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.

SUMMARY1. Metacommunity ecology addresses the situation where sets of local communities are connected by the dispersal of a number of potentially interacting species. Aquatic systems (e.g. lentic versus lotic versus marine) differ from each other in connectivity and environmental heterogeneity, suggesting that metacommunity organisation also differs between major aquatic systems. Here, we review findings from observational field studies on metacommunity organisation in aquatic systems. 2. Species sorting (i.e. species are 'filtered' by environmental factors and occur only at environmentally suitable sites) prevails in aquatic systems, particularly in streams and lakes, but the degree to which dispersal limitation interacts with such environmental control varies among different systems and spatial scales. For example, mainstem rivers and marine coastal systems may be strongly affected by 'mass effects' (i.e. where high dispersal rates homogenise communities to some degree at neighbouring localities, irrespective of their abiotic and biotic environmental conditions), whereas isolated lakes and ponds may be structured by dispersal limitation (i.e. some species do not occur at otherwise-suitable localities simply because sites with potential colonists are too far away). Flow directionality in running waters also differs from water movements in other systems, and this difference may also have effects on the role of dispersal in different aquatic systems. 3. Dispersal limitation typically increases with increasing spatial distance between sites, mass effects potentially increase in importance with decreasing distance between sites, and the dispersal ability of organisms may determine the spatial extents at which species sorting and dispersal processes are most important. 4. A better understanding of the relative roles of species sorting, mass effects and dispersal limitation in affecting aquatic metacommunities requires the following: (i) characterising dispersal rates more directly or adopting better proxies than have been used previously; (ii) considering the nature of aquatic networks; (iii) combining correlative and experimental approaches; (iv) exploring temporal aspects of metacommunity organisation and (v) applying past approaches and statistical methods innovatively for increasing our understanding of metacommunity organisation.

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.

During low water levels, habitats in river-floodplain systems are isolated from each other and from the main river. Oppositely, floods tend to connect water bodies with distinct hydrological characteristics and, as a result, ecological processes and biological communities tend to be more similar among the distinct habitats that comprise a river-floodplain system. Based on a literature review and also using unpublished data obtained in tropical floodplains, the aim of this paper is to highlight the effects of floods as a process that reduce spatial variability. The usual negative relationship between the coefficient of variation of any ecological indicator (e.g., chlorophyll-a or total phosphorus) and water level is the main result demonstrating a reduction in spatial variability due to floods. Considering physical, chemical or biotic data gathered in distinct habitats within the floodplain, this pattern was found in temperate and tropical regions, subjected to distinct levels of anthropogenic impacts, and at different spatial extents. The main mechanism that accounts for this pattern may be stated as follow. During low water level, the biological communities of each habitat in the floodplain (e.g., lagoons, backwater, streams) follow distinct temporal trajectories due to the effects of local driving forces (e.g., an efficient predator trapped in a lagoon but not in another). Management plans and biodiversity conservation in river floodplain systems will benefit by considering the effects of flood homogenization and increased connectivity peculiar to these unique ecosystems.

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/).

Forecasts of species range shifts under climate change are fraught with uncertainties and ensemble forecasting may provide a framework to deal with such uncertainties. Here, a novel approach to partition the variance among modeled attributes, such as richness or turnover, and map sources of uncertainty in ensembles of forecasts is presented. We model the distributions of 3837 New World birds and project them into 2080. We then quantify and map the relative contribution of different sources of uncertainty from alternative methods for niche modeling, general circulation models (AOGCM), and emission scenarios. The greatest source of uncertainty in forecasts of species range shifts arises from using alternative methods for niche modeling, followed by AOGCM, and their interaction. Our results concur with previous studies that discovered that projections from alternative models can be extremely varied, but we provide a new analytical framework to examine uncertainties in models by quantifying their importance and mapping their patterns.

Current climate and Pleistocene climatic changes are both known to be associated with geographical patterns of diversity. We assess their associations with the European Scarabaeinae dung beetles, a group with high dispersal ability and well-known adaptations to warm environments. By assessing spatial stationarity in climate variability since the last glacial maximum (LGM), we find that current scarab richness is related to the location of their limits of thermal tolerance during the LGM. These limits mark a strong change in their current species richness-environment relationships. Furthermore, northern scarab assemblages are nested and composed of a phylogenetically clustered subset of large-range sized generalist species, whereas southern ones are diverse and variable in composition. Our results show that species responses to current climate are limited by the evolution of assemblages that occupied relatively climatically stable areas during the Pleistocene, and by post-glacial dispersal in those that were strongly affected by glaciations.

Within a metacommunity, both environmental and spatial processes regulate variation in local community structure. The strength of these processes may vary depending on species traits (e.g., dispersal mode) or the characteristics of the regions studied (e.g., spatial extent, environmental heterogeneity). We studied the metacommunity structuring of three groups of stream macroinvertebrates differing in their overland dispersal mode (passive dispersers with aquatic adults; passive dispersers with terrestrial adults; active dispersers with terrestrial adults). We predicted that environmental structuring should be more important for active dispersers, because of their better ability to track environmental variability, and that spatial structuring should be more important for species with aquatic adults, because of stronger dispersal limitation. We sampled a total of 70 stream riffle sites in three drainage basins. Environmental heterogeneity was unrelated to spatial extent among our study regions, allowing us to examine the effects of these two factors on metacommunity structuring. We used partial redundancy analysis and Moran's eigenvector maps based on overland and watercourse distances to study the relative importance of environmental control and spatial structuring. We found that, compared with environmental control, spatial structuring was generally negligible, and it did not vary according to our predictions. In general, active dispersers with terrestrial adults showed stronger environmental control than the two passively dispersing groups, suggesting that the species dispersing actively are better able to track environmental variability. There were no clear differences in the results based on watercourse and overland distances. The variability in metacommunity structuring among basins was not related to the differences in the environmental heterogeneity and spatial extent. Our study emphasized that (1) environmental control is prevailing in stream metacommunities, (2) dispersal mode may have an important effect on metacommunity structuring, and (3) some factors other than spatial extent or environmental heterogeneity contributed to the differences among the basins.