Search

Aim: To assess the relative relevance of dispersal limitation and species sorting as drivers of spatial turnover between spider faunas of European territories. Location: Continental Europe. Time period: Present. Major taxa studied: Spiders (Order Araneae). Methods: We analysed how distance‐decay patterns differ between northern and southern Europe (broadly, territories covered vs. not covered by ice sheets during the last glacial maximum, respectively) in 15 spider families, using standardized distances to allow a direct comparison between parameters (i.e. slope and intercept) of climatic and spatial distance‐decay models. Thus, we assessed North–South differences in parameters of spatial and, independently, climatic distance‐decay models, and whether those differences are explained by family‐specific traits related to dispersal ability. Results: Climatic and spatial distance‐decay patterns are very similar in northern Europe, where climatic and spatial distances are highly correlated. In contrast, slopes are steeper in spatial than in climatic distance‐decay curves in southern Europe, where climatic and spatial distances are decoupled. Moreover, family traits related to dispersal ability explained the North–South difference in spatial distance‐decay slopes, as well as the amount of nestedness‐resultant dissimilarity between southern and northern spider faunas. Main conclusions: Our results suggest that differences in beta diversity patterns between northern and southern Europe reflect the strength of dispersal limitation in spiders, which varies across families and leads to different degrees of disequilibrium with current climatic conditions depending on the taxon. Moreover, in the South of Europe, where spatial and climatic distances are uncorrelated, spatial distance‐decay models are steeper and have larger explanatory power than climatic distance‐decay models, which suggests that dispersal limitation is the main factor shaping current beta diversity patterns of European spiders at the continental scale. [ABSTRACT FROM AUTHOR]

Diplura is the sister group to insects and one of the three basal hexapod groups with unique entognathan mouthparts. The order is divided into 10 families, which include 1008 species in 141 genera, with a high proportion of monotypic genera. They are ubiquitous in soils and subsurface terrestrial habitats, as well as have an important role in overall biogeochemical cycles.We present the first comprehensive review of the global biodiversity and ecology of Diplura. We highlight four aspects of this basal hexapod group: diversity in morphological body plans and sizes; ecology in terrestrial environments from soil to caves; food preference and trophic levels, and their biogeographical and paleobiogeographical significance.Diplura depends on high humidity and moderate temperatures. They are presumably very sensitive to anthropogenic pressures and climate change, and therefore are a suitable model for ecophysiological studies and evident priority targets for conservation.We conclude that the future efforts should focus on establishing a molecular phylogeny to clarify the relationships between and within families, as well as to reveal global biogeographical patterns. This will require an increase in sampling effort in several regions of the globe, especially in tropical regions. [ABSTRACT FROM AUTHOR]

Aim: Local suitability estimated with species distribution models (SDMs) could indicate the maximum abundance attainable by species. Often the abundance–suitability relationship is wedge‐shaped because species do not reach their maximum potential in every suitable location. We explore how SDM performance, the amount of information lost when converting continuous abundance into presence–absence data and species prevalence influence the abundance–suitability relationship. Location: Undefined. Time period: Undefined. Major taxa: Virtual species. Methods: Different abundance scenarios were simulated and converted into presence–absence maps. SDMs were parameterized using simulated predictors with different explanatory capacities. Quantile regressions were performed to characterize the abundance–suitability relationship. The strength of the wedge‐shaped pattern was estimated using the difference in slopes between the 90th and 50th quantile lines, the goodness‐of‐fit of the 90th quantile regressions was calculated, and variation of both parameters was analysed as a function of SDM performance, prevalence and maximum abundance. Results: The degree of wedge shape was directly related to maximum abundance. It also depended on SDM quality; the higher the discrimination capacity, the less wedge‐shaped the abundance–suitability relationship and higher the goodness‐of‐fit of the 90th quantile regressions. Prevalence exerted a significant effect on the shape of the relationship by influencing—together with maximum abundance—the extent of information loss. Main conclusions: SDMs cannot predict actual abundance even when the true determinants of abundance are included as predictors. Because presence–absence and abundance data are related but are different variables, wedge‐shaped patterns are unavoidable. Discrimination capacity and prevalence affect the strength of the wedge‐shaped pattern, so understanding their effects is necessary before any biological explanation is provided for the abundance–suitability relationship. Suitability maps derived from SDMs may transmit a false sense of precision at a local scale and should not be used as a perfect surrogate when abundance information is required. [ABSTRACT FROM AUTHOR]

