The influence of sulfur and hair growth on stable isotope diet estimates for grizzly bears
Bibliographic record
Abstract
Stable isotope ratios of grizzly bear (Ursus arctos) guard hair collected from bears on the lower Stikine River, British Columbia (BC) were analyzed to: 1) test whether measuring δ34S values improved the precision of the salmon (Oncorhynchus spp.) diet fraction estimate relative to δ15N as is conventionally done, 2) investigate whether measuring δ34S values improves the separation of diet contributions of moose (Alces alces), marmot (Marmota caligata), and mountain goat (Oreamnos americanus) and, 3) examine the relationship between collection date and length of hair and stable isotope values. Variation in isotope signatures among hair samples from the same bear and year were not trivial. The addition of δ34S values to mixing models used to estimate diet fractions generated small improvement in the precision of salmon and terrestrial prey diet fractions. Although the δ34S value for salmon is precise and appears general among species and areas, sulfur ratios were strongly correlated with nitrogen ratios and therefore added little new information to the mixing model regarding the consumption of salmon. Mean δ34S values for the three terrestrial herbivores of interest were similar and imprecise, so these data also added little new information to the mixing model. The addition of sulfur data did confirm that at least some bears in this system ate marmots during summer and fall. We show that there are bears with short hair that assimilate >20% salmon in their diet and bears with longer hair that eat no salmon living within a few kilometers of one another in a coastal ecosystem. Grizzly bears are thought to re-grow hair between June and October however our analysis of sectioned hair suggested at least some hairs begin growing in July or August, not June and, that hair of wild bears may grow faster than observed in captive bears. Our hair samples may have been from the year of sampling or the previous year because samples were collected in summer when bears were growing new hair. The salmon diet fraction increased with later hair collection dates, as expected if samples were from the year of sampling because salmon began to arrive in mid-summer. Bears that ate salmon had shorter hair and δ15N and δ34S values declined with hair length, also suggesting some hair samples were grown the year of sampling. To be sure to capture an entire hair growth period, samples must be collected in late fall. Early spring samples are also likely to be from the previous year but the date when hair begins to grow appears to vary. Choosing the longest hair available should increase the chance the hair was grown during the previous year and, maximize the period for which diet is measured.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".