Steep slopes, shallow angles: mountain ungulates create their own topography through movements
Bibliographic record
Abstract
Travel is considered to account for a substantial proportion of endothermic species energy expenditure. However, transport costs depend on speed of the animal and slope angle of the terrain. We used biologging data from six ungulate species within the French mountains, combined with mapping data, to examine how these different species reacted to slopes by varying travel speed, and chosen ascent and descent angles, in relation to vectoral dynamic body acceleration (VeDBA; as a proxy for energy expenditure). As predicted by theory and as seen in pumas, animals travelled obliquely so that the angle that any individual experienced was lower than that of the topography. Travel speed affected the VeDBA-based proxy for cost of transport (COT) even though most species moved slower on steeper inclines. Models that considered speed, COT, slope, and habitat type showed clear relationships between COT and slope with variation across habitat types and according to species. Species-specific choice of travel speeds and slope chosen by animals underpins fundamental differences in species physiology and ecology via links in heat production and time spent per altitude. Understanding these interrelations points to the complexity of factors affecting space use by mountain ungulates and is crucial for conservation efforts, especially in fast-changing environments where energy expenditure, temperature changes, and resource accessibility impact population wellbeing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".