Jonathan W. Moore

TL;DR: A Bayesian-mixing model is developed that estimates probability distributions of source contributions to a mixture while explicitly accounting for uncertainty associated with multiple sources, fractionation and isotope signatures.

TL;DR: Stable isotope mixing models are increasingly used to quantify consumer diets, but may be misused and misinter- preted, and major challenges to their effective application are addressed.

TL;DR: In this article, stable isotope mixing models (SIMMs) are used to quantify the proportional contributions of various sources to a mixture, and a compositional component of the model is based on the isometric log ratio (ilr) transform of Egozcue (2003).

TL;DR: This paper proposes a compositional mixture of the food sources corrected for various metabolic factors based on the isometric log‐ratio transform, which can apply a range of time series and non‐parametric smoothing relationships.

TL;DR: One of the most spectacular phenomena in nature is the annual return of millions of salmon to spawn in their natal streams and lakes along the Pacific coast of North America The salmon die after spawning, and the nutrients and energy in their bodies, derived almost entirely from marine sources, are deposited in the freshwater ecosystems This represents a vital input to the ecosystems used as spawning grounds as mentioned in this paper.