Using Bayesian stable isotope mixing models to estimate wolf diet in a multi‐prey ecosystem
Bibliographic record
Abstract
Abstract 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 δ 13 C and δ 15 N 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 δ 13 C and δ 15 N 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".