Next Article in Journal
Activation of BmToll9-1 in Silkworm (Bombyx mori) Larval Midgut by Escherichia coli and Regulation of Growth
Previous Article in Journal
Histopathological and Transcriptional Changes in Silkworm Larval Gonads in Response to Chlorfenapyr Exposure
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Insects
Volume 16
Issue 6
10.3390/insects16060620
insects-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Invasion Dynamics and Migration Patterns of Fall Armyworm (Spodoptera frugiperda) in Shaanxi, China

by
Zhanfeng Yan
1,2,†,
Xiaojun Feng
3,†,
Xing Wang
1,
Xiangqun Yuan
1,
Yongjun Zhang
4,
Daibin Yang
4,
Kanglai He
4,
Feizhou Xie
3,
Zhenying Wang
4,* and
Yiping Li
1,*
1
Key Laboratory of Plant Protection Resources and Pest Management of Ministry of Education, Key Laboratory of Integrated Pest Management on Crops in Northwestern Loess Plateau of Ministry of Agriculture and Rural Affairs, College of Plant Protection, Northwest A&F University, Yangling 712100, China
2
State Key Laboratory of Crop Germplasm Innovation and Molecular Breeding, Syngenta Biotechnology (China) Co., Ltd., Beijing 102206, China
3
Plant Protection Station of Shaanxi Province, Xi’an 710003, China
4
State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Insects 2025, 16(6), 620; https://doi.org/10.3390/insects16060620
Submission received: 30 April 2025 / Revised: 5 June 2025 / Accepted: 9 June 2025 / Published: 11 June 2025
(This article belongs to the Section Insect Pest and Vector Management)
Browse Figures
Review Reports Versions Notes

Simple Summary

The fall armyworm (FAW) is a serious invasive pest that has caused significant damage to crops in China since its arrival in 2019. This study investigates how FAW spreads and migrates in Shaanxi Province over five years, using field surveys, computer simulations, genetic analyses, and experiments. We found that FAW’s appearance in Shaanxi has been delayed each year, and its migration routes and source regions vary annually. The main sources of FAW in Shaanxi are from neighboring provinces like Sichuan, Chongqing, Hubei, Guizhou, and Henan. Genetic analysis showed that FAW populations in Shaanxi have high diversity, with different strains mixing together. Overwintering experiments revealed that FAW pupae cannot survive the winter in Shaanxi, indicating that the region cannot support year-round breeding of this pest. Our findings help us understand FAW’s behavior and provide valuable information for developing better pest management strategies to protect crops and food security in China and beyond.

Abstract

The fall armyworm (Spodoptera frugiperda) is a highly invasive agricultural pest that has caused significant damage to maize and other crops since its initial detection in China in 2019. Understanding its invasion dynamics, migration patterns, genetic diversity, and overwintering capacity is crucial for developing effective pest management strategies. This study investigates these aspects in Shaanxi Province, a critical transitional zone between northern and southern climates in China, from 2019 to 2023. We conducted field surveys in six cities across Shaanxi to monitor the initial infestation of FAW. Migration trajectories were simulated using the HYSPLIT model, integrating pest occurrence data and meteorological information. Genetic analyses were performed on 113 FAW individuals from 12 geographical populations using mitochondrial COI and nuclear Tpi genes. Additionally, an overwintering experiment was conducted to assess the survival of FAW pupae under local winter conditions. The first detection dates of FAW in Shaanxi showed significant interannual variation, with a trend of delayed infestation each year. Three primary migration routes into Shaanxi were identified, originating from Sichuan, Hubei-Chongqing, and Henan. Genetic analysis revealed a predominance of the rice-strain FAW in Shaanxi, with some corn-strain variants in northern regions. The overwintering experiment indicated that FAW pupae could not survive the winter in Shaanxi, suggesting that the region does not support year-round breeding of this pest. This study provides comprehensive insights into the spatiotemporal dynamics and migration patterns of FAW in Shaanxi. The findings highlight the importance of integrated pest management approaches, including monitoring migration routes and genetic diversity, to develop targeted control measures. The inability of FAW to overwinter in Shaanxi suggests that regional climate conditions play a significant role in limiting its year-round presence, which is valuable information for designing early warning systems and sustainable pest management strategies.

