Prey habitat selection under shared predation: tradeoffs between risk and competition?
Bibliographic record
Abstract
An individual's choice of habitat should optimize amongst conflicting demands in a way that maximizes its fitness. Habitat selection by one species will often be influenced by presence and abundance of competitors that interact directly and indirectly with each other (such as through shared predators). The optimal habitat choice will thus depend on competition for resources by other species that can also modify predation risk. It may be possible to disentangle these two effects with careful analysis of density‐dependent habitat selection by a focal prey species. We tested this conjecture by calculating habitat isodars (graphs of density assuming ideal habitat selection) of chital deer living in two adjoining dry‐forest habitats in Gir National Park and Sanctuary, western India. The habitats differed only in presence (Sanctuary) and absence (National Park) of domestic prey (cattle and buffalo). Both species are preyed on by Asiatic lions. The habitat isodar revealed at low densities, that chital live in small groups and prefer habitat co‐occupied by livestock that reduce food resources, but also reduce predation risk. At higher densities, chital form larger groups and switch their preference toward risky habitat without livestock. The switch in chital habitat use is consistent with theories predicting that prey species should trade off safety in favor of food as population density increases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".