Temporal dynamics in the foraging decisions of large herbivores
Bibliographic record
Abstract
The foraging decisions involved in acquiring a meal can have an impact on an animal’s spatial distribution, as well as affect other animal species and plant communities. Thus, understanding how the foraging process varies over space and time has broad ecological implications, and optimal foraging theory can be used to identify key factors controlling foraging decisions. Optimality models are based on currencies, options and constraints. Using examples from research on free-ranging bison (Bison bison), we show how variations in these model elements can yield strong spatio-temporal variation in expected foraging decisions. First, we present a simple optimal foraging model to investigate the temporal scale of foraging decisions. On the basis of this model, we identify the foraging currency and demonstrate that such a simple model can be successful at predicting animal distribution across ecosystems. We then modify the model by changing (1) the forager’s option, from the selection of individual plants to the selection of food bites that may include more than one plant species, (2) its constraints, from being omniscient to having incomplete information of resource quality and distribution and (3) its currency, from the maximisation of energy intake rate (E) to the maximisation of the ratio between E and mortality risk (u).We also show that, where the maximisation of E fails, the maximisation of E/u can explain the circadian rhythm in the diet and movements of bison. Simple optimal foraging-theory models thus can explain changes in dietary choice of bison within a foraging patch and during the course of a day.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".