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MixSIAR: A Bayesian stable isotope mixing model for characterizing intrapopulation niche variation
Citation:
Semmens, B., B. Stock, E. Ward, J. Moore, A. Parnell, A. Jackson, D. Phillips, S. Bearhop, AND R. Inger. MixSIAR: A Bayesian stable isotope mixing model for characterizing intrapopulation niche variation. Presented at Ecological Society of America, Minneapolis, MN, August 04 - 09, 2013.
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 science of stable isotope mixing models has tended towards the development of modeling products (e.g. IsoSource, MixSIR, SIAR), where methodological advances or syntheses of the current state of the art are published in parity with software packages. However, while mixing model theory has recently been extended to incorporate hierarchical structure in mixture populations (e.g. tropic niche partitioning across levels of population structure), no existing mixing model tool currently accounts for such structure. Here we introduce MixSIAR, a new GUI tool based on the R statistical computing platform. MixSIAR is unique in that it incorporates both fixed and random effects associated with the mixture population. Results/Conclusions MixSIAR provides researchers a consolidated analytic framework for addressing hierarchical structure in mixing model analyses. To demonstrate the tool, we show an application to a source/mixture 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 also outline “best practices” associated with data collection and analysis when applying mixing models studies to systems with hierarchical structure.