LEADER 05057nam 22005775 450 001 9910300104803321 005 20220627184429.0 010 $a3-319-71404-X 024 7 $a10.1007/978-3-319-71404-2 035 $a(CKB)4100000002892129 035 $a(MiAaPQ)EBC5347221 035 $a(DE-He213)978-3-319-71404-2 035 $a(PPN)225551217 035 $a(EXLCZ)994100000002892129 100 $a20180319d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNumerical Ecology with R /$fby Daniel Borcard, François Gillet, Pierre Legendre 205 $a2nd ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (xv, 435 pages) $cillustrations 225 1 $aUse R!,$x2197-5736 311 $a3-319-71403-1 327 $aChapter 1. Introduction -- Chapter 2. Exploratory Data Analysis -- Chapter 3. Association Measures and Matrices -- Chapter 4. Cluster Analysis -- Chapter 5. Unconstrained Ordination -- Chapter 6. Canonical Ordination -- Chapter 7. Spatial Analysis of Ecological Data -- Chapter 8. Community Diversity. 330 $aThis new edition of Numerical Ecology with R guides readers through an applied exploration of the major methods of multivariate data analysis, as seen through the eyes of three ecologists. It provides a bridge between a textbook of numerical ecology and the implementation of this discipline in the R language. The book begins by examining some exploratory approaches. It proceeds logically with the construction of the key building blocks of most methods, i.e. association measures and matrices, and then submits example data to three families of approaches: clustering, ordination and canonical ordination. The last two chapters make use of these methods to explore important and contemporary issues in ecology: the analysis of spatial structures and of community diversity. The aims of methods thus range from descriptive to explanatory and predictive and encompass a wide variety of approaches that should provide readers with an extensive toolbox that can address a wide palette of questions arising in contemporary multivariate ecological analysis. The second edition of this book features a complete revision to the R code and offers improved procedures and more diverse applications of the major methods. It also highlights important changes in the methods and expands upon topics such as multiple correspondence analysis, principal response curves and co-correspondence analysis. New features include the study of relationships between species traits and the environment, and community diversity analysis. This book is aimed at professional researchers, practitioners, graduate students and teachers in ecology, environmental science and engineering, and in related fields such as oceanography, molecular ecology, agriculture and soil science, who already have a background in general and multivariate statistics and wish to apply this knowledge to their data using the R language, as well as people willing to accompany their disciplinary learning with practical applications. People from other fields (e.g. geology, geography, paleoecology, phylogenetics, anthropology, the social and education sciences, etc.) may also benefit from the materials presented in this book. Users are invited to use this book as a teaching companion at the computer. All the necessary data files, the scripts used in the chapters, as well as extra R functions and packages written by the authors of the book, are available online (URL: http://adn.biol.umontreal.ca/~numericalecology/numecolR/). 410 0$aUse R!,$x2197-5736 606 $aStatistics  606 $aEcology  606 $aEnvironmental sciences 606 $aR (Computer program language) 606 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020 606 $aTheoretical Ecology/Statistics$3https://scigraph.springernature.com/ontologies/product-market-codes/L19147 606 $aMath. Appl. in Environmental Science$3https://scigraph.springernature.com/ontologies/product-market-codes/U24005 615 0$aStatistics . 615 0$aEcology . 615 0$aEnvironmental sciences. 615 0$aR (Computer program language). 615 14$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aTheoretical Ecology/Statistics. 615 24$aMath. Appl. in Environmental Science. 676 $a574.50151 700 $aBorcard$b Daniel$4aut$4http://id.loc.gov/vocabulary/relators/aut$0767866 702 $aGillet$b François$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aLegendre$b Pierre$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910300104803321 996 $aNumerical Ecology with R$92031359 997 $aUNINA