1. Introduction

The fall armyworm (FAW), Spodoptera frugiperda, is a highly significant pest that poses a considerable threat to global food security, particularly in regions where maize cultivation is prevalent [1,2]. Since its initial detection in Africa in 2016 and its subsequent invasion into China in 2019, understanding the migration ecology of FAW has become crucial for developing science-based management strategies [3]. Monitoring insect migration is essential for predicting and managing pest outbreaks. As Johnson [4] first comprehensively described, insect migration is a complex phenomenon influenced by various factors, including environmental cues and physiological states. For highly mobile pests like FAW, effective monitoring of their long-distance movements is vital for timely interventions and sustainable management practices [5,6]. The importance of migration monitoring in FAW management cannot be overstated. Early detection through monitoring systems enables rapid responses, potentially preventing widespread crop damage and reducing the need for extensive pesticide use [7,8]. Furthermore, understanding migration patterns aids in predicting future infestation areas, allowing for proactive rather than reactive management strategies [9].
In China, FAW has exhibited two primary migration routes: the western route, originating in Myanmar and Yunnan and passing through Guizhou, Chongqing, Sichuan, and Shaanxi, and the eastern route, starting from northern Thailand, Laos, Vietnam, Guangxi, and Guangdong and proceeding through southeastern and east-central provinces to the Huang-Huai-Hai and Northeast Regions [10,11]. However, a critical gap remains in our understanding of FAW’s invasion dynamics, particularly in transitional climatic zones. Previous studies have mainly focused on the general migration patterns and genetic characteristics of FAW in China. Yet, the complex interactions between migration routes, genetic diversity, and overwintering ability in specific transitional regions have not been adequately addressed. Additionally, the potential for genetic exchange between the eastern and western routes and its implications for FAW’s adaptability and management strategies remain underexplored.
This study hypothesizes that the unique migration patterns and genetic diversity of FAW in Shaanxi contribute to its invasive success and that the region’s climate significantly limits its overwintering ability. To fill these knowledge gaps, this study comprehensively investigates FAW’s invasion dynamics in Shaanxi Province from 2019 to 2023. By integrating field surveys, trajectory simulations, molecular analysis, and overwintering experiments, we aim to achieve the following objectives: (1) determine the annual variation in FAW occurrence and identify its migration routes and source regions; (2) assess the genetic diversity and strain composition of FAW populations in Shaanxi and surrounding areas; and (3) evaluate FAW’s overwintering ability in Shaanxi and its implications for regional pest management. Our research not only enhances the understanding of FAW’s behavior in a critical transitional zone but also challenges existing assumptions by revealing the complexity of population mixing and genetic exchange. These findings provide valuable insights for refining pest management strategies and improving early warning systems to protect agricultural productivity and food security in China and beyond.

2. Materials and Methods

2.1. Survey of FAW Initial Infestation in Shaanxi

This study was conducted in six cities of Shaanxi, Hanzhong, Ankang, Shangluo, Baoji, Xianyang, and Weinan, encompassing multiple counties within each city. Field surveys employed a “W”-shaped five-point sampling method, as described by Ren et al. [12]. At each sampling point, the following metrics were recorded: (1) infestation rate: percentage of damaged plants per 100 sampled plants, calculated as I n f e s t a t i o n   r a t e   ( % ) = N u m b e r   o f   d a m a g e d   p l a n t s T o t a l   p l a n t s   s a m p l e d × 100 ; (2) larval density: mean number of larvae per 100 plants, expressed as L a r v a e   p e r   100   p l a n t s = T o t a l   l a r v a e   c o u n t e d T o t a l   p l a n t s   s a m p l e d × 100 ; (3) damage severity: rated on a 0–9 scale (0 = no damage; 9 = complete plant destruction) based on leaf defoliation and heartbreak feeding symptoms [8]. Data for 2019–2021 were provided by the Plant Protection Station of Shaanxi Province Agriculture and Rural Affairs Department, while 2022–2023 data were jointly collected by our research team and local plant protection.

2.2. Simulation of FAW Invasion Routes in Shaanxi

To simulate the invasion pathways of FAW in Shaanxi, we utilized pest occurrence data from 2019 to 2023, sourced from the Shaanxi Provincial Plant Protection Station, combined with tour field surveys. Adult invasion times were estimated based on observed larval instar, local daily mean temperatures, and FAW effective accumulated temperature. Meanwhile, adult emergence timing was based on the observed larval instar, local daily mean temperatures, and the effective accumulated temperature required for FAW development. Specifically, we used the third-instar larvae as the basis for back-calculating the timing of adult emergence. We adopted a threshold temperature of 11.3 °C and a degree-day constant (thermal constant) of 344.5 day·degrees for the estimation, which were determined based on previous research [9,13].
To simulate FAW migration pathways, HYSPLIT [14] runs were initialized at 1500 m above ground level (AGL). This altitude accounts for observed FAW flight behavior and topographic influences. Radar studies indicate FAW migrations occur in layered flows between 300–1500 m AGL, with concentration peaks varying by region and season [6,15]. Additionally, the Qinling Mountains (elevation up to 3767 m) likely force ascending airflows, as documented in other Noctuids (e.g., Mythimna separata) that adopt higher flight altitudes (1000–2000 m) to cross mountain barriers [16,17,18]. The simulation assumed two consecutive nights of flight, with the endpoint of the first night serving as the starting point for the second night. Trajectory data were processed in ArcGIS 10.7, where effective trajectories were selected based on height, direction, and source area pest status. These were then overlaid on provincial administrative maps obtained from the National Geomatics Center of China. By analyzing the simulated trajectories from 2019 to 2023, we identified potential source regions for FAW populations invading Shaanxi, providing crucial insights into their invasion dynamics.

2.3. Genetic Characterization of FAW Populations from Different Geographical Regions

A total of 113 FAW individuals from 12 different geographical populations were collected during 2022–2023, including multiple regions within Shaanxi Province and samples from Yunnan, Henan, and Hubei provinces (Table 1). DNA was extracted using QIAGEN DNA extraction kits following the manufacturer’s protocol. PCR amplification targeted the mitochondrial COI gene (F:TTCGAGCTGAATTAGGGACTC; R:GATGTAAAATATGCTCGTGT) and nuclear Tpi gene (F:GGTGAAATCTCCCCTGCTATG; R:AATTTTATTACCTGCTGTGG), using primers and conditions described by Zhang [19]. The COI gene was selected due to its high variability and maternal inheritance, making it suitable for identifying strain-specific maternal lineages. The Tpi gene, a nuclear marker, was chosen to provide insights into paternal lineages and potential hybridization events. This combination of markers allows for a comprehensive analysis of genetic diversity and population structure. In addition, we acknowledge that our phylogenetic analysis is not designed to definitively infer the geographic origins of FAW populations due to potential gaps in COI reference sequences from regions like Yunnan or Guangxi. Future research incorporating more comprehensive reference data could further clarify the geographic origins of FAW in Shaanxi.
PCR products were sequenced commercially, and sequences were analyzed using Geneious R11 software [20]. MEGA X software [21] was used for sequence alignment and construction of Neighbor-Joining phylogenetic trees with 1000 bootstrap replicates.

