Patterns and potential drivers of intraspecific variability in the body elemental 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 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 whole-body chemical composition of snowshoe hare Lepus americanus , providing one of the few studies of patterns of stoichiometric variability and its potential drivers for a terrestrial vertebrate. Based on snowshoe hare ecology, we expected higher P and N concentrations in females, as well as in larger and older individuals. We obtained whole-body C, N, and P concentrations and C:N, C:P, N:P ratios from a sample of 50 snowshoe hares. We then used general linear models to test for evidence of a relationship between age, sex, or body size and stoichiometric variability in hares. We found considerable variation in the C, N, and P concentrations and elemental ratios within our sample. Contrary to our predictions, we found evidence of N content decreasing with age. As expected, we found evidence of P content increasing with body size. As well, we found no support for a relationship between sex and N or P content, nor for variability in C content and any of our predictor variables. Despite finding considerable stoichiometric variability in our sample, we found no substantial support for age, sex, or body size to relate to this variation. The weak relationship between body N concentration and age may suggest varying nutritional requirements of individuals at different ages. Conversely, P’s weak relationship to body size appears in line with recent evidence of the potential importance of P in terrestrial systems. Snowshoe hares are a keystone herbivore in the boreal forest of North America. 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 | 0.000 | 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".