Time and space catch up with restoration programs that ignore ecosystem service trade-offs
Bibliographic record
Abstract
In response to extreme societal consequences of ecosystem degradation and climate change, attention to ecological restoration is increasing globally. In China, investments in restoration exceeded USD 378.5 billion over the past decade. However, restoration programs are experiments that can cause marked unintended consequences, with trade-offs across space and time that have undergone little empirical examination. We quantified the long-term effects of large-scale afforestation for soil erosion and sandstorm prevention in semiarid China. We found that soil erosion was notably reduced by afforestation but surface runoff declined significantly, after a time lag of 18 years, limiting overall benefit. While forest area also increased, forest quality declined, interacting with reduced surface water runoff. Crucially, increased forest water consumption accelerated downstream groundwater depletion, thus intensifying conflicts over water use. The time lags and spatial trade-offs revealed by this case study provide critical lessons for large-scale restoration programs globally.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".