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Record W2161679768 · doi:10.1111/jeb.12762

The association between the emergence of cooperative breeding and clutch size

2015· article· en· W2161679768 on OpenAlexafffund
Cody Koykka, Geoff Wild

Bibliographic record

VenueJournal of Evolutionary Biology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyAvian clutch sizeClutchFecundityCooperative breedingBreeder (animal)BroodPopulationEcologyReproductionDemographyEngineering

Abstract

fetched live from OpenAlex

Previous theoretical work has suggested that smaller brood sizes helped facilitate the emergence of cooperative breeding in birds. However, recent empirical evidence has found no statistically significant difference between the clutch sizes of cooperative breeders and that of noncooperative breeders. One explanation for this finding is that while small clutch sizes may predispose species to cooperative breeding, the emergence of cooperative breeding itself may influence the evolution of clutch size. Here, we develop a set of models using population dynamics to describe how the emergence of cooperative breeding influences clutch size. We find, in contrast to previous theoretical work, that the emergence of cooperative breeding does not necessarily decrease (and under certain conditions may actually increase) clutch size. In particular, clutch size may increase after the emergence of cooperative breeding if helpers--philopatric individuals that assist their breeding relatives--are able to substantially improve breeder fecundity at low costs to their own survival, and if the association between breeder and helper is brief. In many cases, clutch size increases following the emergence of cooperative breeding not because it is optimal for the breeder, but as the result of breeder-helper conflict over resource allocation.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.154

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.273
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2015
Admission routes2
Has abstractyes

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