Integrating plant stoichiometry and feeding experiments: state-dependent forage choice and its implications on body mass
Bibliographic record
Abstract
Abstract Intraspecific feeding choices account for a large portion of herbivore foraging in many ecosystems. Plant resource quality is heterogeneously distributed, affected by nutrient availability and growing conditions. Herbivores navigate landscapes, making feeding decisions according to food qualities, but also energetic and nutritional demands. We test three non-exclusive foraging hypotheses using the snowshoe hare ( Lepus americanus ): 1) herbivores feeding choices and body conditions respond to intraspecific plant quality variation, 2) feeding responses are mitigated when energetic demands are high, and 3) feeding responses are inflated when nutritional demands are high. We measured black spruce ( Picea mariana ) nitrogen, phosphorus, and terpene compositions, as indicators of quality, within a snowshoe hare trapping grid and found plant growing conditions to explain spruce quality variation (R 2 < 0.36). We then offered two qualities of spruce (H1) from the trapping grid to hares in cafeteria-style experiments and measured their feeding and body condition responses (n = 75). We proxied energetic demands (H2) with ambient temperature and coat insulation (% white coat) and nutritional demands (H3) with the spruce quality (nitrogen and phosphorus content) in home ranges. Hares that preferred higher-quality spruce lost less weight during experiments (p = 0.018). The results supported our energetic predictions: hares in colder temperatures and with less-insulative coats (lower % white) consumed more spruce and were less selective towards high-quality spruce. Collectively, we found variation in plant growing conditions within herbivore home ranges substantial enough to affect herbivore body conditions, but any plant-herbivore interactions are also mediated by animal energetic states.
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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 | 0.000 | 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".