LEADER 05122nam 22005653u 450 001 9910779568203321 005 20210114035523.0 010 $a1-118-56252-6 010 $a1-299-46461-0 010 $a1-118-56253-4 035 $a(CKB)2550000001019379 035 $a(EBL)1165084 035 $a(OCoLC)841908107 035 $a(MiAaPQ)EBC1165084 035 $a(PPN)178195472 035 $a(EXLCZ)992550000001019379 100 $a20131230d2013|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 200 10$aData Analysis in Vegetation Ecology$b[electronic resource] 205 $a2nd ed. 210 $aHoboken $cWiley$d2013 215 $a1 online resource (332 p.) 300 $aDescription based upon print version of record. 311 $a1-118-56254-2 311 $a1-118-38403-2 327 $aCover; Title Page; Copyright; Contents; Preface to the second edition; Preface to the first edition; List of figures; List of tables; About the companion website; Chapter 1 Introduction; Chapter 2 Patterns in vegetation ecology; 2.1 Pattern recognition; 2.2 Interpretation of patterns; 2.3 Sampling for pattern recognition; 2.3.1 Getting a sample; 2.3.2 Organizing the data; 2.4 Pattern recognition in R; Chapter 3 Transformation; 3.1 Data types; 3.2 Scalar transformation and the species enigma; 3.3 Vector transformation; 3.4 Example: Transformation of plant cover data 327 $aChapter 4 Multivariate comparison4.1 Resemblance in multivariate space; 4.2 Geometric approach; 4.3 Contingency measures; 4.4 Product moments; 4.5 The resemblance matrix; 4.6 Assessing the quality of classifications; Chapter 5 Classification; 5.1 Group structures; 5.2 Linkage clustering; 5.3 Average linkage clustering; 5.4 Minimum-variance clustering; 5.5 Forming groups; 5.6 Silhouette plot and fuzzy representation; Chapter 6 Ordination; 6.1 Why ordination?; 6.2 Principal component analysis; 6.3 Principal coordinates analysis; 6.4 Correspondence analysis; 6.5 Heuristic ordination 327 $a6.5.1 The horseshoe or arch effect6.5.2 Flexible shortest path adjustment; 6.5.3 Nonmetric multidimensional scaling; 6.5.4 Detrended correspondence analysis; 6.6 How to interpret ordinations; 6.7 Ranking by orthogonal components; 6.7.1 RANK method; 6.7.2 A sampling design based on RANK (example); Chapter 7 Ecological patterns; 7.1 Pattern and ecological response; 7.2 Evaluating groups; 7.2.1 Variance testing; 7.2.2 Variance ranking; 7.2.3 Ranking by indicator values; 7.2.4 Contingency tables; 7.3 Correlating spaces; 7.3.1 The Mantel test; 7.3.2 Correlograms 327 $a7.3.3 More trends: `Schlaenggli' data revisited7.4 Multivariate linear models; 7.4.1 Constrained ordination; 7.4.2 Nonparametric multiple analysis of variance; 7.5 Synoptic vegetation tables; 7.5.1 The aim of ordering tables; 7.5.2 Steps involved in sorting tables; 7.5.3 Example: ordering Ellenberg's data; Chapter 8 Static predictive modelling; 8.1 Predictive or explanatory?; 8.2 Evaluating environmental predictors; 8.3 Generalized linear models; 8.4 Generalized additive models; 8.5 Classification and regression trees; 8.6 Building scenarios; 8.7 Modelling vegetation types 327 $a8.8 Expected wetland vegetation (example)Chapter 9 Vegetation change in time; 9.1 Coping with time; 9.2 Temporal autocorrelation; 9.3 Rate of change and trend; 9.4 Markov models; 9.5 Space-for-time substitution; 9.5.1 Principle and method; 9.5.2 The Swiss National Park succession (example); 9.6 Dynamics in pollen diagrams (example); Chapter 10 Dynamic modelling; 10.1 Simulating time processes; 10.2 Simulating space processes; 10.3 Processes in the Swiss National Park; 10.3.1 The temporal model; 10.3.2 The spatial model; Chapter 11 Large data sets: wetland patterns; 11.1 Large data sets differ 327 $a11.2 Phytosociology revisited 330 $a The first edition of Data Analysis in Vegetation Ecology provided an accessible and thorough resource for evaluating plant ecology data, based on the author's extensive experience