Ecological security warning in Central Asia: Integrating ecosystem services protection under SSPs-RCPs scenarios

Abstract

Ecological security patterns (ESPs) are designed to enhance ecosystem structure and functionality while preserving vital ecosystem services (ESs). This study not only integrated the ES trade-offs related to ecological security warning, but also considered the effects of future climate changes and human activities on ESPs. By combining the revised universal soil loss equation (RUSLE), the revised wind erosion equation (RWEQ), the dry sedimentation (DS) model, the recreation opportunity map (ROM) and the integrated valuation of ESs and trade-offs (InVEST) model, this study projected provisioning services, regulation services and cultural services in Central Asia (CA) for historical periods (1995–2014) and future scenarios (2021–2099). An ecological security early-warning (source – corridor – barriers) framework was constructed based on the protection of ESs under the SSP126, SSP245 and SSP585 scenarios. The ordered weighted averaging method (OWA) was applied to this framework to identify ecological sources. The Minimum cumulative resistance model (MCR) and circuit theory were used to construct ecological corridors and barriers. Our results revealed that ES hotspot areas will decrease by 11.75 % to 16.42 % in CA under the SSP126, SSP245, and SSP585 scenarios. Under the ecological warning framework, the ecological source warning area will reach 792 km2–1942 km2 and 6591 km2–17,465 km2 under the SSP126 and SSP585 scenarios, respectively. In particular, in the 2050s under the SSP245 scenario, the number of key ecological corridor warnings will exceed 50 % of the total number of corridors. We found that ecological barrier warnings will mainly be distributed in desert areas with low vegetation coverage in southwestern CA. Building upon the reorganization of ESs within the ESP framework, we propose an ecological early warning strategy referred to as “one axis, two belts, two cores, and three zones”. This novel approach aims to enhance our ability to predict and respond to ecological threats and challenges.