled the distribution of the species in the Anthurium genus (53 species) and the Bromeliaceae family (89 species) using six modelling techniques. We combined all of the predictions for the same species in ensemble models based on two different criteria: the average of the rescaled predictions by all techniques and the average of the best techniques. The rescaled predictions were then reclassified into binary predictions (presence/absence). By stacking either the original predictions or binary predictions for both ensemble procedures, we obtained four different species richness models per taxa. The gamma and alpha diversity per elevation band (500 m) was also computed. To evaluate the prediction abilities for the four predictions of species richness and gamma diversity, the models were compared with the real data along an elevation gradient that was independently compiled by specialists. Finally, we also tested whether our richness models performed better than a null model of altitudinal changes of diversity based on the literature. Stacking of the ensemble prediction of the individual species models generated richness models that proved to be well correlated with the observed alpha diversity richness patterns along elevation and with the gamma diversity derived from the literature. Overall, these models tend to overpredict species richness. The use of the ensemble predictions from the species models built with different techniques seems very promising for modelling of species assemblages. Stacking of the binary models reduced the over-prediction, although more research is needed. The randomisation test proved to be a promising method for testing the performance of the stacked models, but other implementations may still be developed.     

Species richness coincidence: conservation strategies based on predictive modelling

The present-day geographic distribution of individual species of five taxonomic groups (plants, dragonflies, butterflies, herpetofauna and breeding birds) is relatively well-known on a small scale (5 × 5 km squares) in Flanders (north Belgium). These data allow identification of areas with a high diversity within each of the species groups. However, differences in mapping intensity and coverage hamper straightforward comparisons of species-rich areas among the taxonomic groups. To overcome this problem, we modelled the species richness of each taxonomic group separately using various environmental characteristics as predictor variables (area of different land use types, biotope diversity, topographic and climatic features). We applied forward stepwise multiple regression to build the models, using a subset of well-surveyed squares. A separate set of equally well-surveyed squares was used to test the predictions of the models. The coincidence of geographic areas with high predicted species richness was remarkably high among the four faunal groups, but much lower between plants and each of the four faunal groups. Thus, the four investigated faunal groups can be used as relatively good indicator taxa for one another in Flanders, at least for their within-group species diversity. A mean predicted species diversity per mapping square was also estimated by averaging the standardised predicted species richness over the five taxonomic groups, to locate the regions that were predicted as being the most species-rich for all five investigated taxonomic groups together. Finally, the applicability of predictive modelling in nature conservation policy both in Flanders and in other regions is discussed.     

Pteridophyte richness, climate and topography in the Iberian Peninsula: comparing spatial and nonspatial models of richness patterns

Aim To describe the spatial variation in pteridophyte species richness; evaluate the importance of macroclimate, topography and within‐grid cell range variables; assess the influence of spatial autocorrelation on the significance of the variables; and to test the prediction of the mid‐domain effect. Location The Iberian Peninsula. Methods We estimated pteridophyte richness on a grid map with c. 2500 km² cell size, using published geocoded data of the individual species. Environmental data were obtained by superimposing the grid system over isoline maps of precipitation, temperature, and altitude. Mean and range values were calculated for each cell. Pteridophyte richness was related to the environmental variables by means of nonspatial and spatial generalized least squares models. We also used ordinary least squares regression, where a variance partitioning was performed to partial out the spatial component, i.e. latitude and longitude. Coastal and central cells were compared to test the mid‐domain effect. Results Both spatial and nonspatial models showed that pteridophyte richness was best explained by a second‐order polynomial of mean annual precipitation and a quadratic elevation‐range term, although the relative importance of these two variables varied when spatial autocorrelation was accounted for. Precipitation range was weakly significant in a nonspatial multiple model (i.e. ordinary regression), and did not remain significant in spatial models. Richness is significantly higher along the coast than in the centre of the peninsula. Main conclusions Spatial autocorrelation affects the statistical significance of explanatory variables, but this did not change the biological interpretation of precipitation and elevation range as the main predictors of pteridophyte richness. Spatial and nonspatial models gave very similar results, which reinforce the idea that water availability and topographic relief control species richness in relatively high‐energy regions. The prediction of the mid‐domain effect is falsified.     

Water–energy,land‐cover and heterogeneity drivers of the distribution of plant species richness in a mountain region of the European Alps

