Scenario-based analysis reveals that climate and land-use changes jointly threaten shorebird habitats, emphasizing the need for conservation measures.
Accelerated urbanization and land reclamation have caused widespread loss of coastal wetlands, reduced habitat availability and increased migration costs for shorebirds. However, the interactive effects of climate change and land-use transitions on habitat networks remain insufficiently understood, especially in rapidly developing bay regions. We developed a scenario-based framework to disentangle climate and land-use impacts on shorebird habitat networks. Integrating species distribution models, landscape connectivity assessments and circuit theory, we mapped potential habitat networks in the Guangdong-Hong Kong-Macao Greater Bay Area and projected dynamics under three scenarios: Current Trend Scenario (CTS), Sustainable Development Scenario (SDS), and Ecological Protection Scenario (EPS) for 2030-2070. Each scenario included two sub-scenarios to compare climate change and land-use effects. Results show that EPS initially expands habitat network over a short period but declines thereafter; SDS shows a steady decline throughout the projection period; and CTS generates the most severe habitat loss. Comparison of sub-scenarios revealed that land-use change influences climate impacts based on development pathways. Under EPS and SDS, incorporating land-use change enhanced habitat connectivity in early decades, whereas under CTS, land-use change consistently accelerated habitat loss. Our findings reveal that climate and land-use change jointly drive irreversible degradation of shorebird habitat networks, characterized by patch loss, corridor simplification, and network collapse. While ecologically oriented land-use can slow this trajectory and sustain connectivity in the short term, it cannot offset long-term climate pressures. This demonstrates the need for more ambitious conservation measures that integrate climate mitigation and land-use adaptation to sustain habitat networks under global change.
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Huang et al. (2026) studied this question.
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