Fine-scale Prey and Foraging Behaviour of Humpback Whales in Southern British Columbia
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".