Evaluating the sampling bias in pattern of subterranean species richness: combining approaches

We investigated the pattern of species richness of obligate subterranean (troglobiotic) beetles in caves in the northwestern Balkans, given unequal and biased sampling. On the regional scale, we modeled the relationship between species numbers and sampling intensity using an asymptotic Clench (Michaelis–Menten) function. On the local scale, we calculated Chao 2 species richness estimates for 20 × 20 km grid cells, and investigated the distribution of uniques, species found in only one cave within the grid cell. Cells having high positive residuals, those with above average species richness than expected according to the Clench function, can be considered true hotspots. They were nearly identical to the observed areas of highest species richness. As sampling intensity in a grid cell increases the expected number of uniques decreases for any fixed number of species in the grid cell. High positive residuals show above average species richness for a certain level of sampling intensity within a cell, so further sampling has the most potential for additional species. In some cells this was supported by high numbers of uniques, also indicating insufficient sampling. Cells with low negative residuals have fewer species than would be expected, and some of them also had a low number of uniques, both indicating sufficient sampling. By combining different analyses in a novel way we were able to evaluate observed species richness pattern as well as identify, where further sampling would be most beneficial. Approach we demonstrate is of broad interest to study of biota with high levels of endemism, small distribution ranges and low catchability.     

Optimizing performance of nonparametric species richness estimators under constrained sampling

Understanding the functional relationship between the sample size and the performance of species richness estimators is necessary to optimize limited sampling resources against estimation error. Nonparametric estimators such as Chao and Jackknife demonstrate strong performances, but consensus is lacking as to which estimator performs better under constrained sampling. We explore a method to improve the estimators under such scenario. The method we propose involves randomly splitting species‐abundance data from a single sample into two equally sized samples, and using an appropriate incidence‐based estimator to estimate richness. To test this method, we assume a lognormal species‐abundance distribution (SAD) with varying coefficients of variation (CV), generate samples using MCMC simulations, and use the expected mean‐squared error as the performance criterion of the estimators. We test this method for Chao, Jackknife, ICE, and ACE estimators. Between abundance‐based estimators with the single sample, and incidence‐based estimators with the split‐in‐two samples, Chao2 performed the best when CV < 0.65, and incidence‐based Jackknife performed the best when CV > 0.65, given that the ratio of sample size to observed species richness is greater than a critical value given by a power function of CV with respect to abundance of the sampled population. The proposed method increases the performance of the estimators substantially and is more effective when more rare species are in an assemblage. We also show that the splitting method works qualitatively similarly well when the SADs are log series, geometric series, and negative binomial. We demonstrate an application of the proposed method by estimating richness of zooplankton communities in samples of ballast water. The proposed splitting method is an alternative to sampling a large number of individuals to increase the accuracy of richness estimations; therefore, it is appropriate for a wide range of resource‐limited sampling scenarios in ecology.     

Species richness, area and climate correlates

Aim Species richness–area theory predicts that more species should be found if one samples a larger area. To avoid biases from comparing species richness in areas of very different sizes, area is often controlled by counting the numbers of co‐occupying species in near‐equal area grid cells. The assumption is that variation in grid cell size accrued from working in a three‐dimensional world is negligible. Here we provide a first test of this idea. We measure the surface area of c. 50 × 50 km and c. 220 × 220 km grid cells across western Europe. We then ask how variation in the area of grid cells affects: (1) the selection of climate variables entering a species richness model; and (2) the accuracy of models in predicting species richness in unsampled grid cells. Location Western Europe. Methods Models are developed for European plant, breeding bird, mammal and herptile species richness using seven climate variables. Generalized additive models are used to relate species richness, climate and area. Results We found that variation in the grid cell area was large (50 × 50 km: 8–3311 km²; 220 × 220: 193–55,100 km²), but this did not affect the selection of variables in the models. Similarly, the predictive accuracy was affected only marginally by exclusion of area within models developed at the c. 50 × 50 km grid cells, although predictive accuracy suffered greater reductions when area was not included as a covariate in models developed for c. 220 × 220 km grid cells. Main conclusions Our results support the assumption that variation in near‐equal area cells may be of second‐order importance for models explaining or predicting species richness in relation to climate, although there is a possibility that drops in accuracy might increase with grid cell size. The results are, however, contingent on this particular data set, grain and extent of the analyses, and more empirical work is required.     

