Genotype diversity promotes the persistence of <i>Daphnia</i> populations exposed to severe copper stress
Bibliographic record
Abstract
When environmental stressors of high intensity are sustained for long periods of time, populations face high probabilities of being extirpated. However, depending on the intensity of the stressor, large populations with sufficient genetic diversity may persist. We report the results of an experiment that tracked the persistence of Daphnia populations exposed to copper contamination. We assessed whether genotypic diversity reduced the risk of extinction. We created monoclonal and multiclonal populations and monitored their population sizes during a 32-week experiment. Cu was applied at a sub-lethal concentration and then increased every week until the population sizes dropped to about 10% of the carrying capacity (Cu at 180 μg/L). The concentration was then increased up to 186 μg/L and held stable until the end of the experiment. A survival analysis showed that clonal diversity extended the persistence of Daphnia populations, but copper contamination caused a substantial genetic erosion followed by population extirpation. However, some Cu-treated populations, mostly multiclonal, showed U-shaped patterns of growth consistent with evolutionary rescue but these did not lead to lasting population recovery. These results highlight the importance of genetic variation for population persistence, but they also show how quickly it can be lost in contaminated 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 | 0.000 |
| 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) | 0.001 | 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".