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Article: Study on the influence of landscape pattern on the spread of pine wilt disease from a multi-scale perspective
| Title | Study on the influence of landscape pattern on the spread of pine wilt disease from a multi-scale perspective |
|---|---|
| Authors | |
| Keywords | Geographical detector Landscape pattern Multi-scale Nonlinear enhancement Pine wilt disease |
| Issue Date | 15-Sep-2024 |
| Publisher | Elsevier |
| Citation | Forest Ecology and Management, 2024, v. 568 How to Cite? |
| Abstract | Since the introduction of pine wilt disease (PWD) into Chongqing, China, it has been continuously spreading, posing a significant threat to the local forest ecosystem dominated by pine forests. Utilizing data spanning the last two decades on the PWD epidemic subcompartment and host coniferous forest in Chongqing, this study employs a geographic detector model to scrutinize the impact of forest landscape patterns on PWD dissemination at the county, township, and 1 km grid scales. The findings reveal three distinct stages of the PWD epidemic in Chongqing from 2001 to 2020: diffusion, stabilization, and outbreak. The western and central regions of Chongqing experienced severe impacts, while the southern part exhibited relatively milder effects. Across the three spatial scales, various landscape pattern indices exhibit robust and consistent impacts on PWD occurrence. Notably, patch area emerges as the most influential factor, with an explanatory power reaching 0.228. Additionally, landscape shape index, number of patches, patch density, river and road exert substantial influence on PWD incidence. Conversely, the impact of edge density, landscape dominance, landscape separation, and landscape fragmentation on PWD occurrence is comparatively weak. Moreover, the interaction of different landscape pattern factors nonlinearly enhances the explanatory power of PWD occurrence. Among these interactions, the synergy between patch area and landscape dominance proves to have the highest explanatory power for the spatial differentiation of PWD at 0.718. This study explores the relationship between the invasion and spread of PWD and landscape patterns, offering useful insights for the prevention and early warning of PWD. Additionally, this research provides valuable reference points for promoting the healthy and sustainable development of forest resources in China. |
| Persistent Identifier | http://hdl.handle.net/10722/359223 |
| ISSN | 2023 Impact Factor: 3.7 2023 SCImago Journal Rankings: 1.197 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lv, Yimeng | - |
| dc.contributor.author | Huang, Jixia | - |
| dc.contributor.author | Fang, Guofei | - |
| dc.contributor.author | Wu, Jin | - |
| dc.contributor.author | Yin, Yuanyuan | - |
| dc.contributor.author | Zhou, Yantao | - |
| dc.contributor.author | Zhao, Chong | - |
| dc.date.accessioned | 2025-08-26T00:30:14Z | - |
| dc.date.available | 2025-08-26T00:30:14Z | - |
| dc.date.issued | 2024-09-15 | - |
| dc.identifier.citation | Forest Ecology and Management, 2024, v. 568 | - |
| dc.identifier.issn | 0378-1127 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/359223 | - |
| dc.description.abstract | <p>Since the introduction of pine wilt disease (PWD) into Chongqing, China, it has been continuously spreading, posing a significant threat to the local forest ecosystem dominated by pine forests. Utilizing data spanning the last two decades on the PWD epidemic subcompartment and host coniferous forest in Chongqing, this study employs a geographic detector model to scrutinize the impact of forest landscape patterns on PWD dissemination at the county, township, and 1 km grid scales. The findings reveal three distinct stages of the PWD epidemic in Chongqing from 2001 to 2020: diffusion, stabilization, and outbreak. The western and central regions of Chongqing experienced severe impacts, while the southern part exhibited relatively milder effects. Across the three spatial scales, various landscape pattern indices exhibit robust and consistent impacts on PWD occurrence. Notably, patch area emerges as the most influential factor, with an explanatory power reaching 0.228. Additionally, landscape shape index, number of patches, patch density, river and road exert substantial influence on PWD incidence. Conversely, the impact of edge density, landscape dominance, landscape separation, and landscape fragmentation on PWD occurrence is comparatively weak. Moreover, the interaction of different landscape pattern factors nonlinearly enhances the explanatory power of PWD occurrence. Among these interactions, the synergy between patch area and landscape dominance proves to have the highest explanatory power for the spatial differentiation of PWD at 0.718. This study explores the relationship between the invasion and spread of PWD and landscape patterns, offering useful insights for the prevention and early warning of PWD. Additionally, this research provides valuable reference points for promoting the healthy and sustainable development of forest resources in China.</p> | - |
| dc.language | eng | - |
| dc.publisher | Elsevier | - |
| dc.relation.ispartof | Forest Ecology and Management | - |
| dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
| dc.subject | Geographical detector | - |
| dc.subject | Landscape pattern | - |
| dc.subject | Multi-scale | - |
| dc.subject | Nonlinear enhancement | - |
| dc.subject | Pine wilt disease | - |
| dc.title | Study on the influence of landscape pattern on the spread of pine wilt disease from a multi-scale perspective | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1016/j.foreco.2024.122128 | - |
| dc.identifier.scopus | eid_2-s2.0-85198134329 | - |
| dc.identifier.volume | 568 | - |
| dc.identifier.eissn | 1872-7042 | - |
| dc.identifier.issnl | 0378-1127 | - |
