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Record W2100092940 · doi:10.1371/journal.pone.0050096

Cost of Living Dictates what Whales, Dolphins and Porpoises Eat: The Importance of Prey Quality on Predator Foraging Strategies

2012· article· en· W2100092940 on OpenAlexaff
Jérôme Spitz, Andrew W. Trites, Vanessa Becquet, Anik Brind’Amour, Yves Cherel, Robert Galois, Vincent Ridoux

Bibliographic record

VenuePLoS ONE · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
FundersLa Rochelle UniversitéAgence Nationale de la Recherche
KeywordsPredationForagingBiologyOptimal foraging theoryEcologyApex predatorPredatorMarine ecosystemEcosystemZoology

Abstract

fetched live from OpenAlex

Understanding the mechanisms that drive prey selection is a major challenge in foraging ecology. Most studies of foraging strategies have focused on behavioural costs, and have generally failed to recognize that differences in the quality of prey may be as important to predators as the costs of acquisition. Here, we tested whether there is a relationship between the quality of diets (kJ · g(-1)) consumed by cetaceans in the North Atlantic and their metabolic costs of living as estimated by indicators of muscle performance (mitochondrial density, n = 60, and lipid content, n = 37). We found that the cost of living of 11 cetacean species is tightly coupled with the quality of prey they consume. This relationship between diet quality and cost of living appears to be independent of phylogeny and body size, and runs counter to predictions that stem from the well-known scaling relationships between mass and metabolic rates. Our finding suggests that the quality of prey rather than the sheer quantity of food is a major determinant of foraging strategies employed by predators to meet their specific energy requirements. This predator-specific dependence on food quality appears to reflect the evolution of ecological strategies at a species level, and has implications for risk assessment associated with the consequences of changing the quality and quantities of prey available to top predators in marine ecosystems.

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 score0.004
Threshold uncertainty score0.335

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 communication0.0000.001
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 head0.098
GPT teacher head0.284
Teacher spread0.187 · 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

Citations131
Published2012
Admission routes1
Has abstractyes

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