2.4. Preliminary Investigation of FAW Pupal Overwintering in Shaanxi

Laboratory-reared FAW pupae were used for the experiment. Thirty pupae of the same age were placed in 1500 mL plastic containers filled with 10 cm of soil, following the method of Li et al. [22]. On 10 December 2022, these containers were placed in fallow fields at six test sites across Shaanxi (Hantai District (106°55′ E, 33°09′ N) and Ningqiang (106°03′ E, 33°57′ N) County in Hanzhong, Xunyang (109°32′ E, 33°06′ N) County and Zhenping (109°26′ E, 31°51′ N) County in Ankang, and Danfeng (110°06′ E, 33°46′ N) County and Shanyang (110°16′ E, 33°27′ N) County in Shangluo. Pupal survival was checked every five days, following a nondestructive protocol. Pupae were initially identified within the topsoil layer (0–10 cm depth) without physical disturbance, with viability preliminarily assessed through intact morphology observation and gentle tactile response (e.g., leg twitching). Intact pupae were then carefully excavated using sterile tools, placed individually in ventilated containers with native soil substrate, and transferred to a climate-controlled chamber (25 ± 1 °C, 70 ± 5% RH, 12:12 L:D photoperiod) to monitor emergence. Mortality was confirmed if no movement or adult emergence occurred within 48 h of acclimatization at 20–22 °C. Three replicates (10 pupae per replicate) were processed per site, along with sterilized soil controls to account for environmental contamination. Data were statistically analyzed using SPSS software (Version 27.0) [23], with significant differences in pupal survival rates among sites were analyzed using one-way ANOVA followed by Tukey’s honestly significant difference (HSD) post hoc test (α = 0.05).

3. Results

3.1. Initial Occurrence of FAW in Shaanxi from 2019 to 2023

First detection dates showed significant interannual variation (ANOVA, F = 7.32, p < 0.01), progressing from 31 May (2019) to late June (2021–2023) (Table 2). In 2019, FAW was first detected on 31 May in Yangxian County, Hanzhong City, marking its initial invasion into Shaanxi Province with a relatively low infestation level. The first occurrence in 2020 was delayed to 28 June in Zhenping County, Ankang City, but with increased severity compared to 2019. From 2021 to 2023, initial detections consistently occurred between late June and early July, with a general declining trend in infestation severity. The specific survey data from different years are shown in the Supplement Files.

3.2. Simulation of FAW Migration Trajectories in Shaanxi

HYSPLIT model simulations of migration trajectories revealed that the main source regions for FAW invading Shaanxi from 2019 to 2023 included Sichuan, Chongqing, Hubei, Guizhou, and Henan (Figure 1). Three primary migration routes were identified: (1) the Sichuan route, mainly invading the Hanzhong area; (2) the Hubei-Chongqing route, primarily affecting the Ankang area; and (3) the Henan-Hubei route, mainly invading the Shangluo area. Notably, migration patterns varied across years. All three routes coexisted in 2019–2020; the Henan-Hubei and Sichuan routes predominated in 2021; and, in 2022–2023, an additional intra-provincial migration route emerged alongside the existing pathways. These variations may be attributed to inter-annual differences in meteorological conditions and source population sizes. For a detailed visualization of these trajectories, please refer to the Supplementary Materials, where the HYSPLIT trajectory analysis results are provided. The suggested migration routes are considered tentative due to the lack of empirical data confirming the actual migration routes.

3.3. Molecular Characterization of FAW Biotypes from Different Geographical Populations

COI sequences revealed 85% rice-strain predominance, with corn-strain variants concentrated in northern Shaanxi (χ2 = 6.54, p = 0.01). Tpi gene analysis showed unexpected monomorphism across all populations (Table 3). Phylogenetic analysis showed that COI-strain 1 (rice strain) clustered with rice strains from Yunnan, India, South Africa, and the United States, while COI-strain 2 (corn strain) grouped with corn strains from the United States and Jiangsu (Figure 2). These results illuminate the genetic diversity and potential origins of FAW populations in Shaanxi.

3.4. Preliminary Investigation of FAW Overwintering in Shaanxi

To evaluate the overwintering potential of FAW in Shaanxi, pupal survival was monitored across six locations from 15 December 2022 to 14 January 2023. One-way ANOVA revealed significant differences in survival rates among locations. Post-hoc pairwise comparisons using Tukey’s HSD test confirmed that pupae failed to emerge at all sites, with survival rates declining progressively until complete mortality was observed by 14 January 2023 (Table 4 and Figure 3). No significant pairwise differences were detected between specific locations (all p > 0.05, Tukey’s HSD), suggesting uniformly lethal effects of low temperatures during the experimental period. These results indicate that Shaanxi’s winter conditions are unlikely to support FAW pupal overwintering or year-round breeding. However, it should be noted that the absence of continuous soil temperature data limits the explanatory power of this experiment regarding pupal mortality. Future studies would benefit from incorporating environmental data, such as soil temperature, to better understand the factors influencing FAW pupal survival.

