Opportunistic sightings from fisheries surveys inform habitat suitability for northern bottlenose whales Hyperoodon ampullatus and sperm whales Physeter macrocephalus in Baffin Bay and Davis Strait, Canadian Arctic
Bibliographic record
Abstract
Knowledge of spatial habitat patterns is critical for understanding the ecology of cetaceans and informing conservation efforts. However, this can be difficult to obtain for species that live in deep, offshore Arctic waters where they are not easily observed. We investigated habitat suitability for 2 cetacean species, northern bottlenose whaleHyperoodon ampullatusand sperm whalePhyseter macrocephalus, in Baffin Bay and Davis Strait, eastern Canadian Arctic. Presence locations were obtained from a unique marine mammal sightings data set where observations were opportunistically recorded during annual government-led fisheries surveys (1999-2017). Environmental variable data were used as predictors in presence-only habitat suitability modelling in Maxent software. A total of 12 sperm whales were observed at 9 unique locations, and 282 northern bottlenose whales were observed at 66 unique locations. The best habitat suitability model for sperm whale (area under the curve [AUC] = 0.72) and for northern bottlenose whale (AUC = 0.88) indicated higher suitability for both species in the central portion of the study area; higher suitability for sperm whales was also present in the southern part of the study area. A future projections scenario using environmental data from 2021 forecasted an increase in suitability in northern regions for both species. Post-model comparisons identified significant relationships between survey effort and habitat suitability, and squid biomass and habitat suitability for both species, although the variance explained by these models was low. We discuss the importance of monitoring cetacean range expansion of temperate whales in the Arctic and how this could lead to shifts in ecosystem dynamics and increased conflict with commercial fisheries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".