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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]

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]

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]

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]

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]

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]

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]