4. Discussion

The FAW invasion in China has rapidly spread since 2019, posing a significant threat to agricultural production, particularly corn crops [24,25,26]. Our study reveals that FAW infestation dynamics in Shaanxi varied annually from 2019 to 2023, with a trend of delayed occurrence each year. The first detection in Shaanxi Province occurred on 31 May 2019, approximately 20 days later than in Hubei Province at a similar latitude [27]. Understanding FAW migration patterns and source regions is crucial for effective prediction and control [28]. Our study used the HYSPLIT platform to simulate FAW migration trajectories, a method widely applied in insect migration studies [29,30]. The results indicate that FAW migration routes and source regions invading Shaanxi varied annually from 2019 to 2023. Our findings have several direct implications for pest management strategies in Shaanxi and adjacent regions. The consistent genetic composition dominated by the rice strain suggests that monitoring and control efforts should focus on this strain. Additionally, the overwintering experiment results highlight that Shaanxi’s climate acts as a natural barrier to FAW’s year-round establishment. This implies that regional pest management could prioritize early-season monitoring and intervention, as FAW populations are likely to be re-invading each year rather than overwintering locally. Specifically, integrated pest management approaches could incorporate the timing of FAW migrations to optimize the deployment of control measures such as targeted insecticide applications or the release of natural enemies in alignment with the pest’s arrival. Furthermore, the stable strain composition observed could inform the selection of resistant maize varieties suited to counteract the predominant rice-strain FAW, thereby enhancing agricultural resilience against this invasive pest. Previous studies have identified two main migration routes for FAW in China, the western and eastern routes [10,11], and our findings largely support this pattern but also reveal more complex dynamics. For instance, we observed potential mixing of populations from eastern and western routes, particularly in Henan province. This complexity aligns with recent studies suggesting that while the two migration routes are generally distinct, some interchange occurs [9,31]. The migration patterns we observed in Shaanxi show similarities to those reported in neighboring regions. For example, Zhang et al. [32] found that FAW populations in Sichuan could migrate to Shaanxi, which our results corroborate. Similarly, our observation of FAW migrations from Hubei and Henan into Shaanxi aligns with the findings of Sun et al. [31] and Tan et al. [33], who reported complex migration patterns in central China. The genetic diversity and potential hybridization observed in our study have significant implications for FAW management. Understanding these genetic dynamics is crucial for developing effective pest management strategies, as emphasized by Westbrook et al. [6]. The high genetic diversity in Henan underscores its pivotal role in FAW population dynamics, suggesting it should be a focal point for intensive monitoring and targeted control efforts. These findings enhance our understanding of FAW population genetics and highlight the need for adaptive and region-specific management strategies.
Genetic analysis of FAW populations provides crucial insights into their complex migration patterns. Our comprehensive study, examining 113 FAW samples from 12 distinct geographical populations using COI and Tpi molecular markers, revealed a notable discrepancy between mitochondrial and nuclear markers. This finding aligns with previous research by Zhang et al. [19] and Wang et al. [34], suggesting a consistent pattern across different studies. Meanwhile, the research results also indicated that the FAW population in Shaanxi was predominantly the rice strain, with a certain proportion of the corn strain in northern Shaanxi. This finding is consistent with the results of Li et al. [9], which highlighted that the migration routes and population genetic structure of FAW in China are shaped by various factors, including geographic origin and migration pathways. According to Li et al. [9], the western migration route originates in Myanmar and Yunnan, passing through Guizhou, Chongqing, Sichuan, and Shaanxi before merging with the eastern route, while the eastern route starts from northern Thailand, Laos, Vietnam, Guangxi, and Guangdong, proceeding through southeastern and east-central provinces to ultimately reach the Huang-Huai-Hai and Northeast Regions. This suggests that FAW populations in Shaanxi may be influenced by both western and eastern routes, resulting in a mix of rice and corn strains. Further analysis found that FAW populations in Henan exhibited the highest genetic diversity, likely due to Henan’s position at the intersection of eastern and western migration routes, making it a hot spot for genetic exchange. This aligns with our observations of genetic mixing in Shaanxi’s FAW populations, indicating some degree of gene flow among populations from different regions. While our results suggest that Shaanxi might serve as a mixing zone for FAW populations, we caution that these interpretations are preliminary and would be strengthened by the inclusion of additional COI reference sequences from underrepresented regions such as Yunnan or Guangxi. Without these data, our analysis provides valuable insights, but cannot conclusively determine geographic origins.
The uniform results obtained from the Tpi gene analysis suggest a high level of genetic similarity across the sampled FAW populations. This finding may imply a recent population expansion or a bottleneck event that has reduced genetic diversity. It also has important implications for pest management, as higher genetic uniformity may indicate a more cohesive population that could respond similarly to control measures. However, this interpretation is limited by the scope of our sampling, which primarily focused on Shaanxi and the surrounding regions. Future studies should expand the sampling to include a broader geographic range to better assess the overall genetic diversity and structure of FAW populations in China.
The results of our overwintering experiment indicate that FAW pupae could not successfully overwinter at any of the test sites in Shaanxi. This finding is consistent with previous research, which has established that FAW lacks diapause capability, rendering it unable to endure extended periods of cold [10]. However, recent observations have reported the persistence of FAW larvae well into the winter months of November and December in Hubei and Henan provinces [34,35]. This discrepancy between our experimental results and field observations raises intriguing questions about the potential for rapid adaptation in FAW populations or the existence of microhabitats that might offer refuge from harsh winter conditions. It also underscores the complexity of FAW’s adaptive potential and highlights a critical gap in our understanding of its overwintering capabilities, particularly in transitional climate zones. It should be noted that our study did not directly measure soil temperature at the overwintering sites, which could influence the interpretation of pupal survival. Historical soil temperature data would provide valuable additional context to our conclusions on pupal mortality. We recommend that future research consider incorporating such environmental variables to further explore the factors influencing FAW pupal survival in different regions. In addition, our study’s sampling scope, while extensive within Shaanxi, does have limitations that should be acknowledged. The sampled regions may not fully represent the entire range of FAW genetic diversity in China, particularly given the pest’s known ability to migrate over long distances. This limitation underscores the need for collaborative research efforts across multiple regions to capture a more complete picture of FAW population dynamics.

