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Analyzing Ecological Data

E-BookPDF1 - PDF WatermarkE-Book
672 Seiten
Englisch
SPRINGER USerschienen am29.08.20072007
This book provides a practical introduction to analyzing ecological data using real data sets. The first part gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling techniques), multivariate analysis, time series analysis, and spatial statistics. The second part provides 17 case studies. The case studies include topics ranging from terrestrial ecology to marine biology and can be used as a template for a reader's own data analysis. Data from all case studies are available from www.highstat.com. Guidance on software is provided in the book.


Grad students, researchersmehr
Verfügbare Formate
BuchGebunden
EUR283,50
BuchKartoniert, Paperback
EUR192,59
E-BookPDF1 - PDF WatermarkE-Book
EUR181,89

Produkt

KlappentextThis book provides a practical introduction to analyzing ecological data using real data sets. The first part gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling techniques), multivariate analysis, time series analysis, and spatial statistics. The second part provides 17 case studies. The case studies include topics ranging from terrestrial ecology to marine biology and can be used as a template for a reader's own data analysis. Data from all case studies are available from www.highstat.com. Guidance on software is provided in the book.


Grad students, researchers
Details
Weitere ISBN/GTIN9780387459721
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2007
Erscheinungsdatum29.08.2007
Auflage2007
ReiheStatistics for Biology and Health
Seiten672 Seiten
SpracheEnglisch
IllustrationenXXVI, 672 p.
Artikel-Nr.1423506
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Preface;7
2;Contents;10
3;Contributors;17
4;1 Introduction;25
4.1;1.1 Part 1: Applied statistical theory;25
4.2;1.2 Part 2: The case studies;27
4.3;1.3 Data, software and flowcharts;30
5;2 Data management and software;31
5.1;2.1 Introduction;31
5.2;2.2 Data management;32
5.3;2.3 Data preparation;33
5.4;2.4 Statistical software;37
6;3 Advice for teachers;41
6.1;3.1 Introduction;41
7;4 Exploration;47
7.1;4.1 The first steps;48
7.2;4.2 Outliers, transformations and standardisations;62
7.3;4.3 A final thought on data exploration;71
8;5 Linear regression;73
8.1;5.1 Bivariate linear regression;73
8.2;5.2 Multiple linear regression;91
8.3;5.3 Partial linear regression;97
9;6 Generalised linear modelling;102
9.1;6.1 Poisson regression;102
9.2;6.2 Logistic regression;111
10;7 Additive and generalised additive modelling;120
10.1;7.1 Introduction;120
10.2;7.2 The additive model;124
10.3;7.3 Example of an additive model;125
10.4;7.4 Estimate the smoother and amount of smoothing;127
10.5;7.5 Additive models with multiple explanatory variables;131
10.6;7.6 Choosing the amount of smoothing;135
10.7;7.7 Model selection and validation;138
10.8;7.8 Generalised additive modelling;143
10.9;7.9 Where to go from here;147
11;8 Introduction to mixed modelling;148
11.1;8.1 Introduction;148
11.2;8.2 The random intercept and slope model;151
11.3;8.3 Model selection and validation;153
11.4;8.4 A bit of theory;158
11.5;8.5 Another mixed modelling example;160
11.6;8.6 Additive mixed modelling;163
12;9 Univariate tree models;166
12.1;9.1 Introduction;166
12.2;9.2 Pruning the tree;172
12.3;9.3 Classification trees;175
12.4;9.4 A detailed example: Ditch data;175
13;10 Measures of association;185
13.1;10.1 Introduction;185
13.2;10.2 Association between sites: Q analysis;186
13.3;10.3 Association among species: R analysis;193
13.4;10.4 Q and R analysis: Concluding remarks;198
13.5;10.5 Hypothesis testing with measures of association;201
14;11 Ordination - First encounter;210
14.1;11.1 Bray- Curtis ordination;210
15;12 Principal component analysis and redundancy analysis;214
15.1;12.1 The underlying principle of PCA;214
15.2;12.2 PCA: Two easy explanations;215
