Carbon and nitrogen discrimination factors of wolves and accuracy of diet inferences using stable isotope analysis
Bibliographic record
Abstract
ABSTRACT Dietary inferences using stable isotope analysis rely on comparing stable carbon and nitrogen isotope content of consumer to prey tissues by modeling discrimination between these tissues. Diet–tissue discrimination factors applied in these models for wild populations must be obtained from controlled feeding studies where consumer diets are known. Species‐specific discrimination factors are lacking for wolves ( Canis lupus ), and most researchers assessing the diet of free‐ranging wolves have used discrimination factors derived from red foxes ( Vulpes vulpes ) fed a commercial pellet diet. We calculated diet–tissue discrimination factors for various tissues from captive wolves fed a controlled diet of horse ( Equus caballus ) meat and also assessed the feasibility of seasonal delineation of diet through the partitioning of metabolically inactive tissues such as guard hairs and whiskers. Stable carbon isotopic discrimination in wolves was highest in whiskers (4.31‰), followed by guard hair (4.25‰), and lowest in serum (2.21‰) and red blood cells (2.16‰). Stable nitrogen isotopic discrimination was highest in serum (4.54‰), guard hair (3.09‰), and whiskers (3.05‰), and lowest in red blood cells (2.99‰). Using these values, we demonstrated the sensitivity of estimated wolf diet proportions to choice of discrimination factors. We also documented a decrease in growth rate of hair and whiskers from summer through autumn, which cautions estimating temporally explicit diet of mammals based on stable isotope analysis of discrete sections of hair and whiskers. We conclude that species‐specific discrimination estimates should be used in dietary assessments based on stable isotope analyses to limit inaccuracies in diet interpretation of wild populations. © 2015 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 | 0.000 | 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".