The Effects of Plant and Soil Characteristics on Partitioning Different Rainfalls to Soil in a Subtropical Chinese Fir Forest Ecosystem
Bibliographic record
Abstract
The climate-induced changes in soil water patterns pose a serious threat to subtropical plantations. Mixed species stands have been advocated as an efficient way to enhance ecosystem stability. However, little is known about their possible impact on the soil water-holding capacity in the subtropics. In this study, we employed a stable hydrogen isotope to assess the contribution of rainfall to soil water (CRSW) in a pure Chinese fir (Cunninghamia lanceolata) plantation and in two mixtures of Chinese fir with Cinnamomum camphora or with Alnus cremastogyne after three different magnitudes of rainfall events in subtropical China. Furthermore, we used structure equation modeling (SEM) to quantify the relative importance of vegetation and soil properties on the CRSW. The results indicated that the CRSW did not differ among these three Chinese fir plantations after light rainfall, whereas the CRSW of moderate and heavy rainfall to soil water were 15.95% and 26.06% higher in Chinese fir plantation with Cinnamomum camphora, and 22.67% and 22.93% higher in Chinese fir plantation with Alnus cremastogyne than that in the pure Chinese fir plantation, respectively. SEM analysis showed that the vegetation biomass and soil properties significantly affected the CRSW following light rainfall, but the soil properties were the most important factors influencing the CRSW under moderate and heavy rainfall. Our findings demonstrate that the mixed conifer–broad-leaved plantation is a more effective strategy for improving the soil water-holding capacity than the pure conifer plantation in subtropical regions, which is conducive to coping with the frequent seasonal droughts and extreme precipitation events.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".