Safety from predators or competitors? Interference competition leads to apparent predation risk
Bibliographic record
Abstract
Prey often react to predation risk by foraging preferentially in the safety of cover rather than in more risky open patches. Yet this pattern of patch use also can be caused by dominant interspecific competitors. We develop a simple theory of this form of apparent predation risk that describes the patch use of an optimal forager confronted with dominant individuals. The theory predicts that subordinate animals should increase their use of safe foraging patches as the density of nearby dominants increases. We tested the theory with meadow voles (Microtus pennsylvanicus) and southern red-backed voles (Myodes gapperi). We used dyadic encounters to confirm that meadow voles are dominant over red-backed voles. We then evaluated their respective foraging patterns in pairs of covered and open patches in 4 adjacent subgrids in an old-field enclosure. Subordinate red-backed voles foraged indifferently between covered and open patches when few meadow voles were present. Red-backed voles increased their use of both patches as the number of nearby meadow voles increased. Giving-up densities were lowest, and harvesting efficiency highest, in covered patches when the number of nearby meadow voles was high. These results document competition between the 2 species and suggest that vigilance toward dominant meadow voles magnifies the risk experienced by red-backed voles in open patches. Investigators assessing foraging behavior between “safe” and “risky” patches might misinterpret the competitive effect as predation risk unless they 1st account for competition among foraging individuals.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".