Exploring contemporary and historic effective population sizes in Atlantic beluga whale populations
Bibliographic record
Abstract
Abstract Ongoing global environmental changes will require animals to adapt to new conditions, as their necessary resources for survival may shift or decline. Understanding population dynamics in wildlife informs conservation strategies by providing context for genetic diversity and current population abundances. Here, we use whole genome data to reconstruct past demographic trends and estimate contemporary effective population sizes in the beluga whale ( Delphinapterus leucas ), an Arctic endemic species with significant cultural importance, that is an integral part of the marine ecosystem along the Canadian coast. In line with previous work, we identified six genetic populations: 1) St. Lawrence Estuary, 2) Eastern High Arctic, 3) James Bay, 4) Cumberland Sound, 5) Hudson Bay-Strait Complex and 6) Little and Great Whale Rivers. Demographic history in the deep (up to 1,000,000 years) past revealed all but the Hudson Bay-Strait Complex population declined in effective population sizes following the last glacial period. Within the last several centuries, the Eastern High Arctic and Hudson Bay-Strait Complex populations have shown evidence for an increase in effective population size and have contemporary estimates greater than 2,000. The other four populations have contemporary estimates below 400. The very low effective population size estimates in Cumberland Sound, Little and Great Whale Rivers and James Bay, as well as the high inbreeding in the St. Lawrence Estuary indicate that these populations are in need of protective measures for long-term sustainability. These results have advanced our understanding of the dynamics of beluga genetics across eastern Canada and provide valuable metrics for conservation strategies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".