Aim: It is still debated whether allometry, the relationship between body size and body parts, entails merely an evolutionary constraint or can itself evolve. Recently, a hypothesis has been proposed that states that static allometry (allometry measured across individuals at the same developmental stage) can evolve from differences in the developmental pathways between pairs of traits under different nutritional environments. A macroecological prediction stemming from this hypothesis is that allometric coefficients (scaling and allometric factors) should covary with ecosystem productivity. Here, we tested this prediction using a worldwide database of mass–length allometric equations. Location: Worldwide, data distributed across the entire globe. Time period: 1967–2017. Major taxa studied: Soil arthropods. Methods: We fitted general linear models with the allometric coefficients (the scaling a and allometric b factors) as the dependent variables. The target independent variable was the normalized difference vegetation index, as a proxy of ecosystem productivity. Longitude, absolute latitude and altitude, as well as mean annual temperature and mean annual precipitation were included as both covariates and additional variables of interest. We also included body bauplan (obtained from geometric morphometrics), taxonomic affiliation (a proxy of phylogenetic relationships) and the reciprocal allometric coefficient as covariates in the model. Results: We found a strong negative association between both allometric factors and the productivity of the ecosystems, and the effect for the allometric factor b was stronger at lower trophic levels. We also detected strikingly similar effects of geographic and climatic predictors on both allometric factors, suggesting the occurrence of similar selective regimes. Main conclusions: The fact that productivity, geography and climate affect the value of mass–length allometric coefficients has important consequences not only to understand the evolution of allometries, but also for how energy is processed in soil ecosystems across the globe. [ABSTRACT FROM AUTHOR]

The continuing advancement of new technologies is making available novel sources of occurrence data, such as photo‐sharing platforms. Using three Iberian Argiope spider species as models, this study aimed to compare the quality of three data sources – existing scientific literature, a biodiversity‐focused photo‐sharing citizen science project, and a photo‐sharing social network – and the results derived from niche and distribution analyses.Data sources were compared in terms of sample size, spatial clustering, and coverage, degree of climatic niche completeness, and in their consistency in the characterisation of response curves. Possible biases towards big cities and protected natural areas were identified, and species distribution models were parameterised and compared.Photo records provided the largest number of occurrences, whereas data from scientific literature showed the worst spatial coverage. Records from the citizen science project yielded the highest degree of niche completeness. All data sources showed some bias towards big cities and protected areas. Data from existing scientific literature yielded the worst range representations and, accordingly, the most different distribution models, whereas the models based on two photo‐vouchered sources were very similar.Range and niche analyses using records from different sources may yield disparate results. Occurrence data from existing scientific literature may not be enough to develop robust niche analyses nor to characterise recent range changes of poorly studied taxa. Photo‐sharing platforms provide updated chorological information and have the potential to increase sample sizes dramatically, which is an important data property in distribution and niche modelling. [ABSTRACT FROM AUTHOR]

The aim of this study was to unravel the relative role played by speleogenesis (i.e., the process in which a cave is formed), landscape-scale variables, and geophysical factors in the determination of species richness in caves. Biological inventories from 21 caves located in the southeastern Iberian Peninsula along with partial least square ( PLS) regression analysis were used to assess the relative importance of the different explanatory variables. The caves were grouped according to the similarity in their species composition; the effect that spatial distance could have on similarity was also studied using correlation between matrices. The energy and speleogenesis of caves accounted for 44.3% of the variation in species richness. The trophic level of each cave was the most significant factor in PLS regression analysis, and epigenic caves (i.e., those formed by the action of percolating water) had significantly more species than hypogenic ones (i.e., those formed by the action of upward flows in confined aquifers). Dissimilarity among the caves was very high (multiple-site βsim = 0.92). Two main groups of caves were revealed through the cluster analysis, one formed by the western caves and the other by the eastern ones. The significant-but low-correlation found between faunistic dissimilarity and geographical distance ( r = .16) disappeared once the caves were split into the two groups. The extreme beta-diversity suggests a very low connection among the caves and/or a very low dispersal capacity of the species. In the region under study, two main factors are intimately related to the richness of terrestrial subterranean species in caves: the amount of organic material (trophic level) and the formation process (genesis). This is the first time that the history of a cave genesis has been quantitatively considered to assess its importance in explaining richness patterns in comparison with other factors more widely recognized. [ABSTRACT FROM AUTHOR]

