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Record W2030589701 · doi:10.1007/s11284-008-0487-z

Effects of body size, age and maturity stage on diet in a large shark: ecological and applied implications

2008· article· en· W2030589701 on OpenAlexaff
Luis O. Lucifora, Verónica B. García, Roberto Carlos Menni, Alicia H. Escalante, Natalia M. Hozbor

Bibliographic record

VenueEcological Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsDalhousie University
FundersConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsPredationBiologyPelagic zonePredatorEcologyApex predatorAllometryZoology

Abstract

fetched live from OpenAlex

Abstract Ontogenetic diet shifts are a widespread phenomenon among vertebrates, although their relationships with life history traits are poorly known. We analyzed the relative importance of body size, age and maturity stage as determinants of the diet of a marine top predator, the copper shark, Carcharhinus brachyurus , by examining stomach contents using a multiple‐hypothesis modeling approach. Copper sharks shifted their diet as size and age increased and as they became sexually mature, incorporated larger prey as they grew, and had a discrete shift in diet with body size, with only individuals larger than ≈200 cm total length able to prey on chondrichthyans. Body size was the most important trait explaining the consumption of chondrichthyans, while age determined the consumption of pelagic teleosts. Pelagic teleosts were consumed mostly by medium‐aged sharks, a result, probably, of a risk‐reducing feeding strategy at young ages coupled with either a senescence‐related decline in performance or a change in sensory capabilities as sharks age. Copper sharks of all sizes were able to cut prey in pieces, implying that gape limitation (i.e., the impossibility of eating prey larger than a predator's mouth) did not play a role in producing the diet shift. Our results suggest that, contrary to the current practice of setting minimum but not maximum size limits in catches, any plan to conserve or restore the ecological function of sharks, through their predatory control of large prey, should aim to maintain the largest individuals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.034
GPT teacher head0.334
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), 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

Citations127
Published2008
Admission routes1
Has abstractyes

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