Translator Disclaimer
1 June 2016 Population Variability of Spodoptera frugiperda (Lepidoptera: Noctuidae) in Maize (Poales: Poaceae) Associated with the Use of Chemical Insecticides
Jose Ricardo Perez-Zubiri, Ernesto Cerna-Chavez, Luis Alberto Aguirre-Uribe, Jeronimo Landeros-Flores, Marvin K. Harris, Raul Rodriguez-Herrera
Author Affiliations +
Abstract

The objective of this research was to determine the genetic variability of Spodoptera frugiperda Smith & Abbot (Lepidoptera: Noctuidae) larvae from populations collected on maize (Poales: Poaceae) crops from several locations (Sinaloa, Jalisco, Coahuila, and Durango) in Mexico grown under various types of pest management using inter-simple sequence repeats (ISSR) type molecular markers. These ISSR markers amplified 105 loci, with 92.59% being polymorphic, which genetically characterized these populations and related this variability with factors such as pest control management. The most divergent population was from Tlajomulco, Jalisco. This location had the most intense use of chemical insecticides with diverse modes of action, which may account for much of the difference in genetic variability observed among sites in this study.

The fall armyworm, Spodoptera frugiperda Smith & Abbot (Lepidoptera: Noctuidae), is a major pest in the western hemisphere, capable of causing substantial damage to corn, sorghum, fodder, grass, rice, cotton, and peanut crops (Nagoshi & Meagher 2004). Approximately 3,000 tons of active ingredient per year are used to control this single pest (Blanco et al. 2014). In several regions of the world, insecticide overuse has resulted in the development of populations highly resistant to these chemicals (Yu et al. 2003; Ahmad & Arif 2010). Consequently, collateral effects such as negative impacts on biological control organisms also occur (Romeis et al. 2006).

Populations of the fall armyworm have been studied in various environments that show various factors can affect their level of resistance to insecticides. Population monitoring of the fall armyworm resistance status has shown that management strategies can minimize the environmental impact of insecticide use (Zenner de Polanía et al. 2007). Insects have demonstrated great genetic plasticity, with more than 500 species now resistant to one or more insecticides (Mc-Gaughey 1985; Ferré & Van Rie 2002). In designing resistance management strategies for S. frugiperda, we must take into account a few factors. First, we need to genetically characterize the population of interest. Second, studies should be conducted to examine the origins of possible biotypes within the population associated with certain environmental conditions. Third, we need to examine the dynamics of a population's genetic variability through generations.

Molecular markers have become a powerful tool to evaluate the degree of genetic variation in a given population with respect to a given factor over the years. Several types of relevant molecular markers have been identified in insect populations (Batley et al. 2004; Clark et al. 2007; Martinelli et al. 2007; Lindroth 2011), including the intersimple sequence repeats (ISSR). The ISSR (Zietkiewicz et al. 1994) is a molecular marker that uses microsatellites as primers to amplify multiple loci, mainly repeated nucleotide sequences of several sizes. Some advantages of these types of molecular markers include high reproducibility, acceptable cost, no need for previous sequence information, and high polymorphism (Semagn et al. 2006). The objective of our research was to study the genetic variability of S. frugiperda in maize (Poales: Poaceae) from México under various crop management strategies using ISSR molecular markers.

Maize fields in localities of Coahuila, Durango, Jalisco, and Sinaloa states that differed in their pest management strategies were selected, and the results of larval testing were used to compare and contrast the impact of management strategies on the genetic variability. The locations from where S. frugiperda populations were collected and the pest management tactics utilized are shown in Fig. 1. A modified protocol of Doyle & Doyle (1991) was used for DNA extraction from the cut mid-section of every selected larva. Polymerase chain reaction (PCR) analysis was performed using ISSR molecular markers. All PCR products were separated on 1% agarose gels for 1.3 h at 65 V. The gels were viewed and photographed under UV illumination and images digitalized in a UVP Mini Darkroom. Amplified bands from every individual were put in a single absence—presence matrix for data organization. Analysis of molecular variance (AMOVA) was carried out using the InfoGen version 2014 statistical software (Balzarini & Di Rienzo 2014). In addition to genetic diversity, Nei's unbiased heterozygosis, mean allele number, and the number of effective alleles were obtained. Data were analyzed to obtain frequency dendrograms.