Aim Niche-based species distribution models (SDMs) are commonly used to predict impacts of global change on biodiversity, but the reliability of these predictions in space and time depends on their transferability. We tested how the strategy used to choose predictors impacts the transferability of SDMs at a cross-continental scale. Location North America, Eurasia and Australia. Method We used a systematic approach including 50 Holarctic plant invaders and 27 initial predictor variables, considering 10 different strategies for variable selection, accounting for the proximality, multicollinearity and climate analogy of predictors. We compared the average performance of each strategy, some of which used a large number of predictor combinations. Next, we looked for the single best model for each species across all the predictor combinations retained in the analysis. Transferability was considered as the predictive success of SDMs calibrated in the native range and projected onto the invaded range. Results Two strategies showed better SDM transferability on average: a set of predictors known for their ecologically meaningful effects on plant distribution, and the two first axes of a principal component analysis calibrated on all predictor variables (Spc2). From the more than 2000 combinations of predictors per species across strategies, the best set of predictors yielded SDMs with good transferability for 45 species (90%). These best combinations consisted of eight randomly assembled (39 species) or uncorrelated predictors (6 species) and Spc2 (5 species). We also found that internal cross-validation was not sufficient to give full information about the transferability of a SDM to a distinct range. Main conclusion Transferring SDMs at the macroclimatic scale, and thus anticipating invasions, is possible for the large majority of invasive plants considered in this study, but the accuracy of the predictions relies strongly on the choice of predictors. From our results, we recommend including either proximal and state-of-the-art variables or a reduced and orthogonalized set to obtain robust SDM projections. [ABSTRACT FROM AUTHOR]

Aim Statistical and ecological mechanisms shape species abundance distributions (SADs). A lack of correlation between ecological gradients and SAD shape would suggest that SADs are caused by purely statistical reasons. We evaluated the variation in the shape of SADs for communities in landscapes of differing variable connectivity, environmental heterogeneity, species niches overlap and productivity. Location Rainforests in the Madidi region (Bolivia). Methods We compiled biological and environmental information on 65 sites (a site being a group of two to six 0.1-ha plots where woody plants of a diameter at breast height ≥ 2.5 cm were inventoried). We built unveiled (complete) SADs for each site and fitted Gambin models to those SADs. The Gambin α parameter served as a metric of SAD shape. Low α values characterize logseries-like SADs, while high α values characterize lognormal-like SADs. For each site, we estimated landscape roughness, environmental heterogeneity, species niche overlap and productivity. These variables were related to SAD shape by means of variation partitioning. Results SADs changed from logseries-like to lognormal-like along the elevational gradient. Many of our predictor variables were correlated: 40.4% of the variation in SAD shape could not be attributed to specific factors. However, 50.62% of the variation in the SAD shape could be assigned to individual predictor matrices: 28.4% was explained exclusively by niche overlap, 15.41% exclusively by environmental heterogeneity, 5.20% exclusively by landscape roughness and 1.6% exclusively by productivity. Main conclusions Ecological processes related to the topographical/environmental complexities that vary across the elevational gradient are correlated with the SAD shape. Purely statistical mechanisms are apparently not sufficient to explain the changes in SAD shape. The most important factor is the mean overlap of the niches of the species of an assemblage: avoiding competition with co-occurring species could be the most important mechanism driving species relative success at the ≤100 km2 scale. [ABSTRACT FROM AUTHOR]

Aim Spatial analysis of the distribution and density of species is of continuing interest within theoretical and applied ecology. Species distribution models (SDMs) are being increasingly used to analyse count, presence-absence and presence-only data sets. There is a growing literature on dynamic SDMs (which incorporate temporal variation in species distribution), joint SDMs (which simultaneously analyse the correlated distribution of multiple species) and geostatistical models (which account for similarity between nearby sites caused by unobserved covariates). However, no previous study has combined all three attributes within a single framework. Innovation We develop spatial dynamic factor analysis for use as a 'joint, dynamic SDM' (JDSDM), which uses geostatistical methods to account for spatial similarity when estimating one or more 'factors'. Each factor evolves over time following a density-dependent (Gompertz) process, and the log-density of each species is approximated as a linear combination of different factors. We demonstrate a JDSDM using two multispecies case studies (an annual survey of bottom-associated species in the Bering Sea and a seasonal survey of butterfly density in the continental USA), and also provide our code publicly as an R package. Main conclusions Case study applications show that that JDSDMs can be used for species ordination, i.e. showing that dynamics for butterfly species within the same genus are significantly more correlated than for species from different genera. We also demonstrate how JDSDMs can rapidly identify dominant patterns in community dynamics, including the decline and recovery of several Bering Sea fishes since 2008, and the 'flight curves' typical of early or late-emerging butterflies. We conclude by suggesting future research that could incorporate phylogenetic relatedness or functional similarity, and propose that our approach could be used to monitor community dynamics at large spatial and temporal scales. [ABSTRACT FROM AUTHOR]

