Bibliographic record
Abstract
AbstractCompetition drives evolutionary change across taxa, but our understanding of how competitive differences among species directs the evolution of interspecific interactions remains incomplete. Verbal models assume that interspecific competition will select for reducing a species' sensitivity to competition with their opponent; however, they do not consider the potential for other demographic components of competitive ability to evolve, specifically, interspecific effects, intraspecific interactions, and intrinsic growth rates. To better understand how competitive ability evolves, we set out to explore how each component has evolved and whether their evolution has been constrained by trade-offs. By setting sympatric and allopatric populations of an annual grass in competition with a dominant invader, we demonstrate (1) that in response to interspecific competition, populations can evolve increased competitive ability through either reduced interspecific or, surprisingly, reduced intraspecific competition; (2) that trade-offs do not always constrain the evolution of competitive ability but rather that parameters may correlate in ways that mutually beget higher competitive ability; and (3) that the evolution of one species can influence the competitive ability of its opponent, a consequence of how competitive ability is defined ecologically. Overall, our results reveal the complexity with which demographic components evolve in response to interspecific competition and the impact past evolution can have on present-day interactions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".