Moose browsing and forage availability: a scale-dependent relationship?
Bibliographic record
Abstract
Scale dependence is a fundamentally important topic in ecology because it determines whether results can be generalized over different spatial scales. We studied the relationship between forage consumption by moose ( Alces alces (L., 1758)) and forage availability across six nested spatial scales in south-central Sweden. By using multiple regression, we concluded that the amount of available forage was the best single variable explaining absolute consumption, irrespectively of scale. Forage species diversity, site productivity, and moose density were also important for predicting forage consumption, but their effects differed across the different spatial scales. A multiple regression including forage availability, moose density, site productivity, and forage diversity explained between 31% and 49% of the variation in forage consumption. The importance of a moose index as an explanatory variable decreased with increasing spatial scale, whereas the importance of site productivity increased. According to model selection based on Akaike's information criterion, the same model was ranked highest at the four smallest spatial scales, whereas the top-ranked models at the two largest spatial scales differed. Furthermore, the relationship between consumption and forage availability changed from underutilization at small scales to proportional use at the home range level. Thus, for a comprehensive understanding of moose browsing in relation to food resources, we conclude that a multi-scale approach is necessary.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".