Evidence for size and sex‐specific dispersal in a cooperatively breeding cichlid fish
Bibliographic record
Abstract
African Great Lake cichlid populations are divided into thousands of genetic subpopulations. The low gene flow between these subpopulations is thought to result from high degrees of natal philopatry, heavy predation pressure, and a patchy distribution of preferred habitats. While predation pressure and habitat distribution are fairly straightforward to assess, data on dispersal distances and rates are scarce. In fishes, direct observations of dispersal events are unlikely, but dispersal can be studied using molecular markers. Using seven microsatellite loci, we examined dispersal in the cooperatively breeding cichlid fish, Neolamprologus pulcher. As this species is found in well-defined groups clustered into subpopulations, we could assess dispersal on a narrow (within subpopulation) and broad (between subpopulation) scale. While fish were generally more related to others in their own subpopulation than they were to fish from other subpopulations, large males diverged from this pattern. Large males were more related to other large males from different subpopulations than they were to large males from their own subpopulation, suggesting more frequent dispersal by large males. Across subpopulations, relatedness between large males was higher than the relatedness among large females; this pattern was not detected in small males and small females. Within a subpopulation, individuals appeared to be preferentially moving away from relatives, and movement was unrestricted by the physical distance between groups. Our results highlight the importance of examining multiple spatial scales when studying individual dispersal biases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".