The results of PCR analysis using ISSR molecular markers produced a total of 105 bands or loci, of which 92.59% were polymorphic and provided enough information to group populations into several nodes. Results from AMOVA (Table 1) show that P-values were small (P = 0.0175 and 0.0075) and genetic variability was greater within populations (96.27%) than among populations (3.73%). These data indicate that the collected populations were essentially panmictic, and there was little genetic differentiation among them, further suggesting that there was high gene flow among the populations. However, the ISSR molecular markers showed a great number of polymorphic loci, which supported placement of populations in separate groups. This genetic plasticity in S. frugiperda was noted in other studies using amplified fragment length polymorphism-type molecular markers for investigation of S. frugiperda population genetic structure (Martinelli et al. 2007; Lobo-Hernández & Saldamando-Benjumea 2012). Other workers on genetic variability in S. frugiperda and other widely distributed species reported that greater variability occurs within than between populations (Clark et al. 2007; Roux et al. 2007; Fuentes-Contreras et al. 2008), which agrees with results found in our research.

The dendrogram (Fig. 2) shows the formation of 4 groups, each containing between 1 and 3 populations (POP): group 1 (POP 1); group 2 (POP 2 and POP 3); group 3 (POP 4, POP 5, and POP 6), and group 4 (POP 7). Population 7 had a genetic composition significantly different from other populations in this study. Population 3 was collected from a maize crop without insect control, whereas population 7 was collected from a corn crop where insecticides of various toxicological groups from organophosphates to some relatively new materials, such as neonicotinoids, had intensively been used for years (INIFAP 2007).

Fig. 1.

Spodoptera frugiperda populations in cultivated maize in various parts of Mexico from which larvae were collected to study molecular genetic variation.

f01_329.jpg

This insecticide use may not be completely responsible for all of the genetic differences observed in population 7, although reports on other Lepidoptera exposed to continuous insecticide exposures show similar genetic changes (Endersby et al. 2006; Franck et al. 2007). Population 4 (Rancho Loma Bonita, Coahuila) and population 6 (Culiacán, Sinaloa) were collected on crops with intensive use of pyrethroid insecticides for pest control, and both populations were sorted into the same group. These management differences suggest that populations intensively exposed to insecticides are more genetically diverse; such variability suggests that chemical treatment affects population allele diversity, which has also been shown previously (Franck et al. 2007). The herein used ISSR molecular markers provide new tools to investigate complex resistance problems under field conditions, and finer-grained interpretations of results using them are expected as larger databases become available.

Table 1.

Analysis of molecular variance (AMOVA) for genetic variability in Spodoptera frugiperda populations using 3 inter-simple sequence repeat (ISSR) molecular markers.

t01_329.gif

Fig. 2.

Dendrogram of genetic distance among Spodoptera frugiperda populations analyzed using ISSR molecular markers.

f02_329.jpg

References Cited

1.

Ahmad M , Arif MI . 2010. Resistance of beet armyworm Spodoptera exigua (Lepidoptera: Noctuidae) to endosulfan, organophosphorus and pyrethroid insecticides in Pakistan. Crop Protection 29: 1428–1433. Google Scholar

2.

Balzarini MG , Di Rienzo JA . 2014. InfoGen versión 2014. FCA, Universidad Nacional de Córdoba, Argentina.  http://www.info-gen.com.ar (last accessed 14 Mar 2016). Google Scholar

3.

Batley JK , Edwards J , Barker JHA , Dawson KJ , Wiltshire CW , Glen DM , Karp A . 2004. Population structure of the beetle pests Phyllodecta vulgatissima and P. vitellinae on UK willow plantations. Insect Molecular Biology 13: 413–421. Google Scholar

4.

Blanco CA , Pellegaud JG , Nava-Camberos U , Lugo-Barrera D , Vega-Aquino P , Coello J , Terán-Vargas AP , Vargas-Camplis J . 2014. Maize pests in Mexico and challenges for the adoption of an integrated pest management program. Journal of Integrated Pest Management 5(4): 1–9. Google Scholar

5.

Clark PL , Molina-Ochoa J , Martinelli S , Skoda SR , Isenhour DJ , Lee DJ , Krumm JT , Foster JE . 2007. Population variation of the fall armyworm, Spodoptera frugiperda, in the western hemisphere. Journal of Insect Science 7: article 5. Google Scholar

6.

Doyle JJ , Doyle JL . 1987. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemistry Bulletin 19: 11–15. Google Scholar

7.

