Inbreeding depression along a life‐history continuum in the great tit
Bibliographic record
Abstract
Inbreeding resulting from the mating of two related individuals can reduce the fitness of their progeny. However, quantifying inbreeding depression in wild populations is challenging, requiring large sample sizes, detailed knowledge of life histories and study over many generations. Here we report analyses of the effects of close inbreeding, based on observations of mating between relatives, in a large, free-living noninsular great tit (Parus major) population monitored over 41 years. Although mating between close relatives (f > or = 0.125) was rare (1.0-2.6% of matings, depending on data set restrictiveness), we found pronounced inbreeding depression, which translated into reduced hatching success, fledging success, recruitment to the breeding population and production of grand offspring. An inbred mating at f = 0.25 had a 39% reduction in fitness relative to that of an outbred nest, when calculated in terms of recruitment success, and a 55% reduction in the number of fledged grand offspring. Our data show that inbreeding depression acts independently at each life-history stage in this population, and hence suggest that estimates of the fitness costs of inbreeding must focus on the entire life cycle.
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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 | 0.001 |
| 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 | 0.001 |
| 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".