Aim Spatial autocorrelation in ecological data can inflate Type I errors in statistical analyses. There has also been a recent claim that spatial autocorrelation generates 'red herrings', such that virtually all past analyses are flawed. We consider the origins of this phenomenon, the implications of spatial autocorrelation for macro-scale patterns of species diversity and set out a clarification of the statistical problems generated by its presence.Location To illustrate the issues involved, we analyse the species richness of the birds of western/central Europe, north Africa and the Middle East.Methods Spatial correlograms for richness and five environmental variables were generated using Moran's I coefficients. Multiple regression, using both ordinary least-squares (OLS) and generalized least squares (GLS) assuming a spatial structure in the residuals, were used to identify the strongest predictors of richness. Autocorrelation analyses of the residuals obtained after stepwise OLS regression were undertaken, and the ranks of variables in the full OLS and GLS models were compared.Results Bird richness is characterized by a quadratic northsouth gradient. Spatial correlograms usually had positive autocorrelation up to c . 1600 km. Including the environmental variables successively in the OLS model reduced spatial autocorrelation in the residuals to non-detectable levels, indicating that the variables explained all spatial structure in the data. In principle, if residuals are not autocorrelated then OLS is a special case of GLS. However, our comparison between OLS and GLS models including all environmental variables revealed that GLS de-emphasized predictors with strong autocorrelation and long-distance clinal structures, giving more importance to variables acting at smaller geographical scales. ConclusionAlthough spatial autocorrelation should always be investigated, it does not necessarily generate bias. Rather, it can be a useful tool to investigate mechanisms operating on richness at different spatial scales. Claims that analyses that do not take into account spatial autocorrelation are flawed are without foundation.

Ecologists and evolutionary biologists are increasingly using big-data approaches to tackle questions at large spatial, taxonomic, and temporal scales. However, despite recent efforts to gather two centuries of biodiversity inventories into comprehensive databases, many crucial research questions remain unanswered. Here, we update the concept of knowledge shortfalls and review the tradeoffs between generality and uncertainty. We present seven key shortfalls of current biodiversity data. Four previously proposed shortfalls pinpoint knowledge gaps for species taxonomy (Linnean), distribution (Wallacean), abundance (Prestonian), and evolutionary patterns (Darwinian). We also redefine the Hutchinsonian shortfall to apply to the abiotic tolerances of species and propose new shortfalls relating to limited knowledge of species traits (Raunkiaeran) and biotic interactions (Eltonian). We

Aim We surveyed the empirical literature to determine how well six diversity hypotheses account for spatial patterns in species richness across varying scales of grain and extent.Location Worldwide.Methods We identified 393 analyses ('cases') in 297 publications meeting our criteria. These criteria included the requirement that more than one diversity hypothesis was tested for its relationship with species richness. We grouped variables representing the hypotheses into the following 'correlate types': climate/ productivity, environmental heterogeneity, edaphics/nutrients, area, biotic interactions and dispersal/history (colonization limitation or other historical or evolutionary effect). For each case we determined the 'primary' variable: the one most strongly correlated with taxon richness. We defined 'primacy' as the proportion of cases in which each correlate type was represented by the primary variable, relative to the number of times it was studied. We tested for differences in both primacy and mean coefficient of determination of the primary variable between the hypotheses and between categories of five grouping variables: grain, extent, taxon (animal vs. plant), habitat medium (land vs. water) and insularity (insular vs. connected).Results Climate/productivity had the highest overall primacy, and environmental heterogeneity and dispersal/history had the lowest. Primacy of climate/ productivity was much higher in large-grain and large-extent studies than at smaller scales. It was also higher on land than in water, and much higher in connected systems than in insular ones. For other hypotheses, differences were less pronounced. Throughout, studies on plants and animals showed similar patterns. Coefficients of determination of the primary variables differed little between hypotheses and across the grouping variables, the strongest effects being low means in the smallest grain class and for edaphics/nutrients variables, and a higher mean for water than for land in connected systems but vice versa in insular systems. We highlight areas of data deficiency. Main conclusionsOur results support the notion that climate and productivity play an important role in determining species richness at large scales, particularly for non-insular, terrestrial habitats. At smaller extents and grain sizes, the primacy of the different types of correlates appears to differ little from null expectation. In our analysis, dispersal/history is rarely the best correlate of species richness, but this may reflect the difficulty of incorporating historical factors into regression models, and the collinearity between past and current climates. Our findings are consistent with the view that climate determines the capacity for species richness. However, its influence is less evident at smaller spatial scales, probably because (1) studies small in extent tend to sample little climatic range, and (2) at large

