Environmental drivers of beluga whale distribution in a changing climate: a case study of summering aggregations in the Mackenzie Estuary and Tarium Niryutait Marine Protected Area
Bibliographic record
Abstract
During summer, the Eastern Beaufort Sea beluga whale ( Delphinapterus leucas (Pallas, 1776)) population aggregates in the waters of the Mackenzie Estuary and Tarium Niryutait Marine Protected Area (TNMPA). Guided by local communities’ priorities, this study aimed to better understand beluga summer habitat selection and to examine whether shifts in beluga distribution are expected under a changing climate. We used a resource selection function (RSF) based on aerial survey data and satellite remote sensing images to estimate the likelihood of beluga presence as a function of environmental conditions. The RSF revealed that belugas selected warm and turbid waters, with suspended particulate matter concentrations and sea surface temperatures ranging above average estuarine values. These specific conditions support hypotheses on the ecological roles of estuaries for belugas such as providing a thermal advantage for their calves or for beluga epidermal moulting. Using a diachronic analysis, we found a distribution shift towards coastal and inshore waters, areas already experiencing effects of climate change. Thus, the current distribution may reflect beluga responses to a changing climate, selecting warmer and more turbid areas. Our finding provides insight into current and evolving beluga habitat and habitat selection under a changing climate, which may help inform beluga management in the TNMPA.
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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 | 0.001 | 0.001 |
| 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".