Impact of heatwave and thinning on tree growth and soil water content in young lodgepole pine forests
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Bibliographic record
Abstract
Extreme climate events (e.g., heatwaves and droughts) are becoming increasingly frequent due to global climate change, which inevitably affects tree growth and various other ecological processes. While the impacts of droughts on these processes have been widely evaluated, the effects of heatwaves on tree growth and soil water content (SWC) remain poorly understood, particularly those related to thinning treatment. In this study, we evaluated the impacts of the 2021 Pacific Northwest Heatwave and thinning on forest growth and SWC, as well as assessed how thinning might mitigate the heatwave's impacts in lodgepole pine forests in British Columbia, Canada. We measured meteorological data (air temperature, rainfall, solar radiation (SR), relative humidity (RH), and wind speed ( W s )), sap flow, SWC, soil temperature ( T s ), and tree diameters at the breast height (DBH) during the growing season (June–September) in the control (27,000 stems·ha −1 ), lightly thinned (4,500 stems·ha −1 ), and heavily thinned (1,100 stems·ha −1 ) experimental plots from 2018 to 2024. We found that thinning persistently and significantly ( p < 0.05) increased individual tree growth, with the most pronounced effects in the heavily thinned stands. The 2021 Pacific Northwest Heatwave led to an exceptionally hot growing season, significantly ( p < 0.05) reducing forest growth and SWC across all plots. Forest growth recovered in 2022 in the thinned plots but remained suppressed in the unthinned plots, suggesting that thinning effectively mitigated the impact of the heatwave on forest growth, while the heatwave's impacts were persistent in the unthinned plots. Our study highlights that thinning is a practical management strategy for improving tree growth and supporting climate change adaptation to extreme climate events.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it