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Numerical Ecology with R provides a long-awaited bridge between a textbook in Numerical Ecology and the implementation of this discipline in the R language. After short theoretical overviews, the authors accompany the users through the exploration of the methods by means of applied and extensively commented examples. Users are invited to use this book as a teaching companion at the computer. The travel starts with exploratory approaches, proceeds with the construction of association matrices, then addresses three families of methods: clustering, unconstrained and canonical ordination, and spatial analysis. All the necessary data files, the scripts used in the chapters, as well as the extra R functions and packages written by the authors, can be downloaded from a web page accessible through the Springer web site(http://adn.biol.umontreal.ca/~numericalecology/numecolR/).

This book is aimed at professional researchers, practitioners, graduate students and teachers in ecology, environmental science and engineering, and in related fields such as oceanography, molecular ecology, agriculture and soil science, who already have a background in general and multivariate statistics and wish to apply this knowledge to their data using the R language, as well as people willing to accompany their disciplinary learning with practical applications. People from other fields (e.g. geology, geography, paleoecology, phylogenetics, anthropology, the social and education sciences, etc.) may also benefit from the materials presented in this book.

The three authors teach numerical ecology, both theoretical and practical, to a wide array of audiences, in regular courses in their Universities and in short courses given around the world. Daniel Borcard is lecturer of Biostatistics and Ecology and researcher in Numerical Ecology at Université de Montréal, Québec, Canada. François Gillet is professor of Community Ecology and Ecological Modelling at Université de Franche-Comté, Besançon, France. Pierre Legendre is professor of Quantitative Biology and Ecology at Université de Montréal, Fellow of the Royal Society of Canada, and ISI Highly Cited Researcher in Ecology/Environment.

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• Product Details
• Table of Contents
• Reviews

• File Size: 11364 KB
• Print Length: 312 pages
• Publisher: Springer; 2011 edition (January 14, 2011)
• Sold by: Amazon Digital Services, Inc.
• Language: English
• ASIN: B008BBAI48
• Text-to-Speech: Enabled
• X-Ray:
  Not Enabled
• Lending: Not Enabled
• Amazon Best Sellers Rank: #749,920 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
  • #66 in Kindle Store > Kindle eBooks > Nonfiction > Science > Nature & Ecology > Natural Resources > Forests & Forestry
  • #66 in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Professional Science > Agricultural Sciences > Forestry
• #66 in Kindle Store > Kindle eBooks > Nonfiction > Science > Nature & Ecology > Natural Resources > Forests & Forestry
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Download Numerical Ecology with R PDF

This book can be seemed as an extension of Legendre's classic "Numerical Ecology". More emphasis is put on the R part. The book starts with data exploration and clustering, then the middle piece is the heavy subject-ordination. Code samples are complete and if you type the code letter for letter, I am sure you will learn a great deal.

However, I get lost in the last two chapters. Canonical ordination and spatial analysis are perhaps too abstract for me. It is a pity that even Google couldn't help me much on them. So, equip yourself with enough linear algebra and statistics, especially the first one, before you go head on into the last two.

Overall, if anyone needs R for ecology, this book is the one.

By dgg32

This is a very practical book that gives excellent examples of how to use many of the Numerical Ecology techniques. It's really a partner book to Legendre & Legendre (1998). Many of the techniques there have been written into R packages, and this book guides you through how to utilize them. A must for anyone who is going beyond basic stats in to the realm of multivariate or spatial statistics.

By J. Becker