Ecological statistics: contemporary theory and application
Gespeichert in:
| Weitere Verfasser: | , , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford
Oxford University Press
[2015]
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xv, 389 Seiten Illustrationen, Diagramme |
| ISBN: | 9780199672554 9780199672547 |
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Datensatz im Suchindex
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| adam_text | Ecological Statistics:
Contemporary Theory
and Application
Edited By
GORDON A FOX
University of South Florida
SIMONETA NEGRETE-YANKELEVICH
Institute de Ecologia A C
VINICIO J SOSA
Instituto de Ecologia A C
OXFORD
UNIVERSITY PRESS
Ecological Statistics: Contemporary Theory and Application First Edition Edited by Gordon A Fox,
Simoneta Negrete-Yankelevich, and Vinicio J Sosa © Oxford University Press 2015
Published in 2015 by Oxford University Press
Contents
List of contributors xiii
Introduction Vinicio J Sosa, Simoneta Negrete-Yankelevich, and Gordon A Fox 1
Why another book on statistics for ecologists? 1
Relating ecological questions to statistics 5
A conceptual foundation: the statistical linear model 7
What we need readers to know 12
How to get the most out of this book 13
1 Approaches to statistical inference Michael A McCarthy 15
1 1 Introduction to statistical inference 15
12A short overview of some probability and sampling theory 16
1 3 Approaches to statistical inference 19
131 Sample statistics and confidence intervals 20
132 Null hypothesis significance testing 21
133 Likelihood 27
134 Informatii m-theoretic methods 30
135 Bayesian methods 33
136 Non-parametric methods 39
1 4 Appropriate use of statistical methods 39
2 Having the right stuff: the effects of data constraints
on ecological data analysis Earl D McCoy 44
2 1 Introduction to data constraints 44
2 2 Ecological data constraints 45
221 Values and biases 45
222 Biased behaviors in ecological research 47
2 3 Potential effects of ecological data constraints 48
231 Methodological underdetermination and cognitive biases 48
232 Cognitive biases in ecological research? 49
2 4 Ecological complexity, data constraints, flawed conclusions 50
241 Patterns and processes at different scales 51
242 Discrete and continuous patterns and processes 52
243 Patterns and processes at different hierarchical levels 54
2 5 Conclusions and suggestions 56
3 Likelihood and model selection Shane A Richards 58
3 1 Introduction to likelihood and model selection 58
3 2 Likelihood functions 59
viii CONTENTS
321 Incorporating mechanism into models 61
322 Random effects 63
3 3 Multiple hypotheses 65
331 Approaches to model selection 67
332 Null hypothesis testing 68
333 An information-theoretic approach 70
334 Using A1C to select models 73
335 Extending the AIC approach 74
336A worked example 76
3 4 Discussion 78
4 Missing data: mechanisms, methods, and messages
Shinichi Nakagawa 81
4 1 Introduction to dealing with missing data 81
4 2 Mechanisms of missing data 83
421 Missing data theory, mechanisms, and patterns 83
422 Informal definitions of missing data mechanisms 83
423 Formal definitions of missing data mechanisms 84
424 Consequences of missing data mechanisms: an example 86
4 3 Diagnostics and prevention 88
431 Diagnosing missing data mechanisms 88
432 How to prevent MNAR missingness 90
4 4 Methods for missing data 92
441 Data deletion, imputation, and augmentation 92
442 Data deletion 92
443 Single imputation 92
444 Multiple imputation techniques 94
445 Multiple imputation steps 95
446 Multiple imputation with multilevel data 98
447 Data augmentation 101
448 Non-ignorable missing data and sensitivity analysis 101
4 5 Discussion 102
451 Practical issues 102
452 Reporting guidelines 103
453 Missing data in other contexts 104
454 Final messages 105
5 What you don t know can hurt you: censored and truncated
data in ecological research Gordon A Fox 106
5 1 Censored data 106
511 Basic concepts 106
512 Some common methods you should not use 107
513 Types of censored data 109
514 Censoring in study designs 111
515 Format of data 113
516 Estimating means with censored data 113
517 Regression for censored data 116
CONTENTS ix
5 2 Truncated data 124