Performance of nonparametric species richness estimators in a high diversity plant community

Abstract. The efficiency of four nonparametric species richness estimators — first‐order Jackknife, second‐order Jackknife, Chao2 and Bootstrap — was tested using simulated quadrat sampling of two field data sets (a sandy ‘Dune’ and adjacent ‘Swale’) in high diversity shrublands (kwongan) in south‐western Australia. The data sets each comprised > 100 perennial plant species and > 10 000 individuals, and the explicit (x‐y co‐ordinate) location of every individual. We applied two simulated sampling strategies to these data sets based on sampling quadrats of unit sizes 1/400th and 1/100th of total plot area. For each site and sampling strategy we obtained 250 independent sample curves, of 250 quadrats each, and compared the estimators’ performances by using three indices of bias and precision: MRE (mean relative error), MSRE (mean squared relative error) and OVER (percentage overestimation). The analysis presented here is unique in providing sample estimates derived from a complete, field‐based population census for a high diversity plant community. In general the true reference value was approached faster for a comparable area sampled for the smaller quadrat size and for the swale field data set, which was characterized by smaller plant size and higher plant density. Nevertheless, at least 15–30% of the total area needed to be sampled before reasonable estimates of S_t (total species richness) were obtained. In most field surveys, typically less than 1% of the total study domain is likely to be sampled, and at this sampling intensity underestimation is a problem. Results showed that the second‐order Jackknife approached the actual value of S_t more quickly than the other estimators. All four estimators were better than S_obs (observed number of species). However, the behaviour of the tested estimators was not as good as expected, and even with large sample size (number of quadrats sampled) all of them failed to provide reliable estimates. First‐ and second‐order Jackknives were positively biased whereas Chao2 and Bootstrap were negatively biased. The observed limitations in the estimators’ performance suggests that there is still scope for new tools to be developed by statisticians to assist in the estimation of species richness from sample data, especially in communities with high species richness.     

Testing species richness estimation methods using museum label data on the Danish Asilidae

Museum collections are treasure troves of biodiversity information thatcan potentially be used for species richness estimation. Using label data on theDanish Asilidae (Diptera), we test eight species richness estimation techniques(abundance-based coverage estimator (ACE), ICE, Chao1, Chao2, first and secondorder Jackknife, Bootstrap and MMMeans) by comparing the estimates to the numberof species likely to occur in Denmark based on distributional information,expert opinion, and a species–area curve. We are investigating which ofthe estimators are most suited for the task. Furthermore, through theuse of four different subsampling schemes we study which kind of label information isnecessary in order to apply these estimation procedures. The first and secondorder Jackknife estimators yield the most accurate estimate of the number ofcollectable species in Denmark, while ACE, Bootstrap and Chao1 only provideslight improvements over observed values. We find that all estimatorsunderestimate the true diversity of Danish Asilidae and speculate that thisperformance is due to a discrepancy between the total and the collectable faunain the region. Finally, we discuss the implications for species richnessestimation and emphasize that for most terrestrial arthropod taxa thesediscrepancies are of such a magnitude that estimated species richness values maybe dangerously low and of limited use in conservation decision making.     

Regional species richness in an obligate subterranean dwelling fauna – epikarst copepods

Aim  The diversity of the obligate cave-dwelling fauna has proved difficult to measure because of the highly localized distributions of most species. We investigated: (1) the local and regional diversity patterns of a major component of the obligate cave-dwelling fauna living in the epikarst zone, the karst layer closest to the surface; (2) variations in local and regional patterns of species richness; and (3) sampling sufficiency at multiple scales.
Location  Caves in the Dinaric Mountains of Slovenia.
Methods  We sampled continuously the abundance of 37 species of copepods dislodged from the epikarst from 35 ceiling drips in six caves for a period of one year. Copepods were collected in a specially designed net that allowed continuous collection.
Results  Based on species accumulation curves and Chao estimates of total diversity, we determined that 3–4 months of continuous sampling were sufficient to find 90% of the species in a drip, that five drips were sufficient to find 90% of the species in a cave, and that five caves were sufficient to find 90% of the species in a region.
Main conclusions  The epikarst copepod fauna is a significant part of the aquatic cave fauna, contributing about 20% at the regional level. Because of the scale of variation, much of which occurs within a cave, and because of the availability of continuous sampling devices, the epikarst component of subterranean diversity seems to be more thoroughly and accurately measured than do other components.     