of research and analysis in this field. Now, the Second Edition expands on this by not only describing how to analyse data, but also enabling readers to follow the step-by-step case studies themselves using the freely available statistical package R. The addition of R in this new edition has allowed coverage of additional methods for classification and ordination, and also logistic regression, GLM 606 $aPlant communities -- Data processing 606 $aPlant communities -- Mathematical models 606 $aPlant ecology -- Data processing 606 $aPlant ecology -- Mathematical models 615 4$aPlant communities -- Data processing. 615 4$aPlant communities -- Mathematical models. 615 4$aPlant ecology -- Data processing. 615 4$aPlant ecology -- Mathematical models. 676 $a581.70285 700 $aWildi$b Otto$0953188 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910779568203321 996 $aData analysis in vegetation ecology$92154905 997 $aUNINA LEADER 03620nam 2200985 a 450 001 9910788991503321 005 20230617034307.0 010 $a1-282-76289-3 010 $a9786612762895 010 $a0-520-93711-2 024 7 $a10.1525/9780520937116 035 $a(CKB)3390000000006973 035 $a(EBL)837170 035 $a(OCoLC)773564888 035 $a(SSID)ssj0000444782 035 $a(PQKBManifestationID)11293206 035 $a(PQKBTitleCode)TC0000444782 035 $a(PQKBWorkID)10482009 035 $a(PQKB)11326551 035 $a(MiAaPQ)EBC837170 035 $a(DE-B1597)520981 035 $a(OCoLC)1114790967 035 $a(DE-B1597)9780520937116 035 $a(Au-PeEL)EBL837170 035 $a(CaPaEBR)ebr10676266 035 $a(CaONFJC)MIL276289 035 $a(EXLCZ)993390000000006973 100 $a20020925d2003 uy 0 101 0 $aeng 135 $aurnn#---|u||u 181 $ctxt 182 $cc 183 $acr 200 10$aWhy, why not$b[electronic resource] /$fMartha Ronk 210 $aBerkeley $cUniversity of California Press$d2003 215 $a1 online resource (101 p.) 225 1 $aNew California poetry ;$vv. 8 300 $aDescription based upon print version of record. 311 0 $a0-520-23623-8 311 0 $a0-520-23811-7 327 $tFront matter --$tContents --$t1. Perplexities --$t2. Why Knowing Is --$t3. Why/Why Not --$tAcknowledgments 330 $aWhy/Why Not presents a speaker caught in quandaries created by changing perspectives, fervors, and locales. Why do we act one way here and another there; why can't a mind stay made up; why do we hate and love at the same time; why does memory fade or insist; why does the ordinary seem so uncanny? These questions are captured in lines that collide and merge, in irreverent and offhand jibes, and in plaintive repetitions.Why/Why Not moves across a vivid terrain-the stage of Hamlet, Phillip Marlowe's Los Angeles, Prague, paintings and gardens-to push through a tangle of ways to make sense of the world. Martha Ronk's poetic language is that of the everyday slightly skewed, as if pieces of an ordinary sentence were missing. Ronk's poems use the repetitive and the banal to explore ways in which language is intertwined with thought and experience. 410 0$aNew California poetry ;$vv. 8. 606 $aAmerican poetry$y21st century 610 $aamerican poets. 610 $abeauty. 610 $achanging perspectives. 610 $achanging places. 610 $acomplex. 610 $acontemporary poetry. 610 $aexploration of language. 610 $afamous poets. 610 $afemale speaker. 610 $ahamlet. 610 $ahuman experience. 610 $ahuman thought. 610 $aintellectual poetry. 610 $airreverent. 610 $aliterature students. 610 $alos angeles. 610 $amaking sense. 610 $amemory. 610 $anarrative poetry. 610 $aphillip marlowe. 610 $aphilosophy. 610 $apoetic language. 610 $apoetry collection. 610 $apoetry. 610 $aprague. 610 $aquestioning. 610 $arealism. 610 $arepetition. 610 $athought provoking. 610 $atouching. 610 $awomen authors. 615 0$aAmerican poetry 676 $a811/.54 700 $aRonk$b Martha Clare$01564379 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910788991503321 996 $aWhy, why not$93833378 997 $aUNINA