5. Conclusions

Our comprehensive study on the invasion dynamics, migration patterns, genetic diversity, and overwintering capacity of FAW in Shaanxi Province from 2019 to 2023 provides critical insights for developing effective pest management strategies. The observed interannual variations in FAW occurrence and the identified migration routes highlight the complex nature of its invasion dynamics. The genetic diversity and strain composition of FAW populations in Shaanxi, characterized by a predominance of the rice strain, underscore the need for region-specific management approaches. The inability of FAW pupae to survive the winter in Shaanxi suggests that climatic conditions act as a natural barrier to its year-round establishment. This finding is particularly valuable for designing early warning systems and targeted control measures, emphasizing the importance of integrating ecological, genetic, and climatic data in pest management frameworks. However, the questions raised by the discrepancies in overwintering capabilities and the implications of the observed genetic uniformity highlight areas where further research is needed. Continued investigation into these aspects will be crucial for developing effective and sustainable management strategies for this invasive pest.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/insects16060620/s1, Table S1: The first occurrence of FAW in different sites of Shaanxi in 2019; Table S2: The first occurrence of FAW in different sites of Shaanxi in 2020; Table S3: The first occurrence of FAW in different sites of Shaanxi in 2021; Table S4: The first occurrence of FAW in different sites of Shaanxi in 2022; Table S5: The first occurrence of FAW in different sites of Shaanxi in 2023. Figure S1: Backward trajectories of FAW in different Areas of Hanzhong City; Figure S2: Backward trajectories of FAW in different Areas of Ankang City; Figure S3: Backward trajectories of FAW in different Areas of Shangluo City; Figure S4: Backward trajectories of FAW in different Areas of Weinan City; Figure S5: Backward trajectories of FAW in different Areas of Hangzhong City; Figure S6: Backward trajectories of FAW in different Areas of Ankang City; Figure S7: Backward trajectories of FAW in different Areas of Shangluo City; Figure S8: Backward trajectories of FAW in different Areas of Hangzhong City; Figure S9: Backward trajectories of FAW in different Areas of Ankang City; Figure S10: Backward trajectories of FAW in different Areas of Shangluo City; Figure S11: Backward trajectories of FAW in different Areas of Hanzhong City; Figure S12: Backward trajectories of FAW in different Areas of Ankang City; Figure S13: Backward trajectories of FAW in different Areas of Shangluo City; Figure S14: Backward trajectories of FAW in different Areas of Hanzhong City; Figure S15: Backward trajectories of FAW in different Areas of Ankang City.

Author Contributions

Conceptualization, X.W. and Z.Y.; methodology, X.Y. and X.F.; software, X.F.; validation, X.Y., Y.Z., D.Y. and F.X.; formal analysis, Z.Y.; investigation, X.F.; resources, X.F.; data curation, Z.Y. and F.X.; writing—original draft preparation, Z.Y.; writing—review and editing, Y.L., Z.W. and K.H.; supervision, Y.L.; project administration, X.Y.; funding acquisition, Y.L. and Z.W. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Key Research and Development Program of China (2021YFD1400700); Shaanxi Province key research and development plan project (2023-ZDLNY-52).

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

Author Zhanfeng Yan was employed by the company Syngenta Biotechnology (China) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
FAWFall armyworm
AGLAbove ground level

