Small‐scale spatial distributions of long‐finned pilot whales change over time, but foraging hot spots are consistent: Significance for marine wildlife tourism management
Bibliographic record
Abstract
Abstract Data collected opportunistically aboard marine wildlife tourism vessels are an inexpensive source of spatial information on the target species. Although these data are often challenging to analyze, they can be used to monitor spatiotemporal changes in species distribution and behavior. Disruptions from whale‐watching vessels to behaviors such as foraging can be particularly harmful to cetaceans, but impacts could be reduced if areas essential for these sensitive behaviors are identified. We used data collected onboard whale‐watching vessels to explore space‐use patterns in long‐finned pilot whales (Globicephala melas) off northern Cape Breton Island, Canada, an area where tourism is essential. Encounters with pilot whales between 2011–2016 occurred twice as far offshore than during 2003–2006 and 2008, and foraging activity decreased. Despite the changes in distribution and activity budgets, we identified two hot spots of foraging activity that persisted through time. These identified foraging hot spots comprised only a small proportion (20 km2) of the range used by whale‐watching vessels. Adaptive local management (e.g., voluntary codes of conduct) focused on limiting interactions in these energetically important areas may help reduce any potential impacts from whale‐watching and promote the continued viability of the whale population and the tourism industry that relies on it.
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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 | 0.000 | 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".