Assessing spatial discreteness of Hudson Bay polar bear populations using telemetry and genetics
Bibliographic record
Abstract
Abstract Identifying biologically meaningful populations is essential to the conservation and management of at‐risk species. Natural populations can be delineated using a variety of methods including tag recoveries, telemetry, stable isotopes, and population genetics, but understanding the processes that lead to and maintain the demographic and genetic distinctiveness of populations is also important. We combined telemetric and genetic data from three adjacent polar bear (Ursus maritimus) populations in Hudson Bay, Canada, to compare two methods of defining structure. We compared the population structure inferred from utilization distributions (UDs) of 62 adult female polar bears tracked by satellite telemetry during the mating season by grouping individuals in two ways: (1) by the management population in which individuals were sampled (capture location), and (2) by population genetic assignment of individuals using marker data (genetic assignment). We found that space‐use overlap varied depending on how individuals were grouped. We found 19.1–34.4% UD overlap when capture locations were used to group individuals, but there was no UD overlap for bears across different genetic groupings. Wildlife management objectives should include consideration of genetic diversity and differentiation, and we found that using genetic assignment to augment analyses from telemetric data provided additional insights on population delineation.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".