Genomic and phenotypic architecture of a spruce hybrid zone (<i><scp>P</scp>icea sitchensis </i>× <i><scp>P</scp>. glauca</i>)
Bibliographic record
Abstract
Interspecific hybridization may enhance the capacity of populations to adapt to changing environments, and has practical implications for reforestation. We use genome-wide estimates of admixture and phenotypic traits for trees in a common garden to examine the extent and direction of gene flow across a Picea hybrid zone, testing assumptions of the bounded hybrid superiority and tension zone models of hybrid zone maintenance. Seeds were collected from the ecological transition zone spanning from maritime to continental climates across the Picea sitchensis-P. glauca contact zone, and 721 trees were planted in a common garden experiment within the hybrid zone. Individuals were genotyped using a panel of 384 candidate-gene single nucleotide polymorphisms (SNPs) putatively associated with adaptive traits in Picea, and phenotyped at age ten for height and autumn cold hardiness. Low interspecific heterozygosity in hybrids indicated that intrinsic reproductive barriers were too weak to prevent widespread recombination, although introgression appeared asymmetric with P. sitchensis dominating the zone. Whereas marker-based hybrid index was strongly correlated with climate and geography, phenotypic traits exhibited weak or no significant clines. Our results indicated that exogenous selection appeared to play a strong role in the distribution and structure of this hybrid zone, indicative of an environmentally determined bounded hybrid superiority model of hybrid zone maintenance, although endogenous mechanisms could not be ruled out. This study provides insight into the mechanisms underlying adaptation across ecologically transitional hybrid zones that will ultimately provide an additional tool in managing these economically important tree species.
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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 | 0.000 |
| 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".