Spatial arrangement of prey affects the shape of ratio‐dependent functional response in strongly antagonistic predators
Bibliographic record
Abstract
Predators play a key role in shaping natural ecosystems, and understanding the factors that influence a predator's kill rate is central to predicting predator-prey dynamics. While prey density has a well-established effect on predation, it is increasingly apparent that predator density also can critically influence predator kill rates. The effects of both prey and predator density on the functional response will, however, be determined in part by their distribution on the landscape. To examine this complex relationship we experimentally manipulated prey density, predator density, and prey distribution using a tadpole (prey)-dragonfly nymph (predator) system. Predation was strongly ratio-dependent irrespective of prey distribution, but the shape of the functional response changed from hyperbolic to sigmoidal when prey were clumped in space. This sigmoidal functional response reflected a relatively strong negative effect of predator interference on kill rates at low prey: predator ratios when prey were clumped. Prey aggregation also appeared to promote stabilizing density-dependent intraguild predation in our system. We conclude that systems with highly antagonistic predators and patchily distributed prey are more likely to experience stable dynamics, and that our understanding of the functional response will be improved by research that examines directly the mechanisms generating interference.
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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 | 0.001 |
| 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) | 0.001 | 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".