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Record W2334740899 · doi:10.3354/meps11260

The paradox of the pelagics: why bluefin tuna can go hungry in a sea of plenty

2015· article· en· W2334740899 on OpenAlexaffabout
WJ Golet, NR Record, Sigrid Lehuta, Molly Lutcavage, Benjamin Galuardi, AB Cooper, AJ Pershing

Bibliographic record

VenueMarine Ecology Progress Series · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTunaFisheryPredationFisheries scienceThunnusMarine conservationBycatchApex predatorFisheries managementGeographyEcologyBiologyFishingFish <Actinopterygii>

Abstract

fetched live from OpenAlex

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 527:181-192 (2015) - DOI: https://doi.org/10.3354/meps11260 The paradox of the pelagics: why bluefin tuna can go hungry in a sea of plenty Walter J. Golet1,2,*,**, Nicholas R. Record3,**, Sigrid Lehuta2, Molly Lutcavage4, Benjamin Galuardi4, Andrew B. Cooper5, Andrew J. Pershing1,2,** 1School of Marine Sciences, University of Maine, College Road, Orono, ME 04469, USA 2Gulf of Maine Research Institute, 350 Commercial Street, Portland, ME 04101, USA 3Bigelow Laboratory for Ocean Sciences, East Boothbay, ME 04544, USA 4Department of Environmental Conservation, Marine Fisheries Institute, University of Massachusetts Amherst, PO Box 3188, Gloucester, MA 01931, USA 5School of Resource and Environmental Management, Simon Fraser University, 8888 University Drive, Burnaby, V5A 1S6, BC, Canada *Corresponding author: walter.golet@maine.edu**These authors contributed equally to this work ABSTRACT: Large marine predators such as tunas and sharks play an important role in structuring marine food webs. Their future populations depend on the environmental conditions they encounter across life history stages and the level of human exploitation. Standard predator-prey relationships suggest favorable conditions (high prey abundance) should result in successful foraging and reproductive output. Here, we demonstrate that these assumptions are not invariably valid across species, and that somatic condition of Atlantic bluefin tuna Thunnus thynnus in the Gulf of Maine declined in the presence of high prey abundance. We show that the paradox of declining bluefin tuna condition during a period of high prey abundance is explained by a change in the size structure of their prey. Specifically, we identified strong correlations between bluefin tuna body condition, the relative abundance of large Atlantic herring Clupea harengus, and the energetic payoff resulting from consuming different sizes of herring. This correlation is consistent with optimal foraging theory, explaining why bluefin tuna condition suffers even when prey is abundant. Furthermore, optimal foraging principles explain a shift in traditional bluefin tuna foraging areas, toward regions with a higher proportion of large herring. Bluefin tuna appear sensitive to changes in the size spectrum of prey rather than prey abundance, impacting their distribution, reproduction and economic value. Fisheries managers will now face the challenge of how to manage for high abundance of small pelagic fish, which benefits benthic fishes and mammalian predators, and maintain a robust size structure beneficial for top predators with alternative foraging strategies. KEY WORDS: Bluefin tuna · Herring · Optimal foraging · Condition · Thunnus thynnus Full text in pdf format PreviousNextCite this article as: Golet WJ, Record NR, Lehuta S, Lutcavage M, Galuardi B, Cooper AB, Pershing AJ (2015) The paradox of the pelagics: why bluefin tuna can go hungry in a sea of plenty. Mar Ecol Prog Ser 527:181-192. https://doi.org/10.3354/meps11260 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 527. Online publication date: May 07, 2015 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2015 Inter-Research.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score1.000

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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.

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

Citations55
Published2015
Admission routes2
Has abstractyes

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