Rapid Exploiter‐Victim Coevolution: The Race Is Not Always to the Swift
Bibliographic record
Abstract
The modeling of coevolutionary races has traditionally been dominated by methods invoking a timescale separation between ecological and evolutionary dynamics, the latter assumed to be much slower than the former. Yet it is becoming increasingly clear that in many cases the two processes occur on similar timescales and that such "rapid" evolution can have profound implications for the dynamics of communities and ecosystems. After briefly reviewing the timescale separations most common in coevolution theory, we use a general model of exploiter-victim coevolution to confront predictions from slow-evolution analysis with Monte Carlo simulations. We show how rapid evolution radically alters the dynamics and outcome of coevolutionary arms races. In particular, a fast-evolving exploiter can enable victim diversification and thereby lose a race it is expected to win. We explain simulation results, using mathematical analysis with relaxed timescale separations. Unusual mutation parameters are not required, since rapid evolution naturally emerges from slow competitive exclusion. Our results point to interesting consequences of exploiter rapid evolution and experimentally testable patterns, while indicating that more attention should be paid to rapid evolution in evolutionary theory.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".