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MixSIAR: advanced stable isotope mixing models in R
Citation:
Stock, B., B. Semmens, E. Ward, J. Moore, A. Parnell, A. Jackson, D. Phillips, S. Bearhop, AND R. Inter. MixSIAR: advanced stable isotope mixing models in R. Presented at Ecological Society of Amerca, Sacramento, CA, August 10 - 15, 2014.
Impact/Purpose:
A consortium of scientists from U.S., Canada, Ireland, and United Kingdom academic institutions, as well as EPA, have developed a new model that can provide insight into how ecological niches vary within a population. The model uses information on the ratios of different stable (non-radioactive) isotopes of an element in animals and in the foods that they eat. With these data the model quantifies the diet composition of each individual animal and how this varies within different segments of the population (e.g., by age, sex, geographic location). This represents an advance in the use of isotopic mixing models to provide further information on the functional structure of wildlife populations which can be important for their conservation and management.
Description:
Background/Question/Methods The development of stable isotope mixing models has coincided with modeling products (e.g. IsoSource, MixSIR, SIAR), where methodological advances are published in parity with software packages. However, while mixing model theory has recently been extended to incorporate hierarchical population structure (e.g. tropic niche partitioning, and continuous covariates (e.g. length), no existing mixing model tool currently accounts for such structure. Here we demonstrate MixSIAR, a new GUI tool in the R statistical computing platform. MixSIAR is unique in that it incorporates fixed, random, and continuous effects associated with the mixture population. Results/Conclusions MixSIAR provides researchers a consolidated analytic framework for addressing hierarchical structure and continuous covariates in mixing model analyses. We show an application of MixSIAR to a system with multiple levels of structure in the mixing population. Through this example, we outline novel mixing model approaches for characterizing intrapopulation niche variation though variance decomposition. We briefly demonstrate MixSIAR’s ability to analyze stable isotope systems with continuous covariates, and conclude by outlining “best practices” in the use of stable isotope mixing models such as MixSIAR.