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Analyzing Ecological Data

:	Alain Zuur, Elena N. Ieno, Graham M. Smith
:	Analyzing Ecological Data
:	Springer-Verlag
:	9780387459721
:	1
:	CHF 153.50
:

:	Ökologie
:	English

:	672
:	Wasserzeichen
:	PC/MAC/eReader/Tablet
:	PDF

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, researchers

	Preface	7
	Contents	10
	Contributors	17
	1 Introduction	25
	1.1 Part 1: Applied statistical theory	25
	1.2 Part 2: The case studies	27
	1.3 Data, software and flowcharts	30
	2 Data management and software	31
	2.1 Introduction	31
	2.2 Data management	32
	2.3 Data preparation	33
	2.4 Statistical software	37
	3 Advice for teachers	41
	3.1 Introduction	41
	4 Exploration	47
	4.1 The first steps	48
	4.2 Outliers, transformations and standardisations	62
	4.3 A final thought on data exploration	71
	5 Linear regression	73
	5.1 Bivariate linear regression	73
	5.2 Multiple linear regression	91
	5.3 Partial linear regression	97
	6 Generalised linear modelling	102
	6.1 Poisson regression	102
	6.2 Logistic regression	111
	7 Additive and generalised additive modelling	120
	7.1 Introduction	120
	7.2 The additive model	124
	7.3 Example of an additive model	125
	7.4 Estimate the smoother and amount of smoothing	127
	7.5 Additive models with multiple explanatory variables	131
	7.6 Choosing the amount of smoothing	135
	7.7 Model selection and validation	138
	7.8 Generalised additive modelling	143
	7.9 Where to go from here	147
	8 Introduction to mixed modelling	148
	8.1 Introduction	148
	8.2 The random intercept and slope model	151
	8.3 Model selection and validation	153
	8.4 A bit of theory	158
	8.5 Another mixed modelling example	160
	8.6 Additive mixed modelling	163
	9 Univariate tree models	166
	9.1 Introduction	166
	9.2 Pruning the tree	172
	9.3 Classification trees	175
	9.4 A detailed example: Ditch data	175
	10 Measures of association	185
	10.1 Introduction	185
	10.2 Association between sites: Q analysis	186
	10.3 Association among species: R analysis	193
	10.4 Q and R analysis: Concluding remarks	198
	10.5 Hypothesis testing with measures of association	201
	11 Ordination — First encounter	210
	11.1 Bray- Curtis ordination	210
	12 Principal component analysis and redundancy analysis	214
	12.1 The underlying principle of PCA	214
	12.2 PCA: Two easy explanations	215
	12.3 PCA: Two technical explanations	217
	12.4 Example of PCA	218
	12.5 The biplot	221
	12.6 General remarks	226
	12.7 Chord and Hellinger transformations	227
	12.8 Explanatory variables	229
	12.9 Redundancy analysis	231
	12.10 Partial RDA and variance partitioning	240
	12.11 PCA regression to deal with collinearity	242
	13 Correspondence analysis and canonical correspondence analysis	246
	13.1 Gaussian regression and extensions	246
	13.2 Three rationales for correspondence analysis	252
	13.3 From RGR to CCA	259
	13.4 Understanding the CCAtriplot	261
	13.5 When to use PCA, CA, RDA or CCA	263
	13.6 Problems with CA and CCA	264
	14 Introduction to discriminant analysis	266
	14.1 Introduction	266
	14.2 Assumptions	269
	14.3 Example	271
	14.4 The mathematics	275
	14.5 The numerical output for the sparrow data	276
	15 Principal coordinate analysis and non-metric multidimensional scaling	280
	15.1 Principal coordinate analysis	280
	15.2 Non-metric multidimensional scaling	282
	16 Time series analysis — Introduction	286
	16.1 Using what we have already seen before	286
	16.2 Auto-regressive integrated moving average models with exogenous variables	302
	17 Common trends and sudden changes	310
	17.1 Repeated LOESS smoothing	310
	17.2 Identifying the seasonal component	314
	17.3 Common trends: MAFA	320
	17.4 Common trends: Dynamic factor analysis	324
	17.5 Sudden changes: Chronological clustering	336
	18 Analysis and modelling of lattice data	342
	18.1 Lattice data	342
	18.2 Numerical representation of the lattice structure	344
	18.3 Spatial correlation	348
	18.4 Modelling lattice data	352
	18.5 More exotic models	355
	18.6 Summary	359
	19 Spatially continuous data analysis and modelling	361
	19.1 Spatially continuous data	361
	19.2 Geostatistical functions and assumptions	362
	19.3 Explora