Can we measure the benefits of help in cooperatively breeding birds: the case of superb fairy‐wrens <i>Malurus cyaneus</i>?
Bibliographic record
Abstract
1. Correlational studies of reproductive success are plagued by difficulty over the direction of causation. For example, improved reproductive success with age can result from increased experience or reproductive effort, or selection against low-quality phenotypes that survive poorly. An association between supernumeraries and reproductive success in cooperative breeders can arise either because supernumeraries boost productivity, or productive territories accumulate supernumeraries. 2. Paired comparisons of parents sampled with and without supernumeraries have recently been widely applied to quantify help. However, Dickinson & Hatchwell (2004) have argued that this approach is flawed. They conjectured that those groups that gain supernumeraries are a biased superior sample of those that initially lack supernumeraries, while groups that lose supernumeraries will be a sample of inferior cooperative groups. They predict that these biased comparisons will underestimate the effect of help. 3. This conjecture has neither been explored theoretically, nor empirically tested. We use data from a 19-year study of the superb fairy-wren Malurus cyaneus to examine the conjecture and derive predictors of annual reproductive success in this species. 4. We introduce statistical models of reproductive success based on a zero-inflated Poisson link function to identify three strong correlates of reproductive success: high spring rainfall, progress from the first to later years of life, and acquisition of supernumeraries. 5. First year females that died after breeding and those that survived to breed again had similar productivity. As female productivity improves with age, increased reproductive skill or effort is implicated rather than selection against inferior phenotypes. 6. We argue that the Dickinson-Hatchwell conjecture does not constrain paired comparisons in M. cyaneus. The dominant male and breeding female gain no immediate fecundity advantage from supernumeraries. 7. Effects on the future survival of dominants are even more difficult, as while helpers could enhance survival of dominants, a territory that facilitates survival should also accumulate philopatric supernumeraries. Males, the philopatric sex, did not survive better on territories with supernumeraries. However, females, the dispersive sex, had higher survival as the number of supernumeraries increased, because helpers allowed them to reduce the costs of reproduction. These data exacerbate the paradox posed by previously reported costs that supernumeraries impose on dominant males.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".