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Analysing Ecological Data

Zuur, Alain F., Ieno, Elena N., Smith, Graham M. 
Springer  2007  



Hardcover  672 pp  ISBN 9780387459677      £66.00
This book provides a practical introduction to analysing ecological data using real data sets collected as part of postgraduate ecological studies or research projects.

The first part of the book gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modelling techniques), multivariate analysis, time series analysis (e.g. common trends) and spatial statistics. The second part provides 17 case studies, mainly written together with biologists who attended courses given by the first authors. The case studies include topics ranging from terrestrial ecology to marine biology. The case studies can be used as a template for your own data analysis; just try to find a case study that matches your own ecological questions and data structure, and use this as starting point for you own analysis. Data from all case studies are available from www.highstat.com. Guidance on software is provided in Chapter 2.

Written for: Graduate students, researchers

Contents

Contributors
1 Introduction
1.1 Part 1: Applied statistical theory, 1.2 Part 2: The case studies, 1.3 Data, software and flowcharts

2 Data management and software
2.1 Introduction, 2.2 Data management,2.3 Data preparation, 2.4 Statistical software

3 Advice for teachers,
3.1 Introduction,

4 Exploration
4.1 The first steps, 4.2 Outliers, transformations and standardisations 4.3 A final thought on data exploration

5 Linear regression
5.1 Bivariate linear regression , 5.2 Multiple linear regression , 5.3 Partial linear regression

6 Generalised linear modelling
6.1 Poisson regression, 6.2 Logistic regression

7 Additive and generalised additive modelling
7.1 Introduction, 7.2 The additive model, 7.3 Example of an additive model, 7.4 Estimate the smoother and amount of smoothing, 7.5 Additive models with multiple explanatory variables, 7.6 Choosing the amount of smoothing, 7.7 Model selection and validation , 7.8 Generalised additive modelling , 7.9 Where to go from here

8 Introduction to mixed modelling
8.1 Introduction, 8.2 The random intercept and slope model, 8.3 Model selection and validation , 8.4 A bit of theory, 8.5 Another mixed modelling example, 8.6 Additive mixed modelling

9 Univariate tree models
9.1 Introduction, 9.2 Pruning the tree, 9.3 Classification trees, 9.4 A detailed example: Ditch data

10 Measures of association
10.1 Introduction, 10.2 Association between sites: Q analysis , 10.3 Association among species: R analysis, 10.4 Q and R analysis: concluding remarks, 10.5 Hypothesis testing with measures of association

11 Ordination - First encounter
11.1 Bray-Curtis ordination

12 Principal component analysis and redundancy analysis
12.1 The underlying principle of PCA,
12.2 PCA: Two easy explanations 12.3 PCA: Two technical explanations, 12.4 Example of PCA, 12.5 The biplot, 12.6 General remarks, 12.7 Chord and Hellinger transformations, 12.8 Explanatory variables , 12.9 Redundancy analysis , 12.10 Partial RDA and variance partitioning, 12.11 PCA regression to deal with collinearity

13 Correspondence analysis and canonical correspondence analysis
13.1 Gaussian regression and extensions, 13.2 Three rationales for correspondence analysis , 13.3 From RGR to CCA , 13.4 Understanding the CCA triplot , 13.5 When to use PCA, CA, RDA or CCA , 13.6 Problems with CA and CCA

14 Introduction to discriminant analysis
14.1 Introduction, 14.2 Assumptions , 14.3 Example , 14.4 The mathematics , 14.5 The numerical output for the sparrow data

15 Principal coordinate analysis and non-metric multidimensional scaling
- 15.1 Principal coordinate analysis ,15.2 Non-metric multidimensional scaling

16 Time series analysis
Introduction, 16.1 Using what we have already seen before , 16.2 Auto-regressive integrated moving average models with exogenous Variables

17 Common trends and sudden changes
17.1 Repeated LOESS smoothing, 17.2 Identifying the seasonal component, 17.3 Common trends: MAFA, 17.4 Common trends: Dynamic factor analysis , 17.5 Sudden changes: Chronological clustering

18 Analysis and modelling of lattice data
18.1 Lattice data, 18.2 Numerical representation of the lattice structure , 18.3 Spatial correlation , 18.4 Modelling lattice data , 18.5 More exotic models , 18.6 Summary,

19 Spatially continuous data analysis and modelling
19.1 Spatially continuous data , 19.2 Geostatistical functions and assumptions , 19.3 Exploratory variography analysis , 19.4 Geostatistical modelling: Kriging , 19.5 A full spatial analysis of the bird radar data

