Gene expression responses of black spruce (Picea mariana) to global climate change conditions
Bibliographic record
Abstract
Global climate change conditions (elevated CO2 and atmospheric temperatures) are subjecting our forests, especially Boreal and temperate forests, to significant abiotic stresses, such as drought. This can affect health, productivity and fitness of our forests. Therefore, it is imperative to understand genomic and eco-physiological responses of forest trees to global climate change. We are addressing this aspect in black spruce (Picea mariana) - a transcontinental, ecologically and economically important tree species of the North American Boreal forest. Our objective was to determine gene expression and physiological responses and their inter-relationships in black spruce to elevated CO2, drought and co-stressed conditions. We have used NGS whole transcriptome sequencing, cDNA-AFLP and qPCR analyses to identify, annotate and characterize genes expressed differentially in response to elevated CO2, drought and combined elevated CO2 and drought conditions in black spruce using the cloned material. Photosynthetic rate and stomatal conductance were measured simultaneously with tissue collection for RNA extraction. Thousands of transcripts (genes) showed differential expression (no expression, up-regulation or down-regulation) in response to elevated CO2, drought and/or their combined conditions, with over 1600 genes from several pathways showing >10-folds gene expression differences between control and treated plants. A number of genes showed 100 to 500 folds up or down regulation in response to elevated CO2, drought or their combined conditions. Responses to each treatment at the gene expression and physiological levels were correlated well among different genotypes. We will present these results which contribute significantly to our understanding of tree’s responses to global climate change.
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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 | 0.000 | 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) | 0.000 | 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".