15.3;12.3 PCA: Two technical explanations;217
15.4;12.4 Example of PCA;218
15.5;12.5 The biplot;221
15.6;12.6 General remarks;226
15.7;12.7 Chord and Hellinger transformations;227
15.8;12.8 Explanatory variables;229
15.9;12.9 Redundancy analysis;231
15.10;12.10 Partial RDA and variance partitioning;240
15.11;12.11 PCA regression to deal with collinearity;242
16;13 Correspondence analysis and canonical correspondence analysis;246
16.1;13.1 Gaussian regression and extensions;246
16.2;13.2 Three rationales for correspondence analysis;252
16.3;13.3 From RGR to CCA;259
16.4;13.4 Understanding the CCAtriplot;261
16.5;13.5 When to use PCA, CA, RDA or CCA;263
16.6;13.6 Problems with CA and CCA;264
17;14 Introduction to discriminant analysis;266
17.1;14.1 Introduction;266
17.2;14.2 Assumptions;269
17.3;14.3 Example;271
17.4;14.4 The mathematics;275
17.5;14.5 The numerical output for the sparrow data;276
18;15 Principal coordinate analysis and non-metric multidimensional scaling;280
18.1;15.1 Principal coordinate analysis;280
18.2;15.2 Non-metric multidimensional scaling;282
19;16 Time series analysis - Introduction;286
19.1;16.1 Using what we have already seen before;286
19.2;16.2 Auto-regressive integrated moving average models with exogenous variables;302
20;17 Common trends and sudden changes;310
20.1;17.1 Repeated LOESS smoothing;310
20.2;17.2 Identifying the seasonal component;314
20.3;17.3 Common trends: MAFA;320
20.4;17.4 Common trends: Dynamic factor analysis;324
20.5;17.5 Sudden changes: Chronological clustering;336
21;18 Analysis and modelling of lattice data;342
21.1;18.1 Lattice data;342
21.2;18.2 Numerical representation of the lattice structure;344
21.3;18.3 Spatial correlation;348
21.4;18.4 Modelling lattice data;352
21.5;18.5 More exotic models;355
21.6;18.6 Summary;359
22;19 Spatially continuous data analysis and modelling;361
22.1;19.1 Spatially continuous data;361
22.2;19.2 Geostatistical functions and assumptions;362
22.3;19.3 Exploratory variography analysis;366
22.4;19.4 Geostatistical modelling: Kriging;378
22.5;19.5 A full spatial analysis of the bird radar data;383
23;20 Univariate methods to analyse abundance of decapod larvae;393
23.1;20.1 Introduction;393
23.2;20.2 The data;394
23.3;20.3 Data exploration;397
23.4;20.4 Linear regression results;399
23.5;20.5 Additive modelling results;401
23.6;20.6 How many samples to take?;403
23.7;20.7 Discussion;405
24;21 Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugal;409
24.1;21.1 Introduction;409
24.2;21.2 Data and materials;410
24.3;21.3 Data exploration;412
24.4;21.4 Classification trees;415
24.5;21.5 Generalised additive modelling;417
24.6;21.6 Generalised linear modelling;418
24.7;21.7 Discussion;421
25;22 Crop pollination by honeybees in Argentina using additive mixed modelling;423
25.1;22.1 Introduction;423
25.2;22.2 Experimental setup;424
25.3;22.3 Abstracting the information;424
25.4;22.4 First steps of the analyses: Data exploration;427
25.5;22.5 Additive mixed modelling;428
25.6;22.6 Discussion and conclusions;434
26;23 Investigating the effects of rice farming on aquatic birds with mixed modelling;436
26.1;23.1 Introduction;436
26.2;23.2 The data;438
26.3;23.3 Getting familiar with the data: Exploration;439
26.4;23.4 Building a mixed model;443
26.5;23.5 The optimal model in terms of random components;446
26.6;23.6 Validating the optimal linear mixed model;449
26.7;23.7 More numerical output for the optimal model;450
26.8;23.8 Discussion;452
27;24 Classification trees and radar detection of birds for North Sea wind farms;454
27.1;24.1 Introduction;454
27.2;24.2 From radars to data;455
27.3;24.3 Classification trees;457
27.4;24.4 A tree for the birds;459
27.5;24.5 A tree for birds, clutter and more clutter;464
27.6;24.6 Discussion and conclusions;466
28;25 Fish stock identification through neural network analysis of parasite fauna;468
28.1;25.1 Introduction;468
