Research Article | | Peer-Reviewed

Analysis of the Evolution of Scale-Free Properties in the Complex Network of American White Moths in Liaoning Area

Received: 7 October 2023     Accepted: 1 November 2023     Published: 17 November 2023
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Abstract

Pest infestation is the most severe forestry biological disaster, causing not only huge economic losses worldwide but also damaging the ecological environment. Evaluating the mechanism and spatial structure changes of pest population distribution in forests scientifically is crucial for understanding and solving the problem of forest pest disasters. With the continuous deepening of research on the niche principle and spatial patterns of insect populations, a research orientation that emphasizes dynamic-multidimensional-three-dimensional and cross-disciplinary integration has become the trend in future forestry biological disaster research. Applying network science analysis methods to the field of forest pest research can help explore the distribution characteristics, affected areas, and temporal and spatial changes in the spread and transmission of forest pests, effectively curb their rampant spread, and provide scientific data support for predicting and forecasting forest pests. This will have far-reaching implications for the comprehensive implementation of integrated pest management strategies. In this paper, based on a spatial influence domain network model, we selected monitoring data of the globally quarantined harmful organism, American white moth in Liaoning Province from 2009 to 2013 as the data source. We constructed a complex network of American white moth pest relationships and focused on studying the overall network's indegree and outdegree network characteristic quantities, as well as the network evolution of the peak larval months and the average degree of the network.

Published in American Journal of Agriculture and Forestry (Volume 11, Issue 6)
DOI 10.11648/j.ajaf.20231106.13
Page(s) 228-232
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

American White Moth, Complex Network, Scale-Free Properties, Evolutionary Analysis

References
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[4] Zhang Wenbo. Research on the Life Characteristics of the Macroscopic Topology of Internet, Doctoral Dissertation, Northeastern University, 2006.
[5] Mi Xue. Research on Key Issues of Embedded Internet Time Characteristics Based on Complex Networks, Doctoral Dissertation, Northeastern University, 2014.
[6] Caldarelli G, Capocci A. Scale-free networks from varying vertex intrinsic fitness [J]. Phys Rev Lett. 2002, 89 (25): 258702.
[7] Newman M E J. The spread of epidemic disease on network [J]. Complexity Digest, 2002, 23, 420-425.
[8] Kim B J, Yoon C N. Path finding strategies in scale-free networks [J]. Physics Review E, 2002, 65.
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[10] Li Guangguang; Zhao Hai et al. Feature analysis of seismic activity network based on k-core decomposition [J], Journal of Earthquake Science, 2015, 37 (2): 239-248.
[11] He Xuan, Zhao Hai et al. Construction method of earthquake network based on spatiotemporal influence domain [J], Journal of Northeastern University (Natural Science Edition), 2014, 35 (10): 1395-1399.
[12] Liu Xiao, Zhao Hai, Feng Ying et al. Network science analysis method for forest pest big data, Journal of Northeast University (Natural Science Edition), 2016, 37 (10): 1255-1257.
[13] Wang Jinfa, et al. Construction and Analysis of Pest Relationship Network Based on Spatial Influence Domain, Journal of Northeast University (Natural Science Edition), 2016, 37 (12): 1701-1704.
[14] Feng Ying. Evolutionary analysis of network nodes in the American white moth pests, Fujian Computer, 2020 (4): 42-46.
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Cite This Article
  • APA Style

    Xiaojuan, S., Ying, F., Jun, L. (2023). Analysis of the Evolution of Scale-Free Properties in the Complex Network of American White Moths in Liaoning Area. American Journal of Agriculture and Forestry, 11(6), 228-232. https://doi.org/10.11648/j.ajaf.20231106.13

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    ACS Style

    Xiaojuan, S.; Ying, F.; Jun, L. Analysis of the Evolution of Scale-Free Properties in the Complex Network of American White Moths in Liaoning Area. Am. J. Agric. For. 2023, 11(6), 228-232. doi: 10.11648/j.ajaf.20231106.13

