Broad‐scale genetic monitoring suggests density‐dependent dispersal in a large carnivore
Bibliographic record
Abstract
Characterizing patterns and drivers of dispersal is fundamental to our understanding of animal ecology and ultimately informing species conservation and management strategies. In this study, we used microsatellite data from 3941 individual black bears Ursus americanus occupying 73 spatially distinct sampling areas across a large heterogeneous landscape to characterize dispersal via gene flow directionality. We fit spatial models to quantified gene flow to test hypotheses regarding drivers of putative dispersal patterns. Specifically, we tested the relative influence of food productivity gradients, bear density, and bear harvest on dispersal. We also evaluated differences in gene flow patterns within and between sexes to assess sex‐biased dispersal. We found evidence suggestive of positive density‐dependent, male‐biased dispersal. Our data show evidence of a relationship between dispersal and broad food productivity gradients. Specifically, male bears displayed preferential dispersal towards mixed deciduous forests with higher food productivity relative to less productive boreal forests. Given the dense sampling scheme across a continuous population, occupying a large heterogeneous landscape, these results provide key insight as to the likely drivers of dispersal patterns in a wide‐ranging mammal.
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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 | 0.000 | 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".