Data from: Resource exploitation collapses the home range of an apex predator
Bibliographic record
Abstract
Optimizing energy acquisition and expenditure is a fundamental trade-off for consumers, strikingly reflected in how mobile organisms use space. Several studies have established that home range size decreases as resource density increases, but the balance of costs and benefits associated with exploiting a given resource density is unclear. We evaluate how the ability of consumers to exploit their resources through movement (termed “resource exploitation”) interacts with resource density to influence home range size. We then contrast two hypotheses to evaluate how resource exploitation influences home range size across a vast gradient of productivity and density of human-created linear features (roads and seismic lines) that are known to facilitate animal movements. Under the Diffusion Facilitation Hypothesis, linear features are predicted to lead to more diffuse space use and larger home ranges. Under the Exploitation Efficiency Hypothesis, linear features are predicted to increase foraging efficiency, resulting in less space being required to meet energetic demands and therefore smaller home ranges. Using GPS telemetry data from 142 wolves (Canis lupus) distributed over more than 500,000 km2, we found that wolf home range size was influenced by the interaction between resource density and exploitation efficiency. Home range size decreased as linear feature density increased, supporting the Exploitation Efficiency Hypothesis. However, the effect of linear features on home range size diminished in more productive areas, suggesting that exploitation efficiency is of greater importance when resource density is low. These results suggest that smaller home ranges will occur where both linear feature density and primary productivity are higher, thereby increasing regional wolf density.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| 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 teacher head, 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".