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| Preface | |
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| Introduction | |
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| Logic in determining the presence or absence of a species | |
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| Estimation of a mean | |
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| Concluding remarks | |
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| Critiques of statistical methods | |
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| Introduction | |
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| Sex ratio of koalas | |
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| Null hypothesis significance testing | |
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| Information-theoretic methods | |
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| Bayesian methods | |
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| Estimating effect sizes | |
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| Concluding remarks | |
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| Analysing averages and frequencies | |
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| The average | |
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| The Poisson distribution with extra variation | |
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| Estimating differences | |
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| Required sample sizes when estimating means | |
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| Estimating proportions | |
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| Multinomial models | |
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| Concluding remarks | |
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| How good are the models? | |
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| How good is the fit? | |
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| How complex is the model? | |
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| Combining measures of fit and simplicity | |
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| The Bayes factor and model probabilities | |
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| Evaluating the shape of distributions | |
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| Concluding remarks | |
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| Regression and correlation | |
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| Regression | |
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| Correlation | |
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| Concluding remarks | |
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| Analysis of variance | |
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| One-way ANOVA | |
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| Coding of variables | |
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| Fixed and random factors | |
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| Two-way ANOVA | |
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| Interaction terms in ANOVA | |
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| Variance partitioning | |
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| An example of ANOVA: effects of vegetation removal on a marsupial | |
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| Analysis of covariance | |
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| ANCOVA: a case study | |
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| Log-linear models for contingency tables | |
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| Concluding remarks | |
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| Case Studies | |
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| Mark-recapture analysis | |
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| Methods | |
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| Effects of marking frogs | |
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| Logistic regression | |
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| Model A | |
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| Models B and C | |
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| Population dynamics | |
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| Mountain pygmy possums | |
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| Subjective priors | |
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| Eliciting probabilities | |
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| Handling differences of opinion | |
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| Using subjective judgements | |
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| Using the consensus of experts | |
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| Representing differences of opinion with subjective priors | |
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| Using Bayesian networks to represent expert opinion | |
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| Concluding remarks | |
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| Conclusion | |
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| Prior information | |
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| Flexible statistical models | |
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| Intuitive results | |
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| Bayesian methods make us think | |
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| A Bayesian future for ecology | |
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| Appendices | |
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| A tutorial for running WinBUGS | |
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| A summary of steps for running WinBUGS | |
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| The steps in more detail | |
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| How to write WinBUGS code | |
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| Probability distributions | |
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| Discrete random variables | |
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| Continuous random variables | |
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| Univariate discrete distributions | |
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| Univariate continuous distributions | |
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| Multivariate discrete distributions | |
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| Multivariate continuous distributions | |
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| Conjugacy | |
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| MCMC algorithms | |
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| Why does it work? | |
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| References | |
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| Index | |