While ecological effects on short-term population dynamics are well understood, their effects over millennia are difficult to demonstrate and convincing evidence is scant. Using coalescent methods, we analysed past population dynamics of three lizard species ( Psammodromus hispanicus, P. edwardsianus, P. occidentalis) and linked the results with climate change data covering the same temporal horizon (120 000 years). An increase in population size over time was observed in two species, and in P. occidentalis, no change was observed. Temporal changes in temperature seasonality and the maximum temperature of the warmest month were congruent with changes in population dynamics observed for the three species and both variables affected population density, either directly or indirectly (via a life-history trait). These results constitute the first solid link between ecological change and long-term population dynamics. The results moreover suggest that ecological change leaves genetic signatures that can be retrospectively traced, providing evidence that ecological change is a crucial driver of genetic diversity and speciation. [ABSTRACT FROM AUTHOR]

Aim The way in which environmental conditions determine the distribution and abundance of species is a crucial topic in ecology, biogeography and conservation. It is especially important to understand the nature of this relationship regarding threatened species. The ability to forecast local densities over the geographic range of species provides a way to link population ecology and biogeography. Particularly, our aim was to test whether predictions derived from species distribution modelling data provide useful information on spatial patterns of abundance and, consequently, may act as surrogates for local density estimates. Location Andalusia (southern Spain). Methods Logistic regression and the favourability function were applied as modelling tools to presence-absence data to compare the predicted results with current abundance. This approach is useful in the identification of local priorities for the target species whenever broad-scale surveys need to be performed. Results The model included variables related to topography, vegetation and spatial location to explain the presence of Bonelli's eagle. A positive relationship was found between both probability and favourability and the density of this species, but a triangular fit only with favourability, suggesting that the physiological and ecological requirements of this species in the study area are better reflected in the favourability model. Main conclusions We suggest that favourability models derived from presence-absence data provide insights into abundance data and valuable information on carrying capacity over large scales. To minimize costs, maximize output and optimize results, priority could be given to detecting the presence of the species, instead of investing resources aimed at estimating abundance, which is more expensive. [ABSTRACT FROM AUTHOR]

Aim When faced with dichotomous events, such as the presence or absence of a species, discrimination capacity (the ability to separate the instances of presence from the instances of absence) is usually the only characteristic that is assessed in the evaluation of the performance of predictive models. Although neglected, calibration or reliability (how well the estimated probability of presence represents the observed proportion of presences) is another aspect of the performance of predictive models that provides important information. In this study, we explore how changes in the distribution of the probability of presence make discrimination capacity a context-dependent characteristic of models. For the first time, we explain the implications that ignoring the context dependence of discrimination can have in the interpretation of species distribution models. Innovation In this paper we corroborate that, under a uniform distribution of the estimated probability of presence, a well-calibrated model will not attain high discrimination power and the value of the area under the curve will be 0.83. Under non-uniform distributions of the probability of presence, simulations show that a well-calibrated model can attain a broad range of discrimination values. These results illustrate that discrimination is a context-dependent property, i.e. it gives information about the performance of a certain algorithm in a certain data population. Main conclusions In species distribution modelling, the discrimination capacity of a model is only meaningful for a certain species in a given geographic area and temporal snapshot. This is because the representativeness of the environmental domain changes with the geographical and temporal context, which unavoidably entails changes in the distribution of the probability of presence. Comparative studies that intend to generalize their results only based on the discrimination capacity of models may not be broadly extrapolated. Assessment of calibration is especially recommended when the models are intended to be transferred in time or space. [ABSTRACT FROM AUTHOR]

