Numerical Ecology, Volume 24 (Developments in Environmental Modelling)

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Numerical Ecology, Volume 24 (Developments in Environmental Modelling)

Numerical Ecology, Volume 24 (Developments in Environmental Modelling)


Numerical Ecology, Volume 24 (Developments in Environmental Modelling)


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Numerical Ecology, Volume 24 (Developments in Environmental Modelling)

The book describes and discusses the numerical methods which are successfully being used for analysing ecological data, using a clear and comprehensive approach. These methods are derived from the fields of mathematical physics, parametric and nonparametric statistics, information theory, numerical taxonomy, archaeology, psychometry, sociometry, econometry and others.An updated, 3rd English edition of the most widely cited book on quantitative analysis of multivariate ecological dataRelates ecological questions to methods of statistical analysis, with a clear description of complex numerical methodsAll methods are illustrated by examples from the ecological literature so that ecologists clearly see how to use the methods and approaches in their own researchAll calculations are available in R language functions

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Product details

Series: Developments in Environmental Modelling (Book 24)

Paperback: 1006 pages

Publisher: Elsevier; 3 edition (August 20, 2012)

Language: English

ISBN-10: 0444538682

ISBN-13: 978-0444538680

Product Dimensions:

7.2 x 1.5 x 9.2 inches

Shipping Weight: 3.8 pounds (View shipping rates and policies)

Average Customer Review:

4.5 out of 5 stars

12 customer reviews

Amazon Best Sellers Rank:

#320,784 in Books (See Top 100 in Books)

This book is an extraordinary reference that covers many important topics in a lucid way. It's mathematically accurate, though not heavily so, but also has a lot of important "lore" that's missing from many books to allow potential users to bring methods in practice. Just to pick two topics, the chapter on ecological resemblance provides many really useful caveats that prospective users need to know and the treatment of redundancy analysis is similarly lucid and much better than any other description I have read. Highly recommended for anyone who makes use of multivariate analysis techniques in ecology, biology, psychology, sociology, data science, etc. The examples are from ecology but none of them appear to be so heavy that if you don't have an ecology background you couldn't figure out what's going on with a bit of time on Wikipedia. The fact that this is a Third Edition is evident: The text is all quite smooth and clean.The only worry I have is that a 1000 page paperback might get beat up and fall apart, but I'll take the risk for this quality of content.

This is a fantastic book. It helped me understand the stats and analyses I needed to complete my PhD.

If you are doing mutivariate and spatial analyses, this is the book to have. The new version has examples in R. Get it and the Numerical Ecology With R book by Borcard et al. (2011) if you use R or Multivariate Analysis of Ecological Data by Leps and Similauer if you use CANOCO as companions.

A must have for advanced ecological research/statistics.

It’s the Bible of commmunity Ecology. Recommended!!

One of the best balances between a text focused to a non statistician group (ecologists and biologists) and a in deep explanations of mathematical and logical principles of statistics. Major focus in multivariate analysis, but with several important concepts applied to univariate problems in ecology.

It is a great bookI would like it contained complete examples in R, as well as r code in the book. But the theory and the explanations to the use of different methods are great and clear.

I would just like to first state that the material covered in the book is fantastic. The reason I am rating it so low is because after a few months the cover is pealing off, there are pages falling out, and some pages were cut folded over. Make sure you check the structure of this book well on delivery.

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Numerical Ecology, Volume 24 (Developments in Environmental Modelling)


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