Connectivity in brown bear populations: an assessment of gene flow in coastal British Columbia
Bibliographic record
Abstract
Brown bear (Ursus arctos) populations have experienced declines in both number and range due to changes in land use and persecution. Identifying and protecting areas with adequate gene flow between populations is now of fundamental importance to the survival of some populations. This study assessed genetic variance and relatedness between individuals from two coastal regions of British Columbia. Samples were analysed at 8 microsatellite loci to determine individual genotypes for statistical analysis. The mean expected heterozygosity (He) of all individuals was 0.69. A difference in He was highlighted between genders, with females displaying homozygosity for 2 out of 8 loci. Genetic differentiation was low (FST = 0.06) between coastal individuals. Dispersal distances of bears in the area would suggest the possibility of gene flow between the two regions. Genetic distance estimates, through kinship coefficients and the proportion of shared alleles, further reiterated a link between the two densely populated areas. Data from this study indicates dispersal via gene flow between the brown bears of southwest coastal British Columbia. Comparisons can now be made with European populations, regarding relatedness and assessment of the connectivity of landscapes. Implications for the conservation of this species in Europe’s fragmented landscape will be discussed.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".