Water Budgets Control the Resilience of Large‐Scale Ecological Restoration
Bibliographic record
Abstract
Abstract Large‐scale ecological restoration is a key nature‐based solution to environmental challenges. However, the resilience of such initiatives is debated due to increasing water consumption. This study examined China's Three‐North Shelterbelt Forest Program, the world's largest ecological restoration project, to assess how water regulated vegetation resilience from 2001 to 2022 and projected future vegetation suitability by integrating meteorological observations, remote sensing data, and Global Circulation Models. We found that approximately 48.2% of vegetation, particularly forests, experienced declining resilience despite greening. Resilience increased with productivity within water resources carrying capacity but decreased when exceeded. Forest resilience peaked when precipitation was fully utilized, whereas grassland resilience was lowest at this equilibrium. By 2050, 1.8% of the area is projected to face degradation risks, with an additional 11.1% at potential risk under the SSP2‐4.5 scenario. Overall, our findings highlight the necessity of integrating water resources constraints into ecological restoration strategies for sustained effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".