Balancing Water Yield and Water Use Efficiency Between Planted and Natural Forests: A Global Analysis
Bibliographic record
Abstract
ABSTRACT Climate warming is projected to affect hydrological cycle in forest ecosystems and makes the forest–water relationship more controversial. Currently, planted forests are gaining more public attention due to their role in carbon sequestration and wood production relative to natural forests. However, little is known about how the global patterns and drivers of water yield and water‐use efficiency (WUE) differ between planted and natural forests. Here, we conduct a global analysis to compare water yield and WUE in planted and natural forests using 946 observations from 112 published studies. The results showed that global average water yield coefficient was 0.29 for planted forests and 0.34 for natural forests. Planted forests exhibited lower water yield coefficient ( p < 0.05) in three climatic regions (arid, dry subhumid, and humid regions), but higher ( p < 0.01) WUE only in arid region, compared with natural forests. Both water yield coefficient and WUE in planted forests were significantly lower ( p < 0.05) than that in natural forests for stand characteristic groups (stand density, average tree height, leaf area index [LAI], and basal area). Additionally, stand density within the ranging between 1000 to 2000 stem ha −1 can maximize the water yield and WUE in planted and natural forests. Water yield coefficient in planted forests was primarily controlled by the factors related to tree growth (i.e., tree height, DBH), while that of natural forest mainly affected by stand structure (i.e., LAI, stand density, DBH). WUE in planted forest was more sensitive to climate than in natural forests. This work highlights the critical role of natural forests in water supply and the importance of tree species selection and stand management (e.g., stand density adjustment) in plantations in future forest restoration policies and climate change mitigation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".