Long‐Term Temporal Divergence in Post‐Drought Resilience Decline Between Deciduous and Evergreen Tree Species
Bibliographic record
Abstract
Severe drought increasingly threatens the resilience, productivity, and distribution of forest biomes worldwide. Understanding the evolution of tree drought resilience over the past century, along with its geographical and taxonomic relationships, is essential for predicting future forest dynamics. Using a tree-ring database from Canadian forests, encompassing 40,147 trees across 4558 plots and 23 species, we analyzed temporal and spatial patterns of drought resilience. We examined how leaf habit, prior drought exposure, and site- and tree-level factors influence growth resistance (immediate drought response), growth recovery (post-drought growth resumption), and overall resilience. Our findings indicate that most major Canadian tree species exhibit low and declining drought resilience. Mean temperature, moisture availability, and elevation emerged as critical factors in shaping tree responses to drought. At high elevation, drought impacts were buffered by cool temperatures, enabling trees to maintain stable growth rates. Deciduous species showed a significant decline in recovery and resilience throughout the 20th century, whereas evergreen species displayed stable but low resilience and recovery. Summer droughts particularly reduced resistance and recovery in deciduous species compared to evergreens. However, prior drought exposure mitigated negative drought responses over a tree's lifetime, suggesting an adaptive capacity in both evergreen and deciduous species. Older forests unaccustomed to severe droughts appear especially vulnerable, potentially leading to shifts in ecosystem composition and reduced biodiversity. The declining resilience of deciduous species, combined with the low resilience of evergreens, suggests major changes for Canadian forests, including reduced productivity and altered species composition. Our results emphasize the importance of proactive forest management strategies to preserve forest productivity and biodiversity in the context of a changing 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 | 0.000 | 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 | 0.000 | 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".