Do stressful conditions make adaptation difficult? Guppies in the oil‐polluted environments of southern Trinidad
Bibliographic record
Abstract
The ability of populations to rapidly adapt to new environments will determine their future in an increasingly human-modified world. Although meta-analyses do frequently uncover signatures of local adaptation, they also reveal many exceptions. We suggest that particular constraints on local adaptation might arise when organisms are exposed to novel stressors, such as anthropogenic pollution. To inform this possibility, we studied the extent to which guppies (Poecilia reticulata) show local adaptation to oil pollution in southern Trinidad. Neutral genetic markers revealed that paired populations in oil-polluted versus not-polluted habitats diverged independently in two different watersheds. Morphometrics revealed some divergence (particularly in head shape) between these environments, some of which was parallel between rivers. Reciprocal transplant experiments in nature, however, found little evidence of local adaptation based on survival and growth. Moreover, subsequent laboratory experiments showed that the two populations from oil-polluted sites showed only weak local adaptation even when compared to guppies from oil-free northern Trinidad. We conclude that guppies show little local adaptation to oil pollution, which might result from the challenges associated with adaptation to particularly stressful environments. It might also reflect genetic drift owing to small population sizes and/or high gene flow between environments.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".