國立虎尾科技大學 |

Multivariate analysis of ecological data using CANOCO /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multivariate analysis of ecological data using CANOCO // Jan Leps̆ & Petr S̆milauer.
Author:	Lepš, Jan,
other author:	Šmilauer, Petr,
Description:	1 online resource (xi, 269 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Subject:	Ecology - Statistical methods. -
Online resource:	https://doi.org/10.1017/CBO9780511615146
ISBN:	9780511615146 (ebook)

Multivariate analysis of ecological data using CANOCO /
Lepš, Jan,1953-

Multivariate analysis of ecological data using CANOCO /Jan Leps̆ & Petr S̆milauer. - 1 online resource (xi, 269 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

This book is primarily written for ecologists needing to analyse data resulting from field observations and experiments. It will be particularly useful for students and researchers dealing with complex ecological problems, such as the variation of biotic communities with environmental conditions or the response of biotic communities to experimental manipulation. Following a simple introduction to ordination methods, the text focuses on constrained ordination methods (RDA, CCA) and the use of permutation tests on statistical hypotheses of multivariate data. An overview of classification methods, or modern regression methods (GLM, GAM, loess), is provided and guidance on the correct interpretation of ordination diagrams is given. Seven case studies of varying difficulty help to illustrate the suggested analytical methods, using the Canoco for Windows software. The case studies utilise both the descriptive and manipulative approaches, and they are supported by data sets and project files available from the book website.

ISBN: 9780511615146 (ebook)Subjects--Topical Terms:

897018
Ecology
--Statistical methods.

LC Class. No.: QH541.15.S72 / L47 2003

Dewey Class. No.: 577/.07/27

Multivariate analysis of ecological data using CANOCO /
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