Fine-scale Prey and Foraging Behaviour of Humpback Whales in Southern British Columbia
Notice bibliographique
Résumé
The North Pacific humpback whale Megaptera novaeangliae is showing strong recovery from commercial over-exploitation, and is recolonizing traditional feeding areas in Pacific Canadian waters now occupied by shipping lanes and high concentrations of people in coastal regions. Meeting and maintaining recovery conservation objectives, therefore, will require accurate information on important prey species and whale foraging behaviour. My dissertation evaluates three data-intensive sampling tools for collecting subsurface information in humpback whale-selected feeding areas in southern British Columbia. Systematic, small-vessel surveys, using active acoustics, enabled comparison of the spatial distribution of prey proximal to, and in areas without, humpback whales off Vancouver Island, British Columbia (BC). My objective was to use active acoustics to broadly separate fish from zooplankton in areas used by humpback whales in southern BC, and to determine if one of these functional prey groups is associated with whale presence more than the other. Surveyed areas in which humpback whales were present were associated with higher zooplankton than fish biomass. I recommend using 38, 125, and 200 kHz frequencies with concurrent net-sampling to improve acoustical classification of co-existing taxa in whale feeding areas. Kinematic diversity in rorqual feeding is manifest over space and time because different prey types are encountered by individual whales. I use a CATS Diary suction cup tag attached to a humpback whale in Juan de Fuca Strait, concurrently with acoustic prey mapping, to describe the prey and estimate the feeding performance of the whale. The tag sensor data suggested that the whale was feeding on krill, while the prey data determined that the whale was in fact feeding on fish, likely walleye pollock, using a “krill-like” lunge-feeding behaviour. A faecal sample from the whale revealed high DNA read abundance and bones from walleye pollock. Depending on the nature of the prey, inferences based solely on whale tag data may be vulnerable to incorrect assumptions about the prey type being targeted. Prey species actually ingested by rorquals are extremely difficult to determine, and therefore a large gap persists in understanding rorqual feeding ecology. I use molecular and visual analyses of faecal samples to illustrate a complementary approach to humpback whale diet analysis, with each method providing unique insight into prey diversity. DNA-metabarcoding of 14 humpback whale faecal samples revealed a fine-scale diversity of prey species detected in the faeces, but DNA detections from exogenous contaminants and secondary predation may influence the results. Accumulating evidence indicates that humpback whales are highly adaptable predators, which often associate with complex prey communities. Data collection must therefore be adaptable to changing spatial and temporal dimensions and include most of the water column. In my experience and opinion, calibrated multifrequency echosounders on small vessels enables data collection on a regular basis, and is among the most promising quantitative tool to meet the challenges of subsurface prey assessments for large whales in BC.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 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 ».