Picturing local adaptation: Spectral and structural traits from drone remote sensing reveal clinal responses to climate transfer in common‐garden trials of interior spruce (<i>Picea engelmannii</i> × <i>glauca</i>)
Bibliographic record
Abstract
Abstract Common‐garden trials of forest trees provide phenotype data used to assess growth and local adaptation; this information is foundational to tree breeding programs, genecology, and gene conservation. As jurisdictions consider assisted migration strategies to match populations to suitable climates, in situ progeny and provenance trials provide experimental evidence of adaptive responses to climate change. We used drone technology, multispectral imaging, and digital aerial photogrammetry to quantify spectral traits related to stress, photosynthesis, and carotenoids, and structural traits describing crown height, size, and complexity at six climatically disparate common‐garden trials of interior spruce (Picea engelmannii × glauca) in western Canada. Through principal component analysis, we identified key components of climate related to temperature, moisture, and elevational gradients. Phenotypic clines in remotely sensed traits were analyzed as trait correlations with provenance climate transfer distances along principal components (PCs). We used traits showing clinal variation to model best linear unbiased predictions for tree height (R2 = .98–.99, root mean square error [RMSE] = 0.06–0.10 m) and diameter at breast height (DBH, R2 = .71–.97, RMSE = 2.57–3.80 mm) and generated multivariate climate transfer functions with the model predictions. Significant (p < .05) clines were present for spectral traits at all sites along all PCs. Spectral traits showed stronger clinal variation than structural traits along temperature and elevational gradients and along moisture gradients at wet, coastal sites, but not at dry, interior sites. Spectral traits may capture patterns of local adaptation to temperature and montane growing seasons which are distinct from moisture‐limited patterns in stem growth. This work demonstrates that multispectral indices improve the assessment of local adaptation and that spectral and structural traits from drone remote sensing produce reliable proxies for ground‐measured height and DBH. This phenotyping framework contributes to the analysis of common‐garden trials towards a mechanistic understanding of local adaptation to climate.
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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 | 0.001 | 0.001 |
| 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".