Frequency-dependent conspecific attraction to food patches
Bibliographic record
Abstract
In many ecological situations, resources are difficult to find but become more apparent to nearby searchers after one of their numbers discovers and begins to exploit them. If the discoverer cannot monopolize the resources, then others may benefit from joining the discoverer and sharing their discovery. Existing theories for this type of conspecific attraction have often used very simple rules for how the decision to join a discovered resource patch should be influenced by the number of individuals already exploiting that patch. We use a mechanistic, spatially explicit model to demonstrate that individuals should not necessarily simply join patches more often as the number of individuals exploiting the patch increases, because those patches are likely to be exhausted soon or joining them will intensify future local competition. Furthermore, we show that this decision should be sensitive to the nature of the resource patches, with individuals being more responsive to discoveries in general and more tolerant of larger numbers of existing exploiters on a patch when patches are resource-rich and challenging to locate alone. As such, we argue that this greater focus on underlying joining mechanisms suggests that conspecific attraction is a more sophisticated and flexible tactic than currently appreciated.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".