Adaptation and competition in deteriorating environments
Bibliographic record
Abstract
Abstract Evolution might rescue populations from extinction in changing environments. Using experimental evolution with microalgae, we investigated if competition influences adaptation to an abiotic stressor, and vice versa, if adaptation to abiotic change influences competition. In a first set of experiments, we propagated monocultures of five species with and without increasing salt stress for ~180 generations. When assayed in monoculture, two of the five species showed signatures of adaptation, that is, lines with a history of salt stress had higher population growth rates at high salt than lines without prior exposure to salt. When assayed in mixtures of species, however, only one of these two species had increased population size at high salt, indicating that competition can alter how adaptation to abiotic change influences population dynamics. In a second experiment, we cultivated two species in monocultures and in pairs, with and without increasing salt. While we found no effect of competition on adaptation to salt, our experiment revealed that evolutionary responses to salt can influence competition. Specifically, one of the two species had reduced competitive ability in the no-salt environment after long-term exposure to salt stress. Collectively, our results highlight the complex interplay of adaptation to abiotic change and competitive 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".