Coupling Coordination Spatial Pattern of Habitat Quality and Human Disturbance and Its Driving Factors in Southeast China

Assessing habitat quality and quantifying human disturbance are fundamental prerequisites for ecological conservation. However, existing studies predominantly focus on single dimensions. There is an urgent need to integrate habitat quality and human disturbance, and quantify their spatially coupled coordination relationships to address the disconnect between them in current research. As a critical ecological reserve in southeastern China, Fujian Province faces threats of ecological degradation. This study employed the InVEST model to evaluate habitat quality in Fujian from 1980 to 2020, utilized a human disturbance index to quantify spatiotemporal patterns of anthropogenic activities, analyzed their changes using landscape indices, and applied coupling coordination analysis to examine their interrelationships. Machine learning and geographically weighted regression were further integrated to identify driving factors of coupling coordination patterns. The results showed that: (1) Habitat quality remained relatively high while human disturbance levels stayed low throughout 1980–2020, though both showed gradual deterioration over time, particularly during 2010–2020, with riverine and coastal eastern regions exhibiting the lowest habitat quality and highest disturbance levels. (2) Coupling coordination relationships predominantly exhibited synergy, with moderate imbalance zones concentrated in the eastern coastal areas. Temporally, regions with lower imbalance expanded significantly during 2010–2020. (3) Landscape metric analysis revealed declining dominance of high-quality habitat/low-disturbance/synergistic zones, contrasted by increased fragmentation of low-quality habitat/high-disturbance/imbalanced zones. (4) Socioeconomic factors exerted stronger influence on coupling coordination patterns than natural environmental variables, proximity to urban areas, road density, and nighttime light indices. Each driver displayed spatially variable positive/negative effects. The results enhance our understanding of human–nature sustainable development dynamics, urban expansion–ecological conservation trade-offs, and provide methodological insights for regional ecological management and achieving sustainable development goals.