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 nonmathematical 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 BrayCurtis 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
nonmetric multidimensional scaling 
15.1 Principal coordinate analysis ,15.2 Nonmetric multidimensional scaling
16 Time series analysis
Introduction,
16.1 Using what we have already seen before ,
16.2 Autoregressive 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, nonmetric
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 SouthAmerican 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 NewfoundlandLabrador
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 VolzhskoKamsky
reserve
37.1 Introduction, 37.2 Study area ,
37.3 Data exploration,
37.4 Models of boreality without spatial autocorrelation ,
37.5 Models of boreality with spatial autocorrelation ,
37.6 Conclusion ,
References
Index
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