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    AMA Style

    Xiaojuan S, Ying F, Jun L. Analysis of the Evolution of Scale-Free Properties in the Complex Network of American White Moths in Liaoning Area. Am J Agric For. 2023;11(6):228-232. doi: 10.11648/j.ajaf.20231106.13

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  • @article{10.11648/j.ajaf.20231106.13,
      author = {Sun Xiaojuan and Feng Ying and Liu Jun},
      title = {Analysis of the Evolution of Scale-Free Properties in the Complex Network of American White Moths in Liaoning Area},
      journal = {American Journal of Agriculture and Forestry},
      volume = {11},
      number = {6},
      pages = {228-232},
      doi = {10.11648/j.ajaf.20231106.13},
      url = {https://doi.org/10.11648/j.ajaf.20231106.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajaf.20231106.13},
      abstract = {Pest infestation is the most severe forestry biological disaster, causing not only huge economic losses worldwide but also damaging the ecological environment. Evaluating the mechanism and spatial structure changes of pest population distribution in forests scientifically is crucial for understanding and solving the problem of forest pest disasters. With the continuous deepening of research on the niche principle and spatial patterns of insect populations, a research orientation that emphasizes dynamic-multidimensional-three-dimensional and cross-disciplinary integration has become the trend in future forestry biological disaster research. Applying network science analysis methods to the field of forest pest research can help explore the distribution characteristics, affected areas, and temporal and spatial changes in the spread and transmission of forest pests, effectively curb their rampant spread, and provide scientific data support for predicting and forecasting forest pests. This will have far-reaching implications for the comprehensive implementation of integrated pest management strategies. In this paper, based on a spatial influence domain network model, we selected monitoring data of the globally quarantined harmful organism, American white moth in Liaoning Province from 2009 to 2013 as the data source. We constructed a complex network of American white moth pest relationships and focused on studying the overall network's indegree and outdegree network characteristic quantities, as well as the network evolution of the peak larval months and the average degree of the network.
    },
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Analysis of the Evolution of Scale-Free Properties in the Complex Network of American White Moths in Liaoning Area
    AU  - Sun Xiaojuan
    AU  - Feng Ying
    AU  - Liu Jun
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    DO  - 10.11648/j.ajaf.20231106.13
    T2  - American Journal of Agriculture and Forestry
    JF  - American Journal of Agriculture and Forestry
    JO  - American Journal of Agriculture and Forestry
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    EP  - 232
    PB  - Science Publishing Group
    SN  - 2330-8591
    UR  - https://doi.org/10.11648/j.ajaf.20231106.13
    AB  - Pest infestation is the most severe forestry biological disaster, causing not only huge economic losses worldwide but also damaging the ecological environment. Evaluating the mechanism and spatial structure changes of pest population distribution in forests scientifically is crucial for understanding and solving the problem of forest pest disasters. With the continuous deepening of research on the niche principle and spatial patterns of insect populations, a research orientation that emphasizes dynamic-multidimensional-three-dimensional and cross-disciplinary integration has become the trend in future forestry biological disaster research. Applying network science analysis methods to the field of forest pest research can help explore the distribution characteristics, affected areas, and temporal and spatial changes in the spread and transmission of forest pests, effectively curb their rampant spread, and provide scientific data support for predicting and forecasting forest pests. This will have far-reaching implications for the comprehensive implementation of integrated pest management strategies. In this paper, based on a spatial influence domain network model, we selected monitoring data of the globally quarantined harmful organism, American white moth in Liaoning Province from 2009 to 2013 as the data source. We constructed a complex network of American white moth pest relationships and focused on studying the overall network's indegree and outdegree network characteristic quantities, as well as the network evolution of the peak larval months and the average degree of the network.
    
    VL  - 11
    IS  - 6
    ER  - 

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Author Information
  • Scientific Research Department, Liaoning Vocational College of Ecological Engineering, Shenyang, China

  • Scientific Research Department, Liaoning Vocational College of Ecological Engineering, Shenyang, China

  • Sankuaishi Forest Farm of Fushun County, Fushun, China

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