Bayesian Methods for EcologyThe 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. |
Contents
| 4 | |
Estimation of a mean | 20 |
Concluding remarks | 29 |
Bayesian methods | 35 |
Estimating effect sizes | 45 |
Concluding remarks | 61 |
The Poisson distribution with extra variation | 71 |
Multinomial models | 88 |
Regression and correlation | 119 |
Analysis of variance | 158 |
CASE STUDIES | 195 |
Effects of marking frogs | 207 |
Population dynamics | 217 |
Subjective priors | 225 |
Conclusion | 244 |
B Probability distributions | 255 |
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Common terms and phrases
000 samples ANOVA approximately assumed Bayes Bayesian analysis Bayesian methods Bernoulli beta distribution body mass Burgman calculate capture rate coefficient confidence interval considered correlation credible interval detection deviance diameter of trees DIC values dnorm dnorm(0 ecologists ecology equal example explanatory variables fish kill frequentist frequentist methods gamma distribution given helmeted honeyeater hypothesis significance testing individuals initial values interaction terms koalas likelihood linear locations logging Markov chain McCarthy mean diameter mulgara normal distribution null hypothesis significance null hypothesis testing occur p-value parameter estimates Pfiesteria PLOs plot Poisson distribution population possible posterior distribution posterior probability powerful owls Pr(D prec precision predicted present prior distribution prior information prior probability probability distribution proportion provides quadrats Quinn and Keough random variable reference class relationship relative resighting return rate sex ratio species specified standard deviation subjective judgement toe clipping toe removed uncertainty uninformative prior variance variation WinBUGS code zero
Popular passages
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