Spatial Heterogeneity in Ecological Thresholds Enables Targeted Restoration in China’s Yellow River Basin

Abstract

Current restoration practices apply uniform management thresholds across entire regions, overlooking spatial heterogeneity and limiting efficiency. We tested whether distinct spatial clustering patterns in China’s Yellow River Basin require different ecological thresholds. Integrating 20-year land use data (2000 ~ 2020) with spatial clustering and machine learning, we quantified driver thresholds for five ecosystem services across four spatial patterns: High-High (HH) synergy, Low-Low (LL) weak synergy, and High-Low (HL) / Low-High (LH) trade-off zones. Vegetation thresholds (NDVI) differed by 33% between HH zones (0.5 ~ 0.7) and LL zones (0.4 ~ 0.5), while driver contributions varied 1.9-fold across patterns. Dominant drivers shifted from population density (2000) to temperature (2020), indicating a transition from anthropogenic to climate-driven dynamics. These findings demonstrate that uniform thresholds misallocate resources—over-investing in resilient HH zones while under-investing in vulnerable LL zones. We propose pattern-specific strategies: maintaining NDVI > 0.5 in synergy zones, surpassing NDVI > 0.4 breakpoints in transitional zones, and implementing dual climate-population controls in degraded areas. This framework advances restoration from uniform prescriptions toward spatially targeted approaches, providing actionable guidance for precision management in degraded drylands globally.

Keywords: ecosystem services, spatial heterogeneity, ecological thresholds, restoration strategies, Yellow River Basin