SAM (Spatial Analysis in Macroecology) is a freeware application that offers a comprehensive array of spatial statistical methods, focused primarily on surface pattern spatial analysis. SAM is a compact, but powerful stand-alone software, with a user-friendly, menu-driven graphical interface. The methods available in SAM are the most commonly used in macroecology and geographical ecology, and range from simple tools for exploratory graphical analysis (e.g. mapping and graphing) and descriptive statistics of spatial patterns (e.g. autocorrelation metrics), to advanced spatial regression models (e.g. autoregression and eigenvector filtering). Download of the software, along with the user manual, can be downloaded online at the SAM website: / (permanent URL at /).Today there are many software applications and packages available for spatial statistical analysis. Some of them are stand-alone applications that offer several methods (e.g.

Because most macroecological and biodiversity data are spatially autocorrelated, special tools for describing spatial structures and dealing with hypothesis testing are usually required. Unfortunately, most of these methods have not been available in a single statistical package. Consequently, using these tools is still a challenge for most ecologists and biogeographers. In this paper, we present  (Spatial Analysis in Macroecology), a new, easy-to-use, freeware package for spatial analysis in macroecology and biogeography. Through an intuitive, fully graphical interface, this package allows the user to describe spatial patterns in variables and provides an explicit spatial framework for standard techniques of regression and correlation. Moran's I autocorrelation coefficient can be calculated based on a range of matrices describing spatial relationships, for original variables as well as for residuals of regression models, which can also include filtering components (obtained by standard trend surface analysis or by principal coordinates of neighbour matrices).  also offers tools for correcting the number of degrees of freedom when calculating the significance of correlation coefficients. Explicit spatial modelling using several forms of autoregression and generalized least-squares models are also available. We believe this new tool will provide researchers with the basic statistical tools to resolve autocorrelation problems and, simultaneously, to explore spatial components in macroecological and biogeographical data. Although the program was designed primarily for the applications in macroecology and biogeography, most of  's statistical tools will be useful for all kinds of surface pattern spatial analysis. The program is freely available at www.ecoevol.ufg.br/sam (permanent URL at http://purl.oclc.org/sam/).

Documenting and exploring the patterns of diversity of life on Earth has always been a central theme in biology. Species richness despite being the most commonly used measure of diversity in macroecological studies suffers from not considering the evolutionary and ecological differences among species. Phylogenetic diversity (PD) and functional diversity (FD) have been proposed as alternative measures to overcome this limitation. Although species richness, PD and FD are closely related, their relationships have never been investigated on a global scale. Comparing PD and FD with species richness corroborated the general assumptions of surrogacy of the different diversity measures. However, the analysis of the residual variance suggested that the mismatches between the diversity measures are influenced by environmental conditions. PD increased relative to species richness with increasing mean annual temperature, whereas FD decreased with decreasing seasonality relative to PD. We also show that the tropical areas are characterized by a FD deficit, a phenomenon, that suggests that in tropical areas more species can be packed into the ecological space. We discuss potential mechanisms that could have resulted in the gradient of spatial mismatch observed in the different biodiversity measures and draw parallels to local scale studies. We conclude that the use of multiple diversity measures on a global scale can help to elucidate the relative importance of historical and ecological processes shaping the present gradients in mammalian diversity.

It is widely believed that contemporary climate determines large-scale patterns of species richness. An alternative view proposes that species richness reflects biotic responses to historic climate changes. These competing ''contemporary climate'' vs ''historic climate'' hypotheses have been vigorously debated without reaching consensus. Here, we test the proposition that European species richness of reptiles and amphibians is driven by climate changes in the Quaternary. We find that climate stability between the Last Glacial Maximum (LGM) and the present day is a better predictor of species richness than contemporary climate; and that the 08C isotherm of the LGM delimits the distributions of narrow-ranging species, whereas the current 08C isotherm limits the distributions of wide-ranging species. Our analyses contradict previous studies of large-scale species richness patterns and support the view that ''historic climate'' can contribute to current species richness independently of and at least as much as contemporary climate.

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