Gender-specific dispersal distances of grizzly bears estimated by genetic analysis
Bibliographic record
Abstract
Natal dispersal is difficult to quantify, and long-distance events are often undetected, leading to biased estimates. Following offspring from their natal home range to their postdispersal adult breeding home range is challenging, and gathering sufficient data for large mammals with long generation times is particularly difficult. Here we measure average sex-specific dispersal distances in grizzly bears (Ursus arctos L., 1758) using individual-based genetic analysis. We genetically sampled and generated 15-locus microsatellite genotypes for 711 grizzly bears over a range of 100 000 km2in southwestern Canada. Microsatellite markers are inherited in a Mendelian fashion, allowing us to use likelihood-based parentage analyses to estimate parent–offspring dyads. We used the distance between individually captured females of parent–offspring pairs (i.e., mother–daughter) to estimate female natal dispersal distances and found that, on average, females dispersed 14.3 km from the center of their natal home range. We used the distance between males of parent–offspring pairs (i.e., father–son) to estimate average male dispersal distances and found that males dispersed, on average, 41.9 km from their natal, or maternal, home range (mother–son dispersal distance). We used a simulation model to estimate the bias associated with measuring the father–son (male–male) distance as an estimate of the mother–son distance.
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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".