Genetic analysis reveals demographic fragmentation of grizzly bears yielding vulnerably small populations
Bibliographic record
Abstract
Ecosystem conservation requires the presence of native carnivores, yet in North America, the distributions of many larger carnivores have contracted. Large carnivores live at low densities and require large areas to thrive at the population level. Therefore, if human-dominated landscapes fragment remaining carnivore populations, small and demographically vulnerable populations may result. Grizzly bear range contraction in the conterminous USA has left four fragmented populations, three of which remain along the Canada-USA border. A tenet of grizzly bear conservation is that the viability of these populations requires demographic linkage (i.e. inter-population movement of both sexes) to Canadian bears. Using individual-based genetic analysis, our results suggest this demographic connection has been severed across their entire range in southern Canada by a highway and associated settlements, limiting female and reducing male movement. Two resulting populations are vulnerably small (< or =100 animals) and one of these is completely isolated. Our results suggest that these trans-border bear populations may be more threatened than previously thought and that conservation efforts must expand to include international connectivity management. They also demonstrate the ability of genetic analysis to detect gender-specific demographic population fragmentation in recently disturbed systems, a traditionally intractable yet increasingly important ecological measurement worldwide.
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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 | 0.000 | 0.000 |
| 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".