Aggregation, defence and warning signals: the evolutionary relationship
Bibliographic record
Abstract
In a seminal contribution, Fisher argued how distastefulness could incrementally evolve in a prey species that was distributed in family groups. Many defended prey species occur in aggregations, but did aggregation facilitate the evolution of defence as Fisher proposed or did the possession of a defence allow individuals to enjoy the benefits of group living? Contemporary theory suggests that it can work both ways: pre-existing defences can make the evolution of gregariousness easier, but gregariousness can also aid the evolution of defence and warning signals. Unfortunately, the key phylogenetic analyses to elucidate the ordering of events have been hampered by the relative rarity of gregarious species, which in itself indicates that aggregation is not a pre-requisite for defence. Like the underlying theory, experimental studies have not given a definitive answer to the relative timing of the evolution of defence and aggregation, except to demonstrate that both orderings are possible. Conspicuous signals are unlikely to have evolved in the absence of a defence and aggregated undefended prey are likely to be vulnerable to predation in the absence of satiation effects. It therefore seems most likely that defence generally preceded the evolution of both aggregation and signalling, but alternative routes may well be possible.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".