Size‐, species‐, and site‐specific tree growth responses to climate variability in old‐growth subalpine forests
Bibliographic record
Abstract
Abstract Tree‐ring data have become widely used to model tree growth responses to climate variability and gain insight about the potential effects of global warming on forests. We capitalized on a rare opportunity to develop growth–climate models using tree‐ring data collected from all trees (>4 cm in diameter at breast height) within 50 × 50 m plots established in subalpine old‐growth forests of western Canada. Our objective was to determine how tree growth responses to climate vary among tree size classes, species, and sites. We modeled relationships between times series of annual basal area increment (ΔBA) and yearly climate variables for individual trees; this approach obviated key statistical criticisms of “traditional” tree‐ring analysis methods. Time series of annual basal area increment were detrended a priori for size, age, legacy, and competition effects. We found that the overall climate signal in our time series of ΔBA was weak; <6% of the interannual variance was explained by climate variables. Nevertheless, there were clear patterns in climate–growth relationships related to tree size and species. Relationships between ΔBA and five climate variables increased in strength with tree size class; large trees were most sensitive to annual climate fluctuations and accounted for ~71% of the overall climate effect on growth across all trees and sites. In all stands, ΔBA variance explained by climate variables was stable over the 20th century for large trees but decreased in the 1940s for small trees, indicating a temporal reduction in sensitivity to annual fluctuations in five climate variables. In coastal forests, relationships between ΔBA and climate for Callitropsis nootkatensis were significantly different in direction and magnitude than those of co‐occurring Pinaceae species. The effect of climate on tree growth was idiosyncratic among stands and could not be discriminated by forest type (coastal vs. interior). Our individual‐tree modeling approach adds to a growing body of research providing novel insights about the complexities of tree growth responses to climate variability and the challenges associated with predicting future tree growth and forest productivity using tree‐ring data.
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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 | 0.001 | 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".