Resource polygon geometry predicts Bayesian stable isotope mixing model bias

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How important are terrestrial organic carbon inputs for secondary production in freshwater ecosystems (2017, background): “For example, when the resource datasets considered in Bayesian SIMMs are highly variable or the models are mathematically under-determined, these analyses tend to produce outcomes that are strongly influenced by the prior generalist assumption (Brett, 2014b; Fry, 2013a,b).”
Diet tracing in ecology: Method comparison and selection (2017, background): “The high disparity between visual and stable isotope analyses applied to datasets with diverse resources likely resulted from the mixing models inability to differentiate among many more resources relative to the number of dietary tracers used (Brett, 2014).”