Bibliographic record
Abstract
This article uses several models to explore the potential indirect evolutionary interaction between two prey species that share one or more predator species. It asks how the antipredator adaptations of a focal prey species are likely to evolve following the introduction of a second prey species that shares one or more of the same predator species. The interactions are represented by differential equation models of homogeneous populations, and evolutionary change occurs in a single quantitative trait within each prey species. Models differ in assumptions about the number of predator types, the costs of antipredator traits, and in how predator and prey traits combine to determine a per capita capture rate. Parallel change in the antipredator characteristics of two species usually occurs when the general risk of predation is changed by the introduction of the second prey. This may be a parallel increase or decrease, depending on whether the ecological interaction is apparent competition or apparent mutualism. Divergence in trait values is most often associated with the presence of two or more distinct types of predators with some form of trade‐off in the prey’s ability to avoid different predators. Divergence may also occur with a single predator type, when two or more strategies exist for reducing predation risk. Convergence is a possible outcome when there are two or more predator types. Coevolution may also produce trait and population cycles in models, and the nature of character displacement is often changed markedly by such instability. The limited evidence for evolutionary indirect effects mediated by shared predation is reviewed. Possible reasons for the disparity between theory and observation are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".