Individual-based dendrogenomic analysis of forest dieback driven by extreme droughts
Bibliographic record
Abstract
Droughts driven by global change are triggering worldwide forest dieback, a phenomenon that is predicted to worsen. We combined genome-wide single nucleotide polymorphisms (SNPs) and dendrochronological approaches to assess genetically-based individual tree vulnerability to past extreme droughts that caused massive mortality of coihue (Nothofagus dombeyi (Mirb.) Blume) forests in northern Patagonia, Argentina. We collected fresh leaves and wood cores from pairs of trees, one with a healthy crown (HC) and another with a partially affected crown (PA), at four sites impacted by droughts in 1998, 2008, and 2014. We used dendrochronological techniques to estimate parameters in terms of growth trends due to drought and genomic analysis to assess the relationship of genomic variation with water stress. While 5155 neutral loci did not differentiate PA from HC trees, a set of 33 adaptive SNPs did, 8 of which were related to water stress. Association analysis between genomic variants and dendrophenotypic traits yielded 6 SNPs that were associated with a growth measure as resilience to cope with drought. Our preliminary results indicate that susceptibility to drought in N. dombeyi could be determined at the genomic level. The combination of these approaches provides a framework for the identification and analysis of candidate genes for stress response in non-model 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 | 0.001 |
| 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) | 0.000 | 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".