References

  1. Sparks, A.N. A review of the biology of the fall armyworm. Fla. Entomol. 1979, 62, 82–87. [Google Scholar] [CrossRef]
  2. Day, R.; Abrahams, P.; Bateman, M.; Beale, T.; Clottery, V.; Cock, M.; Colmenarez, Y.; Corniani, N.; Early, R.; Godoin, J.; et al. Fall armyworm: Impacts and implications for Africa. Outlooks Pest Manag. 2017, 28, 196–201. [Google Scholar] [CrossRef]
  3. Wu, Q.L.; He, L.M.; Shen, X.J.; Jiang, Y.Y.; Liu, J.; Hu, G.; Wu, K.M. Estimation of the potential infestation area of newly-invaded fall armyworm Spodoptera frugiperda in the Yangtze River Valley of China. Insects 2019, 10, 298. [Google Scholar] [CrossRef]
  4. Johnson, C.G. Migration and Dispersal of Insects by Flight; Methuen & Co., Ltd.: London, UK, 1969. [Google Scholar]
  5. Chapman, J.W.; Reynolds, D.R.; Wilson, K. Long-range seasonal migration in insects: Mechanisms, evolutionary drivers and ecological consequences. Ecol. Lett. 2015, 18, 287–302. [Google Scholar] [CrossRef] [PubMed]
  6. Westbrook, J.K.; Nagoshi, R.N.; Meagher, R.L.; Jiang, Y.Y.; Liu, J.; Hu, G.; Wu, K.M. Modeling seasonal migration of fall armyworm moths. Int. J. Biometeorol. 2016, 60, 255–267. [Google Scholar] [CrossRef]
  7. Early, R.; González-Moreno, P.; Murphy, S.T.; Day, R. Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm. NeoBiota 2018, 40, 25–50. [Google Scholar] [CrossRef]
  8. Prasanna, B.M.; Huesing, J.E.; Eddy, R.; Peschke, V.M. Fall Armyworm in Africa: A Guide for Integrated Pest Management; CIMMYT: Texcoco, Mexico, 2018. [Google Scholar]
  9. Li, X.J.; Wu, M.F.; Ma, J.; Gao, B.Y.; Wu, Q.L.; Chen, A.D.; Liu, J.; Jiang, Y.Y.; Zhai, B.P.; Early, R.; et al. Prediction of migratory routes of the invasive fall armyworm in eastern China using a trajectory analytical approach. Pest Manag. Sci. 2020, 76, 454–463. [Google Scholar] [CrossRef] [PubMed]
  10. Jiang, Y.Y.; Liu, J.; Xie, M.C.; Li, Y.H.; Yang, J.J.; Zhang, M.L.; Qiu, K. Observation on law of diffusion damage of Spodoptera frugiperda in China with 2019. Plant Prot. 2019, 45, 10–19. [Google Scholar] [CrossRef]
  11. Chen, R.; Jiang, L.Y.; Chen, J.; Qiao, G.X. DNA barcoding reveals a mysterious high species diversity of conifer-feeding aphids in the mountains of southwest China. Sci. Rep. 2020, 10, 1–13. [Google Scholar] [CrossRef]
  12. Ren, H.Y.; Gui, W.Y.; Bai, Y.F. Long-term effects of grazing and topography on extra-radical hyphae of arbuscular mycorrhizal fungi in semi-arid grasslands. Mycorrhiza 2018, 28, 117–127. [Google Scholar] [CrossRef]
  13. Goergen, G.; Kumar, P.L.; Sankung, S.B.; Togola, A.; Tamò, M. First report of outbreaks of the fall armyworm Spodoptera frugiperda (JE Smith) (Lepidoptera, Noctuidae), a new alien invasive pest in West and Central Africa. PLoS ONE 2016, 11, e0165632. [Google Scholar] [CrossRef] [PubMed]
  14. Stein, A.F.; Draxler, R.R.; Rolph, G.D.; Stunder, B.J.B.; Cohen, M.D.; Ngan, F. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 2015, 96, 2059–2077. [Google Scholar] [CrossRef]
  15. Reynolds, A.M.; Reynolds, D.R.; Sane, S.P.; Hu, G.; Chapman, J.W. Orientation in high-flying migrant insects in relation to flows: Mechanisms and strategies. Philos. Trans. R. Soc. B Biol. Sci. 2016, 371, 20150382. [Google Scholar] [CrossRef]
  16. Alerstam, T.; Chapman, J.W.; Backman, J.; Smith, A.D.; Karlsson, H.; Nilsson, C.; Reynolds, D.R.; Klaassen, R.H.G.; Hill, J.K. Convergent patterns of long-distance nocturnal migration in noctuid moths and passerine birds. Proc. R. Soc. B Biol. Sci. 2011, 278, 2524–2530. [Google Scholar] [CrossRef]
  17. Wu, Q.L.; Jang, Y.Y.; Liu, Y.; Liu, J.; Ma, J.; Hu, G.; Yang, M.J.; Wu, K.M. Migration Pathway of Spodoptera frugiperda in Northwestern China. Sci. Agric. Sin. 2022, 55, 1949–1960. [Google Scholar] [CrossRef]
  18. Wu, Q.L.; Shen, X.J.; He, L.M.; Jiang, Y.Y.; Liu, J.; Hu, G.; Wu, K.M. Windborne migration routes of newly-emerged fall armyworm from Qinling Mountains-Huaihe River region, China. J. Integr. Agric. 2021, 20, 694–706. [Google Scholar] [CrossRef]
  19. Zhang, L.; Jin, M.H.; Zhang, D.D.; Jiang, Y.Y.; Liu, Y.Y.; Wu, K.M.; Xiao, Y.T. Molecular identification of invasive fall armyworm Spodoptera frugiperda in Yunnan Province of China. Plant Prot. 2019, 45, 19–24. [Google Scholar] [CrossRef]
