Predators reduce niche overlap between sympatric prey
Bibliographic record
Abstract
Predators may alter niche overlap between prey species by eliciting divergent anti‐predator behavior. Accordingly, we exploited heterogeneous gray wolf Canis lupus presence in Washington, USA, to contrast patterns of resource and dietary overlap between mule Odocoileus hemionus and white‐tailed deer O. virginianus at sites with and without resident packs. Mule deer run (stot) in a way that is less effective as a means of fleeing from predators than the galloping gait of white‐tailed deer. Consequently, mule deer manage risk from coursing predators like wolves by avoiding encounters, whereas white‐tailed deer respond to such predators by exploiting areas where they are most likely to escape pursuit. Thus, under the ‘refuge partitioning hypothesis' whereby predators reduce prey niche overlap by eliciting use of different refugia, we predicted wolf exposure to 1) decrease resource and dietary overlap between these ungulates, and 2) induce segregation consistent with each species using different parts of the landscape to reduce their wolf risk. At the home range scale, the ways in which resource overlap diminished in the wolf areas were consistent with the prey species reducing their respective risks, particularly with respect to slope, with mule deer separating from white‐tailed deer by seeking steeper areas where wolf encounters are less likely. At the within‐home range scale, the manner in which spatial overlap decreased in relation to forest cover was consistent with species‐specific risk management, with mule deer avoiding wolf encounters by shifting toward this resource. Reduced resource overlap between the deer in areas occupied by wolves did not correspond with dietary divergence. Our findings suggest that wolf risk mediates spatial but not necessarily dietary overlap between sympatric ungulates, divergent anti‐predator behavior is a non‐consumptive pathway by which predators can reduce interspecific competition among prey, and use of disparate refugia by prey may not result in dietary divergence.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".