Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > Ecological science, the Biosphere
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Community Ecology - Analytical Methods Using R and Excel (Paperback, New)
Loot Price: R1,174
Discovery Miles 11 740
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Community Ecology - Analytical Methods Using R and Excel (Paperback, New)
Series: Data in the Wild
Expected to ship within 12 - 17 working days
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Interactions between species are of fundamental importance to all
living systems and the framework we have for studying these
interactions is community ecology. This is important to our
understanding of the planets biological diversity and how species
interactions relate to the functioning of ecosystems at all scales.
Species do not live in isolation and the study of community ecology
is of practical application in a wide range of conservation issues.
The study of ecological community data involves many methods of
analysis. In this book you will learn many of the mainstays of
community analysis including: diversity, similarity and cluster
analysis, ordination and multivariate analyses. This book is for
undergraduate and postgraduate students and researchers seeking a
step-by-step methodology for analysing plant and animal communities
using R and Excel. Microsoft's Excel spreadsheet is virtually
ubiquitous and familiar to most computer users. It is a robust
program that makes an excellent storage and manipulation system for
many kinds of data, including community data. The R program is a
powerful and flexible analytical system able to conduct a huge
variety of analytical methods, which means that the user only has
to learn one program to address many research questions. Its other
advantage is that it is open source and therefore completely free.
Novel analytical methods are being added constantly to the already
comprehensive suite of tools available in R. Mark Gardener is both
an ecologist and an analyst. He has worked in a range of ecosystems
around the world and has been involved in research across a
spectrum of community types. His knowledge of R is largely
self-taught and this gives him insight into the needs of students
learning to use R for complicated analyses.
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