Genetic and geographic determinants of nitrogen isotope discrimination in black cottonwood (<i>Populus trichocarpa</i>)
Bibliographic record
Abstract
Genotypic variation in nitrogen-use traits remains largely unexplored in trees on a range-wide scale, either in field studies or under controlled experiments. Understanding natural variation in nitrogen-related traits and their relationships to climate is essential for studying local adaptation and advancing breeding efforts. In this study, we took advantage of a large collection of black cottonwood genotypes covering a major portion of the species' natural range, to study the genetic variation in nitrogen isotope discrimination (Δ15N). Nearly 350 unrelated wild genotypes were grown under steady-state hydroponic conditions and analyzed for growth and Δ15N-related traits. Differences in biomass, root-to-shoot ratio, whole-plant and organ-level nitrogen percentages, and Δ15N were found between genotypes and populations. Leaf nitrogen percentage and root-to-shoot ratio were significantly correlated to geographic and climatic variables, implying natural selection for lower leaf nitrogen and lower root-to-shoot ratio in regions with longer growing seasons and a lower risk of drought. Root Δ15N and (less so) leaf Δ15N correlated with geographic and climatic variables, and measurements from either tissue provide a reasonable indication of plant nitrogen uptake efficiency. A genome-wide association study was conducted on leaf and root Δ15N, leaf nitrogen percentage and root-to-shoot ratio. The analysis identified a candidate gene encoding glutaminyl-tRNA synthetase linked to root Δ15N, but found no significant associations with genes involved in nitrate transport or assimilation. However, multiple associations were detected for root-to-shoot ratio and leaf nitrogen percentage, both of which affect isotope-based calculations of root nitrogen efflux/influx and leaf nitrogen assimilation activity.
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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".