Endersby NM , McKechnie SW , Ridland PM , Weeks AR . 2006. Microsatellites reveal a lack of structure in Australian populations of the diamondback moth, Plutella xylostella (L.). Molecular Ecology 15: 107–118. Google Scholar

8.

Ferré J , Van Rie J . 2002. Biochemistry and genetics of insect resistance to Bacillus thuringiensis. Annual Review of Entomology 47: 501–533. Google Scholar

9.

Franck P , Reyes M , Olivares J , Sauphanor B . 2007. Genetic architecture in codling moth populations: comparison between microsatellite and insecticide resistance markers. Molecular Ecology 16: 3554–3564. Google Scholar

10.

Fuentes-Contreras E , Espinoza JL , Lavandero B , Ramírez CC . 2008. Population genetic structure of codling moth (Lepidoptera: Tortricidae) from apple orchards in Central Chile. Journal of Economic Entomology 101: 190–198. Google Scholar

11.

INIFAP (Instituto Nacional de Investigaciones Forestales y Agricolas y Pecuarias). 2007. Paquetes tecnológicos para maíz de temporal (ciclo Agrícola primavera- verano) para condiciones de alto, medio y bajo potencial productivo. INIFAP, México D.F. Google Scholar

12.

Lindroth EJ . 2011. Population genetics of the western bean cutworm (Striacosta albicosta Smith) across the United States. Ph.D. thesis. University of Nebraska, Lincoln, Nebraska. Google Scholar

13.

Lobo-Hernández MI , Saldamando-Benjumea CI . 2012. Molecular characterization and genetic differentiation of Spodoptera frugiperda J. E. Smith (Lepidoptera: Noctuidae) in maize, rice, and cotton fields of Colombia with AFLP. Southwestern Entomologist 37: 193–207. Google Scholar

14.

Martinelli S , Clark PL , Zucchi MI , Silva-Filho MC , Foster JE , Omoto C . 2007. Genetic structure and molecular variability of Spodoptera frugiperda (Lepidoptera: Noctuidae) collected in maize and cotton fields in Brazil. Bulletin of Entomological Research 97: 225–231. Google Scholar

15.

McGaughey WH . 1985. Insect resistance to the biological insecticide Bacillus thuringiensis. Science 229: 193–195. Google Scholar

16.

Nagoshi RN , Meagher RL . 2004. Behavior and distribution of the two fall armyworm host strains in Florida. Florida Entomologist 87: 440–448. Google Scholar

17.

Romeis J , Meissle M , Bigler F . 2006. Transgenic crops expressing Bacillus thuringiensis toxins and biological control. Nature Biotechnology 24: 63–71. Google Scholar

18.

Roux O , Gevrey L , Arvanitakis L , Gers C , Bordat D , Legal L . 2007. ISSR-PCR: tool for discrimination and genetic structure analysis of Plutella xylostella populations native to different geographical areas. Molecular Phylogenetics and Evolution 43: 240–250. Google Scholar

19.

Semagn K , Bjørnstad Å, Ndjiondjop MN . 2006. Review: an overview of molecular marker methods for plants. African Journal of Biotechnology 5: 2540–2568. Google Scholar

20.

Yu SJ , Nguyen SN , Abo-Elghar GE . 2003. Biochemical characteristics of insecticide resistance in the fall armyworm, Spodoptera frugiperda (J. E. Smith). Pesticide Biochemistry and Physiology 77: 1–11. Google Scholar

21.

Zenner de Polanía I , Arévalo HA , Mejía R . 2007. El gusano cogollero del maíz Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae) y algunas plantas transgénicas. Revista Colombiana de Ciencias Hortícolas 1: 103–113. Google Scholar

22.

Zietkiewicz E , Rafalski A , Labuda D . 1994. Genome fingerprinting by simple sequence repeat (ssr)-anchored polymerase chain reaction amplification. Genomics 20: 176–183. Google Scholar
Jose Ricardo Perez-Zubiri, Ernesto Cerna-Chavez, Luis Alberto Aguirre-Uribe, Jeronimo Landeros-Flores, Marvin K. Harris, and Raul Rodriguez-Herrera "Population Variability of Spodoptera frugiperda (Lepidoptera: Noctuidae) in Maize (Poales: Poaceae) Associated with the Use of Chemical Insecticides," Florida Entomologist 99(2), 329-331, (1 June 2016). https://doi.org/10.1653/024.099.0233
Published: 1 June 2016
JOURNAL ARTICLE
3 PAGES


SHARE
ARTICLE IMPACT
Back to Top