Recent climatic drying leads to age‐independent growth reductions of white spruce stands in western Canada
Bibliographic record
Abstract
Abstract Since 2001, climatic conditions have been notably drier than normal across large areas of the western Canadian interior, leading to widespread impacts on the forests of this region. This poses a major concern for the future, given climate change projections for continued warming and drying. We conducted tree‐ring analysis in 75 pure stands of white spruce (Picea glauca) across Alberta and west‐central Saskatchewan to examine the effects of recent climatic drying on the growth of this important boreal tree species. Allometric equations were used to calculate annual growth in aboveground tree biomass (GBM) from ring width measurements. Results showed an increasing trend in GBM from the 1960s to the 1990s, followed by a sharp decline during the severe drought of 2001–2002. Of the 75 stands, only 18 recovered sufficiently to cause an increase in mean GBM from the predrought decade of 1991–2000 to the subsequent decade of 2001–2010. The remaining 57 stands exhibited a decline in mean GBM between these decades. Climatic drying was a major cause of the growth decline, as shown by the significant stand‐level relationship between percentage change in decadal mean GBM and the change in decadal mean values of a climate moisture index from 1991–2000 to 2001–2010. Subsequent analyses of boreal stands sampled across Alberta during 2015 revealed that white spruce growth had declined even further as drought conditions intensified during 2014–2015. Overall, there was a 38% decrease in mean GBM between 1997 and 2015, but surprisingly, the percentage decrease was not significantly different for young, productive stands compared with older, less productive stands. Thus, stand ageing cannot explain the observed decline in white spruce growth during the past quarter century, suggesting that these forests are at risk if the trend towards more frequent, severe drought continues in the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".