Ungulate foraging strategies: energy maximizing or time minimizing?
Bibliographic record
Abstract
Summary Many classical models of ungulate foraging are premised on energy maximization, yet limited empirical evidence and untested currency assumptions make the choice of currency a non‐trivial issue. The primary constraints on forage intake of ungulates are forage quality and availability. Using a model that incorporates these two constraints, we predicted the optimal biomass of forage patches for ungulate grazers using an energy maximizing vs. a time minimizing strategy. We tested these predictions on wood bison ( Bison bison athabascae Rhoads) grazing naturally occurring sedge ( Carex atherodes Spreng). The digestive constraint was determined by a series of ad libitum feeding trials using sedge at different stages of growth. Sedge digestibility declined with biomass. Ad libitum intake of sedge by bison declined with sedge digestibility and thus decreased with sedge biomass. On the other hand, short‐term sedge intake rates of wood bison increased with biomass. Incorporation of these constraints resulted in the prediction that daily energy gain of bison should be maximized by grazing patches with a biomass of 10 g m −2 , whereas a bison could minimize daily foraging time needed to fulfil its energy requirement by cropping patches with a biomass of 279 g m −2 . To test these quantitative predictions, we used a staggered mowing regime to convert even‐aged stands of sedge to a mosaic of patches varying in quality and quantity. Observations of bison grazing these mosaics indicated that patches of biomass below 120 g m −2 were avoided, while patches of biomass 156 and 219 g m −2 were highly preferred, with the greatest preference for the latter. These results indicate that bison were behaving as time minimizers rather than energy maximizers. Daily cropping times of free‐ranging bison from the literature corroborate our results.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".