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Life history evolution in cichlids 2: directional evolution of the trade-off between egg number and egg size

2005· article· en· W2103896834 on OpenAlexaff
Niclas Kolm, Nicholas B. Goodwin, Sigal Balshine, John D. Reynolds

Bibliographic record

VenueJournal of Evolutionary Biology · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsMcMaster University
FundersNatural Environment Research CouncilVetenskapsrådet
KeywordsBiologyEvolutionary biologyZoologyBiological evolutionLife historyEcologyGenetics

Abstract

fetched live from OpenAlex

The negative relationship between offspring number and offspring size provides a classic example of the role of trade-offs in life history theory. However, the evolutionary transitions in egg size and clutch size that have produced this negative relationship are still largely unknown. Since body size may affect both of these traits, it would be helpful to understand how evolutionary changes in body size may have facilitated or constrained shifts in clutch and egg size. By using comparative methods with a database of life histories and a phylogeny of 222 genera of cichlid fishes, we investigated the order of evolutionary transitions in these traits in relation to each other. We found that the ancestral large-bodied cichlids first increased egg size, followed by a decrease in both body size and clutch size resulting in the common current combination of a small-bodied cichlid with a small clutch of large eggs. Furthermore, lineages that deviated from the negative relationship between clutch and egg size underwent different transitions in these traits according to their body size (large bodied genera have moved towards the large clutch/small egg end of the continuum and small bodied genera towards the small clutch/large egg end of the continuum) to reach the negative relationship between clutch size and egg size. Our results show that body size is highly important in shaping the negative relationship between clutch size and egg size.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.150
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.236
Teacher spread0.217 · 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

Citations31
Published2005
Admission routes1
Has abstractyes

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