521 Introduction to truncated data 124
522 Sweeping the issue under the rug 125
523 Estimation 125
524 Regression for truncated data 127
5 3 Discussion 129
6 Generalized linear models Yvonne M Buckley 131
6 1 Introduction to generalized linear models 131
6 2 Structure of a GLM 135
621 The linear predict! r 135
622 The error structure 136
623 The link function 136
6 3 Which error distribution and link function are suitable for my data? 137
631 Binomial distribution 138
632 Poisson distribution 141
633 Overdispersion 143
6 4 Model fit and inference 145
6 5 Computational methods and convergence 146
6 6 Discussion 147
7 A statistical symphony: instrumental variables reveal
causality and control measurement error Bruce E Kendall 149
7 1 Introduction to instrumental variables 149
7 2 Endogeneity and its consequences 151
721 Sources of endogeneity 152
722 Effects of endogeneity propagate to other variables 154
7 3 The solution: instrumental variable regression 154
731 Simultaneous equation models 158
7 4 Life-history trade-offs in Florida scrub-jays 158
7 5 Other issues with instrumental variable regression 161
7 6 Deciding to use instrumental variable regression 163
7 7 Choosing instrumental variables 165
7 8 Conclusion 167
8 Structural equation modeling: building and evaluating
causal models James B Grace, Samuel M Scheiner,
and Donald R Schoolmaster, Jr 168
8 1 Introduction to causal hypotheses 168
811 The need for SEM 168
812 An ecological example 169
813A structural equation modeling perspective 171
8 2 Background to structural equation modeling 173
821 Causal modeling and causal hypotheses 173
x CONTENTS
822 Mediators, indirect effects, and conditional independence 174
823A key causal assumption: lack of confounding 175
824 Statistical specifications 175
825 Estimation options: global and local approaches 176
826 Model evaluation, comparison, and selection 178
8 3 Illustration of structural equation modeling 179
831 Overview of the modeling process 179
832 Conceptual models and causal diagrams 180
833 Classic global-estimation modeling 181
834A graph-theoretic approach using local-estimation methods 186
835 Making informed choices about: model form and estimation method 190
836 Computing queries and making interpretations 193
837 Reporting results 196
8 4 Discussion 197
9 Research synthesis methods in ecology Jessica Gurevitch
and Shinichi Nakagawa 200
9 1 Introduction to research synthesis 200
911 Generalizing from results 200
912 What is research synthesis? 201
913 What have ecologists investigated using research syntheses? 201
914 Introduction to worked examples 202
9 2 Systematic reviews: making reviewing a scientific process 203
921 Defining a research quest ion 204
922 Identifying and selecting papers 204
9 3 Initial steps for meta-analysis in ecology 204
931 What not to do 205
932 Data: What do you need, and how do you get it? 205
933 Software for meta-analysis 207
934 Exploratory data analysis 207
9 4 Conceptual and computational tools for meta-analysis 210
941 Effect size metrics 210
942 Fixed, random and mixed models 210
943 Heterogeneity 211
944 Meta-regression 213
945 Statistical inference 213
9 5 Applying our tools: statistical analysis of data 214
951 Plant responses to elevated CO2 214
952 Plant growth responses ti 1 ectomycorrhizal (ECM) interactions 220
953 Is there publication bias, and how much does it affect the results? 221
954 Other sensitivity analyses 222
955 Reporting results of a meta-analysis 223
9 6 Discussion 224
961 Objections to meta-analysis 224
962 Limitations to current practice in ecological meta-analysis 226
963 More advanced issues and approaches 226
CONTENTS xi
10 Spatial variation and linear modeling of ecological data
Simoneta Negrete-Yankelevich and Gordon A Fox 228
10 1 Introduction to spatial variation in ecological data 228
10 2 Background 232
10 2 1 Spatially explicit data 232
10 2 2 Spatial structure 232
10 2 3 Scales of ecological processes and scales of studies 236
10 3 Case study: spatial structure of soil properties in a milpa plot 237
10 4 Spatial exploratory data analysis 238
10 5 Measures and models of spatial autocorrelation 239
10 5 1 Moran s I and correlograms 240
10 5 2 Semi-variance and the variogram 242
