Multiscale relationships between humpback whales and forage species hotspots within a large marine ecosystem
Bibliographic record
Abstract
Fluctuations in prey abundance, composition, and distribution can impact predators, and when predators and fisheries target the same species, predators become essential to ecosystem-based management. Because of the difficulty in collecting concomitant predator-prey data at appropriate scales in patchy environments, few studies have identified strong linkages between cetaceans and prey, especially across large geographic areas. During summer 2018, a line-transect survey for cetaceans and coastal pelagic species was conducted over the continental shelf and slope of British Columbia, Canada, and the US West Coast, allowing for a large-scale investigation of predator-prey spatial relationships. We report on a case study of humpback whales (Megaptera novaeangliae) and their primary prey-Pacific herring (Clupea pallasii), northern anchovy (Engraulis mordax), and krill-using generalized additive models to explore the relationships between whale abundance on 10-km transect segments and prey metrics. Prey metrics included direct measures of biomass densities on segments and an original hotspot metric. For each prey species, segments in the upper fifth percentile for biomass density (across all segments) were designated hotspots, and whale counts on a segment were evaluated for their relationship to number of hotspot segments (species-specific and multispecies) within 25, 50, or 100 km. Whale abundance was not strongly related to direct measures of biomass densities, whereas models using hotspot metrics were more effective at describing variation in whale abundance, underscoring that evaluating prey at relevant and measurable scales is critical in patchy, dynamic marine environments. Our analysis highlighted differences in the distribution and prey availability for three humpback whale distinct population segments (DPSs) as defined under the US Endangered Species Act, including threatened and endangered DPSs that forage within the California Current Large Marine Ecosystem. These linkages provide insights into which prey species whales may be targeting in different regions and across multiple scales and, consequently, how climatic variability and anthropogenic risks may differentially impact these distinct predator-prey assemblages. By identifying scale-appropriate prey hotspots that co-occur with humpback whale aggregations, and with targeted, consistent prey sampling and estimations of potential consumption rates by whales, these findings can help inform the conservation and management of humpback whales within an ecosystem-based management framework.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".