Energy and the Scaling of Animal Space Use
Bibliographic record
Abstract
Daily animal movements are usually limited to a discrete home range area that scales allometrically with body size, suggesting that home-range size is shaped by metabolic rates and energy availability across species. However, there is little understanding of the relative importance of the various mechanisms proposed to influence home-range scaling (e.g., differences in realm productivity, thermoregulation, locomotion strategy, dimensionality, trophic guild, and prey size) and whether these extend beyond the commonly studied birds and mammals. We derive new home-range scaling relationships for fishes and reptiles and use a model-selection approach to evaluate the generality of home-range scaling mechanisms across 569 vertebrate species. We find no evidence that home-range allometry varies consistently between aquatic and terrestrial realms or thermoregulation strategies, but we find that locomotion strategy, foraging dimension, trophic guild, and prey size together explain 80% of the variation in home-range size across vertebrates when controlling for phylogeny and tracking method. Within carnivores, smaller relative prey size among gape-limited fishes contributes to shallower scaling relative to other predators. Our study reveals how simple morphological traits and prey-handling ability can profoundly influence individual space use, which underpins broader-scale patterns in the spatial ecology of vertebrates.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".