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Data analysis in vegetation ecology.
Evolving from years of teaching experience by one of the top experts in vegetation ecology, Data Analysis in Vegetation Ecology, 2nd edition explains the background and basics of mathematical (mainly multivariate) analysis of vegetation data. The new edition now includes practical examples of how to...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Wiley,
2013.
|
| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000Mu 4500 | ||
|---|---|---|---|
| 001 | EBOOKCENTRAL_ocn841908107 | ||
| 003 | OCoLC | ||
| 005 | 20240329122006.0 | ||
| 006 | m o d | ||
| 007 | cr |n|---||||| | ||
| 008 | 130418s2013 nyu o 000 0 eng d | ||
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| 020 | |a 9781118562529 | ||
| 020 | |a 1118562526 | ||
| 029 | 1 | |a DEBBG |b BV044050164 | |
| 029 | 1 | |a DEBSZ |b 397519443 | |
| 029 | 1 | |a DEBSZ |b 431378533 | |
| 035 | |a (OCoLC)841908107 | ||
| 050 | 4 | |a QK911 .W523 2013 | |
| 082 | 0 | 4 | |a 581.70285 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Wildi, Otto. | |
| 245 | 1 | 0 | |a Data analysis in vegetation ecology. |
| 250 | |a 2nd ed. | ||
| 260 | |a New York : |b Wiley, |c 2013. | ||
| 300 | |a 1 online resource (332 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a Evolving from years of teaching experience by one of the top experts in vegetation ecology, Data Analysis in Vegetation Ecology, 2nd edition explains the background and basics of mathematical (mainly multivariate) analysis of vegetation data. The new edition now includes practical examples of how to use the R computer software for analysing vegetation data. | ||
| 505 | 0 | |a Cover; 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. | |
| 505 | 8 | |a Chapter 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. | |
| 505 | 8 | |a 6.5.1 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. | |
| 505 | 8 | |a 7.3.3 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. | |
| 505 | 8 | |a 8.8 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. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Plant communities |x Data processing. | |
| 650 | 0 | |a Plant communities |x Mathematical models. | |
| 650 | 0 | |a Plant ecology |x Data processing. | |
| 650 | 0 | |a Plant ecology |x Mathematical models. | |
| 650 | 6 | |a Écologie végétale |x Informatique. | |
| 650 | 6 | |a Écologie végétale |x Modèles mathématiques. | |
| 650 | 7 | |a Plant communities |x Data processing |2 fast | |
| 650 | 7 | |a Plant communities |x Mathematical models |2 fast | |
| 650 | 7 | |a Plant ecology |x Data processing |2 fast | |
| 650 | 7 | |a Plant ecology |x Mathematical models |2 fast | |
| 776 | 0 | 8 | |i Print version: |z 9781118384039 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1165084 |z Texto completo |
| 994 | |a 92 |b IZTAP | ||