Intraspecific diversity is critical to population-level risk assessments
Bibliographic record
Abstract
Environmental risk assessment (ERA) is critical for protecting life by predicting population responses to contaminants. However, routine toxicity testing often examines only one genotype from surrogate species, potentially leading to inaccurate risk assessments, as natural populations typically consist of genetically diverse individuals. To evaluate the importance of intraspecific variation in translating toxicity testing to natural populations, we quantified the magnitude of phenotypic variation between 20 Daphnia magna clones exposed to two levels of microcystins, a cosmopolitan cyanobacterial toxin. We observed significant genetic variation in survival, growth, and reproduction, which increased under microcystins exposure. Simulations of survival showed that using a single genotype for toxicity tolerance estimates on average failed to produce accurate predictions within the 95% confidence interval over half of the time. Whole genome sequencing of the 20 clones tested for correlations between toxicological responses and genomic divergence, including candidate loci from prior gene expression studies. We found no overall correlations, indicating that clonal variation, rather than variation at candidate genes, predicts population-level responses to toxins. These results highlight the importance of incorporating broad intraspecific genetic variation, without focusing specifically on variation in candidate genes, into ERAs to more reliably predict how local populations will respond to contaminants.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".