Inferring dispersal and migrations from incomplete geochemical baselines: analysis of population structure using Bayesian infinite mixture models
Article first published online: 5 AUG 2013
© 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society
Methods in Ecology and Evolution
Volume 4, Issue 9, pages 836–845, September 2013
How to Cite
Neubauer, P., Shima, J. S., Swearer, S. E. (2013), Inferring dispersal and migrations from incomplete geochemical baselines: analysis of population structure using Bayesian infinite mixture models. Methods in Ecology and Evolution, 4: 836–845. doi: 10.1111/2041-210X.12076
- Issue published online: 2 SEP 2013
- Article first published online: 5 AUG 2013
- Accepted manuscript online: 4 JUN 2013 08:31AM EST
- Manuscript Accepted: 27 MAY 2013
- Manuscript Received: 10 JAN 2013
- Royal Society of New Zealand Marsden
- Bayesian mixture;
- Dirichlet process;
- geochemical tags;
- stock mixture
- Geochemical and stable isotope tags are often used to attribute individual animals in a sample of mixed origins to distinct sources, be it spawning, overwintering or foraging habitats. In order for individuals to be uniquely classified to one source, modelling approaches generally assume that all potential sources have been characterized in terms of their geochemical signature. This assumption is rarely met in applications of geochemistry in environments where species distributions and spawning grounds are poorly known; statistical methods that can accommodate this problem are therefore essential.
- We develop nonparametric Bayesian mixture models for geochemical signatures that estimate the most likely number of sources represented in a mixed sample, both in the absence and presence of baseline data. We then use a marginal clustering framework to evaluate the probability that a fish comes from a particular source.
- Using both simulations and a previously analysed data set, we illustrate the method and highlight the potential merits and difficulties. These examples reveal how our interpretations of geochemistry data sets can change when potentially un-sampled sources are taken into account.