Predator‐mediated Allee effects in multi‐prey systems
Bibliographic record
Abstract
Allee effects can have significant consequences for small populations and understanding the causal mechanisms for such effects is important for guiding conservation actions. One proposed mechanism is through predation, in which a type II functional response leads to increasing predation rates as prey numbers decline. However, models to support this mechanism have incorporated only a single declining prey species in the functional response, which is probably an oversimplification. We reevaluated the potential for predator-mediated Allee effects in a multi-prey system using Holling's disc equation. We also used empirical data on a large herbivore to examine how grouping behavior may influence the potential for predation-mediated Allee effects. Results based on a multi-prey expression of the functional response predict that Allee effects caused by predation on relatively rare secondary prey may not occur because handling time of the abundant prey dominates the functional response such that secondary prey are largely "bycatch". However, a predator-mediated Allee effect can occur if secondary prey live in groups and if, as the population declines, their average group size declines (a relationship seen in several species). In such a case, the rate at which the number of groups declines is less than the rate at which the population declines. Thus the rate at which a predator encounters a group remains relatively stable, but when a predator kills one animal from smaller groups, the predation rate increases. These results highlight the need to evaluate risks associated with potential changes in group size as populations decline.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".