Aim The extent of the study area (geographical background, GB) can strongly affect the results of species distribution models (SDMs), but as yet we lack objective and practicable criteria for delimiting the appropriate GB. We propose an approach to this problem using trend surface analysis (TSA) and provide an assessment of the effects of varying GB extent on the performance of SDMs for four species. Location Mainland Spain. Methods Using data for four well known wild ungulate species and different GBs delimited with a TSA, we assessed the effects of GB extent on the predictive performance of SDMs: specifically on model calibration (Miller's statistic) and discrimination (area under the curve of the receiver operating characteristic plot, AUC; sensitivity and specificity), and on the tendency of the models to predict environmental potential when they are projected beyond their training area. Results In the training area, discrimination significantly increased and calibration decreased as the GB was enlarged. In contrast, as GB was enlarged, both discriminatory power and calibration decreased when assessed in the core area of the species distributions. When models trained using small GBs were projected beyond their training area, they showed a tendency to predict higher environmental potential for the species than those models trained using large GBs. Main conclusions By restricting GB extent using a geographical criterion, model performance in the core area of the species distribution can be significantly improved. Large GBs make models demonstrate high discriminatory power but are barely informative. By delimiting GB using a geographical criterion, the effect of historical events on model parameterization may be reduced. Thus purely environmental models are obtained that, when projected onto a new scenario, depict the potential distribution of the species. We therefore recommend the use of TSA in geographically delimiting the GB for use in SDMs. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim The area under the receiver operating characteristic (ROC) curve (AUC) is a widely used statistic for assessing the discriminatory capacity of species distribution models. Here, I used simulated data to examine the interdependence of the AUC and classical discrimination measures (sensitivity and specificity) derived for the application of a threshold. I shall further exemplify with simulated data the implications of using the AUC to evaluate potential versus realized distribution models. Innovation After applying the threshold that makes sensitivity and specificity equal, a strong relationship between the AUC and these two measures was found. This result is corroborated with real data. On the other hand, the AUC penalizes the models that estimate potential distributions (the regions where the species could survive and reproduce due to the existence of suitable environmental conditions), and favours those that estimate realized distributions (the regions where the species actually lives). Main conclusions Firstly, the independence of the AUC from the threshold selection may be irrelevant in practice. This result also emphasizes the fact that the AUC assumes nothing about the relative costs of errors of omission and commission. However, in most real situations this premise may not be optimal. Measures derived from a contingency table for different cost ratio scenarios, together with the ROC curve, may be more informative than reporting just a single AUC value. Secondly, the AUC is only truly informative when there are true instances of absence available and the objective is the estimation of the realized distribution. When the potential distribution is the goal of the research, the AUC is not an appropriate performance measure because the weight of commission errors is much lower than that of omission errors. [ABSTRACT FROM AUTHOR]

The funnelweb spider Macrothele calpeiana is endemic to the southern half of the Iberian Peninsula, but recent occurrence records from localities in Spain, North Africa and other regions of Europe, which are distant from its native populations, suggest human-mediated dispersal, probably associated with the commercial export of olive trees. The main goal of this study was to assess the environmental suitability of these new records and to discuss the spider's potential to become an invasive species, mainly in new regions across Central Europe and the Mediterranean Basin. Central Europe, Mediterranean Basin. Using presence points from the Iberian native populations of M. calpeiana and a set of climatic variables, four presence-only algorithms (BIOCLIM, DOMAIN, GARP and Maxent) were applied to model the potential distribution of the spider. The models were transferred to Central Europe and the Mediterranean Basin, and the locations of the new records in both the occupied and potential environmental spaces were screened. The four models were generally congruent in predicting the existence of a suitable climate for the species across the Mediterranean Basin, although BIOCLIM and DOMAIN yielded more constrained predictions than GARP and Maxent. Whereas the new records from Central Europe were located far from the occupied and potential climatic spaces, those from the Iberian Peninsula were not. Climatic suitability together with propagule pressure owing to human activities will certainly enhance the opportunities for M. calpeiana to colonize new areas across the Mediterranean Basin. The species has invaded areas beyond its native range, and those new locations located in the Iberian Peninsula and North Africa show environmental suitability for the spider and deserve long-term monitoring. Although the new locations in Central Europe were not predicted by the climate models and the persistence of the species seems improbable, the possibility of rapid evolution or phenotypic plasticity processes raises the need for caution over the possibility of a future spread of M. calpeiana across Europe. Stronger controls over the transport of trees must be applied, and further studies on the ecology of the spider are imperative to assess the possible impact on the invaded ecosystems. [ABSTRACT FROM AUTHOR]