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.     

Assessing the performance of nonparametric estimators of species richness in meadows

To accurately measure the number of species in a biological community, a complete inventory should be performed, which is generally unfeasible; hopefully, estimators of species richness can help. Our main objectives were (i) to assess the performance of nonparametric estimators of plant species richness with real data from a small set of meadows located in the Basque campiña (northern Spain), and (ii) to apply the best estimator to a larger dataset to test the effects on plant species richness caused by environmental conditions and human practices. Two non-asymptotic and seven asymptotic accumulation functions were fitted to a randomized sample-based rarefaction curve computed with data from three well sampled meadows, and information theoretic methods were used to select the best fitting model; this was the Morgan-Mercer-Flodin, and its asymptote was taken as our best guess of true richness. Then, five nonparametric estimators were computed: ICE, Chao 2, Jackknife 1 and 2, and Bootstrap; MMRuns and MMMeans were also assessed. According to the criteria set for our performance assessment (i.e., bias, precision, and accuracy), the best estimator was Jackknife 1. Finally, Jackknife 1 was applied to assess the effects of terrain slope and soil parent material, and also fertilization, grazing, and mowing, on plant species richness from a larger dataset (20 meadows). Results suggested that grass cutting was causing a loss of richness close to 30%, as compared to unmowed meadows. It is concluded that the use of nonparametric estimators of species richness can improve the evaluation of biodiversity responses to human management practices.     

The net result: evaluating species richness extrapolation techniques for littoral pond invertebrates

1. Total species richness for an assemblage or site is a valuable measure in conservation monitoring and assessment, but protocols for sampling and species richness determination in wetland habitats such as ponds, bogs or mires remain largely unrefined. 2. Techniques for estimation of total richness of an assemblage based upon replicated sampling offer the opportunity to derive useful estimates of total richness based upon small numbers of samples, and limit sampling‐derived disturbance which can be particularly problematic in small aquatic habitats. 3. We quantified the performance of eight of the most commonly encountered estimators of species richness for a variety of littoral zone macrofauna from ponds, comparing estimated richness to maximum richness derived from sampling. 4. Estimates using non‐parametric techniques based on species incidence provided the most accurate and precise estimates. The estimators Chao 2 and incidence‐based coverage estimator (ICE) from this category were reliable and consistent slight over‐estimators; the abundance‐based estimator Chao1 also performed well. 5. Species inventory based on relatively small numbers of samples might be significantly improved by use of non‐parametric estimators for quantification of species richness. 6. Use of non‐parametric estimators of species richness can assist biodiversity inventory by preventing erroneous rankings of habitat richness based upon observed species numbers from limited sampling.     

Biodiversity change is scale‐dependent: an example from Dutch and UK hoverflies (Diptera,Syrphidae)

We test whether temporal change in species richness (ΔS [%]) is scale‐dependent, using data on hoverflies from the UK and the Netherlands. We analysed ΔS between pre‐1980 and post‐1980 periods using 5 grid resolutions (10×10, 20×20, 40×40, 80×80 and 160×160 km). We also tested the effect of data quality and of unequal survey periods on ΔS estimates, and checked for spatial autocorrelation of ΔS estimates. Using data from equal survey periods, we found significant increases in hoverfly species richness in the Netherlands at fine scales, but no significant change at coarser scales indicating a decrease in beta diversity. In the UK, ΔS was negative at fine scale, near zero at intermediate scales, and positive at coarse scales, indicating that the degree of spatial beta diversity increased between the time periods. The use of unequal survey periods (using longer periods in the past to compensate for lower survey intensity) tended to inflate past species richness, biasing ΔS estimates downwards. High data quality thresholds sometimes obscured dynamics by reducing sample size, but never reversed trends. There was little spatial autocorrelation of ΔS, implying that local drivers (land use change or environmental noise) are important in dynamics of hoverfly diversity. A second, sample agglomeration approach to measure scaling resulted in greater noise in ΔS, obscuring the NL pattern, while still showing strong evidence of fine‐scale richness loss in the UK. Our results indicate that explicit considerations of spatial (and temporal) scale are essential in studies documenting past biodiversity change, or projecting change into the future.     

