Depletion of deep marine food patches forces divers to give up early
Notice bibliographique
Résumé
Many optimal foraging models for diving animals examine strategies that maximize time spent in the foraging zone, assuming that prey acquisition increases linearly with search time. Other models have considered the effect of patch quality and predict a net energetic benefit if dives where no prey is encountered early in the dive are abandoned. For deep divers, however, the energetic benefit of giving up is reduced owing to the elevated energy costs associated with descending to physiologically hostile depths, so patch residence time should be invariant. Others consider an asymptotic gain function where the decision to leave a patch is driven by patch-depletion effects - the marginal value theorem. As predator behaviour is increasingly being used as an index of marine resource density and distribution, it is important to understand the nature of this gain function. We investigated the dive behaviour of the world's deepest-diving seal, the southern elephant seal Mirounga leonina, in response to patch quality. Testing these models has largely been limited to controlled experiments on captive animals. By integrating in situ measurements of the seal's relative lipid content obtained from drift rate data (a measure of foraging success) with area-restricted search behaviour identified from first-passage time analysis, we identified regions of high- and low-quality patches. Dive durations and bottom times were not invariant and did not increase in regions of high quality; rather, both were longer when patches were of relatively low quality. This is consistent with the predictions of the marginal value theorem and provides support for a nonlinear relationship between search time and prey acquisition. We also found higher descent and ascent rates in high-quality patches suggesting that seals minimized travel time to the foraging patch when quality was high; however, this was not achieved by increasing speed or dive angle. Relative body lipid content was an important predictor of dive behaviour. Seals did not schedule their diving to maximize time spent in the foraging zone in higher-quality patches, challenging the widely held view that maximizing time in the foraging zone translates to greater foraging success.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».