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Luis Mauricio Bini

Bio: Luis Mauricio Bini is an academic researcher from Universidade Federal de Goiás. The author has contributed to research in topics: Species richness & Biodiversity. The author has an hindex of 59, co-authored 257 publications receiving 14069 citations. Previous affiliations of Luis Mauricio Bini include University of Guelph & Universidade Estadual de Maringá.


Papers
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Journal ArticleDOI
TL;DR: SAM (Spatial Analysis in Macroecology) as discussed by the authors ) is a freeware application that offers a comprehensive array of spatial statistical methods, focused primarily on surface pattern spatial analysis.
Abstract: 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 ).

1,123 citations

Journal ArticleDOI
TL;DR: In this article, the authors analyzed the species richness of the birds of western/central Europe, north Africa and the Middle East using Moran's I coefficients and multiple regression, using both ordinary least-squares (OLS) and generalized least squares (GLS) assuming a spatial structure in the residuals, to identify the strongest predictors of richness.
Abstract: 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 north-south 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. Conclusion Although 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.

989 citations

Journal ArticleDOI
TL;DR: In this article, the authors highlight the effects of floods as a process that reduce spatial variability in tropical floodplains and highlight that during low water levels, habitats in river-floodplain systems are isolated from each other and from the main river.
Abstract: 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.

713 citations

Journal ArticleDOI
TL;DR: A better understanding of the relative roles of species sorting, mass effects and dispersal limitation in affecting aquatic metacommunities requires the following: characterising dispersal rates more directly or adopting better proxies than have been used previously; considering the nature of aquatic networks; and combining correlative and experimental approaches.
Abstract: Summary 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. 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. 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. 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.

664 citations

Journal ArticleDOI
TL;DR: Sam as discussed by the authors is a freeware package for spatial analysis in macroecology and biogeography, which allows the user to describe spatial patterns in variables and provides an explicit spatial framework for standard techniques of regression and correlation.
Abstract: 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 sam (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). sam 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 sam's statistical tools will be useful for all kinds of surface pattern spatial analysis. The program is freely available at http://www.ecoevol.ufg.br/sam (permanent URL at http://purl.oclc.org/sam/).

638 citations


Cited by
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Journal ArticleDOI
TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

14,171 citations

Book
21 Mar 2002
TL;DR: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data is as discussed by the authors, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced.
Abstract: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

9,509 citations

Journal ArticleDOI

6,278 citations

01 Jan 2016
TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.
Abstract: Thank you very much for downloading modern applied statistics with s. As you may know, people have search hundreds times for their favorite readings like this modern applied statistics with s, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. modern applied statistics with s is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern applied statistics with s is universally compatible with any devices to read.

5,249 citations

Journal ArticleDOI
TL;DR: Species distribution models (SDMs) as mentioned in this paper are numerical tools that combine observations of species occurrence or abundance with environmental estimates, and are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time.
Abstract: Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.

5,076 citations