Aim To evaluate the relative importance of water–energy, land‐cover, environmental heterogeneity and spatial variables on the regional distribution of Red‐Listed and common vascular plant species richness. Location Trento Province (c. 6200 km²) on the southern border of the European Alps (Italy), subdivided regularly into 228 3′ × 5′ quadrants. Methods Data from a floristic inventory were separated into two subsets, representing Red‐Listed and common (i.e. all except Red‐Listed) plant species richness. Both subsets were separately related to water–energy, land‐cover and environmental heterogeneity variables. We simultaneously applied ordinary least squares regression with variation partitioning and hierarchical partitioning, attempting to identify the most important factors controlling species richness. We combined the analysis of environmental variables with a trend surface analysis and a spatial autocorrelation analysis. Results At the regional scale, plant species richness of both Red‐Listed and common species was primarily related to energy availability and land cover, whereas environmental heterogeneity had a lesser effect. The greatest number of species of both subsets was found in quadrants with the largest energy availability and the greatest degree of urbanization. These findings suggest that the elevation range within our study region imposes an energy‐driven control on the distribution of species richness, which resembles that of the broader latitude gradient. Overall, the two species subsets had similar trends concerning the relative importance of water–energy, land cover and environmental heterogeneity, showing a few differences regarding the selection of some predictors of secondary importance. The incorporation of spatial variables did not improve the explanatory power of the environmental models and the high original spatial autocorrelation in the response variables was reduced drastically by including the selected environmental variables. Main conclusions Water–energy and land cover showed significant pure effects in explaining plant species richness, indicating that climate and land cover should both be included as explanatory variables in modelling species richness in human‐affected landscapes. However, the high degree of shared variation between the two groups made the relative effects difficult to separate. The relatively low range of variation in the environmental heterogeneity variables within our sampling domain might have caused the low importance of this complex factor.     

Regional‐scale patterns and predictors of species richness and abundance across twelve major tropical inter‐reef taxa

Species richness and abundance are biodiversity metrics widely used to describe and estimate changes in biodiversity. Studies of marine species richness and abundance typically focus on one, or just a few, taxa. Consequently, it is currently not possible to understand the performance of predictors of species richness and abundance across marine taxa. Using a taxonomically comprehensive dataset of twelve major taxa of flora and fauna from eight phyla sampled from the inter‐reef seabed region of the Great Barrier Reef, Australia, we used boosted regression trees to test the performance of fourteen environmental and spatial predictors of species richness and abundance. Sediment composition predicted richness best for all taxa: gravel contributed up to 39% relative influence for one group and all taxa had low richness in muddy habitats. Sea surface temperature, seabed current shear stress, depth and latitude were also influential predictors for species richness for eight groups. Sediment was frequently an influential predictor for abundance also, while distance to domain (reef/coast) and longitude were relatively influential for six taxa. Within‐site richness was correlated between nearly all pairs of taxa, as was within‐site abundance, however ρ values were low. Overall, model performance was high, explaining up to 62% deviance of species richness, and 38% of abundance. Typically, deviance explained was greater for richness than abundance and may indicate that some drivers of species richness operate independently of any effects on species richness mediated by their effect on abundance. Deviance explained differed most between richness and abundance for bryozoans (23.3% difference) and soft corals (15.2% difference). While sediments were consistently the best predictors across all taxa, the inconsistent influence of all other predictors across taxonomic groups, as well as the low correlation of richness and abundance across taxonomic groups, cautions against predicting regional patterns of species richness and abundance from few taxa.     

The role of regional and local scale predictors for plant species richness in Mediterranean forests

Abstract

Both local and regional predictors play a role in determining plant community structure and composition. Climate, soil features as well as different local history and management affect forest understorey and tree species composition, but to date their specific role is relatively unknown. Few studies have addressed the importance of these predictors, especially in the Mediterranean area, where environmental conditions and human impacts have generated heterogeneous forest communities. In this study, the relationships between environmental variables and species richness of different groups of vascular plants (vascular species, woody species and open habitat species) and bryophytes were investigated in Tuscan forests. A total of 37 environmental variables were used by generalised linear model fitting in order to find parsimonious sub-sets of environmental factors (predictors) that are able to explain species diversity patterns at the local scale. Moreover, the role of regional and local variable groups on species richness of the considered plant groups was estimated by using the variance partitioning approach. We found that local variables, such as forest management and structure, explained more variance than regional variables for total species richness, open habitat species richness and bryophyte species richness. On the other hand, regional variables (such as elevation) played a central role for woody species richness.     

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

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

The effects of scale and sample size on the accuracy of spatial predictions of tiger beetle (Cicindelidae) species richness

We apply geostatistical modeling techniques to investigate spatial patterns of species richness. Unlike most other statistical modeling techniques that are valid only when observations are independent, geostatistical methods are designed for applications involving spatially dependent observations. When spatial dependencies, which are sometimes called autocorrelations, exist, geostatistical techniques can be applied to produce optimal predictions in areas (typically proximate to observed data) where no observed data exist. Using tiger beetle species (Cicindelidae) data collected in western North America, we investigate the characteristics of spatial relationships in species numbers data, First, we compare the accuracy of spatial predictions of species richness when data from grid squares of two different sizes (scales) are used to form the predictions. Next we examine how prediction accuracy varies as a function of areal extent of the region under investigation. Then we explore the relationship between the number of observations used to build spatial prediction models and prediction accuracy. Our results indicate that, within the taxon of tiger beetles and for the two scales we investigate, the accuracy of spatial predictions is unrelated to scale and that prediction accuracy is not obviously related lo the areal extent of the region under investigation. We also provide information about the relationship between sample size and prediction accuracy, and, finally, we show that prediction accuracy may be substantially diminished if spatial correlations in the data are ignored.