Patterns and potential drivers of intraspecific variability in the body C, N, and P composition of a terrestrial consumer, the snowshoe hare ( <i>Lepus americanus</i> )
Bibliographic record
Abstract
Abstract Intraspecific variability in ecological traits is widespread in nature. Recent evidence, mostly from aquatic ecosystems, shows individuals differing at the most fundamental level, that of their chemical composition. Age, sex, or body size and condition may be key drivers of intraspecific variability in the body concentrations of carbon (C), nitrogen (N), and phosphorus (P). However, we still have a rudimentary understanding of the patterns and drivers of intraspecific variability in chemical composition of terrestrial consumers, particularly vertebrates. Here, we investigate the elemental composition of the snowshoe hare Lepus americanus . Based on snowshoe hare ecology, we predicted older, larger individuals to have higher concentration of N or P and lower C content compared with younger, smaller individuals. We also predicted females to have higher concentrations of N, P, and lower C than males due to the higher reproductive costs they incur. Finally, we predicted that individuals in better body condition would have higher N and P than those in worse condition, irrespective of age. We obtained C, N, and P concentrations and ratios from a sample of 50 snowshoe hares. We then used general linear models to test our predictions on the relationship between age, sex, body size or condition and stoichiometric variability in hares. We found considerable variation in C, N, and P stoichiometry within our sample. Contrary to our predictions, we found weak evidence of N content decreasing with age. As well, sex appeared to have no relationship with hare body elemental composition. Conversely, as expected, P content increased with body size and condition. Finally, we found no relationship between variability in C content and any of our predictor variables. Snowshoe hare stoichiometry does not appear to vary with individual age, sex, body size, or condition. However, the weak relationship between body N concentration and age may suggest varying nutritional requirements of individuals at different ages. Conversely, body P's weak relationship to body size and condition appears in line with this limiting element's importance in terrestrial ecosystems. Snowshoe hares are keystone herbivores in the boreal forest of North America, and the substantial stoichiometric variability we find in our sample could have important implications for nutrient dynamics, in both boreal and adjacent ecosystems.
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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 | 0.000 | 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".