10 6 Adding spatial structures to linear models 246
10 6 1 Generalized least squares models 247
10 6 2 Spatial autoregressive models 250
10 7 Discussion 259
11 Statistical approaches to the problem of phylogenetically
correlated data Marc J Lajeunesse and Gordon A Fox 261
11 1 Introduction to phylogenetically correlated data 261
11 2 Statistical assumptions and the comparative phylogenetic method 262
11 2 1 The assumptions of conventional linear regression 263
11 2 2 The assumption of independence and phylogenetic correlations 265
11 2 3 What are phylogenetic correlations and how do they affect data? 266
11 2 4 Why are phylogenetic correlations important for regression? 272
11 2 5 The assumption of hoinoscedasticity and evolutionary models 278
11 2 6 What happens when the incorrect model of evolution is assumed? 280
11 3 Establishing confidence with the comparative phylogenetic method 281
11 4 Conclusions 283
12 Mixture models for overdispersed data Jonathan R Rhodes 284
12 1 Introduction to mixture models for overdispersed data 284
12 2 Overdispersion 286
12 2 1 What is overdispersion and what causes it? 286
12 2 2 Detecting overdispersion 288
12 3 Mixture models 289
12 3 1 What is a mixture model? 289
12 3 2 Mixture models used in ecology 292
12 4 Empirical examples 293
12 4 1 Using binomial mixtures to model dung decay 293
12 4 2 Using Poisson mixtures to model lemur abundance 299
12 5 Discussion 306
13 Linear and generalized linear mixed models Benjamin M Bolker 309
13 1 Introduction to generalized linear mixed models 309
13 2 Running examples 310
xii CONTENTS
13 3 Concepts 311
13 3 1 Model definition 311
13 3 2 Conditional, marginal, and restricted likelihood 319
13 4 Setting up a GLMM: practical considerations 322
13 4 1 Response distribution 322
13 4 2 Link function 323
13 4 3 Number and type of random effects 323
13 5 Estimation 323
13 5 1 Avoiding mixed models 324
13 5 2 Method of moments 324
13 5 3 Deterministic/frequentist algorithms 324
13 5 4 Stochastic/Bayesian algorithms 325
13 5 5 Model diagnostics and troubleshooting 326
13 5 6 Examples 327
13 6 Inference 328
13 6 1 Approximations for inference 328
13 6 2 Methods of inference 329
13 6 3 Reporting the GLMM results 331
13 7 Conclusions 333
Appendix 335
Glossary 345
References 354
Index 379
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| spelling | Ecological statistics contemporary theory and application edited by Gordon A. Fox, Simoneta Negrete-Yankelevich, Vinicio J. Sosa Oxford Oxford University Press [2015] xv, 389 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Ecology / Statistical methods Ökologie Statistik (DE-588)4056995-0 gnd rswk-swf Methode (DE-588)4038971-6 gnd rswk-swf Ökologie (DE-588)4043207-5 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Ökologie (DE-588)4043207-5 s Statistik (DE-588)4056995-0 s Methode (DE-588)4038971-6 s DE-604 Fox, Gordon A. 1952- (DE-588)132252740 edt Negrete-Yankelevich, Simoneta edt Sosa, Vinicio J. edt HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027805671&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Ecological statistics contemporary theory and application |
| title_auth | Ecological statistics contemporary theory and application |
| title_exact_search | Ecological statistics contemporary theory and application |
| title_full | Ecological statistics contemporary theory and application edited by Gordon A. Fox, Simoneta Negrete-Yankelevich, Vinicio J. Sosa |
| title_fullStr | Ecological statistics contemporary theory and application edited by Gordon A. Fox, Simoneta Negrete-Yankelevich, Vinicio J. Sosa |
| title_full_unstemmed | Ecological statistics contemporary theory and application edited by Gordon A. Fox, Simoneta Negrete-Yankelevich, Vinicio J. Sosa |
| title_short | Ecological statistics |
| title_sort | ecological statistics contemporary theory and application |
| title_sub | contemporary theory and application |
| topic | Ecology / Statistical methods Ökologie Statistik (DE-588)4056995-0 gnd Methode (DE-588)4038971-6 gnd Ökologie (DE-588)4043207-5 gnd |
| topic_facet | Ecology / Statistical methods Ökologie Statistik Methode Aufsatzsammlung |
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