Water-use strategies of Chinese fir to simulated precipitation change in a subtropical region
Bibliographic record
Abstract
Abstract Altered precipitation regimes due to climate change influence plant–water interactions through shifts in soil moisture dynamics, highlighting the need for a mechanistic understanding of diverse water-use strategies and plant adaptations. In this study, we adopted an integrated approach combining measurements of stable hydrogen and oxygen isotopes in soil, groundwater, and xylem water, alongside sap flow and tree growth using dendrometers, to investigate the water-use strategies of Chinese fir (Cunninghamia lanceolata) under varying drought intensities. The experimental design included a control (C) and three precipitation reduction treatments (−30%, −50%, and −80%). This study analyzed data collected from both the wet and dry seasons of 2022, with precipitation exclusion devices installed and functioning since October 2021. The results indicated that during the wet season, Chinese fir primarily used shallow soil water (0–20 cm), with uptake proportions of 54.30%, 87.90%, 86.00%, and 63.70% under the C, −30%, −50%, and −80% treatments, respectively. In the dry season, as shallow soil water became increasingly scarce, water uptake gradually shifted toward deeper soil layers (40–60 cm), accounting for 49.30%, 79.10%, 68.50%, and 32.40%, respectively, and to groundwater sources, with 37.60%, 6.90%, 21.30%, and 61.50%, respectively. As expected, all precipitation reduction treatments reduced growth and water consumption (transpiration) compared with the C group. Notably, Chinese fir under the extreme drought treatment maintained adequate transpiration by relying heavily on groundwater throughout both seasons. This enabled increased growth during the wet season, though it also induced early growth cessation during the dry season. These findings suggest that Chinese fir exhibits substantial plasticity in its water acquisition strategies, allowing dynamic adjustment of water uptake between soil layers and groundwater sources depending on moisture availability. Our 1-year study demonstrates that Chinese fir can regulate water use and maintain radial growth under varying precipitation reduction treatments and seasonal conditions. Continuous long-term monitoring is essential to assess the sustained effects of drought on these ecohydrological processes.
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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".