Exploring dynamic spatiotemporal relationships among multiple ecosystem systems to identify priority restoration areas: a case study in the Chinese Loess Plateau
Bibliographic record
Abstract
Abstract Ecological restoration has significantly improved ecosystem services (ESs) in the Chinese Loess Plateau. Identifying spatial priority restoration areas based on ESs plays a key role in future ecological restoration, as dynamic trade-off relationships exist in multiple ESs. This study examined the dynamic spatial and temporal relationships among soil erosion, carbon storage, and habitat quality from 1988 to 2020 and explored the spatial priority restoration areas in Yan’an, the Loess Plateau. We found that ecological restoration has improved soil erosion, carbon storage, and habitat quality in the entire Yan’an over the past three decades, but low values of ES areas were concentrated in north Yan’an. Trade-offs occurred in soil erosion, carbon storage, and habitat quality from 1988 to 2020. Significant trade-off relationship areas moved from south to north Yan’an, concentrating on Wuqi, Zhidan, Ansai counties, and north Baota district. Moreover, a high level of vegetation cover was maintained in Yan’an in 2015 and 2020, but we did not find a significant improvement for three ESs in 2020 in comparison to 2015. Thus, a focus should be on the maintenance of the level of vegetation in 2020 and priority restoration areas tend to be clustered in four counties located in north Yan’an. However, knowledge on what vegetation threshold is compatible with a good level of ES is missing. Future research may investigate the threshold of vegetation cover for providing multiple ES on a regional scale, even expanding to the entire Loess Plateau, and further identifying spatial priority restoration areas across the Loess Plateau.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".