Supplemental data for: Combining stable isotope ratios with elemental concentrations to improve the estimation of terrestrial carnivore diets
Bibliographic record
Abstract
Stable isotopes of animal tissue have been used to estimate diet for many consumer species and places, but the ability to assign contributions to all food items is limited by the number of tracers used, and the separation of the tracer data among the dietary sources. We tested whether we could detect caribou (Rangifer tarandus) in the diet of individual predators in southwest Canada, where caribou are endangered. The separation between caribou tissue and that of other common prey was minimal using C13 and N15 isotope ratios and our confidence in assigning the caribou diet fraction correctly was low despite testing several fractionation values and priors. We measured the concentration of a suite of elements in the tissue of lichen, large prey and predators to investigate whether we could use an elemental concentration as a diet tracer and better assign the caribou diet fraction, because several of these elements were known to be more abundant in lichen, a major food for caribou in winter. Strontium and cesium had higher concentrations, when normalized by a common salt (we chose calcium), in caribou tissue than the other prey species we measured; this was also true for strontium isotope ratios. The elemental tracers appeared to overestimate caribou in the diet however, we suggest that the addition of either cation could yield finer and more accurate estimates of diet for large terrestrial predators after further investigation. The addition of a strontium isotope ratio tracer to a diet investigation may be equally informative and require less pre-work, because one ratio (Sr87/Sr86) has already been well studied. This dataset contains the raw data and analysis code used to produce the results in the paper titled "Combining stable isotope ratios with elemental concentrations to improve the estimation of terrestrial carnivore diets" published in Global Ecology and Conservation in 2023. The raw data are in a text format (csv) and the scripts are in R.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.608 | 0.142 |
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".