Data from: Small population size and low genomic diversity have no effect on fitness in experimental translocations of a wild fish
Bibliographic record
Abstract
Little empirical work in nature has quantified how wild populations with varying effective population sizes and genetic diversity perform when exposed to a gradient of ecologically important environmental conditions. To achieve this, juvenile brook trout from 12 isolated populations or closed metapopulations that differ substantially in population size and genetic diversity were transplanted to previously fishless ponds spanning a wide gradient of ecologically important variables. We evaluated the effect of genome-wide variation, effective population size (Ne), pond habitat, and initial body size on two fitness correlates (survival and growth). Genetic variables had little effect on either fitness correlate, which were determined primarily by habitat (pond temperature, depth, and pH) and initial body size. These results suggest that some vertebrate populations with low genomic diversity, low Ne and long-term isolation can represent important sources of variation and be capable of maintaining fitness in, and ultimately persisting and adapting to, changing environments. Our results also reinforce the paramount importance of improving available habitat and slowing habitat degradation for species conservation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.044 |
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".