Foraging decisions at multiple spatial and temporal scales, a bison perspective
Bibliographic record
Abstract
This thesis is an investigation of the behavioural response of free-ranging bison ('Bison bison') to resource distribution and abundance across spatio-temporal scales. From 1996 to 1999, I examined the searching behaviour, diet selection and habitat use of bison in Prince Albert National Park, Saskatchewan. Analysis of winter searching/foraging paths revealed that bison used area-restricted search to find food underneath the snow. Bison perception of resource quality varied with their short-term sampling experience. Computer simulations based on bison behaviour and habitat characteristics indicated that searching efficiency should increase as more sampling information is used to assess resource quality, but this increase should rapidly level off. Simulations further suggest that bison can normally maximize their searching efficiency by considering the information gathered within a foraging bout. This result provides a potential explanation for the flexibility in bison assessment of resource quality observed under field conditions. Despite such flexibility in perception of resource quality, the diet choice of bison was limited to only few plant species. Contingency models, which I developed based on the maximization of short- and long-term gains, revealed dietary choices more consistent with short-term goals. In summer, bison diet was similar in all meadows. In winter, diet choice was still consistent with short-term goals, but bison exhibited frequency-dependent selection for the two plant species providing the highest short-term profitability, in contrast with theoretical predictions. Meadow selection by bison was not directly related to the distribution of the plant they most often consumed, suggesting scale-sensitivity in selection criteria. In summer, the presence of nearby meadows and water areas increased the probability of use of a given meadow. In winter, snow depth within meadows was a dominant factor related to the probability of use. Larger bison herds were more likely to be observed in meadows close to other meadows. I conclude that prediction of resource and habitat selection at a given spatial and temporal scale cannot be readily inferred from knowledge at other scales, due to trade-offs among individual requirements of bison.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".