Ecological network identification and connectivity robustness evaluation in the Yellow River Basin under a multi-scenario simulation

Dan Men and Jinghu Pan

Ecological Modelling, 2023, vol. 482, issue C

Abstract: The deterioration of the environment has been aggravated by the rapid growth of cities and economies, and inter-regional political, economic, scientific, and technological frictions caused by ecological problems are increasing. To safeguard the ecological environment and design healthy urban development, it is crucial to assess the structure and quality of ecological networks (ENs), which serve as links between landscape structure and ecological processes. This paper simulates land use in 2035 under three development scenarios for the Yellow River Basin (YRB), identifying ENs, and assessing the robustness of network connectivity for each scenario. We found that in the ecological protection scenario (ES), forest and water Land expanded the most, increasing by 4.16% and 6.98%, respectively. The ES also produced the most ecological patches (53) and important ecological corridors (141), with the important corridors forming an east–west spatial pattern. In the baseline scenario (BS), important corridors formed a north–south pattern, and in the urban development scenario (US), the important corridors were characterised by small-scale aggregation. The initial connectivity robustness of ENs was 0.5, 0.46, and 0.45 in the ES, BS, and US, respectively. Whether using deliberate or random attack models, the robustness of network connectivity was strongest in the ES. This study provides a theoretical reference for the future planning of eco-friendly development and ecological security patterns in the YRB.

Keywords: Multi-scenario simulation; Ecological networks; Connectivity robustness; The Yellow River Basin (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:482:y:2023:i:c:s0304380023001151

DOI: 10.1016/j.ecolmodel.2023.110384

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