Inferring dispersal and migrations from incomplete geochemical baselines: Analysis of population structure using Bayesian infinite mixture models

Philipp Neubauer, Jeffrey S. Shima, Stephen E. Swearer

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)836-845
Number of pages10
JournalMethods in Ecology and Evolution
Volume4
Issue number9
DOIs
Publication statusPublished - Sept 2013
Externally publishedYes

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