The Yellow River Delta is one of China’s most ecologically fragile regions, experiencing prolonged pressures from rapid urbanization and ecological degradation. Existing research, however, has predominantly focused on constructing ecological security patterns under single scenarios, with limited systematic multi-scenario comparisons and insufficient statistical support. To address this gap, this study proposes an integrated framework of “land use simulation—multi-scenario ecological security pattern construction—statistical comparative analysis.” Using the PLUS model, three scenarios were constructed—Business-as-Usual (BAU), Priority Urban Development (PUD), and Priority Ecological Protection (PEP)—to simulate land use changes by 2040. Habitat quality assessment, Multi-Scale Pattern Analysis (MSPA), landscape connectivity, and circuit theory were integrated to identify ecological source areas, corridors, and nodes, incorporating a novel hexagonal grid partitioning method. Statistical significance was evaluated using parametric tests (ANOVA, t-test) and non-parametric tests (permutation test, PERMANOVA). Analysis indicated significant differences in ecological security patterns across scenarios. Under the PEP scenario, ecological source areas reached 3580.42 km² (12.39% of the total Yellow River Delta), corresponding to a 14.85% increase relative to the BAU scenario and a 32.79% increase relative to the PUD scenario. These gains are primarily attributable to stringent wetland and forestland protection policies, which successfully limited the encroachment of construction land into ecological space. Habitat quality and connectivity markedly improved, resulting in the highest ecosystem stability. By contrast, the PUD scenario experienced an 851.46 km² expansion of construction land, resulting in the shrinkage of ecological source areas and intensified fragmentation, consequently increasing ecological security risks. The BAU scenario demonstrated moderate outcomes, with a moderately balanced spatial configuration. In conclusion, this study introduces an ecological restoration strategy of “five zones, one belt, one center, and multiple corridors” based on multi-scenario ecological security patterns. This provides a scientific foundation for ecological restoration and territorial spatial planning in the Yellow River Delta, while the proposed multi-scenario statistical comparison method provides a replicable methodological framework for ecological security pattern research in other delta regions.

Arid region ecosystems are among the most fragile ecological types worldwide. They depend heavily on limited water resources and are strongly influenced by intensive human activities, leading their ecosystem services to exhibit nonlinear and threshold responses to driving factors. Identifying the thresholds of ecosystem services under the combined influence of natural and socio-economic interactive drivers is of great significance for regional ecological risk warning and differentiated management. Taking the Tarim River Basin as a case study, this research establishes an integrated analytical framework that combines causal inference, interaction term construction, interpretable machine learning (XGBoost-SHAP), and piecewise linear regression. The framework is used to evaluate the variations in four types of ecosystem services in 2000, 2010, and 2023, to analyze the interactive effects of driving factors, and to identify their thresholds influencing ecosystem service functions. The results indicate that (1) different types of ecosystem service functions exhibited distinct trends from 2000 to 2023, with habitat quality and water yield showing declining tendencies, while soil conservation and Windbreak and sand fixation demonstrated gradual increases; (2) Causal Screening and interaction modeling revealed that the interaction between precipitation and population density (Pre × Pop) served as the key synergistic driver of changes in the four ecosystem service functions. Both the ecosystem services and the coupled natural–social driving processes exhibited pronounced nonlinear characteristics, with evident trend shifts occurring within specific threshold intervals. (3) The precise coupling thresholds of different ecosystem services under natural–social drivers were identified, intuitively revealing the coupling threshold characteristics of various ecosystem services; (4) The integration of causal inference with interpretable machine learning enhances the reliability of threshold identification, revealing the heterogeneous response mechanisms of different services and providing a quantitative basis for the zoning regulation and differentiated management of regional ecosystems. The findings offer a transferable methodological framework to support ecological governance in arid regions.