Patterns of species richness hotspots and estimates of their protection are sensitive to spatial resolution

Aim

Species richness is a measure of biodiversity often used in spatial conservation assessments and mapped by summing species distribution maps. Commission errors inherent those maps influence richness patterns and conservation assessments. We sought to further the understanding of the sensitivity of hotspot delineation methods and conservation assessments to commission errors, and choice of threshold for hotspot delineation.

Location

United States.

Methods

We created range maps and 30‐m and 1‐km resolution habitat maps for terrestrial vertebrates in the United States and generated species richness maps with each dataset. With the richness maps and the GAP Protected Areas Dataset, we created species richness hotspot maps and calculated the proportion of hotspots within protected areas; calculating protection under a range of thresholds for defining hotspots. Our method allowed us to identify the influence of commission errors by comparing hotspot maps.

Results

Commission errors from coarse spatial grain data and lack of porosity in the range data inflated richness estimates and altered their spatial patterns. Coincidence of hotspots from different data types was low. The 30‐m hotspots were spatially dispersed, and some were very long distances from the hotspots mapped with coarser data. Estimates of protection were low for each of the taxa. The relationship between estimates of hotspot protection and threshold choice was nonlinear and inconsistent among data types (habitat and range) and grain size (30‐m and 1‐km).

Main conclusions

Coarse mapping methods and grain sizes can introduce commission errors into species distribution data that could result in misidentifications of the regions where hotspots occur and affect estimates of hotspot protection. Hotspot conservation assessments are also sensitive to choice of threshold for hotspot delineation. There is value in developing species distribution maps with high resolution and low rates of commission error for conservation assessments.     

Could we obtain better estimates of plot species richness from multiple‐observer plant censuses?

Question: Could we better estimate plot species richness by asking several botanists to survey the same plots and using non‐parametric estimators of richness? Location: Two French deciduous forests. Methods: Using replicated, independent censuses made by 11 professional botanists on the same eight 100‐m² forest plots, the relative performance of different richness estimators (Lincoln‐Petersen, Jackknife 1&2, Chao 1&2, Bootstrap, Chao Mth, Darroch) and the variation in their performance with the number of botanists involved (teams with two to eight botanists) were investigated. The sensitivity of these estimators to the presence of misidentifications in the data was also assessed. Results: When misidentifications are removed, Chao Mth estimators converged fastest to true richness, but none of the tested estimators correctly accounted for differences in exhaustiveness between the teams. Finally, all estimators were highly sensitive to misidentifications. Conclusions: Richness estimators are of little help in the presence of misidentifications and are ineffective at removing between‐team discrepancies, thus strongly limiting their usefulness in practice. Methods are presented to show how surveys can be designed to remove misidentifications and limit between‐team discrepancies. A sensible sampling design for 100‐m² plots in temperate forests would involve triplets of botanists and correcting data with the Chao N1. Pairs of botanists would already significantly improve the richness estimates, but such estimates would still be biased low. However, further research is needed to design new richness estimators that are more robust to observer effects.     

Phylogeography of subterranean and surface populations of water lice Asellus aquaticus (Crustacea: Isopoda)

The water louse Asellus aquaticus is a widespread, euryoecious species, mostly uniform throughout its range. However, six subspecies are known from the Dinaric karst in the northwestern Balkans. They include some specialized subterranean populations. The pattern of genetic variation among subterranean and surface populations in this hydrographically highly fragmented karst region was investigated using a 653 bp fragment of the mitochondrial gene (COI). Sequencing of 168 individuals from 25 localities revealed 72 haplotypes. amova and methods of phylogenetic reconstruction all uncovered hydrographic structuring of genetic variation of the populations. Nested clade analysis pointed out several fragmentation events, along with some range expansions within hydrographical systems. By superimposing the subterranean mode of life on the phylogeographical pattern, three independent cave colonizations could be inferred within a distance of < 100 km. Caves were invaded after the ancestral surface populations became isolated through vicariant fragmentation. A possible scenario of hydrographic history of the region was constructed combining the molecular data with palaeogeographical information.     

