Carnivore-specific stable isotope variables and variation in the foraging ecology of modern and ancient wolf populations: case studies from Isle Royale, Minnesota, and La Brea
Bibliographic record
Abstract
We use carbon and nitrogen isotope data collected from two North American gray wolf ( Canis lupus L., 1758) populations (Isle Royale and northern Minnesota) to both calculate carnivore-specific isotopic variables and investigate wolf foraging ecology. The isotopic enrichments of 13 C and 15 N that occur between mammalian carnivores and their prey have not been well defined in modern populations. We use bone collagen from the Isle Royale National Park wolf, moose ( Alces alces (L., 1758)), and beaver ( Castor canadensis Kuhl, 1820) populations to determine trophic enrichment factors of 1.3‰ ± 0.6‰ for δ 13 C and 4.6‰ ± 0.7‰ for δ 15 N. We apply these carnivore-specific fractionation factors to a case study from the fossil record, and reconstruct the diets of late-Pleistocene dire wolves ( Canis dirus (Leidy, 1858)) from the La Brea tar pits. We use the Minnesota wolf tissue (collagen, hair, muscle) isotopic data to estimate carnivore population subsample sizes needed to replicate the mean values of the whole population within one standard deviation. Finally, we compare the Isle Royale and Minnesota collagen and hair isotopic data to published δ 13 C and δ 15 N values for North American gray wolf populations. We find that interpopulation differences in isotope variances provide insight into wolf foraging ecology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".