Using Bayesian stable isotope mixing models to estimate wolf diet in a multi-prey ecosystem

Stable isotope analysis (SIA) of wolf (Canis lupus) tissues can be used to estimate diet and intra-population diet variability when potential prey have distinct δ¹³C and δ¹⁵N values. We tested this technique using guard hairs collected from 44 wolves in 12 northwestern Montana packs, summer 2009. We used hierarchical Bayesian stable isotope mixing models to determine diet and scales of diet variation from δ¹³C and δ¹⁵N of wolves and potential prey, white-tailed deer (Odocoileus virginianus), mule deer (Odocoileus hemionus), elk (Cervus canadensis), moose (Alces alces), snowshoe hare (Lepus americanus), and other prey. As a check on SIA results, we conducted a separate diet analysis with temporally matched scats (i.e., collected in summer 2008) from 4 of the same packs. Wolves were centered on the ungulate prey in the isotope mixing space. Both methods revealed differences among pack diets and that wolves may consume moose in greater proportions than predicted by available biomass. Stable isotope analysis, and scat results were not entirely concordant; assumptions related to tissues of use in SIA, hair growth period in wolves, and scat sampling may have contributed to a mismatch between methods. Incorrect fractionation values, insufficient separation of prey in the isotope mixing space, choice of prior information in the Bayesian mixing models, and unexplained factors may have distorted diet estimates. However, the consistently high proportion of moose in pack diets suggests that increased population monitoring would benefit management of moose and wolves. Our results also support suggestions of other researchers that species-specific fractionation values should be used whenever possible, and that SIA may sometimes only provide indices of use for general groups of prey (e.g., large ungulates). © 2012 The Wildlife Society.