Effects of wetland foraging and body size on bison hair sulfur, nitrogen, and carbon isotope compositions: Implications for wildlife conservation, archaeology, and palaeoecology
Bibliographic record
Abstract
Palaeoecological and archaeological reconstructions using stable isotopes rely on understanding isotopic processes in modern ecosystems. This study examines how sulfur, nitrogen, and carbon isotope values ( δ 34 S, δ 15 N, and δ 13 C) of modern plains and wood bison ( Bison bison bison and Bison bison athabascae ) hair records foraging selectivity and size-related metabolic differences in a C 3 -dominated mixed woodland environment: Elk Island National Park (EINP), Alberta, Canada. Terrestrial grasses on the margins of wetlands in EINP had substantially lower δ 34 S and higher δ 15 N than grasses in dry environments, signalling the potential for modern and ancient herbivore tissue δ 34 S and δ 15 N values to record wetland versus dryland foraging. Bison hair δ 34 S and δ 15 N differed between wood and plains bison subspecies, with lower δ 34 S and higher δ 15 N values in wood bison hair suggesting they consumed more plants growing in wetland habitats. Adult females and juveniles consumed a greater proportion of wetland plants than adult males, reflecting differential group dynamics and foraging decisions. Bison body size was positively (linearly) correlated with hair δ 13 C and negatively (non-linearly) correlated with hair δ 15 N, but uncorrelated with hair δ 34 S. We hypothesize that increased body size requires greater reliance on microbial proteins to build body tissues (increasing hair δ 13 C), and that larger-bodied individuals have greater nitrogen use efficiency and lower-quality diets (decreasing hair δ 15 N). Our results suggest that (palaeo)ecological and archaeological researchers should consider the extent to which habitat selectivity and metabolic effects related to sex, age and body size can influence herbivore tissue isotopic compositions.
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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 | 0.000 | 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) | 0.001 | 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".