Call number:
AWI S2-23-95184
Description / Table of Contents:
A comprehensive and practical guide to analysing ecological data based on courses given to researchers, environmental consultants and post graduate students.
Provides comprehensive introductory chapters together with 17 detailed case study chapters written jointly with former course attendants.
Each case study explores the statistical options most appropriate to the ecological questions being asked and will help the reader choose the best approach to analysing their own data.
A non-mathematical, but modern approach (GLM, GAM, mixed models, tree models, neural networks) is used throughout the book, making it ideally suited to practicing ecologists and environmental scientists as well as professional statisticians.
All data sets from the case studies are available for downloading from www.highstat.com
Type of Medium:
Monograph available for loan
Pages:
XXVI, 672 Seiten
,
Illustrationen
,
24 cm
ISBN:
0387459677 (hbk)
,
9780387459677 (hbk)
,
978-0-387-45967-7
,
0387459723 (electronic)
,
9780387459723 (electronic)
Series Statement:
Statistics for biology and health
Language:
English
Note:
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 CCA13.4 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
23.8 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
26.7 Discussion
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
Location:
AWI Reading room
Branch Library:
AWI Library
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