Strong spatial genetic structure in peripheral but not core populations of Sitka spruce [<i>Picea sitchensis</i> (Bong.) Carr.]
Bibliographic record
Abstract
We examined spatial genetic structure within eight populations of Sitka spruce classified as core or peripheral based on ecological niche, and continuous or disjunct based on species distribution. In each population, 200 trees were spatially mapped and genotyped for eight cDNA-based sequence tagged site (STS) codominant markers. Spatial autocorrelation was assessed by estimating p(ij), the average co-ancestry coefficient, between individuals within distance intervals. The distribution of alleles and genotypes within core populations was almost random, with nonsignificant co-ancestry values among trees as close as 50 m in core populations. In contrast, the distribution of alleles and genotypes within peripheral populations revealed an aggregation of similar multilocus genotypes, with co-ancestry values greater than 0.20 among trees up to 50 m apart and significant, positive values between trees up to 500 m. The relatively high density of reproductive adults in core populations may lead to highly overlapping seed shadows that limit development of spatial genetic structure. However, in peripheral populations with a lower density of adults, the distribution of alleles and genotypes was highly structured, likely due to offspring establishment near maternal trees and subsequent biparental inbreeding, as well as more recent population establishment at the leading edge of post-Pleistocene range expansion. Conserving genetic diversity in peripheral populations may require larger reserves for in situ conservation than required in core populations. These data on spatial genetic structure can be used to provide guidance for sampling strategies for both ex situ conservation and research collections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".