Geographic distributions of species are constrained by several factors acting at different scales, with climate assumed to be a major determinant at broad extents. Recent studies, however, have challenged this statement and indicated that climate may not dominate among the factors governing geographic distributions of species. Here, we argue that these results are misleading due to the lack of consideration of the geographic area that has been accessible to the species. North America. We generated null distributions for 75 North American endemic and 19 non-endemic bird species. For each species, climatic envelopes of observed and null distributions were modelled using neural networks and generalized linear models, and seven climatic predictors. Values of the area under the receiver-operating characteristic curve (AUC) based on models of observed distributions were compared with corresponding AUC values for the null distributions. More than 82% of the endemic species showed AUC higher for the observed than for the null distributions, while 63% of the non-endemic species showed such a pattern. We demonstrate a dominant climatic signal in shaping North American bird distributions. Our results attest to the importance of climate in determining species distributions and support the use of climate-envelope models for estimating potential distributional areas at the appropriate spatial scales. [ABSTRACT FROM AUTHOR]

1. The importance of dispersal capacity in structuring beta-diversity gradients has been pointed out in a number of recent studies, making geographical distance (GD) an important predictor of the composition of assemblages apart from environment. In this study, we analyse the relative importance of climate, habitat complexity and GD in four spider families with different dispersal tendencies in the centre of the Iberian Peninsula. 2. Regional beta-diversity was calculated for the four families and values were bootstrapped to test for significant difference between groups. Mantel tests assessed the explanatory capacity of four potential predictors – climatic similarity, similarity of vegetation complexity, GD and cost distance (CD) considering topography – on faunistic similarity. Simple regressions were used to compare decay rates, and bootstrapping tested for significant differences in coefficients. 3. Regional beta-diversity values were higher in less vagile families. Climatic distance was strongly correlated with faunistic dissimilarity in the four families, whereas GD had no explanatory power. Except in one family, CD was correlated with faunistic dissimilarity but its independent association was negligible when controlling for climate. 4. Both the pattern of regional beta-diversity among families and the pattern of variation in species composition explained by climate are consistent with the hypothesis that dispersion capacity influences the composition of the assemblages. However, climate distance was the main factor determining species sorting in space while GD had a weak effect. Differences in the degree of specialisation or nonlinear relationships between species composition and space may obscure the signal of GD at the scale of work. [ABSTRACT FROM AUTHOR]

Aim Nowadays, large amounts of species distribution data and software for implementing different species distribution modelling methods are freely available through the internet. As a result, methodological works that analyse the relative performance of modelling techniques, as well as those that study which species characteristics affect their performance, are necessary. We discuss three important topics that must be kept in mind when modelling species distributions, namely (i) the distinction between potential and realized distribution, (ii) the effect of the relative occurrence area of the species on the results of the evaluation of model performance, and (iii) the general inaccuracy of the predictions of the realized distribution provided by species distribution modelling methods. Location Unspecific. Methods Using some recent papers as a basis, we illustrate the three issues mentioned above and discuss the negative implications of neglecting them. Results Considering a potential-realized distribution gradient, different modelling methods may be arranged along this gradient according to their ability to model any concept. Complex techniques may be more suitable to model the realized distribution than simple ones, which may be more appropriate to estimate the potential distribution. Comparisons among techniques must consider this scenario. The relative occurrence area of the species conditions the results of the evaluation scores, implying that models of rare species will unavoidably yield higher discrimination values. Moreover, discrimination values that are usually reported in the literature may imply considerable over or underestimations of the distribution of the species. Main conclusions It is extremely important to establish a solid conceptual and methodological framework on which the emergent field of species distribution modelling can stand and develop. [ABSTRACT FROM AUTHOR]

