A field assessment of the SpalingerHobbs mechanistic foraging model: free-ranging moose in winter
Bibliographic record
Abstract
The mechanistic foraging models introduced by Spalinger and Hobbs in 1992 have been very influential in studies of herbivory at a variety of scales. However, almost no field study has evaluated whether the assumption regarding invariability of parameters with time holds for large herbivores with long foraging bouts, and most studies have obtained the model parameters from very short trials. We used free-ranging moose, Alces alces (L., 1758), to test this assumption of invariability and to compare intake calculated by the SpalingerHobbs model using parameters obtained from 10-min trials with intake calculated using data obtained from entire bouts. Our results revealed that the invariance assumption was not fully met: moose increased bite and chew rates and took smaller bites the longer a bite or chew sequence lasted, which resulted in declining intake rates. As a result, the original model misestimated intake by more than double for mountain birch (Betula pubescens ssp. czerepanovii (Orlova) Hämet-Ahti) and by up to 23% for willow (Salix spp.). Compared with data from entire foraging bouts, parameters derived from only the first 10 min of a bout overestimated intake of mountain birch by 31% and underestimated intake of willow by up to 24%. Our results suggest that for herbivores with long foraging bouts, one could modify the model to allow some parameters to vary with time but, more simply, one should parameterize the model using data from entire foraging bouts.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".