海岛型城市红火蚁发生程度空间格局及驱动因子——以福建海坛岛为例

doi:10.17520/biods.2022501

生物多样性 ›› 2023, Vol. 31 ›› Issue (5): 22501. DOI: 10.17520/biods.2022501

所属专题：昆虫多样性与生态功能

• 研究报告: 生物安全与自然保护 • 上一篇下一篇

海岛型城市红火蚁发生程度空间格局及驱动因子——以福建海坛岛为例

陈宏¹, 冼晓青², 陈宜雪³, 林娜¹, 王苗苗¹, 李志鹏¹, 赵健¹^,^*()

1.福建省农业科学院数字农业研究所, 福州 350001
2.中国农业科学院植物保护研究所, 北京 100193
3.平潭综合实验区农业农村发展服务中心, 福建平潭 350400

收稿日期:2022-08-30 接受日期:2022-12-08 出版日期:2023-05-20 发布日期:2023-05-19
通讯作者: * E-mail: zhaojian@faas.cn
基金资助:
国家重点研发计划(2021YFC2600403);福建省农业科学院引导性科技创新项目(YDXM2021003);福建省农业科学院科技创新团队建设项目(CXTD2021012-3)

Spatial pattern and driving factors on the prevalence of red imported fire ant (Solenopsis invicta) in island cities: A case study of Haitan Island, Fujian

Hong Chen¹, Xiaoqing Xian², Yixue Chen³, Na Lin¹, Miaomiao Wang¹, Zhipeng Li¹, Jian Zhao¹^,^*()

1. Institute of Digital Agriculture Research, Fujian Academy of Agricultural Sciences, Fuzhou 350001
2. Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193
3. Agricultural and Rural Development Service Center of Pingtan Comprehensive Experimental Area, Pingtan, Fujian 350400

Received:2022-08-30 Accepted:2022-12-08 Online:2023-05-20 Published:2023-05-19
Contact: * E-mail: zhaojian@faas.cn

摘要/Abstract

摘要：

红火蚁(Solenopsis invicta)是极具危险性的外来入侵物种, 研究海岛在城市化发展中红火蚁发生程度的空间格局及驱动因子, 对保护海岛生态安全具有重要意义。本研究以福建省东部的海坛岛为例, 运用核密度、空间自相关模型分析红火蚁发生程度空间格局, 进一步运用地理探测器揭示环境因子(8种)、社会经济因子(10种)以及两类因子交互作用对红火蚁发生程度的影响。结果显示: 研究区红火蚁发生程度空间密度表现为不均匀聚集特征, 农田发生程度最为严重(高-高聚集)、园林绿化用地次之(高-低聚集和低-低聚集)、居民区周边最轻, 且发生区域呈现正向空间自相关关系。18种影响因子对红火蚁发生程度空间分异解释度的q值范围为0.014-0.278。从整体上看, 两类因子对红火蚁发生程度的影响存在差异, 且社会经济因子q值平均数高于环境因子。对红火蚁发生程度影响最大的经济因子是农村人口数量(q = 0.278), 其次为乡镇面积(q = 0.268)。在环境因子中, 土壤类型(q = 0.172)和年均降水量(q = 0.149)的影响力较强。环境因子与社会经济因子两两叠加作用都将正向增强红火蚁的发生程度, 其中, 乡镇面积与邻近景区距离、年均降水量与邻近景区距离两组因子交互作用对红火蚁发生程度的影响力最大(q = 0.466)。本研究结果表明农业耕作、城镇化建设中绿化苗木引种、道路交通运输对海岛型城市红火蚁发生程度的空间格局起到关键性作用。因此, 在防控中不仅要加强对调运苗木和往来货物的检疫, 还应采取因地适宜的防控措施, 有效抑制红火蚁种群数量和扩散速度。

关键词: 红火蚁, 海岛型城市, 空间格局, 地理探测器, 海坛岛

Abstract

Aims: The red imported fire ant (RIFA), Solenopsis invicta, is one of the world’s worst invasive species, both environmentally and ecologically. In this study, we aim to analyze the spatial pattern of RIFA and the interactive mechanism of driving factors of its population density in island cities.
Methods: We used the Haitan Island as an example, the biggest island in Fujian Province, to analyze the spatial pattern of RIFA populations using Kernel density estimation and spatial autocorrelation models. We also used geographical detector to elucidate the individual and interactive effects of both environmental factors (8 types) and socio-economic factors (10 types) on RIFA prevalence.
Results: The spatial density of RIFA populations in the study area was characterized by uneven distribution, with the highest population density occurring in farmland (high-high aggregation), followed by landscaping and greening land (high-low and low-low aggregation), and the lowest density around residential areas. There were positive spatial autocorrelations within the population range. The q value for the interpretation of 18 impact factors on the spatial differentiation ranged from 0.014 to 0.278. Overall, there were differences between the effects of the two types of factors on RIFA occurrence, and the mean q value of the socio-economic factors were higher than those of the environmental factors. The socio-economic factors that had the greatest impact on RIFA occurrence were rural population size (q = 0.278) and township area (q = 0.268). The soil classification (q = 0.172) and average annual precipitation (q = 0.149) were stronger than other environmental factors. The interaction between township area and distance to nearby scenic spot, and average annual precipitation and distance to nearby scenic spot, had the greatest impact on the occurance of RIFA (q = 0.466). The combined effect of environmental and socio-economic factors positively enhanced the occurance of RIFA.
Conclusion: Our study indicates that agricultural cultivation, seedling introduction in urban greening construction, and transportation significantly affect the spatial pattern of RIFA prevalence in island cities. As far as its prevention and control strategy is concerned, we should not only implement strict plant quarantine regulations on imported seedlings and cargo, but also take locally-appropriate measures to effectively reduce the population size and prevent the spread of RIFA.

