Temporal change in inbreeding depression in life-history traits in captive populations of guppy (Poecilia reticulata): evidence for purging?
Bibliographic record
Abstract
Inbreeding depression, which generally affects the fitness of small populations, may be diminished by purging recessive deleterious alleles when inbreeding persists over several generations. Evidence of purging remains rare, especially because of the difficulties of separating the effects of various factors affecting fitness in small populations. We compared the expression of life-history traits in inbred populations of guppy (Poecilia reticulata) with contemporary control populations over 10 generations in captivity. We estimated inbreeding depression as the difference between the two types of populations at each generation. After 10 generations, the inbreeding coefficient reached a maximum value of 0.56 and 0.16 in the inbred and control populations, respectively. Analysing changes in the life-history traits across generations showed that inbreeding depression in clutch size and offspring survival increased during the first four to six generations in the populations from the inbred treatment and subsequently decreased as expected if purging occurred. Inbreeding depression in two other traits was weaker but showed similar changes across generations. The loss of six populations in the inbred treatment indicates that removal of deleterious alleles also occurred by extinction of populations that presumably harboured high genetic load.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".