Journal
ENVIRONMETRICS
Volume 24, Issue 6, Pages 387-399Publisher
WILEY
DOI: 10.1002/env.2221
Keywords
stable isotope analysis; mixing models; Bayesian hierarchical model; compositional data; time series
Categories
Funding
- US Environmental Protection Agency
Ask authors/readers for more resources
In this paper, we review recent advances in stable isotope mixing models (SIMMs) and place them into an overarching Bayesian statistical framework, which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixture. The most widely used application is quantifying the diet of organisms based on the food sources they have been observed to consume. At the centre of the multivariate statistical model we propose is a compositional mixture of the food sources corrected for various metabolic factors. The compositional component of our model is based on the isometric log-ratio transform. Through this transform, we can apply a range of time series and non-parametric smoothing relationships. We illustrate our models with three case studies based on real animal dietary behaviour. Copyright (c) 2013 John Wiley & Sons, Ltd.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available