Intrapopulation variability in wolf diet revealed using a combined stable isotope and fatty acid approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Naturally occurring stable isotope ratios and fatty acids are two types of chemical biomarkers frequently used to quantitatively estimate consumer diets. Stable isotope values in animal tissues and diets have been evaluated using Bayesian mixing models to provide dietary estimates of consumers in both terrestrial and marine ecosystems. Fatty acids have primarily been used to examine diets of marine species. Using muscle and adipose tissue, we combined the two biomarkers in a Bayesian mixing model to generate quantitative diet estimates for gray wolves ( Canis lupus , n = 78) in the southern Northwest Territories, Canada. Simulation experiments showed that the combined dataset led to more accurate and precise diet estimates than stable isotopes alone. Overall, bison ( Bison bison athabascae ) dominated the winter diet (63–96%) of wolves. In one region where bison were not readily available, wolf diet was more variable, with substantial contributions from boreal caribou ( Rangifer tarandus caribou ), moose ( Alces alces ), snowshoe hare ( Lepus americanus ), and beaver ( Castor canadensis ). Surprisingly, fish also comprised 5–26% of wolf diet in this region. Wolves likely scavenged on scraps left behind by commercial ice fishing operations on Great Slave Lake. Our investigation underlines the power of combining these two major analytical tools to investigate diet in an elusive and opportunistic predator.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it