28.2;25.2 Horse mackerel in the northeast Atlantic;469
28.3;25.3 Neural networks;471
28.4;25.4 Collection of data;474
28.5;25.5 Data exploration;475
28.6;25.6 Neural network results;476
28.7;25.7 Discussion;479
29;26 Monitoring for change: Using generalised least squares, non- metric multidimensional scaling, and the Mantel test on western Montana grasslands;482
29.1;26.1 Introduction;482
29.2;26.2 The data;483
29.3;26.3 Data exploration;486
29.4;26.4 Linear regression results;491
29.5;26.5 Generalised least squares results;495
29.6;26.6 Multivariate analysis results;498
29.7;26.7 Discussion;502
30;27 Univariate and multivariate analysis applied on a Dutch sandy beach community;504
30.1;27.1 Introduction;504
30.2;27.2 The variables;505
30.3;27.3 Analysing the data using univariate methods;506
30.4;27.4 Analysing the data using multivariate methods;513
30.5;27.5 Discussion and conclusions;518
31;28 Multivariate analyses of South-American zoobenthic species - spoilt for choice;521
31.1;28.1 Introduction and the underlying questions;521
31.2;28.2 Study site and sample collection;522
31.3;28.3 Data exploration;524
31.4;28.4 The Mantel test approach;527
31.5;28.5 The transformation plus RDA approach;530
31.6;28.6 Discussion and conclusions;530
32;29 Principal component analysis applied to harbour porpoise fatty acid data;532
32.1;29.1 Introduction;532
32.2;29.2 The data;532
32.3;29.3 Principal component analysis;534
32.4;29.4 Data exploration;535
32.5;29.5 Principal component analysis results;535
32.6;29.6 Simpler alternatives to PCA;541
32.7;29.7 Discussion;543
33;30 Multivariate analyses of morphometric turtle data - size and shape;545
33.1;30.1 Introduction;545
33.2;30.2 The turtle data;546
33.3;30.3 Data exploration;547
33.4;30.4 Overview of classic approaches related to PCA;550
33.5;30.5 Applying PCA to the original turtle data;552
33.6;30.6 Classic morphometric data analysis approaches;553
33.7;30.7 A geometric morphometric approach;558
34;31 Redundancy analysis and additive modelling applied on savanna tree data;563
34.1;31.1 Introduction;563
34.2;31.2 Study area;564
34.3;31.3 Methods;564
34.4;31.4 Results;567
34.5;31.5 Discussion;575
35;32 Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico;577
35.1;32.1 Introduction;577
35.2;32.2 The study area;578
35.3;32.3 The data;579
35.4;32.4 Data exploration;581
35.5;32.5 Canonical correspondence analysis results;584
35.6;32.6 African star grass;587
35.7;32.7 Discussion and conclusion;589
36;33 Estimating common trends in Portuguese fisheries landings;591
36.1;33.1 Introduction;591
36.2;33.2 The time series data;592
36.3;33.3 MAFA and DFA;595
36.4;33.4 MAFA results;596
36.5;33.5 DFA results;598
36.6;33.6 Discussion;603
37;34 Common trends in demersal communities on the Newfoundland- Labrador Shelf;605
37.1;34.1 Introduction;605
37.2;34.2 Data;606
37.3;34.3 Time series analysis;607
37.4;34.4 Discussion;614
38;35 Sea level change and salt marshes in the Wadden Sea: A time series analysis;616
38.1;35.1 Interaction between hydrodynamical and biological factors;616
38.2;35.2 The data;618
38.3;35.3 Data exploration;620
38.4;35.4 Additive mixed modelling;622
38.5;35.5 Additive mixed modelling results;625
38.6;35.6 Discussion;628
39;36 Time series analysis of Hawaiian waterbirds;630
39.1;36.1 Introduction;630
39.2;36.2 Endangered Hawaiian waterbirds;631
39.3;36.3 Data exploration;632
39.4;36.4 Three ways to estimate trends;634
39.5;36.5 Additive mixed modelling;641
39.6;36.6 Sudden breakpoints;645
39.7;36.7 Discussion;646
40;37 Spatial modelling of forest community features in the Volzhsko- Kamsky reserve;647
40.1;37.1 Introduction;647
40.2;37.2 Study area;649
40.3;37.3 Data exploration;650
40.4;37.4 Models of boreality without spatial auto-correlation;652
40.5;37.5 Models of boreality with spatial auto-correlation;654
40.6;37.6 Conclusion;660
41;Index;681mehr

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