Exploring spatio-temporal heterogeneity of ecosystem service interactions in rapidly urbanizing areas: Trade-offs/synergistic changes and their driving mechanisms
Bibliographic record
Abstract
With rapid economic development and urbanization, the natural ecosystems and ecosystem services (ESs) in the Lower Yellow River Region (LYRR) have undergone irreversible destruction, intensifying the conflict between ecological conservation and socioeconomic development. This study utilized multi-source spatial data from 1990 to 2020 and employed the InVEST and RUSLE models to quantify water yield (WY), carbon storage (CS), soil conservation (SC), and food production (FP). Spearman correlation and geographically weighted regression (GWR) were applied to analyze trade-offs and synergies, while random forest and partial least squares structural equation modeling were used to identify driving factors and their pathways. The results revealed significant changes in the spatial pattern of WY, whereas the other three ESs remained relatively stable. Significant spatiotemporal heterogeneity and scale effects were observed in ES interactions, leading to discrepancies between Spearman and GWR. The strongest trade-off between WY and CS, peaking at −0.42*** in 2010. Driving mechanisms showed that LUCC, Pre, DEM, and PET dominated WY; LUCC primarily drove CS; DEM strongly influenced SC; and LUCC, NDVI, and POP majorly affected FP. Over the 30-year period, the direction and intensity of drivers' impacts on ESs varied significantly. For instance, in 1990, Pre (0.734***) exerted the strongest positive effect on WY, while LUCC (−0.934***) had the most significant negative impact on CS. However, their indirect effects through intermediary pathways remained weak. These findings offer a scientific foundation for ecological management and sustainable development in rapidly urbanizing regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".