Foraging behaviour of Thick-billed Murres (Uria lomvia) in northern Hudson Bay
Notice bibliographique
Résumé
The foraging behaviour of seabirds has been well-studied, but the role of energy costs and prey type in determining foraging behaviour is still poorly known. To investigate how energy costs influence the foraging behaviour of Thick-billed Murres, a generalist seabird, at Coats Island, Nunavut, I attached positively (n=9), negatively (n=10) and neutrally (n=9) buoyant handicaps and drag handicaps of cross-sectional area equivalent to three (2.8 cm2;n=8) and six (5.6 cm2;n=6) percent of murre body cross-sectional area. To investigate how murres modify their foraging behaviour for different prey types, I attached time-depth-temperature recorders to chick-rearing murres (n=23 in 2004;n=33 in 2005;n= 60 in 2006) and monitored dive behaviour on the dive bout preceding the delivery of prey items observed at the colony. When buoyancy was altered, or drag increased; murres reduced dive depth, dive duration, ascent rates, descent rates and time spent diving. Handicapped murres did not alter surface pause duration, but surface pause duration increased for a given dive duration, agreeing with predictions from foraging theory. Thus, murres altered dive behaviour in response to increasing energy costs. Dive behaviour for the following prey: fish doctor, squid, amphipods, daubed shanny, sand lance and Arctic shanny was discriminated from each other at the 80% or 95% confidence level by minimum convex polygons on a discriminant analysis of dive variables and, therefore, were considered "specialist" prey items. Specifically, amphipods were captured after V-shaped dives near the colony with a slow descent rate, squid were captured after deep V-shaped dives and fish doctor were captured after a long series of U-shaped dives in warm water far from the colony. Dive behaviour for Arctic cod, capelin and sculpin, overlapped both with each other and with the behaviour associated with other prey items and, therefore, were classified as "generalist" prey items. In general, V-shaped dives preceded deliveries of pelagic prey items and U-shaped dives preceded deliveries of benthic prey items. The relationship between surface pause, dive depth and dive duration also varied with prey type. For example, surface pause duration decreased weakly (but significantly) with prey mass (R2=0.01-0.04) and was unrelated to prey type (schooling vs. benthic); dive diration for a given depth increased with prey mass (R2=0.17) and was longer for benthic items, presumably because benthic dives involved less energy expenditure. Thus, dive behaviour clearly reflected prey type and, therefore, perceived energy gain. Distance flown for a given prey item and average mass of prey items declined over the season, suggesting that murres depleted prey from waters near the colony. This conclusion was also supported by a tradeoff between depth and distance and a trend towards increasing prey mass with flight distance. Consequently, I concluded that seabird foraging behaviour is influenced by energy costs, prey type and degree of prey depletion. A thorough understanding of these issues is necessary to use seabird foraging behaviour as an indicator for prey abundance or distribution.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».