No evidence for inbreeding avoidance in a great reed warbler population
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".