Endoparasite Species Richness of Iberian Carnivores: Influences of Host Density and Range Distribution

We investigate the determinants of macroparasite species richness of Iberian carnivores. For this, we used the parasitological data collected on 14 species of carnivores over a 10-year period. These previously unpublished data permitted to estimate parasite species richness using estimators of species richness, i.e. Jackknife first order and Chao 2. Most of the parasite species were rare, with low prevalence. Potential determinants were investigated as possible factors explaining the variability of parasites species richness among carnivores host body mass, host geographical range, host longevity and host density. Using independent contrasts, we found positive relationships between residuals of estimates of parasite species richness and residuals in host density, and between residuals of estimates of parasite species richness and residuals in host range. These results are discussed in terms of risk of extinction and invasion abilities related to a possible investment in immune defences correlated with parasite diversity.     

Multi-extent analysis of the relationship between pteridophyte species richness and climate

Aim To determine the relationship between the distribution of climate, climatic heterogeneity and pteridophyte species richness gradients in Australia, using an approach that does not assume potential relationships are spatially invariant and allows for scale effects (extent of analysis) to be explicitly examined. Location Australia, extending from 10° S to 43° S and 112° E to 153° E. Method Species richness within 50 × 50 km grid cells was determined using point distribution data. Climatic surfaces representing the distribution and availability of water and energy at 1 km and 5 km cell resolutions were obtained. Climate at the 50 km resolution of analysis was represented by their mean and standard deviation in that area. Relationships were assessed using geographically weighted linear regression at a range of spatial bandwidths to investigate scale effects. Results The parameters and the predictive strength of all models varied across space at all extents of analysis. Overall, climatic variables representing water availability were more highly correlated to pteridophyte richness gradients in Australia than those representing energy. Their variance in cells further increased the strength of the relationships in topographically heterogeneous regions. Relationships with water were strong across all extents of analysis, particularly in the tropical and subtropical parts of the continent. Water availability explained less of the variation in richness at higher latitudes. Main conclusions This study brings into question the ability of aspatial and single‐extent models, searching for a unified explanation of macro‐scaled patterns in gradients of diversity, to adequately represent reality. It showed that, across Australia, there is a positive relationship between pteridophyte species richness and water availability but the strength and nature of the relationship varies spatially with scale in a highly complex manner. The spatial variance, or actual complexity, in these relationships could not have been demonstrated had a traditional aspatial global regression approach been used. Regional scale variation in relationships may be at least as important as more general relationships for a true understanding of the distribution of broad‐scale diversity.     

How island size and isolation affect bee and wasp ensembles on small tropical islands: a case study from Kepulauan Seribu,Indonesia

Aim To study the effects of isolation and size of small tropical islands on species assemblages of bees (superfamily Apoidea) and wasps (superfamily Vespoidea). Location Twenty islands in the Kepulauan Seribu Archipelago off the coast of west Java, Indonesia. The size of surveyed islands ranges between 0.75 and 41.32 ha; their distance from the coast of Java varies between 3 and 62 km. Methods Field work was conducted from February to May 2005. Bees and wasps were caught with a sweep net during sampling units of 15 min, continuing until four consecutive samples revealed no new species. Total species richness was quantified by the estimators Chao 2, first‐order jackknife and Michaelis–Menten. The software binmatnest was used to test for nestedness of species assemblages. Similarities of species composition between islands were quantified by Sørensen’s similarity index. Results Eighty‐two species were recorded on the 20 surveyed islands. Species richness declined with increasing isolation of islands from the source area, Java. Although the size of the largest island exceeded that of the smallest island by a factor of almost 60, island size only very weakly affected species richness of bees; no effect of island size was found for wasps. Mean body size of species decreased with increasing island isolation. Nestedness of island faunas was only weakly developed. Species composition of both superfamilies was affected by island isolation, but not by island size. Main conclusions While the species–isolation relationship on the very small islands of Kepulauan Seribu followed the prediction of MacArthur and Wilson’s equilibrium theory, the absence of a species–area relationship indicated a weak ‘small‐island effect’, at least in wasps. The combination of an only weakly developed pattern of nested species subsets, the shift in species compositions and the decline of mean body size with increasing island isolation from the source area indicates that biotic interactions and different species traits contribute to the shaping of communities of bees and wasps within the archipelago. The potential of biotic interactions for generating distribution patterns of species within the archipelago is also emphasized by the observed restriction of some species with apparently high dispersal abilities to outer islands.     

