Delineating genetic groupings in continuously distributed species across largely homogeneous landscapes: a study of American black bears (<i>Ursus americanus</i>) in Ontario, Canada
Bibliographic record
Abstract
There is a crucial need to understand the genetic consequences of landscape modifications on continuous populations that could become fragmented, and to evaluate the degree of differentiation of isolated populations that were historically part of the core. Using 15 microsatellite loci, we evaluated the genetic structure of American black bears ( Ursus americanus Pallas, 1780) across a vast, contiguous Ontario landscape (>1 × 106km2) that largely represents their pre-European settlement distribution. Because geographic barriers are absent, we predicted that isolation by distance would drive genetic structure. We identified three genetic clusters (Northwest, Southeast, and Bruce Peninsula) that were less differentiated than when assessed with mtDNA, suggesting the influence of male-biased dispersal on large-scale genetic differentiation. Isolation by distance (r = 0.552, P = 0.001) was supported by a weak, clinal variation between Northwest and Southeast, illustrating the challenges to delineate populations in wide-ranging taxa. The Bruce Peninsula cluster, confined to a small area under strong anthropogenic pressures, was more differentiated from neighbouring clusters (FST > 0.13, P < 0.0001), with a genetic diversity corresponding to disjunct populations of black bears. Our results could be used in landscape genetics models to project the evolution of population differentiation based on upcoming landscape modifications in northern regions of North America.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".