No evidence for inbreeding avoidance in a great reed warbler population
Why this work is in the frame
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Bibliographic record
Abstract
Inbreeding depression may drive the evolution of inbreeding avoidance through dispersal and mate choice. In birds, many species show female-biased dispersal, which is an effective inbreeding avoidance mechanism. In contrast, there is scarce evidence in birds for kin discriminative mate choice, which may, at least partly, reflect difficulties detecting it. First, kin discrimination may be realized as dispersal, and this is difficult to distinguish from other causes of dispersal. Second, even within small, isolated populations, it is often difficult to determine the potential candidates available to a female when choosing a mate. We sought evidence for inbreeding avoidance via kin discrimination in a breeding population of great reed warblers (Acrocephalus arundinaceus) studied over 17 years. Inbreeding depression is strong in the population, suggesting that it would be adaptive to avoid relatives as mates. Detailed data on timing of settlement and mate search movements made it possible to identify candidate mates for each female, and long-term pedigrees and resolved parentage enabled us to estimate relatedness between females and their candidate mates. We found no evidence for kin discrimination: mate choice was random with respect to relatedness when all mate-choice events were considered, and, after correction for multiple tests, also in all breeding years. We suggest that dispersal is a sufficient inbreeding avoidance mechanism in most situations, although the lack of kin discriminative mate choice has negative consequences for some females, because they end up mating with closely related males that lowers their fitness.
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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 it