Year: 2012
Type: preprint
Abstract: In this paper we review recent advances in Stable Isotope Mixing Models (SIMMs) and place them into an over-arching 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 (ilr) transform of Egozcue (2003). Through this transform we can apply a range of time series and non-parametric smoothing relationships. We illustrate our models with 3 case studies based on real animal dietary behaviour.
Source: arXiv (Cornell University)
Institutions: University College Dublin, Environmental Protection Agency, University of Exeter, Scripps Institution of Oceanography, University of California San Diego, National Oceanic and Atmospheric Administration, NOAA National Marine Fisheries Service Northwest Fisheries Science Center, Simon Fraser University, Trinity College Dublin
Language: English
Topic: Isotope Analysis in Ecology
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2952799240