Apparent predation risk: tests of habitat selection theory reveal unexpected effects of competition
Bibliographic record
Abstract
Questions: Does reducing density of one species increase habitat use by its competitor? If so, can the competitive effect mimic predation risk? Hypotheses: A competing species should increase its use of secondary habitat as the density of its competitor in that habitat declines. And it should forage in safe sites more readily when its competitor is abundant than when it is sparse. Organisms: Two co-existing species of northern voles (Myodes gapperi and Microtus pennsylvanicus) known to have distinct habitat preferences. Field site: Two pairs of interconnected rodent-proof enclosures in field and forest habitat at the Lakehead University Habitron near Thunder Bay, Ontario, Canada. Methods: I predicted density-dependent habitat use from first principles, then measured the density of Myodes in the two habitats as well as its quitting-harvest rate in artificial food patches. I tested the predictions by contrasting treatments where I reduced the density of Microtus in the presence of Myodes, versus controls where I reduced an equal density of Myodes existing alone. Results: Myodes used its preferred forest habitat more at high Microtus density than at low Microtus density. But Microtus occupied both habitats at all densities. Myodes used safe foraging sites more intensely in the treatment where Microtus was present than in the control where it was absent. Conclusions: Competition between these two vole species is reduced by density-dependent habitat selection. But Myodes also trades off food for safety to avoid competition with larger Microtus. Ecologists must first eliminate competition if they are to accurately estimate the effects of predation risk on species co-existence.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".