Predicting potential global and future distributions of the African armyworm (Spodoptera exempta) using species distribution models

被引:9
|
作者
Gomez-Undiano, Irene [1 ]
Musavi, Francis [2 ]
Mushobozi, Wilfred L. [3 ,4 ]
David, Grace M. [4 ]
Day, Roger [5 ]
Early, Regan [6 ]
Wilson, Kenneth [1 ]
机构
[1] Univ Lancaster, Lancaster Environm Ctr, Lancaster, England
[2] NARL Kabete, State Dept Crop Dev & Agr Res, Waiyaki Way, Nairobi, Kenya
[3] Crop Biosci Solut Ltd, Arusha, Tanzania
[4] Minist Agr & Food Secur, Pest Control Serv, Arusha, Tanzania
[5] CABI, Nairobi, Kenya
[6] Univ Exeter, Ctr Ecol & Conservat, Penryn, Cornwall, England
基金
英国生物技术与生命科学研究理事会;
关键词
LOW-DENSITY POPULATIONS; LEPIDOPTERA-NOCTUIDAE; WALKER LEPIDOPTERA; CLIMATE-CHANGE; FALL ARMYWORM; SAMPLE-SIZE; OUTBREAKS; RANGE; MOTH; MANAGEMENT;
D O I
10.1038/s41598-022-19983-y
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Invasive species have historically been a problem derived from global trade and transport. To aid in the control and management of these species, species distribution models (SDMs) have been used to help predict possible areas of expansion. Our focal organism, the African Armyworm (AAW), has historically been known as an important pest species in Africa, occurring at high larval densities and causing outbreaks that can cause enormous economic damage to staple crops. The goal of this study is to map the AAW's present and potential distribution in three future scenarios for the region, and the potential global distribution if the species were to invade other territories, using 40 years of data on more than 700 larval outbreak reports from Kenya and Tanzania. The present distribution in East Africa coincides with its previously known distribution, as well as other areas of grassland and cropland, which are the host plants for this species. The different future climatic scenarios show broadly similar potential distributions in East Africa to the present day. The predicted global distribution shows areas where the AAW has already been reported, but also shows many potential areas in the Americas where, if transported, environmental conditions are suitable for AAW to thrive and where it could become an invasive species.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Predicting potential global and future distributions of the African armyworm (Spodoptera exempta) using species distribution models
    Irene Gómez-Undiano
    Francis Musavi
    Wilfred L. Mushobozi
    Grace M. David
    Roger Day
    Regan Early
    Kenneth Wilson
    Scientific Reports, 12
  • [2] Potential rates of increase of solitarious and gregarious phases of the African armyworm Spodoptera exempta (Lepidoptera: Noctuidae)
    Cheke, RA
    ECOLOGICAL ENTOMOLOGY, 1995, 20 (04) : 319 - 325
  • [3] SEASONAL AND GEOGRAPHICAL VARIATION IN THE MIGRATORY POTENTIAL OF OUTBREAK POPULATIONS OF THE AFRICAN ARMYWORM MOTH, SPODOPTERA-EXEMPTA
    WILSON, K
    GATEHOUSE, AG
    JOURNAL OF ANIMAL ECOLOGY, 1993, 62 (01) : 169 - 181
  • [4] SURVIVAL AND DEVELOPMENT OF THE AFRICAN ARMYWORM SPODOPTERA-EXEMPTA (WLK) (LEPIDOPTERA, NOCTUIDAE) ON SOME GRASS SPECIES (GRAMINEAE)
    YARRO, JG
    INSECT SCIENCE AND ITS APPLICATION, 1984, 5 (01): : 1 - 5
  • [5] SEASONAL CHANGES IN DISTRIBUTION OF AFRICAN ARMYWORM SPODOPTERA EXEMPTA (WLK) (LIP NOCTUIDAE) WITH SPECIAL REFERENCE TO EASTERN AFRICA
    BROWN, ES
    BETTS, E
    RAINEY, RC
    BULLETIN OF ENTOMOLOGICAL RESEARCH, 1969, 58 : 661 - +
  • [8] Global Potential Geographical Distribution of the Southern Armyworm (Spodoptera eridania) under Climate Change
    Zhang, Yu
    Zhao, Haoxiang
    Qi, Yuhan
    Li, Ming
    Yang, Nianwan
    Guo, Jianyang
    Xian, Xiaoqing
    Liu, Wanxue
    BIOLOGY-BASEL, 2023, 12 (07):
  • [9] Predicting the distributions of under-recorded Odonata using species distribution models
    Hassall, Christopher
    INSECT CONSERVATION AND DIVERSITY, 2012, 5 (03) : 192 - 201
  • [10] Predicting the Global Potential Suitable Distribution of Fall Armyworm and Its Host Plants Based on Machine Learning Models
    Huang, Yanru
    Dong, Yingying
    Huang, Wenjiang
    Guo, Jing
    Hao, Zhuoqing
    Zhao, Mingxian
    Hu, Bohai
    Cheng, Xiangzhe
    Wang, Minghao
    REMOTE SENSING, 2024, 16 (12)