Exploring species attributes and site characteristics to assess plant invasions in Spain

Aim Biological invasions are a major component of global change with increasing effects on natural ecosystems and human societies. Here, we aim to assess the relationship between plant invader species attributes and the extent of their distribution range size, at the same time that we assess the association between environmental factors and plant invader species richness. Location Spain, Mediterranean region. Methods From the species perspective, we calculated the distribution range size of the 106 vascular plant invaders listed in a recently published atlas of alien plant species in Spain. Range size was used as an estimation of the degree of invasion success of the species. To model variation in range size between species as a function of a set of species attributes, we adopted the framework of the generalized linear mixed models because they allow the incorporation of taxonomic categories as nested random factors to control for phylogenetic relationships. From the invaded site perspective, we determined invader plant species richness as the number of species for each 10 × 10 km Universal Transverse Mercator (UTM) grid. For each grid cell, we estimated variables concerning landscape, topography, climate and human settlement. Then, we performed a generalized linear mixed model incorporating a defined spatial correlation structure to assess the relationship between plant invader richness and the environmental predictors. Results From the species perspective, wind dispersal and minimum residence time appeared to favour invasion success. From the invaded site perspective, we identified high anthropogenic disturbance, low altitude, short distance to the coastline and dry, hot weather as the main correlates to UTM grid cell invader richness. Main conclusions According to these results, an increasing importance of man‐modified ecosystems and global warming in the Mediterranean region should facilitate the expansion of plant invaders, especially wind‐dispersed species, leading to the accumulation of invasive species in some sites (i.e. invasion hot spots).     

Estimating species richness: still a long way off!

Xu et al., in this issue of the Journal of Vegetation Science, compare several species richness estimators. All the non‐parametric estimators, such as Chao and jackknife estimators, underestimated the true number, whereas all the area‐based models, based on species–area curves, overestimated it. No reliable method yet exists to predict the number of species in an area that is appreciably larger than the one(s) sampled.     

Downscaling European species atlas distributions to a finer resolution: implications for conservation planning

Aim One of the limitations to using species’ distribution atlases in conservation planning is their coarse resolution relative to the needs of local planners. In this study, a simple approach to downscale original species atlas distributions to a finer resolution is outlined. If such a procedure yielded accurate downscaled predictions, then it could be an aid to using available distribution atlases in real‐world local conservation decisions. Location Europe. Methods An iterative procedure based on generalized additive modelling is used to downscale original European 50 × 50 km distributions of 2189 plant and terrestrial vertebrate species to c. 10 × 10 km grid resolution. Models are trained on 70% of the original data and evaluated on the remaining 30%, using the receiver operating characteristic (ROC) procedure. Fitted models are then interpolated to a finer resolution. A British dataset comprising distributions of 81 passerine‐bird species in a 10 × 10 km grid is used as a test bed to assess the accuracy of the downscaled predictions. European‐wide, downscaled predictions are further evaluated in terms of their ability to reproduce: (1) spatial patterns of coincidence in species richness scores among different groups; and (2) spatial patterns of coincidence in richness, rarity and complementarity hotspots. Results There was a generally good agreement between downscaled and observed fine‐resolution distributions for passerine species in Britain (median Jaccard similarity = 70%; lower quartile = 36%; upper quartile = 88%). In contrast, the correlation between downscaled and observed passerine species richness was relatively low (rho = 0.31) indicating a pattern of error propagation through the process of overlaying downscaled distributions for many species. It was also found that measures of model accuracy in fitting original data (ROC) were a poor predictor of models’ ability to interpolate distributions at fine resolutions (rho = ?0.10). Although European hotspots were not fully coincident between observed and modelled coarse‐resolution data, or between modelled coarse resolution and modelled downscaled data, there was evidence that downscaled distributions were able to maintain original cross‐taxon coincidence of species‐richness scores, at least for terrestrial vertebrate groups. Downscaled distributions were also able to uncover important environmental gradients otherwise blurred by coarse‐resolution data. Main conclusions Despite uncertainties, downscaling procedures may prove useful to identify reserves that are more meaningfully related to local patterns of environmental variation. Potential errors arising from the presence of false positives may be reduced if downscaled‐distribution records projected to occur outside the range of original coarse‐resolution data are excluded. However, the usefulness of this procedure may be limited to data‐rich regions. If downscaling procedures are applied to data‐poor regions, then there is a need to undertake further research to understand the structure of error in models. In particular, it would be important to investigate which species are poorly modelled, where and why. Without such an assessment it is difficult to support unsupervised use of downscaled data in most real‐world situations.