  20. Kearse, M.; Moir, R.; Wilson, A.; Stones-Havas, S.; Cheung, M.; Sturrock, S.; Buxton, S.; Cooper, A.; Markowitz, S.; Duran, C.; et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 2012, 28, 1647–1649. [Google Scholar] [CrossRef] [PubMed]
  21. Kumar, S.; Stecher, G.; Li, M.; Knyaz, C.; Tamura, K. MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms. Mol. Biol. Evol. 2018, 35, 1547–1549. [Google Scholar] [CrossRef]
  22. Li, G.P.; Liu, B.; Sun, X.; Huang, J.R.; Qiu, F.; Feng, H.Q. Field evaluation of Bt corn expressing cry1A.105+cry2Ab2+cp4epsps against Lepidoptera pests and tolerance to glyphosate. Plant Prot. 2019, 45, 142–147. [Google Scholar] [CrossRef]
  23. IBM Corp. IBM SPSS Statistics for Windows, Version 27.0; IBM Corp.: Armonk, NY, USA, 2020.
  24. Wang, L.; Chen, K.W.; Zhong, G.H.; Xiao, J.D.; He, X.F.; Lu, Y.Y. Progress for occurrence and management and the strategy of the fall armyworm Spodoptera frugiperda (Smith). J. Econ. Entomol. 2019, 41, 479–487. [Google Scholar] [CrossRef]
  25. Yang, P.Y.; Zhu, X.M.; Guo, J.F.; Wang, Z.Y. Strategy and advice for managing the fall armyworm in China. Plant Prot. 2019, 45, 1–6. [Google Scholar] [CrossRef]
  26. Jing, D.P.; Guo, J.F.; Jiang, Y.Y.; Zhao, J.Z.; Sehi, A.; He, K.L.; Wang, Z.Y. Initial detections and spread of invasive Spodoptera frugiperda in China and comparisons with other noctuid larvae in cornfields using molecular techniques. Insect Sci. 2020, 27, 780–790. [Google Scholar] [CrossRef] [PubMed]
  27. Yang, J.J.; Guo, Z.P.; Luo, H.G.; Jiang, Y.Y.; Huang, J.X.; Zhang, K.X.; Liu, Q.; Yuan, H. Research on the law of infestation and monitoring technology of Spodoptera frugiperda in Hubei province in 2019. Plant Prot. 2020, 46, 247–253. [Google Scholar] [CrossRef]
  28. Guo, J.F.; Zhao, J.Z.; He, K.L.; Zhang, F.; Wang, Z.Y. Potential invasion of the crop-devastating insect pest fall armyworm Spodoptera frugiperda to China. Plant Prot. 2019, 44, 1–10. [Google Scholar] [CrossRef]
  29. Jia, H.R.; Guo, J.L.; Wu, Q.L.; Hu, C.X.; Li, X.K.; Zhou, X.Y.; Wu, K.M. Migration of invasive Spodoptera frugiperda (Lepidoptera: Noctuidae) across the Bohai Sea in northern China. J. Integr. Agric. 2021, 20, 685–693. [Google Scholar] [CrossRef]
  30. Lu, Y.H.; Wu, K.M.; Jiang, Y.Y.; Guo, Y.Y.; Desneux, N. Widespread adoption of Bt cotton and insecticide decrease promotes biocontrol services. Nature 2013, 487, 362–365. [Google Scholar] [CrossRef]
  31. Sun, X.X.; Hu, C.X.; Jia, H.R.; Wu, Q.L.; Shen, X.J.; Zhao, S.Y.; Jiang, Y.Y.; Wu, K.M. Case study on the first immigration of fall armyworm, Spodoptera frugiperda invading into China. J. Integr. Agric. 2021, 20, 664–672. [Google Scholar] [CrossRef]
  32. Zhang, L.; Liu, B.; Liu, C.; Zhang, D.D.; Zhao, S.Y.; Li, Z.Y.; Xu, P.J.; Wilson, K.; Withers, A.; Jones, C.M. Genetic structure and insecticide resistance characteristics of fall armyworm populations invading China. Mol. Ecol. Resour. 2020, 20, 1682–1696. [Google Scholar] [CrossRef]
  33. Tan, S.Q.; Wang, Z.X.; He, J.X.; Ma, Z.N.; Yin, L.; Zhang, Z.B.; Zhang, Y.S. Analysis of the migration path and source areas of fall armyworm Spodoptera frugiperda in Hunan Province in 2021. J. Plant Prot. 2024, 51, 132–140. [Google Scholar] [CrossRef]
  34. Wang, R.L.; Jiang, C.X.; Guo, X.; Chen, D.D.; Chao, Y.; Zhang, Y.; Wang, M.T.; Li, Q. Potential distribution and genetic diversity of fall armyworm in China. Glob. Ecol. Conserv. 2020, 21, e00865. [Google Scholar] [CrossRef]
  35. Jiang, C.X.; Zhang, X.Y.; Xie, W.Q.; Wang, R.L.; Feng, C.H.; Ma, L.; Li, Q.; Yang, Q.F.; Wang, H.J. Predicting the potential distribution of the fall armyworm Spodoptera frugiperda (J.E. Smith) under climate change in China. Glob. Ecol. Conserv. 2022, 33, e01994. [Google Scholar] [CrossRef]
Insects 16 00620 g001
Figure 1. Invasion paths of FAW into Shaanxi Province from different migration routes in 2019–2023.
Figure 1. Invasion paths of FAW into Shaanxi Province from different migration routes in 2019–2023.
Insects 16 00620 g001
Insects 16 00620 g002
Figure 2. Phylogenetic tree inferred from COI gene fragments.
Figure 2. Phylogenetic tree inferred from COI gene fragments.
Insects 16 00620 g002
Insects 16 00620 g003
Figure 3. Daily average temperature in Hanzhong, Ankang, and Shangluo, respectively, in the winter of 2022–2023.
Figure 3. Daily average temperature in Hanzhong, Ankang, and Shangluo, respectively, in the winter of 2022–2023.
Insects 16 00620 g003
Table 1. Sampling information for different geographical populations of FAW.