20 Univariate methods to analyse abundance of decapod larvae
20.1 Introduction, 20.2 The data , 20.3 Data exploration, 20.4 Linear regression results , 20.5 Additive modelling results,20.6 How many samples to take? , 20.7 Discussion

21 Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugal
21.1 Introduction , 21.2 Data and materials , 21.3 Data exploration , 21.4 Classification trees, 21.5 Generalised additive modelling , 21.6 Generalised linear modelling , 21.7 Discussion

22 Crop pollination by honeybees in Argentina using additive mixed modelling
22.1 Introduction , 22.2 Experimental setup , 22.3 Abstracting the information , 22.4 First steps of the analyses: Data exploration, 22.5 Additive mixed modelling , 22.6 Discussion and conclusions

23 Investigating the effects of rice farming on aquatic birds with mixed Modelling,
23.1 Introduction , 23.2 The data , 23.3 Getting familiar with the data: Exploration , 23.4 Building a mixed model, 23.5 The optimal model in terms of random components , 23.6 Validating the optimal linear mixed model , 23.7 More numerical output for the optimal model, Discussion

24 Classification trees and radar detection of birds for North Sea wind Farms
24.1 Introduction , 24.2 From radars to data ,24.3 Classification trees, 24.4 A tree for the birds, 24.5 A tree for birds, clutter and more clutter, 24.6 Discussion and conclusions

25 Fish stock identification through neural network analysis of parasite Fauna,
25.1 Introduction , 25.2 Horse mackerel in the northeast Atlantic, 25.3 Neural networks, 25.4 Collection of data, 25.5 Data exploration, 25.6 Neural network results , 25.7 Discussion

26 Monitoring for change: Using generalised least squares, non-metric multidimensional scaling, and the Mantel test on western Montana grasslands
26.1 Introduction, 26.2 The data , 26.3 Data exploration, 26.4 Linear regression results , 26.5 Generalised least squares results, 26.6 Multivariate analysis results ,

27 Univariate and multivariate analysis applied on a Dutch sandy beach Community
27.1 Introduction, 27.2 The variables, 27.3 Analysing the data using univariate methods, 27.4 Analysing the data using multivariate methods , 27.5 Discussion and conclusions

28 Multivariate analyses of South-American zoobenthic species - spoilt for choice
28.1 Introduction and the underlying questions, 28.2 Study site and sample collection, 28.3 Data exploration, 28.4 The Mantel test approach, 28.5 The transformation plus RDA approach , 28.6 Discussion and conclusions

29 Principal component analysis applied to harbour porpoise fatty acid data
29.1 Introduction, 29.2 The data , 29.3 Principal component analysis , 29.4 Data exploration, 29.5 Principal component analysis results , 29.6 Simpler alternatives to PCA, 29.7 Discussion

30 Multivariate analyses of morphometric turtle data - size and shape
30.1 Introduction, 30.2 The turtle data , 30.3 Data exploration , 30.4 Overview of classic approaches related to PCA, 30.5 Applying PCA to the original turtle data , 30.6 Classic morphometric data analysis approaches, 30.7 A geometric morphometric approach

31 Redundancy analysis and additive modelling applied on savanna tree data
31.1 Introduction , 31.2 Study area , 31.3 Methods , 31.4 Results , 31.5 Discussion

32 Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico
32.1 Introduction , 32.2 The study area, 32.3 The data , 32.4 Data exploration , 32.5 Canonical correspondence analysis results , 32.6 African star grass , 32.7 Discussion and conclusion

33 Estimating common trends in Portuguese fisheries landings
33.1 Introduction , 33.2 The time series data , 33.3 MAFA and DFA, 33.4 MAFA results , 33.5 DFA results, 33.6 Discussion

34 Common trends in demersal communities on the Newfoundland-Labrador Shelf
34.1 Introduction , 34.2 Data, 34.3 Time series analysis, 34.4 Discussion

35 Sea level change and salt marshes in the Wadden Sea: A time series analysis
35.1 Interaction between hydrodynamical and biological factors ,35.2 The data , 35.3 Data exploration , 35.4 Additive mixed modelling , 35.5 Additive mixed modelling results, 35.6 Discussion

36 Time series analysis of Hawaiian waterbirds
36.1 Introduction. 36.2 Endangered Hawaiian waterbirds . 36.3 Data exploration. 36.4 Three ways to estimate trends. 36.5 Additive mixed modelling . 36.6 Sudden breakpoints, 36.7 Discussion

37 Spatial modelling of forest community features in the Volzhsko-Kamsky reserve
37.1 Introduction, 37.2 Study area , 37.3 Data exploration, 37.4 Models of boreality without spatial auto-correlation , 37.5 Models of boreality with spatial auto-correlation , 37.6 Conclusion ,
References

Index

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