Disturbance and habitat use: is edge more important than area?
Bibliographic record
Abstract
In their efforts to maximize fitness while reducing the probability of dying, animals must decide which patches to forage in, when to forage, and how long to forage in each patch. Each decision will be modified by habitat and habitat disturbance. We evaluate the effects of habitat disturbance on foraging behaviour by imagining an initially homogeneous environment that is altered to create patches of different sizes. Disturbance increases predation risk, or otherwise alters patch profitability. Foragers can respond by changing their pattern of foraging, or by reducing their activity. We develop predictions for each scenario. We then test the predictions with data on the abundance and foraging activity of meadow voles ( Microtus pennsylvanicus ) in and around four sizes of circular disturbed patches. We created the patches by mowing vegetation in an abandoned hay field in northern Ontario, Canada. The treatments had no effect on vole density, and there was no consistent relationship between vole activity and distance from the edge of disturbed patches. Incidental predation of sunflower seeds, our measure of vole foraging behaviour, declined linearly with increasing patch circumference (edge). Seed consumption by meadow voles, and predation by voles on lower food levels, correlates with the length of edge habitat rather than with the area disturbed. Adaptive behaviour can thereby explain edge effects that, under current priorities emphasizing area, would appear at odds with conservation ecology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".