Discrimination factors of carbon and nitrogen stable isotopes from diet to hair in captive large Arctic carnivores of conservation concern
Bibliographic record
Abstract
Rationale Stable isotope analysis is widely used to reconstruct diet, delineate trophic interactions, and determine energy pathways. Such ecological inferences are based on the idea that animals are, isotopically, what they eat but with a predictable difference between the isotopic ratio of a consumer and that of its diet, coined as the discrimination factor. Providing correct estimates of diet‐consumer isotopic discrimination in controlled conditions is key for a robust application of the stable isotopes technique in the wild. Methods Using a Finnigan Mat Delta Plus isotope‐ratio mass spectrometer, we investigated isotopic discrimination of carbon and nitrogen isotope ratios (δ 13 C and δ 15 N values) in guard hairs of four Arctic predators; the wolf ( n = 7), the wolverine ( n = 2), the grizzly bear ( n = 2), and the polar bear ( n = 3). During a 3‐month trial, carnivores were fed a mixed diet. The δ 13 C and δ 15 N values, and the mass (g) of diet items, were monitored weekly for each individual to determine their Total Diet Average ratios. Results Diet‐hair isotopic discrimination (Δx) varied according to species, ranging [1.88 ± 0.69‰: 3.2 ± 0.69‰] for δ 13 C values, and [1.58 ± 0.17‰: 3.81 ± 0.22‰] for δ 15 N values. Adult wolves Δ 13 C average (2.03 ± 0.7‰) was lower than that of young wolves (2.60 ± 0.8‰) and any other species (combined average of 2.59 ± 0.28‰), except for the wolverine (2.12 ± 0.23‰). Wolves Δ 15 N averages (juveniles: 3.51 ± 0.34‰, adults: 3.68 ± 0.28‰) were higher than those of any other species (combined average: 2.50 ± 0.58‰). Conclusions The discrimination factors for δ 13 C and δ 15 N values calculated in this study could be used in ecological studies dealing with free‐ranging animals, with implications for non‐invasive research approaches. As in other controlled discrimination studies, we recommend caution in applying our discrimination factors when the population structure is heterogeneous.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".