Coupling dynamics of human-environment systems in the large lake basin: Spatiotemporal interactions and driving mechanisms of ecosystem services and human footprint intensity
Bibliographic record
Abstract
• Opposing ESs-HFI dynamics emerge: ESs contract upstream vs HFI hotspots cluster downstream. • Polarized CCD pattern with degradation in transitional zones between stable ecological barriers and urban cores. • Synergistic eco-development systems amplify nonlinear HES coupling via carbon-water-NDVI synergy and GDP-population pressure. • Spatial statistics-CCD-XGBoost-SHAP integration offers replicable framework diagnosing lake basin sustainability. High-intensity human activities are reshaping the pattern of ecosystem services (ESs) and threatening the sustainability of human–environment systems (HES) in large lake basins. To elucidate the coupling mechanisms of such systems, this study constructs a model based on ESs and human footprint intensity (HFI). First, the Sen-MK trend and center-of-gravity migration models are used to characterize the spatiotemporal dynamics of the system. Next, exploratory spatial data analysis (ESDA) is combined with the coupling coordination degree (CCD) model to examine the spatial dependence structure and the coupled evolution patterns of the HES. Finally, the XGBoost-SHAP machine learning framework is applied to reveal the nonlinear interaction mechanisms among key driving factors. Using the Dongting Lake Basin as a case study, the results show the following. First, between 2000 and 2020, ESs and HFI exhibited significant spatial mismatches. The total ESs initially declined and then rebounded to 33 %, concentrating in upstream areas. In contrast, high HFI zones expanded from 13 % to 25 %, continuously clustering in downstream urban regions. Second, the HES exhibited a typical “contraction in the middle, expansion at both ends” pattern. Medium-coordination zones decreased by 10.9 %, while low-coordination zones increased by 135 %. Upstream ecological barrier areas and downstream urban clusters remained relatively stable, whereas the coordination in midstream transitional zones significantly deteriorated. Third, carbon storage, NDVI, and water yield constitute the ecological gain system of ESs, while population and GDP form the development pressure system. Significant synergistic enhancement exists among ecological elements, amplifying their positive effects on system coupling. Overall, the sustainable development of HES requires a comprehensive basin assessment and a systems coupling perspective. This study provides a scientific basis for hierarchical governance in large lake basins.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".