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Spatial early-warning assessment of ecological security in the Poyang Lake Basin based on PSR and Spatial Durbin Modeling
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  • Published: 24 April 2026

Spatial early-warning assessment of ecological security in the Poyang Lake Basin based on PSR and Spatial Durbin Modeling

  • HaiXiang Guo1,2,
  • CongLe Hu1,2,
  • MeiYi Xin1,2 &
  • …
  • Qi Wu1,2 

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Subjects

  • Ecology
  • Environmental sciences
  • Environmental social sciences

Abstract

Constructing a basin-level ecological security early-warning (ESEW) system is critical for overcoming the fragmentation of administrative management and achieving holistic watershed governance. Focusing on the Poyang Lake Basin, this study develops a spatial ESEW assessment framework based on the Pressure-State-Response (PSR) model. By integrating 1 km grid-scale data with the Spatial Durbin Model (SDM), we investigate the spatiotemporal evolution of ecological security and quantify the cross-regional spillover effects of key drivers from 2000 to 2020. The results reveal a structural “deterioration-then-recovery” trajectory: the area of “Moderate Warning” expanded from 52.40% in 2000 to a peak of 64.53% in 2010 due to rapid industrialization, before dropping to 37.93% in 2020 as “Light Warning” areas became dominant (62.00%). Spatial analysis confirms significant clustering of ecological risks. Crucially, the SDM identifies a strong spatial spillover effect, indicating that ecological degradation is highly contagious across county borders. Specifically, industrial sulfur dioxide emissions and urbanization intensity (NUADI) in neighboring areas exert significant negative spillovers on local ecological security, whereas farmland-to-forest conversion generates positive externalities. Based on these findings, we propose a differentiated governance strategy, including “Source Control” for high-risk pollution clusters and market-oriented ecological compensation mechanisms to internalize the benefits of upstream protection, providing a scientific basis for cross-regional collaborative governance.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China [grant number 72074198]; the Foundation for Innovative Research Groups of Hubei Province [grant number 2024AFA015]; the National Social Science Fund of China [grant number 23AZD072]; and the Fundamental Research Funds for the Central Universities at China University of Geosciences (Wuhan) [grant number CUG2642022006].

Author information

Authors and Affiliations

  1. School of Economics and Management, China University of Geosciences, Wuhan, 430074, China

    HaiXiang Guo, CongLe Hu, MeiYi Xin & Qi Wu

  2. The Laboratory of Natural Disaster Risk Prevention and Emergency Management of China University of Geosciences, Wuhan, 430074, China

    HaiXiang Guo, CongLe Hu, MeiYi Xin & Qi Wu

Authors
  1. HaiXiang Guo
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  2. CongLe Hu
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  3. MeiYi Xin
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  4. Qi Wu
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Corresponding author

Correspondence to CongLe Hu.

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The authors declare no competing interests.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Cite this article

Guo, H., Hu, C., Xin, M. et al. Spatial early-warning assessment of ecological security in the Poyang Lake Basin based on PSR and Spatial Durbin Modeling. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44479-4

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  • Received: 20 October 2025

  • Accepted: 11 March 2026

  • Published: 24 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-44479-4

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Keywords

  • Ecological security
  • Early-warning
  • PSR model
  • SDM model
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