Mutually exclusive feeding yields Holling type <scp>III</scp> functional response
Bibliographic record
Abstract
Abstract Many generalist predators, including fish, insects and mammals, temporarily focus on their search behaviour and can switch between prey species. Flexible and rapid behavioural plasticity enables a predator to forage optimally, as guided by search image formation and prey switching in response to changing prey abundances. More than half a century of studies have used Holling type III functional response to model the impact of this feeding behaviour on population dynamics, yet a mechanistic understanding for the pathway between individual behaviour and feeding rates remains poorly understood. To understand this common feeding behaviour, we developed three mechanistic models using first‐principles to provide direct derivations for the relationship between observed individual behaviour and feeding rates. The derived models yield three novel, generalised functional responses for predators characterised by prey switching and mutually exclusive feeding (i.e. feeding is limited to one prey species at a time). We show how these functional responses act as Holling type III response, and how they can be used to predict predators' diet compositions. Our results demonstrate that several behavioural forms of prey switching, as displayed in a wide range of predator species, consistently produce type III conforming functional responses. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".