It is well known that biodiversity data from historical inventories presents important geographic and taxonomic biases. Due to this, current knowledge on the distribution of most species could be incomplete and biased. We assess how the biases in historical biodiversity data might affect the description of the environmental niche of the species, using exhaustive data on the distribution of dung beetles in Madrid as a case study. We describe the historical process of survey and compare such historical data with the results of an exhaustive survey, identifying the environmental biases in the historical surveys during different periods, and assessing the completeness of the environmental niche of the species provided by historical data through time. Events like the Spanish Civil War affect the tempo and spread of surveys, but the exhaustive work since 1970 provides a good, though incomplete, coverage of the region by 1998. In spite of this, the biases in historical data result in a limited knowledge about the niche of an important number of species. Although nearly a half of the species had the 100% of their niche covered by data in 1998, roughly a third had less than 75%, nearly a fourth less than 50%, and 18 species had to be excluded from the analyses due to the lack of data. Our results point out that data from non-standardized inventories often provide an incomplete description of the environmental responses of most species. Due to this, we highlight that currently predictive models of species distributions present some limitations, since the results of models based in partial information about the environmental niche of the species will be compromised. Therefore, the biases in the available data must be evaluated before constructing predictive maps of species distributions, and taken into account when drawing conclusions or conservation strategies from these maps. [ABSTRACT FROM AUTHOR]

The area under the receiver operating characteristic (ROC) curve, known as the AUC, is currently considered to be the standard method to assess the accuracy of predictive distribution models. It avoids the supposed subjectivity in the threshold selection process, when continuous probability derived scores are converted to a binary presence–absence variable, by summarizing overall model performance over all possible thresholds. In this manuscript we review some of the features of this measure and bring into question its reliability as a comparative measure of accuracy between model results. We do not recommend using AUC for five reasons: (1) it ignores the predicted probability values and the goodness-of-fit of the model; (2) it summarises the test performance over regions of the ROC space in which one would rarely operate; (3) it weights omission and commission errors equally; (4) it does not give information about the spatial distribution of model errors; and, most importantly, (5) the total extent to which models are carried out highly influences the rate of well-predicted absences and the AUC scores. [ABSTRACT FROM AUTHOR]

Different distribution maps can be obtained for the same species if localities where species are present are mapped at different times. We analysed the accumulation of information over time for a group of dung beetle species in the Iberian Peninsula. To do this, we used all available information about the distribution of the group as well as data on selected species to examine if the process of discovery of species distribution has occurred in a climatically or spatially structured fashion. Our results show the existence of a well-defined pattern of temporal growth in distributional information; due to this, the date of capture of each specimen can be explained by the environmental and spatial variables associated to the collection sites. We hypothesize that such temporal biases could be the rule rather than the exception in most distributional data. These biases could affect the weighting of environmental factors that influence species distributions, as well as the accuracy of predictive distribution models. Systematic surveys should be a priority for the description of species geographical ranges in order to make robust predictions about the consequences of habitat and climate change for their persistence and conservation. [ABSTRACT FROM AUTHOR]

1. The effect and relative importance of both environmental and geographic factors on species turnover of Chrysomelidae assemblages in the Iberian Peninsula was explored. 2. Ordination and classification analysis of species lists from well-sampled local areas identified two main biogegraphic groups of inventories, discriminating between Eurosiberian and Mediterranean faunas. 3. After describing this pattern, the underlying determinants were explored, separating pure from combined effects of environment and geography by means of partial canonical correspondence analysis. 4. Results indicate that pure effects of environment and geographic position are of similar magnitude (about 20% of the variation from each), and quite important in comparison with combined effects. 5. The power of the pure effect of geographical variables, together with beta-diversity independence of species richness gradients, point to the inadequacy of environmental measurement explanation of beta-diversity patterns, and to the existence of a major component of geographical variation independent of environment. [ABSTRACT FROM AUTHOR]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

1. The habitat heterogeneity hypothesis states that the more complex the habitat, the higher the species richness. The present study analyzes the effect of local factors on regional spider ( Araneidae and Thomisidae) richness. The main objective is to disentangle the relative importance of habitat structure and other environmental variables. 2. Fifteen territorial units of 1 km2 were sampled to obtain reliable and comparable inventories of the two spider families. Richness values were modelled using general regression models and a set of climate, topographic and vegetation structure variables. Pure and joint effects were computed via variation partitioning. 3. The results highlight the great importance of vegetation complexity, especially of grass and sub-shrub cover, in determining spider species richness. 4. The maximum temperature is the only climate variable significantly related to species richness, although its effect is combined with that of spatial and vegetation structure variables. 5. These results support the habitat heterogeneity hypothesis, and highlight the importance of taking vegetation complexity into account when managing habitats and where spider conservation is desired. [ABSTRACT FROM AUTHOR]