Sustainability, Vol. 18, Pages 1779: Assessing Nonlinear Effects of Landscape Patterns on Habitat Quality in the Yellow River Basin: An Integrated Framework Combining Interpretable Machine Learning and Spatial Autocorrelation

Sustainability doi: 10.3390/su18041779

Authors:
Faming Li
Kaiting Yang
Tianming Sun
Yuming Shao
Yanhong Huo
Yiqing Liu

In the context of accelerating worldwide urbanization and ecosystem decline, deciphering the interactions between landscape patterns and habitat quality is essential for biodiversity preservation, particularly within ecologically sensitive zones like the Yellow River Basin. This research investigates the spatiotemporal dynamics, spatial linkages, and nonlinear relationships connecting landscape patterns and habitat quality across the basin. Utilizing land use datasets spanning 1980–2023, we combined the InVEST model, landscape pattern indices, spatial autocorrelation analysis, the XGBoost algorithm, and SHAP interpretability methods. The results show that: (1) Landscape patterns underwent a clear transition around 1995, shifting from regularization and connectivity toward fragmentation and heterogeneity, evidenced by increases in PD, LSI, and SHEI, alongside decreases in LPI and CONTAG. (2) Mean habitat quality progressively declined, exhibited a spatial distribution characterized by “higher in the west, lower in the east.” Low-quality habitat areas expanded from 2.12% to 3.76%, whereas high-quality areas decreased from 23.12% to 22.45%, with better habitats largely maintained in western headwaters and the Qinling Mountains. (3) Significant spatial correlations were observed: LPI positively correlated with habitat quality, while PD, LSI, SHEI, and CONTAG showed negative correlations. Two dominant spatial aggregations emerged—namely “high connectivity–high quality” in the west and “high fragmentation–low quality” in the east. (4) CONTAG was identified as the dominant factor influencing habitat quality, with all landscape indices exhibiting distinct threshold effects. The proposed framework, which integrates spatial statistics, machine learning, and interpretability methods, offers a novel approach for deciphering complex ecological processes. Moreover, the identified thresholds and zonal management strategies offer a scientific foundation for ecological conservation and spatial planning in the Yellow River Basin and other vulnerable river systems worldwide.

Source link

Faming Li www.mdpi.com