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Enregistrement W2271552226 · doi:10.1242/jeb.112656

The fittest fish escape trawling

2015· article· en· W2271552226 sur OpenAlexaff
Erin S. McCallum

Notice bibliographique

RevueJournal of Experimental Biology · 2015
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueFish Ecology and Management Studies
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésTrawlingPredationFisheryFish <Actinopterygii>PopulationBiologyVulnerability (computing)EcologyDemographyComputer science

Résumé

récupéré en direct d'OpenAlex

Humans have used trawling nets to catch fish for hundreds of years, making us one of the most effective predators for certain fish species. It is well known that we can shape characteristics of fish populations through ‘fishery-induced evolution’, where we catch the most desirable and largest fish from the population while trawling. Much less is known about an individual fish's vulnerability to trawling. For example, are some fish consistently more likely to be captured? And, might this capture vulnerability be due to aspects of their physiology already known to be important for evading natural predators in the wild, like metabolic rate and swimming performance?Shaun Killen, Julie Nati and Cori Suski, of the University of Glasgow, UK, and the University of Illinois at Urbana-Champaign, USA, set out to answer these questions using a population of minnows in a swim tunnel outfitted with a trawling net. While you might think it is less optimal to test such questions using a surrogate species in the laboratory, Killen and colleagues point out that these questions would have been impossible to answer with large-scale trawling in the wild.Knowing that quick, anaerobically powered movements such as darting are often used by fish to avoid predators and could be used to avoid oncoming trawls, the team measured various aspects of metabolism after exhaustively exercising fish. They measured how much extra oxygen each fish consumed after exercise before returning to their baseline metabolic rate (metabolism for everyday functions). Then, the researchers grouped fish in shoals and assessed their ability to resist capture in the trawl when the water flow was set to a constant speed of 38 cm s−1. Finally, the authors assessed swimming performance when they increased the water flow rate to simulate the fish trying to escape and measured the speed at which the fish switched from aerobic swimming (smooth and sustained movement) to anaerobic swimming (burst and glide movement). This switch was a measure of aerobic or endurance performance, and could be important for fish out-swimming a trawl net. They also measured the speed at which the fish failed to sustain anaerobic burst swimming and was unable to continue swimming in order to evaluate the animal's anaerobic power.The researchers first found that a fish's vulnerability to trawl capture was highly repeatable as the same fish were captured repeatedly across multiple trawling sessions. The fish that avoided capture also had greater anaerobic capacity and anaerobic swimming performance, they consumed more oxygen after exercise and were able to sustain burst and glide swimming at greater speeds: so anaerobic movements could be important for escaping an approaching trawl. In addition to increased anaerobic performance, fish that avoided capture had greater aerobic swimming performance and they maintained smooth locomotion at higher water speeds. The authors suggest that aerobic endurance could be important for out-swimming trawls after their initial approach.Killen's team is the first to identify that physiological traits can determine a fish's vulnerability to capture by a trawling net and that this vulnerability is very consistent. While more research is needed to fully understand the relationship between various aspects of exercise physiology and capture, they have provided an intriguing first investigation. Their results suggest that fisheries have the potential to shape physiological traits in heavily trawled fish populations. Indeed, by capturing all the ‘slow-pokes’ we might be causing our most consumed fish to be better at escaping our trawl sweeps.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0120,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,023
Tête enseignante GPT0,274
Écart entre enseignants0,251 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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