Table 1. Sampling information for different geographical populations of FAW.
Sample No.CollectionCollection TimeHostStageNumber of Samples
1Baoji, Shaanxi Province (107°96′ E, 34°48′ N)10/12/2023Cornlarvae13
2Hanzhong, Shaanxi Province
(107°12′ E, 34°48′ N)		7/20/2023Cornlarvae13
3Ankang, Shaanxi Province
(109°86′ E, 36°66′ N)		7/23/2023Cornlarvae7
4Ankang, Shaanxi Province
(109°26′ E, 32°52′ N)		8/28/2023Cornlarvae6
5Ruili, Yunnan Province
(97°49′ E, 23°58′ N)		4/25/2023Cornlarvae8
6Xinxiang, Henan Province
(114°08′ E, 32°10′ N)		7/5/2023Cornlarvae12
7Shangluo, Shaanxi Province
(110°21′ E, 34°01′ N)		8/3/2023Cornlarvae13
8Wuhan, Hubei Province
(114°06′ E, 30°34′ N)		6/30/2023Cornlarvae11
9Xi’an, Shaanxi Province
(109°08′ E, 34°25′ N)		9/7/2022Cornlarvae10
10Hanzhong, Shaanxi Province
(106°55′ E, 32°09′ N)		7/21/2022Cornlarvae7
11Shangluo, Shaanxi Province
(110°30′ E, 33°59′ N)		8/1/2022Cornlarvae6
12Ankang, Shaanxi Province
(108°36′ E, 32°50′ N)		7/26/2022Cornlarvae7
Table 2. Summary of the earliest locations in Shaanxi and the damages of FAW in different years.
Table 2. Summary of the earliest locations in Shaanxi and the damages of FAW in different years.
Survey YearCountyInitial Occurrence DateOccurrence Area (mu)Average Rate of Damaged Plants (%)Average Larval Number per 100 PlantsLarval InstarEstimated
Immigration
Date
2019Yangxian
(107°44′ E, 33°07′ N)		31 May0.377.132–421 May
2020Zhenping
(109°26′ E, 31°51′ N)		28 June407151–415 June
2021Chenggu
(107°11′ E, 33°27′ N)		22 June72.643–58 June
2022Ningqiang
(106°03′ E, 33°57′ N)		11 July3.72.2922–428 June
2023Mian
(106°54′ E, 33°09′ N)		20 June1.21.0822–51 June
Table 3. Strain identification of FAW based on different molecular markers.
Table 3. Strain identification of FAW based on different molecular markers.
Sample No.Collect LocationsCOI MakerTpi Maker
Corn
Strain		Rice
Strain		Corn
Strain		Rice
Strain
1Baoji, Shaanxi Province49130
2Hanzhong, Shaanxi Province013130
3Ankang, Shaanxi Province0770
4Xianyang, Shaanxi Province2460
5Ruili, Yunnan Province0880
6Xinxiang, Henan Province75120
7Shangluo, Shaanxi Province49130
8Wuhan, Hubei Province011110
9Xi’an, Shaanxi Province010100
10Hanzhong, Shaanxi Province *0770
11Shangluo, Shaanxi Province *0660
12Ankang, Shaanxi Province *0770
* indicate that the larvae samples collected in 2022, other unmarked larvae samples collected in 2023.
Table 4. Survival rates of pupae of FAW at different dates in Shaanxi (from December 2022 to January 2023).
Table 4. Survival rates of pupae of FAW at different dates in Shaanxi (from December 2022 to January 2023).
Collect LocationsSurvival Rates (%) of Pupae at Different Dates
15 December20 December25 December30 December4 January9 January14 January
Hanzhong100 ± 0.00 a95.55 ± 1.11 a83.33 ± 1.92 ab61.11 ± 2.94 ab36.67 ± 1.93 ab16.67 ± 1.93 a0
Ankang98.89 ± 1.11 a96.67 ± 1.93 a87.78 ± 1.11 a64.59 ± 1.04 ab37.78 ± 1.11 ab6.67 ± 3.84 a0
Shngluo98.89 ± 1.11 a88.89 ± 1.11 b72.22 ± 1.11 c55.55 ± 4.01 b31.11 ± 2.22 ab7.78 ± 1.14 a0
Note: Data in the table are means ± SE. Means followed by different letters are significantly different (Tukey’s HSD test).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yan, Z.; Feng, X.; Wang, X.; Yuan, X.; Zhang, Y.; Yang, D.; He, K.; Xie, F.; Wang, Z.; Li, Y. Invasion Dynamics and Migration Patterns of Fall Armyworm (Spodoptera frugiperda) in Shaanxi, China. Insects 2025, 16, 620. https://doi.org/10.3390/insects16060620

AMA Style

Yan Z, Feng X, Wang X, Yuan X, Zhang Y, Yang D, He K, Xie F, Wang Z, Li Y. Invasion Dynamics and Migration Patterns of Fall Armyworm (Spodoptera frugiperda) in Shaanxi, China. Insects. 2025; 16(6):620. https://doi.org/10.3390/insects16060620

Chicago/Turabian Style

Yan, Zhanfeng, Xiaojun Feng, Xing Wang, Xiangqun Yuan, Yongjun Zhang, Daibin Yang, Kanglai He, Feizhou Xie, Zhenying Wang, and Yiping Li. 2025. "Invasion Dynamics and Migration Patterns of Fall Armyworm (Spodoptera frugiperda) in Shaanxi, China" Insects 16, no. 6: 620. https://doi.org/10.3390/insects16060620

APA Style

Yan, Z., Feng, X., Wang, X., Yuan, X., Zhang, Y., Yang, D., He, K., Xie, F., Wang, Z., & Li, Y. (2025). Invasion Dynamics and Migration Patterns of Fall Armyworm (Spodoptera frugiperda) in Shaanxi, China. Insects, 16(6), 620. https://doi.org/10.3390/insects16060620

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop