Macroecological data are usually structured in space, so taking into account spatial autocorrelation in regression and correlation analyses is essential for a better understanding of patterns and processes. Many methods are available to deal with spatial autocorrelation, but there are some difficulties when one is dealing with huge geographical extents and fine-scale data. So, we propose a relatively simple and fast computer-intensive approach to deal with Principal Coordinate of Neighbor Matrices (PNCM)/Moran’s Eigenvector Mapping (MEM) analyses for large datasets, using global richness pattern of sharks as a model. We performed a variance partitioning approach by regressing species richness against environmental variables and spatial eigenvectors derived from PCNM. Due to the large number of ocean grid cells (> 9000), we ran the analyses 1000 timesby randomly subsampling each time 50 to 4500 cells and compared the distribution of the variance partitioning components, as well as the slopes of the environmental variables. We also estimated Moran’s I coefficients for regression residuals to check if spatial eigenvectors took into account spatial autocorrelation. Comparing statistics of analyses with different sample sizes, we note that although the environmental component increases linearly, other components (unique space and shared) of the most important variables stabilize with about 1000 cells, whereas all other smaller effects tend to stabilize between 2500 and 3000 cells. Besides that, PCNM eigenvectors were able to control spatial autocorrelation very well. We showed that shark richness patterns are strongly and positively correlated with temperature range, according to the well-known pattern of distribution for the taxon, and strong negatively correlated with oxygen supplies, which are higher in polar zones where  ice acts as a barrier to sharks. Our approach clearly shows that it is possible to perform a robust evaluation of global patterns of diversity using eigenvector approaches based on a resampling strategy and allows effective computation of the variance partitioning even when dealing with large datasets.

Macroecology focuses on ecological questions at broad spatial and temporal scales, providing a statistical description of patterns in species abundance, distribution and diversity. More recently, historical components of these patterns have begun to be investigated more deeply. We tentatively refer to the practice of explicitly taking species history into account, both analytically and conceptually, as ‘evolutionary macroecology’. We discuss how the evolutionary dimension can be incorporated into macroecology through two orthogonal and complementary data types: fossils and phylogenies. Research traditions dealing with these data have developed more‐or‐less independently over the last 20–30 years, but merging them will help elucidate the historical components of diversity gradients and the evolutionary dynamics of species’ traits. Here we highlight conceptual and methodological advances in merging these two research traditions and review the viewpoints and toolboxes that can, in combination, help address patterns and unveil processes at temporal and spatial macro‐scales.

There have been numerous claims in the ecological literature that spatial autocorrelation in the residuals of ordinary least squares (OLS) regression models results in shifts in the partial coefficients, which bias the interpretation of factors influencing geographical patterns. We evaluate the validity of these claims using gridded species richness data for the birds of North America, South America, Europe, Africa, the ex-USSR, and Australia. We used richness in 110x110 km cells and environmental predictor variables to generate OLS and simultaneous autoregressive (SAR) multiple regression models for each region. Spatial correlograms of the residuals from each OLS model were then used to identify the minimum distance between cells necessary to avoid short-distance residual spatial autocorrelation in each data set. This distance was used to subsample cells to generate spatially independent data. The partial OLS coefficients estimated with the full dataset were then compared to the distributions of coefficients created with the subsamples. We found that OLS coefficients generated from data containing residual spatial autocorrelation were statistically indistinguishable from coefficients generated from the same data sets in which short-distance spatial autocorrelation was not present in all 22 coefficients tested. Consistent with the statistical literature on this subject, we conclude that coefficients estimated from OLS regression are not seriously affected by the presence of spatial autocorrelation in gridded geographical data. Further, shifts in coefficients that occurred when using SAR tended to be correlated with levels of uncertainty in the OLS coefficients. Thus, shifts in the relative importance of the predictors between OLS and SAR models are expected when small-scale patterns for these predictors create weaker and more unstable broad-scale coefficients. Our results indicate both that OLS regression is unbiased and that differences between spatial and nonspatial regression models should be interpreted with an explicit awareness of spatial scale.

Species are the currency of most biodiversity studies. However, many shortfalls and biases remain in our biodiversity estimates, preventing a comprehensive understanding of the eco-evolutionary processes that have shaped the biodiversity currently available on Earth. Biased biodiversity estimates also jeopardize the effective implementation of data-driven conservation strategies, ultimately leading to biodiversity loss. Here, we delve into the concept of the Latitudinal Taxonomy Gradient (LTG) and show how this new idea provides an interesting conceptual link between the Linnean (i.e., our ignorance of how many species there are on Earth), Darwinian (i.e., our ignorance of species evolutionary relationships), and Wallacean (i.e., our ignorance on species distribution) shortfalls. More specifically, we contribute to an improved understanding of LTGs and establish the basis for the development of new methods that allow us to: (i) better account for the integration between different shortfalls and, (ii) estimate how these interactions may affect our understanding about the evolutionary components of richness gradients at macroecological scales.

Whether species are capable of adapting to rapid shifts in climate raises considerable interest. Analyses based on niche models often assume niche conservatism and equilibrium with climate, implying that species will persist only in regions where future climatic conditions match their current conditions and that they will colonize these regions promptly. However, species may adapt to changing climate and persist where future climates differ from their current optimum. Here, we provide a first macroecological generalization to the approach of evolutionary rescue, by comparing the expected shift in mean temperature within the geographic range of 7193 species of amphibians worldwide, under alternative warming scenarios. Expected evolutionary change is expressed in units of standard deviations of mean temperature, per generation (Haldanes) and compared with theoretical models defining the maximum sustainable evolutionary rates (MSER) for each species. For the pessimistic emission scenario RCP8.5, shifts in mean temperature vary between near-zero and 6°C within the geographic ranges for most species, with a median equal to 3.75°C. The probability of evolutionary rescue in temperature peaks is higher than 0.05 for about 55% of the species and higher than 0.95 for only 12% of the species. Therefore, the predicted shift in mean temperature would be too extreme to deal with for almost half of the species. When evolutionary plasticity is incorporated, this scenario becomes more optimistic, with about 44% of the species being likely to shift their thermal peaks tracking future warming. These figures are not random in geographical space: evolutionary rescue would be unlikely in the tropics, especially in South America (Amazonia), parts of Africa, Indonesia, and in the Mediterranean region. Given the uncertainty in demographic and genetic parameters for species’ responses to climate change, we caution that it remains difficult to assess the realism of the macroecological generalization. In any case, it may be precautionary to assume that our results are not liberal, showing low probability of adaptation for most of the species and thus that the persistence of populations by evolutionary rescue may, in general, be unlikely in the long term.