Deng Jun, Li Junjie, Zhang Xinrui, Zeng Lingda, Guo Yanqing, Wang Xu, Chen Zijing, Zhou Jiali, Huang Xiaolei
State Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops, College of Plant Protection, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Insects. 2023 Jun 21;14(7):572. doi: 10.3390/insects14070572.
In the present study, a global presence/absence dataset including 2486 scale insect species in 157 countries was extracted to assess the establishment risk of potential invasive species based on a self-organizing map (SOM). According to the similarities in species assemblages, a risk list of scale insects for each country was generated. Meanwhile, all countries in the dataset were divided into five clusters, each of which has high similarities of species assemblages. For those countries in the same neuron of the SOM output, they may pose the greatest threats to each other as the sources of potential invasive scale insect species, and therefore, require more attention from quarantine departments. In addition, normalized ζ values were used to measure the uncertainty of the SOM output. In total, 9 out of 63 neurons obtained high uncertainty with very low species counts, indicating that more investigation of scale insects should be undertaken in some parts of Africa, Asia and Northern Europe.
在本研究中，提取了一个包含157个国家2486种介壳虫物种的全球存在/缺失数据集，以基于自组织映射（SOM）评估潜在入侵物种的定殖风险。根据物种组合的相似性，生成了每个国家的介壳虫风险列表。同时，数据集中的所有国家被分为五个聚类，每个聚类的物种组合具有高度相似性。对于SOM输出中处于同一神经元的那些国家，作为潜在入侵介壳虫物种的来源，它们可能对彼此构成最大威胁，因此，需要检疫部门给予更多关注。此外，使用归一化的ζ值来衡量SOM输出的不确定性。在总共63个神经元中，有9个获得了高不确定性且物种数量非常少，这表明在非洲、亚洲和北欧的一些地区应加强对介壳虫的调查。
