On the role of enemies in divergence and diversification of prey: a review and synthesis
Bibliographic record
Abstract
Understanding the contribution of ecological interactions to the origin and maintenance of diversity is a fundamental challenge for ecologists and evolutionary biologists, and one that is currently receiving a great deal of attention. Natural enemies (e.g., predators, parasites, and herbivores) are ubiquitous in food webs and are predicted to have significant impacts on phenotypic diversity and on speciation, and extinction rates of their prey. Spurred by the development of a theoretical framework beginning in the late 1970s, there is now a growing body of literature that addresses the effects of enemy–prey interactions on the evolution of prey. A number of theoretical models predict that enemies can produce phenotypic divergence between closely related species, even in the absence of interspecific competition for resources. Effects on diversification of prey are more variable, and enemies may either enhance or depress speciation and extinction rates of their prey. Empirical evidences from a number of study systems, notably those involving predators and prey in aquatic environments and interactions between insects and flowering plants, confirm both predictions. There is now considerable evidence for the role of enemies, especially those that are size-selective or use visual cues when identifying suitable prey, on phenotypic divergence of sympatric and allopatric taxa. Enemies may spur diversification rates in certain groups under some circumstances, and hinder diversification rates in other cases. I suggest that further research should focus on the role of enemies in diversification of prey, with significant insights likely to be the product of applying traditional experimental approaches and emerging comparative phylogenetic methods.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".