Data Analysis in Vegetation Ecology 1st Edition by Otto Wildi ISBN 047066102X 9780470661024

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ISBN 10: 047066102X 
ISBN 13: 9780470661024
Author: Otto Wildi

Evolving from years of teaching experience by one of the top experts in vegetation ecology, Data Analysis in Vegetation Ecology aims to explain the background and basics of mathematical (mainly multivariate) analysis of vegetation data.

The book lays out the basic operations involved in the analysis, the underlying hypotheses, aims and points of views. It conveys the message that each step in the calculations has a specific, straightforward meaning and that patterns and processes known by ecologists often find their counterpart in mathematical operations and functions. The first chapters introduce the elementary concepts and operations and relate them to real-world phenomena and problems. Later chapters concentrate on combinations of methods to reveal surprising features in data sets. Showing how to find patterns in time series, how to generate simple dynamic models, how to reveal spatial patterns and related occurrence probability maps.

Data Analysis in Vegetation Ecology 1st Table of contents:

Part I: Setting the Scene

• 1. What is Numerical Ecology?
  • Introduction to quantitative methods in ecology.
  • Role of computers and software (especially R).
  • Types of ecological data.
• 2. A Short Introduction to the R Environment
  • Getting started with R (installation, basic commands).
  • Data structures in R (vectors, matrices, data frames, lists).
  • Importing and exporting data.
  • Basic plotting in R.
• 3. The Structure of Ecological Data
  • Variables and observations.
  • Data matrices (species by sites, environmental variables by sites).
  • Data quality and errors.

Part II: Univariate and Bivariate Statistics in Ecology

• 4. Basic Statistics
  • Measures of central tendency (mean, median, mode).
  • Measures of dispersion (variance, standard deviation).
  • Histograms and boxplots.
• 5. Introduction to Hypotheses Testing
  • Null and alternative hypotheses.
  • P-values and significance.
  • Type I and Type II errors.
• 6. Comparison of Two Samples
  • t-tests (independent and paired).
  • Non-parametric alternatives (Mann-Whitney U, Wilcoxon signed-rank).
• 7. Comparison of Many Samples: ANOVA
  • One-way ANOVA.
  • Post-hoc tests.
  • Non-parametric alternatives (Kruskal-Wallis).
• 8. Regression and Correlation
  • Linear regression (simple and multiple).
  • Assumptions of regression.
  • Correlation coefficients (Pearson, Spearman).

Part III: Multivariate Analysis in Ecology

• 9. Data Transformations and Standardization
  • Why transform data (e.g., log, square root).
  • Standardization methods (e.g., z-scores, relativization).
• 10. Measures of Similarity and Dissimilarity
  • Euclidean distance, Manhattan distance.
  • Bray-Curtis, Jaccard, Sørensen indices for ecological data.
• 11. Introduction to Ordination
  • Purpose of ordination (reducing dimensionality, visualizing patterns).
  • Distinction between unconstrained and constrained ordination.
• 12. Unconstrained Ordination: Principal Components Analysis (PCA)
  • Theory and application of PCA.
  • Interpreting PCA biplots.
• 13. Unconstrained Ordination: Correspondence Analysis (CA)
  • Theory and application of CA for species composition data.
  • Interpreting CA biplots.
• 14. Unconstrained Ordination: Non-metric Multidimensional Scaling (NMDS)
  • Theory and application of NMDS.
  • Stress values and interpretation.
• 15. Constrained Ordination: Redundancy Analysis (RDA)
  • Theory and application of RDA.
  • Relationship between species composition and environmental variables.
• 16. Constrained Ordination: Canonical Correspondence Analysis (CCA)
  • Theory and application of CCA.
  • Interpreting CCA triplots.
• 17. Cluster Analysis
  • Hierarchical clustering (agglomerative methods: single, complete, average linkage).
  • Dendrograms and their interpretation.
  • Determining the number of clusters.

Part IV: Advanced Topics and Applications

• 18. Discriminant Analysis
  • Classifying objects into predefined groups.
• 19. Classification and Regression Trees (CART)
  • Decision tree models for ecological data.
• 20. Generalized Linear Models (GLMs)
  • Extending linear models to non-normal distributions (e.g., Poisson, binomial).
• 21. Community Trajectory Analysis
  • Analyzing changes in ecological communities over time or space.
• 22. Spatial Analysis in Ecology
  • Introduction to spatial data.
  • Spatial autocorrelation.
• 23. Time Series Analysis in Ecology
  • Analyzing ecological data over time.
• 24. Model Selection and Evaluation
  • AIC, BIC, cross-validation.

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Tags: Otto Wildi, Data Analysis, Vegetation Ecology