Key words: Solenopsis invicta, island cities, spatial pattern, geographic detector, Haitan Island

陈宏, 冼晓青, 陈宜雪, 林娜, 王苗苗, 李志鹏, 赵健 (2023) 海岛型城市红火蚁发生程度空间格局及驱动因子——以福建海坛岛为例. 生物多样性, 31, 22501. DOI: 10.17520/biods.2022501.

Hong Chen, Xiaoqing Xian, Yixue Chen, Na Lin, Miaomiao Wang, Zhipeng Li, Jian Zhao (2023) Spatial pattern and driving factors on the prevalence of red imported fire ant (Solenopsis invicta) in island cities: A case study of Haitan Island, Fujian. Biodiversity Science, 31, 22501. DOI: 10.17520/biods.2022501.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2022501

https://www.biodiversity-science.net/CN/Y2023/V31/I5/22501

图/表 7

参考文献 35

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交互关系 Interaction relationship	交互作用 Interaction effect
q(x1∩x2) < Min(q(x1), q(x2))	非线性减弱 Nonlinear attenuation
Min(q(x1), q(x2)) < q(x1∩x2) < Max(q(x1), q(x2))	单因子非线性减弱 One-factor nonlinear attenuation
q(x1∩x2) > Max(q(x1), q(x2))	双因子增强 Two-factor enhancement
q(x1∩x2) = q(x1) + q(x2)	独立 Independence
q(x1∩x2) > q(x1) + q(x2)	非线性增强 Nonlinear enhancement

交互关系 Interaction relationship	交互作用 Interaction effect
q(x1∩x2) < Min(q(x1), q(x2))	非线性减弱 Nonlinear attenuation
Min(q(x1), q(x2)) < q(x1∩x2) < Max(q(x1), q(x2))	单因子非线性减弱 One-factor nonlinear attenuation
q(x1∩x2) > Max(q(x1), q(x2))	双因子增强 Two-factor enhancement
q(x1∩x2) = q(x1) + q(x2)	独立 Independence
q(x1∩x2) > q(x1) + q(x2)	非线性增强 Nonlinear enhancement

类别 Category	影响因子 Impact factors	数据来源 Data sources
环境因子 Environmental factors	数字高程模型 Digital elevation model (DEM) (x1)	SRTM (https://srtm.csi.cgiar.org)
	坡度 Slope (x2)	通过分析DEM数据获得 Obtained by analyzing DEM data
	地表覆盖 Land cover (x3)	通过解析Landsat 8卫星影像获得 Obtained by analyzing Landsat 8 satellite images
	归一化植被指数 Normalized difference vegetation index (NDVI) (x4)
	土壤类型 Soil classification (x5)	资源环境科学与数据中心 Resource and Environment Science and Data Center (www.resdc.cn)
	年均降水量 Average annual precipitation (x6)
	年积温 Annual cumulative temperature (x7)
	干燥度 Drought index (x8)
社会经济因子 Socio-economic factors	夜间灯光 Night light (x9)	珞珈一号数据 Luojia-1 Data (http://59.175.109.173:8888/)
	乡镇面积 Township area (x10)	中国县域统计年鉴(乡镇卷): 2018 China Statistical Yearbook (Township): 2018 (国家统计局农村社会经济调查司, 2019)
	村庄数量 Number of villages (x11)
	农村人口数量 Rural population size (x12)
	路网密度 Road network density (x13)	通过分析OSM数据获得 Obtained by analyzing OSM data
	邻近道路距离 Distance to nearby road (x14)
	邻近道路类型 Adjacent road type (x15)
	邻近景区距离 Distance to nearby scenic spot (x16)
	邻近码头距离 Distance to nearby wharf (x17)
	离跨海大桥距离 Distance to the cross-sea bridge (x18)

类别 Category	影响因子 Impact factors	数据来源 Data sources
环境因子 Environmental factors	数字高程模型 Digital elevation model (DEM) (x1)	SRTM (https://srtm.csi.cgiar.org)
	坡度 Slope (x2)	通过分析DEM数据获得 Obtained by analyzing DEM data
	地表覆盖 Land cover (x3)	通过解析Landsat 8卫星影像获得 Obtained by analyzing Landsat 8 satellite images
	归一化植被指数 Normalized difference vegetation index (NDVI) (x4)
	土壤类型 Soil classification (x5)	资源环境科学与数据中心 Resource and Environment Science and Data Center (www.resdc.cn)
	年均降水量 Average annual precipitation (x6)
	年积温 Annual cumulative temperature (x7)
	干燥度 Drought index (x8)
社会经济因子 Socio-economic factors	夜间灯光 Night light (x9)	珞珈一号数据 Luojia-1 Data (http://59.175.109.173:8888/)
	乡镇面积 Township area (x10)	中国县域统计年鉴(乡镇卷): 2018 China Statistical Yearbook (Township): 2018 (国家统计局农村社会经济调查司, 2019)
	村庄数量 Number of villages (x11)
	农村人口数量 Rural population size (x12)
	路网密度 Road network density (x13)	通过分析OSM数据获得 Obtained by analyzing OSM data
	邻近道路距离 Distance to nearby road (x14)
	邻近道路类型 Adjacent road type (x15)
	邻近景区距离 Distance to nearby scenic spot (x16)
	邻近码头距离 Distance to nearby wharf (x17)
	离跨海大桥距离 Distance to the cross-sea bridge (x18)

海岛型城市红火蚁发生程度空间格局及驱动因子——以福建海坛岛为例

Spatial pattern and driving factors on the prevalence of red imported fire ant (Solenopsis invicta) in island cities: A case study of Haitan Island, Fujian

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