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Record W2981172674 · doi:10.3389/fmars.2019.00676

First Reconstruction of Kinship in a Scalloped Hammerhead Shark Aggregation Reveals the Mating Patterns and Breeding Sex Ratio

2019· article· en· W2981172674 on OpenAlexaff
Amandine D. Marie, Christophe M. Herbinger, Philippe Fullsack, Ciro Rico

Bibliographic record

VenueFrontiers in Marine Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBiologyThreatened speciesEndangered speciesPopulationFisheryMatingEcologyZoologyDemography

Abstract

fetched live from OpenAlex

Sharks constitute one of the most threatened clades (Selachimorpha) of all marine fish, and substantial management efforts are required to help the recovery of their populations worldwide. Despite its significant impact on population dynamics and conservation, sharks’ reproductive and philopatric behavior has received little attention in fisheries management. The scalloped hammerhead shark (Sphyrna lewini), an endangered species listed on the IUCN’s Red List and on the CITES’ Appendix II, is an apex predator that potentially exhibits female philopatry to mating grounds. We reconstructed, for the first time in an open ocean species, the relationship among 166 juvenile individuals caught in a recently discovered aggregation of the Rewa Delta, Fiji, and determined the sample population’s mating system using 6,437 SNPs. Using two software packages, COLONY2 and SNP PEDIGREE, results revealed very high consistency in the identification of full and half sib. Moreover, COLONY2 allowed us to identify an equal breeding sex ratio for each cohort analyzed for this population (1.04:1; 1.02:1), as well as several cases of multiple paternity and numerous matings of the same male with different females suggesting polygynandry for this species. These findings reveal additional information about the complex life history of the scalloped hammerhead shark.

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)0.000
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 score0.036
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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
Published2019
Admission routes1
Has abstractyes

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