Association genetics, geography and ecophysiology link stomatal patterning in <i><scp>P</scp>opulus trichocarpa</i> with carbon gain and disease resistance trade‐offs
Bibliographic record
Abstract
Stomata are essential for diffusive entry of gases to support photosynthesis, but may also expose internal leaf tissues to pathogens. To uncover trade-offs in range-wide adaptation relating to stomata, we investigated the underlying genetics of stomatal traits and linked variability in these traits with geoclimate, ecophysiology, condensed foliar tannins and pathogen susceptibility in black cottonwood (Populus trichocarpa). Upper (adaxial) and lower (abaxial) leaf stomatal traits were measured from 454 accessions collected throughout much of the species range. We calculated broad-sense heritability (H(2) ) of stomatal traits and, using SNP data from a 34K Populus SNP array, performed a genome-wide association studies (GWAS) to uncover genes underlying stomatal trait variation. H(2) values for stomatal traits were moderate (average H(2) = 0.33). GWAS identified genes associated primarily with adaxial stomata, including polarity genes (PHABULOSA), stomatal development genes (BRASSINOSTEROID-INSENSITIVE 2) and disease/wound-response genes (GLUTAMATE-CYSTEINE LIGASE). Stomatal traits correlated with latitude, gas exchange, condensed tannins and leaf rust (Melampsora) infection. Latitudinal trends of greater adaxial stomata numbers and guard cell pore size corresponded with higher stomatal conductance (gs ) and photosynthesis (Amax ), faster shoot elongation, lower foliar tannins and greater Melampsora susceptibility. This suggests an evolutionary trade-off related to differing selection pressures across the species range. In northern environments, more adaxial stomata and larger pore sizes reflect selection for rapid carbon gain and growth. By contrast, southern genotypes have fewer adaxial stomata, smaller pore sizes and higher levels of condensed tannins, possibly linked to greater pressure from natural leaf pathogens, which are less significant in northern ecosystems.
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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 | 0.000 | 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) | 0.002 | 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".