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Mate Choice and Hybridization in Lake Malawi Cichlids, <i>Sciaenochromis fryeri</i> and <i>Cynotilapia afra</i>

2007· article· en· W2120548287 on OpenAlexafffund
Robert Gerlai

Bibliographic record

VenueEthology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsBiologyCichlidZoologyMate choiceEcologyReproductive isolationSympatric speciationCaptivityCourtshipCourtship displaySexual selectionSympatryPomacentridaeEthogramHabitatMatingFish <Actinopterygii>Coral reef fishPopulationFisheryDemography

Abstract

fetched live from OpenAlex

Abstract Lake Malawi is home to hundreds of cichlid species, most of which are endemic to the lake. Several of these species exhibit a remarkably similar behavioural repertoire including territorial and reproductive behaviours. Despite their diverse anatomy and body colouration, hybridization among Malawi cichlids has been observed in captivity and has also been reported to occur in nature. Here, possible factors underlying hybridization between two species, Sciaenochromis fryeri , a large piscivore, and Cynotilapia afra (Jalo reef), a small planktivore biocover grazer, were investigated in captivity. In a series of experiments the size of the territory, the size of the males and the availability of conspecific mates were manipulated. It was found that mate selection in these two species under semi‐natural conditions is dependent not only upon species‐specific cues but also upon the status of the male. Larger male body size and larger territory size was generally preferred by females and when these attributes characterized a heterospecific male they also increased the probability of heterospecific courtship. Given that spawning between the above‐mentioned species resulted in fertile offspring and that their geographical location and habitat overlap in nature, the effect of their mate‐choice strategies on hybridization and speciation is considered.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.207

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 head0.016
GPT teacher head0.250
Teacher spread0.235 · 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

Citations11
Published2007
Admission routes2
Has abstractyes

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