Interactive effects of copper contamination and salinization across multiple genotypes of Daphnia magna
Bibliographic record
Abstract
Understanding how organisms respond to multiple environmental stressors is essential for predicting ecosystem impacts in the face of increasing anthropogenic pressures. However, few studies have explicitly examined how genotypes of the same species respond to combined stressors, with the specific objective of disentangling variation both within and across geographic locations. In this study, we examined the individual and combined effects of copper contamination and elevated salinity on multiple genotypes of Daphnia magna from U.S. and French populations. Our findings revealed that copper exposure consistently increased mortality across all genotypes, with U.S. genotypes displaying greater sensitivity than French counterparts. Salinity stress primarily reduced fecundity, and again, U.S. genotypes exhibited lower resilience. Under combined copper and salinity stress, however, U.S. genotypes showed survival benefits, suggesting potential cross-tolerance mechanisms between these stressors. Moreover, there was substantial variation in the response to both stressors within both locations. This genotype-specific variation underscores the necessity of considering genetic factors and genotype-specific sensitivity/tolerance into ecosystem management and conservation strategies, particularly under multiple-stressor scenarios. Further exploration of the genetic pathways and adaptation potential driving these responses will enhance our ability to support biodiversity and ecosystem resilience amid global environmental change.
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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) | 0.000 | 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".