Responses of forest carbon and water coupling to thinning treatments from leaf to stand scales in a young montane pine forest
Bibliographic record
Abstract
Abstract Background Water-use efficiency (WUE) represents the coupling of forest carbon and water. Little is known about the responses of WUE to thinning at multiple spatial scales. The objective of this research was to use field measurements to understand short-term effects of two thinning treatments (T1: 4500 stems ha−1; and T2: 1100 stems ha−1) and the control (NT: 27,000 stems ha−1) on WUE at the three spatial scales (leaf level: the ratio of leaf photosynthesis to leaf transpiration; tree-level: tree growth to tree transpiration; and stand level: net primary production (NPP) to stand transpiration) and intrinsic WUEi (the ratio of leaf photosynthesis to stomatal conductance at leaf-level; and NPP to canopy conductance at stand-level) in a 16-year old natural lodgepole pine forest. Leaf-level measurements were conducted in 2017, while tree- and stand-level measurements were conducted in both 2016 (the normal precipitation year) and 2017 (the drought year). Results The thinning treatments did not significantly affect the tree- and stand-level WUE in the normal year of 2016. However, the thinning significantly affected WUE in the drought year of 2017: T2 exhibited significantly higher tree-level WUE (0.49 mm2 kg−1) than NT (0.08 mm2 kg−1), and compared to NT, the stand-level WUE values in the thinned stands (T1 and T2) were significantly higher, with means of 0.31, 0.56 and 0.70 kg m−3, respectively. However, the leaf-level and stand-level WUEi in the thinned stands in the drought year were significantly lower than those in the unthinned stands. No significant differences in the leaf-level WUE were found among the treatments in 2017. In addition, the thinning did not significantly change the WUE-VPD relationships at any studied spatial scale. Conclusions The thinning treatments did not cause significant changes in all studied WUE metrics in a normal year. However, their effects were significantly promoted under the drought conditions probably due to the decrease in soil water availability, demonstrating that thinning can improve WUE and consequently support forests to cope with the drought effects. The inconsistent results on the effects of the thinning on forest carbon and water coupling at the spatial scales and the lack of the consistent WUE metrics constraint across-scale comparison and transferring of WUE.
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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 | 0.000 |
| 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 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".