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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010192421 | QH541.2 M32 2007 | Open Access Book | Book | Searching... |
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Summary
Summary
The interest in using Bayesian methods in ecology is increasing, however many ecologists have difficulty with conducting the required analyses. McCarthy bridges that gap, using a clear and accessible style. The text also incorporates case studies to demonstrate mark-recapture analysis, development of population models and the use of subjective judgement. The advantages of Bayesian methods, are also described here, for example, the incorporation of any relevant prior information and the ability to assess the evidence in favour of competing hypotheses. Free software is available as well as an accompanying web-site containing the data files and WinBUGS codes. Bayesian Methods for Ecology will appeal to academic researchers, upper undergraduate and graduate students of Ecology.
Author Notes
Michael A. McCarthy is Senior Ecologist at the Royal Botanical Gardens, Melbourne and Senior Fellow in the School of Botany at the University of Melbourne, Australia
Table of Contents
| Preface | p. xi |
| 1 Introduction | p. 1 |
| Example 1 Logic in determining the presence or absence of a species | p. 4 |
| Example 2 Estimation of a mean | p. 20 |
| Concluding remarks | p. 29 |
| 2 Critiques of statistical methods | p. 30 |
| Introduction | p. 30 |
| Sex ratio of koalas | p. 31 |
| Null hypothesis significance testing | p. 35 |
| Information-theoretic methods | p. 45 |
| Bayesian methods | p. 52 |
| Estimating effect sizes | p. 58 |
| Concluding remarks | p. 61 |
| 3 Analysing averages and frequencies | p. 63 |
| The average | p. 63 |
| The Poisson distribution with extra variation | p. 71 |
| Estimating differences | p. 71 |
| Required sample sizes when estimating means | p. 73 |
| Estimating proportions | p. 81 |
| Multinomial models | p. 88 |
| Concluding remarks | p. 92 |
| 4 How good are the models? | p. 94 |
| How good is the fit? | p. 95 |
| How complex is the model? | p. 101 |
| Combining measures of fit and simplicity | p. 105 |
| The Bayes factor and model probabilities | p. 108 |
| Evaluating the shape of distributions | p. 116 |
| Concluding remarks | p. 118 |
| 5 Regression and correlation | p. 119 |
| Regression | p. 119 |
| Correlation | p. 148 |
| Concluding remarks | p. 156 |
| 6 Analysis of variance | p. 158 |
| One-way ANOVA | p. 158 |
| Coding of variables | p. 159 |
| Fixed and random factors | p. 162 |
| Two-way ANOVA | p. 165 |
| Interaction terms in ANOVA | p. 167 |
| Variance partitioning | p. 167 |
| An example of ANOVA: effects of vegetation removal on a marsupial | p. 170 |
| Analysis of covariance | p. 180 |
| ANCOVA: a case study | p. 182 |
| Log-linear models for contingency tables | p. 190 |
| Concluding remarks | p. 193 |
| Case Studies | |
| 7 Mark-recapture analysis | p. 197 |
| Methods | p. 197 |
| 8 Effects of marking frogs | p. 207 |
| Logistic regression | p. 209 |
| Model A | p. 210 |
| Models B and C | p. 211 |
| 9 Population dynamics | p. 217 |
| Mountain pygmy possums | p. 217 |
| 10 Subjective priors | p. 225 |
| Eliciting probabilities | p. 225 |
| Handling differences of opinion | p. 226 |
| Using subjective judgements | p. 227 |
| Using the consensus of experts | p. 227 |
| Representing differences of opinion with subjective priors | p. 230 |
| Using Bayesian networks to represent expert opinion | p. 236 |
| Concluding remarks | p. 243 |
| 11 Conclusion | p. 244 |
| Prior information | p. 244 |
| Flexible statistical models | p. 245 |
| Intuitive results | p. 245 |
| Bayesian methods make us think | p. 245 |
| A Bayesian future for ecology | p. 246 |
| Appendices | |
| A A tutorial for running WinBUGS | p. 249 |
| A summary of steps for running WinBUGS | p. 249 |
| The steps in more detail | p. 249 |
| How to write WinBUGS code | p. 253 |
| B Probability distributions | p. 255 |
| Discrete random variables | p. 255 |
| Continuous random variables | p. 257 |
| Univariate discrete distributions | p. 261 |
| Univariate continuous distributions | p. 266 |
| Multivariate discrete distributions | p. 272 |
| Multivariate continuous distributions | p. 273 |
| Conjugacy | p. 275 |
| C MCMC algorithms | p. 277 |
| Why does it work? | p. 280 |
| References | p. 282 |
| Index | p. 293 |
