Assessing genetic diversity and structure of fragmented populations of eastern white pine (Pinus strobus) and western white pine (P. monticola) for conservation management
Bibliographic record
Abstract
Aims Many pine populations in Canada have fragmented distributions resulting from the effects of glaciations, overharvesting and white pine blister rust infections. Forest fragmentation can modify gene flow and reduce genetic diversity. Selective logging can reduce the density of trees, thereby altering mating patterns and increasing inbreeding. The hypothesis of the present study is that forest fragmentation will not increase inbreeding and will have no effect on genetic diversity parameters in the Canadian Pinus moniticola and P. strobus populations targeted because of (i) the long life span of the pine species, (ii) outbreeding and self-incompatibility of P. monticola and P. strobus and (iii) wind pollination resulting in high gene flow among populations. We studied the genetic diversity of P. strobus across its range in Canada, and we completed a detailed analysis of the genetic structure of P. monticola populations from western Canada using microsatellites genetic markers. Methods Seed samples from 10 P. monticola populations and 10 P. strobus populations were collected from western and eastern Canada, respectively. The mother trees included in seed lots were representative of each stand. Genomic DNA extracted from each sample was amplified with microsatellite primers. The intra- and interpopulation genetic diversity parameters were assessed using Popgene and Genepop softwares and the genetic distances among populations within each species using the PowerMarker software. Important findings Pinus monticola and P. strobus exhibited moderate to high genetic diversity. Also, both species showed low levels of inbreeding despite the geographic isolation and small stand size. Gene flow estimates were high and population differentiation values were relatively low for these fragmented forest sites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".