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Multivariate Analysis of Ecological ...
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Ebooks Corporation.
Multivariate Analysis of Ecological Data using CANOCO.
紀錄類型:
書目-語言資料,印刷品 : 單行本
正題名/作者:
Multivariate Analysis of Ecological Data using CANOCO./
作者:
Leps, Jan.
其他作者:
Milauer, Petr.
出版者:
Cambridge :Cambridge University Press, : 2003.,
面頁冊數:
283 p.
標題:
Ecology. -
電子資源:
Click here to view book
ISBN:
9780511076619 (electronic bk.)
Multivariate Analysis of Ecological Data using CANOCO.
Leps, Jan.
Multivariate Analysis of Ecological Data using CANOCO.
[electronic resource]. - Cambridge :Cambridge University Press,2003. - 283 p.
Cover; Half-title; Title; Copyright; Contents; Preface; 1 Introduction and data manipulation; 2 Experimental design; 3 Basics of gradient analysis; 4 Using the Canoco for Windows 4.5 package; 5 Constrained ordination and permutation tests; 6 Similarity measures; 7 Classification methods; 8 Regression methods; 9 Advanced use of ordination; 10 Visualizing multivariate data; 11 Case study 1: Variation in forest bird assemblages; 12 Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows
This book is primarily written for students and researchers dealing with complex ecological problems. Multivariate statistical methods are described, and advice is given on how best to apply these methods using Canoco software. Data sets and program files for the case studies are provided on a supporting website.
Electronic reproduction.
Available via World Wide Web.
Mode of access: World Wide Web.
ISBN: 9780511076619 (electronic bk.)Subjects--Topical Terms:
493801
Ecology.
Index Terms--Genre/Form:
498447
Electronic books.
LC Class. No.: QH541.15.S72 L47 2003eb
Dewey Class. No.: 577.01519535
Multivariate Analysis of Ecological Data using CANOCO.
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Cover; Half-title; Title; Copyright; Contents; Preface; 1 Introduction and data manipulation; 2 Experimental design; 3 Basics of gradient analysis; 4 Using the Canoco for Windows 4.5 package; 5 Constrained ordination and permutation tests; 6 Similarity measures; 7 Classification methods; 8 Regression methods; 9 Advanced use of ordination; 10 Visualizing multivariate data; 11 Case study 1: Variation in forest bird assemblages; 12 Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows
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13 Case study 3: Separating the effects of explanatory variables14 Case study 4: Evaluation of experiments in randomized complete blocks; 15 Case study 5: Analysis of repeated observations of species composition from a factorial experiment; 16 Case study 6: Hierarchical analysis of crayfish community variation; 17 Case study 7: Differentiating two species and their hybrids with discriminant analysis; Appendix A: Sample datasets and projects; Appendix B: Vocabula
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