The evolution of cooperative breeding in the African cichlid fish, Neolamprologus pulcher
Bibliographic record
Abstract
The conundrum of why subordinate individuals assist dominants at the expense of their own direct reproduction has received much theoretical and empirical attention over the last 50 years. During this time, birds and mammals have taken centre stage as model vertebrate systems for exploring why helpers help. However, fish have great potential for enhancing our understanding of the generality and adaptiveness of helping behaviour because of the ease with which they can be experimentally manipulated under controlled laboratory and field conditions. In particular, the freshwater African cichlid, Neolamprologus pulcher, has emerged as a promising model species for investigating the evolution of cooperative breeding, with 64 papers published on this species over the past 27 years. Here we clarify current knowledge pertaining to the costs and benefits of helping in N. pulcher by critically assessing the existing empirical evidence. We then provide a comprehensive examination of the evidence pertaining to four key hypotheses for why helpers might help: (1) kin selection; (2) pay-to-stay; (3) signals of prestige; and (4) group augmentation. For each hypothesis, we outline the underlying theory, address the appropriateness of N. pulcher as a model species and describe the key predictions and associated empirical tests. For N. pulcher, we demonstrate that the kin selection and group augmentation hypotheses have received partial support. One of the key predictions of the pay-to-stay hypothesis has failed to receive any support despite numerous laboratory and field studies; thus as it stands, the evidence for this hypothesis is weak. There have been no empirical investigations addressing the key predictions of the signals of prestige hypothesis. By outlining the key predictions of the various hypotheses, and highlighting how many of these remain to be tested explicitly, our review can be regarded as a roadmap in which potential paths for future empirical research into the evolution of cooperative breeding are proposed. Overall, we clarify what is currently known about cooperative breeding in N. pulcher, address discrepancies among studies, caution against incorrect inferences that have been drawn over the years and suggest promising avenues for future research in fishes and other taxonomic groups.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".