Data Treatment in Environmental Sciences 1st Edition by Valérie David 0081023464 9780081023464

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Product details:

ISBN 10: 0081023464 

ISBN 13: 9780081023464 

Author: Valérie David

Data Treatment in Environmental Sciences presents the various methods used in the analysis of databases—obtained in the field or in a laboratory—by focusing on the most commonly used multivariate analyses in different disciplines of environmental sciences, from geochemistry to ecology. The book examines the principles, application conditions and implementation (in R software) of various analyses before interpreting them. The wide variety of analyses presented allows users to treat datasets, both large and small, which are often limited in terms of available processing techniques.

The approach taken by the author details (i) the preparation of a dataset prior to analysis, in relation to the scientific strategy and objectives of the study, (ii) the preliminary treatment of datasets, (iii) the establishment of a structure of objects (stations/dates) or relevant variables (e.g. physicochemical, biological), and (iv) how to highlight the explanatory parameters of these structures (e.g. how the physico-chemistry influences the biological structure obtained).

• Proposes tools that can be used to deal with environmental data
• Insists on the adequacy between the scientific objectives and the types of analyses
• Present mathematical principles without going into detail
• Offers a wide range of important analyses

Table of contents:

1: Observing and Preparing a Data Set

Abstract

1.1 Creating a database in relation to our predetermined goals

1.2 Observing the database scrupulously

1.3 Reducing the number of environmental variables

1.4 Eliminating rare species

2: Preliminary Treatment of the Data Set

Abstract

2.1 Abundances, species richness and species diversity

2.2 Transformations

2.3 Association measures and matrices

3: Structure as Groups of Objects/Variables

Abstract

3.1 The most used types of cluster analysis

3.2 Information on the descriptors that generate the groups obtained

4: Structure as Gradients of Objects/Variables

Abstract

4.1 Parametric alternative: unconstrained ordination

4.2 Non-parametric alternative: nMDS

5: Understanding a Structure

Abstract

5.1 Correlating a structure with one or more structures without causal hypothesis

5.2 Explaining a structure

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